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CN-121979076-A - Unmanned express delivery vehicle control system and method based on running state sensing

CN121979076ACN 121979076 ACN121979076 ACN 121979076ACN-121979076-A

Abstract

The invention discloses an unmanned express delivery vehicle control system and method based on running state sensing, and relates to the field of unmanned express delivery vehicle control systems. The method comprises the steps of obtaining vehicle-mounted data in a driving period through a vehicle-mounted sensor array, performing time alignment and validity marking on the vehicle-mounted data to obtain a data quality label, constructing an evidence item set based on the data quality label, generating a sub-portrait label set according to the evidence item set, and generating a portrait parameter set based on the sub-portrait label set through a preset priority rule and a persistence judging strategy. Although this approach is excellent in improving system adaptability, security, dynamic decision capability, censorability, and resource utilization efficiency, under complex or extreme circumstances, problems such as sensor failure, data loss, real-time data processing bottlenecks, and tag interpretation errors can affect the real-time response and decision accuracy of the system, thereby reducing overall performance and stability, especially under high load or low signal conditions.

Inventors

  • YANG CHANGXING

Assignees

  • 利可充(山东)智能科技有限公司

Dates

Publication Date
20260505
Application Date
20260211

Claims (10)

  1. 1. The unmanned express vehicle control method based on the running state sensing is characterized by comprising the following steps of: s1, acquiring vehicle-mounted data in a driving period through a vehicle-mounted sensor array, and performing time alignment and validity marking on the vehicle-mounted data to obtain a data quality label; s2, constructing an evidence item set based on the data quality label, and generating a sub-portrait label set according to the evidence item set; S3, generating an portrait parameter set based on the sub-portrait tag set through a preset priority rule and a persistence judging strategy, and generating an operation state portrait code according to structural codes of the portrait parameter set and the sub-portrait tag set; s4, generating a control constraint packet according to the running state portrait code, determining a control mode, constraining path planning, track generation and execution control based on the control mode, and outputting a vehicle control instruction; and S5, executing a vehicle control instruction, synchronously acquiring execution feedback, performing closed-loop verification on the running state portrait code, triggering a rechecking process when a preset rechecking condition is met, and updating the running state portrait code and the control constraint packet based on a rechecking result.
  2. 2. The unmanned express delivery vehicle control method based on the operation state sensing according to claim 1, wherein the S1 comprises: And carrying out time alignment on the vehicle-mounted data in a cache alignment or alignment maintaining mode, marking the data exceeding a preset aging threshold as outdated, carrying out validity marking on the vehicle-mounted data subjected to time alignment in at least one mode of range verification, jump verification and continuity verification, and generating a data quality label based on the outdated marking and the validity marking.
  3. 3. The unmanned express delivery vehicle control method based on the operation state sensing according to claim 1, wherein the S2 comprises: subsystem grouping is carried out on the vehicle-mounted data according to the data quality label by adopting a preset subsystem mapping table; constructing evidence rules based on a preset evidence rule configuration table aiming at each group of data, executing evidence judgment, discretizing the evidence judgment result and outputting to generate an evidence conclusion; and carrying out stability judgment in a preset sliding time window based on the data quality label and combining the evidence conclusion, generating an evidence strength label, and then collecting the evidence conclusion and the evidence strength label to form an evidence item set.
  4. 4. The unmanned express delivery vehicle control method based on the operation state sensing of claim 3, wherein the S2 further comprises: merging the evidence items in the evidence item set according to the subsystem group, and generating an initial grade of a corresponding sub-portrait label by adopting a preset evidence merging strategy; mapping the initial level into a sub-portrait level through a preset level mapping table, and executing persistence judgment on the sub-portrait level to generate a sub-portrait tag set.
  5. 5. The unmanned express delivery vehicle control method based on the operation state sensing according to claim 1, wherein the S3 comprises: the portrait parameter set comprises a total portrait level, a dominant risk source and a credibility level; determining a dominant risk source in the sub-portrait tag set according to a preset priority rule; gating the sub-portrait tag set according to a preset persistence judging strategy to generate a total portrait level; generating a credibility grade according to the data quality label and the consistency judgment of the evidence item set; and carrying out structural coding on the total portrait level, the dominant risk source, the credibility level and the sub portrait tag set to generate an operation state portrait code.
  6. 6. The unmanned express delivery vehicle control method based on the operation state sensing according to claim 1, wherein the S4 comprises: selecting a control constraint template in a preset control constraint template library by adopting at least one mode of table lookup matching, rule matching and state machine switching based on the total image level in the image parameter set and a dominant risk source; And generating a control constraint packet by a control constraint template based on the running state portrait code and in a parameter instantiation mode, selecting a control mode according to the total portrait level, and associating the control constraint packet with the control mode through a preset constraint association table.
  7. 7. The unmanned express delivery vehicle control method based on the operation state sensing of claim 6, wherein S4 further comprises: limiting candidate routes and candidate maneuvers in a path planning process according to the control constraint packet, generating a planning result by adopting a preset constraint path planning mode, and taking the planning result as a reference path or constraint input for track generation; Applying boundary constraint to the track generation process according to the control constraint packet, generating a target track by adopting a preset track generation mode, and taking the target track as a tracking target for executing control; and executing control quantity constraint processing on the execution control process according to the control constraint packet, and outputting a vehicle control instruction based on the target track.
  8. 8. The unmanned express delivery vehicle control method based on the operation state sensing according to claim 1, wherein the S5 comprises: Executing a vehicle control instruction, and collecting and executing feedback; performing time sequence alignment on the vehicle control instruction and the execution feedback, executing instruction and responding to consistency verification based on the alignment result, and obtaining a consistency result; When the consistency result meets the preset deviation condition, the evidence item set and the evidence item or sub-portrait label associated with the deviation are updated, and the running state portrait code and the control constraint package are regenerated according to the evidence item or sub-portrait label.
  9. 9. The unmanned express delivery vehicle control method based on the operation state sensing of claim 8, wherein S5 further comprises: Analyzing the running state image code to obtain a credibility grade; And judging whether a preset rechecking condition is met or not based on at least one of the credibility level, the data quality label and the running state portrait code history sequence, triggering a rechecking process to acquire rechecking data when the preset rechecking condition is met, reconstructing an evidence item set, a sub portrait label set and a running state portrait code based on the rechecking data, and reconstructing a control constraint packet according to the updated running state portrait code.
  10. 10. An unmanned express delivery vehicle control system based on operation state sensing, for implementing the unmanned express delivery vehicle control method based on operation state sensing as claimed in any one of claims 1 to 9, comprising: The acquisition and marking module is used for acquiring vehicle-mounted data in a driving period through the vehicle-mounted sensor array, and carrying out time alignment and validity marking on the vehicle-mounted data to obtain a data quality label; the sub-portrait generation module is used for constructing an evidence item set based on the data quality label and generating a sub-portrait label set according to the evidence item set; The portrait code synthesis module generates a portrait parameter set based on the sub-portrait tag set through a preset priority rule and a persistence judging strategy, and generates an operation state portrait code according to structural coding of the portrait parameter set and the sub-portrait tag set, wherein the portrait parameter set comprises a total portrait level, a dominant risk source and a credibility level; the portrait drive control module generates a control constraint packet according to the running state portrait code, determines a control mode, constrains path planning, track generation and execution control based on the control mode, and outputs a vehicle control instruction; And the closed loop checking module is used for executing the vehicle control instruction, synchronously acquiring the execution feedback, performing closed loop checking on the running state portrait code, triggering a rechecking process when the preset rechecking condition is met, and updating the running state portrait code and the control constraint packet based on the rechecking result.

Description

Unmanned express delivery vehicle control system and method based on running state sensing Technical Field The invention relates to the field of unmanned express delivery vehicle control systems, in particular to an unmanned express delivery vehicle control system and method based on running state sensing. Background With the continuous development of intelligent technology, unmanned express delivery vehicles are widely applied to modern urban distribution as an important component of automated logistics. Unmanned express delivery car possesses high-efficient, environmental protection's advantage, can promote logistics distribution's speed and quality by a wide margin. However, due to the complex and changeable environment and the stability of system hardware and software, various challenges are still faced in the operation process of the unmanned express delivery vehicle, especially how to ensure the safety and stability of the unmanned express delivery vehicle, currently, the existing unmanned express delivery vehicle control system mainly depends on a single sensor or health index to evaluate the vehicle state and accordingly make control decisions, the traditional method usually only focuses on the health condition of a subsystem, such as the accuracy of a positioning system or the electric quantity of a battery, and the cooperative effect among all subsystems in the system is ignored, along with the continuous development of the unmanned express delivery vehicle technology, the uncertainty in the complex environment is difficult to be dealt with by a control scheme of the single index, the coping capability of the system to abnormal events is poor, and the control strategy often lacks dynamic adjustment capability; In the prior art, although the safety, dynamic decision capability, censorability and resource utilization efficiency of the unmanned express delivery vehicle control system are improved, in practical application, some potential technical defects still exist, firstly, the system highly depends on the health conditions of a plurality of sensors and subsystems, if some of the sensors have faults or data loss, especially in a low-signal environment, critical data cannot be obtained in time by the system, the stability of the whole system is influenced, particularly in the case of sensor failure or data transmission blockage, the system cannot quickly adjust a decision strategy, thus response time can be delayed, vehicles cannot avoid obstacles or adjustment paths in time, potential safety hazards are increased, secondly, although the rechecking process can effectively cope with the fluctuation of abnormal data, however, in environments such as extreme weather conditions or electromagnetic interference, the reliability of the sensor and the accuracy of data may be greatly reduced, which may affect the timely processing and decision optimization of the system for abnormal conditions, increase the risk of errors in the complex environment, and in addition, although the data processing efficiency is improved through multi-layer data processing and resource screening, in the scenes of high-density obstacles, complex path planning or real-time high-frequency data processing, the system may encounter calculation bottlenecks, resulting in delayed response and even failure to timely update the control strategy, thereby affecting the real-time coping ability of the vehicle in the fast-changing environment. Disclosure of Invention Based on the defects of the prior art, the invention aims to provide an unmanned express vehicle control system and method based on running state sensing so as to solve the technical problems. The unmanned express vehicle control method based on the operation state perception comprises the following steps of obtaining vehicle-mounted data in a driving period through a vehicle-mounted sensor array, carrying out time alignment and validity marking on the vehicle-mounted data to obtain a data quality label, constructing an evidence item set based on the data quality label, generating a sub-image label set according to the evidence item set, generating an image parameter set based on the sub-image label set through a preset priority rule and a persistence judging strategy, generating an operation state image code according to the image parameter set and the sub-image label set structural coding, generating a control constraint packet according to the operation state image code, determining a control mode, restraining path planning, track generation and execution control based on the control mode, outputting a vehicle control instruction, executing the vehicle control instruction, synchronously obtaining execution feedback, carrying out closed loop verification on the operation state image code, triggering a rechecking process when a preset rechecking condition is met, and updating the operation state image code and the control constraint packet based on a rechecking result. The meth