A driving training business comprehensive management system

Abstract

This invention relates to the field of data-driven business management system technology, and discloses a comprehensive management system for driver training business, comprising: processing in-vehicle process data streams into behavioral feature tags, diagnosing the long-term health status and performance degradation trend of teaching resources based on their historical distribution, dynamically adjusting instantaneous risk thresholds based on health status, and preemptively compensating scheduling parameters based on degradation trends before resource performance fails. This invention transforms the traditional management system from a passive schedule execution tool into a management hub that can proactively perceive the teaching process and establish a collaborative closed loop between long-term status insight, instantaneous risk decision-making, future trend prediction, and current scheduling intervention, thereby solving the technical problem of mismatch between management behavior and teaching reality.

Description

Technical Field

[0001] This invention relates to a comprehensive management system for driver training operations, belonging to the technical field of data-driven business management systems. Background Technology

[0002] Currently, in the management practice of the driver training industry, using computer systems to schedule, dispatch, and record information on teaching resources such as students, instructors, and training vehicles is a common technical means to ensure the orderly operation of business. Such management systems typically use the static identity information of teaching resources, such as student ID, instructor qualifications, and vehicle license plate numbers, as the basic data units for scheduling and dispatching. Based on the established teaching plan and timetable, they generate and execute dispatch instructions, playing a fundamental role in achieving the standardization and automation of large-scale business processes.

[0003] However, with the expansion of driver training operations and the increasing demands for refined teaching quality, the aforementioned management approach based on static information has encountered a disconnect between the data foundation upon which management instructions rely and the dynamic evolution of the teaching process. The massive, continuous stream of process-related operational data generated by training vehicles during operation, such as changes in engine speed, pedal opening, and steering wheel angle, objectively contains rich information for fine-grained analysis of the teaching process. However, in existing management systems, this data is usually discarded due to its unstructured nature and difficulty in direct interpretation. The system only records the final, single event result, such as training completed or training failed. This systematic neglect of process information leads to management decisions always being based on an incomplete business profile, which in turn causes a series of technical challenges.

[0004] Specifically, existing technologies suffer from the following shortcomings: 1. Vagueness in diagnosis: When trainees repeatedly fail in a training exercise, the system cannot effectively distinguish from the data level whether the root cause is a problem with the trainee's operational skills, a mismatch between the instructor's guidance methods, or the mechanical characteristics or potential malfunctions of the training vehicle itself, resulting in a lack of objective basis for subsequent scheduling adjustments; 2. Lagging management: The system can only respond to teaching failures that have already occurred. It lacks the ability to perceive subtle operational deviations or gradual changes in vehicle status that indicate potential risks or efficiency decline, and therefore cannot provide proactive management intervention; 3. Rigidity in scheduling: Due to the lack of quantitative understanding of the dynamic capabilities and characteristics of teaching resources in actual interaction, resource matching and scheduling can only rely on fixed rules, making it difficult to achieve individualized instruction and optimal resource allocation. Therefore, how to establish a new management and operation mode that transforms traditionally discarded process-based driving behavior data into core data that can directly drive business decisions, and on this basis, construct a closed-loop management logic capable of self-diagnosis and adaptive optimization, becomes the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a comprehensive management system for driver training operations. Its main purpose is to solve the problem that existing management systems, due to their neglect of process data, result in a disconnect between management logic and teaching reality, thus failing to achieve accurate diagnosis of the root causes of problems, predictive intervention of potential risks, and dynamic optimization of resource allocation.

[0006] To achieve the above objectives, the present invention provides a comprehensive management system for driver training operations, comprising: A driving behavior characterization engine is configured to acquire and process time-series data streams, including tachometer pedal opening and steering wheel angle, from a standardized vehicle bus to generate structured driving behavior feature labels. A module for dynamic matching and scheduling of resource demands; A data consistency and root cause diagnosis module is configured to: calculate a long-term health index characterizing the performance evolution of any teaching resource based on the historical distribution of driving behavior feature labels across a resource group consisting of student vehicles and instructors, and fit decay trend data characterizing the direction of performance evolution. A cross-mechanism threshold dynamic adaptive module is configured to receive a long-term health index and adjust a risk trigger threshold for monitoring transient risk events based on the index. A trend extrapolation-based preemptive scheduling compensation module is configured to receive attenuation trend data and adjust the scheduling parameters in the resource demand dynamic matching and scheduling module according to the trend data before the performance status of any teaching resource reaches a preset failure threshold.

[0007] Preferably, the driving behavior feature engine is specifically configured to: perform denoising and feature extraction processing on the time-series data stream within a sliding window of a preset length to generate driving behavior feature labels; wherein, the driving behavior feature labels include at least one of: clutch control stability label, throttle application smoothness label, or steering operation coordination label.

[0008] Preferably, the data consistency and root cause diagnosis module is specifically configured to: establish a statistical baseline for the distribution of driving behavior feature labels of similar resources in the resource group; and generate a long-term health index by calculating the deviation between the feature label distribution associated with any teaching resource and the statistical baseline.

[0009] Preferably, the data consistency and root cause diagnosis module is also configured to generate a predictive maintenance work order or teaching ability assessment suggestion that points to any teaching resource as the root cause of the anomaly when the deviation exceeds a preset diagnostic threshold.

[0010] Preferably, the cross-mechanism threshold dynamic adaptive module is specifically configured to: within a preset control period, based on the received long-term health index... ,according to The calculation rules for risk trigger thresholds Recalculate and set; among which, As a basic risk trigger threshold, As a long-term health index A function that is positively correlated.

[0011] Preferably, the preemptive scheduling compensation module based on trend extrapolation is specifically configured as follows: using a linear regression exponential smoothing or autoregressive moving average model to extrapolate the decay trend data to predict the performance status of any teaching resource in a future time window; and based on the predicted performance status, applying a compensatory bias to one or more scheduling parameters in the resource demand dynamic matching and scheduling module that characterize the operating cost weight or matching priority of any teaching resource.

[0012] Preferably, the system also includes: a teaching resource capability map and scheduling strategy adaptive optimization module, which is configured to: build and dynamically update the internal capability profile of the instructor or training vehicle based on historical driving behavior feature labels and matching scheduling execution results, and adjust the resource matching weight in the resource demand dynamic matching and scheduling module according to this capability profile.

[0013] Preferably, the teaching resource capability map and scheduling strategy adaptive optimization module is specifically configured to: construct the internal capability profile of the instructor by establishing and updating a quantitative model that represents the instructor's efficiency in solving preset driving behavior feature labels.

[0014] Preferably, the instantaneous risk event includes at least one of the following: abnormal engine speed, rapid acceleration, rapid deceleration, or sudden steering wheel turn.

[0015] Preferably, the system also includes: an output interface configured to output the predictive maintenance work order teaching ability assessment suggestions or resource allocation instructions generated based on the internal capability profile to a management terminal.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. By setting up a real-time characteristic engine for driving behavior patterns and a dynamic matching and scheduling module for resource requirements based on behavior feature tags, the continuous unstructured data streams collected from standardized in-vehicle equipment are processed in real time into structured driving behavior feature tags with clear business meanings. These dynamically generated tags are then directly used as the core basis for matching and scheduling between students, instructors, and vehicles. This operating mode changes the traditional management system's logic of relying on static identity information for pre-set scheduling, enabling resource scheduling instructions at the management level to directly respond to real-time specific behaviors at the teaching execution level. This establishes a continuous closed-loop data coupling relationship between management behavior and the teaching process, thereby avoiding resource mismatch caused by the disconnect between management and teaching information.

[0017] 2. Building upon the above, this invention further establishes a data consistency and anomaly root cause diagnosis module for cross-trainee vehicle instructors. This module analyzes the distribution patterns of historical driving behavior feature tags across different teaching resources. When it is found that the frequency of specific negative behavior tags associated with any teaching resource, such as a training vehicle, is systematically higher than that of other similar resources, the system determines it as a potential anomaly of the resource itself, rather than a behavioral problem of the operator. This mechanism extends the application of data originally used to evaluate a single teaching event to the group correlation diagnosis of the health status of all resources. This allows the system to function as an objective, data-driven tool for monitoring the health status of teaching resources and identifying the root causes of faults while performing its core scheduling functions. This capability is not available in traditional management systems.

[0018] 3. This invention further includes a teaching resource capability map and adaptive optimization module for scheduling strategies. This module continuously tracks and quantifies the efficiency of different combinations of instructors or training vehicles in resolving various driving behavior feature tags, and updates the internal capability profile of each teaching resource accordingly. When the dynamic matching and scheduling module encounters similar problems in subsequent work, it will refer to this capability profile to perform biased optimization of resource scheduling. This approach transforms each independent scheduling execution result into empirical data for optimizing all future scheduling decisions, enabling the overall scheduling logic of the system to self-improve and continuously adapt as the running time increases. It also incorporates trend extrapolation. The preemptive resource scheduling compensation module receives performance degradation trend data of teaching resources from the anomaly root cause diagnosis module. Before the actual performance state of a resource reaches a preset failure threshold, it continuously adjusts its relevant scheduling parameters in the dynamic matching and scheduling module based on this trend data. For example, for a vehicle identified as having a slow upward trend in fuel consumption, the system will gradually reduce its weight in long-distance training tasks. This operating mechanism shifts the system's response from passively handling existing problems to proactively intervening in potential problems, actively delaying the performance degradation process and offsetting its potential negative operational impact without interrupting resource usage. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the closed-loop control logic and core data flow of the system of the present invention; Figure 2 This is a comparison chart of the evolution of vehicle health index under different fault modes according to the present invention; Figure 3 This is a schematic diagram of the hierarchical functional architecture of the data-driven system of the present invention. Detailed Implementation

[0020] To facilitate understanding of the present invention, the present invention will be described more fully below in conjunction with embodiments. However, the present invention is not limited to the following embodiments. On the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosure of the present invention. It should be understood that the following embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention.

[0021] This invention provides a comprehensive management system for driver training operations. Its overall system architecture aims to transform data nodes in the business management process from passive and static to proactive and dynamic, establishing closed-loop optimization capabilities. In specific deployment, the system mainly consists of a driving behavior characteristic engine, a resource demand dynamic matching and scheduling module, a data consistency and root cause diagnosis module, a cross-mechanism threshold dynamic adaptive module, and a trend extrapolation-based preemptive scheduling compensation module. These modules work collaboratively to elevate driver training operation management capabilities from scheduling based on static identity information to real-time diagnosis and predictive intervention based on dynamic process data. In the operational scenario of driver training institutions, a technical problem faced by managers is the disconnect between the data upon which scheduling instructions are based and the actual teaching and training process. For example, when students repeatedly fail in a specific training exercise, existing management systems can only record the results and cannot distinguish from a data perspective whether the root cause is the student's skill, the instructor's guidance method, or the vehicle's condition. Subsequent management adjustments lacked objective basis. To address this technical issue, the driving behavior feature engine configured in this system serves as the system's data perception unit. Through a standardized vehicle bus interface, it acquires real-time time-series data streams generated during training vehicle operation, including engine speed, pedal opening, and steering wheel angle. This engine does not merely record data; instead, within a preset sliding time window (e.g., 2 seconds), it performs noise reduction and feature extraction on the data stream. Based on preset business rules, it transforms the unstructured physical signal stream into structured driving behavior feature labels. For example, during hill start conditions, if the engine detects high-frequency oscillations exceeding a preset vibration threshold near the clutch pedal opening point, and the engine speed fails to maintain stability, the system generates a clutch control stability label with an attribute value of unstable. Through this operational mode, the engine translates the teaching process, which is difficult for management to perceive, into quantifiable and analyzable business data, providing data input for subsequent management logic execution.

[0022] After obtaining structured behavioral feature labels, the next step to improve management efficiency is to utilize this dynamic information for the allocation of teaching resources. The limitation of traditional scheduling methods lies in their reliance on fixed, empirical rules. The resource demand dynamic matching and scheduling module in this system operates on the core driving variable of driving behavior feature labels generated in real-time by the driving behavior feature engine, rather than the traditional static identity information of students or instructors. When the system identifies a student frequently exhibiting negative states such as smooth throttle application, this module queries the internal teaching resource capability map. This map is a dynamically updated capability profile of instructors and training vehicles created by the system through analysis of historical data. For example, the map records that Instructor Li has the shortest average time to resolve throttle control related issues, and that Training Vehicle No. 3 is marked as suitable for throttle control practice due to its relatively gentle throttle response curve. Based on this, the scheduling module will prioritize matching the student with Instructor Li and Vehicle No. 3. Thus, each scheduling decision is transformed from a fixed scheduling operation into a dynamic optimization based on real-time problems and historical data, aiming to improve the targeting of resource allocation. Furthermore, the management system needs to be able to diagnose potential systemic problems, especially when facing fluctuations in teaching quality caused by gradual changes in equipment performance, where existing management methods often have a lag in response. The data consistency and root cause diagnosis module in this invention is configured to address this issue. This module does not analyze single training data, but rather periodically (e.g., daily) statistically analyzes the distribution of historical driving behavior feature tags across a resource group consisting of students, vehicles, and instructors. Its deterministic procedure first establishes a statistical baseline for the tag distribution of similar resources. For example, it calculates that the average probability of all training vehicles being associated with stall failure tags in the past 30 days is 2%, which serves as a benchmark for vehicle health. Subsequently, the module calculates the deviation of the distribution of similar feature tags associated with any teaching resource, such as training vehicle No. 5, from the statistical baseline. If the system finds that the probability of stall failure associated with vehicle No. 5 in the past week is 8%, higher than the 2% baseline, and this phenomenon is associated with multiple students of different skill levels, then the system attributes this deviation to the resource itself and generates a long-term health index characterizing the evolution of the vehicle's performance. When this deviation exceeds the preset diagnostic threshold, the system automatically generates a predictive maintenance work order for training vehicle No. 5. Through this correlational diagnostic logic based on group data, the system can proactively monitor the health status of the business system and identify the root cause of the fault.

[0023] After quantifying the long-term health status of resources, another key aspect of refined management is how to use this insight into slow variables to guide instantaneous risk decisions regarding fast variables. Fixed risk monitoring thresholds are difficult to adapt to devices with different health statuses. To address this, the cross-mechanism threshold dynamic adaptive module in the system is configured to receive the long-term health index calculated by the data consistency and root cause diagnosis module. The risk trigger threshold used to monitor instantaneous risk events (such as abnormal engine speed) is adjusted based on this index. Its adjustment algorithm follows these rules: In the formula, This is a real-time risk trigger threshold; The basic risk trigger threshold is calibrated by analyzing the data distribution of experienced instructors under standard driving conditions. As a long-term health index The positively correlated function, in a specific implementation, can be set as follows: For example, if the basic risk trigger threshold for abnormal engine speed... It is rated at 4000 RPM, which is significant for a vehicle with a health index. For vehicles with a real-time risk threshold of 4000 RPM, the health index has decreased due to wear and tear. For vehicles that are flagged as high-risk, the system will automatically adjust their risk trigger threshold to [a higher level]. The speed is specified as RPM. This mechanism enables the system's risk perception to be context-adaptive, allowing for more sensitive monitoring of resources with known potential performance degradation. Ultimately, the management system should have the ability to proactively intervene, rather than simply responding to problems that have already occurred. Traditional management systems, after identifying performance degradation trends, typically generate reports and wait for manual decisions, potentially missing the intervention window. To enable the system to proactively intervene, this invention sets up a preemptive scheduling compensation module based on trend extrapolation. This module receives degradation trend data representing the direction of resource performance evolution output by the root cause diagnosis module. For example, by using a linear regression model, the system analyzes and finds that the average fuel consumption per 100 kilometers of training vehicle No. 2 over the past 30 days shows the following trend: In the formula, The predicted fuel consumption per 100 kilometers is expressed in liters per 100 kilometers. The number of days since the start of the analysis; upon receiving this trend data, the preemptive scheduling compensation module does not wait for fuel consumption to reach the alarm threshold, but immediately executes compensatory intervention. Based on the predicted future performance status, it applies a compensatory bias to one or more scheduling parameters in the resource demand dynamic matching and scheduling module that characterize the operating cost weight or matching priority of vehicle No. 2. This smooth fine-tuning of internal parameters will cause the scheduling system to gradually reduce the probability of allocating vehicle No. 2 to high fuel consumption tasks such as long-distance training in subsequent decisions, thereby proactively offsetting the potential increase in operating costs caused by performance degradation without interrupting normal vehicle use, and shifting the system's management approach from passive response to proactive prevention.

[0024] It should be specifically noted that, in the specific implementation of this invention, the driving behavior data of students and instructors are all managed as personal information. For example, before data collection, the purpose, method, and scope of data collection, including for students and instructors, will be fully informed through appropriate means, and their explicit consent will be obtained. During data processing and analysis, the system may also prioritize the use of anonymization or pseudonymization techniques to desensitize personal information, especially when conducting macro-analysis such as establishing group statistical baselines and diagnosing resource health status, in order to sever the direct association between data and specific natural persons to the greatest extent. For scenarios where identifiable personal information must be used for dynamic matching and scheduling, the system will establish a strict data access control mechanism and security measures to ensure that the data is only used for the predetermined purpose of improving teaching quality and management efficiency, and unauthorized access, disclosure, or use for other commercial purposes is strictly prohibited. All data storage, transmission, and destruction follow the principles of minimization, necessity, and security to protect the legitimate rights and interests of data subjects. All of these are extended implementation methods that can be known by those skilled in the art.

[0025] Example 1: In a driver training operation with dozens of training vehicles, the system faces a situation where a student repeatedly stalls the engine while practicing hill starts in training vehicle #5. The existing management approach simply records this event as a training failure and increases the training time for that item. This approach leads to ineffective consumption of training resources, including increased fuel costs and accelerated wear on the vehicle's clutch system. In this situation, the driver training integrated management system of this invention operates. When the student performs a hill start again in vehicle #5, the system's driving behavior characterization engine acquires real-time data streams from the vehicle bus and processes them to generate a series of structured driving behavior feature labels. These include multiple unstable clutch control stability labels and a momentary risk event of abnormal engine speed. At this point, the output of the data consistency and root cause diagnosis module provides direct input for the operation of the cross-mechanism threshold dynamic adaptive module. The root cause diagnosis module, in its periodic analysis, has already determined the long-term health index of vehicle #5 based on historical data. Updated to 0.8, this index reflects that the vehicle's recent overall performance is worse than the fleet average. It is passed to the cross-mechanism threshold dynamic adaptive module, which is based on The calculation rules have pre-defined the risk trigger threshold for abnormal engine speed events in vehicle No. 5. The speed was reduced from the base value of 4000 rpm to 3200 rpm. As a result, a minor speed anomaly was detected during the student's operation. Without this mechanism, this early risk signal might have been ignored at the standard threshold.

[0026] Furthermore, after receiving the feature labels related to the training event generated by the driving behavior characterization engine, the data consistency and root cause diagnosis module did not directly attribute the problem to the student. Instead, it performed a correlation check. By querying the historical database, this module found that in the past week, the frequency of negative attributes in the clutch control stability category labels associated with all different students who used training vehicle No. 5 was systematically higher than the statistical baseline when these students used other vehicles. Based on this, the module pointed to the root cause of the problem as the teaching resources themselves, rather than the student's operating skills, and generated a predictive maintenance work order indicating a potential mechanical fault in the clutch system of vehicle No. 5. This approach resolved... To address the technical challenge of distinguishing between performance issues caused by human error and equipment malfunction in management, the system compares isolated event data within the context of aggregate data. This shifts the system's approach from superficially addressing why trainees fail to analyzing the root causes of a systemic increase in failure probability. Ultimately, simultaneously with the generation of predictive maintenance work orders, a dynamic resource demand matching and scheduling module is activated to replan subsequent training for the trainee. This module, based on the trainee's currently generated behavioral profile of clutch control instability, queries the teaching resource capability map and matches them with an instructor whose historical data shows the highest efficiency in resolving issues related to that specific skill, as well as a machine with a long-term health index. For training vehicle #8, with a throttle response characteristic of 0.98 and labeled as linear, the system recommended this new resource combination to the operations manager. Subsequently, the trainee completed the hill start exercise using vehicle #8 and under the guidance of the new instructor. Meanwhile, vehicle #5 was inspected by maintenance personnel, who confirmed that its clutch engagement point was unclear and repaired it. The entire process demonstrates that the system, through the collaborative work between modules, transformed an isolated teaching failure into a management action of proactive vehicle maintenance and dynamic optimization of teaching resources, thereby improving the quality of teaching services while avoiding continuous waste of operating costs.

[0027] Example 2: To objectively verify the technical effectiveness of the driver training business integrated management system of the present invention in a real operating environment, a comparative experiment was conducted. This experiment aimed to quantitatively evaluate the performance of the present invention's solution compared to traditional management methods in terms of teaching and training efficiency, resource usage costs, and problem diagnosis accuracy. This experiment was conducted in a driver training institution with 40 training vehicles of the same model and similar conditions, over a period of 90 days. The resources involved in the experiment included 40 instructors with similar teaching experience and 400 students in the same period. All training vehicles were equipped with standardized vehicle bus interfaces, capable of outputting time-series data streams at a frequency of 10Hz. The experimental resources were divided into two groups. The control group included 20 vehicles, 20 instructors, and 200 students. Its daily management work followed the existing static management software of the driver training institution, which was based on manual scheduling. The discovery and handling of operational problems were performed manually by the operations manager. The experimental group also included 20 vehicles, 20 instructors, and 200 students. All management and scheduling work in the experimental group was performed by the driver training business integrated management system of the present invention, which was deployed on a server with corresponding data processing capabilities.

[0028] During the experiment, key operational indicators for both groups were continuously recorded. The main assessment indicators included: average training time per subject (in hours); average fuel consumption per 100 kilometers (in liters / 100 kilometers); fault root cause diagnosis accuracy (in %); and unplanned vehicle downtime (in hours / month). The fault root cause diagnosis accuracy was assessed as follows: an independent third-party maintenance engineer inspected all 40 vehicles weekly, and their findings were used as a baseline. The diagnostic conclusions of manually determined fault causes in the control group and the predictive maintenance work orders generated by the system in the experimental group were compared with this baseline to calculate the accuracy. On day 32 of the experiment, vehicle number 28 in the experimental group exhibited the stalling problem reported by trainees at low speeds. The system's data consistency and root cause diagnosis module captured this pattern and, by analyzing historical driving behavior feature tags, generated intermediate diagnostic data and conclusions as shown in Table 1. (See Table 1.)

[0029] Table 1: Example of fault diagnosis process data for vehicle No. 28.

[0030]

[0031] As shown in Table 1, the data consistency and root cause diagnosis module first calculated the statistical baseline frequency of stall-related negative labels for the entire test group fleet to be 1.8%, while the frequency of this indicator for vehicle No. 28 over the past week was 7.5%, indicating a deviation from the baseline. Based on this, the system determined the vehicle's long-term health index. The calculated value was 0.65, and based on the characteristics of this deviation pattern, the output predictive maintenance work order pointed to the fuel supply system as the root cause. After inspection by an independent maintenance engineer, the problem of vehicle No. 28 was indeed caused by a clogged fuel filter. The system diagnosis was consistent with the facts. In contrast, a vehicle in the control group that exhibited a similar phenomenon continued to operate for two days after being manually judged as having a problem with the trainee's operation. Due to the worsening of the fault, it experienced an unplanned downtime of 4 hours. After a complete 90-day test cycle, the final statistical results of various operational indicators for the two groups are shown in Table 2.

[0032] Table 2: Comparison of 90-day operational indicators between the control group and the experimental group.

[0033]

[0034] Experimental data shows that, compared with the control group using traditional management methods, the experimental group using the technical solution of this invention has improved in the assessed operational indicators; the reduction in the average training time per subject is related to the improved teaching relevance through dynamic resource matching based on behavioral characteristics; the decrease in average fuel consumption per 100 kilometers is related to the system's predictive intervention and optimized scheduling reducing ineffective resource consumption; the improvement in the accuracy of fault root cause diagnosis and the reduction in unplanned vehicle downtime correspond to the operating mechanism of this invention, which transforms passive response into proactive diagnosis to improve the precision of business management and resource availability.

[0035] To further verify from the opposite perspective the decisive technical effect of the core mechanism of the present invention, which is to conduct root cause diagnosis by establishing a historical distribution statistical baseline across a resource group consisting of student vehicles and instructors, the following comparative example 1 is set up.

[0036] Comparative Example 1: This comparative example aims to illustrate the technical limitations of a conventional driver training management system lacking the data consistency and root cause diagnosis modules of this invention when dealing with complex teaching problems caused by the slow degradation of the performance of teaching resources themselves. This conventional management system is completely identical in hardware to the experimental group in Example 2, capable of accessing and processing the onboard bus timing data stream from 20 training vehicles. The system presets a fixed engine speed abnormality risk trigger threshold (4000 rpm). Its only essential difference from the solution of this invention is that this conventional system lacks the ability to analyze the historical distribution of driving behavior feature tags within the resource group; its diagnostic logic only... Limited to recording isolated, momentary risk events, and identifying the responsible party as the student currently driving the vehicle; the test conditions were consistent with Example 2. On the 45th day of the test, training vehicle No. 11 in the control group began to show early fault symptoms of unclear engagement point due to clutch wear; during the subsequent 21-day observation period (day 46 to day 66), the vehicle was scheduled normally and used by 18 different students for hill start training. The routine management system recorded and analyzed all training processes involving vehicle No. 11 during the observation period. The key process data and the final operational impact are shown in Table 3 below.

[0037] Table 3: Data and impact of the routine management system's diagnostic process for potential faults in vehicle No. 11.

[0038]

[0039] The test results show that, due to the lack of a mechanism for consistency analysis and deviation calculation of historical driving behavior feature labels across resource groups, this conventional management system cannot separate the systematic operational anomalies exhibited by different trainees on the same device from the individual behavioral problems of the trainees. It attributes all 43 stalling events to the trainees and fails to identify that vehicle number 11 itself is the cause of the systematic increase in the failure probability. The accuracy rate of fault root cause diagnosis is 0. This diagnostic failure directly leads to the ineffective consumption of training resources and the final unplanned shutdown, proving that this conventional technical approach, which relies solely on isolated event judgment, has defects in solving the problem of hidden resource faults.

[0040] Example 3: This example combines Figures 1 to 3 A description of a comprehensive management system for driver training services, such as... Figure 1As shown, engine speed, pedal opening, and steering wheel angle are input into a driving behavior characterization engine. This engine processes the process data into structured driving behavior feature labels. These labels, along with historical driving behavior feature labels, are then sent to a data consistency and root cause diagnosis module. This module diagnoses the long-term health status and performance degradation trend of teaching resources, outputting a long-term health index and degradation trend data. The long-term health index is passed to a cross-mechanism threshold dynamic adaptation module to dynamically adjust risk trigger thresholds, while the degradation trend data is passed to a trend-based preemptive scheduling compensation module to generate... While proactively compensating for scheduling parameters, structured driving behavior feature tags are sent to the resource demand dynamic matching and scheduling module. This module performs dynamic optimization matching based on real-time behavior tags and resource profiles, and feeds back historical scheduling execution results to the teaching resource capability map and scheduling strategy adaptive optimization module. The latter updates the internal capability profile of the instructor / vehicle and the resource matching weights based on this. Finally, the predictive maintenance work order or teaching capability assessment suggestion generated by the data consistency and root cause diagnosis module, and the resource allocation instruction generated by the resource demand dynamic matching and scheduling module, are sent to the management terminal through an output interface.

[0041] like Figure 2 As shown, the horizontal axis represents the number of operating days, and the vertical axis represents the health index. Among them, vehicle number 8 serves as the benchmark for health status, and its health index... The health index of vehicle #5 has remained stable at a level close to 1.0 for a long period. However, due to a clutch malfunction, its health index is low. The health index showed a continuous and relatively rapid downward trend, while vehicle number 28, due to a fuel system malfunction, had a significantly lower health index. After a slow initial decline, the rate of decay gradually accelerates. These curves intuitively reflect the system's ability to quantitatively diagnose the performance degradation process of different types of resources.

[0042] like Figure 3 As shown, the architecture physically comprises a training vehicle group and an onboard data acquisition unit, and logically consists of four core parts: a real-time data pipeline, a unified data storage, an analysis and modeling engine, and core business services. Massive amounts of real-time process data are processed through the data access gateway and real-time feature generation engine in the real-time data pipeline. The processed time-series data, business data, and data warehouse are managed by the unified data storage module. The analysis and modeling engine includes a health status diagnosis module, a capability graph modeling module, and a decay trend prediction module, responsible for running the core algorithms. The core business service layer encapsulates dynamic scheduling services, predictive intervention services, and external API interfaces, ultimately outputting diagnostic reports and scheduling instructions to the integrated management platform.

[0043] Example 4: Before deploying a specific system, to ensure that the driving behavior characterization engine can stably transform the raw data stream into structured driving behavior feature labels, its internal label generation logic needs to be calibrated offline. The technical challenge of this process is how to establish a deterministic association between a business concept, such as clutch control instability, and a set of quantifiable and measurable physical signal features. To solve this problem, the following standardized calibration procedure is used to determine the generation threshold of clutch control stability category labels. First, a senior driving instructor is selected as the benchmark driver, and a fully maintained training vehicle is selected as the reference vehicle. Then, the benchmark driver is required to repeatedly perform hill start operations at least 50 times in a standard test area. During this process, the system continuously records the clutch pedal opening timing data output from the vehicle bus at a frequency of 10Hz, forming a standard operation database. For each timing data segment in this database, the signal standard deviation within a 2-second time window after the vehicle enters the semi-engaged state is calculated. A baseline stability parameter is obtained by taking the arithmetic mean of the standard deviations calculated from all 50 operations. The threshold for determining stable and unstable states; This is set to a predetermined multiple of the baseline stability parameter to balance the sensitivity and specificity of the diagnosis. , where the coefficient The value of was determined to be 3.5 through analysis of typical erroneous operation samples in the historical student database; therefore, during actual system operation, the driving behavior characterization engine will calculate the signal standard deviation within the corresponding time window for any clutch operation in real time. ,when At that time, a stable class tag for clutch control with an attribute value of unstable is generated.

[0044] Furthermore, regarding the long-term health index in the data consistency and root cause diagnosis module... The calculation method also employs a deterministic procedure, and this index... The aim is to quantify the performance of a teaching resource relative to its peer group, with values ​​normalized to between 0 and 1, where 1 represents the ideal state. The calculation process first establishes a statistical baseline, and the system calculates the statistical baseline frequency of similar resources in the resource group associated with a certain negative behavioral label. Next, calculate the actual frequency with which any teaching resource is associated with the negative label within the same assessment cycle. To convert these two frequency values ​​into a health index, the system uses the following nonlinear mapping relationship for calculation: where, For long-term health index; The actual frequency of negative labels for a specific resource; The baseline frequency for similar resources; This is a decay coefficient, used to adjust the sensitivity of the health index to performance deviations. The calibration principle for this coefficient is that when the performance of a resource is at the group average level, i.e. Its health index is 1, and when its actual negative label frequency reaches 5 times the baseline frequency, its health index should drop to 0.1, a level indicating significant abnormality. Based on this constraint, it can be solved by... To calculate the attenuation coefficient The system transforms a relative performance deviation into a quantifiable health indicator that can be used for subsequent management decisions. Finally, the core of constructing the internal competency profile of instructors in the teaching resource competency map and scheduling strategy adaptive optimization module lies in quantifying problem-solving efficiency. When a student, under the guidance of an instructor, experiences a statistically decreasing trend in the frequency of a previously frequent negative driving behavior characteristic label during consecutive training sessions, the system considers this a problem-solving event. This module records the total training time from the first high-frequency appearance of the negative label to its frequency decreasing below the group average. For each coach and each type of resolvable negative label, the system will continuously calculate and update the corresponding average resolution time. This value is used as a core quantitative indicator to represent the coach's teaching ability in this area and is stored in their internal competency profile. When the resource demand dynamic matching and scheduling module makes decisions, it will prioritize matching trainees with those who address their current primary problems. The coach with the lowest numerical value enables the scheduling logic of the entire system to continuously optimize and adaptively adjust itself based on historical data.

[0045] Example 5: In an operational scenario, when a driver training institution introduces a brand-new training vehicle, to ensure the system can manage it, the new vehicle will enter a data accumulation and baseline establishment phase in the initial stage of operation. During this phase, for example, the first 100 training hours, the system will set an initial long-term health index for the vehicle. The system will be set to version 1.0 and will continue to collect its operational data during this period. However, it will not be included in the calculation of abnormal deviation in the data consistency and root cause diagnosis module for the time being, in order to isolate the impact of atypical data fluctuations generated during the vehicle break-in period on the diagnostic results. After the data accumulation phase is completed, the system will use the collected data to establish a personalized initial performance baseline for the new vehicle and formally incorporate it into the dynamic monitoring and scheduling system of the entire fleet.

[0046] Furthermore, to ensure that various statistical baselines and capability maps in the system can adapt to the long-term evolution of the entire operating environment, such as the general aging of all vehicles or changes in the overall driving habits of the student group, the system adopts a timeliness guarantee and reconstruction mechanism. This mechanism uses a sliding time window to manage all basic data used to calculate statistical baselines. For example, when calculating the frequency of fleet engine shutdown labels, the system only uses data from the past 90 days, and historical data exceeding this time limit will be excluded, thus ensuring that the baseline can reflect the current operational reality. In addition, the system continuously monitors a global management indicator, namely the overall generation rate of predictive maintenance work orders. When this generation rate deviates from its historical average by more than 25% within a statistical period, such as 30 consecutive days, the system determines that a global change in operating conditions may have occurred and automatically triggers mandatory relearning and reconstruction of all core statistical baselines and teaching resource capability maps.

[0047] Example 6: In an operational scenario, the data consistency and root cause diagnosis module has identified that the fuel consumption per 100 kilometers of training vehicle No. 7 has shown a trend over the past 30 days. The linear growth trend, among which, For the predicted fuel consumption per 100 kilometers, The trend data, which is calculated in days, is transmitted to the preemptive scheduling compensation module. At this point, the module needs to transform this predictive trend information into a management intervention action that can be executed by the scheduling system.

[0048] To address this issue, this module employs the following procedure to apply the operating cost weighting parameter in the resource demand dynamic matching and scheduling module. To implement dynamic compensatory adjustments, the system first sets a baseline operating cost weight for healthy vehicles of the same model. Its initial value is 1.0, and a baseline fuel consumption value is set. For example, 8.5 liters per 100 kilometers; dynamic adjustment is calculated using the following formula: In the formula, For vehicle number 7 in the The weight of the operating cost after compensatory adjustment for each day; Weighted by benchmark operating costs; The first predicted by the trend model Fuel consumption per 100 kilometers per day; This is the baseline fuel consumption value; This is a preset business strategy sensitivity coefficient, which is calibrated by operations managers based on cost control strategy requirements. For example, it can be set to... This means that when predicted fuel consumption exceeds the benchmark value by 10%, its operating cost weight increases by 5% accordingly, thereby adjusting the system's level of risk aversion towards costs; Taking today as an example, the system predicts fuel consumption as follows: If the weight of the vehicle's operating cost is calculated per liter per 100 kilometers, the weighting for that day's operating costs will be automatically adjusted accordingly. After this finely adjusted weight value is input into the scheduling module, it will allocate vehicle No. 7 to high-cost tasks such as long-distance or high-intensity training with a low probability during the global scheduling optimization calculation. This achieves a smooth, upfront hedging of potential excess costs without interrupting the vehicle's service.

[0049] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A driving school business integrated management system characterized by comprising: The system comprises: a driving behavior characterization engine configured to obtain and process time-series data streams containing accelerator pedal opening and steering wheel angle from a standardized vehicle bus to generate structured driving behavior feature labels; a resource demand dynamic matching and scheduling module; a data consistency and root cause diagnosis module configured to calculate a long-term health index representing the performance evolution of any teaching resource based on the historical distribution of driving behavior feature labels across a resource group consisting of trainees and instructors, and to fit decay trend data representing the performance evolution direction of the teaching resource; a cross-mechanism threshold dynamic self-adaptive module configured to receive the long-term health index and adjust a risk trigger threshold for monitoring instantaneous risk events according to the index; a preemptive scheduling compensation module based on trend extrapolation configured to receive the decay trend data and adjust scheduling parameters in the resource demand dynamic matching and scheduling module according to the trend data before the performance state of any teaching resource reaches a preset failure threshold.

2. The driving school business comprehensive management system according to claim 1, wherein The driving behavior characterization engine is specifically configured to perform denoising and feature extraction processing on the time-series data streams within a preset length of sliding window to generate driving behavior feature labels; wherein the driving behavior feature labels at least include one of clutch control stability class labels, throttle application smoothness class labels, or steering operation coordination class labels.

3. The driving school business comprehensive management system according to claim 1, wherein The data consistency and root cause diagnosis module is specifically configured to establish a statistical baseline of the driving behavior feature label distribution of the same type of resources in the resource group, and to generate the long-term health index by calculating the deviation of the feature label distribution associated with any teaching resource from the statistical baseline.

4. The driving school business comprehensive management system according to claim 1, wherein The data consistency and root cause diagnosis module is further configured to generate a predictive maintenance work order or teaching ability evaluation suggestion pointing to any teaching resource when the deviation exceeds a preset diagnosis threshold.

5. The driving school business comprehensive management system according to claim 1, wherein The cross-mechanism threshold dynamic self-adaptive module is specifically configured to: in a preset control period, according to the received long-term health index , according to the operation rule of , recalculate and set the risk trigger threshold ; wherein, is a basic risk trigger threshold, is a function positively correlated with the long-term health index .

6. The driving school business comprehensive management system according to claim 1, wherein The preemptive scheduling compensation module based on trend extrapolation is specifically configured to use a linear regression exponential smoothing or autoregressive moving average model to extrapolate the decay trend data to predict the performance state of any teaching resource within a future time window, and to apply a compensatory bias to one or more scheduling parameters in the resource demand dynamic matching and scheduling module representing the operating cost weight or matching priority of the teaching resource.

7. The driving school business comprehensive management system according to claim 1, wherein The system further comprises a teaching resource capability map and scheduling strategy adaptive optimization module configured to build and dynamically update the internal capability profile of the instructor or training vehicle based on historical driving behavior feature labels and matching scheduling execution results, and to adjust the resource matching weight in the resource demand dynamic matching and scheduling module according to the capability profile.

8. The driving school business comprehensive management system according to claim 7, wherein The teaching resource capability map and scheduling strategy adaptive optimization module is specifically configured to build the internal capability profile of the instructor by establishing and updating a quantitative model representing the instructor's solution efficiency for a preset driving behavior feature label.

9. The driving school business comprehensive management system according to claim 1, wherein The instantaneous risk events include at least one of engine speed anomaly, sudden acceleration, sudden deceleration, or sudden steering wheel beating.

10. The driving school business comprehensive management system according to claim 1, wherein The system further comprises an output interface configured to output the predictive maintenance work order teaching capability assessment suggestion or the resource allocation instruction generated according to the internal capability portrait to a management terminal.

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