A real-time intelligent teaching system and method for panoramic driving training

Abstract

This invention provides a real-time intelligent teaching system and method for panoramic driving training, relating to the field of driving training technology. It includes: a task and credential module, an execution and causal reversal module, a reconstruction and snapshot module, a penalty and reputation feedback module, and an audit and compliance module. The system generates oracle credentials bound to an initial success probability estimate through an oracle network; it uses a verifiable delay function to set a time window for completing the signature, enabling locking, unlocking, or rollback of the execution witness; it supports concurrent and symmetric scheduling of mirrored tasks; it calculates staking penalties and updates reputation based on the initial success probability using a convex function mechanism; and finally, it writes all process data into an immutable audit chain and generates zero-knowledge proof compliance credentials for verification by regulatory agencies.

Description

Technical Field

[0001] This invention relates to the field of driver training technology, specifically to a real-time intelligent teaching system and method for panoramic driver training. Background Technology

[0002] The driver training industry is rapidly developing towards intelligence and real-time interaction. With the increasing demand for road traffic safety and the expansion of training scale, the traditional manual teaching model can no longer meet the requirements of efficient, standardized and traceable teaching management. The introduction of intelligent technology to assist driver training has become an industry consensus, and related systems must have high reliability, fairness and compliance.

[0003] Existing driver training systems typically combine electronic records with manual review; some systems have implemented digital allocation of training tasks and tracking of execution status; some solutions assess student and instructor behavior through simple logical judgments and database records; and a few systems have introduced reputation mechanisms and basic audit functions to improve the transparency of the teaching process.

[0004] The shortcomings of existing technologies are: lack of effective multi-party collaboration and status synchronization mechanisms during task execution; difficulty in achieving automatic rollback and responsibility identification in abnormal situations; insufficient guarantee of fairness in task scheduling and resource allocation; reliance on manual intervention in auditing and compliance; and difficulty in achieving a fully verifiable and tamper-proof supervision mechanism. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a real-time intelligent teaching system and method for panoramic driving training, which solves the problems mentioned in the background technology, such as the lack of an effective multi-party collaboration and state synchronization mechanism during task execution; difficulty in achieving automatic rollback and responsibility determination in abnormal situations; insufficient guarantee of fairness in task scheduling and resource allocation; reliance on manual intervention in auditing and compliance; and difficulty in achieving a fully verifiable and tamper-proof supervision mechanism.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a real-time intelligent teaching system and method for panoramic driving training, comprising: a task and credential module, an execution and causal reversal module, a reconstruction and snapshot module, a penalty and reputation feedback module, and an audit and compliance module;

[0009] When a student triggers a driving training task, the task and credential module generates an initial success probability estimate of the driving training task based on the historical reputation data of the student and the instructor extracted from the reputation database, and generates an oracle credential bound to the initial success probability estimate through multi-party threshold signature of an oracle network composed of multiple authorized verification nodes.

[0010] When the execution and causal reversal module receives the oracle certificate, it generates a locked execution witness that is not visible to the outside and is in a locked state, even if not all instructor signatures have been collected. This allows the driving training task to enter the execution entry point. At the same time, it calls a verifiable delay function to generate a signature completion time window that is not expeditable. If the signature completion is completed within the signature completion time window, the locked execution witness is unlocked and the execution result is written to an immutable snapshot. If the signature completion is not completed before the signature completion time window expires, the locked execution witness is invalidated and a rollback is triggered. In concurrent task scenarios, the execution and causal reversal module generates a mirror task for the original task. The mirror task and the original task are both constrained by the signature completion time window and are symmetrically scheduled according to the alternating release rule and resource interlocking constraints.

[0011] The reconstruction and snapshot module maintains a task buffer pool and generates textual reconstruction snapshots according to the buffer pool rules. These snapshots are then solidified by the reconstruction certificate. Tasks are dispatched in snapshot order through a preemption token and woke up one by one by the scheduler. All locked execution witnesses are written into immutable snapshots after unlocking.

[0012] When the signature completion fails, the penalty and reputation feedback module calculates and deducts the penalty amount from the pledge pre-paid by the student or coach based on the initial success probability estimate in the oracle certificate. The penalty amount increases exponentially based on a convex function as the initial success probability estimate increases. The penalty ticket and reputation update record are written into the reputation database, and the reputation update record is fed back to the task and certificate module to affect the initial success probability estimate of subsequent oracle certificates.

[0013] After the task is closed, the audit and compliance module writes the oracle certificate, locked execution witness, completed signature status, reconstructed certificate and reputation update result into the immutable audit chain, and generates a compliance certificate based on zero-knowledge proof for independent verification by regulatory agencies.

[0014] Preferably, through the execution and causal reversal module, after receiving the oracle certificate, if not all instructor signatures are collected, a locked execution witness is generated, allowing the driving training task to enter the execution entry point. Simultaneously, a verifiable delay function is invoked to generate a 120-second signature completion time window to ensure non-acceleration. If all valid instructor signatures are collected within the signature completion time window, the locked execution witness is unlocked, and the execution result is transmitted to the reconstruction and snapshot module to generate a reconstruction snapshot. This snapshot is then permanently written into the immutable snapshot using a reconstruction certificate, and the scheduler wakes up the task according to the order of the preemption token and buffer pool. If the signature completion time window expires and not all signatures are collected, the locked execution witness must not be unlocked, and the lock should be invalidated. The execution witness is determined and a rollback is triggered. The penalty and reputation feedback module automatically deducts the stake penalty amount from the stakes of both the student and the coach based on the initial success probability estimate in the oracle certificate. The stake penalty amount starts with a base penalty rate of 0.01 and is amplified exponentially by a convex function with a parameter of 2.0 to strengthen the economic constraint on users with high reputation values, thereby reducing the incentive to default. In concurrent scenarios, the execution and causal reversal module generates a mirror task for the original task. The mirror task and the original task are both subject to the completion signature time window constraint and are symmetrically scheduled according to the alternating release rule and resource interlock constraint to avoid resource deadlock. The release order log is recorded in the release operation and submitted to the audit and compliance module for fairness assessment.

[0015] Preferably, upon receiving the unlock execution result from the execution and causal reversal module, the reconstruction and snapshot module immediately generates a reconstruction snapshot and records the training data, signature credentials, and task status involved in the execution process line by line. These records must be written sequentially into the reconstruction storage unit, without skipping any fields, to ensure data integrity. After generating the reconstruction snapshot, a reconstruction certificate is used to solidify it. This solidification process includes a step-by-step verification of the snapshot generation timestamp, unique snapshot identifier, and signature chain. The verified snapshot is then written to an immutable snapshot area, preventing subsequent modifications. After the snapshot is solidified, the scheduler wakes up tasks according to the order of the preemption token and the buffer pool. The order in which the preemption token is acquired is determined by the task submission time and task priority. Priority is determined by the system. If two tasks compete simultaneously, the one with higher priority enters the buffer pool queue first, while the remaining tasks enter the waiting area. Tasks cannot bypass the buffer pool and directly enter the execution area. When a task enters the execution area from the buffer pool, the wake-up time, token number, and buffer pool location must be recorded and submitted to the audit and compliance module for archiving, for subsequent evaluation of execution fairness. During the task wake-up process, if it is found that the snapshot has not been solidified within the 120-second time window, the task's entry into the execution area will be immediately terminated, and the penalty and reputation feedback module will impose a stake penalty on the responsible party to prevent unfairness caused by task delays. Through the above mandatory steps, it is ensured that all execution processes in the training scenario can be fully traced, and that the data and task status are verifiable and tamper-proof.

[0016] Preferably, in the penalty and reputation feedback module, a convex function exponential penalty model is used to calculate the pledge penalty amount. The input items are the student's reputation value, the coach's reputation value, the initial success probability estimate in the oracle certificate, and the base penalty rate of 0.01. The first step is to standardize the student's reputation value and the coach's reputation value into penalty weights. The standardization process involves subtracting the minimum reputation value and dividing by the reputation value range to obtain the normalized weight. The second step is to multiply the normalized weight by the base penalty rate and use it as the base for the exponential calculation, setting the exponential amplification factor to 2.0 to obtain the amplified result. The third step is to calculate the final pledge penalty amount as the product of the amplified result and the original pledge amount, rounded to the smallest monetary unit. The difference between the benefits of default and compliance is compared through mathematical expectation. If the expected loss from default is greater than the expected benefit from continuing to trade, The incentive to default is suppressed by economic constraints, thus achieving a stronger constraint effect on high-credit accounts. In the concurrent driver training scenario, the execution and causal reversal module creates a mirror task for each original training task. The mirror task copies the resource usage snapshot of the original task and enters the buffer pool synchronously. The scheduler applies alternating release rules and resource interlock constraints to implement symmetrical scheduling for both the original task and the mirror task. The alternating release rule stipulates that when at least two tasks compete for the same resource set, they take turns releasing their respective resources according to the order of preemption tokens and record the release sequence. The buffer pool saves the release sequence log and submits it to the audit and compliance module for fairness verification. By copying the resource usage status of the mirror task and enforcing the alternating release order, the circular waiting situation can be eliminated, thereby avoiding resource deadlock and improving the efficiency of concurrent scheduling.

[0017] Preferably, in the audit and compliance module, upon receiving the release order log and buffer pool status submitted by the scheduler, the module immediately compares and verifies them according to time order and task priority order. The verification process includes confirming whether the timestamp, preemption token number, and resource release order of each task upon entering the buffer pool strictly comply with the alternating release rules and resource interlocking constraints. If a task is found to have skipped the predetermined token order or failed to follow the rotation release requirements when releasing resources, the task is immediately marked as violating the rules, and the violation record is written to the immutable snapshot area. In the immutable snapshot area, the violation record must include the task number, instructor identifier, student identifier, violation time, violation operation content, and snapshot verification signature to ensure that all violations have a complete chain of evidence. When the verification is successful, the audit and compliance module generates a valid certificate. The data is then returned to the reconstruction and snapshot module as supplementary evidence of the final execution record of the task, ensuring that the task execution result has compliance proof. In concurrent training scenarios, if two or more tasks fail to complete snapshot solidification within a 120-second time window, the audit and compliance module must compare all relevant tasks one by one. If the snapshot fails to solidify due to resource competition, the responsible party is determined according to the order in which the token is claimed, and the penalty and reputation feedback module is triggered to perform pledge deduction processing. Each pledge deduction operation must be synchronously written to the reputation value update unit. The reputation value update unit reduces the student's reputation value and the coach's reputation value according to the deduction amount and immediately feeds it back to the reputation value storage table to ensure the real-time effectiveness of reputation weight calculation in subsequent scheduling processes. Through strict sequence verification, violation record solidification, and real-time reputation updates.

[0018] Preferably, in the reputation value storage table, the reputation values ​​of all trainees and coaches are updated in real time according to the task execution results. The update rule is as follows: when a task completes signature collection and successfully saves a snapshot within a 120-second time window, the reputation value increases by the base reward rate of 0.005 multiplied by the task completion weight. The task completion weight is calculated based on the task duration and resource utilization rate. When a task fails to complete signature collection within the specified time or is marked as a violation by the audit and compliance module due to irregular operations, the reputation value decreases by the pledge penalty amount calculated by the convex function exponential penalty model multiplied by the reputation coefficient. The reputation coefficient is linearly adjusted between 0.5 and 1.5 according to the number of historical violations. The reputation value storage table adopts a dual verification mechanism. The first verification is performed by reconstructing... The first layer of verification is triggered synchronously with the snapshot module after the snapshot is solidified, ensuring that the change in reputation value is consistent with the task result. The second layer of verification is triggered synchronously by the audit and compliance module when the violation record is generated, ensuring that the operation of reducing reputation value is consistent with the evidence of violation. After the reputation value is updated, the reputation value storage table transmits the result to the scheduler. The scheduler calculates the weight according to the latest reputation value in the next task scheduling cycle. The task with the higher weight has a higher priority to enter the buffer pool in the competition for tokens. The reputation value storage table must ensure immutability. Each change requires the generation of a change certificate, which includes the task number, the reputation value before the change, the reputation value after the change, the triggering module number, and a digital signature. All certificates are written to the immutable snapshot area and stored permanently.

[0019] Preferably, in the driving training task submission stage, the driving training task is first bound to a unique task number, student identifier, and instructor identifier upon input. An initial oracle certificate is then generated by the execution and causal reversal module. This oracle certificate records the estimated task completion time, required resource types, required resource quantities, and an initial success probability estimate, calculated based on historical reputation scores and task difficulty coefficients. After submission, the execution and causal reversal module immediately retrieves the reputation score storage table, comparing the student's reputation score with the instructor's reputation score. If the reputation score is below the minimum threshold of 30, the task is directly rejected from entering the execution process, and the rejection reason and task number are written to an immutable snapshot area. If the reputation score is above the minimum threshold of 30, the task is rejected from entering the execution process. If the threshold requirements are met, the Execution and Causal Reversal module initiates the signature collection operation and generates a locked execution witness, which includes the task number, resource requirements, signature status, and timestamp. During the signature collection process, each coach must complete the signature operation within 120 seconds; otherwise, the Execution and Causal Reversal module will transmit the incomplete status to the Penalty and Reputation Feedback module, which will deduct the relevant stake according to the convex function exponential penalty model. When all coaches complete the signature within the specified time, the Execution and Causal Reversal module will transmit the complete signature chain to the Reconstruction and Snapshot module to solidify it into a snapshot and ensure that all submission information, signature credentials, and time records are completely preserved.

[0020] Preferably, during the driving training task execution phase, upon receiving the unlock execution witness, the driving training task is formally introduced into the resource allocation process. Resources in the buffer pool are allocated according to the resource requirements and task priorities recorded in the oracle token. The resource allocation order must strictly follow the order of the preemption token numbers; tasks with smaller numbers get allocation opportunities first. If two tasks have the same number, the order of submission time determines allocation. During allocation, the execution and causal reversal modules invoke the alternating release rule to prevent prolonged resource occupation. The alternating release rule stipulates that if a task holds resources continuously for more than 30 seconds, it must release at least one type of resource for other tasks. Released resources are immediately redistributed by the scheduler and recorded in the buffer pool. Resource release time, resource type, and receiving task number; after all resources are allocated, the execution and causal reversal module will call the reconstruction and snapshot module to generate an execution snapshot. The snapshot records the resource details used by the task, the percentage of execution progress, and the latest signature status. The snapshot generation cycle is once every 10 seconds and cannot be skipped; if a task's snapshot generation is delayed by more than 20 seconds due to resource contention during execution, the audit and compliance section will immediately trigger an anomaly check and write the delay information into the immutable snapshot area. At the same time, the penalty and reputation feedback module will impose a pledge penalty on the responsible party. The pledge penalty amount is calculated according to the convex function exponential penalty model, with an initial penalty rate of 0.01 and an exponential amplification factor of 2.0; the formula for the convex function exponential penalty model is: Where P is the final amount of the pledge penalty, that is, the actual amount deducted from the pledge of the student or coach; The raw, unprocessed reputation values ​​of trainees, directly from the reputation database; The raw, unprocessed reputation values ​​of coaches, directly from the reputation database; The normalized weights for student reputation scores are calculated using the following formula: ,in The student's current reputation score is retrieved from the reputation database. The minimum reputation value allowed by the system is 30. The maximum allowed reputation value is 100. The normalized weights for coach reputation scores are calculated using the following formula: , The coach's current reputation value is read from the reputation database; b is the base penalty rate of 0.01, or 1%; k is the exponential amplification factor; S is the original pledge amount, which is the total amount of pledge prepaid by the student or coach.

[0021] Preferably, during the task completion phase, when the execution and causal reversal module detects that the task progress has reached 100%, it immediately generates a completion status identifier and transmits the task execution result along with the completion status identifier to the reconstruction and snapshot module. The reconstruction and snapshot module then generates a final snapshot. The final snapshot must include the task number, student identifier, instructor identifier, execution start time, execution end time, resource usage records, signature chain, and completion status identifier, ensuring that the snapshot data covers the entire task process. After generating the final snapshot, the reconstruction certificate immediately performs a solidification operation on the snapshot. The solidification operation includes generating a unique snapshot identifier, embedding a completion timestamp, overlaying the signature chain, and performing verification. After successful verification, the snapshot is written to the immutable snapshot area and cannot be modified or deleted subsequently. After completion, the scheduler releases all resources occupied by the task according to the buffer pool sequence. The release operation needs to record the release time, resource type, resource quantity and receiving task number, and submit the record to the audit and compliance department for fairness verification. If there are any violations or timeouts during the execution of the task, the audit and compliance department needs to write the violations and the final snapshot into the immutable snapshot area, and notify the penalty and reputation feedback module to execute the staking penalty. The staking penalty amount is still calculated according to the convex function exponential penalty model. If there are no violations during the entire execution of the task, the penalty and reputation feedback module will increase the reward amount for the student and coach's reputation value according to the initial success probability estimate in the oracle certificate and the basic reward rate of 0.005, and synchronize the reputation value update result to the reputation value storage table.

[0022] Preferably, during the abnormal rollback phase, if the execution and causal reversal module fails to collect all coach signatures by the 120-second signature completion time window, it immediately triggers a rollback operation. The first step of the rollback operation is to invalidate the locked execution witness and generate a rollback snapshot in the immutable snapshot area. The rollback snapshot must record the task number, student identifier, coach identifier, number of incomplete signatures, completed signature chains, and rollback trigger time. The second step is to notify the scheduler to release all occupied resources. The resource release record must include the resource type, resource quantity, release time, and receiving task number, and be submitted to the audit and compliance department for archiving. The third step is to call the penalty and reputation feedback module to perform staking processing. The staking processing rule is based on the initial success probability valuation. The basic penalty amount is calculated using a base penalty rate of 0.01. Then, the penalty result is amplified by a convex function exponential amplification factor of 2.0. The final penalty amount is deducted proportionally from the pledges of trainees and coaches. The proportion is determined based on the completion rate of signatures: if the completion rate is below 50%, the pledges of trainees and coaches are deducted at a 7:3 ratio; if the completion rate is above 50%, the pledges are deducted at a 5:5 ratio. The fourth step is to update the reputation value storage table, synchronizing the pledge penalty result with the reputation value reduction operation, and generating a reputation value change voucher. The voucher includes the task number, the reputation value before the change, the reputation value after the change, the penalty amount, and the digital signature. The voucher is written to the immutable snapshot area for storage. The fifth step is to notify the audit and compliance departments to generate a violation record for the rollback event. The violation record must be archived together with the rollback snapshot.

[0023] Preferably, in concurrent training scenarios, when the execution and causal reversal module receives multiple task submissions, it generates a corresponding mirror task for each original task. The mirror task copies the resource requirements, signature status, and progress snapshot of the original task and enters the buffer pool along with the original task. When allocating resources, the scheduler considers both the original and mirror tasks, performing symmetric scheduling according to the preemption token order. The symmetric scheduling rules stipulate that the original and mirror tasks must occupy resources in an alternating release manner. The release order in each round is determined by the preemption token number, with the smaller number releasing first and the larger number releasing later. The released resources are immediately allocated to the other party, ensuring that there is no circular waiting in the resource allocation process. During the symmetric scheduling process, the buffer pool needs to record the timestamp of each release operation. The system records resource type, release task number, and receiving task number, and submits these records to the audit and compliance department for archiving. If a task fails to release resources within the specified rounds, the scheduler immediately suspends the task, and the audit and compliance department marks it as a violation, writing the violation record to the immutable snapshot area. After all original and mirror tasks have completed resource usage through alternating release, the execution and causal reversal module generates a phased snapshot, recording the resource allocation sequence, task progress percentage, and signature chain in the snapshot, which is then solidified and written to the immutable snapshot area by the reconstructed certificate. Through the replication of mirror tasks and the forced execution of symmetric scheduling, resource deadlock can be avoided and resource utilization improved in driver training scenarios, while ensuring the fairness and traceability of each task's execution in a concurrent environment.

[0024] Preferably, during the execution of resource interlock constraints, when the scheduler detects multiple tasks simultaneously requesting the same set of resources, it immediately activates the resource interlock constraints. These constraints stipulate that all competing tasks must enter the resource usage queue sequentially according to their preemption token numbers, and out-of-order operations are not allowed. Tasks with smaller token numbers occupy resources first, while tasks with larger token numbers enter a waiting state, during which they cannot occupy any resources. During resource usage, the scheduler enforces release order rules, requiring each task to release at least one type of resource after occupying resources for 30 seconds. The buffer pool records the timestamp of the release operation, the resource type, the releasing task number, and the receiving task number. All release order logs are transmitted in real time. The audit and compliance department compares the results. If the audit and compliance department finds that a task has not released resources as required or has skipped a release round, it immediately generates a violation record and writes the violation snapshot to the immutable snapshot area. At the same time, it notifies the penalty and reputation feedback module to calculate the pledge penalty amount according to the convex function exponential penalty model and deduct the relevant pledge. If all tasks strictly follow the release order log, the audit and compliance department will generate a compliance certificate and bind it to the task execution snapshot, which is then solidified into an immutable record by the reconstructed certificate. Through the dual mandatory mechanism of resource interlock constraints and release order log audit, the fairness of resource scheduling in the driver training scenario can be guaranteed, the risk of long-term resource occupation and deadlock can be avoided, and a verifiable execution trajectory can be generated for each task.

[0025] Preferably, after the penalty and reputation feedback module completes the reputation value update, it immediately transmits the latest reputation value to the scheduler. The scheduler dynamically adjusts the task priority based on the reputation value. Tasks submitted by students and coaches with higher reputation values ​​receive greater weight, making it easier to obtain a higher position in the token-grabbing competition. The mapping relationship between reputation value and priority is determined according to segmented rules: tasks with reputation values ​​between 30 and 50 have a weight of 1, tasks with reputation values ​​between 51 and 80 have a weight of 2, and tasks with reputation values ​​between 81 and 100 have a weight of 3. The weight values ​​are directly added to the token-grabbing calculation formula to determine the buffer pool order. When a task is successfully completed and passes the compliance verification of the audit and compliance section, the penalty and reputation feedback module... The system will increase the reputation value by 0.005 based on the base reward rate and write it to the reputation value storage table. An increase in reputation value directly increases the weight of subsequent tasks. When a task is rolled back due to timeout, violation, or incomplete signature, the penalty and reputation feedback module will calculate the penalty amount through a convex function exponential penalty model and reduce the reputation value accordingly. A decrease in reputation value will reduce the weight of subsequent tasks. At the beginning of each scheduling cycle, the scheduler must read the latest reputation value storage table and reorder the task queue according to the weight to ensure the priority of participants with high reputation values ​​in resource allocation, thereby incentivizing compliant execution. All reputation value change vouchers and task priority adjustment records must be written to the immutable snapshot area and solidified by the reconstructed certificate to ensure the traceability and immutability of the reward and penalty linkage process.

[0026] Preferably, during the snapshot solidification and evidence chain overlay verification process, when the reconstruction and snapshot module receives task data from the execution and causal reversal module or the audit and compliance section, it immediately generates a snapshot. The snapshot content includes the task number, student identifier, instructor identifier, resource allocation details, signature chain, task progress percentage, and event timestamp. The snapshot is then transmitted to the reconstruction certificate for solidification. During the solidification process, the reconstruction certificate must generate a unique snapshot identifier and overlay a timestamp and digital signature onto the snapshot. Subsequently, the signature chain is verified level by level, with the student signature first and the instructor signature second. If a signature is missing or incorrect, the snapshot is immediately marked as invalid and the system returns to the execution and causal reversal module to trigger a rollback. If all signatures pass verification... The reconstructed certificate writes snapshots to an immutable snapshot area, which cannot be modified or deleted. Snapshots stored in the immutable snapshot area must be linked in a chain structure. Each new snapshot superimposes its own hash value and signature onto the hash value of the previous snapshot, forming an evidence chain to ensure the sequential order and immutability of the snapshot data. During the chain superposition verification process, the audit and compliance departments will periodically check the integrity of the snapshot chain. If a chain break or hash verification failure is found, a violation record will be immediately generated and the penalty and reputation feedback module will be notified for pledge deduction. The deduction amount is still calculated according to the convex function exponential penalty model. Through the enforcement of snapshot solidification, evidence chain superposition, and hierarchical verification, the integrity, continuity, and mandatory constraints of driver training tasks at the data storage and traceability level can be guaranteed.

[0027] Preferably, during the verifiable delay function and time window control process, the execution and causal reversal module, while generating the locked execution witness, immediately calls the verifiable delay function to create a 120-second signature completion time window. The delay function ensures that the computation cannot be accelerated through a sequential chain of operations; each operation step depends on the previous result, and any skipping or parallel processing will lead to verification failure. During the execution of the delay function, all coaches must complete the signing operation within 120 seconds. After the signature is submitted, the execution and causal reversal module verifies the validity of each signature and updates the signature chain status in real time. If all signatures are collected before the 120-second time window expires, the execution and causal reversal module immediately unlocks the locked execution witness and sends it to the reconstruction and snapshot module to generate a snapshot. If there are still incomplete signatures when the 120-second time window expires... The execution and causal reversal module immediately invalidates the locked execution witness and triggers a rollback. Simultaneously, the rollback operation calls the penalty and reputation feedback module to deduct relevant pledges based on the convex function exponential penalty model. Throughout the process, the audit and compliance department must verify the integrity of the delayed function execution in real time. The verification method is to compare the final output value of the delayed function with the preset verification value. If an inconsistency is found, a violation record is immediately generated and written to the immutable snapshot area. All delayed function execution records and time window verification results must generate snapshots and be solidified into part of the evidence chain by the reconstructed certificate, ensuring that every signature collection and time control is verifiable and tamper-proof. Through the mandatory execution of verifiable delayed functions and time window controls, fairness, reliability, and mandatory constraints can be ensured during the signature collection process of driver training tasks.

[0028] Preferably, during the execution of the preemption token and buffer pool scheduling rules, when the scheduler receives multiple tasks entering the buffer pool, it must sort the tasks according to their preemption token numbers, with tasks having higher priority in resource allocation. If two tasks have the same preemption token number, the scheduler sorts them according to their submission time, with the earlier-submitted task entering the execution area first. After sorting, the scheduler allocates resources to the tasks sequentially and records the task number, preemption token number, allocation time, and resource type in the buffer pool. All records are transmitted in real time to the audit and compliance department for comparison. During task execution, the scheduler must adhere to the alternating release rule, requiring each task to release at least one type of resource if it holds resources for more than 30 seconds. The release operation must record the release time and the released task number. The scheduler records the resource allocation number, resource type, and receiving task number, submitting them to the immutable snapshot area for permanent storage via a reconstructed certificate. If a task fails to release resources within the specified time, the scheduler immediately suspends task execution and notifies the audit and compliance department to generate a violation record. This violation record is simultaneously transmitted to the penalty and reputation feedback module, which calculates the pledge penalty amount according to the convex function exponential penalty model and executes the penalty. If all tasks complete resource allocation and alternating release in sequence, the scheduler generates a scheduling completion certificate and binds it to the task execution snapshot. This certificate is written to the immutable snapshot area, forming part of the evidence chain. Through the prioritization of token numbers, buffer pool resource records, alternating release rules, and the mandatory enforcement of violation handling, the fairness, transparency, and traceability of driver training tasks during the scheduling phase can be guaranteed.

[0029] (III) Beneficial Effects

[0030] This invention provides a real-time intelligent teaching system and method for panoramic driving training. It has the following beneficial effects:

[0031] 1. This invention generates a locked execution witness through the execution and causal reversal module and binds it to a 120-second signature completion time window; the execution witness can only be unlocked by collecting all signatures within the specified time; if the collection is not completed, a rollback is triggered and a convex function exponential penalty is executed; this can effectively reduce the default motivation of high-reputation users; and ensure that the entire training task process is traceable and subject to mandatory constraints.

[0032] 2. This invention introduces mirrored tasks and adopts symmetric scheduling and alternating release rules in concurrent training scenarios; all original tasks and mirrored tasks are synchronously constrained by the signature completion time window; the scheduler executes resource interlock constraints according to the order of preemption tokens; resource deadlock is avoided and resource utilization is improved; and the fairness and compliance of task execution are guaranteed. Detailed Implementation

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] This invention provides a real-time intelligent teaching system and method for panoramic driving training. The system includes a task and credential module that immediately performs data preparation when a student triggers a driving training task on a training terminal. The task and credential module reads the student's reputation value and the instructor's reputation value from a reputation database and calculates the task difficulty coefficient. Subsequently, it performs multi-party threshold signatures through multiple authorized verification nodes in an oracle network to generate an oracle credential bound to an initial success probability estimate. The oracle credential includes a task number, student identifier, instructor identifier, resource type and quantity, estimated completion time, and initial success probability estimate. After the oracle credential is generated, it is received by the execution and causal reversal module. If not all instructor signatures are collected, a locked execution witness is generated. Simultaneously, the execution and causal reversal module calls a verifiable delay function to generate a non-accelerable completion signature time window, with a completion signature time window length of 120 seconds. The execution and causal reversal module binds the locked execution witness to the completion signature time window and allows the task to enter a buffer pool to wait for resource allocation. The buffer pool is maintained by the reconstruction and snapshot module, which maintains the task order according to buffer pool rules.

[0035] The refactoring and snapshot module periodically generates an in-process snapshot every 10 seconds and records the details of the resources used by the task, the percentage of execution progress, and the signature chain. The scheduler distributes preemption tokens in order according to the preemption token and the snapshot order table to wake up tasks. When allocating resources, the scheduler enforces resource interlock constraints and alternating release rules. The alternating release rule stipulates that when a task continuously occupies resources for more than 30 seconds, it must release at least one type of resource for competing tasks to use. The release operation is recorded by the buffer pool with the release timestamp, the type of resource released, and the receiving task number, and the release order log is submitted to the audit and compliance module. After receiving the release order log, the audit and compliance module verifies it according to the time order and task priority order. The verification includes confirming whether the preemption token number and the resource release round are consistent. After the verification is passed, the audit and compliance module instructs the refactoring certificate to solidify the refactoring snapshot. The solidification steps of the refactoring certificate include generating a unique snapshot identifier, embedding a completion timestamp, superimposing a signature chain, and writing a chained snapshot record in the immutable snapshot area. The snapshot chain is connected in a way that superimposes the hash of the previous snapshot with the hash of the next snapshot and includes a signature to ensure immutability.

[0036] If all coach signatures are collected within the signature completion time window, the execution and causal reversal module, after unlocking and locking the execution witness, will send the execution result to the reconstruction and snapshot module to generate a final snapshot, which will then be permanently written to the immutable snapshot area by the reconstruction certificate. If the signature completion time window expires before the signatures are completed, the execution and causal reversal module will invalidate the locked execution witness and trigger a rollback process. The rollback process includes generating a rollback snapshot record of the number of incomplete signatures and the chain of completed signatures, notifying the scheduler to release the occupied resources and submitting the release record to the audit and compliance module, and the penalty and reputation feedback module based on the oracle certificate. The initial success probability estimate is calculated using a convex function exponential penalty model to determine the amount of the pledge penalty, which is automatically deducted from the pledges of trainees and coaches. The pledge penalty calculation is based on a base penalty rate of 0.01, and is amplified by an exponential amplification factor of 2.0. The penalty is rounded to the smallest monetary unit. The penalty notes and rollback records are written to the reputation database to update the reputation data and are fed back to the task and voucher module to affect the subsequent initial success probability estimate. At the same time, the audit and compliance module writes all rollback snapshots, penalty notes and reputation update records to the immutable audit chain and generates compliance vouchers for verification by regulatory agencies.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real-time intelligent teaching system for panoramic driving training, characterized in that, Comprise: a task and credential module, an execution and causal reversal module, a reconstruction and snapshot module, a punishment and reputation feedback module, and an audit and compliance module; the task and credential module generates an initial success probability estimate of a driving training task based on historical reputation data of a student and a coach extracted from a reputation library when the student triggers the driving training task, and generates a prediction credential binding the initial success probability estimate through multi-party threshold signature of an oracle network composed of multiple authorized verification nodes; the execution and causal reversal module generates a locked execution witness that is invisible to the outside and in a locked state without collecting all coach signatures, and allows the driving training task to enter the execution entrance, while calling a verifiable delay function to generate a non-acceleratable completion signature time window, and if the completion signature is completed within the completion signature time window, the locked execution witness is unlocked and the execution result is written into an immutable snapshot, if the completion signature is not completed at the expiration of the completion signature time window, the locked execution witness is invalidated and rollback is triggered, and the execution and causal reversal module generates a mirror task for the original task in a concurrent task scenario, the mirror task and the original task are bound by the completion signature time window, and are symmetrically scheduled according to the alternate release rule and the resource interlocking constraint; the reconstruction and snapshot module maintains a task buffer pool, generates a text-based reconstruction snapshot according to the buffer pool rule, and solidifies it by a reconstruction certificate, dispatches the snapshot in order by preemption token and wakes up the task one by one by a scheduler, and all locked execution witnesses are written into an immutable snapshot after unlocking; the punishment and reputation feedback module calculates and deducts a pledge penalty amount from the pledge pre-paid by the student or the coach according to the initial success probability estimate in the prediction credential when the completion signature fails, the pledge penalty amount is exponentially amplified based on a convex function as the initial success probability estimate increases, the punishment ticket and the reputation update record are written into the reputation library, and the reputation update record is fed back to the task and credential module to affect the initial success probability estimate of the subsequent prediction credential; the audit and compliance module writes the prediction credential, the locked execution witness, the completion signature state, the reconstruction certificate and the reputation update result into an immutable audit chain after the task is closed, and generates a compliance credential based on zero-knowledge proof for independent verification by regulatory agencies.

2. The real-time intelligent teaching system for panoramic driving training according to claim 1, characterized in that: The task and credential module first extracts the historical reputation data of the student and the coach from the reputation library when the student triggers the driving training task, and calculates the initial success probability estimate of the driving training task, writes the initial success probability estimate into the prediction credential, and then generates the prediction credential binding the initial success probability estimate through multi-party threshold signature of multiple nodes of the oracle network, the prediction credential once generated is the only credential for the task to enter the execution entrance.

3. The real-time intelligent teaching system for panoramic driving training according to claim 2, characterized in that: The execution and causal reversal module generates a locked execution witness in a locked state and allows the driving training task to enter the execution entrance without collecting all the coach signatures when receiving the prophecy certificate, the locked execution witness cannot be validated before being unlocked, the execution and causal reversal module simultaneously calls the verifiable delay function to generate a complete signature time window, the complete signature time window is bound to the locked execution witness, if all the coach valid signatures are received within the complete signature time window, the locked execution witness is unlocked and transferred to the reconstruction and snapshot module for snapshot solidification; If the complete signature time window expires and the complete signature is not completed, the locked execution witness is invalidated and triggers rollback, the rollback event is handed over to the punishment and reputation feedback module.

4. The real-time intelligent teaching system for panoramic driving training according to claim 3, characterized in that: The execution and causal reversal module immediately unlocks the locked execution witness in the locked state when monitoring that all the coach valid signatures are received within the complete signature time window, and synchronously writes the execution result of the driving training task into the immutable snapshot generated by the reconstruction and snapshot module, if all the valid signatures cannot be collected when the complete signature time window expires, the locked execution witness is immediately invalidated and triggers state rollback, the rollback event and its related credentials are transmitted to the punishment and reputation feedback module to calculate the staking penalty amount and update the reputation.

5. The real-time intelligent teaching system for panoramic driving training according to claim 4, characterized in that: The execution and causal reversal module generates a corresponding mirror task while processing the original task, the mirror task generates the locked execution witness together with the original task, and is subject to the complete signature time window, if the original task completes the complete signature and unlocks the locked execution witness, the locked execution witness of the mirror task is unlocked synchronously; if the original task does not complete the complete signature, the locked execution witnesses of the original task and the mirror task are invalidated together, in the release operation after the completion of the task execution, according to the alternate release rule and the resource interlocking constraint, the original task and the mirror task are symmetrically and alternately scheduled, and a log containing the mirror task, the original task and the release order thereof is generated, the log is submitted to the audit and integration module for fairness evaluation.

6. The real-time intelligent teaching system for panoramic driving training according to claim 5, characterized in that: The reconstruction and snapshot module generates a textual reconstruction snapshot after each driving training task is unlocked and completed, and is solidified by a reconstruction certificate, the immutable snapshot is only generated when the complete signature time window is closed and the complete signature is completed, otherwise the driving training task state is not written into the immutable snapshot, the reconstruction and snapshot module rearranges the driving training task order in the buffer pool according to the buffer pool rule and generates a snapshot order table, the preemption token is distributed according to the snapshot order table, and the scheduler wakes up the driving training tasks one by one according to the preemption token, all the immutable snapshots contain the correspondence between the locked execution witness and the reconstruction certificate.

7. The real-time intelligent teaching system for panoramic driving training according to claim 6, characterized in that: The punishment and reputation feedback module calculates a pledge penalty amount according to the initial success probability estimate in the prediction voucher when all the coach effective signatures are not collected before the completion of the completion signature time window, the pledge penalty amount is exponentially amplified based on a convex function as the initial success probability estimate increases, the pledge penalty amount is automatically deducted from the pledge of the relevant party, the punishment ticket and the rollback record are generated at the same time and written into the reputation library, the reputation library updates the reputation data of the relevant party accordingly, and the reputation update record is fed back to the task and voucher module.

8. A method for implementing the real-time intelligent teaching system for panoramic driving training according to claim 1, characterized in that, The method comprises the following steps: Step one: when the student triggers the driving training task, the initial success probability estimate of the driving training task is generated based on the historical reputation data of the student and the coach extracted from the reputation library, and the prediction voucher binding the initial success probability estimate is generated through multi-party threshold signature of the oracle network composed of multiple authorized verification nodes; Step two: when receiving the prediction voucher, the lock execution witness in a locked state which is invisible to the outside and is generated under the condition that all the coach signatures are not collected, and the driving training task is allowed to enter the execution entrance, and a verifiable delay function is called to generate a completion signature time window with non-accelerability, if the completion signature is completed within the completion signature time window, the lock execution witness is unlocked and the execution result is written into the immutable snapshot, if the completion signature is not completed before the completion signature time window expires, the lock execution witness is invalidated and the rollback is triggered; Step three: an image task is generated for the original task in a concurrent task scenario, the image task is bound by the completion signature time window together with the original task, and is symmetrically scheduled according to the alternate release rule and the resource interlocking constraint; Step four: a task buffer pool is maintained, a text reconstruction snapshot is generated according to the buffer pool rule and is solidified by a reconstruction certificate, the tasks are dispatched in the snapshot order by preemption tokens and are awakened one by one by the scheduler, and all the lock execution witnesses are written into the immutable snapshot after being unlocked; Step five: when the completion signature fails, the pledge penalty amount is calculated and deducted from the pledge of the student or the coach according to the initial success probability estimate in the prediction voucher, the pledge penalty amount is exponentially amplified based on a convex function as the initial success probability estimate increases, the punishment ticket and the reputation update record are written into the reputation library, and the reputation update record is fed back to the task and voucher module to affect the initial success probability estimate of the subsequent prediction voucher; Step six: after the task closed loop, the prediction voucher, the lock execution witness, the completion signature state, the reconstruction certificate and the reputation update result are written into the immutable audit chain, and the compliance voucher based on zero-knowledge proof is generated for independent verification by the supervisory agency.