A cable construction equipment cluster collaborative control system based on wireless transmission

Abstract

This invention relates to the field of cable installation technology and discloses a collaborative control system for a cluster of cable construction equipment based on wireless transmission. The system includes a leader device that broadcasts its normalized output power as a power consumption benchmark, and each follower device that dynamically tracks its output power to this benchmark by adjusting its own operating speed in real time. This invention transforms the goal of collaborative control from traditional speed replication to tracking the physical state of uniform power consumption, enabling the entire equipment cluster to operate as a collaborative system. Its control mechanism can continuously transform changes in local physical resistance along the line into dynamic self-balancing of global tension, thereby improving the smoothness and safety of long-distance cable installation operations under complex conditions and giving the system the ability to diagnose conditions and predict risks.

Description

Technical Field

[0001] This invention relates to a cluster collaborative control system for cable construction equipment based on wireless transmission, belonging to the field of cable installation technology. Background Technology

[0002] Currently, when using traction machines and conveyor clusters for long-distance, complex cable laying operations, a common technical challenge that affects the long-term service life of cables is how to maintain a uniform tension distribution throughout the cable in a dynamically changing construction environment. Due to the continuous changes in local physical resistance such as friction and slope along the path, the force actually applied to the cable by each construction device while following a unified speed command is not equal. This can cause stress concentration and slight relaxation inside the cable, which may lead to a decline in insulation performance or physical damage in the long run.

[0003] The current situation leading to the above problems is the commonly used collaborative control, which is based on the central controller sending preset kinematic commands to each equipment unit and expecting each unit to execute them accurately through wireless communication. The limitation of this approach is that its control is based on abstract kinematic commands rather than the actual physical load state of the construction site. It treats a mechanically continuously coupled physical system as multiple independently controllable digital nodes. Therefore, it lacks direct perception and adaptability to actual local resistance changes, and its control logic is prone to tension imbalance.

[0004] To compensate for this deficiency, the industry has attempted to acquire more comprehensive data by adding external sensors, such as deploying a large number of tension gauges, in order to achieve more precise control. However, this approach has not provided a universal solution, mainly due to: 1. Significantly increasing the hardware cost and complexity of deployment and maintenance; 2. High-frequency, multi-point data transmission placing high demands on the wireless communication environment at the construction site, reducing system reliability; 3. It is still a passive adjustment based on external compensation, failing to address the lack of perception of physical state changes by the control core. Therefore, the technical problem to be solved by this invention is to find a control method that does not rely on a large number of external sensors, enabling each unit in the equipment cluster to self-adjust and coordinate based on a unified internal state quantity that reflects the overall system load, thereby actively achieving global dynamic tension balance. Summary of the Invention

[0005] This invention provides a wireless transmission-based collaborative control system for cable construction equipment clusters. Its main purpose is to solve the problem of systemic tension imbalance and hidden cable damage caused by the inability to sense and adapt to real physical load changes due to the reliance on discrete motion command replication in the existing collaborative control of cable construction equipment clusters.

[0006] To achieve the above objectives, the present invention provides a wireless transmission-based collaborative control system for cable construction equipment clusters, the system comprising:

[0007] At least one navigator device and at least one follower device, each of the navigator device and the follower device including its own motor and motor driver, and the navigator device and the corresponding follower device communicating via a wireless module;

[0008] The navigator device is configured to: upon startup from a standstill, enter an initialization state. In this initialization state, the power consumption reference broadcast by the navigator device is a power value generated based on a power envelope function that monotonically increases over time, stored in the navigator device, until the rate of change of the actual output power of the navigator device's motor driver falls below a rate of change threshold for a certain period of time, at which point it switches to a normal operating state. In the normal operating state, it operates at a target speed determined by received external instructions or internally stored construction task parameters, and acquires the normalized output power of its own motor driver in real time, using this normalized output power as the power consumption reference, which is periodically broadcast via the wireless module.

[0009] The follower device is configured to: receive a power consumption reference; acquire the normalized output power of its own motor driver in real time; and, through a control algorithm, use the difference between the power consumption reference and the normalized output power of its own motor driver as input to generate a speed adjustment command for adjusting its own motor operating speed in real time. The control algorithm is configured to generate a speed adjustment command aimed at reducing the difference.

[0010] Preferably, the control algorithm is a proportional-integral-derivative (PID) controller, and the follower device is configured to generate speed adjustment commands based on the difference between the power consumption reference and the normalized output power of its own motor driver through the proportional-integral-derivative PID controller.

[0011] Preferably, the navigator and follower devices are also configured to perform an idle power self-calibration after system startup and before entering operation. The idle power self-calibration specifically includes: all navigator devices and all follower devices in the system running in an idle state for a configurable calibration period and recording the idle output power of their respective motor drivers as a zero-point reference. The normalized output power is obtained by subtracting the zero-point reference from the real-time output power of the motor driver and then dividing by the rated power of the motor driver.

[0012] Preferably, the follower device is also configured to automatically switch to a power hold mode when no new power consumption reference is received within a configurable timeout period in the system. In this power hold mode, the follower device adjusts the operating speed of its own motor to maintain the output power of its own motor driver when it last successfully received the power consumption reference.

[0013] Preferably, the leader device is also configured to: calculate in real time a systematic drag coefficient characterizing the overall drag resistance experienced by the device cluster, and broadcast the systematic drag coefficient along with a power consumption reference; the systematic drag coefficient... The calculation method is as follows: ,in, For system drag coefficients, For the normalized output power of the motor driver in the Navigator device, The target speed for the motor of the Navigator device.

[0014] Preferably, the follower device is further configured to: perform catastrophic physical event prediction and avoidance; the catastrophic physical event prediction and avoidance specifically includes: calculating in real time the first-order and second-order time rate of change of the normalized output power of its own motor driver; constructing a power consumption state feature vector from the instantaneous value of the normalized output power, the first-order time rate of change, and the second-order time rate of change; when the power consumption state feature vector matches the cable breakage event feature template or the device jamming event feature template stored in the follower device, unconditionally suspending speed adjustment based on the power consumption reference, and executing a safety avoidance procedure including immediately applying mechanical braking to its own motor and broadcasting a cluster stop command.

[0015] Preferably, the follower device is further configured to: self-tune the control parameters of the proportional-integral-derivative (PID) controller; the self-tune of the control parameters specifically includes: continuously acquiring the difference signal between the power consumption reference and the normalized output power of the follower device's own motor driver within a sliding time window to form a difference signal sequence; performing statistical feature extraction on the difference signal sequence to calculate the error variance characterizing the fluctuation amplitude of the difference signal and the error zero-crossing rate characterizing the fluctuation frequency of the difference signal, which together constitute a control error feature spectrum; and based on the control error feature spectrum, querying a two-dimensional lookup table stored in the follower device to obtain the adjustment strategy for the proportional, integral, and derivative gains of the PID controller, and adjusting the control parameters of the PID controller accordingly.

[0016] Preferably, the system also includes a distributed mechanical impedance tomography diagnostic mode; in this diagnostic mode, the leader device is configured to superimpose a periodic sinusoidal disturbance signal with internally stored frequency and amplitude values ​​onto its broadcast power consumption reference, the amplitude of which is less than five percent of the average value of the power consumption reference under normal operating conditions; and each follower device is configured to extract the response amplitude and phase delay corresponding to the frequency value from the normalized output power signal of its own motor driver through a digital phase-locked loop amplification algorithm; the response amplitude and phase delay are used as local mechanical impedance characteristics and transmitted back to the central monitoring platform to generate a mechanical impedance response spectrum along the cable laying path.

[0017] Preferably, the navigator device is further configured to: calculate the time change rate of the power consumption reference in real time, and broadcast the time change rate as a feedforward control signal along with the power consumption reference; the follower device is further configured to: introduce a feedforward control path into its control algorithm, which compensates for the speed adjustment command based on the received time change rate of the power consumption reference, in order to suppress system tension oscillations caused by the navigator device performing acceleration or deceleration operations.

[0018] Preferably, the system also includes a navigator handover mechanism. When the navigator device stops broadcasting the power consumption reference due to a fault, the follower device located at the forefront of the cable laying path, which is designated as the first backup navigator device, will automatically switch roles to become the new navigator device and begin to run at the target speed determined by the task parameters stored in its internal memory, as well as broadcast the normalized output power of its own motor driver.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. The system provided by this invention constructs a cooperative control method, in which the leader device broadcasts the normalized output power of its own motor driver as the power consumption benchmark of the entire device cluster. The core control objective of each follower device is no longer to copy a speed or torque command, but to adjust the running speed of its own motor in real time so that the normalized output power of its own motor driver tracks the power consumption benchmark. In this way, the entire physically separated device cluster is unified into a whole at the control logic level. The local physical resistance change at any point along the cable caused by factors such as friction, bending or slope will be directly and timely reflected as the power consumption change of the device at that point. Through its tracking behavior of the power consumption benchmark, it will automatically and continuously trigger speed adjustment, which will then be transmitted to adjacent devices through the physical tension of the cable itself. Finally, a tension self-balancing endogenous mechanism without central decision-making is achieved in the entire system, thereby avoiding the tension unevenness and cumulative damage caused by communication delay and command mismatch in traditional control methods.

[0021] 2. Building upon the core mechanism of power consumption synchronization, this invention further establishes a layered system optimization and security assurance system based on in-depth analysis of power consumption signals. On the one hand, by continuously analyzing the statistical characteristics of power consumption tracking error signals, the system can identify the degree of matching between the current control behavior and the physical operating conditions, and accordingly self-tunes the key parameters of its own controller. This enables the system not only to adapt to the environment but also to self-tune the key parameters of its own controller to optimize its dynamic response performance and smooth out minor tension fluctuations that may damage cable life under normal operating conditions. On the other hand, by calculating the time change rate characteristics of power consumption signals in real time, the system can capture preset signal patterns before catastrophic physical events such as cable breakage or equipment jamming. Based on this, it unconditionally suspends the conventional tracking control logic and executes the highest priority safety avoidance procedure, enabling the system to perform efficient collaborative operations while possessing the ability to protect based on state prediction.

[0022] 3. This invention also integrates the control path and diagnostic function of the equipment cluster in principle, enabling the system to assess macroscopic working conditions and locate local faults without adding additional physical sensors. The leader device calculates a systematic coefficient characterizing the overall drag resistance of the cluster in real time by the relationship between its own operating speed and output power, providing construction managers with an intuitive macroscopic perspective to quantify the overall construction difficulty. In a specific diagnostic mode, the leader device can superimpose a weak periodic disturbance signal on the power consumption benchmark, while each follower device analyzes the response amplitude and phase delay of its own power consumption to this disturbance and transmits back the local mechanical impedance characteristics of its respective section, thereby jointly generating a distributed fault diagnosis map along the cable path, realizing the location of hidden problems such as high friction sections or cable accumulation. Attached Figure Description

[0023] Figure 1 This is a flowchart of the collaborative control logic based on power consumption reference tracking in this invention;

[0024] Figure 2 This is a schematic diagram of the physical architecture and data interaction of the system of the present invention;

[0025] Figure 3 This is a schematic diagram illustrating the switching of multiple operating modes of the device of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other. 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.

[0027] This invention discloses a wireless transmission-based collaborative control system for cable construction equipment clusters. The system architecture includes at least one construction device configured as a leader and at least one device configured as a follower. Both the leader and follower devices include their own motors and motor drivers, and communicate via a wireless module. The core operation method of this system involves the leader device broadcasting the normalized output power of its own motor driver as a dynamic power consumption reference. All follower devices then use this received power consumption reference as a control target, adjusting the operating speed of their own motors through a control algorithm to achieve their respective power consumption targets. The normalized output power of the motor driver tracks this power consumption benchmark, thereby unifying the entire equipment cluster into a cooperative system that responds based on the same physical state quantity in terms of control logic. During long-distance cable laying operations, variations in the coefficient of friction, slope, or curvature along the cable path can lead to uneven forces exerted on the cable by each piece of equipment if all construction equipment operates only according to a uniform speed command. This can potentially cause stress concentration or cable accumulation. To address this, the navigator device in this invention is configured to operate at a target speed determined by an external command or an internal preset task, based on the navigator device's motor. Run and acquire the normalized output power of its motor driver in real time. ,Should The value reflects the local physical drag that the navigator needs to overcome to maintain its current speed; the navigator then... The value is defined as a power consumption reference. And it broadcasts periodically via a wireless module at a frequency of, for example, 1 to 2 Hz; accordingly, each follower device receives a power consumption reference. Then, its control algorithm will... With the normalized output power of its own motor driver acquired in real time Compare and obtain the power difference. Based on this difference, a speed adjustment command is generated to adjust the operating speed of its own motor. The continuous effect of this speed adjustment command is to make the power difference approach zero. As a result, the output power of each follower device automatically tracks the load level perceived by the leader device. This mechanism enables local resistance changes along the line to be automatically converted into a dynamic balance of tension in the entire system.

[0028] To implement the power point tracking control logic described above, the control algorithm in the follower device can be configured as a proportional-integral-derivative (PID) controller. This PID controller will calculate the power difference... As input, through the calculations of its proportional, integral, and differential components, specific adjustment commands for motor speed are generated. Furthermore, to eliminate inconsistencies in power consumption characteristics caused by individual differences or mechanical wear among different devices, the system is configured to perform a no-load power self-calibration before startup and operation. This calibration procedure is as follows: all construction equipment operates uniformly under no-load conditions for a preset calibration duration, such as 10 seconds, and the average output power of each device during this period is recorded and stored as its respective no-load output power zero-point reference. In subsequent operations, the normalized output power is calculated by subtracting the stored zero-point reference from the real-time output power of the motor driver and then dividing by the rated power of the motor driver. This method allows devices of different power levels to coordinate on a unified power consumption dimension. Considering the instant the system starts from a standstill, the navigation... The navigator device's motor generates an impact power higher than normal operating power due to overcoming static friction. If this impact power is directly broadcast as the power consumption reference, it may cause synchronization issues in the cluster. Therefore, the navigator device is configured to enter an initialization state upon startup from a standstill. In this state, the power consumption reference broadcast by the navigator device is not its actual output power, but rather a power value generated based on a power envelope function stored internally that monotonically increases with time. Simultaneously, the navigator device continuously monitors the rate of change of its own motor driver's actual output power. Only when this rate of change falls below a preset threshold for a certain period of time, such as 2 seconds, does the system switch from the initialization state to the normal operating state. At this point, the navigator device broadcasts its now stable and true normalized output power, thus ensuring a smooth system startup process.

[0029] Under unstable wireless communication conditions, follower devices may be unable to receive new power consumption references for a period of time. To address this, follower devices are configured to automatically switch to power-holding mode if no new power consumption reference is received within a configurable timeout period, such as 5 seconds. In this mode, the follower device adjusts the operating speed of its own motor to maintain the output power value of its motor driver when it last successfully received the power consumption reference. This maintenance of local tension is a more reliable strategy than speed holding or equipment shutdown. To provide the work management platform with a quantitative assessment of the construction status, the leader device is also configured to calculate in real time a systematic drag coefficient characterizing the overall drag resistance experienced by the equipment cluster. The formula for calculating this coefficient is: ,in, For the normalized output power of the motor driver in the Navigator device, The target speed of the navigator's motor; the calculated systematic drag coefficient. Will be compared with power consumption benchmark The simultaneous broadcasting provides data support for managers to intuitively assess the overall construction difficulty; when the Navigator equipment performs dynamic operations such as acceleration or deceleration, the simple proportional-integral-derivative PID controller feedback control may cause system tension oscillations due to its lag; to suppress such oscillations, the Navigator equipment is also configured to calculate the time change rate of its broadcast power consumption reference in real time. The time change rate is broadcast as a feedforward control signal along with the power consumption reference. Correspondingly, a feedforward control path is also introduced into the control algorithm of each follower device. This path compensates for the speed adjustment command generated by the proportional-integral-derivative PID controller based on the received power consumption reference time change rate, so that the follower device can respond to the dynamic changes of the leader in advance and effectively suppress system tension oscillation.

[0030] To enable the system to anticipate and mitigate specific catastrophic physical events, such as cable breakage or equipment jamming, the follower device is also configured to execute a safety monitoring logic in parallel. This logic specifically includes: real-time calculation of the first-order time rate of change of the normalized output power of the follower device's own motor driver. and second-order rate of change over time The instantaneous value of the normalized output power, the first-order rate of change over time, and the second-order rate of change over time are combined to form a power consumption state feature vector. When the power consumption state feature vector matches a preset event feature template stored in the follower device, such as a cable breakage template or a device jamming template, the device will suspend the speed adjustment logic based on the power consumption reference and execute a preset safety avoidance procedure. This procedure may include immediately applying mechanical braking to its own motor and broadcasting a cluster emergency stop command. To enable the system's control performance to continuously adapt to changing construction conditions, the follower device can also be configured to perform parameter self-tuning of its own proportional-integral-derivative PID controller. The specific method of this self-tuning mechanism is to continuously collect the power difference between the power consumption reference and the normalized output power of its own motor driver within a sliding time window, such as 10 seconds. The signal sequence of the power difference is then analyzed. Statistical features are extracted from this power difference signal sequence to calculate the error variance characterizing the amplitude of the difference signal fluctuation and the error zero-crossing rate characterizing its fluctuation frequency. These two features together constitute a control error feature spectrum. Finally, based on this control error feature spectrum, a lookup table stored in the follower device is queried to obtain a set of adjustment values ​​for the proportional, integral, and derivative gains of the proportional-integral-derivative PID controller. The parameters of the proportional-integral-derivative PID controller are then updated accordingly, thereby continuously optimizing the dynamic response performance of the system.

[0031] In scenarios requiring the location of localized fault sections along the route, the system can also operate in a distributed mechanical impedance tomography diagnostic mode; in this diagnostic mode, the navigator device is configured to superimpose a preset disturbance frequency onto its broadcast power consumption reference. and amplitude The periodic sinusoidal perturbation signal, its broadcast power consumption reference becomes Among them, amplitude It is set to be less than five percent of the average power consumption baseline to avoid affecting normal construction; at this time, each follower device is configured to: normalize the output power signal of its own motor driver. In this process, a digital lock-in amplification algorithm, such as the Goertzel algorithm, is used to extract the frequency corresponding to the disturbance. Corresponding response amplitude and phase delay Each follower device extracts the response amplitude and phase delay as a local mechanical impedance feature and transmits it back to a central monitoring platform to generate a mechanical impedance response spectrum along the cable laying path that can calibrate problems such as high friction or cable stacking. Finally, to improve the continuity and reliability of the system's operation, the system may also include a leader handover mechanism. When the existing leader device stops broadcasting the power consumption reference due to a failure, a follower device that is pre-designated as the first backup leader device and is usually located at the forefront of the cable laying path will be configured to automatically switch its role as the new leader device. After the switch, the new leader device will start running at the target speed determined by the task parameters stored in its internal memory and broadcast the normalized output power of its own motor driver as the new power consumption reference to maintain the continuity of the entire cluster operation.

[0032] Example 1: In a 1.5-kilometer-long underground utility tunnel cable laying operation, the path includes gentle slopes and bends. A cluster of equipment, consisting of a main traction machine as the navigator and five intermediate conveyors as followers, is operating at a preset speed. Due to nighttime temperature differences, imperceptible condensation forms on the tunnel walls and cable surfaces, causing a slight and non-uniform change in the coefficient of friction between the cables and the conveyor rollers. This introduces a physical disturbance into the system operation that cannot be directly detected by conventional sensing methods. Under these conditions, the navigator equipment maintains its target speed... Its motor driver needs to output a normalized output power that matches the overall physical resistance of its section. ,Should The value is then used as a power consumption reference. Broadcast to all follower devices; a follower device traveling in a section where the coefficient of friction is increased due to more condensation, needs to overcome increased resistance to maintain its original speed, thus increasing the output power of its own motor driver. Higher than the received power consumption reference At this point, the proportional-integral-derivative PID controller inside the follower device will adjust based on this positive power difference. This generates an adjustment command to increase the motor's operating speed, thereby actively accelerating to moderately reduce the cable drag load it bears, until the normalized output power of its motor driver is reached. Decrease and approach power consumption benchmark Conversely, the normalized output power of the motor driver of another follower device traveling in a drier region with a lower coefficient of friction... It will be lower than the power consumption benchmark. Its proportional-integral-derivative (PID) controller generates a command to reduce the motor's operating speed, thereby reducing the tension from the downstream cable and reducing the normalized output power of the motor driver. Rising and approaching the power consumption benchmark .

[0033] The aforementioned series of distributed and independent adjustment processes are no longer controlled by kinematic speed commands, but rather by tracking a unified physical state variable, namely power consumption. Therefore, the problem of uneven friction coefficients at various points along the line, which previously required complex external sensor arrays, has been directly converted and absorbed into the system's core control variables; the Navigator broadcasts a power consumption benchmark. This mechanism sets a uniform load level for the entire system, while each follower device operates based on power differences. The proportional-integral-derivative (PID) controller that adjusts the speed provides each local unit with the ability to autonomously adjust to achieve a uniform load level. The combination of these two technical features enables high-precision physical state coordination to be achieved through a low-frequency and single-value wireless broadcast. Ultimately, despite the existence of dynamically changing non-uniform frictional forces, the tension distribution within the entire cable system tends to be uniform and stable, avoiding the risk of damage caused by excessive local stress or cable slack. This control method based on a unified power consumption benchmark makes the entire physically separated equipment cluster appear as a mechanically continuous whole during operation. External physical disturbances are transmitted through the tension of the cable itself and are adapted and balanced by the system as a whole.

[0034] Example 2: To verify the effectiveness of the cooperative control method of the present invention in suppressing local dynamic load disturbances and maintaining cable tension uniformity, the following comparative experiment was conducted. The experiment aimed to quantify and compare the differences in internal cable tension fluctuations when periodic drag resistance occurred in the laying path of equipment clusters using the power consumption reference synchronization method and the traditional unified speed command method. The test platform consisted of one leader device and two follower devices connected in series along a straight path, with a spacing of 50 meters between the devices, and a power cable passing through all the devices. An electromagnetic eddy current brake with precisely controllable output braking force was installed on the cable path between the first and second follower devices to simulate the dynamic resistance of the local path. At the same time, fiber Bragg grating tension sensors were installed between the leader device and the first follower device, and between the first follower device and the brake, respectively. Their data were collected and recorded by an independent system and did not participate in any control process.

[0035] During the test, the target speed of the Navigator device's motor was... The speed was set to 1.0 m / s. The electromagnetic eddy current brake applied a sinusoidal braking force at a frequency of 0.1 Hz, with a peak value of 500 N. This frequency was designed to simulate a typical obstacle section with gradually changing resistance, approximately 10 meters in length. The data sampling frequency of the tension sensor was set to 10 Hz to ensure distortion-free capture of the system response caused by low-frequency disturbances. In the control group test, all devices executed a uniform speed command of 1.0 m / s. When a periodic disturbance was applied, the tension sensor readings in the section before the disturbance showed violent fluctuations, with a standard deviation of 88.4 N and a peak fluctuation of 352.1 N. In the experimental group test, the system switched to the cooperative control method of this invention, and the navigator device broadcast its power consumption reference. Each follower device tracks the benchmark, and when the exact same perturbation is applied, the standard deviation of tension at the same monitoring point decreases to 11.8 N, while the peak variability decreases to 46.9 N.

[0036] Data shows that in the control group, each device adhered to speed commands and was unable to respond effectively to local load changes, causing disturbances to be amplified into severe tension fluctuations. However, in the test group, when the load applied by the brake was transmitted to the navigator via cable, causing it to broadcast a power consumption benchmark... When the load increases, follower devices that have not yet directly experienced the load will experience a decrease in output power compared to the new load. The proportional-integral-derivative (PID) controller instructs the cable to automatically reduce its speed, thereby increasing its power by bearing more tension. This proactively distributes and absorbs local disturbances rapidly throughout the entire equipment cluster. The test results confirm that, compared to traditional speed command control, the power consumption reference synchronous collaborative control method of this invention can reduce the amplitude of cable tension fluctuations caused by local dynamic resistance changes by more than an order of magnitude. This data verifies the effectiveness of this solution in improving the smoothness and safety of cable laying operations.

[0037] Example 3: This example combines Figures 1 to 3 This describes a collaborative control system for a cluster of cable construction equipment based on wireless transmission, such as... Figure 1 As shown, it divides the equipment cluster into two roles: navigator and follower, and defines their respective operating processes. The navigator equipment runs at a target speed according to received external instructions or preset task parameters, and acquires in real time the normalized output power of its motor driver generated to overcome local physical resistance. This power value was then defined as a unified power consumption benchmark for the entire system. It is broadcast wirelessly, and all follower devices receive this power consumption benchmark. Then, it is compared with the normalized output power of its own motor driver obtained in real time. By comparing the values, the power difference can be calculated. This difference is then input into a PID controller to generate a speed adjustment command. This command continuously adjusts the operating speed of the follower device's own motor to adjust the normalized output power of its motor driver. Approaching and tracking power consumption benchmarks To further enhance system performance and safety, the follower device also runs two high-order logic operations in parallel. One is the self-tuning of the proportional-integral-derivative PID controller parameters, which analyzes the power difference. The statistical characteristics are used to query a two-dimensional lookup table to optimize the proportional-integral-derivative PID parameters and achieve adaptive control. The second is the prediction and avoidance of catastrophic physical events. It calculates the power consumption state feature vector and matches it with the feature templates of preset events such as cable breakage or equipment jamming. Once the match is successful, the safety avoidance program is unconditionally triggered, such as immediately braking and broadcasting a cluster stop command.

[0038] like Figure 2 As shown, the collaborative control system disclosed in this invention physically consists of a cluster of devices deployed at a construction site and a remote central monitoring platform. At the construction site, along the laying path of a power cable, at least one navigator device as the main traction machine and multiple follower devices as intermediate conveyors are deployed. Each device includes hardware such as a motor, driver, and wireless module, and runs software deployed on it. The core software function of the navigator device is a power consumption reference broadcasting program, while the follower devices run core software modules such as collaborative control algorithms, safety avoidance and self-tuning, and local mechanical impedance feature extraction. The core data interaction is that the navigator device periodically broadcasts the power consumption reference to all follower devices, and each device can also transmit diagnostic information, such as local mechanical impedance features, back to the central monitoring platform. The platform then processes the transmitted data through its monitoring and diagnostic software to generate a mechanical impedance spectrum and undertakes the function of construction task management.

[0039] like Figure 3As shown, after power-on, the device first enters the standby / initialization state. Upon receiving the receive start command and power consumption reference, it enters the core working state of the system, namely the power tracking state. In this state, the device will adjust its speed in real time to track the power consumption reference. If the power consumption reference timeout occurs, the device will automatically switch to the power hold state to maintain its most recent output power value until the power consumption reference is restored, and then return to the power tracking state. The system can also switch to the impedance diagnostic state according to the enter diagnostic mode command to extract the response amplitude and phase of the disturbance signal, and return to the power tracking state after receiving the exit diagnostic mode command. According to the figure, in the core power tracking state or the temporary power hold state, once a catastrophic event is detected, the system will forcibly switch to the highest priority safety avoidance state, execute immediate braking and broadcast a cluster stop command until the task ends.

[0040] Example 4: To ensure the performance and safety of the control logic of this invention in specific engineering applications, an offline calibration procedure is established that can be completed before device deployment. This procedure aims to provide a deterministic basis for key control parameters and logic thresholds within the system based on physical testing. On a test platform equipped with a programmable dynamic load simulator, a follower device to be deployed is fixed, and its output is coupled to the load simulator. This platform is used to provide a quantized load input that can be accurately reproduced. The two-dimensional lookup table on which the self-tuning function of the proportional-integral-derivative PID controller inside the follower device depends, and the event feature template on which its catastrophic physical event prediction and avoidance function depends, are calibrated in this procedure. To calibrate the two-dimensional lookup table, a constant power consumption reference within its intermediate power range is first broadcast to the follower device. Using a load simulator, a set of preset typical load curves, including step load, ramp load, and sinusoidal load, are applied to the device, and the data is recorded under each load curve. The device is used for tracking... The resulting power consumption difference Signal sequence; for each recorded The signal sequence is analyzed, and its error variance and zero-crossing rate are calculated to obtain a control error characteristic spectrum. Simultaneously, the proportional-integral-derivative (PID) parameters of the device are adjusted until they reach their optimal values. If the overshoot is less than 5% and the settling time is less than 2 seconds under step response, the adjustment direction of the proportional-integral-derivative PID parameter at this time is associated with the corresponding control error characteristic spectrum and stored as an entry in a two-dimensional lookup table. By systematically traversing all preset typical load curves, a calibrated two-dimensional lookup table covering multiple operating conditions can be generated.

[0041] To define the characteristic template for catastrophic physical events, firstly, with the equipment operating stably under a moderate load, a load simulator is applied to cause the output torque to rise to the motor stall torque within a preset 0.1-second time window, simulating the condition where the equipment is jammed by a foreign object; the power consumption state feature vector during this process is recorded. The instantaneous peak value is recorded, and this set of data is multiplied by a correction factor of 1.2 as a safety margin and stored in the device as a device jamming event feature template. Subsequently, when the device is running stably under medium load, the load simulator instantly unloads the applied load completely to simulate the cable breakage condition, and records the instantaneous valley value of the power consumption state feature vector during this load drop. This set of data is stored in the device as a cable breakage event feature template. By executing the above offline calibration procedure, before deployment, the key logic judgment basis of each device regarding adaptive control and safety protection is transformed from general reference values ​​to engineering parameters that are precisely calibrated based on its own mechanical and electrical characteristics. This process makes the behavior of all units in the entire device cluster have engineering consistency and predictability.

[0042] Example 5: Before deploying equipment to lay a cable with known physical characteristics, and the working path is in an environment with specific wireless channel fading characteristics, the following pre-calibration procedure can be performed to optimize system functional parameters. This procedure provides a deterministic setting basis for the perturbation signal frequency of the distributed mechanical impedance tomography diagnostic mode and the trigger duration of the power hold mode. To calibrate the perturbation signal frequency used in the diagnostic mode, firstly, on the test platform, the navigator device and a follower device are connected through a test cable of the same type and preset length as the actual cable to be laid. The navigator device is set to diagnostic mode, and the frequency of the periodic perturbation signal superimposed on its broadcast power consumption reference is linearly scanned within a range of 0.05Hz to 5.0Hz. During this period, the follower device at the receiving end continuously runs a digital phase-locked amplification algorithm, records the response amplitude of its own power consumption signal at different perturbation frequencies, and sets the frequency corresponding to the maximum response amplitude as the standard perturbation frequency used by all devices in this task when executing the diagnostic mode.

[0043] To set the trigger timeout for power hold mode, the wireless modules of the navigator and follower devices are connected to a wireless channel simulator. This simulator is configured to reproduce the expected signal fading statistical distribution based on the channel characteristics report of the target construction environment. Under this simulated channel, the two devices are continuously operated in power reference synchronization mode, and the follower device at the receiving end records the duration of each continuous communication interruption when it fails to successfully receive the power reference data packet. After the test, all recorded interruption duration data are statistically analyzed, and its 95th percentile value is calculated. This statistical value is then multiplied by a preset correction factor of 1.5. The result is set as the timeout duration for all follower devices to determine when to enter power hold mode in this mission.

[0044] Example 6: This example discloses an offline optimization procedure for determining key parameters in a feedforward control path. The aim is to obtain a feedforward gain coefficient through systematic optimization that minimizes the internal tension oscillations of the equipment cluster when responding to dynamic changes in commands. On a test platform equipped with a dynamic response analysis system, a follower device that has completed other calibration procedures is fixed, and its output is coupled to a load simulator. The control algorithm of the follower device is configured to include a feedforward control path, and the feedforward control output of this path is defined as the received power consumption reference time change rate plus a feedforward gain coefficient to be optimized. The product of; to find the optimal feedforward gain coefficient First, a performance index for quantifying the smoothness of dynamic response is defined. This index is the product of the overshoot and settling time of the tension response; the smaller the value, the better the suppression of tension oscillations. At the beginning of the calibration process, The initial value is set to zero, and a square wave power reference with a periodic step change in power value between 20% and 60% is broadcast to the follower device. The signal is used to simulate the instantaneous large-amplitude speed change operation of the navigator equipment; the overshoot and settling time of the cable tension response measured by the tension sensor under the excitation of this square wave signal are recorded, and the initial values ​​of its performance indicators are calculated.

[0045] Subsequently, the feedforward gain coefficients are adjusted using a preset increment step size. Iterate over the value of , and in each new value Under the given value, the same square wave power consumption reference signal is repeatedly applied, and the corresponding performance indicators are recorded and calculated; all iterative steps are... Record the values ​​and their corresponding performance index values, and select the one that minimizes the performance index. The value is taken as the feedforward gain coefficient value that is ultimately fixed in the device program. This procedure transforms the determination of the feedforward gain coefficient from a process that depends on experience to a reproducible engineering optimization process with the explicit optimization goal of suppressing physical oscillations. After this optimization is completed, the device cluster not only has adaptive capability under steady-state conditions, but its response smoothness to dynamic commands is also quantitatively guaranteed, so that the coordinated control of the entire system is in an optimized state across the entire operating range.

[0046] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A collaborative control system for cable construction equipment clusters based on wireless transmission, characterized in that, The system includes: At least one navigator device and at least one follower device, each of the navigator device and the follower device including its own motor and motor driver, and the navigator device and the corresponding follower device communicating via a wireless module; The Navigator device is configured to: upon startup from a standstill, enter an initialization state. In this initialization state, the power consumption reference broadcast by the Navigator device is a power value generated based on a power envelope function that monotonically increases over time, stored in the Navigator device, until the rate of change of the actual output power of the Navigator device's motor driver falls below a threshold for a certain period of time, at which point it switches to a normal operating state. In the normal operating state, it operates at a target speed determined by received external instructions or internally stored construction task parameters, and acquires the normalized output power of its own motor driver in real time, using this normalized output power as the power consumption reference, which is periodically broadcast via the wireless module. The follower device is configured to: receive a power consumption reference; and acquire the normalized output power of its own motor driver in real time. The control algorithm takes the difference between the power consumption reference and the normalized output power of its own motor driver as input to generate a speed adjustment command for adjusting the motor operating speed in real time. The control algorithm is configured to generate a speed adjustment command designed to reduce the difference.

2. The cable construction equipment cluster collaborative control system based on wireless transmission as described in claim 1, characterized in that, The control algorithm is a proportional-integral-derivative (PID) controller. The follower device is configured to generate speed adjustment commands based on the difference between the power consumption reference and the normalized output power of its own motor driver via the PID controller.

3. The cable construction equipment cluster collaborative control system based on wireless transmission as described in claim 1, characterized in that, The navigator and follower devices are also configured to perform an idle power self-calibration after system startup and before entering operation. The idle power self-calibration specifically includes: all navigator devices and all follower devices in the system run for a configurable calibration period in an idle state and record the idle output power of their respective motor drivers as the zero-point reference. The normalized output power is obtained by subtracting the zero-point reference from the real-time output power of the motor driver and then dividing by the rated power of the motor driver.

4. The wireless transmission-based cable construction equipment cluster collaborative control system as described in claim 1, characterized in that, The follower device is also configured to automatically switch to a power hold mode when no new power reference is received within a configurable timeout period in the system. In this power hold mode, the follower device adjusts the operating speed of its own motor to maintain the output power of its motor driver when it last successfully received the power reference.

5. The cable construction equipment cluster collaborative control system based on wireless transmission as described in claim 1, characterized in that, The leader device is also configured to: calculate in real time a systematic drag coefficient characterizing the overall drag resistance experienced by the device cluster, and broadcast the systematic drag coefficient along with a power consumption reference; the systematic drag coefficient... The calculation method is as follows: in, For system drag coefficients, For the normalized output power of the motor driver in the Navigator device, The target speed for the motor of the Navigator device.

6. The cable construction equipment cluster collaborative control system based on wireless transmission as described in claim 1, characterized in that, The follower device is also configured to: perform prediction and avoidance of catastrophic physical events; the prediction and avoidance of catastrophic physical events specifically includes: calculating the first-order and second-order time-dependent rates of change of the normalized output power of its own motor driver in real time; and constructing a power consumption state feature vector from the instantaneous value of the normalized output power, the first-order time-dependent rate of change, and the second-order time-dependent rate of change.

7. The cable construction equipment cluster collaborative control system based on wireless transmission as described in claim 2, characterized in that, The follower device is also configured to: self-tune the control parameters of the proportional-integral-derivative (PID) controller; the self-tune of the control parameters specifically includes: continuously acquiring the difference signal between the power consumption reference and the normalized output power of the follower device's own motor driver within a sliding time window to form a difference signal sequence; performing statistical feature extraction on the difference signal sequence to calculate the error variance characterizing the amplitude of the difference signal fluctuation and the error zero-crossing rate characterizing the frequency of the difference signal fluctuation, which together constitute a control error feature spectrum; and based on the control error feature spectrum, querying a two-dimensional lookup table stored in the follower device to obtain the adjustment strategy for the proportional, integral, and derivative gains of the PID controller, and adjusting the control parameters of the PID controller accordingly.

8. The cable construction equipment cluster collaborative control system based on wireless transmission as described in claim 1, characterized in that, The system also includes a distributed mechanical impedance tomography diagnostic mode; in this diagnostic mode, the Navigator device is configured to superimpose a periodic sinusoidal disturbance signal with internally stored frequency and amplitude values ​​onto its broadcast power reference, with the amplitude being less than five percent of the average value of the power reference under normal operating conditions. Furthermore, each follower device is configured to extract the response amplitude and phase delay corresponding to the frequency value from the normalized output power signal of its own motor driver through a digital phase-locked amplification algorithm; and to transmit the response amplitude and phase delay as local mechanical impedance characteristics back to a central monitoring platform to generate a mechanical impedance response spectrum along the cable laying path.

9. The wireless transmission-based cable construction equipment cluster collaborative control system as described in claim 1, characterized in that, The navigator device is also configured to: calculate the time change rate of the power consumption reference in real time and broadcast the time change rate as a feedforward control signal along with the power consumption reference; the follower device is also configured to: introduce a feedforward control path into its control algorithm, which compensates for speed adjustment commands based on the received time change rate of the power consumption reference.

10. The cable construction equipment cluster collaborative control system based on wireless transmission as described in claim 1, characterized in that, The system also includes a navigator handover mechanism. When the navigator device stops broadcasting power consumption references due to a malfunction, the follower device located at the front of the cable laying path, which is designated as the first backup navigator device, will automatically switch roles to become the new navigator device and begin running at the target speed determined by the task parameters stored in its internal memory, as well as broadcasting the normalized output power of its own motor driver.

Images