A three-source external man-machine vehicle cooperative steady-state quantitative measurement and control system and method

Abstract

The application discloses an external three-source man-machine-vehicle collaborative steady-state quantitative measurement and control system and method, which is externally mounted with a time-sharing shared bus, and three collecting branches can be split and combined. The system can collect PPG of a human body, CAN of a physical robot and CAN data of a new energy vehicle in parallel, multiplexes an analog-digital channel and a public cache, introduces a bus cache constraint weighting template to respectively calculate original steady-state parameters of three types of carriers, such as accumulation, relaxation and turnover, corrects a baseline through GPS time sequence and weather weight, quantifies man-machine and man-vehicle synchronous matching degrees through a man-machine and man-vehicle coupling algorithm, adaptively adjusts a message collecting period according to the accumulation, turnover and coupling indexes, and forms a hardware regulation closed loop. Based on an embedded bus simulation model derivation, compared with three sets of independent device storage and power consumption theory, the system is reduced by 48% and 41% respectively. The application is only used for man-machine adaptation evaluation, does not develop human disease diagnosis and treatment, is suitable for adaptation, health care, catering robot and new energy vehicle manufacturer paid evaluation, and peripherals can be outsourced, and a simplified implementation idea without GPS correction is recorded.

Description

Technical Field

[0001] This invention belongs to the fields of embedded bus signal acquisition, multi-source heterogeneous data processing, and human-machine collaborative quantitative measurement and control technology. Specifically, it relates to an external integrated steady-state evaluation device that can separately and jointly acquire human physiological signals, physical service robot operation data, and new energy vehicle status data. Background Technology

[0002] The existing engineering technologies for wearable physiological monitoring, performance testing of physical service robots, and overall vehicle status monitoring of new energy electric vehicles are completely fragmented, and suffer from multiple industry deficiencies. The core primary pain point is the lack of a quantitative system for human-machine and human-vehicle interaction matching, while the others are secondary optimization deficiencies.

[0003] 1. Existing equipment can only perform steady-state scoring on human bodies, robots, and vehicles individually. There are no objective and quantifiable human-machine collaboration coupling indicators or human-vehicle driving-riding collaboration coupling indicators. Imbalance in human-machine interaction and disconnect in driving-riding experience can only be judged subjectively by humans. There is no unified objective numerical standard to measure the degree of synchronization between humans and equipment.

[0004] 2. Physical service robots are natively equipped with CAN bus and can automatically output motor torque messages. However, existing robot detection equipment is fragmented, with each type of robot equipped with independent acquisition hardware. Based on the simulation conditions of embedded bus bandwidth, the storage usage of three independent acquisition links is theoretically 48% higher and the power consumption of the whole machine is theoretically 41% higher than that of a time-sharing shared bus.

[0005] 3. The existing built-in monitoring of the robot is only a post-statistical subroutine within the main control and large model. There is no external independent data acquisition hardware link. The calculation results cannot be used to dynamically adjust the message reporting cycle. The numerical conversion is only done through post-mathematical processing and cannot interfere with the physical operating parameters of the hardware.

[0006] 4. Even if some monitoring schemes take environmental factors into account, their calibration baselines are only for a single device, and there is a lack of a unified GPS timing and meteorological baseline compensation mechanism to eliminate synchronization deviations between humans and multiple devices.

[0007] 5. Traditional quantitative indicators only involve basic weighting, linear inversion, and proportional normalization, without introducing bus cache constraint coefficients. The indicators only represent surface-level data statistics and cannot characterize underlying bus blocking or hardware occupancy. They are merely post-mathematical transformations and have no value in hardware control engineering.

[0008] 6. Existing multi-source testing equipment cannot flexibly separate or combine data collection. It only supports fixed dual or triple carriers working simultaneously and cannot evaluate a single type of equipment as needed, resulting in poor deployment flexibility.

[0009] In summary, the most critical shortcoming of existing technologies is the lack of quantitative benchmarks for human-machine and human-vehicle collaboration and coupling, making it impossible to objectively assess the degree of synchronization and matching between humans and service robots, and between humans and vehicles. At the same time, there are secondary defects such as fragmented testing equipment, lack of closed-loop control in built-in monitoring, lack of hardware constraints on quantitative indicators, lack of synchronous timing correction for multiple carriers, and inflexible combination and separation of data acquisition links.

[0010] Purpose of the invention

[0011] To address the shortcomings of existing single-channel smart glasses, such as low signal-to-noise ratio of physiological acquisition waveforms, circuit crosstalk due to software-based time-division multiplexing of pulses and acquisition, high error due to single-layer baseline compensation, mutual coupling of physiological regulation indicators, fixed pulse output levels, and lack of hardware-level privacy protection, this invention provides a multi-sensor physiological characteristic balance three-dimensional quantitative smart glasses system and evaluation application method. Through the design of dual-channel high-sensitivity acquisition architecture, hardware-level electrical isolation, four-layer series baseline correction, and decoupled indicator regression, high-precision physiological monitoring under lightweight daily wear conditions is achieved. Summary of the Invention

[0012] 1. Technical problem to be solved by the present invention

[0013] To address the shortcomings of existing technologies, such as the inability to evaluate single devices independently, the lack of unified quantitative standards for human-machine / human-vehicle collaboration, redundant data acquisition hardware resources, static post-processing data statistics, inability to eliminate baseline deviations across multiple carriers due to time and geographical factors, and the inflexible deployment of acquisition carriers, this invention provides an external time-sharing shared bus three-source steady-state quantitative measurement and control scheme. It collects human PPG, physical robot CAN, and new energy vehicle CAN data in parallel. These three carriers can be activated independently, combined in pairs, or all three can be fully operational. An independent steady-state calculation formula with hardware cache constraints is added, and GPS time-series meteorological correction is introduced to uniformly correct the baselines of multiple carriers. A coupling algorithm quantifies the synchronization and adaptability of human-machine and human-vehicle interactions, and the hardware acquisition frequency is dynamically adjusted based on the quantitative indicators.

[0014] The human physiological data collected in this invention is only used for human-machine collaboration and adaptation assessment, and is not used for the diagnosis, treatment, or prevention of human diseases.

[0015] 2. Overview of Technical Solution

[0016] This invention provides an external three-source human-machine-vehicle collaborative steady-state quantitative measurement and control system. An external time-sharing bus carries three types of independently startable and stopable acquisition branches, which respectively acquire human physiological waveforms, robot operation messages, and vehicle power and cabin messages. The bus uniformly reuses analog-to-digital conversion and caching resources. After preprocessing, the original steady-state parameters are calculated for the three types of carriers using a cached constraint weighted template. Baseline offset is eliminated through GPS timing and meteorological parameter correction. Based on the corrected steady-state parameters, the human-machine coupling matching degree and the human-vehicle coupling matching degree are calculated respectively. The system adaptively adjusts the message acquisition frequency of each branch according to the steady-state indicators and coupling matching degree, forming a complete hardware closed-loop measurement and control system.

[0017] This invention also provides a supporting measurement and control method that supports arbitrary combinations of single-branch, dual-branch, and triple-branch data acquisition; those skilled in the art can omit the GPS correction module to achieve a low-cost and simplified implementation architecture.

[0018] 3. Beneficial effects

[0019] 3.1 External time-sharing shared acquisition bus with time-sharing multiplexing of external analog-to-digital converter and buffer hardware. The three types of acquisition branches can be flexibly separated and combined, supporting arbitrary combination evaluation of single carrier, dual carrier, and triple carrier. Based on the simulation conditions of the embedded bus standard, compared with three independent acquisition devices, the overall storage resources are theoretically reduced by 48% and the overall operating power consumption is theoretically reduced by 41%. The simulation benchmark is a scenario of three-way parallel 50Hz acquisition, 16M shared buffer, and 12-bit analog-to-digital converter acquisition.

[0020] 3.2 Independent composite calculation formulas with cache constraints are set for human body, robot and vehicle. The indicators synchronously represent the operating status of the equipment and the load of the bus hardware. The numerical calculation directly controls the acquisition hardware and does not belong to simple mathematical transformation.

[0021] 3.3 Two sets of quantitative algorithms, namely human-machine collaborative coupling and human-vehicle collaborative coupling, are added. They only run when the corresponding two carriers collect data at the same time, objectively quantifying the degree of synchronization between humans and equipment, filling the gap of existing equipment lacking a two-way interactive objective quantitative benchmark.

[0022] 3.4 Coupling indicators are linked to hardware acquisition and scheduling. When human-machine or human-vehicle collaboration is unbalanced, the corresponding device message sampling is automatically encrypted to accurately capture the timing of interaction imbalance.

[0023] 3.5 Relying on the native CAN bus of the physical robot, hardware operation messages are automatically output, eliminating the need to develop separate test and acquisition scripts for each type of robot, thus reducing the difficulty of implementation.

[0024] The 3.6 standard solution incorporates GPS time-series meteorological correction to uniformly eliminate baseline offsets from multiple carriers. At the same time, it can reduce GPS peripherals to form a simplified implementation mode. One architecture can adapt to the needs of both high-precision commercial and low-cost small customers.

[0025] 3.7 The entire set of external equipment does not occupy the built-in main control computing power of the robot or vehicle. It is only used for human-machine adaptation assessment and does not carry out human disease diagnosis and treatment. It can provide standardized paid assessment services to multiple industries. Attached Figure Description

[0026] Figure 1 This is a block diagram of the overall system architecture of the present invention, which includes an external three-source time-division acquisition bus, time-series regional correction, human-machine-vehicle dual-coupling operation, and hardware closed-loop scheduling.

[0027] Figure 2 This is a complete flowchart of the external bypass acquisition, composite operation, timing correction, coupled calculation, and hardware control of the present invention;

[0028] Figure 3 This is a schematic diagram of the multi-dimensional curve output of the time-series steady state, human-machine coupling, and human-vehicle coupling of the present invention.

[0029] Explanation of reference numerals in the attached figures

[0030] 1: Independent external shared time-sharing acquisition bus;

[0031] 2: Human body photoelectric acquisition unit;

[0032] 3: CAN data acquisition unit for physical robots;

[0033] 4: Electric vehicle CAN acquisition unit;

[0034] 5: GPS meteorological auxiliary data acquisition unit;

[0035] 6: Unified data preprocessing module;

[0036] 7: Composite constraint steady-state calculation module;

[0037] 8: Time-series regional correction module;

[0038] 9: Human-machine collaborative response coupled computation submodule;

[0039] 10: Human-vehicle collaborative response and coupled computation submodule;

[0040] 11: Three-source collaborative hardware scheduling and control unit;

[0041] 12: Time series analysis and report output module. Detailed Implementation

[0042] As shown in Figure 1, the system of the present invention includes an independent external shared time-sharing acquisition bus. The bus is connected to a human photoelectric acquisition unit, a physical robot CAN acquisition unit, an electric vehicle CAN acquisition unit, and a GPS meteorological auxiliary acquisition unit. The four signals are sent to the preprocessing, steady-state calculation, timing correction, and coupling calculation modules. Finally, the scheduling unit controls the acquisition bus in reverse, and the timing module outputs the evaluation report.

[0043] As shown in Figure 2, the method flow of the present invention is as follows: the corresponding acquisition branch is activated as needed to collect raw data and auxiliary correction data, the data is cleaned and normalized, the original steady-state parameters are calculated for each carrier, the corrected steady-state parameters are obtained by GPS time-series meteorological correction, the coupling matching degree is calculated when the corresponding carrier is collected simultaneously, the acquisition cycle is dynamically adjusted according to the indicators, the time-series data is stored for a long time and alarms and evaluation reports are output.

[0044] As shown in Figure 3, the time-series curve includes three steady-state solid lines for siltation, expansion, and flow, two dashed lines for matching degree for human-machine coupling and human-vehicle coupling, two horizontal benchmark threshold lines for siltation and collaborative imbalance, and divides multiple equipment load and collaborative state zones.

[0045] General Pre-Declaration for Simulation Implementation

[0046] The hardware models, sampling parameters, weighting coefficients, judgment thresholds, and calculated values ​​described in this section are all simulated exemplary values, used only to clearly explain the operation logic and scheduling process of this invention, and are not intended to limit the scope of protection of this invention. Those skilled in the art can flexibly replace and adjust various parameters according to actual usage scenarios and acquisition accuracy requirements. All calculation results are derived based on embedded bus simulation models, and the entire algorithm and scheduling process can be completely reproduced without physical prototype testing.

[0047] Example 1: Standard high-precision simulation implementation method (all three channels open, equipped with GPS correction logic)

[0048] 1. Exemplary simulation hardware parameters

[0049] The shared bus analog-to-digital converter is a 12-bit successive approximation type, with a global simulation sampling rate of 50Hz; the simulation shared cache is 16M bytes; the exemplary main processor is an STM32H743;

[0050] Human body PPG simulation sampling rate 100Hz; physical robot CAN simulation baud rate 500kbps; vehicle CAN simulation baud rate 250kbps; GPS example module NEO-M8N, with matching simulated temperature, humidity, atmospheric pressure and meteorological parameters;

[0051] An example weight configuration is w1=0.35, w2=0.35, w3=0.3, which satisfies w1+w2+w3=1; the bus cache constraint coefficient C is calculated in the simulation as follows: C = current cache occupancy bytes / total cache bytes, with a value range of 0~0.5;

[0052] Exemplary judgment criteria: siltation judgment criterion value 40, coupling adaptation judgment criterion value 60.

[0053] 2. Simulation Calculation Example

[0054] The simulation scene bus cache occupies 8M, C=8 / 16=0.5;

[0055] The sum of the simulation mean values ​​for the three types of physiological characteristics of the human body is 0.5, the sum of the simulation mean values ​​for the three types of operational characteristics of the robot is 0.57, and the sum of the simulation mean values ​​for the three types of vehicle characteristics is 0.46.

[0056] Z1=20 simulation sampling period, Z2=8 simulation sampling period;

[0057] The simulation timing correction weight Kt=1.12, and the simulation environment compensation coefficient Ke=0.96;

[0058] Simulation results of original human body stasis parameters:

[0059] D0h=100×[0.35×0.5÷3 + 0.5×max (0.35,0.35,0.3)]=23.33

[0060] Simulation results of original human body stretching parameters:

[0061] S0h=100×[1-0.35×0.5÷3]×(1-0.5÷2)=70.63

[0062] Simulation results of original human body circulation parameters:

[0063] T0h=100×(1-8÷(20+8))×(1-0.5)=35.71

[0064] Corrected human simulation parameters:

[0065] Dh = 23.33 × 1.12 × 0.96 = 25.07

[0066] Sh = 70.63 × 1.12 × 0.96 = 75.89

[0067] Th = 35.71 × 1.12 × 0.96 = 38.26

[0068] The robot and vehicle independently deduce and correct steady-state simulation parameters using the same computational template; the simulation values ​​of human-machine coupling degree Hm and human-vehicle coupling degree Hv are solved by substituting them into the coupling algorithm formula.

[0069] The simulation yielded Th=38.26<40, indicating that the flow was obstructed on the human side. The bus scheduling unit switched the time-sharing transmission slots to stagger the message transmission of the three types of tested objects.

[0070] 3. Simulation of scheduling logic derivation

[0071] With simulation coupling value Hm=54<60, the simulation acquisition cycle of joint torque messages for the encrypted rehabilitation manipulator and lower limb exoskeleton is reduced to 10ms; with simulation Hv=52<60, the simulation reporting cycle of the vehicle CAN is shortened to 50ms.

[0072] Example 2: Two-way combined simulation implementation (human body + robot, equipped with GPS correction logic)

[0073] The vehicle-mounted CAN acquisition branch is turned off, and w3 is set to 0; only human and robot data are collected in the simulation, and only the human-machine coupling algorithm is run. Hv is not calculated; the simulation hardware parameters, calculation formulas, correction procedures, and scheduling rules are consistent with those in Example 1.

[0074] Example 3: Single-carrier simulation implementation (physical robot only)

[0075] The human body and vehicle-mounted data acquisition branches are closed, with w1=0 and w3=0. Only the robot's own D0m, S0m, and T0m are simulated, without performing any coupled calculations, and only the robot's steady-state simulation evaluation data is output. In the single-carrier simulation mode, the maximum weight is w2, and the C term participates in the calculation. The system has built-in dynamic weight normalization logic to ensure that the index scale is consistent under different combination modes.

[0076] Simplify the implementation approach

[0077] In application scenarios where the accuracy requirements for evaluation are low and there is no need to eliminate time-series regional baseline offset, GPS and meteorological acquisition peripherals can be removed, the GPS time-series meteorological correction step can be skipped, and the original steady-state parameters can be used directly to complete the coupling matching degree calculation. The hardware acquisition, weighted calculation, and scheduling rules remain unchanged, with only the multi-carrier baseline compensation function missing, thus reducing hardware and computing power investment.

[0078] Simulation effect description

[0079] The resource optimization effect of this invention is calculated based on a simulation model of a three-channel parallel 50Hz acquisition system, a 16M shared cache, and a 12-bit analog-to-digital converter standard embedded bus. The comparison scheme consists of three independent acquisition hardware systems for human body, robot, and vehicle, each with an independent 4M cache and an independent analog-to-digital converter unit. The simulation output of the three independent schemes occupies a total storage of 48M, while the shared cache of this invention is only 16M, theoretically reducing storage by 48%. The total power consumption of the three independent MCU simulation is 1.2W, while the power consumption of the single processor simulation of this invention is 0.708W, theoretically reducing power consumption by 41%.

Claims

1. An external three-source human-machine-vehicle collaborative steady-state quantitative measurement and control system, characterized in that, include: Independent external shared time-sharing acquisition bus, unified data preprocessing module, composite constraint steady-state calculation module, time-series regional correction module, human-machine collaborative response coupling calculation submodule, human-vehicle collaborative response coupling calculation submodule, three-source collaborative hardware scheduling and control unit, time-series analysis and report output module; The independent external shared time-division acquisition bus is not embedded inside the device under test. It time-division multiplexes a unified analog-to-digital conversion channel and a common buffer. Three types of bypass acquisition units that can be started and stopped independently are connected to the bus: human body photoelectric acquisition unit, physical robot acquisition unit, and electric vehicle CAN acquisition unit. The three types of acquisition units support parallel acquisition in any combination of single, two, or three channels. GPS and meteorological acquisition units are auxiliary calibration peripherals and are not included in the three types of test object sources. The physical robots include catering, health care humanoids, rehabilitation robotic arms, and lower limb assistive exoskeletons. The robots automatically output operation messages through the native CAN bus, eliminating the need for manually writing a large number of test scripts. The human photoelectric acquisition unit is used to acquire the raw PPG waveform signal of the human body and extract multiple types of human physiological characteristics from it; The physical robot acquisition unit is used to read robot CAN bus messages and extract multiple types of robot motion interaction features; The electric vehicle CAN acquisition unit is used to read vehicle bus messages and extract various vehicle operating characteristics; Z1 is the average system state switching interval, and Z2 is the convergence time of abnormal states; C is the shared bus real-time cache constraint coefficient, with a value range of 0 to 0.5; w1 is the weight of the human branch, w2 is the weight of the robot branch, and w3 is the weight of the vehicle branch, with a value range of 0 to 1. w1+w2+w3=1, and the weight of the branch is automatically set to 0 if it is not enabled. The unified data preprocessing module performs noise reduction, baseline correction and feature normalization on the heterogeneous raw data streams of the three types of carriers, and outputs standardized feature vectors. The composite constraint steady-state calculation module introduces bus cache constraint coefficients for human body, robot and vehicle respectively, and performs independent calculations using the same composite weighted template to obtain the original siltation parameters, original resource expansion parameters and original flow parameters for human body, robot and vehicle respectively. General composite operation template for single-type carriers: D0 = 100 × [w × mean of the three carrier characteristics ÷ 3 + C × max (w1, w2, w3)] S0 = 100 × [1 - w × mean of three carrier characteristics ÷ 3] × (1 - C ÷ 2) T0=100×(1 - Z2 ÷ (Z1+Z2)) × (1 - C) The time-series regional correction module includes a GPS astronomical time-series correction subunit and a local meteorological compensation subunit. It calculates the local real time series using GPS latitude and longitude and the system clock to obtain the time-series correction weight Kt, and generates an environmental compensation coefficient Ke using temperature, humidity, and seasonal segmented data. Kt and Ke range from 0.7 to 1.

3. Kt and Ke are used to correct the original steady-state parameters of the human body, robot, and vehicle, respectively, resulting in corrected steady-state parameters for the human body, robot, and vehicle. The correction calculation is: D = D0 × Kt × Ke, S = S0 × Kt × Ke, T = T0 × Kt × Ke. The human-machine collaborative response coupling calculation submodule and the human-vehicle collaborative response coupling calculation submodule receive the correction steady-state parameters and perform coupling calculations only when the two acquisition units are enabled at the same time, respectively obtaining the human-machine coupling matching degree Hm and the human-vehicle coupling matching degree Hv; Human-machine collaborative response coupling algorithm: Hm = 100 × [ 1 - 0.4×|Dh-Dm|÷100 - 0.3×|Sh-Sm|÷100 - 0.3×|Th-Tm|÷100 ] Human-vehicle collaborative response coupling algorithm: Hv = 100 × [ 1 - 0.4×|Dh-Dv|÷100 - 0.3×|Sh-Sv|÷100 - 0.3×|Th-Tv|÷100 ] Hm and Hv range from 0 to 100. The higher the value, the higher the synchronization and compatibility between the person and the device. The three-source collaborative hardware scheduling and control unit is bidirectionally connected to the composite constraint steady-state operation module, the timing and regional correction module, and the dual-coupling operation submodule. Based on the single-carrier accumulation parameter D, the flow parameter T, the human-machine coupling degree Hm, and the human-vehicle coupling degree Hv, it dynamically adjusts the corresponding device message acquisition cycle, forming a hardware closed-loop measurement and control link of acquisition-operation-timing correction-coupling evaluation-adaptive adjustment. The system presets the siltation judgment benchmark value and the collaborative imbalance coupling benchmark value. The time series analysis and report output module stores 7-day, 30-day, and 90-day multi-cycle time series data and outputs integrated steady-state and collaborative matching evaluation reports. When the flow index of any carrier after correction is lower than the siltation judgment benchmark value and the corresponding siltation D continues to exceed the standard, or when the human-machine / human-vehicle coupling degree is lower than the collaborative imbalance coupling benchmark value, bus siltation and human-machine / human-vehicle collaborative imbalance alarms are pushed respectively.

2. The system according to claim 1, characterized in that, The stagnation judgment benchmark value is 40, and the collaborative imbalance coupling benchmark value is 60; the hierarchical control rules of the three-source collaborative hardware scheduling and control unit include: 1) When the single-carrier accumulation parameter D > 60, encrypt the message acquisition cycle of the device corresponding to the carrier and expand the unified bus cache; 2) When the single carrier resource expansion parameter S > 70, reduce the acquisition frequency of the carrier device and reclaim the idle bus buffer; 3) When any carrier transfer parameter T < 40, switch the bus time-division transmission time slot to stagger the transmission of the three types of carriers under test and eliminate bus data conflicts. 4) When the human-machine coupling Hm < 60, automatically increase the frequency of collecting interaction logs for rehabilitation robotic arms, lower limb exoskeletons, or catering and companion robots. 5) When the vehicle-human coupling Hv < 60, encrypt the collection of CAN messages for the vehicle's power battery and cabin temperature control. 6) For rehabilitation robotic hands and lower limb exoskeletons, when the self-corrected congestion parameter Dm is consistently high, the acquisition of joint torque is intensified; for catering and companion humanoid robots, when the self-corrected flow parameter Tm is low, the capture of interaction logs is intensified.

3. An external three-source human-machine-vehicle collaborative steady-state quantitative measurement and control method, characterized in that, Includes the following steps: S1: Independent external shared time-sharing acquisition bus enables single, two, or three acquisition units as needed, time-sharing bypass acquisition of human PPG waveforms, physical robot CAN native messages, and electric vehicle CAN messages, synchronously acquiring bus buffer usage data and GPS weather-assisted correction data; physical robot native hardware messages are automatically uploaded, eliminating the need for manual maintenance of numerous test scripts; S2: Perform noise reduction, baseline correction, and dynamic window feature extraction on the heterogeneous data stream of the enabled unit, and normalize it to the dimensionless range of 0~1; S3: For enabled human, robot, and vehicle units, a composite weighted template with bus cache constraints is used to independently calculate their original accumulation, expansion, and flow steady-state parameters; unenabled units do not participate in the calculation and their corresponding weights are reset to 0. S4: Perform GPS astronomical timing and regional meteorological dual correction, use Kt and Ke to correct the original parameters of the enabled units respectively, and output the corrected steady-state parameters of the human body, robot and vehicle. S5: If both the human and robot acquisition units are activated simultaneously, the human-robot collaborative response coupling algorithm is run to calculate Hm; if both the human and vehicle acquisition units are activated simultaneously, the human-vehicle collaborative response coupling algorithm is run to calculate Hv; single-unit acquisition only outputs its own steady-state parameters. S6: Based on the D and T indicators of each carrier, the human-machine coupling degree Hm, and the human-vehicle coupling degree Hv, dynamically adjust the corresponding device message acquisition cycle to form a hardware acquisition adaptive closed loop. S7: Stores 7-day, 30-day, and 90-day time-series data, and statistically analyzes the long-term trends of steady-state indicators and coupling degree of each carrier. S8: The system presets the congestion judgment benchmark value and the collaborative coupling benchmark value. Based on the corresponding benchmark, it judges the bus congestion and human-machine / human-vehicle collaboration imbalance, and outputs a standardized third-party evaluation report and corresponding alarm.

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