Remote ad hoc network mobile control system and method for intelligent cone barrel

Abstract

The invention belongs to the technical field of intelligent cone barrel control, and particularly relates to a remote ad hoc network mobile control system and method for an intelligent cone barrel, and the system comprises an intelligent control terminal, a distributed ad hoc network communication module, a group cooperative control decision module, and a plurality of intelligent cone barrel monomers. An intelligent control terminal serves as an instruction core and an interaction hub, a distributed ad hoc network communication module constructs a low-delay and anti-fault two-way communication link, and a group cooperative control decision module generates an executable moving scheme through global path planning, local obstacle avoidance and a formation control algorithm. The intelligent cone barrel single body is used as an execution main body to realize scheme landing, the motion abnormity of the single cone barrel can be accurately quantified, the cone barrel formation motion management and control stability hidden danger degree can be reasonably evaluated, the deployment efficiency, the moving precision and the operation safety of the cone barrel formation are greatly improved, intelligent support is provided for scenes such as road construction and accident disposal, and the practicability is high. And the working difficulty of management personnel is obviously reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent cone control technology, specifically to a remote self-organizing network mobile control system and method for intelligent cones. Background Technology

[0002] In scenarios such as road construction barriers, traffic accident scene handling, and temporary traffic control, traffic cones serve as core safety control facilities. Their deployment efficiency, warning effect, and control accuracy directly affect the safety of workers and the order of road traffic. Traditional traffic cones rely on manual handling and placement, which is not only labor-intensive and time-consuming to deploy, but also exposes workers to traffic risks in complex environments such as highways, nighttime, or inclement weather. Furthermore, manual placement makes it difficult to ensure uniform spacing and standardized formation of traffic cones, which can easily lead to secondary accidents due to blind spots. With the penetration of IoT and automation technologies, preliminary intelligent traffic cone products have emerged in the industry.

[0003] For example, Chinese patent CN216809676U discloses "An intelligent alarm cone with Beidou positioning and uploading function." This technical solution clearly focuses on the field of intelligent cones. By integrating a Beidou positioning module, a wireless communication module, an audible and visual alarm module, and a battery component inside the cone, it achieves real-time acquisition and uploading of cone location information and audible and visual warnings in abnormal states. It also supports remote backend monitoring of the cone's working status. Compared with traditional manual cones, it significantly improves the automation level of positioning and warnings and reduces the frequency of manual inspections. However, this technical solution still reveals the following limitations in actual complex operation scenarios:

[0004] First, the communication mechanism has bottlenecks. Although it supports location data upload, it lacks a self-organizing network architecture among the cones. When a large number of cones are deployed or the work area is large, signal interruptions and data transmission delays often occur, making it impossible to achieve command synchronization and status exchange among the cone group. Second, it lacks group collaborative control capabilities, only enabling independent positioning and alarms for individual cones. Path planning, formation adjustment, and obstacle avoidance algorithms are not designed. If the cone arrangement needs to be changed according to operational requirements, manual movement and adjustment are still required, which is inefficient and exposes workers to hazardous working environments again. Finally, it lacks full-process control functions, making it impossible to assess the cone movement status and overall formation stability in real time. This is detrimental to ensuring the safe, stable, and efficient movement of the cone formation and significantly reducing its management difficulty.

[0005] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a remote self-organizing network mobile control system and method for intelligent cones, so as to solve the technical defects mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a remote self-organizing network mobile control system for intelligent cones, comprising an intelligent control terminal, a distributed self-organizing network communication module, a group collaborative control decision module, and several intelligent cone units; wherein, each intelligent cone unit has a built-in embedded controller, a positioning fusion module, an environmental perception module, an intelligent warning module, a high-efficiency power management module, a drive precision control module, and a tracked mobile execution module, and the embedded controller adopts an ARM Cortex-A series or RISC-V architecture, serving as the core of the intelligent cone unit to coordinate the operation of each built-in module;

[0008] The intelligent control terminal receives user operation commands and outputs them in a standardized manner, while also receiving and displaying feedback data. The distributed self-organizing network communication module uses self-organizing network technology to construct a "core-follower" hybrid network topology, which is used to establish bidirectional communication links between intelligent cones and between intelligent cones and the intelligent control terminal. It also uses a custom application layer protocol to transmit cone IDs, status information, and control commands. The group collaborative control decision module receives commands from the intelligent control terminal and, in conjunction with positioning and environmental data, outputs path planning and formation control commands to each individual intelligent cone.

[0009] Furthermore, the intelligent control terminal has a built-in software operation unit, a voice control unit, and a command verification unit. The software operation unit integrates electronic maps, formation template libraries, task planning, and manual control functions. The voice control unit integrates offline / online voice recognition components and noise suppression components, activates the intelligent cone unit through a preset wake-up word, and triggers corresponding actions based on the command word.

[0010] The instruction verification unit performs legality verification on the structured instructions generated by the two types of input methods. After the verification is passed, the instruction is encapsulated into a standardized format and output.

[0011] Furthermore, the distributed self-organizing network communication module adopts Wi-FiMesh, ZigBee or LoRa self-organizing network technology to construct a "core-follower" hybrid network topology, with some smart cones acting as core nodes and the rest of the smart cones acting as follower nodes; the distributed self-organizing network communication module uses a custom application layer protocol to transmit data frames containing cone ID, status data and control commands.

[0012] Furthermore, the group collaborative control decision-making module includes a global path planning unit, a local path planning and obstacle avoidance unit, and a formation control unit; the global path planning unit adopts... Algorithm or The algorithm plans the overall movement path of the cone formation; the local path planning obstacle avoidance unit uses the artificial potential field method or the dynamic window method to generate real-time local trajectories to avoid obstacles; the formation control unit uses the leader-follower method or the virtual structure method.

[0013] Furthermore, in the navigator-follower method, the core cone acts as the navigator, sending its own pose data in real time, while the follower cones adjust their movement state according to preset relative position parameters; the virtual structure method treats the entire cone formation as a rigid body, with each intelligent cone corresponding to a fixed point on the rigid body, and adjusting its position synchronously according to the movement trajectory of the rigid body.

[0014] Furthermore, in the intelligent cone unit, the positioning fusion module integrates an RTK high-precision positioning unit and an UWB ultra-wideband positioning unit, and fuses multi-source data through a Kalman filter algorithm to output sub-meter-level lane-level pose information; the environmental perception module integrates an inertial measurement unit, a visual camera, and a lidar to monitor the cone's own attitude and collect and identify information about the surrounding environment.

[0015] The intelligent warning module includes an LED flashing unit and an audible and visual alarm, supporting programmable multi-mode warning output and triggering an alarm when the cone malfunctions; the high-efficiency power management module consists of a high-performance lithium battery pack, a fast charging circuit, a power management unit, and a low-power control program; the drive precision control module uses a DC servo motor or a geared motor; the tracked mobile execution module adopts a low center of gravity and a large bottom edge structure, with IP65 waterproof and dustproof capabilities and anti-tipping performance against wind speeds of up to 10m / s.

[0016] Furthermore, the distributed self-organizing network communication module communicates with the cone motion capture and evaluation module. The cone motion capture and evaluation module acquires all intelligent cone units in the cone formation, marks the corresponding intelligent cone unit as target unit i, where i is a natural number greater than 1; it captures the motion information of target unit i and evaluates its motion performance, and sends the motion performance evaluation results to the intelligent control terminal. The specific evaluation and analysis process is as follows:

[0017] The motion parameters that need to be monitored during the movement of the target unit i are obtained. Based on the monitoring data of the corresponding motion parameters, it is determined whether an abnormality has occurred. If the corresponding motion parameter is abnormal, the abnormal state of the corresponding motion parameter is timed until it recovers to the normal state. Based on this, the single abnormality statistical duration is obtained. The statistical duration of all single abnormalities of the corresponding motion parameter within a unit time is summed to obtain the motion parameter abnormality statistical duration value. The total number of times the corresponding motion parameter is abnormal within a unit time is marked as the motion parameter abnormality frequency value. The number of times the single abnormality statistical duration of the corresponding motion parameter exceeds the corresponding preset single abnormality statistical duration threshold within a unit time is marked as the motion parameter abnormality recurrence value.

[0018] The characteristic value of the operation parameter is obtained by weighted summation of the time value of the operation parameter anomaly, the frequency value of the operation parameter anomaly, and the risk value of the operation parameter anomaly. The ratio of the characteristic value of the operation parameter to the corresponding preset characteristic threshold of the operation parameter is calculated to obtain the risk ratio coefficient of the operation parameter.

[0019] Each motion parameter is pre-set to correspond to a set of preset parameter weight values. The motion parameter risk ratio coefficient of the corresponding motion parameter is multiplied by the corresponding preset weight value to obtain the motion parameter risk table coefficient. The motion parameter risk table coefficients of all motion parameters are summed to obtain the motion evaluation decision value of target unit i. The motion evaluation decision value is compared with the preset motion evaluation decision threshold. If the motion evaluation decision value exceeds the preset motion evaluation decision threshold, a motion instability signal of target unit i is generated. If the motion evaluation decision value does not exceed the preset motion evaluation decision threshold, a motion qualified signal of target unit i is generated.

[0020] Furthermore, the cone motion capture and evaluation module is connected to the formation stability and hidden danger output module. The cone motion capture and evaluation module sends the motion performance evaluation results of all intelligent cones in the cone formation to the formation stability and hidden danger output module. The formation stability and hidden danger output module analyzes the control and hidden danger status of the cone formation and generates a formation stability early warning signal or a formation stability normal signal through analysis. The formation stability early warning signal or formation stability normal signal is sent to the intelligent control terminal. When the intelligent control terminal receives the formation stability early warning signal, it issues a corresponding warning.

[0021] Furthermore, the specific analysis process of the formation stability hazard output module is as follows:

[0022] The system acquires individual smart cones corresponding to motion instability signals and marks them as unstable cones. If there are unstable cones in the core cone, a formation stability warning signal is generated. If there are no unstable cones in the core cone, the proportion of unstable cones in the cone formation is marked as the formation instability measurement value. The formation instability measurement value is compared with a preset formation instability measurement threshold. If the formation instability measurement value exceeds the preset formation instability measurement threshold, a formation stability warning signal is generated.

[0023] If the formation instability measurement value does not exceed the preset formation instability measurement threshold, the average motion evaluation decision value of all intelligent cones in the cone formation is used to calculate the formation operation and control characterization value. The motion evaluation decision value with the largest value in the cone formation is marked as the formation operation and control abnormal amplitude value. The formation stability alarm value is calculated by weighted summation of the formation instability measurement value, the formation operation and control characterization value, and the formation operation and control abnormal amplitude value. If the formation stability alarm value exceeds the preset formation stability alarm threshold, a formation stability warning signal is generated; otherwise, a formation stability normal signal is generated.

[0024] This invention also proposes a remote self-organizing network mobile control method for intelligent cones, comprising the following steps:

[0025] Step 1: Input and standardization of control commands;

[0026] Step 2: Self-organizing network construction and command transmission;

[0027] Step 3: Group collaborative decision-making and instruction generation;

[0028] Step 4: Monitoring and feedback of cone parameters;

[0029] Step 5: Execution and control of cone movement.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. In this invention, an intelligent control terminal serves as the command core and interaction hub, and a distributed self-organizing network communication module constructs a low-latency, fault-resistant bidirectional communication link. An executable movement plan is generated through global path planning, local obstacle avoidance, and formation control algorithms. The intelligent cone unit acts as the execution subject to implement the plan, which greatly improves the deployment efficiency, movement accuracy, and operational safety of the cone formation. It provides intelligent support for scenarios such as road construction and accident handling, and significantly reduces the workload of management personnel.

[0032] 2. In this invention, the cone motion capture and evaluation module accurately quantifies the motion anomalies of individual cones and provides individual basis for formation stability control. The formation stability hazard output module reasonably evaluates the degree of hazard in the stability control of cone formation motion, forming a dual stability control mechanism of "individual assessment - overall judgment". This is conducive to ensuring the safety and consistency of cone formation operation and further reducing the difficulty of cone formation management. Attached Figure Description

[0033] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0034] Figure 1 This is a system block diagram of the present invention;

[0035] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1: As Figure 1 As shown, the remote self-organizing network mobile control system for intelligent cones proposed in this invention includes an intelligent control terminal, a distributed self-organizing network communication module, a group collaborative control decision module, and several intelligent cone units.

[0038] The intelligent cone unit incorporates an embedded controller, a positioning fusion module, an environmental perception module, an intelligent warning module, a high-efficiency power management module, a drive precision control module, and a tracked mobile execution module. The embedded controller uses an ARM Cortex-A series or RISC-V architecture and serves as the core of the intelligent cone unit to coordinate the operation of each built-in module. Thus, the embedded controller coordinates the working sequence of each module through its built-in logic control program to ensure the orderly operation of the intelligent cone unit.

[0039] That is, after receiving the control commands issued by the group collaborative control decision module, the embedded controller parses out parameters such as speed and direction and distributes them to the drive precision control module, intelligent warning module, etc. At the same time, it collects positioning data from the positioning fusion module, attitude and obstacle data from the environmental perception module, and power data from the high-efficiency power management module in real time. After performing data format conversion and validity verification, it feeds back to the distributed self-organizing network communication module. The embedded controller supports multi-threaded processing and the data processing latency is ≤50ms.

[0040] Furthermore, the positioning fusion module integrates BeiDou / GPSRTK high-precision positioning units and UWB ultra-wideband positioning units, and fuses multi-source data through the Kalman filter algorithm to output sub-meter-level lane-level pose information (it should be noted that the Kalman filter algorithm filters the data from each sensor by establishing state equations and observation equations, suppressing noise interference, and outputting stable pose data, providing basic support for formation control and precise movement).

[0041] Among them, the BeiDou / GPSRTK high-precision positioning unit provides centimeter-level absolute positioning accuracy and can obtain the latitude and longitude coordinates of individual smart cones; the UWB ultra-wideband positioning unit achieves relative position measurement of ≤10cm through distance measurement between cones, which is used to calibrate the spacing between cones.

[0042] The environmental perception module integrates an inertial measurement unit (IMU), a visual camera, and a lidar to monitor the cone's own attitude and collect and identify information about the surrounding environment. The inertial measurement unit collects the cone's acceleration and angular velocity data, calculates attitude parameters such as pitch and roll angles, and can monitor the cone's tilt status in real time. In the event of satellite signal loss (such as in tunnels or when tall buildings block the signal), it provides short-term, high-precision displacement and attitude estimation for ≤30 seconds.

[0043] The visual camera and LiDAR work together. The visual camera captures images of the surrounding environment, while the LiDAR acquires 3D point cloud data. After fusion, the two can identify obstacles (such as vehicles, pedestrians, and scattered objects) and road conditions (such as smoothness and water accumulation) within a 5-meter range, with an accuracy rate of ≥95%. Furthermore, the environmental perception module transmits the collected attitude data, obstacle data, and road data to the embedded controller. After preprocessing, the data is used to trigger local warnings (such as activating an alarm when the tilt angle is too large) and sent to the group collaborative control decision module to provide data support for local obstacle avoidance decisions.

[0044] The intelligent warning module includes an LED flashing unit and an audible and visual alarm, supporting programmable multi-mode warning output and triggering an alarm when the cone malfunctions. Specifically, the LED flashing unit uses high-brightness LED beads with a brightness ≥500cd / m², supporting programmable multi-mode output such as constant light, strobe, and alternating flashing. Different modes are adapted to different operating scenarios (e.g., strobe mode for construction scenarios and alternating flashing mode for accident scenarios). In low-visibility environments such as nighttime, rain, snow, and fog, the warning distance of the LED light is ≥200 meters. Furthermore, the intelligent warning module controls the flashing frequency and brightness of the LED light through PWM signals and controls the start and stop of the audible and visual alarm through level signals, with a response time ≤100ms.

[0045] The high-efficiency power management module consists of a high-performance lithium battery pack, a fast charging circuit, a power management unit, and a low-power control program. It provides stable power supply for each module of the smart cone. The lithium battery pack has an energy density of ≥200Wh / kg and can support 12 hours of continuous operation of the cone after a single full charge. The fast charging circuit adopts DC fast charging technology, which can restore 8 hours of battery life in 30 minutes of charging, reducing charging waiting time.

[0046] The power management unit monitors battery voltage, current, and power data in real time with a measurement error of ≤5%. It transmits power information to the embedded controller via the CAN bus and features overcharge, over-discharge, and overcurrent protection to prevent battery damage. The low-power control program adjusts power consumption according to the cone's working status. When the smart cone is in standby mode, it automatically shuts down the non-core functions of the positioning fusion module and the environmental perception module, reducing standby power consumption to ≤5W. In addition, the high-efficiency power management module supports modular battery replacement, allowing for quick battery replacement without disassembling the smart cone, thus improving operational efficiency.

[0047] The drive precision control module uses a DC servo motor or a geared motor as the power source, and with the help of a PID closed-loop control algorithm, it can achieve precise control of the cone's movement. Specifically, the motor speed adjustment range is 0.1-1m / s, and the output torque is ≥5N・m, which can drive the cone to move stably on a road surface with a slope of ≤15°.

[0048] The PID control algorithm adjusts the motor's output voltage and current by comparing the difference between the target speed and the actual speed in real time, keeping the speed error within ≤±0.02m / s. After receiving speed, torque, and other commands from the embedded controller, the drive precision control module converts the commands into motor control signals through the motor drive circuit. At the same time, it collects the motor's speed and current data and feeds them back to the embedded controller, forming a closed-loop control.

[0049] The tracked mobile execution module adopts a tracked chassis design, combined with the low center of gravity and large bottom edge structure of the cone body, to achieve stable movement and anti-interference capabilities. Specifically, the tracks are made of rubber with anti-slip patterns on the surface, and the ground contact area is significantly increased compared to traditional wheeled chassis, improving passability on complex road surfaces such as mud and gravel. The center of gravity height of the intelligent cone unit is ≤15cm, and the bottom edge diameter is ≥50cm. With the support of the tracked chassis, the wind resistance reaches 10m / s (equivalent to a level 5 wind), which can effectively prevent it from being blown over by the wind.

[0050] Furthermore, the outer shell adopts a waterproof and dustproof design with a protection level of IP65, which can work normally in rainy and dusty environments; a buffer device is installed at the bottom of the chassis to reduce the impact of road bumps on the internal modules; by driving the power provided by the precise control module, it can realize forward, backward, and turning actions, with a turning radius of ≤0.5 meters, which can meet the movement needs in narrow spaces.

[0051] The intelligent control terminal is used to receive user operation commands and output them in a standardized manner. It also receives and displays feedback data (such as the position, power, speed, and warning status of each intelligent cone). It can not only provide a precise and safe command source for group collaborative control and adapt to remote planning and rapid on-site operation scenarios, but also help to understand the operating status of each intelligent cone in detail and allow for manual intervention control.

[0052] Specifically, the intelligent control terminal has a built-in software operation unit, voice control unit, and command verification unit. The software operation unit integrates electronic map, formation template library, task planning, and manual control functions. The electronic map is used to display the location information of all intelligent cones in real time. The formation template library has preset common formations such as line, arc, and rectangle. The task planning function (path drawing tool) allows users to draw the movement path of the cone formation or set target points. The manual control function (manual panel) allows for fine-tuning of individual intelligent cones.

[0053] Users can view the location coordinates and working status of all cones in real time through an electronic map, call the target formation with one click through the formation template library, or set the movement trajectory and target point through the path drawing tool. They can also adjust the speed, direction and other parameters of one or more cones through the manual panel.

[0054] The voice control unit integrates offline / online voice recognition components and noise suppression components. It activates the intelligent cone unit via a preset wake-up word, and the user's voice commands are filtered for environmental interference (vehicle horns, wind noise, etc.) by the noise suppression component. The offline / online voice recognition component then extracts features and performs semantic matching to convert them into structured commands. Based on the command words, corresponding actions are triggered (e.g., voice input such as "Cones, be careful, form an arc to cover the accident area," "Advance in a single line for 30 meters," "Emergency stop," etc.). The voice recognition function achieves an accuracy rate of ≥90% when the ambient noise is ≤60dB, adapting to the needs of road work sites. Furthermore, if the recognition confidence level is ≤85%, the user is prompted to re-enter the voice command via voice feedback.

[0055] The instruction verification unit performs legality checks on structured instructions generated by the two types of input methods, such as whether the target location exceeds the operation range, whether the movement speed is lower than the safety threshold, and whether the formation parameters meet the road width restrictions. After the verification is passed, the instruction is encapsulated into a standardized format and output, for example, encapsulated into a standardized format of "instruction ID-execution object-target parameter-timestamp".

[0056] The distributed self-organizing network communication module uses self-organizing network technology to construct a "core-follower" hybrid network topology, which is used to establish bidirectional communication links between smart cones and between smart cones and smart control terminals. It also uses a custom application layer protocol to transmit cone ID, status information and control commands.

[0057] It should be noted that the distributed self-organizing network communication module adopts Wi-FiMesh, ZigBee or LoRa self-organizing network technology to build a "core-follower" hybrid network topology, with some smart cone units acting as core nodes and the rest of the smart cone units acting as follower nodes.

[0058] Among them, the core nodes account for 10% to 20% of the total number of cones. They are equipped with enhanced communication, computing and sensing functions and can serve as regional communication hubs. They are responsible for receiving instructions from intelligent control terminals and forwarding them to follower nodes, while also performing global collaborative computing. The follower nodes adopt a lightweight design, retaining only basic communication and data feedback functions. They receive and execute instructions forwarded by the core nodes, while feeding back their own status data (location, power, speed, etc.) to the core nodes.

[0059] The distributed self-organizing network communication module adopts a custom application layer protocol. The data frame structure includes cone ID, status data (covering location, power, speed information, etc.), and control commands (including movement direction, speed, formation parameters, etc.) to reduce data transmission volume. Communication latency is controlled within ≤100ms, and packet loss rate is ≤0.5%, ensuring efficient transmission of commands and data. Furthermore, when the number of cones increases or decreases, the distributed self-organizing network communication supports dynamic networking, completing network topology adjustments within ten seconds. If a single node fails, data can automatically switch to a backup link to achieve fault redundancy.

[0060] The group collaborative control decision module is used to receive instructions from the intelligent control terminal and, in combination with positioning and environmental data, output path planning and formation control instructions to each intelligent cone unit; specifically, the group collaborative control decision module includes a global path planning unit, a local path planning and obstacle avoidance unit, and a formation control unit;

[0061] Among them, the global path planning unit adopts Algorithm or The algorithm plans the overall movement path of the cone formation, that is, it uses... Algorithm or The algorithm, by combining information such as road boundaries and restricted areas in the electronic map, plans the overall movement path of the cone formation from the starting point to the target point. The algorithm also selects the optimal path by evaluating parameters such as path length and road surface smoothness, avoiding invalid movement across lanes.

[0062] The local path planning obstacle avoidance unit uses the artificial potential field method or the dynamic window method to generate a real-time local trajectory to avoid obstacles. Specifically, it uses the artificial potential field method or the dynamic window method (DWA) to receive obstacle data (such as accident vehicles and scattered objects) transmitted by the environmental perception module in real time and generate a local adjustment trajectory within 500ms. By calculating the distance between the cone and the obstacle, it adjusts the direction and speed of movement to achieve obstacle avoidance.

[0063] The formation control unit adopts either the pilot-follower method or the virtual structure method (i.e., two control modes). It should be noted that in the pilot-follower method, the core cone (i.e., the core node) acts as the navigator, sending its own pose data in real time, while the following cones (i.e., the following nodes) adjust their own movement state according to preset relative position parameters (such as Δx, Δy). The virtual structure method treats the entire cone formation as a rigid body, with each intelligent cone corresponding to a fixed point on the rigid body. The cones adjust their positions synchronously according to the movement trajectory of the rigid body to ensure the overall movement consistency of the formation.

[0064] Example 2: Figure 1As shown, the difference between this embodiment and embodiment one is that the distributed self-organizing network communication module is connected to the cone motion capture and evaluation module. The motion information of each smart cone is transmitted to the cone motion capture and evaluation module in real time through the distributed self-organizing network communication module. The cone motion capture and evaluation module obtains all smart cones in the cone formation and marks the corresponding smart cone as target cone i, where i is a natural number greater than 1.

[0065] The motion information of target unit i is captured and its motion performance is evaluated. The evaluation results are then sent to the intelligent control terminal. This not only facilitates enhanced management and timely inspection and maintenance of the corresponding intelligent cone units, but also provides individual status information for the overall management of the cone formation, enabling effective supervision of all intelligent cone units and ensuring the stable, efficient, and safe movement of the cone formation. The specific evaluation and analysis process is as follows:

[0066] The motion parameters (such as motion speed, pitch angle, roll angle, yaw angle, etc.) that need to be monitored during the motion of the target unit i are obtained. Based on the monitoring data of the corresponding motion parameters, it is determined whether there is any abnormality. If the corresponding motion parameter is abnormal, the abnormal state of the corresponding motion parameter is timed until it returns to the normal state, and the duration of a single abnormality is obtained accordingly.

[0067] Furthermore, the statistical duration of all single anomalies of the corresponding motion parameters within a unit time is summed to obtain the statistical duration value of motion parameter anomalies. The total number of times the corresponding motion parameters are abnormal within a unit time is marked as the frequency value of motion parameter anomalies. The number of times the statistical duration of a single anomaly of the corresponding motion parameters exceeds the corresponding preset threshold for the statistical duration of a single anomaly within a unit time is marked as the recurrence value of motion parameter anomalies.

[0068] The operational parameter characteristic value is obtained by weighted summation of the time value, frequency value, and risk value of operational parameter anomalies. Specifically, each of these values ​​is assigned a pre-defined weight coefficient, and each value is multiplied by its corresponding pre-defined weight coefficient. The sum of these three products is then marked as the operational parameter characteristic value. The operational parameter risk ratio coefficient is calculated by comparing the operational parameter characteristic value with the corresponding pre-defined operational parameter characteristic threshold. It should be noted that a larger operational parameter risk ratio coefficient indicates a worse overall performance of the corresponding motion parameters of target unit i.

[0069] Each motion parameter is pre-set to correspond to a set of preset parameter weight values ​​with values ​​greater than zero. The greater the safety risk caused by the abnormality of the corresponding motion parameter, the greater the value of the preset parameter weight value. The motion parameter risk ratio coefficient of the corresponding motion parameter is multiplied by the corresponding preset weight value to obtain the motion parameter risk table coefficient. The motion parameter risk table coefficients of all motion parameters are summed to obtain the motion assessment decision value of target unit i. It should be noted that the larger the value of the motion assessment decision value, the worse the overall motion condition of target unit i is.

[0070] The motion assessment decision value is compared with the preset motion assessment decision threshold. If the motion assessment decision value exceeds the preset motion assessment decision threshold, it indicates that the overall motion condition of target unit i is poor, and an unstable motion signal for target unit i is generated. If the motion assessment decision value does not exceed the preset motion assessment decision threshold, it indicates that the overall motion condition of target unit i is good, and a qualified motion signal for target unit i is generated.

[0071] Example 3: Figure 1 As shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the cone motion capture and evaluation module is connected to the formation control and stability hazard output module. The cone motion capture and evaluation module sends the motion performance evaluation results of all intelligent cones in the cone formation to the formation control and stability hazard output module. The formation control and stability hazard output module analyzes the control and hazard status of the cone formation and generates a formation control and stability early warning signal or a formation control and stability normal signal through analysis.

[0072] Furthermore, the system sends either a formation stability warning signal or a normal formation stability signal to the intelligent control terminal. Upon receiving the formation stability warning signal, the intelligent control terminal issues a corresponding warning, accurately identifying potential stability issues in formation movement control and sending warnings. This forms a dual stability control mechanism of "individual assessment - overall judgment," further ensuring the safety and consistency of cone formation operation and significantly reducing the difficulty of cone formation management. The specific analysis process of the formation stability issue output module is as follows:

[0073] The intelligent cone unit corresponding to the motion instability signal is obtained and marked as an unstable cone. If there is an unstable cone in the core cone, it indicates that the motion control stability of the cone formation is high per unit time, and a formation stability warning signal is generated.

[0074] If there are no unstable cones in the core cone, the proportion of unstable cones in the cone formation is marked as the formation instability measurement value. The formation instability measurement value is compared with the preset formation instability measurement threshold. If the formation instability measurement value exceeds the preset formation instability measurement threshold, it indicates that the motion control stability of the cone formation is high per unit time, and a formation stability warning signal is generated.

[0075] If the formation instability measurement value does not exceed the preset formation instability measurement threshold, the average value of the motion evaluation decision values ​​of all intelligent cones in the cone formation is calculated to obtain the formation operation and control characterization value, and the motion evaluation decision value with the largest value in the cone formation is marked as the formation operation and control abnormal amplitude value.

[0076] The formation stability alarm value is calculated by weighting and summing the formation instability measurement value, formation operation control characterization value, and formation operation control anomalous amplitude value. Specifically, each of these values ​​is assigned a corresponding preset weight coefficient, and then multiplied by its respective preset weight coefficient. The sum of these three products is then marked as the formation stability alarm value. It should be noted that the larger the formation stability alarm value, the higher the overall degree of potential stability risks in the movement control of the cone formation per unit time.

[0077] The formation stability alarm value is compared with the preset formation stability alarm threshold. If the formation stability alarm value exceeds the preset formation stability alarm threshold, it indicates that the overall level of potential stability risks in the movement control of the cone formation is relatively high within a unit of time, and a formation stability warning signal is generated. If the formation stability alarm value does not exceed the preset formation stability alarm threshold, it indicates that the overall level of potential stability risks in the movement control of the cone formation is relatively low within a unit of time, and a normal formation stability signal is generated.

[0078] Example 4: Figure 2 As shown, the difference between this embodiment and Embodiments 1, 2, and 3 is that the remote self-organizing network movement control method for the intelligent cone proposed in this invention includes the following steps:

[0079] Step 1: Control command input and standardization:

[0080] Control commands are input through intelligent control terminals and standardized into a unified format.

[0081] Step 2: Self-organizing network construction and command transmission:

[0082] The system automatically constructs a "core-follower" topology. The core node receives terminal instructions and forwards them to all follower nodes with low latency through self-organizing network technology, while establishing bidirectional communication links between cones.

[0083] Step 3: Group Collaborative Decision-Making and Instruction Generation

[0084] By combining location data and environmental data, three types of instructions are generated: path planning, local obstacle avoidance, and formation control, which are then distributed to each intelligent cone unit.

[0085] Step 4: Cone Parameter Monitoring and Feedback

[0086] Parameters of each intelligent cone are monitored, and the preprocessed data is output and fed back in real time.

[0087] Step 5: Cone Movement Execution and Control:

[0088] The movement of the intelligent cone is driven by control commands and adaptively adjusted according to parameter deviations.

[0089] The working principle of this invention is as follows: In use, the intelligent control terminal serves as the command core and interaction hub. The distributed self-organizing network communication module constructs a low-latency, fault-resistant bidirectional communication link using a "core-follower" hybrid topology and a custom protocol. The group collaborative control decision module generates an executable movement plan through global path planning, local obstacle avoidance, and formation control algorithms. The intelligent cone acts as the execution subject to implement the plan. Furthermore, the cone motion capture and evaluation module accurately quantifies the motion anomalies of individual cones, providing individual basis for formation stability control. The formation stability hazard output module reasonably assesses the degree of hazard in the stability control of the cone formation. This not only solves the problems of low efficiency and difficult control of traditional manual cone deployment, but also significantly improves the deployment efficiency, movement accuracy, and operational safety of cone formations through multi-module collaboration to adapt to complex road conditions. It provides intelligent support for road construction, accident handling, and other scenarios, significantly reducing the workload of management personnel.

[0090] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0091] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A remote self-organizing network mobile control system for intelligent cones, characterized in that, It includes an intelligent control terminal, a distributed self-organizing network communication module, a group collaborative control decision module, and several intelligent cone units; among them, each intelligent cone unit has an embedded controller, a positioning fusion module, an environmental perception module, an intelligent warning module, a high-efficiency power management module, a drive precision control module, and a tracked mobile execution module built in, and the embedded controller serves as the core of the intelligent cone unit to coordinate the operation of each built-in module; The intelligent control terminal receives user operation commands and outputs them in a standardized manner, while also receiving and displaying feedback data. The distributed self-organizing network communication module uses self-organizing network technology to construct a "core-follower" hybrid network topology, which is used to establish bidirectional communication links between intelligent cones and between intelligent cones and the intelligent control terminal, and uses a custom application layer protocol to transmit data frames. The group collaborative control decision module receives commands from the intelligent control terminal and, in combination with positioning and environmental data, outputs path planning and formation control commands to each individual intelligent cone.

2. The remote self-organizing network mobile control system for the intelligent cone according to claim 1, characterized in that, The intelligent control terminal has a built-in software operation unit, a voice control unit, and a command verification unit. The software operation unit integrates electronic maps, formation template libraries, task planning, and manual control functions. The voice control unit integrates offline / online voice recognition components and noise suppression components, activates the intelligent cone unit through a preset wake-up word, and triggers corresponding actions based on command words. The command verification unit performs legality verification on structured commands generated by the two types of input methods. After the verification is successful, the command is encapsulated into a standardized format and output.

3. The remote self-organizing network mobile control system for the intelligent cone according to claim 1, characterized in that, The distributed self-organizing network communication module adopts Wi-FiMesh, ZigBee or LoRa self-organizing network technology to build a "core-follower" hybrid network topology, with some smart cones serving as core nodes and the rest of the smart cones serving as follower nodes; the distributed self-organizing network communication module uses a custom application layer protocol to transmit data frames containing cone ID, status data and control commands.

4. The remote self-organizing network mobile control system for the intelligent cone according to claim 1, characterized in that, The group collaborative control decision-making module includes a global path planning unit, a local path planning and obstacle avoidance unit, and a formation control unit. The global path planning unit employs... Algorithm or The algorithm plans the overall movement path of the cone formation; the local path planning obstacle avoidance unit uses the artificial potential field method or the dynamic window method to generate real-time local trajectories to avoid obstacles; the formation control unit uses the leader-follower method or the virtual structure method.

5. The remote self-organizing network mobile control system for the intelligent cone according to claim 4, characterized in that, In the pilot-follower method, the core cone acts as the navigator, sending its own pose data in real time, while the follower cones adjust their movement state according to preset relative position parameters. The virtual structure method treats the entire cone formation as a rigid body, with each intelligent cone corresponding to a fixed point on the rigid body, and adjusts its position synchronously according to the movement trajectory of the rigid body.

6. The remote self-organizing network mobile control system for the intelligent cone according to claim 1, characterized in that, In the intelligent cone unit, the positioning fusion module integrates an RTK high-precision positioning unit and an UWB ultra-wideband positioning unit, and fuses multi-source data through a Kalman filter algorithm; the environmental perception module integrates an inertial measurement unit, a visual camera, and a lidar; the intelligent warning module includes an LED flashing unit and an audible and visual alarm; the high-efficiency power management module consists of a high-performance lithium battery pack, a fast charging circuit, a power management unit, and a low-power control program; and the drive precision control module uses a DC servo motor or a geared motor.

7. The remote self-organizing network mobile control system for the intelligent cone according to claim 1, characterized in that, The distributed self-organizing network communication module communicates with the cone motion capture and evaluation module. The cone motion capture and evaluation module acquires all intelligent cone units in the cone formation, marks the corresponding intelligent cone unit as target unit i, where i is a natural number greater than 1; it captures the motion information of target unit i and evaluates its motion performance. The specific evaluation and analysis process is as follows: The motion parameters that need to be monitored during the movement of target unit i are obtained. The motion parameter risk ratio coefficient of the corresponding motion parameter is multiplied by the corresponding preset weight value to obtain the motion parameter risk table coefficient. The motion parameter risk table coefficients of all motion parameters are summed to obtain the motion evaluation decision value of target unit i. If the motion evaluation decision value exceeds the preset motion evaluation decision threshold, the motion instability signal of target unit i is generated; otherwise, the motion qualification signal of target unit i is generated.

8. The remote self-organizing network mobile control system for the intelligent cone according to claim 7, characterized in that, The cone motion capture and evaluation module communicates with the formation control and stability hazard output module. The formation control and stability hazard output module analyzes the control and hazard status of the cone formation and sends the formation control and stability early warning signal or the formation control and stability normal signal to the intelligent control terminal.

9. The remote self-organizing network mobile control system for the intelligent cone according to claim 8, characterized in that, The specific analysis process of the formation stability risk output module is as follows: If there are unstable cones in the core cone, a formation stability warning signal is generated; if there are no unstable cones in the core cone, the proportion of unstable cones in the cone formation is marked as the formation instability measurement value. If the formation instability measurement value exceeds the preset formation instability measurement threshold, a formation stability warning signal is generated; if the formation instability measurement value does not exceed the preset formation instability measurement threshold, the formation stability alarm value is calculated by weighted summation of the formation instability measurement value, the formation operation and control characterization value, and the formation operation and control abnormal amplitude value. If the formation stability alarm value exceeds the preset formation stability alarm threshold, a formation stability warning signal is generated; otherwise, a normal formation stability signal is generated.

10. A remote self-organizing network mobile control method for intelligent cones, characterized in that, Includes the following steps: Step 1: Control command input and standardization; Step 2: Self-organizing network construction and command transmission; Step 3: Group collaborative decision-making and command generation; Step 4: Cone parameter monitoring and feedback; Step 5: Cone movement execution and control.

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