Edge computing scheduling method and system for heterogeneous multi-source sensor

Abstract

The invention relates to the technical field of data scheduling, in particular to an edge computing scheduling method and system for a heterogeneous multi-source sensor. The method comprises the following steps: collecting original data by using a heterogeneous sensor built in the intelligent safety helmet; performing data fingerprint generation on the original data to obtain an encrypted heterogeneous data set; performing task type division on the heterogeneous data set to generate a standardized task queue; monitoring node states of the edge computing nodes to construct a resource dynamic sensing matrix; performing resource allocation security isolation on the standardized task queue according to the resource dynamic sensing matrix, and generating a resource scheduling log; the resource scheduling log is input into an AI edge computing security coprocessor for multi-objective optimization, and a task scheduling strategy is generated; and carrying out credibility verification on the task scheduling strategy. According to the method, through intelligent data encryption, dynamic resource scheduling, security and credibility verification and anomaly detection, the credibility and stability of task scheduling are improved.

Description

Technical Field

[0001] The present invention relates to the field of data scheduling technology, and in particular to an edge computing scheduling method and system for heterogeneous multi-source sensors. Background Art

[0002] Early edge computing was mainly used for simple sensor data processing, but with the increase in heterogeneous sensors, how to reasonably schedule data from different sensors and make full use of the advantages of edge computing has become a new challenge. In recent years, researchers have proposed a variety of edge computing scheduling methods, mainly focusing on how to dynamically schedule according to the data types, computing requirements and network conditions of different sensors. The initial scheduling methods were relatively simple and often relied on fixed rules and static configurations. With the deepening of research, more and more dynamic scheduling methods based on artificial intelligence and machine learning have been proposed, which can optimize task allocation in real time and improve the overall efficiency and resource utilization of the system. However, traditional task scheduling often ignores multi-objective optimization and the trusted verification of task scheduling strategies, which can easily lead to unstable task execution or tampering of scheduling strategies, resulting in low credibility and stability of task scheduling. Summary of the invention

[0003] Based on this, it is necessary to provide an edge computing scheduling method and system for heterogeneous multi-source sensors to solve at least one of the above technical problems.

[0004] To achieve the above object, an edge computing scheduling method for heterogeneous multi-source sensors is provided, the method comprising the following steps: Step S1: using the built-in heterogeneous sensor of the smart helmet to collect raw data; generating data fingerprints for the raw data to obtain an encrypted heterogeneous data set; dividing the heterogeneous data set into task types to generate a standardized task queue; Step S2: Monitor the node status of edge computing nodes to build a resource dynamic perception matrix; perform resource allocation security isolation on the standardized task queue according to the resource dynamic perception matrix, and generate a resource scheduling log; Step S3: Input the resource scheduling log into the AI ​​edge computing security coprocessor for multi-objective optimization to generate a task scheduling strategy; perform trustworthy verification on the task scheduling strategy, and deploy the task scheduling strategy to the target edge node based on the trustworthy verification result to generate the final scheduling instruction set; Step S4: Collect node scheduling process data for the final scheduling instruction set to obtain task execution data; detect abnormal execution types in the task execution data, and dynamically adjust the task scheduling strategy, generate a task execution report and a security audit log and synchronize them to the emergency command center.

[0005] The present invention provides an efficient and secure task scheduling and execution scheme by combining a smart helmet with edge computing technology. First, the original data is collected by the heterogeneous sensor built into the smart helmet, and a data fingerprint is generated and encrypted to obtain a heterogeneous data set. Then, these data sets are divided into task types to form a standardized task queue. By monitoring the status of the edge computing node, a resource dynamic perception matrix is ​​constructed, and resources are allocated and securely isolated for the standardized task queue according to the matrix, and a resource scheduling log is generated. Subsequently, the resource scheduling log is input into the AI ​​edge computing security coprocessor for multi-objective optimization, a task scheduling strategy is generated, and the strategy is trusted and verified. Finally, the task scheduling strategy is deployed to the target edge node according to the verification result to generate the final scheduling instruction set. During the task execution process, the system will collect data on the node scheduling process, obtain task execution data, and detect abnormal execution types. If an abnormality is detected, the system will dynamically adjust the task scheduling strategy, generate a task execution report and a security audit log, and synchronize them to the emergency command center in real time. Through this process, the efficiency of task scheduling can be improved, the security and reliability of the task execution process can be enhanced, resource allocation can be optimized, the stable operation of the system in a complex environment can be ensured, and emergency response can be made quickly when abnormal situations occur. Therefore, the present invention improves the credibility and stability of task scheduling through intelligent data encryption, dynamic resource scheduling, secure and reliable verification, and abnormality detection.

[0006] Preferably, step S1 comprises the following steps: Step S11: collecting raw data using the heterogeneous sensors built into the smart helmet; Step S12: preprocessing the raw data to generate a standard heterogeneous data set, wherein the preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S13: marking the acquisition timestamp of the standard heterogeneous data set; extracting the device identification of the standard heterogeneous data set; performing data hash encryption on the standard heterogeneous data set based on the device identification to generate an encrypted heterogeneous data set; Step S14: dividing the encrypted heterogeneous data set into task types to generate heterogeneous data task types; Step S15: sorting heterogeneous data task types by collecting timestamps to generate a standardized task queue.

[0007] The present invention effectively optimizes task management, improves data security, and enhances the stability and scalability of the system through multi-level data processing and task sorting. First, the raw data is collected by the heterogeneous sensors built into the smart helmet, providing rich collection information for subsequent processing. Then, the raw data is preprocessed, including data cleaning, denoising, missing value filling and standardization, to ensure the quality and availability of the data. Then, by marking the timestamp of the data, extracting the device identification, and performing data hash encryption, the security and integrity of the data are guaranteed, and the risk of data tampering and leakage is prevented. Subsequently, by dividing the encrypted heterogeneous data sets into task types and sorting the tasks, the execution order of the tasks is effectively organized, ensuring the efficiency of task scheduling and the orderly operation of the system. This series of processing methods not only improves the security, traceability and reliability of the data, but also ensures the efficiency and stability of the task scheduling process, greatly improving the overall performance and adaptability of the smart helmet system.

[0008] Preferably, step S2 comprises the following steps: Step S21: deploy edge computing nodes; Step S22: Perform node status monitoring on the edge computing node to generate edge computing node status data, wherein the node status monitoring includes computing load, storage margin, and network bandwidth status; Step S23: construct a resource dynamic perception matrix based on the edge computing node status data; calculate the task priority of the standardized task queue, and adjust the task order of the standardized task queue according to the task priority to generate a dynamic priority queue; Step S24: Perform resource allocation security isolation on the dynamic priority queue through the trusted execution environment and generate a resource scheduling log.

[0009] The present invention optimizes the efficiency of resource allocation and task scheduling by deploying edge computing nodes and monitoring their status in real time, and improves the stability and security of the system. First, the deployment of edge computing nodes provides powerful computing and storage support for task execution. Then, by monitoring the status of the nodes, data such as computing load, storage margin and network bandwidth status are collected to ensure that the system maintains efficient operation under the use of various resources. Based on these node status data, a resource dynamic perception matrix is ​​constructed, which can reflect the resource status of the node in real time, thereby providing a reliable basis for task priority calculation. Subsequently, the standardized task queue is adjusted according to the calculated task priority, and a dynamic priority queue is generated to ensure that important tasks are given priority, thereby improving the response speed and efficiency of task execution. Finally, the task queue is securely isolated for resource allocation through a trusted execution environment, and a resource scheduling log is generated. This process strengthens the security of the system and ensures the stability of resources during the allocation process and the integrity of data. Overall, this process optimizes resource management and task scheduling through real-time monitoring, dynamic priority adjustment and security isolation mechanisms, and improves the adaptability, stability and security of the system in complex environments.

[0010] Preferably, step S24 includes the following steps: Step S241: Initialize the trusted execution environment: the secure memory size is 512MB to 16GB, the encrypted storage space ranges from 256MB to 4TB, the number of CPU trusted execution cores is between 1 and 8 cores, the TEE OS startup time is about 50 to 500ms, the encryption algorithm throughput can reach 100 to 5000Mbps, the remote authentication time is about 10 to 200ms, the trusted measurement time is 5 to 100ms, the key management module storage capacity is between 128KB and 32MB, and the maximum number of concurrent trusted applications can support 10 to 1000; Step S242: verifying the integrity of the trusted execution environment and generating environment verification data; performing security policy loading on the environment verification data and generating policy configuration data; performing resource boundary division on the policy configuration data and generating isolation domain data; Step S243: extracting the task attributes of the dynamic priority queue, and performing queue scheduling on the dynamic priority queue to generate scheduling sequence data; performing resource demand analysis on the scheduling sequence data to generate resource mapping data; Step S244: Use the isolation domain data to perform resource security isolation and allocation on the resource mapping data, and generate a resource scheduling log.

[0011] The present invention introduces a trusted execution environment (TEE) to ensure data security and high efficiency of resource management in the task scheduling process, effectively improving the security, reliability and flexibility of the system. First, initialize the trusted execution environment, and set appropriate parameters such as secure memory, encrypted storage space and computing resources to ensure that TEE has sufficient processing power to meet the security and resource requirements of different tasks. Multiple parameter settings in this process, such as encryption algorithm throughput and key management module storage capacity, ensure that the system can efficiently process large-scale tasks and perform encrypted storage management. Then, by verifying the integrity of the trusted execution environment, generating environment verification data and loading security policies, the system's protection capabilities are further enhanced to avoid potential security vulnerabilities. Subsequently, by extracting task attributes and scheduling queues for dynamic priority queues, generating scheduling sequence data, and combining resource demand analysis to generate resource mapping data, it is ensured that the priority of tasks and required resources are reasonably allocated. Finally, the resource mapping data is securely isolated using the isolation domain data to ensure data security during resource allocation and generate resource scheduling logs. This process not only strengthens the security of the task scheduling process and prevents potential resource leakage or data tampering, but also optimizes the system's resource utilization efficiency and task execution efficiency through dynamic adjustment and isolation mechanisms. Overall, this process greatly improves the adaptability, stability, and security of the task scheduling system in a high-security environment.

[0012] Preferably, the step S3 of inputting the resource scheduling log into the AI ​​edge computing security coprocessor for multi-objective optimization includes: Input the resource scheduling log into the AI ​​edge computing security coprocessor to set the optimization target and generate the optimization target setting data, where the optimization target setting data includes the latency target, energy consumption target and security risk target; The delay minimization optimization calculation is performed on the delay target to obtain the delay minimization optimization data, where the delay minimization optimization calculation formula is as follows:

[0013] In the formula, is the optimized overall delay, For the The computational latency of a task, For the The communication delay of each task, is the total number of tasks; Energy consumption balance optimization calculation is performed on the energy consumption target to obtain energy consumption balance optimization data, where the energy consumption balance optimization calculation formula is as follows:

[0014] In the formula, is the overall energy consumption after optimization, For the The computing energy consumption of each module is For the The communication energy consumption of each module is and It is a module The weight coefficient of computing energy consumption and communication energy consumption, is the total number of modules; The security risk suppression optimization calculation is performed on the security risk target to obtain the security risk suppression optimization data, wherein the security risk suppression optimization calculation formula is as follows:

[0015] In the formula, For optimized security risks, For the The vulnerability risk of each task, For the The invasion risk of each task, and is the weight coefficient of vulnerability risk and intrusion risk, is the total number of tasks; According to the delay minimization optimization data, energy consumption balancing optimization data and security risk suppression optimization data, the resource scheduling log is used to schedule tasks and generate a task scheduling strategy.

[0016] The present invention significantly improves the efficiency, security and resource utilization of task scheduling through the multi-objective optimization mechanism of the AI ​​edge computing security coprocessor, ensuring that the system effectively reduces latency, energy consumption and security risks while running efficiently. First, by using the optimization target setting, the AI ​​edge computing security coprocessor converts the resource scheduling log into multi-dimensional optimization target data, including latency, energy consumption and security risks. This process provides a scientific basis for subsequent scheduling decisions through clear optimization targets. Then, by minimizing the latency, the calculation and communication delays during task execution can be reduced, ensuring that the system's response speed and task processing efficiency are optimal. Through energy consumption balancing optimization, the system can effectively balance the computing and communication energy consumption when processing tasks, reduce unnecessary energy consumption, thereby improving the overall energy efficiency performance, which is particularly important in resource-constrained environments. Finally, through security risk suppression optimization, the system can effectively control the potential vulnerabilities and intrusion risks of tasks, reduce security risks, and ensure the security of data and task execution processes. This series of optimization calculations not only improves the stability and security of the system, but also ensures the efficiency of resource scheduling. Finally, by integrating the optimization data of these three aspects, the system can generate a task scheduling strategy that meets the requirements of latency, energy consumption and safety, thereby achieving the optimal allocation of resources and efficient execution of tasks while ensuring system safety. Overall, this optimization process significantly improves the system's adaptability in complex environments, reduces latency and energy consumption, and enhances the security of the task execution process.

[0017] Preferably, the trustworthy verification of the task scheduling strategy in step S3 includes: Extract the source code and configuration files of the task scheduling strategy for static analysis, including verifying the logical correctness of the task scheduling algorithm, policy constraints and security control measures, and generating static verification data for the task scheduling strategy; Use the trusted execution environment to dynamically verify the task scheduling strategy, simulate the scheduling process, monitor the behavior and resource usage of the task scheduling strategy in the real operating environment, and generate dynamic verification data for the task scheduling strategy; Integrate the static verification data of task scheduling strategy and the dynamic verification data of task scheduling strategy into a trusted verification result.

[0018] The present invention combines static analysis and dynamic verification to ensure the logical correctness, execution security and rationality of resource utilization of task scheduling strategy before implementation, thereby greatly enhancing the trust and reliability in the task scheduling process. First, by extracting the source code and configuration file of the task scheduling strategy for static analysis, the logical correctness of the task scheduling algorithm can be systematically checked to ensure that the constraints and safety control measures of the strategy are not ignored. This static verification ensures the correctness of the task scheduling strategy in the design stage and avoids potential loopholes or errors. The static verification data of the task scheduling strategy generated by this analysis provides a basis for subsequent verification. The task scheduling strategy is dynamically verified using a trusted execution environment. By simulating the scheduling process and monitoring the performance of the strategy in a real operating environment, the behavior, resource usage and potential risk points of the task scheduling strategy in actual operation can be more realistically reflected. The dynamic verification data generated by this process can further verify the effectiveness and security of the strategy at runtime. The static verification data and the dynamic verification data are combined to generate a trusted verification result, thereby ensuring the security and feasibility of the task scheduling strategy during actual deployment. Through this trusted verification process, potential risks and behaviors that do not meet expectations can be identified before task scheduling, reducing the failure rate and potential safety hazards in system execution. Overall, the trusted verification process greatly enhances the reliability of the task scheduling strategy and ensures that the system can operate safely, stably and efficiently in complex environments.

[0019] Preferably, the step S3 of deploying the task scheduling strategy to the target edge node according to the result of the trusted verification includes: According to the results of trusted verification, the task scheduling strategy is deployed to the target edge node to detect the node status and generate node status data; Plan the deployment path for the node status data and generate deployment plan data; Perform network connection test on node status data through deployment plan data to generate network quality data; Establish a secure channel for network quality data and generate channel configuration data; The channel configuration data is used to distribute task instructions to the deployment plan data and generate scheduling instruction data.

[0020] The present invention ensures that the task scheduling strategy can be efficiently and safely deployed to the target edge node by combining the trusted verification results, node status monitoring, network testing and secure channel construction. First, the task scheduling strategy is deployed to the target edge node according to the trusted verification results, and the node status detection is performed to generate the node status data. This process ensures that the task scheduling strategy can be reasonably deployed according to the actual node resource situation. Next, the node status data is deployed for path planning, and the deployment plan data is generated to ensure that the task can be executed on the appropriate path and avoid resource conflicts. The node status is tested for network connection through the deployment plan data to generate network quality data to ensure that the network connection is stable during the task deployment process and avoid task delays caused by poor network quality. Subsequently, based on the network quality data, a secure channel is established to generate channel configuration data, thereby ensuring security during data transmission and preventing data leakage or tampering. Finally, the deployment plan is distributed with task instructions using the channel configuration data to generate scheduling instruction data to ensure that the task can be smoothly executed in the target node. Overall, this step optimizes the deployment efficiency, security and reliability of task scheduling and ensures that the task is smoothly executed in the edge node as expected.

[0021] Preferably, step S4 comprises the following steps: Step S41: collecting node scheduling process data for the final scheduling instruction set, including real-time recording of task execution time, resource consumption, task status and node response information, and generating task execution data; Step S42: performing abnormal execution type detection on the task execution data to generate abnormal task execution data; Step S43: According to the abnormal task execution data, the task scheduling strategy is used to allocate task computing power, generate a task execution report and a security audit log, and synchronize them to the emergency command center.

[0022] The present invention realizes real-time monitoring and dynamic optimization of the task execution process through step S4 to ensure the accuracy of scheduling and the efficient execution of tasks. First, in step S41, the node scheduling process data of the final scheduling instruction set is collected, including real-time recording of task execution time, resource consumption, task status and node response information, so as to generate detailed task execution data. Then, in step S42, the task execution data is detected for abnormal execution types, including task timeout exceptions, resource excess exceptions, task failure exceptions and node response delay exceptions, etc., to ensure accurate identification of potential problems and generate abnormal task execution data. Finally, in step S43, the system performs task computing power allocation scheduling on the task scheduling strategy according to the abnormal task execution data, and generates a detailed task execution report and security audit log, which are synchronized to the emergency command center for further analysis and decision-making. The implementation of this step can effectively improve the stability of the system, the reliability of task execution and the response speed of exception handling, and provide all-round security protection for intelligent task scheduling.

[0023] Preferably, step S42 includes the following steps: Step S421: When any of the following situations occurs, it is determined as a task timeout exception and task timeout exception data is obtained: the task execution time deviates from the predetermined maximum execution time by more than ±10%, or the task fails to be completed within the specified time; Step S422: when any of the following situations occurs, it is determined as a resource over-limit exception, and resource over-limit exception data is obtained: CPU usage exceeds 95%, memory usage exceeds 90%, or storage usage exceeds 85%; Step S423: When the following conditions occur at the same time, it is determined as a task failure exception, and task failure exception data is obtained: the number of task failures exceeds 3 times in a row, and the number of retries after each failure does not exceed 2 times; Step S424: When the following conditions occur at the same time, it is determined that the node response delay is abnormal, and the node response delay abnormal data is obtained: the node response time exceeds 5 seconds continuously, and the duration of the node non-response exceeds 5 minutes; Step S425: Integrate task timeout exception data, resource excess exception data, task failure exception data and node response delay exception data to obtain abnormal task execution data.

[0024] The present invention can effectively monitor and identify problems in the task execution process by determining and collecting data on different abnormal situations that occur during the task execution process, ensuring timely processing and optimizing system performance. First, step S421 can timely identify task timeout anomalies by detecting deviations from the task execution time or failure to complete on time, ensuring that the task is completed within the predetermined time frame. Step S422 monitors resource usage and timely identifies resource overrun anomalies to avoid system performance degradation or collapse due to excessive resource occupancy. Step S423 monitors task failures to ensure that multiple task failures can be identified and corresponding emergency measures can be taken to avoid long-term failure of task execution. Step S424 monitors node response delay anomalies, and can identify long-term node non-response or delay situations, thereby taking measures to avoid system performance bottlenecks or fault expansion. Finally, step S425 integrates various types of abnormal data, generates abnormal task execution data, and helps system managers quickly locate and solve problems that occur during task execution, thereby improving the stability and reliability of the task scheduling system.

[0025] In this specification, an edge computing scheduling system for heterogeneous multi-source sensors is provided, which is used to execute the above-mentioned edge computing scheduling method for heterogeneous multi-source sensors. The edge computing scheduling system for heterogeneous multi-source sensors includes: The data acquisition module is used to collect raw data using the heterogeneous sensors built into the smart helmet; generate data fingerprints for the raw data to obtain encrypted heterogeneous data sets; divide the heterogeneous data sets into task types to generate a standardized task queue; The task allocation module is used to monitor the node status of edge computing nodes and build a resource dynamic perception matrix; according to the resource dynamic perception matrix, the standardized task queue is allocated and safely isolated, and a resource scheduling log is generated; The resource scheduling module is used to input the resource scheduling log into the AI ​​edge computing security coprocessor for multi-objective optimization and generate a task scheduling strategy; perform trusted verification on the task scheduling strategy, and deploy the task scheduling strategy to the target edge node based on the trusted verification results to generate the final scheduling instruction set; The anomaly detection module is used to collect node scheduling process data for the final scheduling instruction set to obtain task execution data; detect abnormal execution types in task execution data, and dynamically adjust the task scheduling strategy, generate task execution reports and security audit logs and synchronize them to the emergency command center.

[0026] The beneficial effect of the present invention is that the data acquisition module uses the built-in heterogeneous sensors of the smart helmet to collect raw data, and generates and encrypts the data to ensure the security and integrity of the data. Through the division of task types and the generation of standardized task queues, clear guidance can be provided for subsequent task allocation. The task allocation module builds a resource dynamic perception matrix by monitoring the edge computing node status, and allocates resources and isolates security for the task queue based on the matrix, generates a resource scheduling log, and provides resource guarantee for task scheduling. The resource scheduling module inputs the resource scheduling log into the AI ​​edge computing security coprocessor for multi-objective optimization, generates a task scheduling strategy, and verifies the strategy with credibility. Finally, the task scheduling strategy is deployed to the target edge node based on the verification result, and a scheduling instruction set is generated to ensure that the task is executed according to the predetermined plan. The anomaly detection module monitors the task execution process in real time, collects task execution data, promptly discovers abnormal situations in task execution, and dynamically adjusts the task scheduling strategy, generates a task execution report and a security audit log, and synchronizes them to the emergency command center. The system effectively improves the efficiency, reliability and security of task execution through the optimization of multiple links such as data acquisition, task allocation, resource scheduling and anomaly detection. Therefore, the present invention improves the credibility and stability of task scheduling through intelligent data encryption, dynamic resource scheduling, secure and reliable verification, and anomaly detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of the steps of an edge computing scheduling method for heterogeneous multi-source sensors; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0029] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0030] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0031] To achieve this, please refer to Figures 1 to 3 , an edge computing scheduling method for heterogeneous multi-source sensors, the method comprising the following steps: Step S1: using the built-in heterogeneous sensor of the smart helmet to collect raw data; generating data fingerprints for the raw data to obtain an encrypted heterogeneous data set; dividing the heterogeneous data set into task types to generate a standardized task queue; Step S2: Monitor the node status of edge computing nodes to build a resource dynamic perception matrix; perform resource allocation security isolation on the standardized task queue according to the resource dynamic perception matrix, and generate a resource scheduling log; Step S3: Input the resource scheduling log into the AI ​​edge computing security coprocessor for multi-objective optimization to generate a task scheduling strategy; perform trustworthy verification on the task scheduling strategy, and deploy the task scheduling strategy to the target edge node based on the trustworthy verification result to generate the final scheduling instruction set; Step S4: Collect node scheduling process data for the final scheduling instruction set to obtain task execution data; detect abnormal execution types in the task execution data, and dynamically adjust the task scheduling strategy, generate a task execution report and a security audit log and synchronize them to the emergency command center.

[0032] The present invention provides an efficient and secure task scheduling and execution scheme by combining a smart helmet with edge computing technology. First, the original data is collected by the heterogeneous sensor built into the smart helmet, and a data fingerprint is generated and encrypted to obtain a heterogeneous data set. Then, these data sets are divided into task types to form a standardized task queue. By monitoring the status of the edge computing node, a resource dynamic perception matrix is ​​constructed, and resources are allocated and securely isolated for the standardized task queue according to the matrix, and a resource scheduling log is generated. Subsequently, the resource scheduling log is input into the AI ​​edge computing security coprocessor for multi-objective optimization, a task scheduling strategy is generated, and the strategy is trusted and verified. Finally, the task scheduling strategy is deployed to the target edge node according to the verification result to generate the final scheduling instruction set. During the task execution process, the system will collect data on the node scheduling process, obtain task execution data, and detect abnormal execution types. If an abnormality is detected, the system will dynamically adjust the task scheduling strategy, generate a task execution report and a security audit log, and synchronize them to the emergency command center in real time. Through this process, the efficiency of task scheduling can be improved, the security and reliability of the task execution process can be enhanced, resource allocation can be optimized, the stable operation of the system in a complex environment can be ensured, and emergency response can be made quickly when abnormal situations occur. Therefore, the present invention improves the credibility and stability of task scheduling through intelligent data encryption, dynamic resource scheduling, secure and reliable verification, and abnormality detection.

[0033] In the embodiment of the present invention, reference Figure 1 As shown, it is a schematic diagram of the step flow of an edge computing scheduling method for heterogeneous multi-source sensors of the present invention. In this example, the edge computing scheduling method for heterogeneous multi-source sensors includes the following steps: Step S1: using the built-in heterogeneous sensor of the smart helmet to collect raw data; generating data fingerprints for the raw data to obtain an encrypted heterogeneous data set; dividing the heterogeneous data set into task types to generate a standardized task queue; In an embodiment of the present invention, a plurality of sensors are built into a smart helmet, such as an accelerometer, a gyroscope, a temperature and humidity sensor, an environmental gas detector (such as a CO2, O2 concentration sensor), a heart rate monitor, a GPS module, etc. The sensor continuously collects environmental and physiological data at a fixed frequency (for example, once per second). For example, the temperature and humidity sensor collects the ambient temperature and humidity once per second, and the heart rate monitor collects the wearer's heart rate data every 1 minute. The collected raw data will first be stored in the local cache, and then uploaded to the edge computing node through the Bluetooth or Wi-Fi module. The raw data is encrypted using a hash algorithm (such as SHA-256) to generate a "fingerprint" of the data. The fingerprint is unique, and after encryption, data tampering can be prevented and data integrity can be guaranteed. The encrypted data fingerprint is stored in the local database and uploaded to the data storage system of the edge node. The fingerprint of each data packet will be accompanied by a timestamp and a sensor ID for subsequent verification. The system classifies the collected data based on the data type, for example, physiological data (such as heart rate, temperature) is divided into health monitoring tasks, and environmental data (such as temperature, humidity, gas concentration) is divided into environmental monitoring tasks. According to the task priority, processing time requirements and other factors, the tasks are sorted by type to generate a standardized task queue. The format of the standardized queue includes information such as task ID, task type, task description, priority, and required resources.

[0034] Step S2: Monitor the node status of edge computing nodes to build a resource dynamic perception matrix; perform resource allocation security isolation on the standardized task queue according to the resource dynamic perception matrix, and generate a resource scheduling log; In an embodiment of the present invention, a resource monitoring module is installed on each edge node to monitor the processing capacity (CPU usage, memory usage), storage space, network bandwidth and other resource states of the node in real time. By regularly collecting the resource status data of the node, a resource dynamic perception matrix is ​​generated. Each item in the matrix represents the resource usage of a node. The matrix update cycle can be set to once per minute to ensure that the system perceives resource changes in real time. In order to improve the accuracy of resource monitoring, the resource data of multiple nodes will be processed by a data fusion algorithm to eliminate redundant and erroneous data and form a comprehensive resource perception matrix. According to the information in the resource dynamic perception matrix, the system allocates resources to the standardized task queue. For example, computing resources are preferentially allocated to health monitoring tasks that require high computing power, and storage resources are allocated to environmental monitoring data that requires long-term storage. Virtualization technology (such as Docker containers) is used to isolate each task to ensure that resources between different tasks do not interfere with each other and prevent data leakage or conflict. After each task scheduling, the system records the resource allocation (such as task ID, allocated node, resource type, etc.) and generates a detailed resource scheduling log. This log will be used for subsequent analysis and auditing.

[0035] Step S3: Input the resource scheduling log into the AI ​​edge computing security coprocessor for multi-objective optimization to generate a task scheduling strategy; perform trustworthy verification on the task scheduling strategy, and deploy the task scheduling strategy to the target edge node based on the trustworthy verification result to generate the final scheduling instruction set; In an embodiment of the present invention, the task scheduling log is input into the AI ​​coprocessor, and a multi-objective optimization algorithm (such as reinforcement learning, particle swarm optimization, etc.) is used for scheduling optimization. The optimization objectives include resource utilization, task completion time, network delay, etc. The AI ​​coprocessor dynamically adjusts the execution order and node allocation of tasks according to the real-time node load and task requirements to complete task scheduling in the best way. The generated task scheduling strategy is verified for security using a trusted computing module. This includes verifying whether the task scheduling complies with preset security rules (such as data encryption, access control, etc.) to ensure that no data leakage or unauthorized access occurs during task scheduling. The scheduling strategy is encrypted and authenticated by a digital signature to ensure that the task scheduling strategy has not been tampered with and can be traced back to the source. Once the scheduling strategy passes the trusted verification, the system will deploy the task scheduling instruction set to the target edge node. The deployment process involves encryption and decryption of the instruction set to ensure that the scheduling instructions are not tampered with during transmission. Finally, the scheduling instruction set will be transmitted to the target edge node through the network to start executing the corresponding task.

[0036] Step S4: Collect node scheduling process data for the final scheduling instruction set to obtain task execution data; detect abnormal execution types in the task execution data, and dynamically adjust the task scheduling strategy, generate a task execution report and a security audit log and synchronize them to the emergency command center.

[0037] In the embodiment of the present invention, when the task is executed on the target edge node, the system will collect task execution data in real time, including execution time, resource consumption, task status, etc. The task execution data of all nodes will be uploaded to the data center in real time for subsequent data analysis and auditing. The system uses an anomaly detection algorithm based on machine learning to monitor the task execution data, discover abnormal situations in task execution (such as execution timeout, abnormal resource consumption, etc.), and generate an alarm. Once an abnormality is detected, the system will dynamically adjust by rescheduling tasks, adjusting resource allocation, etc. For example, if the CPU usage of a node is too high, the system will migrate some tasks to other nodes. Based on the task execution data, the system will generate a detailed task execution report, and the report content includes resource consumption, execution time, abnormal situations, etc. during task execution. For the scheduling and execution of each task, the system generates a detailed security audit log, including scheduling decisions, task execution, resource allocation and other information. The log will be synchronized to the emergency command center for real-time monitoring and decision-making. All task execution reports and security audit logs will be synchronized to the emergency command center through a secure channel for management personnel to monitor and process.

[0038] Preferably, step S1 comprises the following steps: Step S11: collecting raw data using the heterogeneous sensors built into the smart helmet; Step S12: preprocessing the raw data to generate a standard heterogeneous data set, wherein the preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S13: marking the acquisition timestamp of the standard heterogeneous data set; extracting the device identification of the standard heterogeneous data set; performing data hash encryption on the standard heterogeneous data set based on the device identification to generate an encrypted heterogeneous data set; Step S14: dividing the encrypted heterogeneous data set into task types to generate heterogeneous data task types; Step S15: sorting heterogeneous data task types by collecting timestamps to generate a standardized task queue.

[0039] In an embodiment of the present invention, a plurality of sensors (such as accelerometers, gyroscopes, temperature and humidity sensors, heart rate monitors, GPS modules, etc.) are built into the smart helmet, and these sensors continuously collect data at preset time intervals (such as every second or every minute). The data collected by the sensor includes but is not limited to: accelerometer and gyroscope data: used to monitor the wearer's motion state (such as acceleration, angular velocity, etc.). Environmental sensor data: temperature and humidity sensors, gas concentration sensors (such as CO2, O2), etc., used to monitor changes in the surrounding environment. Physiological data: monitoring of physiological states such as heart rate and body temperature. Location information: GPS module records the wearer's geographic location in real time. The collected raw data is cleaned to remove abnormal values, invalid data or inconsistent data. For example, filter out heart rate values ​​that do not meet expectations (such as heart rate values ​​that are too low or too high), remove data anomalies caused by sensor failure, use statistical methods (such as the median method) to replace outliers or abnormal data, and use filtering algorithms (such as low-pass filters, Kalman filters) to remove noise in the raw data. For example, environmental sensor data may be affected by external electromagnetic interference, and filters are used to smooth the data to reduce the impact of noise on data quality. Process missing values, use interpolation methods (such as linear interpolation, spline interpolation) or predict missing values ​​based on historical data to ensure data continuity and integrity. Standardize the collected data, convert data collected by different sensors (such as acceleration, temperature, humidity, etc.) to a unified scale, and use standardization algorithms (such as Z-Score standardization or Min-Max standardization) to normalize the data to a range of 0 to 1 to ensure consistency in subsequent processing. Add a timestamp to each data in the standard heterogeneous data set. The timestamp is automatically generated by the system and marks the exact time of data collection. Each data record is accompanied by the collection time to ensure that the data can be analyzed and processed in chronological order. The timestamp format can use UNIX timestamps to ensure the accuracy and consistency of time records. Each smart helmet is equipped with a unique device identifier (such as device ID). When collecting data, the system automatically extracts the device identifier and stores it with the data. Based on the device identifier and data content, the standard heterogeneous data set is encrypted using a hash encryption algorithm (such as SHA-256). The purpose of hash encryption is to generate irreversible encrypted data to ensure data integrity and privacy. The hashed data is stored in an encrypted storage system to ensure that the data cannot be tampered with by unauthorized personnel. According to the different sources and contents of the data, the encrypted heterogeneous data sets are divided into different task types. For example: Health monitoring tasks: involving physiological data such as heart rate and body temperature. Environmental monitoring tasks: involving environmental data such as temperature, humidity, and gas concentration. Motion state tasks: involving motion data such as accelerometer and gyroscope data. Depending on the task type, each type of data set will be passed to different processing units for task processing. Each encrypted data set is labeled according to the task type.The system assigns a unique task ID to each data set and records the task type, which ensures that the task type is clear and facilitates subsequent processing and scheduling. The heterogeneous data task types are sorted in chronological order according to the acquisition timestamp, which helps the system prioritize tasks according to their timing. If the task type involves real-time processing (such as health monitoring tasks), the system will prioritize data with closer timestamps to ensure that the tasks are processed on time. The sorted tasks are generated into a standardized task queue according to attributes such as priority and task type. Each task in the task queue will contain information such as task ID, task type, timestamp, and device identification. The task queue will be further processed or assigned to the edge computing node for execution according to the preset scheduling strategy.

[0040] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: deploy edge computing nodes; Step S22: Perform node status monitoring on the edge computing node to generate edge computing node status data, wherein the node status monitoring includes computing load, storage margin, and network bandwidth status; Step S23: construct a resource dynamic perception matrix based on the edge computing node status data; calculate the task priority of the standardized task queue, and adjust the task order of the standardized task queue according to the task priority to generate a dynamic priority queue; Step S24: Perform resource allocation security isolation on the dynamic priority queue through the trusted execution environment and generate a resource scheduling log.

[0041] In an embodiment of the present invention, by deploying edge computing nodes at predetermined edge computing locations (such as construction sites, production sites, or data centers), it is ensured that the nodes can effectively process when receiving data streams from smart helmets. Each edge computing node is equipped with appropriate computing resources (such as CPU, GPU, memory, storage) to ensure that the collected massive data can be processed and stored. The edge computing node is connected to the local network to ensure that the node can receive heterogeneous data sets from the smart helmet in real time and can interact with other edge nodes or central servers. Configure the operating system and software environment of the edge computing node, including installing relevant computing frameworks and task scheduling management tools. Configure the firewalls and security measures of each node to ensure security during data transmission. Set up an access control mechanism for the edge computing node to ensure that only authorized devices or users can access the computing and storage resources of the node. Configure the communication interface between the edge computing node and the smart helmet. Common communication protocols include Wi-Fi, Bluetooth, or 5G, etc., to ensure the reliability and low latency of the data transmission process. Configure the interface between the node and the upper scheduling system or cloud platform for subsequent task scheduling and resource optimization. Deploy resource monitoring tools on edge computing nodes to collect computing load data (such as CPU and GPU usage, memory usage, etc.) in real time. These data help understand whether the computing power of the node is sufficient and whether load balancing is required. By regularly sampling computing load data, monitor whether the node is in an overloaded state, and then decide whether to expand computing resources or redistribute tasks. Monitor the storage usage of the node, including hard disk space, cache storage, database storage, etc. This monitoring ensures that data will not be lost or cause system crashes due to insufficient storage capacity. Generate storage margin data through the storage monitoring system to adjust the task data storage strategy in time. Monitor the network bandwidth of the edge computing node in real time to ensure that the node can communicate with other nodes or cloud platforms unimpeded. The monitoring content includes bandwidth usage, latency, data transmission rate, etc. If the network bandwidth is found to be insufficient, it will affect the timely execution of the task, so the bandwidth usage needs to be dynamically optimized. Aggregate the monitoring data such as computing load, storage margin, network bandwidth, etc. to the central monitoring system or the local storage of the edge node to form the edge computing node status data. The status data includes the computing resource usage, storage resource status, network bandwidth, etc. of each node for subsequent task scheduling and resource allocation. Based on the node status data (such as computing load, storage margin, network bandwidth), a resource dynamic perception matrix is ​​constructed. This matrix can reflect the current resource status of each edge computing node and the load capacity of each node. The dynamic perception matrix will contain the resource status of each node (such as real-time data of load, storage and bandwidth) so that the task scheduling system can optimize the scheduling according to the current resource situation. The priority of each task in the standardized task queue is calculated based on factors such as task type, urgency, and estimated execution time.Priority calculation considers the following factors: Task urgency: High-priority tasks such as real-time health monitoring tasks and environmental monitoring tasks should be processed first. Task complexity: Computationally complex tasks (such as large-scale data processing tasks) require more computing resources, so their priorities will be dynamically adjusted based on resource availability. Task dependencies: If there are dependencies between tasks (for example, task A must be executed before task B), the system needs to adjust the order of tasks based on the dependencies. According to the task priority calculation results, the task order of the standardized task queue is adjusted to generate a dynamic priority queue. The queue will contain information such as the task ID, task type, priority, and expected execution time. The adjusted queue ensures that the most urgent and important tasks can be executed first, while those that are not urgent will be delayed to avoid overloading system resources. Deploy a trusted execution environment (TEE) on each edge computing node, such as Intel SGX or ARM TrustZone. TEE is used to ensure the security and privacy of task execution during resource scheduling. Through TEE, tasks are securely isolated to ensure that resource access between different tasks will not conflict or leak sensitive data. Through the trusted execution environment, tasks in the task queue are allocated resources according to their priority and resource requirements. Resource allocation includes computing resources (such as CPU, GPU), storage resources, network bandwidth, etc. The task allocation process is isolated through TEE to ensure that task data is not interfered with or accessed by other tasks, preventing potential security threats (such as malicious attacks or data leakage). Each step in the resource scheduling process is recorded to generate a detailed resource scheduling log. The scheduling log includes task ID, computing resources assigned to the task, storage resources, network bandwidth, priority, timestamp, and other information.

[0042] Preferably, step S24 includes the following steps: Step S241: Initialize the trusted execution environment: the secure memory size is 512MB to 16GB, the encrypted storage space ranges from 256MB to 4TB, the number of CPU trusted execution cores is between 1 and 8 cores, the TEE OS startup time is about 50 to 500ms, the encryption algorithm throughput can reach 100 to 5000Mbps, the remote authentication time is about 10 to 200ms, the trusted measurement time is 5 to 100ms, the key management module storage capacity is between 128KB and 32MB, and the maximum number of concurrent trusted applications can support 10 to 1000; Step S242: verifying the integrity of the trusted execution environment and generating environment verification data; performing security policy loading on the environment verification data and generating policy configuration data; performing resource boundary division on the policy configuration data and generating isolation domain data; Step S243: extracting the task attributes of the dynamic priority queue, and performing queue scheduling on the dynamic priority queue to generate scheduling sequence data; performing resource demand analysis on the scheduling sequence data to generate resource mapping data; Step S244: Use the isolation domain data to perform resource security isolation and allocation on the resource mapping data, and generate a resource scheduling log.

[0043] In an embodiment of the present invention, the size of the secure memory required by the trusted execution environment (TEE) is configured, which is usually between 512MB and 16GB. The secure memory is used to temporarily store data when executing tasks and ensure the confidentiality and integrity of the data. The size of the encrypted storage space is set to a range of 256MB to 4TB for storing sensitive data and encrypted task information. The size of the encrypted storage space should be adjusted according to the storage requirements of the specific task. The number of CPU trusted execution cores supported by the TEE is configured, usually 1 to 8 cores, which will be used to execute trusted tasks and ensure the security of the tasks. Configure an appropriate number of cores to improve computing performance and support multi-task parallel execution. The startup time of the TEE should be between 50 and 500ms to ensure that the trusted execution environment can be quickly started and ready to execute tasks when task scheduling begins. The encryption algorithm throughput should reach 100 to 5000Mbps to ensure the efficiency of encryption operations to meet the encryption requirements of large data volume tasks. The remote authentication time is controlled within 10 to 200ms to ensure that edge nodes and other remote devices can quickly verify and establish trusted communication connections. The trust measurement time is controlled within 5 to 100ms to ensure that the trust measurement of tasks and environments can be completed in a short time so that security verification can be performed quickly. The storage capacity of the key management module is configured between 128KB and 32MB to ensure that the keys used in encryption and decryption operations can be stored securely. The maximum number of concurrent trusted applications supported is 10 to 1000 to ensure that the trusted execution environment can handle multiple parallel tasks and applications at the same time. By using technologies such as hash values, signatures, and cryptographic verification, the integrity of the TEE environment is verified to ensure that it has not been tampered with. The verification content includes the hardware configuration, operating system, and application of the TEE. The environment verification process should be automatically executed during the startup process to ensure that the TEE environment of each node is secure and consistent. After verification, a set of environment verification data is generated to record information during the verification process, such as verification time, verification results, and the algorithms used. The environment verification data will be used for subsequent policy loading and task execution to ensure that all operations during task execution are performed in a trusted environment. Load the corresponding security policy according to the environment verification data to ensure that each task in the TEE environment is securely controlled. Security policies include access control policies, task execution policies, data protection policies, etc., to ensure that all resource allocation and task scheduling are performed in an environment that meets security requirements. Through the loaded security policies, policy configuration data is generated, including task execution permissions, resource usage restrictions, data access control, etc. The policy configuration data will be dynamically adjusted according to the environment verification data to ensure that the policy matches the current environment and resource status. Based on the policy configuration data, the boundaries of resource usage are divided. For example, CPU, memory, storage and other resources are divided into different security isolation areas (isolation domain data) according to task type and priority to prevent interference between different tasks or leakage of sensitive information.Extract the relevant attributes of each task from the dynamic priority queue, including task type, priority, computing resource requirements, storage requirements, execution time, etc. The extraction of task attributes helps to understand the execution characteristics of each task, thereby providing a basis for subsequent resource allocation and scheduling. Queue scheduling is performed on tasks based on their priority, resource requirements, and execution time. According to the priority order of tasks, the execution order of tasks is dynamically adjusted to ensure that high-priority tasks are executed first. In the process of task scheduling, factors such as dependencies between tasks and resource conflicts must also be considered to ensure the rational use of resources and efficient execution of tasks. The scheduled tasks are used to generate scheduling sequence data, which includes the execution order, allocated resources, and estimated execution time of each task. The scheduling sequence data will serve as a reference for task scheduling, guiding each edge computing node to execute tasks in the specified order and resource allocation. Perform resource requirement analysis on each task in the scheduling sequence to evaluate the requirements of each task for computing resources, storage resources, and network bandwidth. The results of the resource requirement analysis will help the system dynamically adjust the resource allocation strategy to ensure that all tasks can be executed efficiently within the allocated resource range. Based on the results of the resource requirement analysis, resource mapping data is generated to record in detail the computing, storage, and network resources required for each task. Use the isolation domain data to securely isolate the resource mapping data of the task, ensuring that each task can only access the resources assigned to it, but cannot access the resources of other tasks. The isolation domain data can be configured according to the policy to ensure that the data and resources between different tasks will not interfere with each other, ensuring the security and data privacy during the task execution process. According to the resource mapping data and isolation domain data, resources are allocated. Appropriate computing resources, storage space and network bandwidth are allocated to each task to ensure that the task can be executed smoothly. During the resource allocation process, the system needs to adjust the resource allocation strategy in real time to cope with dynamically changing resource requirements and task priorities. During the resource allocation process, the detailed information of each step of resource allocation is recorded to generate a resource scheduling log. The log content includes the task ID, the type of allocated resources, the number of resources, the timestamp of resource allocation, etc.

[0044] It is particularly important that step S244 further includes the following steps: Step S2441: Perform security trust evaluation on the isolated domain data to generate isolated domain security trust evaluation data; Step S2442: Perform dynamic access control mapping on the resource mapping data to generate access control optimization data; Step S2443: performing multi-dimensional resource conflict detection on the resource mapping data to generate resource conflict prediction data, wherein the multi-dimensional resource conflict detection includes analyzing the timing trend of computing resources and predicting resource bottleneck points within the next 30 minutes; Step S2444: Integrate the isolation domain security trust assessment data, access control optimization data and resource conflict prediction data, perform adaptive security isolation and optimal allocation of resources, and generate a resource scheduling log.

[0045] In the embodiment of the present invention, the trust of the isolated domain is evaluated by adopting a method based on the Zero Trust Architecture (ZTA). The evaluation parameters include: the integrity of the computing environment (such as the TEE environment verification result), the security level of the task (based on the sensitivity of the task data), the historical security event records (such as whether unauthorized access has occurred), the execution of the access control policy (whether there is access that violates the policy), and the security trust score range (0-100) is set. The calculation formula is as follows: ;in The TEE environment integrity assessment score. is the security level of the task (such as high, medium, low), is the impact factor of historical security events, The access control policy execution status. , , , The weight parameter is used to classify the isolation domain according to the trust score: high security trust (80-100): tasks can be executed directly, medium security trust (50-79): additional security verification (such as multi-factor authentication) is required, and low security trust (0-49): task execution is isolated and rejected, trust evaluation logs are generated, and stored in the trusted database. The role-based access control (RBAC) + attribute-based access control (ABAC) method is adopted: resource access rights (such as administrator, ordinary user, guest) are assigned based on the role of the user to whom the task belongs. Access rights are dynamically adjusted based on the attributes of the task (such as priority, data sensitivity, and computing resource requirements). Compute access rights requirements: whether the task requires high-priority computing resources; whether the data of the task requires additional encryption; whether the execution time of the task affects the overall scheduling of the system; adopt the principle of least privilege (Least Privilege) to restrict the task from accessing unnecessary resources and reduce security risks. Record the optimized access control rules: the allowed resource range (CPU, storage, bandwidth), access control level (execute only, read only, read and write), and access time window (when the task can access resources). Use time series analysis methods (such as ARIMA and LSTM neural networks) to model computing resource usage trends. Mainly focus on indicators: CPU utilization trend, storage usage growth trend, and network bandwidth consumption change trend. Use regression analysis to predict the peak time of computing resources: ;in For future time The resource bottleneck prediction value within Different types of resources (CPU, memory, storage, bandwidth) are allocated at different times The utilization rate within is the weight parameter, As the error term, set resource usage thresholds: CPU utilization > 80% causes computing resource contention, storage utilization > 90% causes storage bottlenecks, and network bandwidth utilization > 85% causes communication delays. Generate resource conflict prediction data and mark the time point and task ID when the bottleneck occurs. Adjust resource access permissions based on the isolation domain security trust assessment data: For high-trust tasks, allow direct access to required resources. For medium-trust tasks, add security checks (such as dynamic password authentication). For low-trust tasks, block access or perform security isolation. Adjust the execution order of tasks based on access control optimization data: high-priority tasks are given priority in allocating computing resources. Low-priority tasks are executed when resource pressure is low to avoid resource conflicts. Adjust the resource scheduling strategy for the next 30 minutes based on resource conflict prediction data: if resource bottlenecks are predicted, postpone the execution of some low-priority tasks. If computing resources are tight, dynamically adjust task allocation to avoid resource contention. Record the final resource allocation plan for all tasks and generate a resource scheduling log.

[0046] Preferably, the step S3 of inputting the resource scheduling log into the AI ​​edge computing security coprocessor for multi-objective optimization includes: Input the resource scheduling log into the AI ​​edge computing security coprocessor to set the optimization target and generate the optimization target setting data, where the optimization target setting data includes the latency target, energy consumption target and security risk target; The delay minimization optimization calculation is performed on the delay target to obtain the delay minimization optimization data, where the delay minimization optimization calculation formula is as follows:

[0047] In the formula, is the optimized overall delay, For the The computational latency of a task, For the The communication delay of each task, is the total number of tasks; Energy consumption balance optimization calculation is performed on the energy consumption target to obtain energy consumption balance optimization data, where the energy consumption balance optimization calculation formula is as follows:

[0048] In the formula, is the overall energy consumption after optimization, For the The computing energy consumption of each module is For the The communication energy consumption of each module is and It is a module The weight coefficient of computing energy consumption and communication energy consumption, is the total number of modules; The security risk suppression optimization calculation is performed on the security risk target to obtain the security risk suppression optimization data, wherein the security risk suppression optimization calculation formula is as follows:

[0049] In the formula, For optimized security risks, For the The vulnerability risk of each task, For the The invasion risk of each task, and is the weight coefficient of vulnerability risk and intrusion risk, is the total number of tasks; According to the delay minimization optimization data, energy consumption balancing optimization data and security risk suppression optimization data, the resource scheduling log is used to schedule tasks and generate a task scheduling strategy.

[0050] In an embodiment of the present invention, by inputting the resource scheduling log into the AI ​​edge computing security coprocessor, key scheduling information such as task execution time, communication overhead, computing resource consumption, security event records, etc. is extracted. Based on task requirements and system constraints, optimization goals are set, including: reducing task execution time and improving scheduling efficiency; balancing the energy consumption of computing and communication to improve overall energy utilization; reducing vulnerabilities and intrusion risks and improving system security. According to the optimization target setting data, the optimization problem is constructed to form a parameter set for input optimization calculation. Parse the task calculation time and communication time in the resource scheduling log, perform the following optimization calculations, and obtain the optimal delay: In the formula, is the optimized overall delay, For the The computational latency of a task, For the The communication delay of each task, is the total number of tasks; use the multi-objective optimization algorithm in the AI ​​coprocessor (such as dynamic programming, reinforcement learning, or evolutionary algorithm) to solve the optimal task allocation solution to reduce the total delay and generate delay minimization optimization data for subsequent scheduling decisions. Extract the computing energy consumption and communication energy consumption in the resource scheduling log, and perform the following optimization calculations to balance the computing and communication energy consumption: In the formula, is the overall energy consumption after optimization, For the The computing energy consumption of each module is For the The communication energy consumption of each module is and It is a module The weight coefficient of computing energy consumption and communication energy consumption, is the total number of modules; adjust the task allocation strategy to avoid overloading of certain computing nodes, reduce the centralized distribution of high-energy consumption tasks, generate energy consumption balancing optimization data, and provide support for scheduling strategies. Based on log data, calculate the vulnerability risk and intrusion risk of each task, and perform the following optimization calculations to reduce the overall security risk: In the formula, For optimized security risks, For the The vulnerability risk of each task, For the The invasion risk of each task, and is the weight coefficient of vulnerability risk and intrusion risk, is the total number of tasks; adjust task allocation to reduce the centralized execution of high-risk tasks. Take proactive defense strategies, such as dynamic access control and anomaly detection mechanisms, to generate security risk suppression optimization data for developing safer task scheduling plans. Build an optimized task scheduling plan based on latency minimization optimization data, energy consumption balancing optimization data, and security risk suppression optimization data. Use weighted multi-objective optimization methods to weigh the importance of different optimization goals. Dynamically adjust task allocation strategies through AI models (such as reinforcement learning, neural networks, etc.). Assign tasks to the optimal computing nodes. Adjust computing and communication resources to achieve energy consumption balance. Use security protection measures to reduce vulnerabilities and intrusion risks. Deploy task scheduling strategies to edge computing environments to achieve dynamic task optimization execution.

[0051] Preferably, the trustworthy verification of the task scheduling strategy in step S3 includes: Extract the source code and configuration files of the task scheduling strategy for static analysis, including verifying the logical correctness of the task scheduling algorithm, policy constraints and security control measures, and generating static verification data for the task scheduling strategy; Use the trusted execution environment to dynamically verify the task scheduling strategy, simulate the scheduling process, monitor the behavior and resource usage of the task scheduling strategy in the real operating environment, and generate dynamic verification data for the task scheduling strategy; Integrate the static verification data of task scheduling strategy and the dynamic verification data of task scheduling strategy into a trusted verification result.

[0052] In an embodiment of the present invention, the core scheduling algorithm code, rule configuration file, security policy definition, etc. are obtained from the task scheduling system. The code structure is parsed to extract the scheduling logic, decision rules and constraints. Formal verification (such as Hoare logic, model detection) is used to verify the logical completeness of the scheduling algorithm. Check whether there are problems such as dead loops, infinite recursion, and resource competition. Ensure that task scheduling complies with predefined priority rules, delay thresholds, and resource allocation strategies. Use rule checking tools (such as ESLint and Clang Static Analyzer) to automatically verify whether the scheduling strategy meets the standards. Analyze whether the task scheduling strategy includes access control, authority management, and defense mechanisms. Use code security auditing tools (such as SonarQube and Flawfinder) to detect whether there are security vulnerabilities (such as SQL injection and buffer overflow). Record code inspection results, logic analysis reports, and security assessment data to form a static verification data set. The Trusted Execution Environment (TEE) provides an isolated computing environment to ensure that the task scheduling strategy is executed in a secure environment to avoid external interference. Run scheduling policies in TEE platforms such as Intel SGX, AMD SEV, or ARM TrustZone to ensure that scheduling policies are not maliciously tampered with. Track the execution time of tasks to ensure that latency constraints are met. Monitor resource usage such as CPU, memory, and network bandwidth to ensure balanced resource scheduling. Detect whether there are problems such as unauthorized access, abnormal termination, deadlock, memory leak, etc. during task execution. Identify abnormal patterns through log analysis and anomaly detection algorithms (such as LSTM anomaly detection models). Monitor whether access control policies are effective to prevent unauthorized task execution. Detect whether there are unauthorized API calls or file access behaviors. Record task execution logs, performance monitoring data, and anomaly detection reports to form a dynamic verification data set. Compare static verification data with dynamic verification data to analyze whether there are conflicts or inconsistencies. Analyze historical scheduling data through machine learning models (such as decision trees and random forests) to optimize trusted verification results. Set the credibility scoring formula: ; Score the logical correctness, Score constraint compliance, Score security control compliance, Score the dynamic running stability, , , , It is the weight coefficient and can be adjusted according to business needs.

[0053] Preferably, the step S3 of deploying the task scheduling strategy to the target edge node according to the result of the trusted verification includes: According to the results of trusted verification, the task scheduling strategy is deployed to the target edge node to detect the node status and generate node status data; Plan the deployment path for the node status data and generate deployment plan data; Perform network connection test on node status data through deployment plan data to generate network quality data; Establish a secure channel for network quality data and generate channel configuration data; The channel configuration data is used to distribute task instructions to the deployment plan data and generate scheduling instruction data.

[0054] In an embodiment of the present invention, the CPU, memory, storage, bandwidth, running tasks and other status data of the target edge node are collected. The current task load and resource utilization are obtained to form a node resource information table. The collected node resource information is analyzed to detect whether there are abnormal conditions such as overload, failure, packet loss, etc. A node health status report is generated using threshold detection and machine learning anomaly detection methods. According to the health status report, node status data is generated, including availability score, running status, load status, etc. The task scheduling strategy after trusted verification is parsed to extract scheduling constraints (computing requirements, bandwidth requirements, delay requirements, etc.). Combined with the node status data, a candidate node set is determined. The shortest path algorithm based on graph theory (such as Dijkstra) is used to calculate the optimal deployment path. Combined with the real-time network topology, ensure that the path selection meets the QoS (Quality of Service) requirements. Deployment plan data is formed, including target node selection, task migration path, load balancing strategy, etc. According to the deployment plan data, a connectivity test (Ping, TCP handshake, etc.) is performed on the candidate edge node. Monitor network parameters such as packet loss rate and RTT (Round Trip Time) to ensure that the node is reachable. Use tools such as iPerf to evaluate bandwidth performance and ensure that task data can be transmitted stably. Calculate the throughput between nodes and select the appropriate network path based on task requirements. Combine the results of connectivity tests and bandwidth tests to generate network quality data, including indicators such as latency, jitter, and packet loss rate. Use TLS (Transport Layer Security) or IPSec to establish end-to-end encrypted channels, and use Zero Trust Architecture (ZTA) for multi-factor identity authentication. Establish secure channels through VPN, SSH Tunnel, TLS, etc. to ensure data transmission security, configure data encryption policies, and select AES-256 or higher encryption algorithms. Record the configuration information of the secure channel, including encryption protocols, authentication methods, key exchange information, etc. Encrypt and encapsulate task scheduling instructions based on channel configuration data to ensure instruction integrity and tamper resistance. Use blockchain technology to sign and verify task instructions to improve credibility. Use message queue technologies such as MQTT and Kafka to ensure that instructions are reliably transmitted to the target node. Implement low-latency instruction delivery through WebSocket or gRPC. Record the task instruction distribution results and confirm whether each target edge node correctly receives and executes the task. Form scheduling instruction data, including task allocation log, execution status, abnormal conditions, etc.

[0055] As an example of the present invention, refer to Figure 3 As shown, in this example, step S4 includes: Step S41: collecting node scheduling process data for the final scheduling instruction set, including real-time recording of task execution time, resource consumption, task status and node response information, and generating task execution data; Step S42: performing abnormal execution type detection on the task execution data to generate abnormal task execution data; Step S43: According to the abnormal task execution data, the task scheduling strategy is used to allocate task computing power, generate a task execution report and a security audit log, and synchronize them to the emergency command center.

[0056] In an embodiment of the present invention, the start time, end time, running time and task response delay of the task are recorded. A distributed log system (such as ELK, Prometheus) is used to continuously monitor the task execution time. The deviation range of the execution time is calculated in combination with the node load. The consumption of resources such as CPU, memory, disk IO, and network bandwidth is monitored. Performance monitoring tools (such as cAdvisor and Grafana) are used to collect the node operation status in real time. A resource consumption model is formed to evaluate the scheduling efficiency. The task execution status (success, failure, timeout, abnormal termination, etc.) is recorded. The heartbeat signal, system log, and load status of the node are collected to ensure the normal execution of the task. The WebSocket or MQTT protocol is used to implement a low-latency data reporting mechanism. Task execution data is generated by combining task execution time, resource consumption, task status, and node response data. Abnormal detection is performed on the task execution data, including: timeout abnormality (execution time exceeds the preset threshold), resource overflow abnormality (CPU, memory, bandwidth exceeds the available range), task failure abnormality (return error code or task crash), and communication abnormality (node ​​unresponsive for a long time or packet loss rate is too high). Use LSTM (Long Short-Term Memory Network) or Isolation Forest for anomaly detection, distinguish between normal execution and abnormal execution, train the model to learn historical task execution data, and dynamically adjust the detection threshold. Record abnormal task information, including task ID, abnormal type, abnormal cause, impact range, etc., to generate abnormal task execution data. According to the abnormal task execution data, dynamically adjust the task scheduling strategy, including: load balancing optimization (migrating high-load tasks to low-load nodes). Computing power allocation optimization (increasing the computing power priority of abnormal tasks to avoid resource bottlenecks). Real-time task retry (reallocating failed tasks to spare nodes). Record the overall situation of task execution, including: total number of tasks, success rate, failure rate, average task execution time, resource utilization, type distribution of abnormal tasks and optimization effect. Record detailed logs of the task scheduling process to ensure traceability, including: task execution changes (scheduling adjustment records), abnormal response records (corrective measures and recovery processes), security event records (whether attacks or illegal operations are involved), and use blockchain technology to ensure that audit logs cannot be tampered with. Task execution reports and security audit logs are synchronized to the emergency command center through Kafka message queues or RESTful APIs. AES-256 encryption is used to transmit data to ensure security. The command center can perform abnormal warnings, decision optimization, and historical data backtracking.

[0057] It is particularly important that the task computing power allocation and scheduling of the task scheduling strategy according to the abnormal task execution data also includes: Dynamically perceive edge node resources based on abnormal task execution data for task scheduling strategies, and generate edge computing resource status perception data; Perform image modeling on edge computing resource status perception data to generate computing resource images; Use the federated reinforcement learning framework to perform resource fusing on computing resource profiles and generate a trusted policy fusing mechanism. Use the trusted policy fuse mechanism to audit the resource allocation of task scheduling policies and generate task execution reports and security audit logs.

[0058] In an embodiment of the present invention, the resources of the edge node are dynamically sensed based on various abnormal information (such as task timeout, resource overrun, task failure, etc.) extracted from the abnormal task execution data. This process generates edge computing resource status perception data by monitoring the resource usage of the edge node, including real-time data such as CPU, memory, storage, and network bandwidth. These data will reflect the current load situation, resource consumption level, and operation status of each edge node, and help the system to evaluate the resource capacity and load situation of each node in real time. Based on the edge computing resource status perception data, the system performs computing resource portrait modeling. This process uses the collected various resource consumption data, task execution status, and historical performance data to construct a computing resource portrait of each edge node through machine learning methods (such as cluster analysis, principal component analysis, etc.). The computing resource portrait includes the processing capacity, resource consumption mode, load balancing capability, and other characteristics of the node, and is classified according to the different working states of the node (such as high load, low load, idle state, etc.). After the computing resource portrait is constructed, the system will perform resource fuse on the computing resource portrait through the federated reinforcement learning framework. The federated reinforcement learning framework allows multiple edge nodes to share information while maintaining data privacy, and optimizes resource scheduling strategies through collaborative learning. Under this framework, the system evaluates the resource fuse threshold by simulating different task scheduling schemes and resource allocation strategies, that is, how to quickly and effectively adjust resource allocation in the event of overload or resource bottleneck to prevent system crashes or task failures. The resource fuse mechanism is generated based on the node load, task priority, and task resource requirements to ensure that the system can dynamically adjust under high load or abnormal conditions to avoid single point overload problems. The task scheduling strategy is audited using the generated trusted policy fuse mechanism. The trusted policy fuse mechanism automatically adjusts the task scheduling strategy based on the computing resource profile and resource fuse mechanism in the case of multiple resource anomalies to ensure that resources are reasonably allocated among nodes. By auditing the task scheduling strategy, the system can evaluate the effectiveness of the current scheduling strategy and make adjustments to abnormal task execution data. For example, if some node resources exceed the standard, the system will migrate the relevant tasks to nodes with lower loads, or split the computing tasks into smaller subtasks for parallel processing. Finally, the adjustment of the task scheduling strategy will generate a task execution report and a security audit log. The task execution report will record in detail the changes in various indicators during the task execution process, including task success rate, execution time, resource consumption, task failure, etc.; the security audit log includes detailed records of all resource allocation, scheduling decisions and change operations during the task scheduling process to ensure transparency and traceability of operations.

[0059] Preferably, step S42 includes the following steps: Step S421: When any of the following situations occurs, it is determined as a task timeout exception and task timeout exception data is obtained: the task execution time deviates from the predetermined maximum execution time by more than ±10%, or the task fails to be completed within the specified time; Step S422: when any of the following situations occurs, it is determined as a resource over-limit exception, and resource over-limit exception data is obtained: CPU usage exceeds 95%, memory usage exceeds 90%, or storage usage exceeds 85%; Step S423: When the following conditions occur at the same time, it is determined as a task failure exception, and task failure exception data is obtained: the number of task failures exceeds 3 times in a row, and the number of retries after each failure does not exceed 2 times; Step S424: When the following conditions occur at the same time, it is determined that the node response delay is abnormal, and the node response delay abnormal data is obtained: the node response time exceeds 5 seconds continuously, and the duration of the node non-response exceeds 5 minutes; Step S425: Integrate task timeout exception data, resource excess exception data, task failure exception data and node response delay exception data to obtain abnormal task execution data.

[0060] In the embodiment of the present invention, when the task execution time deviates from the predetermined maximum execution time by more than ±10%, or the task fails to be completed within the specified time, it is determined as a task timeout exception. At this time, the system will monitor the execution time of the task and calculate the difference between it and the predetermined maximum execution time. If the difference exceeds ±10%, it is considered as a timeout; or if the task is not completed within the specified time range, it will also trigger the task timeout exception determination. The task timeout exception data will include the task ID, the task start time, the predetermined maximum execution time, the actual execution time, and the timeout reason. When the CPU usage exceeds 95%, the memory usage exceeds 90%, or the storage usage exceeds 85% during the task execution, it is determined as a resource overrun exception. This step monitors the consumption of node resources in real time. Once it is found that the resource usage exceeds the set threshold, the system will immediately mark it as a resource overrun exception and generate corresponding resource overrun exception data, which will include the task ID, CPU usage, memory usage, storage usage, and the specific threshold of the resource overrun. When the task fails more than 3 times in a row, and the number of retries after each failure does not exceed 2 times, it is determined as a task failure exception. This step monitors the execution status of the task and records the failure of the task. If a task fails more than three times in a row during execution and no more than two retries are performed after each failure, the task will be marked as a failure exception. The task failure exception data will include detailed information such as the task ID, number of failures, number of retries, and failure reasons. When the response time of a node exceeds 5 seconds continuously and the duration of the node's non-response exceeds 5 minutes, it is considered a node response delay exception. By monitoring the response time of the node, if the response time exceeds 5 seconds and the node has not responded in the past 5 minutes, it is considered a node response delay exception. The node response delay exception data will record information such as the node ID, continuous response time, duration of non-response, and node status. In the above steps, all the abnormal data generated will be integrated. Task timeout exception data, resource excess exception data, task failure exception data, and node response delay exception data will be summarized into a complete abnormal task execution data.

[0061] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0062] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An edge computing scheduling method for heterogeneous multi-source sensors, characterized in that: The following steps are involved: Step S1: Collecting raw data using heterogeneous sensors built into the smart helmet; Generating data fingerprints for the original data to obtain an encrypted heterogeneous data set; dividing the heterogeneous data set into task types to generate a standardized task queue; Step S2: Monitor the node status of edge computing nodes to build a resource dynamic perception matrix; perform resource allocation security isolation on the standardized task queue according to the resource dynamic perception matrix, and generate a resource scheduling log; Step S3: Input the resource scheduling log into the AI ​​edge computing security coprocessor for multi-objective optimization to generate a task scheduling strategy; Perform trustworthy verification on the task scheduling strategy, and deploy the task scheduling strategy to the target edge node based on the trustworthy verification result to generate the final scheduling instruction set; Step S4: Collect node scheduling process data for the final scheduling instruction set to obtain task execution data; detect abnormal execution types in the task execution data, and dynamically adjust the task scheduling strategy, generate a task execution report and a security audit log and synchronize them to the emergency command center.

2. The edge computing scheduling method for heterogeneous multi-source sensors according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting raw data using the heterogeneous sensors built into the smart helmet; Step S12: preprocessing the raw data to generate a standard heterogeneous data set, wherein the preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S13: marking the acquisition timestamp of the standard heterogeneous data set; extracting the device identification of the standard heterogeneous data set; performing data hash encryption on the standard heterogeneous data set based on the device identification to generate an encrypted heterogeneous data set; Step S14: dividing the encrypted heterogeneous data set into task types to generate heterogeneous data task types; Step S15: sorting heterogeneous data task types by collecting timestamps to generate a standardized task queue.

3. The edge computing scheduling method for heterogeneous multi-source sensors according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: deploy edge computing nodes; Step S22: Perform node status monitoring on the edge computing node to generate edge computing node status data, wherein the node status monitoring includes computing load, storage margin, and network bandwidth status; Step S23: construct a resource dynamic perception matrix based on the edge computing node status data; calculate the task priority of the standardized task queue, and adjust the task order of the standardized task queue according to the task priority to generate a dynamic priority queue; Step S24: Perform resource allocation security isolation on the dynamic priority queue through the trusted execution environment and generate a resource scheduling log.

4. The edge computing scheduling method for heterogeneous multi-source sensors according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: Initialize the trusted execution environment: the secure memory size is 512MB to 16GB, the encrypted storage space ranges from 256MB to 4TB, the number of CPU trusted execution cores is between 1 and 8 cores, the TEE OS startup time is about 50 to 500ms, the encryption algorithm throughput can reach 100 to 5000Mbps, the remote authentication time is about 10 to 200ms, the trusted measurement time is 5 to 100ms, the key management module storage capacity is between 128KB and 32MB, and the maximum number of concurrent trusted applications can support 10 to 1000; Step S242: verifying the integrity of the trusted execution environment and generating environment verification data; performing security policy loading on the environment verification data and generating policy configuration data; performing resource boundary division on the policy configuration data and generating isolation domain data; Step S243: extracting the task attributes of the dynamic priority queue, and performing queue scheduling on the dynamic priority queue to generate scheduling sequence data; performing resource demand analysis on the scheduling sequence data to generate resource mapping data; Step S244: Use the isolation domain data to perform resource security isolation and allocation on the resource mapping data, and generate a resource scheduling log.

5. The edge computing scheduling method for heterogeneous multi-source sensors according to claim 1 is characterized in that: Inputting the resource scheduling log into the AI ​​edge computing security coprocessor for multi-objective optimization in step S3 includes: Input the resource scheduling log into the AI ​​edge computing security coprocessor to set the optimization target and generate the optimization target setting data, where the optimization target setting data includes the latency target, energy consumption target and security risk target; The delay minimization optimization calculation is performed on the delay target to obtain the delay minimization optimization data, where the delay minimization optimization calculation formula is as follows: In the formula, is the optimized overall delay, For the The computational latency of a task, For the The communication delay of each task, is the total number of tasks; Energy consumption balance optimization calculation is performed on the energy consumption target to obtain energy consumption balance optimization data, where the energy consumption balance optimization calculation formula is as follows: In the formula, is the overall energy consumption after optimization, For the The computing energy consumption of each module is For the The communication energy consumption of each module is and It is a module The weight coefficient of computing energy consumption and communication energy consumption, is the total number of modules; The security risk suppression optimization calculation is performed on the security risk target to obtain the security risk suppression optimization data, wherein the security risk suppression optimization calculation formula is as follows: In the formula, For optimized security risks, For the The vulnerability risk of each task, For the The invasion risk of each task, and is the weight coefficient of vulnerability risk and intrusion risk, is the total number of tasks; According to the delay minimization optimization data, energy consumption balancing optimization data and security risk suppression optimization data, the resource scheduling log is used to schedule tasks and generate a task scheduling strategy.

6. The edge computing scheduling method for heterogeneous multi-source sensors according to claim 1 is characterized in that: The trusted verification of the task scheduling strategy in step S3 includes: Extract the source code and configuration files of the task scheduling strategy for static analysis, including verifying the logical correctness of the task scheduling algorithm, policy constraints and security control measures, and generating static verification data for the task scheduling strategy; Use the trusted execution environment to dynamically verify the task scheduling strategy, simulate the scheduling process, monitor the behavior and resource usage of the task scheduling strategy in the real operating environment, and generate dynamic verification data for the task scheduling strategy; Integrate the static verification data of task scheduling strategy and the dynamic verification data of task scheduling strategy into a trusted verification result.

7. The edge computing scheduling method for heterogeneous multi-source sensors according to claim 1 is characterized in that: Deploying the task scheduling strategy to the target edge node according to the result of the trusted verification in step S3 includes: According to the results of trusted verification, the task scheduling strategy is deployed to the target edge node to detect the node status and generate node status data; Plan the deployment path for the node status data and generate deployment plan data; Perform network connection test on node status data through deployment plan data to generate network quality data; Establish a secure channel for network quality data and generate channel configuration data; The channel configuration data is used to distribute task instructions to the deployment plan data and generate scheduling instruction data.

8. The edge computing scheduling method for heterogeneous multi-source sensors according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: collecting node scheduling process data for the final scheduling instruction set, including real-time recording of task execution time, resource consumption, task status and node response information, and generating task execution data; Step S42: performing abnormal execution type detection on the task execution data to generate abnormal task execution data; Step S43: According to the abnormal task execution data, the task scheduling strategy is used to allocate task computing power, generate a task execution report and a security audit log, and synchronize them to the emergency command center.

9. The edge computing scheduling method for heterogeneous multi-source sensors according to claim 8 is characterized in that: Step S42 includes the following steps: Step S421: When any of the following situations occurs, it is determined as a task timeout exception and task timeout exception data is obtained: the task execution time deviates from the predetermined maximum execution time by more than ±10%, or the task fails to be completed within the specified time; Step S422: when any of the following situations occurs, it is determined as a resource over-limit exception, and resource over-limit exception data is obtained: CPU usage exceeds 95%, memory usage exceeds 90%, or storage usage exceeds 85%; Step S423: When the following conditions occur at the same time, it is determined as a task failure exception, and task failure exception data is obtained: the number of task failures exceeds 3 times in a row, and the number of retries after each failure does not exceed 2 times; Step S424: When the following conditions occur at the same time, it is determined that the node response delay is abnormal, and the node response delay abnormal data is obtained: the node response time exceeds 5 seconds continuously, and the duration of the node non-response exceeds 5 minutes; Step S425: Integrate task timeout exception data, resource excess exception data, task failure exception data and node response delay exception data to obtain abnormal task execution data.

10. An edge computing scheduling system for heterogeneous multi-source sensors, characterized in that: Used to execute the edge computing scheduling method for heterogeneous multi-source sensors as claimed in claim 1, the edge computing scheduling system for heterogeneous multi-source sensors includes: The data acquisition module is used to collect raw data using the heterogeneous sensors built into the smart helmet; generate data fingerprints for the raw data to obtain encrypted heterogeneous data sets; divide the heterogeneous data sets into task types to generate a standardized task queue; The task allocation module is used to monitor the node status of edge computing nodes and build a resource dynamic perception matrix; according to the resource dynamic perception matrix, the standardized task queue is allocated and safely isolated, and a resource scheduling log is generated; The resource scheduling module is used to input the resource scheduling log into the AI ​​edge computing security coprocessor for multi-objective optimization and generate a task scheduling strategy; perform trusted verification on the task scheduling strategy, and deploy the task scheduling strategy to the target edge node based on the trusted verification results to generate the final scheduling instruction set; The anomaly detection module is used to collect node scheduling process data for the final scheduling instruction set to obtain task execution data; detect abnormal execution types in task execution data, and dynamically adjust the task scheduling strategy, generate task execution reports and security audit logs and synchronize them to the emergency command center.

Images