An internet of things data transmission method

Abstract

The application discloses an Internet of Things data transmission method, comprising the following steps: in the Internet of Things data transmission, credit nodes are elected in each group when the system starts, a semantic label library is constructed to distinguish data credibility and assign power; a cross-group request is initiated by a device, the system analyzes the content and adds labels, the credit nodes dynamically adjust the topology in combination with the environment and trust scores; the request must be verified by double signature, and if successful, the trust score of the source device is increased, and if false, the score is reduced and isolated; at the same time, the system locates the conflict signal by using space-time coordinates, and through dynamic time slot allocation, phase compensation and vertical isolation strategy, the signal transmission is adjusted in real time and adaptively, the interference is effectively suppressed, and the conflict probability is reduced; the scheme improves the reliability and stability of the Internet of Things communication, and guarantees the safe and efficient transmission of data.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more particularly to an IoT data transmission method. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), massive numbers of devices connecting to networks create complex data interaction scenarios. Traditional IoT data transmission often relies on centralized architectures, which pose single-point-of-failure risks and make data security difficult to guarantee. Especially in cross-group communication, the data quality from different devices varies greatly, and the lack of an effective trust assessment mechanism can easily lead to malicious data propagation or data tampering. While existing technologies attempt to improve security through encryption or access control, they do not fully consider the dynamic changes in device behavior and the impact of environmental factors on trust, making it difficult to achieve adaptive inter-group communication management. Therefore, a dynamic transmission method based on credit assessment is urgently needed.

[0003] In existing IoT data transmission solutions, cross-group communication often employs a static topology, with fixed trust relationships between devices and a lack of real-time verification. This model is prone to communication interruptions or data leaks when faced with device failures, network attacks, or environmental interference. Furthermore, traditional methods rely solely on device identifiers to distinguish data sources, failing to effectively assess data trustworthiness and leading to low-quality or malicious data consuming network resources. In addition, the lack of dynamic penalty and incentive mechanisms makes it difficult to isolate malicious devices in a timely manner, impacting overall network performance. Therefore, a trust management method combining device behavior monitoring and dynamic topology adjustment is needed to improve the reliability and security of IoT cross-group communication. Summary of the Invention

[0004] This application provides an IoT data transmission method that combines device behavior monitoring with a trust management method based on dynamic topology adjustment, thereby improving the reliability and security of IoT cross-group communication.

[0005] This application provides an Internet of Things (IoT) data transmission method, including:

[0006] S1, when the system starts, each IoT device group elects a local credit node, distinguishes the credibility of the data source according to the semantic tag library defined by the credit node, and sets a weight value;

[0007] S2, When a device generates a cross-group request, the system parses the request content and extracts key semantic features, and adds semantic tags according to the type of the source device;

[0008] S3, the credit node continuously monitors device behavior and dynamically adjusts the communication topology between groups by combining real-time environmental data and trust scores;

[0009] S4, when there is a cross-group request, it must be verified by the dual signature of the source group and the target group's credit node;

[0010] S5: Successfully verified requests increase the trust score of the source device; when a fake request is detected, the trust score of the source device decreases and the device is switched to an isolated topology path.

[0011] Preferably, the simultaneous combination of real-time environmental data and trust scoring specifically includes: the credit node acquiring device behavior data in real time; collecting environmental data in real time through environmental sensors deployed in the system; calculating the values ​​of various monitoring indicators based on the collected device behavior data; normalizing all behavior indicators; and then averaging and weighting all behavior indicators to obtain a behavior evaluation score; the adjustment formula is as follows: ,in, It is the adjusted behavioral assessment score. It is the environmental impact coefficient; It is a behavioral assessment score; This is the value of the current environmental data. It is the average value of historical environmental data. and These are the maximum and minimum values ​​of historical environmental data, respectively; a dynamic update model is used, combining the device's trust score and the currently adjusted behavior assessment score to update the device's trust score; the formula is: ,in This is the updated trust score. This is the trust score before the update. It is a forgetting factor.

[0012] Preferably, the step of distinguishing the credibility of data sources based on the semantic tag library defined by the credit node specifically includes: the credit node is responsible for collecting behavioral data of devices within the group, and updating the trust score of the device according to preset rules based on the device's behavioral performance; wherein, the data transmission accuracy score A is the number of times the device successfully transmits accurate data within a period of time. Total number of transmissions Data transmission accuracy score The task completion timeliness score T is given, where the specified completion time for each task is denoted as T. The actual completion time of the equipment is Define the timeliness factor The number of tasks m completed by the equipment is counted, and the timeliness of task completion is scored. , The timeliness factor corresponds to the i-th task; the rule compliance score C represents the number of times the device violates the rules within the period, v represents the number of times the device violates the rules, and a maximum allowed number of violations is set. Compliance score When v≥ When C=0; the device's trust score is calculated by combining the data transmission accuracy score, task completion timeliness score, and rule compliance score. The device's trust score formula is: , , and These are the weighting coefficients used to calculate the trust scoring formula. .

[0013] Preferably, the credit node includes: the top three devices with the strongest communication capabilities are selected as credit nodes based on the communication capabilities of devices within the group; the credit node is responsible for managing trust data within the group, including updating device trust scores and maintaining the semantic tag library.

[0014] Preferably, when the system starts, each IoT device group elects a local credit node, specifically including:

[0015] The system receives registration signals from all devices, divides autonomous subgroups according to geographical location grids, and elects a master relay node for each subgroup;

[0016] Receive semantic tag libraries from each domain, extract key semantic features, establish a dynamic mapping dictionary, and synchronize the dictionary to all master relay nodes;

[0017] The main relay node receives device data and communication environment data from the subgroup, calculates the current topology maintenance energy consumption, and determines whether the topology structure needs to be reconstructed.

[0018] During cross-domain communication, the sending master relay node retrieves the mapping dictionary, converts the semantic tags, adds cross-domain markers, and sends them to the receiving master relay node via the quantum tunnel protocol; the receiving master relay node parses the cross-domain markers, reverse-maps the tags according to the local semantic dictionary, and broadcasts them to devices within the subgroup.

[0019] Based on historical energy consumption and topology reconfiguration frequency, device trust scores are incorporated into priority topology decisions.

[0020] Preferably, the calculation of the current topology maintenance energy consumption includes: calculating the transmission energy consumption for maintaining the current topology using the Shannon formula extended model, and calculating the energy consumption for node i to send data to relay node j: Let be the transmit power of node i. For data transmission time; Where L is the efficiency of the transmitting circuit, and L is the data packet length; Let B be the target data rate and B be the channel bandwidth; the formula is: The energy consumption of relay node j in receiving data. The power of the receiving circuit is given by the formula: If data needs to be transmitted through multiple hops, the total energy consumption is the sum of the energy consumption of each hop: The total energy consumption is calculated by weighting and summing the path energy consumption from all devices to the relay node, and then calculating the transmission energy consumption and reception energy consumption for each path.

[0021] Preferably, the calculation of the total energy consumption by measuring the transmission energy consumption and receiving energy consumption of each path includes: if the current topology maintenance energy consumption exceeds a preset threshold, then triggering a reconstruction; if reconstruction is triggered, generating a reconstruction instruction, including a candidate relay node list and a switching time window; if not triggered, maintaining the current topology and recording the current state for the next round of judgment.

[0022] Preferably, the step of dividing autonomous subgroups according to geographic location grids specifically involves:

[0023] The system continuously monitors signals sent from each subgroup master relay node, extracts spatial attribute information from the signals, and generates a phase fingerprint for each subgroup master relay signal;

[0024] Based on spatial attribute information, a four-dimensional spatiotemporal coordinate system for the device is constructed using longitude, latitude, and altitude as spatial coordinate axes and a time phase window as the time axis.

[0025] When two or more subgroup main relay signals are detected to have the same coordinates, it is determined that these signals may be in conflict. The time phase difference within different time phase windows is calculated, and orthogonal time slots are dynamically allocated.

[0026] Assess the severity of signal collisions between subgroups and classify them into different collision levels. For signals with different collision levels, generate corresponding phase compensation arrays to process the collision signals and form reverse beams.

[0027] When an overlap of allocated orthogonal time slots is detected, a virtual time slot partition is created;

[0028] Continuously monitor the signal conflict situation after processing, evaluate the spatial cancellation effect in real time, and obtain the altitude data of relevant equipment if the cancellation effect is poor.

[0029] Based on the acquired equipment altitude data, an isolation zone is established in the vertical direction.

[0030] Preferably, the construction of the four-dimensional spatiotemporal coordinate system of the device includes: acquiring the longitude, latitude, and altitude information of each subgroup master relay node; considering the dynamic change characteristics of the signal over time, dividing time into a series of discrete time periods, with each time period serving as a time phase window; using the acquired longitude, latitude, and altitude as spatial coordinate axes, and the defined time phase window as the time axis, to construct a four-dimensional spatiotemporal coordinate system.

[0031] Preferably, the creation of virtual time slot partitions specifically includes: continuously monitoring the usage of allocated orthogonal time slots, and detecting whether there is time slot overlap through time measurement and synchronization techniques; when two or more signals are detected to be allocated to the same time slot and the overlap ratio exceeds a preset threshold, it is determined that there is a time slot overlap problem; for signals with the same spatial coordinates and arriving within the same time phase window, the arrival time differences at different time points are analyzed, and the time phase difference is calculated; based on the calculated time phase difference, an orthogonal time slot is allocated to conflicting signals using a dynamic time slot allocation algorithm.

[0032] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0033] By employing multi-dimensional signal processing and intelligent resource allocation, the problem of signal collisions between subgroups was effectively resolved. Conflicting signals were precisely located using a spatiotemporal coordinate system, and efficient interference suppression was achieved through dynamic time slot allocation and phase compensation techniques. Furthermore, the signal transmission environment was further optimized through a vertical isolation strategy. Real-time monitoring and adaptive adjustment throughout the entire process significantly improved the reliability and stability of signal transmission, reduced the probability of signal collisions, and ensured the efficient operation of IoT communication.

[0034] Efficient management is achieved by dividing autonomous subgroups according to geographical location and electing master relay nodes; a dynamic semantic mapping dictionary is established to ensure accurate cross-domain communication; topology energy consumption is calculated to determine whether to reconstruct and optimize energy consumption; and device trust scores are incorporated into topology decision-making to improve topology stability. These measures comprehensively optimize and improve the system in terms of management, communication, energy consumption, and stability.

[0035] After establishing a vertical isolation zone, the isolation effect is continuously monitored. The actual effectiveness of the vertical isolation is evaluated by measuring parameters such as signal strength and interference levels inside and outside the isolation zone. Based on the monitoring results, the vertical isolation strategy and parameters are dynamically optimized and adjusted. For example, if the isolation effect is found to be unsatisfactory, the antenna elevation angle or transmit power can be further adjusted to improve the vertical isolation effect and ensure that signal conflicts between subgroups are effectively resolved. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating an IoT data transmission method according to an embodiment of the present invention. Detailed Implementation

[0037] To facilitate understanding of the present invention, a more complete description of this application will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the present invention.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0039] Example 1: Figure 1 This is a flowchart illustrating an IoT data transmission method according to an embodiment of the present invention.

[0040] like Figure 1 As shown, an IoT data transmission method includes the following steps:

[0041] S1. When the system starts, each IoT device group elects a local credit node, distinguishes the credibility of the data source according to the semantic tag library defined by the credit node, and sets a weight value.

[0042] The selection criteria for trust nodes are based on the communication capabilities of devices within the group. The top three devices with the strongest communication capabilities are selected as trust nodes. These trust nodes are responsible for managing trust data within the group, including updating device trust scores and maintaining the semantic tag library.

[0043] Specifically, after system startup, all IoT devices are identified and divided into several IoT device groups based on their geographical location, functional attributes, or preset rules. Each group forms a relatively independent trust management unit, assigning corresponding weights to each communication capability indicator (including but not limited to signal strength, data transmission rate, packet loss rate, network latency, etc.) to reflect its importance in the election of trust nodes. The communication capability indicators for each device are normalized, mapping the indicator values ​​to the [0,1] interval. Based on the calculated comprehensive communication capability score, all devices within the group are ranked, and the top three devices with the highest scores are selected as trust nodes.

[0044] Credit nodes define a semantic tag library based on the characteristics of devices within the group and business needs. These tags describe the credibility characteristics of the data source (high-credibility device, medium-credibility device, low-credibility device, unknown-credibility device). Each semantic tag is assigned a weight value to reflect its influence on data credibility. For example, let the weight of the "high-credibility device" tag be... The weight of the "Zhongxin Keji Device" label is The weight of the "low-trust device" label is The weight of the "Unknown Trusted Device" label is Furthermore, these weight values ​​can be adjusted according to the actual business scenario.

[0045] The trust node is responsible for collecting behavioral data of devices within the group (accuracy, timeliness, and compliance of data transmission), and updating the trust score of the device according to preset rules based on the device's behavior.

[0046] Among them, the data transmission accuracy score A is the number of times the device successfully transmits accurate data within a specified period (defined according to the application scenario). Total number of transmissions Data transmission accuracy score Its value range is [0,1].

[0047] The task completion timeliness score T is given, and the specified completion time for each task is denoted as T. The actual completion time of the equipment is Define the timeliness factor. The number of tasks m completed by the equipment is counted, and the timeliness of task completion is scored. The value range is [0,1], where, This represents the timeliness factor corresponding to the i-th task.

[0048] The compliance score is C. The number of times the device violates the rules within a given period is counted (v). A maximum allowed number of violations is set. Compliance score based on adherence to rules When v≥ When C=0, the range of values ​​is [0,1].

[0049] A device's trust score is calculated by combining scores for data transmission accuracy, task completion timeliness, and rule compliance. The value ranges from [0,1]. A larger value indicates a more trustworthy device. , and These are the weighting coefficients used to calculate the trust score formula. These values ​​need to be set appropriately based on the specific application scenario and business requirements. .

[0050] Credit nodes periodically assess the trust status of devices within the group and dynamically adjust their semantic tags based on the devices' trust scores. For example, when a device's trust score exceeds a certain threshold, its semantic tag is upgraded from "medium-trusted device" to "high-trusted device"; when the trust score falls below a certain threshold, its semantic tag is downgraded. Simultaneously, credit nodes expand or modify the semantic tag library based on new business needs or device changes to ensure that the tag library accurately describes the trustworthiness characteristics of the data source.

[0051] S2, when a device generates a cross-group request, the system parses the request content and extracts key semantic features, and adds semantic tags according to the type of the source device.

[0052] Specifically, the system's communication module continuously monitors the network port. When a device initiates a cross-group request, the communication module captures the request data packet and passes it to the request parsing module. The request parsing module first performs an integrity check on the request data packet to ensure that the packet is not corrupted or missing critical information. For example, it checks whether the header and trailer identifiers of the data packet are complete and whether the checksum is correct. If the data packet is incomplete, it returns an error message to the sending device, requesting a retransmission of the request.

[0053] The request is parsed using predefined request syntax rules (based on a specific protocol format, JSON). For example, if the request is in JSON format, the parsing module uses a JSON parsing library to convert the request string into an internal data structure, such as a dictionary or object. It identifies various fields in the request, such as request type (query, update, delete, etc.), target group identifier, and request parameters. Based on business logic and domain knowledge, it performs semantic understanding on the parsed fields. For example, if the request type is "query," it needs further analysis to determine the query target—whether it's querying the status information of a specific device or querying statistical data for a group. A semantic mapping table is established to map field values ​​in the request to specific semantic concepts. For example, "temperature>30" is mapped to "high temperature status query."

[0054] Based on the semantic understanding results, key semantic features (entities, operations, attributes) in the request are identified. Natural language processing techniques (if the request contains a natural language description) or rule matching methods (for structured requests) are used to extract these key semantic features. The extracted key semantic features are then normalized to have a unified representation. For example, device identifiers in different formats are uniformly converted into unique numeric codes, and temperature values ​​are uniformly converted to degrees Celsius while retaining a certain number of decimal places.

[0055] Extract the source device's identification information from the request data packet, such as the device's MAC address, IP address, or unique device identifier. Query the device type corresponding to this device identifier based on the device registration information table in the system. The device registration information table should contain a mapping relationship between device identifiers and device types. Validate the identified device type to ensure its legality and validity. For example, check whether the device type is within the system's supported range and whether the device is in a normal operating state. If the device type is invalid, reject the request and return an error message.

[0056] Based on the source device type and extracted key semantic features, select appropriate semantic tags from a semantic tag library. The semantic tag library should predefine tags corresponding to different device types and combinations of semantic features. For example, for a request to query high-temperature status from a temperature sensor, the tag "Temperature Sensor - High Temperature Query" can be appended. The selected semantic tags are then appended to the request data structure so that subsequent processing modules can perform corresponding operations based on the tags. For example, a "semantic_tags" field can be added to the request object, storing the tags as a list in this field.

[0057] If a similarity-based method is used to identify key semantic features, the cosine similarity formula can be employed. If there is uncertainty in the device identifier resolution process, such as due to ambiguity in the device identifier or incomplete device registration information, a probabilistic method can be used to calculate the probability of the device type. Assume there are m possible device types. , ,..., Based on the device identifier and other relevant information, calculate the probability of each device type. ,and .

[0058] Bayes' theorem can be used to calculate the probability of device type: ,in, This indicates the device identifier and other relevant information. In the given information The device type is The posterior probability, In the device type The following information appears The likelihood probability, The equipment type is Prior probability It is information The marginal probability can be calculated using the law of total probability: In practical applications, these probability values ​​need to be estimated based on the specific business scenario and data distribution.

[0059] S3, the credit node continuously monitors device behavior and dynamically adjusts the communication topology between groups by combining real-time environmental data and trust scores.

[0060] Specifically, based on the system's security requirements and business characteristics, the credit node determines the device behavior indicators (data transmission frequency, data accuracy, response time, number of violations) that need to be monitored, clarifies the types of real-time environmental data that need to be combined (network bandwidth, signal strength, device power consumption, ambient temperature (if device performance is affected by temperature)), constructs the initial inter-group communication topology based on the device's initial trust score and business requirements, and determines the connection relationships and communication paths between devices.

[0061] Credit nodes acquire real-time device behavior data. Environmental sensors deployed within the system collect environmental data in real time. Based on the collected device behavior data, the values ​​of various monitoring indicators are calculated, all behavior indicators are normalized, and a weighted average of all indicators is taken to obtain a behavior evaluation score. For example, data transmission frequency = amount of data successfully transmitted within a certain time period / time interval; data accuracy = amount of accurately transmitted data / total amount of transmitted data. The impact of real-time environmental data on device behavior is analyzed, and the trust score is adjusted accordingly.

[0062] The adjustment formula is as follows: ,in, It is the adjusted behavioral assessment score. It is the environmental impact coefficient, with a value range between [0,1], used to control the degree of influence of environmental data on behavioral assessment; It is a behavioral assessment score; This is the value of the current environmental data. It is the average value of historical environmental data. and These are the maximum and minimum values ​​of historical environmental data, respectively.

[0063] A dynamic update model is used, combining the device's trust score with its currently adjusted behavioral assessment score to update the device's trust score. The formula is: ,in This is the updated trust score. This is the trust score before the update. It is the forgetting factor, with a value between [0,1], used to control the degree of influence of historical trust scores on the current trust score. The larger the value, the greater the impact of historical trust scores; The smaller the value, the greater the impact of the current behavior assessment score.

[0064] Based on the trust scores of the devices, adjustment rules for the inter-group communication topology are set (for example, devices with a trust score higher than 0.8 can act as core nodes, responsible for communicating with other groups; devices with a trust score between 0.5 and 0.8 can act as ordinary nodes, only communicating with devices within the same group; devices with a trust score lower than 0.5 will be isolated and prohibited from participating in inter-group communication). Using relevant algorithms from graph theory, the inter-group communication topology is reconstructed according to the device trust scores and adjustment rules (using Prim's algorithm to construct a minimum spanning tree with the core nodes as vertices, serving as the main communication path between groups). The adjusted communication topology information is sent to each device, and the devices configure communication connections and routing according to the new topology information, completing the dynamic adjustment of the inter-group communication topology.

[0065] S4 requires dual signature verification from both the source group and the target group's credit nodes when making cross-group requests.

[0066] S5: Successfully verified requests increase the trust score of the source device; when a fake request is detected, the trust score of the source device decreases and the device is switched to an isolated topology path.

[0067] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0068] By dynamically electing trust nodes, constructing a semantic tag library, and implementing a dual-signature verification mechanism, the system achieves accurate assessment and dynamic management of trust in IoT devices. The system intelligently adjusts trust scores and communication topologies based on device communication capabilities, behavioral data, and environmental factors, effectively distinguishing the credibility of data sources and isolating malicious devices. Simultaneously, it leverages semantic feature parsing to enhance compliance verification of cross-group requests. This solution significantly improves the security, adaptability, and scalability of IoT systems, providing reliable assurance for device collaboration in low-trust environments.

[0069] Example 2: In Example 1, when IoT devices transmit data, a unified topology decision-making mechanism is used to construct and adjust the data transmission topology based on established communication rules and simple energy consumption assessment. However, in real-world applications, IoT devices are widely distributed and diverse, with significant differences in communication capabilities, energy consumption characteristics, and communication environments across different regions and types. The unified topology decision-making mechanism does not fully consider these complex factors, making it difficult to accurately determine when to perform topology reconfiguration and how to select the optimal relay node when facing different device combinations and communication scenarios. For example, in cross-domain communication scenarios, the semantic labels of devices in different domains differ, and the unified mechanism cannot effectively handle semantic conversion, easily leading to data transmission errors or misunderstandings. Simultaneously, the energy consumption assessment of devices remains at a simple level, failing to deeply integrate historical energy consumption data and topology reconfiguration frequency, resulting in inaccurate and incomplete energy consumption assessments, thus affecting the scientific validity and rationality of topology decisions. Furthermore, with the continuous expansion of the IoT scale and dynamic changes in devices, the unified mechanism lacks flexibility and adaptability, making it difficult to quickly respond to changes in device status and network topology adjustment needs. To address the aforementioned issues, further optimization and improvement of IoT data transmission methods are proposed. Taking into full account factors such as device characteristics, communication scenarios, and historical data, more accurate, efficient, and intelligent data transmission topology decisions are achieved. Example 2 is presented here.

[0070] In some embodiments, each IoT device group elects a local credit node upon system startup, and step S1 further includes:

[0071] S11, the system receives registration signals from all devices, divides autonomous subgroups according to geographical location grids, and elects a master relay node for each subgroup.

[0072] Specifically, the system receives registration signals from all devices (including device ID, geographical location, communication capability, remaining battery power, and domain type), constructs a global device map, and divides the devices into n autonomous subgroups based on a geographical grid (1km×1km). Each subgroup contains approximately n / 100 devices, ensuring that communication latency within each subgroup is below a threshold. A master relay node is elected for each subgroup, selected based on the top 3 devices in terms of communication capability and remaining battery power > 40%. The master relay node is re-elected every 24 hours. If the current master relay's battery power falls below 20% or its communication capability drops out of the top 5, an emergency election is triggered.

[0073] S12 receives the semantic tag library of each domain, extracts key semantic features, establishes a dynamic mapping dictionary, and synchronizes the dictionary to all main relay nodes.

[0074] Specifically, it receives semantic tag libraries from various domains (such as lighting, security, and transportation). Each tag library contains the original semantic tags reported by the devices and their associated attributes (such as "temperature-unit: ℃-range: 0-100"). All tags are standardized into a "core word-attribute-value" structure, duplicate tags are removed, and tags with the same semantics but different expressions are merged, achieved through a predefined thesaurus or contextual similarity matching. The standardized multi-domain semantic tag set is... ,in This is the tag library for the i-th domain. Establish semantic mapping relationships between domains (e.g., "strobe alarm" in the lighting domain → "intrusion alarm" in the security domain), and synchronize the dictionary to all master relay nodes.

[0075] S13, the main relay node receives device data and communication environment data within the subgroup, calculates the current topology maintenance energy consumption, and determines whether the topology structure needs to be reconstructed.

[0076] Specifically, the transmission energy consumption for maintaining the current topology is calculated using the Shannon formula extension model, specifically the energy consumption for node i to send data to relay node j. The transmit power of node i (obtained through device data). Data transmission time (determined by data packet size and transmission rate); L represents the efficiency of the transmitting circuit (typically 0.5~0.8), and L represents the data packet length (bits). Let B be the target data rate (bps) and B be the channel bandwidth (Hz); the formula is:

[0077]

[0078] The energy consumption of relay node j in receiving data. The power of the receiving circuit (typically 10~50mW) is given by the following formula:

[0079]

[0080] If data needs to be transmitted through multiple hops, the total energy consumption is the sum of the energy consumption of each hop:

[0081]

[0082] The total energy consumption is calculated by weighting and summing the path energy consumption from all devices to the relay node, and then calculating the transmission energy consumption and reception energy consumption for each path.

[0083] If the current topology maintains energy consumption exceeding a preset threshold (e.g., 20% of the average device power), a reconfiguration is triggered. If a reconfiguration is triggered, a reconfiguration instruction is generated, including a list of candidate relay nodes and a switching time window. If no reconfiguration is triggered, the current topology is maintained, and the current state is recorded for the next round of judgment.

[0084] S14, When cross-domain communication occurs, the sending master relay node retrieves the mapping dictionary, converts the semantic tags and adds cross-domain markers, and sends them to the receiving master relay node through the quantum tunnel protocol; the receiving master relay node parses the cross-domain markers, reverse maps the tags according to the local semantic dictionary, and broadcasts them to devices within the subgroup.

[0085] S15 incorporates device trust scores into priority topology decisions based on historical energy consumption and topology reconfiguration frequency.

[0086] Specifically, historical energy consumption data for all devices is continuously collected and stored, including the transmission energy consumption, reception energy consumption, and total energy consumption of each device before and after each topology reconfiguration. The time point and triggering reason for each topology reconfiguration are recorded. Based on historical energy consumption data, energy consumption characteristic indicators for each device are calculated (e.g., the average energy consumption and energy consumption volatility of the device in the most recent topology cycles are calculated; the average energy consumption reflects the long-term energy consumption level of the device, and the energy consumption volatility reflects the stability of the device's energy consumption; the energy consumption volatility can be obtained by calculating the ratio of the standard deviation to the average value of the energy consumption data). Devices with large energy consumption fluctuations indicate that their energy consumption performance is unstable and should be carefully considered in topology decisions. The relationship between device energy consumption and topology stability is analyzed in conjunction with the topology reconfiguration frequency. The number of times each device participates in topology reconfiguration and the number of topology reconfigurations caused by that device are statistically analyzed. If a device frequently triggers topology reconfiguration, such as link interruption due to abnormal energy consumption, it indicates that the device has a negative impact on topology stability and its priority should be reduced in subsequent decisions.

[0087] A device trust score evaluation model is established, which comprehensively considers the energy consumption characteristics of the device and the factors related to topology reconfiguration. A reasonable weight is set for each indicator, and the trust score of each device is calculated by weighted summation. The higher the trust score, the more reliable the device is in terms of energy consumption and topology stability. The devices are then ranked according to their trust scores, and the devices with high trust scores are listed as the priority for topology reconfiguration decisions.

[0088] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0089] Efficient management is achieved by dividing autonomous subgroups according to geographical location and electing master relay nodes; a dynamic semantic mapping dictionary is established to ensure accurate cross-domain communication; topology energy consumption is calculated to determine whether to reconstruct and optimize energy consumption; and device trust scores are incorporated into topology decision-making to improve topology stability. These measures comprehensively optimize and improve the system in terms of management, communication, energy consumption, and stability.

[0090] Example 3: In Example 2, although methods such as dividing autonomous subgroups by geographical location grid, establishing a dynamic semantic mapping dictionary, calculating topology maintenance energy consumption, and incorporating device trust scores into priority topology decisions optimized the IoT data transmission process to some extent, improving system stability and efficiency, in real-world applications, the sheer number and dense distribution of IoT devices make it highly susceptible to signal conflicts between different subgroups, both spatially and temporally. Example 2 lacks an effective mechanism for handling signal conflicts between subgroups. When conflicts occur, they cannot be resolved promptly and accurately, leading to degraded signal transmission quality, data loss or errors, and ultimately affecting the normal operation of the entire IoT system. Especially with the continuous expansion and increasing complexity of IoT application scenarios, signal conflict problems are becoming increasingly prominent, becoming a key factor restricting further improvements in IoT data transmission performance. To effectively solve the signal conflict problem between subgroups and ensure the stability and reliability of IoT data transmission, further optimization and improvement of existing methods are necessary.

[0091] In some embodiments, step S11, which involves dividing autonomous subgroups according to a geographic location grid, further includes:

[0092] S111, the system continuously monitors the signals sent from each subgroup master relay node, extracts the spatial attribute information in the signals, and generates the phase fingerprint of each subgroup master relay signal.

[0093] Specifically, the signal receiving module in the system remains on, continuously scanning the communication frequency band to capture signals sent from each subgroup master relay node. Multi-channel receiving technology is used to simultaneously receive signals from multiple subgroup master relay nodes.

[0094] For each received subgroup master relay signal, a specialized signal processing algorithm is used for spatial attribute analysis. Utilizing the amplitude and phase differences of the received signal across different antennas, a beamforming algorithm is employed to determine the signal's propagation direction, i.e., the angle of arrival (AOA). Combining the signal's propagation time and known propagation speed, the distance between the signal source and the receiving device is calculated, further aiding in determining the signal's spatial location. Multipath effects during propagation are analyzed, and relevant information about the multipath components is extracted to more accurately describe the signal's spatial propagation characteristics.

[0095] Based on the extracted spatial attribute information of the signal, combined with the signal's frequency, time, and other characteristic parameters, a hash algorithm is used to generate the phase fingerprint (a unique identifier of the signal) of each subgroup master relay signal. During the generation process, key features of the signal are quantized and encoded.

[0096] S112, based on spatial attribute information, uses longitude coordinates, latitude coordinates and altitude as spatial coordinate axes, and time phase window as time axis to construct a four-dimensional spatiotemporal coordinate system for the device.

[0097] Specifically, the longitude, latitude, and altitude information of each subgroup's main relay node are obtained through the built-in Global Positioning System (GPS) module of the IoT device or by communicating with other external positioning systems. For some devices that cannot directly obtain precise geographical locations, a signal strength-based positioning algorithm can be used to estimate the device's geographical location by measuring the signal strength between the device and surrounding known location reference nodes, combined with a signal propagation model.

[0098] Considering the dynamic changes of the signal over time, time is divided into a series of discrete time intervals, each of which serves as a time phase window. The length of the time phase window is set appropriately based on the actual application scenario and signal characteristics. For example, in high-speed mobile IoT scenarios, the time phase window can be set shorter to more accurately track signal changes; in static or low-speed mobile scenarios, the time phase window can be appropriately extended. A unique time identifier is assigned to each time phase window.

[0099] Using the obtained longitude, latitude, and altitude coordinates as spatial coordinate axes and the defined time phase window as the time axis, a four-dimensional spatiotemporal coordinate system is constructed. In this coordinate system, each subgroup master relay signal can be represented by a unique spatiotemporal coordinate point, which contains the signal's spatial location and temporal characteristics.

[0100] S113, when it is detected that two or more subgroup main relay signals have the same coordinates, it is determined that these signals may be in conflict, the time phase difference within different time phase windows is calculated, and orthogonal time slots are dynamically allocated.

[0101] Specifically, the system continuously monitors the signal distribution in the spatiotemporal coordinate system. By comparing the spatiotemporal coordinates of the main relay signals from different subgroups, when two or more signals with the same (longitude, latitude, altitude) coordinates are detected, it is determined that these signals may conflict in spatial location. Further analysis of the arrival of these signals within different time phase windows is conducted; if multiple signals are detected within the same time phase window, the possibility of signal conflict is further confirmed.

[0102] For signals with the same spatial coordinates arriving within the same time phase window, signal processing algorithms are used to analyze their arrival time differences at different time points and calculate the time phase difference. It should be noted that the calculation of the time phase difference needs to consider factors such as signal propagation delay and device processing delay.

[0103] Based on the calculated time phase difference, a dynamic time slot allocation algorithm is used to assign orthogonal time slots to conflicting signals. Orthogonal time slots are time slots allocated at different times, allowing these signals to be transmitted at different times, thus avoiding signal overlap and conflict. The allocation algorithm comprehensively considers factors such as signal priority, service type, and signal strength. For example, service signals with high real-time requirements (such as emergency alarm signals) are given higher priority and allocated better time slot resources first; for signals with weaker signal strength, their time slot allocation is appropriately adjusted to improve the reliability of signal transmission.

[0104] S114 assesses the severity of signal collisions between subgroups and classifies them into different collision levels. For signals with different collision levels, a corresponding phase compensation array is generated to process the collision signals and form a reverse beam.

[0105] Specifically, the severity of signal conflicts between subgroups is comprehensively assessed based on factors such as signal strength, time phase difference, service importance, and historical conflict records. A fuzzy comprehensive evaluation model is used to quantify each influencing factor, and a comprehensive score for the conflict level is obtained through weighted calculation. Based on the score results, conflicts are divided into three levels: low, medium, and high.

[0106] For signals with different levels of conflict, corresponding phase compensation arrays are generated using signal processing theory and algorithms. A phase compensation array is a set of parameters used to adjust the signal phase, aiming to change the signal propagation direction and beamform to achieve spatial cancellation and interference suppression. When generating the phase compensation array, factors such as the signal propagation environment and equipment characteristics are considered, and the array parameters are continuously adjusted through optimization algorithms to achieve the best phase compensation effect.

[0107] The generated phase compensation array processes the conflicting signals, and by adjusting the phase and amplitude of the signals, a reverse beam is formed. The reverse beam is then superimposed on the original conflicting signal in space, achieving spatial cancellation. An adaptive beamforming algorithm employs beamforming technology to dynamically adjust the shape and direction of the reverse beam based on real-time monitoring of signal conflict.

[0108] S115, when an overlap of allocated orthogonal time slots is detected, a virtual time slot partition is created.

[0109] Specifically, the usage of allocated orthogonal time slots is continuously monitored, and time slot overlap is detected through time measurement and synchronization techniques. When two or more signals are detected to be allocated to the same time slot, and the overlap ratio exceeds a preset threshold (10%), a time slot overlap problem is determined to exist.

[0110] Once time slot overlap is detected, the system immediately creates virtual time slot partitions. Virtual time slot partitions further subdivide the original time slots, forming smaller time slot units. The size and number of virtual time slot partitions are determined based on the degree of time slot overlap and signal characteristics. For example, time slots with severe overlap can be subdivided into more smaller time slot units to ensure that each signal receives independent transmission time.

[0111] A unique identifier, i.e., entanglement pair ID, is assigned to each split time slot. Based on the signal priority and service requirements, the split time slots are reassigned to conflicting signals to avoid further aggravation of signal conflicts caused by time slot overlap.

[0112] S116 continuously monitors the signal conflict situation after processing, evaluates the spatial cancellation effect in real time, and obtains the altitude data of relevant equipment if the cancellation effect is poor.

[0113] Specifically, the signal interference situation after the above processing is continuously monitored. The spatial cancellation effect is evaluated in real time by measuring parameters such as the strength and signal-to-noise ratio of the remaining signal after processing. Signal detection and measurement equipment is used to accurately measure the signal. At the same time, data analysis algorithms are used to process the measurement data to calculate quantitative indicators of the spatial cancellation effect, such as cancellation rate and residual interference power.

[0114] The quantitative indicators of spatial cancellation effect obtained from real-time evaluation are compared with preset thresholds. If the cancellation effect reaches the preset threshold, i.e., the remaining signal strength is low and the interference is small, the conflict is considered to be effectively resolved, and the current processing strategy continues. If the cancellation effect is poor, i.e., the remaining signal strength is still high and the interference is large, it is determined that further measures need to be taken, triggering the acquisition of altitude data from relevant equipment.

[0115] For situations where the offsetting effect is poor, altitude data can be obtained from the relevant devices by communicating with the barometer built into the IoT device or with an external altitude measurement system. For some mobile devices, the frequency of altitude data updates needs to be increased to reflect changes in the device's actual location.

[0116] S117, based on the acquired equipment altitude data, establishes an isolation zone in the vertical direction.

[0117] Specifically, based on the acquired equipment altitude data, the vertical distribution of different devices is analyzed. A vertical isolation strategy is formulated by considering signal propagation characteristics and interference. The height range and isolation strength for vertical isolation are determined; for example, different isolation height thresholds are set based on the altitude differences between devices. When the altitude difference between devices exceeds the threshold, vertical isolation measures are implemented.

[0118] Vertical isolation zones are established by adjusting parameters such as the device's transmit power and antenna orientation. For example, for IoT devices located on different floors, adjusting the antenna's elevation angle ensures the signal propagates primarily horizontally, reducing signal interference between floors. Smart antenna technology dynamically adjusts the antenna's beam direction and width based on the device's altitude, achieving more precise vertical isolation. Furthermore, combined with power control algorithms, the transmit power of the devices is adjusted appropriately based on the distance and interference levels between them, further enhancing the vertical isolation effect.

[0119] After establishing a vertical isolation zone, the isolation effect is continuously monitored. The actual effectiveness of the vertical isolation is evaluated by measuring parameters such as signal strength and interference levels inside and outside the isolation zone. Based on the monitoring results, the vertical isolation strategy and parameters are dynamically optimized and adjusted. For example, if the isolation effect is found to be unsatisfactory, the antenna elevation angle or transmit power can be further adjusted to improve the vertical isolation effect and ensure that signal conflicts between subgroups are effectively resolved.

[0120] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0121] By employing multi-dimensional signal processing and intelligent resource allocation, the problem of signal collisions between subgroups was effectively resolved. Conflicting signals were precisely located using a spatiotemporal coordinate system, and efficient interference suppression was achieved through dynamic time slot allocation and phase compensation techniques. Furthermore, the signal transmission environment was further optimized through a vertical isolation strategy. Real-time monitoring and adaptive adjustment throughout the entire process significantly improved the reliability and stability of signal transmission, reduced the probability of signal collisions, and ensured the efficient operation of IoT communication.

[0122] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An Internet of Things data transmission method, characterized by, include: S1. When the system starts, each IoT device group elects a local trust node. The trust node distinguishes the credibility of data sources based on the semantic tag library defined by the trust node and sets weight values. The trust node defines a set of semantic tag libraries to describe the credibility characteristics of data sources based on the characteristics of the devices in the group and business needs. Each semantic tag is assigned a weight value to reflect its influence on the credibility of the data. The trust node is responsible for collecting behavioral data of the devices in the group and updating the trust score of the devices according to the behavior of the devices and preset rules. S2, When a device generates a cross-group request, the system parses the request content and extracts key semantic features, and adds semantic tags according to the type of the source device; S3, the credit node continuously monitors device behavior and dynamically adjusts the communication topology between groups by combining real-time environmental data and trust scores; S4, when there is a cross-group request, it must be verified by the dual signature of the source group and the target group's credit node; S5: Successfully verified requests increase the trust score of the source device; when a fake request is detected, the trust score of the source device decreases and the device is switched to an isolated topology path.

2. The IoT data transmission method as described in claim 1, characterized in that, The simultaneous combination of real-time environmental data and trust score specifically includes: the credit node acquires the behavior data of the device in real time, the environmental data is collected in real time through the environmental sensor deployed in the system, the value of each monitoring index is calculated according to the collected device behavior data, all behavior indexes are normalized and processed, and the behavior evaluation score is obtained after the average weighted processing of all behavior indexes; the adjustment formula is Wherein, is the adjusted behavior evaluation score, is the environmental influence coefficient; is the behavior evaluation score; is the value of the current environmental data, is the average value of the historical environmental data, and are the maximum value and the minimum value of the historical environmental data respectively; a dynamic updating model is used to update the trust score of the device in combination with the trust score of the device and the current adjusted behavior evaluation score; the formula is: Wherein is the updated trust score, is the trust score before updating, is the forgetting factor.

3. The IoT data transmission method as described in claim 2, characterized in that, The semantic label library is defined according to the credit node to distinguish the data source credibility, and specifically comprises: the credit node is responsible for collecting the behavior data of the devices in the group, updating the trust score of the device according to the behavior performance of the device according to the preset rules; wherein the data transmission accuracy score A is the number of times of successfully transmitting accurate data by the device within a period of time and the total number of transmissions , the data transmission accuracy score ; the task completion timeliness score T is that the completion time of each task is defined as , the actual completion time of the device is , the timeliness factor is defined as , the number of tasks completed by the device is m, and the task completion timeliness score , , the timeliness factor corresponding to the ith task; the compliance score C of the rule is that the number of times of violating the rules by the device within a period of time is v, a maximum allowed number of rule violations is set as , the compliance score C of the rule is , when v is greater than or equal to , C=0; the trust score of the device is calculated by comprehensively considering the data transmission accuracy score, the task completion timeliness score and the compliance score of the rule, and the trust score formula of the device is: , , and are weight coefficients for calculating the trust score formula, .

4. The IoT data transmission method of claim 3, wherein, Credit nodes are selected based on the communication capabilities of devices within a group. The top three devices with the strongest communication capabilities are chosen as credit nodes. Credit nodes are responsible for managing trust data within the group, including updating device trust scores and maintaining the semantic tag library.

5. The IoT data transmission method of claim 4, wherein, When the system starts up, each IoT device group elects a local credit node, specifically including: The system receives registration signals from all devices, divides autonomous subgroups according to geographical location grids, and elects a master relay node for each subgroup; Receive semantic tag libraries from each domain, extract key semantic features, establish a dynamic mapping dictionary, and synchronize the dictionary to all master relay nodes; The main relay node receives device data and communication environment data from the subgroup, calculates the current topology maintenance energy consumption, and determines whether the topology structure needs to be reconstructed. During cross-domain communication, the sending master relay node retrieves the mapping dictionary, converts the semantic tags, adds cross-domain markers, and sends them to the receiving master relay node via the quantum tunnel protocol; the receiving master relay node parses the cross-domain markers, reverse-maps the tags according to the local semantic dictionary, and broadcasts them to devices within the subgroup. Trust scores are derived based on the historical energy consumption and topology reconfiguration frequency of each device, and these trust scores are used for priority topology decisions.

6. The IoT data transmission method as described in claim 5, characterized in that, The calculation of the current topology maintenance energy consumption includes: the transmission energy consumption of the current topology maintenance energy consumption adopts a Shannon formula extension model, and energy consumption of the node i sending data to the relay node j is calculated: is the transmission power of the node i, is the data transmission time; is the transmission circuit efficiency, and L is the data packet length; is the target data rate, and B is the channel bandwidth; the formula is: is the energy consumption of the relay node j receiving data, is the receiving circuit power, and the formula is: if the data needs to be transmitted through multiple hops, the total energy consumption is the sum of the energy consumption of each hop, and the formula is: the energy consumption of all device-to-relay node paths is weighted and summed to calculate the energy consumption of each path; the total energy consumption is obtained by summing the transmission energy consumption and the receiving energy consumption.

7. The IoT data transmission method as described in claim 6, characterized in that, The calculation of the total energy consumption for each path, including transmission and reception energy consumption, includes: if the current topology maintenance energy consumption exceeds a preset threshold, then a reconstruction is triggered; if a reconstruction is triggered, a reconstruction instruction is generated, including a list of candidate relay nodes and a switching time window; if no reconstruction is triggered, the current topology is maintained, and the current state is recorded for the next round of judgment.

8. The IoT data transmission method as described in claim 5, characterized in that, The division of autonomous subgroups according to geographic location grids is specifically as follows: The system continuously monitors signals sent from each subgroup master relay node, extracts spatial attribute information from the signals, and generates a phase fingerprint for each subgroup master relay signal; Based on spatial attribute information, a four-dimensional spatiotemporal coordinate system for the device is constructed using longitude, latitude, and altitude as spatial coordinate axes and a time phase window as the time axis. When two or more subgroup main relay signals are detected to have the same coordinates, it is determined that these signals may be in conflict. The time phase difference within different time phase windows is calculated, and orthogonal time slots are dynamically allocated. Assess the severity of signal collisions between subgroups and classify them into different collision levels. For signals with different collision levels, generate corresponding phase compensation arrays to process the collision signals and form reverse beams. When an overlap of allocated orthogonal time slots is detected, a virtual time slot partition is created; Continuously monitor the signal conflict situation after processing, evaluate the spatial cancellation effect in real time, and obtain the altitude data of relevant equipment if the cancellation effect is poor. Based on the acquired equipment altitude data, an isolation zone is established in the vertical direction.

9. The Internet of Things data transmission method as described in claim 8, characterized in that, The construction of the four-dimensional spatiotemporal coordinate system of the device includes: acquiring the longitude, latitude, and altitude information of each subgroup master relay node; considering the dynamic change characteristics of the signal over time, dividing time into a series of discrete time periods, with each time period serving as a time phase window; using the acquired longitude, latitude, and altitude as spatial coordinate axes, and the defined time phase window as the time axis, to construct a four-dimensional spatiotemporal coordinate system.

10. The Internet of Things data transmission method as described in claim 8, characterized in that, The creation of virtual time slot partitions specifically includes: continuously monitoring the usage of allocated orthogonal time slots, detecting whether time slot overlap exists through time measurement and synchronization techniques; when two or more signals are detected to be allocated to the same time slot and the overlap ratio exceeds a preset threshold, it is determined that there is a time slot overlap problem; for signals with the same spatial coordinates that arrive within the same time phase window, analyze their arrival time differences at different time points and calculate the time phase difference; based on the calculated time phase difference, use a dynamic time slot allocation algorithm to allocate orthogonal time slots for conflicting signals.

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