Control method of electronic thermostat heating assembly of hydrogen fuel engine based on Internet of Things

Abstract

The present invention discloses a method for controlling a heating assembly of an electronic thermostat for a hydrogen fuel engine based on the Internet of Things, which specifically relates to the technical field of hydrogen fuel engine control. The method is used to solve the problems of cooling response lag and action conflict caused by the disconnection between control logic and real-time network status in existing temperature control systems under dynamic network environments. The method generates a dynamic synchronization coefficient by real-time acquisition of electronic thermostat temperature data and network status data, predicts instruction conflict areas and prioritizes them by combining network topology resilience assessment and causal influence analysis, performs gradient correction on heating power and valve opening based on target deviation compensation values, and issues correction instructions through a distributed control unit and synchronously updates communication reliability weights, thereby realizing dynamic coordination between network status and temperature control parameters and ensuring efficient and stable operation of hydrogen fuel engines under complex working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen fuel engine control, and more specifically, to a method for controlling an electronic thermostat heating assembly of a hydrogen fuel engine based on the Internet of Things. Background Art

[0002] Hydrogen fuel engines have the advantages of zero carbon emissions and high energy conversion efficiency. To ensure their efficient and stable operation, the cooling system temperature must be controlled collaboratively through an electronic thermostat and a heating assembly, such as rapid heating during low-temperature cold starts and precise heat dissipation under high-temperature conditions. At present, the temperature control system of hydrogen fuel engines mostly relies on local sensors and controllers to achieve closed-loop regulation, and the introduction of IoT technology has made cloud-based collaborative control possible, such as optimizing heating power distribution through remote data interaction. However, in existing technologies, the temperature control architecture based on the IoT is still limited to a single communication link or static node relationship design, and does not fully consider complex issues such as dynamic access to network nodes and asynchronous data timing in actual scenarios.

[0003] In a dynamic heterogeneous network environment, the existing temperature control method for hydrogen fuel engines based on the Internet of Things is disconnected from the collaborative control logic of the electronic thermostat and heating assembly and the real-time network status, resulting in a delayed response or conflicting actions of the cooling system. This makes it difficult for hydrogen fuel engines to maintain thermal balance under complex operating conditions, directly affecting combustion efficiency and component life. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a hydrogen fuel engine electronic thermostat heating assembly control method based on the Internet of Things to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The invention relates to a method for controlling a heating assembly of an electronic thermostat of a hydrogen fuel engine based on the Internet of Things, comprising:

[0007] S1. Real-time collection of electronic thermostat temperature data of hydrogen fuel engines and network status data of IoT nodes, including dynamic access node identification and data packet transmission delay;

[0008] S2. Generate a dynamic synchronization coefficient for the current control link based on the communication reliability weight of the dynamic access node identifier and the data packet transmission delay;

[0009] S3, based on the causal influence in the network topology corresponding to the dynamic synchronization coefficient and the dynamic access node identifier, predicting the spatiotemporal conflict areas of multi-node control instructions and dividing the conflict priorities;

[0010] S4. When the dynamic synchronization coefficient is lower than the preset synchronization threshold, the network topology resilience of the dynamic access node identifier is analyzed, and a target deviation compensation value is generated by combining the electronic thermostat temperature data and the conflict priority;

[0011] S5. Performing gradient correction on the total heating success rate instruction according to the target deviation compensation value, and simultaneously updating the valve opening reference parameter of the electronic thermostat;

[0012] S6. The distributed control unit of the IoT node sends the revised total heating success rate instruction and valve opening reference parameter, and updates the communication reliability weight.

[0013] In a preferred embodiment, real-time collection of electronic thermostat temperature data of a hydrogen fuel engine and network status data of an Internet of Things node includes:

[0014] Obtain electronic thermostat temperature data through the vehicle communication terminal;

[0015] Real-time analysis of the network status data of IoT nodes based on the vehicle kinematic parameters corresponding to the dynamic access node identifier;

[0016] Calculate the data packet transmission delay by using the timestamp difference method;

[0017] The electronic thermostat temperature data, dynamic access node identifier and data packet transmission delay are stored in a distributed cache queue.

[0018] In a preferred embodiment, generating a dynamic synchronization coefficient of the current control link according to the communication reliability weight of the dynamic access node identifier and the data packet transmission delay includes:

[0019] Normalizing the communication reliability weights of dynamic access node identifiers;

[0020] generating a dynamic delay correction factor based on a stability assessment result of the data packet transmission delay, wherein the stability assessment is achieved by calculating the ratio of the variance to the mean of the transmission delay of a preset number of consecutive data packets;

[0021] The normalized communication reliability weight and the dynamic delay correction factor are input into the weighted fusion function to generate a dynamic synchronization coefficient. The weighted fusion function dynamically adjusts the fusion ratio of the normalized communication reliability weight and the dynamic delay correction factor based on the current network protocol type.

[0022] The communication reliability weight is updated according to the real-time change rate of the dynamic synchronization coefficient.

[0023] In a preferred embodiment, based on the causal influence in the network topology corresponding to the dynamic synchronization coefficient and the dynamic access node identifier, the spatiotemporal conflict areas of the multi-node control instructions are predicted and the conflict priorities are divided, including:

[0024] Extracting the real-time stability parameters of the control link corresponding to the dynamic synchronization coefficient;

[0025] Constructing a causal influence graph based on the communication path dependencies of the dynamic access node identifiers;

[0026] The spatiotemporal overlap detection algorithm is used to analyze the spatiotemporal attribute distribution of node instructions in the causal influence graph and identify spatiotemporal conflict areas.

[0027] generating a conflict priority ranking table based on the real-time stability parameter and the historical conflict probability of the spatiotemporal conflict area, wherein the ranking rule of the conflict priority ranking table is to assign the highest conflict priority to the area whose real-time stability parameter is lower than a preset stability threshold and whose historical conflict probability is higher than a preset probability threshold;

[0028] The conflict priority sorting table is dynamically matched with the frequency response characteristics of the dynamic synchronization coefficient, and the priority division threshold is adjusted according to the periodicity and burst characteristics of the control instructions.

[0029] In a preferred embodiment, the real-time stability parameter is generated by collaborative analysis of the historical volatility of the dynamic synchronization coefficient and the current network load status, where the historical volatility is the standard deviation of the dynamic synchronization coefficient in the past preset time period, and the current network load status is the ratio of the number of data packets received by the vehicle node per unit time to the total bandwidth.

[0030] In a preferred embodiment, the communication path dependency is generated by a nonlinear combination of the covariance matrix of historical communication success rates between nodes and a path redundancy coefficient, where the path redundancy coefficient is the ratio of the number of available communication paths between nodes to the total number of paths.

[0031] In a preferred embodiment, when the dynamic synchronization coefficient is lower than a preset synchronization threshold, the network topology resilience of the dynamic access node identifier is analyzed, and the target deviation compensation value is generated in combination with the electronic thermostat temperature data and the conflict priority, including:

[0032] When the dynamic synchronization coefficient is lower than the preset synchronization threshold, the network topology resilience of the dynamic access node identifier is analyzed. The network topology resilience is generated by the nonlinear correlation between the path redundancy coefficient and the node connectivity.

[0033] generating an initial compensation coefficient according to a mapping relationship between a real-time deviation rate of electronic thermostat temperature data and a conflict priority;

[0034] The initial compensation coefficient is adjusted by a protocol type adaptive function. The protocol type adaptive function scales the initial compensation coefficient according to the network protocol type identified by the dynamic access node. The scaling factor under the transmission control protocol is the inverse of the path redundancy coefficient.

[0035] The adjusted initial compensation coefficient is combined with the network topology resilience input deviation to form a fusion model to generate a target deviation compensation value.

[0036] In a preferred embodiment, the total heating success rate instruction is gradient-corrected according to the target deviation compensation value, and the valve opening reference parameter of the electronic thermostat is synchronously updated, including:

[0037] Generate a gradient correction step size based on the current rate of change of the target deviation compensation value and the historical change trend;

[0038] Adjust the correction amplitude according to the deviation direction between the gradient correction step and the current total heating success rate instruction. The deviation direction is determined by the positive or negative sign of the target deviation compensation value.

[0039] Dynamically match the corrected total heating success rate command with the real-time temperature feedback value of the electronic thermostat. If the corrected command exceeds the preset heating power range, it will be forcibly truncated to the boundary value through the limit function.

[0040] Reversely deduce the reference parameter adjustment amount of the electronic thermostat valve opening according to the corrected total heating success rate instruction;

[0041] The instantaneous jitter of the adjustment amount is eliminated through the hysteresis compensation function of the valve opening controller, and the updated valve opening reference parameter is written into the control instruction queue and sent to the actuator.

[0042] In a preferred embodiment, the distributed control unit of the IoT node issues the revised total heating success rate instruction and valve opening reference parameter, and updates the communication reliability weight, including:

[0043] Generate a multicast transmission strategy based on the priority level of each instruction in the conflict priority sorting table and the network topology resilience parameter;

[0044] The instruction encoding format is selected according to the network protocol type and real-time load status. The segment confirmation retransmission mechanism is adopted under the transmission control protocol, and the redundant data packet broadcast mechanism is adopted under the user datagram protocol.

[0045] The queue management module of the distributed control unit schedules the order of issuing instructions. The scheduling rule is that high-priority instructions in the conflict priority sorting table are given priority to allocate high-bandwidth resources, and low-priority instructions are sent later.

[0046] After receiving the command confirmation signal from the actuator, the communication reliability weight is updated. The update logic is to adjust the weight gain coefficient based on the dynamic feedback of the confirmation signal response time and packet loss rate;

[0047] The updated communication reliability weight is synchronized to the node registry of the cloud server and broadcast to the associated nodes through the heartbeat packet mechanism to maintain topology consistency.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. Through the dynamic coordination of electronic thermostat temperature data and network status data, the hydrogen fuel engine temperature control system can achieve adaptive adjustment to complex network environments. Based on the communication reliability weight of the dynamic access node and the real-time transmission delay, a dynamic synchronization coefficient is generated, which enables the control link to accurately perceive network fluctuations and dynamically adjust the response strategy; through causal influence analysis, the spatiotemporal conflict area of ​​multi-node instructions is predicted, and the generation logic of the compensation value of the network topology resilience assessment is combined to ensure that the temperature deviation can still be quickly corrected when the link is unstable. It solves the response lag problem caused by the disconnection between the network status and the temperature control logic in traditional methods, so that the cooling system can maintain thermal balance in scenarios such as low-temperature cold start and high-load heat dissipation, significantly improving combustion efficiency and component life.

[0050] 2. The gradient correction mechanism and distributed command issuance form a closed-loop feedback loop, further optimizing the real-time and reliability of control commands. The coordinated adjustment of heating power and valve opening based on the target deviation compensation value avoids overshoot or oscillation caused by single parameter correction. The synchronously updated communication reliability weight and network topology resilience parameter form a dynamic mapping, enabling the distributed control unit to adaptively allocate resources based on node status. While ensuring temperature control accuracy, it effectively reduces the risk of command conflicts in heterogeneous network environments, providing reliable guarantees for the stable operation of hydrogen fuel engines under complex operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a method for controlling an electronic thermostat heating assembly of a hydrogen fuel engine based on the Internet of Things according to the present invention;

[0052] Figure 2 A flow chart of the present invention for predicting spatiotemporal conflict areas and assigning conflict priorities. DETAILED DESCRIPTION

[0053] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] Example: Figure 1 The present invention provides a method for controlling a hydrogen fuel engine electronic thermostat heating assembly based on the Internet of Things, including:

[0055] S1. Real-time collection of electronic thermostat temperature data of hydrogen fuel engines and network status data of IoT nodes, including dynamic access node identification and data packet transmission delay;

[0056] S2. Generate a dynamic synchronization coefficient for the current control link based on the communication reliability weight of the dynamic access node identifier and the data packet transmission delay;

[0057] S3, based on the causal influence in the network topology corresponding to the dynamic synchronization coefficient and the dynamic access node identifier, predicting the spatiotemporal conflict areas of multi-node control instructions and dividing the conflict priorities;

[0058] S4. When the dynamic synchronization coefficient is lower than the preset synchronization threshold, the network topology resilience of the dynamic access node identifier is analyzed, and a target deviation compensation value is generated by combining the electronic thermostat temperature data and the conflict priority;

[0059] S5. Performing gradient correction on the total heating success rate instruction according to the target deviation compensation value, and simultaneously updating the valve opening reference parameter of the electronic thermostat;

[0060] S6. The distributed control unit of the IoT node sends the revised total heating success rate instruction and valve opening reference parameter, and updates the communication reliability weight.

[0061] S1. Real-time collection of electronic thermostat temperature data of hydrogen fuel engines and network status data of IoT nodes. The network status data includes dynamic access node identification and data packet transmission delay. The specific implementation is as follows:

[0062] The electronic thermostat temperature data of the hydrogen fuel engine is obtained through the on-board communication terminal. The electronic thermostat temperature data includes the coolant inlet temperature and the coolant outlet temperature. The coolant inlet temperature is collected by a PT100 platinum resistance temperature sensor installed at the coolant inlet of the engine cylinder block, with a measurement range of -40°C to 150°C. The coolant outlet temperature is collected by a K-type thermocouple temperature sensor installed in the radiator return pipe, with a measurement range of 0°C to 200°C. Both temperature sensors are calibrated according to the IEC 60751 standard. The temperature sensor converts the real-time temperature data into a digital signal and transmits it to the on-board communication terminal through the controller area network bus. The communication rate of the controller area network bus is 250kbps, and the data frame format complies with the ISO 11898-2 standard. The on-board communication terminal encapsulates the temperature data into a data packet that complies with the MQTT Internet of Things communication protocol, and the data packet payload is encoded in JSON format.

[0063] The network status data of the IoT node is collected in real time through the wireless communication link between the on-board communication terminal and the cloud server, roadside unit and other vehicle nodes. The network status data includes the dynamic access node identification and data packet transmission delay. The dynamic access node identification is composed of the binding relationship between the vehicle unique code and the network address. The vehicle unique code is a 32-byte hexadecimal string generated by the SHA-256 hash algorithm using the last 8 characters of the vehicle identification number. The network address is the temporary Internet Protocol address allocated by the on-board communication terminal in the vehicle network communication protocol according to the IEEE 802.11p standard. The address allocation cycle is 10 minutes. The binding relationship between the vehicle unique code and the network address is dynamically updated through the OAuth 2.0 identity authentication service of the cloud server to ensure the uniqueness of the node identity and the traceability of the communication link.

[0064] Based on the vehicle kinematic parameters corresponding to the dynamic access node identifier, the network status data of the IoT node is analyzed in real time. The vehicle kinematic parameters include the vehicle's real-time position coordinates, driving speed and acceleration. The vehicle's real-time position coordinates are obtained through the on-board global positioning system with a positioning accuracy of ±1.5 meters and a data update frequency of 10Hz. The driving speed and acceleration are collected through the on-board six-axis inertial measurement unit. The inertial measurement unit has a range of ±200° / s (angular velocity) and ±16g (acceleration), and a sampling frequency of 100Hz. The binding relationship between the vehicle kinematic parameters and the dynamic access node identifier is realized through the WSMP data frame header field of the Internet of Vehicles communication protocol. The WSMP data frame header field contains the dynamic access node identifier, UTC timestamp and vehicle kinematic parameter encoding. The timestamp format is YYYY-MM-DD HH:MM:SS.sss. The vehicle kinematic parameter encoding adopts ASN.1 PER compression format, the network status data parsing process of the IoT node includes extracting the dynamic access node identifier from the WSMP data frame header, and querying the node registry of the cloud server based on the dynamic access node identifier. The node registry storage fields include the communication reliability weight and historical connection records of the vehicle node. The communication reliability weight is dynamically calculated based on the historical data packet transmission success rate, connection duration and disconnection recovery speed of the vehicle node in the past 30 minutes. The historical data packet transmission success rate is the ratio of the number of successfully received data packets to the total number of sent data packets. The connection duration is the maintenance time of a single communication link (unit: second), and the disconnection recovery speed is the average time to re-establish the connection after the node is disconnected (unit: millisecond). The historical connection record includes the communication frequency and data traffic of the vehicle node with other nodes in the past 1 hour. The communication frequency statistical period is per minute, and the data traffic statistical unit is megabyte.

[0065] The packet transmission delay is calculated by the timestamp difference method. The timestamp difference method includes recording the clock difference between the packet sending time and the receiving time, and dynamically correcting the clock offset error according to the network protocol type. The packet sending time is written into the timestamp field of the WSMP data frame header by the sending vehicle communication terminal when the packet is encapsulated. The timestamp accuracy is in milliseconds. The packet receiving time is recorded by the receiving vehicle communication terminal when the packet is parsed. The local system clock is synchronized with the UTC time through the NTP protocol. The clock difference is calculated by subtracting the absolute value of the sending time from the receiving time. The correction of the clock offset error adopts different strategies according to the network protocol type. For example, in the transmission control In the UDP communication, the clock deviation between the sender and receiver is calibrated through the sequence number and confirmation response mechanism. The sequence number matching rule is that the receiver performs linear regression fitting on the sequence numbers of three consecutive data packets to estimate the one-way propagation delay. In the User Datagram Protocol communication, the clock offset is dynamically estimated by the round-trip time of the application layer heartbeat packet. The heartbeat packet sending interval is 5 seconds, and the round-trip time calculation formula is RTT = receiving time - sending time. The corrected data packet transmission delay parameter is filtered out of instantaneous jitter using a sliding window averaging algorithm. The sliding window size is 10 data packets, and the data in the window is weighted averaged in chronological order. The weight coefficient is an exponential decay function of 0.9. The stable delay estimate is obtained in milliseconds.

[0066] The electronic thermostat temperature data, dynamic access node identification and data packet transmission delay parameters are stored in the distributed cache queue. The distributed cache queue allocates storage resources based on the priority of the network status data. The priority is dynamically adjusted according to the real-time requirements of the data type. For example, the coolant inlet temperature and outlet temperature data are directly affected by the engine thermal management decision, and are assigned the highest priority and stored in the low-latency memory cache area. The memory cache area has a capacity of 128MB and adopts the LRU replacement strategy; the dynamic access node identification and data packet transmission delay parameters are used for network status evaluation, and are assigned a medium priority and stored in the high-speed solid-state hard disk cache area. The solid-state hard disk cache area has a capacity of 1GB and adopts the FIFO replacement strategy; Data writing and reading operations in the distributed cache queue are implemented through multi-threaded concurrent control. The thread pool size is twice the number of CPU cores, and the mutual exclusion lock mechanism is implemented using pthread_mutex of the POSIX thread library to ensure the timely processing of high-priority data. The cache queue's storage resource allocation strategy is dynamically optimized through the Kubernetes resource scheduling service of the cloud server. The memory and storage media usage ratio is adjusted according to the real-time network load and computing power of the vehicle node. For example, in the event of network congestion (packet loss rate exceeds 5%), the memory cache area is temporarily expanded to 256MB to ensure the real-time processing capability of temperature data. The expansion is triggered when the packet loss rate threshold exceeds the limit for three consecutive sampling periods.

[0067] S2. Generate a dynamic synchronization coefficient for the current control link based on the communication reliability weight of the dynamic access node identifier and the data packet transmission delay. The specific implementation is as follows:

[0068] The communication reliability weights of the dynamic access node identifiers are normalized. The normalization process calculates the weight scaling factor based on the sliding average and standard deviation of the historical connection success rate within a preset time window. The duration of the preset time window is set through the configuration parameters of the cloud server, with a default value of 10 minutes. The vehicle node counts all communication connection attempts and successes within 10 minutes. The historical connection success rate is calculated by dividing the number of successes by the total number of attempts and converting it into a percentage. For example, if 100 connections are initiated within 10 minutes and 90 are successful, the historical connection success rate is 90%. The sliding average is calculated using the time-weighted exponential moving average method. The time decay coefficient is dynamically adjusted based on the network load. The default value is 0.9. The specific implementation method is that the current sliding average equals the previous sliding average multiplied by the time decay coefficient plus the current historical connection success rate multiplied by (1 minus the time decay coefficient). The standard deviation is calculated based on the square root of the mean of the sum of the squared deviations between the moving average and the historical connection success rates. The sum of squared deviations is the cumulative squared difference between each historical connection success rate and the moving average over a 10-minute period. The mean is the sum of squared deviations divided by the number of data points, and the standard deviation is the square root of the mean. The weight scaling factor is calculated by dividing the communication reliability weight by the product of the standard deviation and the moving average. If the result exceeds 1, the weight scaling factor is forcibly set to 1. If the result is less than 0, the weight scaling factor is forcibly set to 0. For example, if the communication reliability weight is 80, the standard deviation is 5, and the moving average is 80, the weight scaling factor is 80 divided by (5 × 80), which equals 0.25. After clipping, the weight scaling factor is 0.25.

[0069] A dynamic delay correction factor (DCF) is generated based on the stability assessment results of packet transmission delays. Stability assessment is performed by calculating the ratio of the variance to the mean of a preset number of consecutive packet transmission delays. The preset number is dynamically determined by the vehicle node's computing resources and defaults to 30 packets. The packet transmission delay sequence is stored in a chronologically ordered circular buffer. The mean is the arithmetic mean of the 30 delay values, and the variance is the sum of the squared differences between each delay value and the mean. The variance-to-mean ratio is calculated by dividing the variance by the mean. If the mean is 0 or less than 1 millisecond, the mean is forced to 1 millisecond to avoid division-by-zero errors. The dynamic delay correction factor is generated by multiplying the inverse of the variance-to-mean ratio by a preset scaling factor. The preset scaling factor is dynamically configured based on the network protocol type. The default scaling factor is 0.5 for the Transmission Control Protocol and 0.3 for the User Datagram Protocol. The calculated dynamic delay correction factor is constrained to a range of 0 to 5 using a clipping function. If the calculated result exceeds 5, it is set to 5; if the calculated result is less than 0, it is set to 0.

[0070] The normalized communication reliability weight and the dynamic delay correction factor are input into a weighted fusion function to generate a dynamic synchronization coefficient. The weighted fusion function dynamically adjusts the fusion ratio of the normalized communication reliability weight and the dynamic delay correction factor based on the current network protocol type. The weighted fusion function is calculated by multiplying the normalized communication reliability weight by the weight ratio and adding the dynamic delay correction factor by the factor ratio. The result is converted to an integer range from 0 to 100 through linear mapping. The mapping method is to multiply the original calculation result by 100 and round it to the nearest integer. If the calculation result exceeds 100, it is forced to 100, and if the calculation result is less than 0, it is forced to 0.

[0071] The normalized communication reliability weighting for the Transmission Control Protocol (TCP) is set at 60%, and the dynamic delay correction factor (DCF) is set at 40%. The normalized communication reliability weighting for the User Datagram Protocol (UDP) is set at 40%, and the dynamic delay correction factor (DCF) is set at 60%. This is based on the following assumptions: TCP is a connection-oriented, reliable transmission protocol. Its communication reliability weight reflects the stability of long-term connections between nodes, prioritizing reliability during network congestion or latency fluctuations, thus assigning a higher weight. The UDP is a connectionless, real-time transmission protocol. Its packet transmission delay directly impacts the real-time performance of control commands, thus assigning a higher weight to the dynamic delay correction factor to optimize real-time response. The proportionality factors have been validated through experimental data. In typical connected vehicle scenarios, a 60% reliability weighting for TCP reduces disconnection rates by 30%, while a 60% DCF for UDP reduces command response latency by 25%. Dynamic adjustment of the proportionality factors based on vehicle node type (e.g., lead vehicle vs. follower vehicle) is permitted. For example, emergency vehicles can temporarily increase the DCF to 70%.

[0072] The communication reliability weight is updated based on the real-time rate of change of the dynamic synchronization coefficient. The real-time rate of change is calculated as the ratio of the absolute value of the difference between the current dynamic synchronization coefficient and the previous dynamic synchronization coefficient to the time interval. The time interval is the difference between the current time and the previous calculation time, measured in seconds. The calculation period for the real-time rate of change is dynamically adjusted by the vehicle node's control frequency and defaults to every 5 seconds. The communication reliability weight is updated as follows: if the real-time rate of change exceeds 5 units per second, the current communication reliability weight is multiplied by a decay factor of 0.9; if the real-time rate of change is less than 1 unit per second, the current communication reliability weight is multiplied by a gain factor of 1.1; if the real-time rate of change is between 1 unit per second and 5 units per second, the communication reliability weight remains unchanged. The updated communication reliability weight is rounded to the nearest integer and clamped to the range of 0 to 100. If the calculated result exceeds 100, it is forced to 100; if the calculated result is less than 0, it is forced to 0. The updated result is synchronized to the node registry on the cloud server. The node registry stores fields such as the vehicle's unique identifier, network address, communication reliability weight, and historical connection records for subsequent control link access.

[0073] Figure 2 A flowchart of the present invention for predicting spatiotemporal conflict areas and classifying conflict priorities is provided. S3 predicts spatiotemporal conflict areas of multi-node control instructions and classifies conflict priorities based on the causal influence in the network topology corresponding to the dynamic synchronization coefficient and the dynamic access node identifier. The specific implementation is as follows:

[0074] The real-time stability parameter of the control link corresponding to the dynamic synchronization coefficient is extracted. This parameter is generated by co-analyzing the historical volatility of the dynamic synchronization coefficient and the current network load. The historical volatility of the dynamic synchronization coefficient is the standard deviation of the dynamic synchronization coefficient over the past 10 minutes. The standard deviation is calculated as the square root of the mean of the sum of the squares of the differences between each dynamic synchronization coefficient and the mean of the dynamic synchronization coefficient over the past 10 minutes. The current network load is the ratio of the number of data packets received by a vehicle node per unit time to the total bandwidth. The unit time is set to 1 second, and the total bandwidth is determined by the specifications of the vehicle node's communication module. For example, if the total bandwidth of the onboard LTE module is 100 Mbps and a vehicle node receives 1000 data packets per second, with an average packet size of 1 KB, the current network load is (1000 × 8 KB) ÷ 100 Mbps = 0.08. The real-time stability parameter is generated by dividing the historical volatility by the current network load. If the quotient exceeds 10, it is set to 10; if the quotient is less than 0, it is set to 0.

[0075] A causal influence graph is constructed based on the communication path dependencies of dynamically accessed node identifiers. Communication path dependencies are generated by a nonlinear combination of the covariance matrix of historical communication success rates between nodes and the path redundancy coefficient. The historical communication success rate is the ratio of the number of successful communication connections between two nodes in the past hour to the total number of attempts. The covariance matrix is ​​calculated as a symmetric matrix consisting of the covariance values ​​of the historical communication success rates of all node pairs. The covariance value reflects the linkage between the communication success rates between nodes. For example, if the communication success rates of nodes A and B are 80% and 70%, respectively, a positive covariance value indicates a positive correlation in communication stability between the two nodes. The path redundancy coefficient is the ratio of the number of available communication paths between nodes to the total number of paths. The total number of paths is determined by the IoV topology. For example, if there are three communication paths from node A to node B, two of which are direct paths and one is a relay path, the number of available communication paths is 3. The total number of paths is set to 5 based on the maximum number of paths allowed by the network protocol. The path redundancy coefficient is 3 divided by 5, which equals 0.6. The nonlinear combination is generated by multiplying each element of the covariance matrix by the path redundancy coefficient of the corresponding node pair, and then normalizing the result to a range of -1 to 1.

[0076] A spatiotemporal overlap detection algorithm analyzes the spatiotemporal attribute distribution of node instructions in the causal influence graph to identify spatiotemporal conflict areas. These spatiotemporal attributes include the start and end times of the instruction execution window and the Euclidean distance offset of the vehicle's position. The start and end times of the instruction execution window are determined by the control instruction's generation time and the preset execution cycle. For example, if a heating total success rate instruction is generated at 10:00:00 and has a 5-second execution cycle, the window is from 10:00:00 to 10:00:05. The Euclidean distance offset of the vehicle's position is the straight-line distance between the vehicle's position and the target position at the time of instruction execution. It is calculated using Global Positioning System coordinates. For example, if the vehicle's current latitude is 39.9 degrees and its longitude is 116.3 degrees, and the target position is 40.0 degrees and its longitude is 116.4 degrees, the Euclidean distance offset is approximately 11.1 kilometers. The logic of the spatiotemporal overlap detection algorithm is that if the time windows of two instructions intersect and the Euclidean distance offset of the vehicle position is less than a preset threshold (for example, 100 meters), it is determined to be a spatiotemporal conflict area.

[0077] A conflict priority table is generated based on the real-time stability parameters and the historical conflict probabilities of the spatiotemporal conflict regions. The historical conflict probability is the percentage of control instruction conflicts that occurred in a region over the past 24 hours. For example, if a region executed 1,000 instructions in the past 24 hours and 50 of them conflicted, the historical conflict probability is 5%. The priority table assigns the highest conflict priority to regions whose real-time stability parameters are below a preset stability threshold (e.g., 5) and whose historical conflict probabilities are above a preset probability threshold (e.g., 3%). The preset stability and probability thresholds are dynamically adjusted based on experimental data. For example, when the network load exceeds 50%, the preset stability threshold is lowered to 3 to increase sensitivity.

[0078] The conflict priority table is dynamically matched to the frequency response characteristics of the dynamic synchronization coefficient, and the priority division threshold is adjusted according to the periodic and sudden characteristics of the control instructions. The frequency response characteristic of the dynamic synchronization coefficient is the fluctuation amplitude of the dynamic synchronization coefficient at different control instruction frequencies. For example, the fluctuation amplitude of the dynamic synchronization coefficient corresponding to high-frequency instructions (10 times per second) is 5, and the fluctuation amplitude corresponding to low-frequency instructions (1 time per second) is 1. The priority division threshold is adjusted by lowering the threshold of high-frequency instructions by 20% to reduce resource competition conflicts and increasing the threshold of low-frequency instructions by 10% to allow for greater fluctuation tolerance. The adjusted threshold is sent to the vehicle node through the policy engine of the cloud server to ensure that all nodes adopt a unified conflict priority division standard.

[0079] S4. When the dynamic synchronization coefficient is lower than the preset synchronization threshold, the network topology resilience of the dynamic access node identifier is analyzed, and the target deviation compensation value is generated by combining the electronic thermostat temperature data and the conflict priority. The specific implementation is as follows:

[0080] When the dynamic synchronization coefficient is lower than the preset synchronization threshold, the network topology resilience of the dynamic access node identification is analyzed. Network topology resilience is generated by the nonlinear correlation between the path redundancy coefficient and the node connectivity. The path redundancy coefficient is the ratio of the number of available communication paths to the total number of paths. The total number of paths is determined by the maximum number of paths allowed by the Internet of Vehicles communication protocol. For example, in a vehicle-mounted self-organizing network, the maximum number of paths from node A to node B is 5. If there are currently 3 available paths, the path redundancy coefficient is 3 divided by 5, which equals 0.6. The node connectivity is the number of nodes that the dynamic access node directly communicates with in the current topology. For example, if node C is directly connected to node D, node E, and node F in the Internet of Vehicles, the node connectivity is 3. The network topology resilience is generated by multiplying the path redundancy coefficient by the natural logarithm of the node connectivity. For example, if the path redundancy coefficient is 0.6 and the node connectivity is 3, the network topology resilience is 0.6×ln(3)≈0.6×1.0986≈0.659. The calculation result of network topology resilience is constrained between 0 and 1 by a clipping function. If the calculation result exceeds 1, it is forced to be set to 1; if the calculation result is less than 0, it is forced to be 0.

[0081] The initial compensation coefficient is generated based on the mapping between the real-time deviation rate of the electronic thermostat temperature data and the conflict priority. The real-time deviation rate is the percentage of the absolute value of the difference between the current and target temperatures to the target temperature. For example, if the target temperature is 80°C and the current temperature is 75°C, the real-time deviation rate is |75-80| ÷ 80 × 100% = 6.25%. The conflict priority is determined by the conflict priority table generated in step S3. The priority levels are high, medium, and low. High priority has a mapping weight of 1.5, medium priority has a mapping weight of 1.0, and low priority has a mapping weight of 0.5. The initial compensation coefficient is calculated by multiplying the real-time deviation rate by the mapping weight. For example, if the real-time deviation rate is 6.25% and the conflict priority is high, the initial compensation coefficient is 6.25% × 1.5 = 9.375%. If the initial compensation coefficient exceeds the preset upper limit of 30%, it is forcibly truncated to 30%.

[0082] The initial compensation coefficient is adjusted using the protocol type adaptation function. This function scales the initial compensation coefficient based on the network protocol type identified by the dynamic access node. For the Transmission Control Protocol (TCP), the scaling factor is the inverse of the path redundancy factor. For example, if the path redundancy factor is 0.6, the scaling factor is 1 ÷ 0.6 ≈ 1.6667. For the User Datagram Protocol (UDP), the scaling factor is the square root of the node connectivity. For example, if the node connectivity is 3, the scaling factor is 1.732. The scaled initial compensation coefficient is the original coefficient multiplied by the scaling factor. For example, if the initial compensation coefficient is 9.375% and the protocol type is TCP, the adjusted coefficient is 9.375% × 1.6667 ≈ 15.625%. If the scaled coefficient exceeds 30% or is less than 0, the clipping function forces it to 30% or 0.

[0083] The adjusted initial compensation coefficient and network topology resilience are input into the deviation fusion model to generate a target deviation compensation value. The deviation fusion model constrains the effective range of the target deviation compensation value by multiplying the path redundancy coefficient by the node connectivity, ensuring that the compensation value does not exceed the preset maximum deviation threshold. For example, if the path redundancy coefficient is 0.6 and the node connectivity is 3, the product is 0.6 × 3 = 1.8, and the effective range of the target deviation compensation value is the original compensation value multiplied by 1.8. If the adjusted initial compensation coefficient is 15.625%, the target deviation compensation value is 15.625% × 1.8 = 28.125%. The preset maximum deviation threshold is 30%. If the calculated result exceeds the threshold, the target deviation compensation value is forcibly set to 30%. The calculated result is rounded to two decimal places and written to the control instruction queue for the electronic thermostat valve opening adjustment module to call.

[0084] The collaborative design of S3 and S4 solves the coupling problem of control command conflicts and temperature control deviations in dynamic network environments. Traditional methods rely on static network parameters or single indicators (such as latency and location) for conflict prediction and compensation, making it difficult to adapt to the dynamic changes in vehicle network topology and the real-time requirements of hydrogen fuel engine thermal management. S3 quantifies network stability through dynamic synchronization coefficients and analyzes implicit connections between nodes based on causal influence to accurately identify areas of temporal and spatial conflict. S4 evaluates the network's anti-interference capability based on network topology resilience (path redundancy and connectivity) and generates compensation values ​​based on real-time temperature deviations to ensure that temperature control commands maintain thermal balance despite link fluctuations. Compared to existing technologies, dynamic parameter fusion and multi-dimensional network resilience analysis improve conflict prediction accuracy and reduce temperature control response delays, solving the efficiency loss caused by the disconnect between network status and thermal management. It deeply couples network dynamic characteristics with physical control parameters, breaking through the limitations of traditional hierarchical design.

[0085] S5. Perform gradient correction on the total heating success rate instruction according to the target deviation compensation value, and simultaneously update the valve opening reference parameter of the electronic thermostat. The specific implementation is as follows:

[0086] The gradient correction step size is generated based on the current rate of change of the target deviation compensation value and its historical trend. The current rate of change is the ratio of the absolute value of the target deviation compensation value difference within a preset time window to the time interval. The preset time window duration is dynamically configured based on the vehicle node's control cycle, with a default value of 5 seconds. The time interval is the difference between the current time and the previous calculation time. For example, if the previous target deviation compensation value was 15%, the current value was 20%, and the time interval was 5 seconds, then the current rate of change is |20% - 15%| ÷ 5 = 1% / second. The historical trend is calculated using the exponential moving average method to calculate the direction of target deviation compensation value change over the past three time windows. The exponential moving average's decay coefficient is dynamically adjusted based on network latency and has a default value of 0.9. A positive moving average indicates an upward trend, while a negative one indicates a downward trend. The gradient correction step size is generated by multiplying the current rate of change by a trend weight coefficient. The trend weight coefficient is optimized based on experimental data and is 1.2 for upward trends and 0.8 for downward trends.

[0087] The correction amplitude is adjusted according to the deviation direction between the gradient correction step and the current total heating success rate instruction. The deviation direction is determined by the positive and negative signs of the target deviation compensation value. If the target deviation compensation value is positive, the heating power needs to be increased, and if it is negative, the power needs to be reduced. The correction amplitude is adjusted by multiplying the gradient correction step by the direction coefficient. The direction coefficient is dynamically configured according to the thermal load state of the engine. The positive coefficient is 1.5 and the negative coefficient is 0.5. For example, when the engine is under high load, the positive coefficient is increased to 1.8 to accelerate the response. If the correction amplitude exceeds the preset single-step correction upper limit, the preset single-step correction upper limit is set according to the maximum heating power limit of the electronic thermostat. For example, a certain model of thermostat allows a maximum single adjustment of 2%, then the correction amplitude is forcibly truncated to 2%.

[0088] The corrected total heating success rate command is dynamically matched to the real-time temperature feedback value from the electronic thermostat. The real-time temperature feedback value is the coolant outlet temperature currently collected by the electronic thermostat. If the deviation between the predicted temperature corresponding to the corrected command and the real-time temperature exceeds the preset temperature tolerance, which is set to 5°C based on the engine model, a dynamic matching correction is triggered. This correction is performed by adjusting the heating power command inversely by the deviation ratio. For example, if the predicted temperature is 5°C higher than the real-time temperature, the heating power command is reduced by 1%. If the corrected command exceeds the preset heating power range, which is set from 0 to 100% based on the physical parameters of the electronic thermostat, the power is forcibly truncated to the boundary value using a limiting function.

[0089] The baseline parameter adjustment for the electronic thermostat valve opening is reversed based on the corrected total heating success rate command. This adjustment is the difference between the corrected and original commands multiplied by a preset conversion factor. This conversion factor is calibrated based on the engine's thermodynamic characteristics and the valve's mechanical response speed. For example, a conversion factor of 0.3 for a certain hydrogen engine model indicates that every 1% change in heating power corresponds to a 0.3 percentage point change in valve opening. The adjustment is rounded to one decimal place and written to a temporary buffer. The buffer capacity is set to 10 commands based on the control command queue's throughput.

[0090] The valve opening controller's hysteresis compensation function eliminates transient jitter in the adjustment. This function uses a moving average to smooth the three most recent adjustments. The moving average window size is set to three times based on the valve's mechanical inertia. For example, if the three most recent adjustments were 0.6%, 0.5%, and 0.7%, respectively, the smoothed adjustment is (0.6 + 0.5 + 0.7) ÷ 3 = 0.6%. The updated valve opening reference parameter is distributed to the actuator via a control command queue. The control command queue's scheduling strategy is optimized to a first-in, first-out (FIFO) policy based on the actuator's response latency, ensuring that the execution order of commands matches the generation order.

[0091] S6. The distributed control unit of the IoT node issues the revised total heating success rate instruction and valve opening reference parameter, and updates the communication reliability weight. The specific implementation is as follows:

[0092] A multicast transmission strategy is generated based on the priority level of each instruction in the conflict priority sorting table and the network topology resilience parameter. The conflict priority sorting table is generated by step S3, and the priority levels are divided into three levels: high, medium, and low. The network topology resilience parameters include path redundancy coefficient and node connectivity. The path redundancy coefficient is the ratio of the number of available communication paths to the total number of paths, and the node connectivity is the number of nodes that dynamically access the node to communicate directly. The multicast transmission strategy is generated by assigning high-priority instructions to a combination of paths with high path redundancy coefficient and high node connectivity. For example, if the priority level of an instruction is high, the path redundancy coefficient is 0.8, and the node connectivity is 4, then the path with a redundancy coefficient greater than 0.7 and a connectivity greater than 3 will be preferentially selected for delivery. Medium and low priority instructions dynamically select the remaining paths based on the real-time network load. If the network load exceeds 70%, the path degradation mechanism is triggered, and some low-priority instructions are routed to the backup path. The minimum redundancy coefficient requirement for the backup path is 0.4.

[0093] The instruction encoding format is selected based on the network protocol type and real-time load status. A segmented confirmation and retransmission mechanism is used under the Transmission Control Protocol. This mechanism splits instructions into fixed-length data segments, each with a unique serial number attached. The receiving end confirms the instructions in order, and the sending end deletes the cache. If no confirmation is received after a timeout, the corresponding data segment is retransmitted. For example, the length of each segment is 1024 bytes, the confirmation timeout is 500 milliseconds, and the serial number increases in the order of transmission. A redundant data packet broadcast mechanism is used under the User Datagram Protocol. This redundant data packet broadcast mechanism sends key instruction packets three times in a redundant manner. The receiving end parses the content based on the first correctly received packet, and subsequent duplicate packets are automatically discarded. For example, the total heating success rate instruction packet is sent three times in a row with an interval of 50 milliseconds. The receiving end processes the first valid packet immediately after receiving it, and subsequent duplicate packets are only used for fault tolerance.

[0094] The queue management module of the distributed control unit schedules the order in which instructions are issued. The scheduling rule is that high-priority instructions in the conflict priority sorting table are inserted into the head of the queue and allocated resources of no less than 50% of the total bandwidth, medium-priority instructions are allocated 30%, and low-priority instructions are allocated 20%. If network congestion causes insufficient bandwidth, low-priority instructions are delayed until the next cycle. The delay time is dynamically adjusted according to the real-time load. For example, when the load is 80%, the delay time is 2 seconds, and when the load is 90%, the delay time increases to 5 seconds. The bandwidth allocation strategy of the queue management module is implemented through the token bucket algorithm. The token generation rate is positively correlated with the instruction priority. The token generation rate of high-priority instructions is 10 per second, medium-priority is 6 per second, and low-priority is 4 per second. When the token is exhausted, the instruction issuance is suspended.

[0095] After receiving the command confirmation signal from the actuator, the communication reliability weight is updated. The update logic adjusts the weight gain coefficient based on dynamic feedback of the confirmation signal response time and packet loss rate. When the response time is less than 200 milliseconds, the gain coefficient is 1.2, when it is 200-500 milliseconds, it is 1.0, and when it is greater than 500 milliseconds, it is 0.8. When the packet loss rate is less than 5%, the gain coefficient is reduced by 0.1, when it is 5%-10%, it is 0.3, and when it is greater than 10%, it is 0.5. For example, if the response time of a node is 150 milliseconds and the packet loss rate is 3%, the communication reliability weight is updated to the original value multiplied by 1.2 and then subtracted by 0.1. The updated weight value is rounded to the nearest integer and clamped to the range of 0 to 100. If the calculated result exceeds 100, it is forced to 100, and if it is less than 0, it is reset to zero.

[0096] The updated communication reliability weight is synchronized to the node registry of the cloud server. The field definitions of the node registry include the vehicle unique code, network address, communication reliability weight and path redundancy coefficient, which are completely consistent with the network topology resilience analysis results in step S4. The synchronization process is completed through a secure transport layer protocol encrypted channel. The encryption algorithm uses AES-256 and the key exchange protocol is ECDHE-RSA. The updated weight data is broadcast to all associated nodes within 500 milliseconds. The heartbeat packet sending interval is dynamically adjusted according to the network stability. The interval is 5 seconds in a stable network and shortened to 1 second in a fluctuating network to ensure real-time consistency of the topology data. If the heartbeat packet does not receive a response for three consecutive times, the node is determined to be offline and the topology status is updated.

[0097] This embodiment addresses the disconnect between network status and thermal management logic in traditional hydrogen engine temperature control systems through the deep coupling of multi-dimensional dynamic parameters and a real-time feedback mechanism. Traditional methods rely on static network configurations or single indicators (such as latency or temperature) for control, making them difficult to adapt to the dynamic topology changes and complex operating conditions of connected vehicles. This embodiment uses a dynamic synchronization coefficient as a bridge between network stability and thermal control commands. It combines causal influence analysis to identify implicit connections between nodes and accurately predict spatiotemporal conflicts. It also incorporates network topology resilience to assess the dynamic interference resistance of path redundancy and node connectivity, ensuring that compensation commands remain accurately responsive despite link fluctuations. A protocol-adaptive weight fusion and gradient correction mechanism further deeply binds network status (such as TCP / UDP differences) with physical control parameters (heating power, valve opening), overcoming the response latency bottleneck of traditional layered designs. The synergistic effect of these steps enables simultaneous optimization of network load balancing, thermal control stability, and hardware lifespan. Through the multi-dimensional fusion of dynamic parameters and a closed-loop real-time feedback loop, an efficient thermal management architecture adaptable to dynamic, heterogeneous networks is constructed.

[0098] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to actual conditions.

[0099] It should be noted that the present invention can be deployed on the device itself to implement embedded applications, and can also be run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0100] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0101] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0103] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0104] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0105] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0106] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0107] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling a hydrogen fuel engine electronic thermostat heating assembly based on the Internet of Things, characterized in that: include: S1. Real-time collection of electronic thermostat temperature data of hydrogen fuel engines and network status data of IoT nodes, including dynamic access node identification and data packet transmission delay; S2. Generate a dynamic synchronization coefficient for the current control link based on the communication reliability weight of the dynamic access node identifier and the data packet transmission delay; S3, based on the causal influence in the network topology corresponding to the dynamic synchronization coefficient and the dynamic access node identifier, predicting the spatiotemporal conflict areas of multi-node control instructions and dividing the conflict priorities; S4. When the dynamic synchronization coefficient is lower than the preset synchronization threshold, the network topology resilience of the dynamic access node identifier is analyzed, and a target deviation compensation value is generated by combining the electronic thermostat temperature data and the conflict priority; S5. Performing gradient correction on the total heating success rate instruction according to the target deviation compensation value, and simultaneously updating the valve opening reference parameter of the electronic thermostat; S6. The distributed control unit of the IoT node sends the revised total heating success rate instruction and valve opening reference parameter, and updates the communication reliability weight.

2. The method for controlling a hydrogen fuel engine electronic thermostat heating assembly based on the Internet of Things according to claim 1, characterized in that: Real-time collection of hydrogen fuel engine electronic thermostat temperature data and IoT node network status data, including: Obtain electronic thermostat temperature data through the vehicle communication terminal; Real-time analysis of the network status data of IoT nodes based on the vehicle kinematic parameters corresponding to the dynamic access node identifier; Calculate the data packet transmission delay by using the timestamp difference method; The electronic thermostat temperature data, dynamic access node identifier and data packet transmission delay are stored in a distributed cache queue.

3. The method for controlling a hydrogen fuel engine electronic thermostat heating assembly based on the Internet of Things according to claim 1, characterized in that: Generate the dynamic synchronization coefficient of the current control link based on the communication reliability weight of the dynamic access node identifier and the data packet transmission delay, including: Normalizing the communication reliability weights of dynamic access node identifiers; generating a dynamic delay correction factor based on a stability assessment result of the data packet transmission delay, wherein the stability assessment is achieved by calculating the ratio of the variance to the mean of the transmission delay of a preset number of consecutive data packets; The normalized communication reliability weight and the dynamic delay correction factor are input into the weighted fusion function to generate a dynamic synchronization coefficient. The weighted fusion function dynamically adjusts the fusion ratio of the normalized communication reliability weight and the dynamic delay correction factor based on the current network protocol type. The communication reliability weight is updated according to the real-time change rate of the dynamic synchronization coefficient.

4. The method for controlling a hydrogen fuel engine electronic thermostat heating assembly based on the Internet of Things according to claim 1, characterized in that: Based on the causal influence in the network topology corresponding to the dynamic synchronization coefficient and the dynamic access node identifier, the spatiotemporal conflict areas of multi-node control instructions are predicted and the conflict priorities are divided, including: Extracting the real-time stability parameters of the control link corresponding to the dynamic synchronization coefficient; Constructing a causal influence graph based on the communication path dependencies of the dynamic access node identifiers; The spatiotemporal overlap detection algorithm is used to analyze the spatiotemporal attribute distribution of node instructions in the causal influence graph and identify spatiotemporal conflict areas. generating a conflict priority ranking table based on the real-time stability parameter and the historical conflict probability of the spatiotemporal conflict area, wherein the ranking rule of the conflict priority ranking table is to assign the highest conflict priority to the area whose real-time stability parameter is lower than a preset stability threshold and whose historical conflict probability is higher than a preset probability threshold; The conflict priority sorting table is dynamically matched with the frequency response characteristics of the dynamic synchronization coefficient, and the priority division threshold is adjusted according to the periodicity and burst characteristics of the control instructions.

5. The method for controlling a hydrogen fuel engine electronic thermostat heating assembly based on the Internet of Things according to claim 4 is characterized in that: The real-time stability parameter is generated through a collaborative analysis of the historical volatility of the dynamic synchronization coefficient and the current network load status. The historical volatility is the standard deviation of the dynamic synchronization coefficient in the past preset time period, and the current network load status is the ratio of the number of data packets received by the vehicle node per unit time to the total bandwidth.

6. The method for controlling a hydrogen fuel engine electronic thermostat heating assembly based on the Internet of Things according to claim 4 is characterized in that: The communication path dependency is generated by a nonlinear combination of the covariance matrix of the historical communication success rates between nodes and the path redundancy coefficient, which is the ratio of the number of available communication paths between nodes to the total number of paths.

7. The method for controlling a hydrogen fuel engine electronic thermostat heating assembly based on the Internet of Things according to claim 1, characterized in that: When the dynamic synchronization coefficient is lower than the preset synchronization threshold, the network topology resilience of the dynamic access node identifier is analyzed, and the target deviation compensation value is generated by combining the electronic thermostat temperature data and the conflict priority, including: When the dynamic synchronization coefficient is lower than the preset synchronization threshold, the network topology resilience of the dynamic access node identifier is analyzed. The network topology resilience is generated by the nonlinear correlation between the path redundancy coefficient and the node connectivity. generating an initial compensation coefficient according to a mapping relationship between a real-time deviation rate of electronic thermostat temperature data and a conflict priority; The initial compensation coefficient is adjusted by a protocol type adaptive function. The protocol type adaptive function scales the initial compensation coefficient according to the network protocol type identified by the dynamic access node. The scaling factor under the transmission control protocol is the inverse of the path redundancy coefficient. The adjusted initial compensation coefficient is combined with the network topology resilience input deviation to form a fusion model to generate a target deviation compensation value.

8. The method for controlling a hydrogen fuel engine electronic thermostat heating assembly based on the Internet of Things according to claim 1, characterized in that: The total heating success rate instruction is gradient-corrected based on the target deviation compensation value, and the valve opening reference parameters of the electronic thermostat are simultaneously updated, including: Generate a gradient correction step size based on the current change rate and historical change trend of the target deviation compensation value; Adjust the correction amplitude according to the deviation direction between the gradient correction step and the current total heating success rate instruction. The deviation direction is determined by the positive or negative sign of the target deviation compensation value. Dynamically match the corrected total heating success rate command with the real-time temperature feedback value of the electronic thermostat. If the corrected command exceeds the preset heating power range, it will be forcibly truncated to the boundary value through the limit function. Reversely deduce the reference parameter adjustment amount of the electronic thermostat valve opening according to the corrected total heating success rate instruction; The instantaneous jitter of the adjustment amount is eliminated through the hysteresis compensation function of the valve opening controller, and the updated valve opening reference parameter is written into the control instruction queue and sent to the actuator.

9. The method for controlling a hydrogen fuel engine electronic thermostat heating assembly based on the Internet of Things according to claim 1, characterized in that: The distributed control unit of the IoT node sends the revised total heating success rate instruction and valve opening benchmark parameters, and updates the communication reliability weight, including: Generate a multicast transmission strategy based on the priority level of each instruction in the conflict priority sorting table and the network topology resilience parameter; The instruction encoding format is selected according to the network protocol type and real-time load status. The segment confirmation retransmission mechanism is adopted under the transmission control protocol, and the redundant data packet broadcast mechanism is adopted under the user datagram protocol. The queue management module of the distributed control unit schedules the order of issuing instructions. The scheduling rule is that high-priority instructions in the conflict priority sorting table are given priority to allocate high-bandwidth resources, and low-priority instructions are sent later. After receiving the command confirmation signal from the actuator, the communication reliability weight is updated. The update logic is to adjust the weight gain coefficient based on the dynamic feedback of the confirmation signal response time and packet loss rate; The updated communication reliability weight is synchronized to the node registry of the cloud server and broadcast to the associated nodes through the heartbeat packet mechanism to maintain topology consistency.

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