Photovoltaic-based pile driver collaborative operation method and system, terminal and storage medium

Abstract

The invention relates to a pile driver collaborative operation method and system based on photovoltaic, a terminal and a storage medium, and relates to the technical field of photovoltaic engineering construction.The method comprises the steps that collected construction operation basic parameters are input into a multi-machine system for modeling and a problem definition model for analysis to obtain construction limiting parameters; collecting real-time sampling parameters; analyzing the construction limitation parameters and the real-time sampling parameters to obtain pile driver collaborative operation parameters and multi-dimensional reward parameters; according to the pile driver collaborative operation parameters, pile drivers are controlled to operate; judging whether the collected operation progress is consistent with the construction completion progress or not; if yes, the pile driver is controlled to stop working; if not, the construction limitation parameters, the pile driver collaborative operation parameters, the multi-dimensional award parameters and the collected construction process parameters are analyzed to obtain collaborative operation parameters of the next period; and controlling the pile driver to operate according to the collaborative operation parameters in the next period, and collecting construction process parameters and construction progress for cyclic judgment. The construction method has the effect of improving the construction efficiency and safety.

Description

Technical Field

[0001] This application relates to the technical field of photovoltaic engineering construction, and in particular to a photovoltaic-based method, system, terminal and storage medium for collaborative operation of piling machines. Background Technology

[0002] Photovoltaic piling machines are mechanical devices used in the construction of photovoltaic power plants to drive the foundation piles of photovoltaic brackets into complex terrain such as sandy land and mountains. Photovoltaic piling machines achieve the driving of piles into the soil through hammering, vibration or static pressure, thereby providing stable foundation support for photovoltaic brackets and photovoltaic modules.

[0003] In related technologies, the collaborative operation scheduling technology of multiple pile drivers mainly relies on the experience and judgment of construction personnel, or pre-plans the task allocation and travel path of each pile driver through traditional optimization methods such as genetic algorithms, and pre-formulates static planning schemes.

[0004] Regarding the aforementioned technologies, various obstacles may arise at the construction site, and the construction progress may be affected by factors such as weather. These dynamic changes prevent traditional scheduling methods that rely on manual experience or static planning from adjusting work arrangements in a timely manner to adapt to new situations, resulting in low construction efficiency, extended construction cycles, and increased project costs. At the same time, the lack of an effective coordination mechanism significantly increases the risk of collisions between multiple piling machines. Consequently, in the event of a collision, not only will equipment be damaged and the construction period delayed, but the safety of construction personnel may also be threatened. There is still room for improvement. Summary of the Invention

[0005] To improve construction efficiency and safety, this application provides a photovoltaic-based method, system, terminal, and storage medium for collaborative operation of piling machines.

[0006] Firstly, this application provides a photovoltaic-based method for collaborative operation of piling machines, employing the following technical solution: A photovoltaic-based collaborative operation method for pile drivers includes: Collect the pre-set construction operation parameters of the pile driver; The basic parameters of the construction operation are input into the preset multi-machine system modeling and problem definition model for analysis to generate construction constraint parameters; Collect real-time sampling parameters; Analyze construction constraint parameters and real-time sampling parameters to generate pile driver collaborative operation parameters and multi-dimensional reward parameters; The collaborative operation of the pile drivers is controlled based on the collaborative operation parameters, and construction process parameters and construction progress are collected. Determine whether the work progress is consistent with the preset construction completion schedule; If they match, then control the pile driver to stop working; If there is a discrepancy, the construction constraint parameters, pile driver collaborative operation parameters, multi-dimensional reward parameters, and construction process parameters will be analyzed to generate collaborative operation parameters for the next cycle. The piling machine is controlled to operate collaboratively based on the parameters of the next cycle of collaborative operation, and the construction process parameters and construction progress are collected and cyclically judged.

[0007] Optionally, the steps of analyzing construction constraint parameters and real-time sampling parameters to generate pile driver collaborative operation parameters and multi-dimensional reward parameters include: Real-time sampling parameters and construction limitation parameters are input into a pre-set photovoltaic construction-specific observation model for analysis to generate standard local observation vectors; The standard local observation vectors are input into a preset strategy value network for analysis to generate initial piling machine operation parameters; Find the pile driver observation space, pile driver action space, global construction state transition rules, and global collaborative objective function from the construction constraint parameters; The standard local observation vector, global collaborative objective function, and initial pile driver operation parameters are analyzed to generate multidimensional reward parameters; The observation space, action space, global construction state transition rules, global collaborative objective function, and initial piling machine operation parameters of the piling machine are analyzed to generate collaborative operation parameters of the piling machine.

[0008] Optionally, the steps of analyzing the standard local observation vector, the global cooperative objective function, and the initial pile driver operating parameters to generate multidimensional reward parameters include: The standard local observation vector and the initial pile driver operation parameters are substituted into the preset construction efficiency reward function for calculation to generate construction efficiency reward parameters. The standard local observation vector and the initial pile driver operation parameters are substituted into the preset collision avoidance penalty function for calculation to generate collision avoidance penalty parameters; The standard local observation vector and the initial pile driver operation parameters are substituted into the preset path optimization penalty function for calculation to generate path waste penalty parameters; The construction efficiency reward parameters, collision avoidance penalty parameters, path waste penalty parameters, and global collaborative objective function are input into a preset global reward fusion and gradient propagation model for analysis to generate multidimensional reward parameters.

[0009] Optionally, the steps to generate pile driver collaborative operation parameters include analyzing the pile driver observation space, pile driver action space, global construction state transition rules, global collaborative objective function, and initial pile driver operation parameters: The initial piling machine operation parameters, piling machine observation space, global construction state transition rules, global collaborative objective function, and piling machine motion space are input into a preset task allocation model for analysis to generate a piling machine task allocation matrix. The pile driver task allocation matrix and pile driver observation space are input into a preset load balancing model for analysis to generate pile driver task allocation instructions. The task allocation instructions and observation space of the pile driver are analyzed to generate a collision avoidance scheme for the pile driver. The global construction state transition rules and pile driver collision avoidance schemes are analyzed to generate pile driver collaborative operation parameters.

[0010] Optionally, the steps of analyzing the pile driver task allocation instructions and the pile driver observation space to generate a pile driver collision avoidance scheme include: The observation space of the pile driver is substituted into the preset dynamic safety distance derivation formula for calculation to generate the dynamic safety distance of the pile driver. The dynamic safety distance of the pile driver and the observation space of the pile driver are substituted into the preset speed adjustment derivation formula for calculation to generate the speed adjustment value of the pile driver; Collect real-time spacing between pile drivers; The task allocation instructions for pile drivers, the real-time spacing between pile drivers, the dynamic safety distance of pile drivers, and the movement space of pile drivers are analyzed to generate corrected target pile positions. The dynamic safety distance of the pile driver, the speed adjustment value of the pile driver, and the corrected target pile position are summarized to generate a collision avoidance scheme for the pile driver.

[0011] Optionally, the steps to analyze the global construction state transition rules and pile driver collision avoidance schemes to generate pile driver collaborative operation parameters include: The pile driver collision avoidance scheme, pile driver observation space and global construction state transition rules are input into the preset decision total delay definition model for analysis to generate the pile driver decision total delay time; The total decision delay time of the pile driver and the pile driver collision avoidance scheme are analyzed to generate pile driver collaborative operation parameters.

[0012] Secondly, this application provides a photovoltaic-based collaborative operation system for piling machines, employing the following technical solution: A photovoltaic-based collaborative piling machine system includes: The data acquisition module is used to collect basic construction operation parameters, real-time sampling parameters, construction process parameters, and construction progress. A memory for storing the program of the photovoltaic-based piling machine collaborative operation method as described in any of the above; The processor and the program in the memory can be loaded and executed by the processor to implement the photovoltaic-based piling machine collaborative operation method as described in any of the above.

[0013] Thirdly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the preceding claims for a photovoltaic-based collaborative piling machine operation method.

[0014] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates improvements in construction efficiency and safety, and adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by the aforementioned photovoltaic-based piling machine collaborative operation method.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. By inputting basic construction operation parameters into a multi-machine system modeling and problem definition model for analysis, construction constraint parameters are obtained. Analysis of these parameters and real-time sampling parameters yields pile driver collaborative operation parameters and multi-dimensional reward parameters. Based on these parameters, the collaborative operation of the pile drivers is controlled. Construction process parameters and progress are collected, and it is determined whether the operation progress matches the completion progress. If they match, the pile driver stops working; otherwise, the construction constraint parameters, collaborative operation parameters, multi-dimensional reward parameters, and construction process parameters are analyzed to obtain collaborative operation parameters for the next cycle. Based on these parameters, the collaborative operation of the pile drivers is controlled, and the collection of construction process parameters and progress is repeated cyclically. This achieves real-time control of the pile driver's collaborative operation, improves safety during construction, and enhances the overall construction efficiency of the pile driver. 2. By inputting the initial piling machine operating parameters, piling machine observation space, global construction state transition rules, global collaborative objective function, and piling machine motion space into the task allocation model, a piling machine task allocation matrix is ​​obtained. The piling machine task allocation matrix and piling machine observation space are then input into the load balancing model to obtain piling machine task allocation instructions. The piling machine task allocation instructions and piling machine observation space are analyzed to obtain a piling machine collision avoidance scheme. Finally, the global construction state transition rules and piling machine collision avoidance scheme are analyzed to obtain piling machine collaborative operation parameters, thereby reducing the probability of piling machine collisions and improving safety during construction. 3. The dynamic safety distance of the pile driver is calculated by substituting the observation space of the pile driver into the dynamic safety distance derivation formula. The dynamic safety distance and the observation space of the pile driver are then substituting into the speed adjustment derivation formula to calculate the speed adjustment value of the pile driver. The corrected target pile position is obtained by analyzing the pile driver task allocation instructions, the real-time distance between pile drivers, the dynamic safety distance of the pile driver, and the pile driver action space. The dynamic safety distance, the speed adjustment value of the pile driver, and the corrected target pile position are summarized to obtain the pile driver collision avoidance scheme. This provides data support for determining the collaborative operation parameters of the pile drivers based on the pile driver collision avoidance scheme, thereby reducing the probability of pile driver collisions and improving safety during construction. Attached Figure Description

[0016] Figure 1 This is a flowchart of a photovoltaic-based collaborative operation method for pile drivers in an embodiment of this application.

[0017] Figure 2 This is a flowchart of the steps in this application embodiment to analyze construction constraint parameters and real-time sampling parameters to generate pile driver collaborative operation parameters and multi-dimensional reward parameters.

[0018] Figure 3 This is a flowchart of the steps in this application embodiment to analyze the standard local observation vector, the global collaborative objective function, and the initial pile driver operation parameters to generate multidimensional reward parameters.

[0019] Figure 4 This is a flowchart of the steps in this application embodiment to analyze the pile driver observation space, pile driver action space, global construction state transition rules, global collaborative objective function, and initial pile driver operation parameters to generate pile driver collaborative operation parameters.

[0020] Figure 5 This is a flowchart of the steps in this application embodiment to analyze the pile driver task allocation instruction and the pile driver observation space to generate a pile driver collision avoidance scheme.

[0021] Figure 6 This is a flowchart of the steps in this application embodiment to analyze the global construction state transition rules and pile driver collision avoidance scheme to generate pile driver collaborative operation parameters. Detailed Implementation

[0022] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 6 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0023] This application discloses a photovoltaic-based method for collaborative operation of piling machines. This method primarily addresses the issues of improving the construction efficiency and safety of piling machines. Specifically, it discloses a piling machine, a piling machine status monitoring device, and a processing terminal. The processing terminal is communicatively connected to the piling machine and the piling machine status monitoring device, establishing a communication network between multiple piling machines to enable data transmission. After the piling machine status monitoring device sends basic construction operation parameters to the processing terminal, the processing terminal inputs these parameters into a corresponding algorithm for analysis to obtain construction constraint parameters. After determining the construction constraint parameters, further analysis yields collaborative operation parameters for the piling machines. The processing terminal then controls the piling machines to perform collaborative construction based on these parameters. The piling machine status monitoring device collects construction process parameters and progress data, sending them to the processing terminal. The processing terminal then determines the collaborative operation parameters for the next cycle based on the construction progress and process parameters. This method aims to rapidly and rationally control the piling machines to perform collaborative construction operations, thereby achieving real-time control of the piling machine construction process and improving construction efficiency and safety.

[0024] Reference Figure 1 This application discloses a photovoltaic-based method for collaborative operation of piling machines, including the following steps: Step S100: Collect the pre-set construction operation foundation parameters of the piling machine.

[0025] Among them, the basic parameters of construction operation refer to the data set used to store environmental parameters, number of pile drivers, rated operating parameters of pile drivers and construction planning parameters during the current construction operation. By summarizing the environmental parameters, number of pile drivers, rated operating parameters of pile drivers and construction planning parameters through the processing terminal, the basic parameters of construction operation can be obtained.

[0026] Environmental parameters refer to the basic parameters of the construction area of ​​the pile driver. In one embodiment, the environmental parameters can be obtained by the operator by finding them in the geological survey report and uploading them to the processing terminal.

[0027] The number of pile drivers refers to the total number of pile drivers participating in this construction project; the construction planning parameters refer to the set of parameters such as the location of all piles that need to be driven in the construction area and their corresponding numbers and sequences. In one embodiment, the operator can find the number of pile drivers and the construction planning parameters in the construction design drawings and upload them to the processing terminal.

[0028] The rated operating parameters of a pile driver refer to the factory operating parameters of the pile driver. In one embodiment, these parameters are obtained by the operator by finding them in the technical manual and uploading them to the processing terminal.

[0029] A pile driver is a mechanical device that enables multiple pile drivers to work together in a construction area and drive piles into the ground through hammering, vibration, or static pressure to provide stable foundation support for photovoltaic brackets and photovoltaic modules.

[0030] Step S101: Input the basic parameters of the construction operation into the preset multi-machine system modeling and problem definition model for analysis to generate construction constraint parameters.

[0031] Among them, construction constraint parameters refer to the set of rules and boundary parameters set by the pile driver during the construction operation to ensure the safety of the construction operation and the actual feasibility of the pile driver in the construction operation. These parameters are based on environmental parameters, the number of pile drivers, the rated operating parameters of the pile drivers, and the construction planning parameters. They are used to constrain and guide the collaborative operation of multiple pile drivers. The construction constraint parameters are obtained by inputting the basic parameters of the construction operation into the multi-machine system modeling and problem definition model through the processing terminal.

[0032] The observation space of a pile driver refers to the vector of local state information that each pile driver can collect through its own sensors, including the current position of the pile driver, the task status reflecting the current construction progress of the pile driver, and local point cloud features for sensing the surrounding terrain, obstacles, and other pile drivers; the action space of a pile driver refers to the range of values ​​of the commands that each pile driver can execute during construction operations, including the pile position to be driven by the pile driver, i.e., the coordinates of the target pile position, and the movement speed of the pile driver during construction, i.e., the travel speed of the pile driver; the global construction state transition rules are rules used to describe the changes in the construction environment state as each pile driver's actions change; the global collaborative objective function is a mathematical expression used to quantify the overall efficiency of pile driver collaborative operations.

[0033] Multi-machine system modeling and problem definition model refers to treating N pile drivers as a set of intelligent agents in a photovoltaic construction scenario. A partially observable Markov game model is used to model the entire collaborative piling machine system. The model consists of a data input layer, an agent set layer, an observation space layer, an action space layer, a state transition layer, an objective function definition and mathematical expression layer, and a data output layer. Basic construction operation parameters are input into the data input layer via a processing terminal, allowing these parameters to be broken down and allocated to corresponding layers for analysis. The agent set layer numbers the piling machines based on their number, creating an agent set. Environmental parameters and the piling machine's rated operating parameters are input into the observation space layer for definition, resulting in the piling machine's observation space, including its three-dimensional position. Task status , This refers to the number of completed piles. This refers to the number of remaining piles. This refers to energy consumption and local point cloud features. The observation vector is ,in , This refers to the dimension value of the observed vector. This refers to the feature dimension of the point cloud; by inputting the rated operating parameters and construction planning parameters of the pile driver into the motion space layer, the motion space of the pile driver is obtained, including the coordinates of the target pile position. speed of travel The speed constraint is , and action vectors The process involves inputting environmental parameters, piling machine rated operating parameters, and construction planning parameters into the state transition layer. By introducing belief states, the probability of the agent's influence on the global state is estimated, resulting in global construction state transition rules. Then, the environmental parameters, piling machine rated operating parameters, and construction planning parameters are input into the standard function definition and mathematical expression layer to establish a multi-objective weighted optimization function. By setting a discount factor to balance real-time and future rewards, and then calculating the load coefficient based on the remaining number of piles to be driven for each piling machine, this coefficient is used as the weight of the objective function to obtain the global collaborative objective function. Finally, the data output layer integrates and outputs the data from each level to obtain construction constraint parameters.

[0034] Step S102: Collect real-time sampling parameters.

[0035] Among them, real-time sampling parameters refer to the data collected by the pile driver's own sensors in real time during construction operations, including the pile driver's current position data, raw point cloud data, and task status data. The real-time sampling parameters can be obtained by summarizing the data collected by the pile driver's own sensors through the processing terminal.

[0036] The current position data of the pile driver refers to the coordinates of the pile driver's location in the current cycle. In one embodiment, the current position data of the pile driver is obtained by data acquisition through a GPS receiver and a 6-axis IMU installed on the top of the pile driver.

[0037] Raw point cloud data refers to the local terrain features of the construction area where the pile driver is located. In one embodiment, the data is collected by the lidar on the pile driver.

[0038] Task status data refers to a set of data reflecting the completion status of piling by a piling machine. It includes the number of piles completed, the number of piles remaining, and the energy consumption data of the piling machine. In one embodiment, the piling process is monitored in real time by a controller in the piling machine. When the piling machine reaches the target pile position coordinates in the piling machine's action space and performs a construction action, the controller generates a completion signal, which automatically increments the number of completed piles by 1. When it is necessary to retrieve the number of completed piles in the current cycle, the corresponding data can be extracted to obtain the number of completed piles. Then, the total number of piles driven by the piling machine is subtracted from the number of completed piles by the piling machine through the processing terminal to obtain the number of piles remaining. Finally, the number of completed piles, the number of piles remaining, and the energy consumption data of the piling machine are summarized to obtain the task status data.

[0039] The energy consumption data of a pile driver refers to the energy consumed by the pile driver during construction operations. In one embodiment, the energy consumption data of the pile driver can be obtained by reading the remaining percentage of the battery power in real time through the battery management system in the pile driver.

[0040] Step S103: Analyze the construction constraint parameters and real-time sampling parameters to generate pile driver collaborative operation parameters and multi-dimensional reward parameters.

[0041] Among them, the pile driver collaborative operation parameters refer to the set of parameters used to control the collaborative operation of each pile driver in the current construction cycle; the multi-dimensional reward parameters refer to the quantitative data used to evaluate the construction efficiency, construction safety, and construction path cost of the pile drivers during the construction process in this cycle. The pile driver collaborative operation parameters and multi-dimensional reward parameters can be obtained by analyzing the construction constraint parameters and real-time sampling parameters through the processing terminal. Specific methods are described in [reference needed]. Figure 2 This process reduces the risk of collisions between pile drivers and improves construction efficiency.

[0042] Step S104: Control the collaborative operation of the pile driver according to the collaborative operation parameters of the pile driver, and collect the construction process parameters and construction progress.

[0043] In this process, after the processing terminal determines the collaborative operation parameters of the piling machine, it controls the collaborative operation of the piling machine based on the collaborative operation parameters, and determines the construction process parameters and construction progress, thereby providing data support for determining the collaborative operation parameters of the piling machine in the next cycle.

[0044] Construction process parameters refer to a set of data that reflects the actual energy consumption and actual time required for pile driving during the construction process. In one embodiment, the actual time required for pile driving can be obtained by starting the timer from the start of the current cycle and stopping the timer at the end of the current cycle through the built-in timing module in the pile driver. The actual energy consumption can be obtained by reading the data in real time through the built-in energy consumption acquisition module in the pile driver.

[0045] Construction progress refers to the progress of all construction work completed by all pile drivers within this cycle. Each pile driver actively sends its own pile driver observation space from its construction limit parameters to other pile drivers through the communication network. The processing terminal finds the remaining number of piles to be driven and the total number of piles to be driven for each pile driver in the pile driver observation space. The remaining number of piles to be driven for each pile driver is added together to get the total number of piles to be driven. The total number of piles to be driven for each pile driver is added together to get the total number of piles driven in the construction process. The construction progress is obtained by dividing the total number of piles to be driven by the total number of piles driven in the construction process.

[0046] Step S105: Determine whether the work progress is consistent with the preset construction completion progress.

[0047] The construction completion progress refers to the work progress when all pile drivers have completed their corresponding pile driving tasks in the construction area. In one embodiment, the construction completion progress is 100%.

[0048] The processing terminal determines whether the operation progress is consistent with the construction completion progress, thereby determining whether the pile driver has completed the construction operation.

[0049] Step S1051: If they match, control the pile driver to stop working.

[0050] If the processing terminal determines that the operation progress is consistent with the construction completion progress, it means that the piling machine has completed the construction operation. Therefore, the processing terminal sends a signal to all piling machines to control the piling machines to stop working.

[0051] Step S1052: If there is a discrepancy, analyze the construction constraint parameters, pile driver collaborative operation parameters, multi-dimensional reward parameters, and construction process parameters to generate collaborative operation parameters for the next cycle.

[0052] If the processing terminal determines that the operation progress is inconsistent with the construction completion progress, it means that the pile driver has not completed the construction operation. Therefore, the processing terminal determines the collaborative operation parameters for the next cycle, thereby providing data support for controlling the pile driver to carry out collaborative operation in the next cycle.

[0053] The next cycle collaborative operation parameters refer to the set of parameters used to control the collaborative operation of each piling machine in the next cycle. The construction constraint parameters, piling machine collaborative operation parameters, multi-dimensional reward parameters, and construction process parameters are input into the experience playback and data augmentation model for analysis through the processing terminal. The parameters in the matrix optimization strategy value network are optimized by prioritizing experience playback. After optimization, the process returns to step S102, where the construction constraint parameters and the newly collected real-time sampling parameters are input into the corresponding algorithm for analysis. This yields the next cycle collaborative operation parameters and the next cycle multi-dimensional reward parameters. The parameters in the strategy value network are then optimized based on the multi-dimensional reward parameters. The specific determination method is the same as that used in step S103 above for determining the piling machine collaborative operation parameters and multi-dimensional reward parameters, and will not be elaborated here.

[0054] The experience replay and data augmentation model refers to a model that uses a priority mechanism based on time-series difference error design to perform priority experience replay and bias correction on experience data in order to improve the utilization rate of key samples. It consists of a data input layer, a priority calculation and sampling layer, an experience pool management strategy layer, a gradient correction layer, and a data output layer. The processing terminal inputs empirical data, consisting of construction constraint parameters, pile driver collaborative operation parameters, multi-dimensional reward parameters, and construction process parameters, into the priority calculation and sampling layer via the data input layer. The priority calculation and sampling layer determines the importance of each piece of experience based on temporal difference error, thus determining the priority of each piece of experience. It then determines the probability of each piece of experience being sampled in the experience pool based on its priority. Finally, it calculates the importance sampling weight by combining the total capacity of the experience pool with the sampling probability of each piece of experience. The experience pool management strategy layer employs a segmented storage mechanism, dividing experiences into high, medium, and low priorities, ensuring that high-priority experiences account for 30%. When the experience pool is full, it prioritizes eliminating samples with the smallest temporal difference error among the low-priority samples. The importance sampling weights output from the priority calculation and sampling layer and the experience batches output from the experience pool management strategy layer are input into the gradient correction layer. The importance sampling weights are normalized, and the network training loss is adaptively corrected based on the normalized sampling weights to obtain the gradient correction direction. This gradient correction direction is then used to optimize the parameters in the strategy value network.

[0055] Step S10521: Control the collaborative operation of the piling machine according to the collaborative operation parameters of the next cycle, and continue to collect construction process parameters and construction progress for cyclical judgment.

[0056] In this process, after the processing terminal determines the parameters for the next cycle of collaborative operation, it controls the collaborative operation of the piling machine based on the parameters and continues to collect construction process parameters and construction progress for cyclical judgment, thereby achieving real-time control of the piling machine during construction.

[0057] Reference Figure 2The steps for analyzing construction constraint parameters and real-time sampling parameters to generate pile driver collaborative operation parameters and multi-dimensional reward parameters include: Step S200: Input the real-time sampling parameters and construction restriction parameters into the preset photovoltaic construction-specific observation model for analysis to generate standard local observation vectors.

[0058] The standard local observation vector refers to the data set after standardizing the pile driver observation space in the construction constraint parameters. It includes standardized pile driver current position data, standardized point cloud data, and standardized task status data. The standard local observation vector can be obtained by inputting the real-time sampling parameters and the pile driver observation space in the construction constraint parameters into the photovoltaic construction-specific observation model through the processing terminal.

[0059] Standardized pile driver current location data refers to the coordinates of the pile driver's location in the current cycle after standardization processing; standardized point cloud data refers to the local terrain features of the construction area where the pile driver is located after standardization processing; standardized task status data refers to the data set used to reflect the pile driving completion status of the pile driver after standardization processing, including the number of piles completed by the pile driver, the number of piles remaining by the pile driver, and the energy consumption data of the pile driver.

[0060] The photovoltaic construction-specific observation model refers to a model used to standardize the observation space of the piling machine, making it readable by subsequent models that need to use the piling machine observation space. It consists of a point cloud preprocessing layer, a feature extraction layer, and a feature fusion layer. The processing terminal downsamples and denoises the local point cloud data of the piling machine observation space to clean the local point cloud data. The cleaned local point cloud data is then input into the feature extraction layer, which uses a layered sampling structure of PointNet++ to construct a local region through ball query (radius 0.5m) and calculates the point cloud features and temporal features of each point. The feature fusion layer uses an attention fusion mechanism to fuse the point cloud features and temporal features of each point, and outputs the fused feature matrix to obtain the standard local observation vector.

[0061] Step S201: Input the standard local observation vector into the preset strategy value network for analysis to generate initial piling machine operation parameters.

[0062] The initial piling machine operating parameters refer to the set of parameters that control the piling machine's operation, which are directly output from the standard local observation vector and only meet the most basic mathematical constraints. These parameters include the piling machine's travel speed and the target piling position. The initial piling machine operating parameters can be obtained by inputting the standard local observation vector into the strategy value network for analysis through the processing terminal.

[0063] The strategy value network refers to a network that maps standard local observation vectors to action vectors of pile driver construction operations. It consists of a data input layer and a feature extraction layer, a fully connected layer and a normalization layer, and an output layer. Through the processing terminal, the standard local observation vectors are input into the data input layer and the feature extraction layer for feature extraction. The extracted data is then input into the fully connected layer and the normalization layer for nonlinear transformation and high-level abstraction. Finally, the output layer uses the Tanh activation function to constrain the action range and maps the data output by the Tanh activation function to specific physical meanings. The mapped data is then the initial pile driver operation parameters.

[0064] Step S202: Locate the pile driver observation space, pile driver action space, global construction state transition rules, and global collaborative objective function in the construction constraint parameters.

[0065] In this step, the pile driver observation space, pile driver action space, global construction state transition rules, and global collaborative objective function are consistent with those in step S101 above. By searching in the construction constraint parameters through the processing terminal, the pile driver observation space, pile driver action space, global construction state transition rules, and global collaborative objective function can be obtained.

[0066] Step S203: Analyze the standard local observation vector, global collaborative objective function, and initial pile driver operation parameters to generate multidimensional reward parameters.

[0067] The multidimensional reward parameters in this step are the same as those in step S103 above. These parameters are obtained by analyzing the standard local observation vector, the global cooperative objective function, and the initial pile driver operation parameters using a processing terminal. The specific method is described in [reference needed]. Figure 3 This process provides data support for subsequent optimization of parameters in the policy value network.

[0068] Step S204: Analyze the pile driver observation space, pile driver action space, global construction state transition rules, global collaborative objective function, and initial pile driver operation parameters to generate pile driver collaborative operation parameters.

[0069] In this step, the collaborative operation parameters of the pile driver are the same as those in step S103 above. By analyzing the pile driver observation space, pile driver action space, global construction state transition rules, global collaborative objective function and initial pile driver operation parameters through the processing terminal, the collaborative operation parameters of the pile driver can be obtained. The specific method is the same as step 4, which provides data support for controlling the pile driver to carry out collaborative operation based on the collaborative operation parameters of the pile driver.

[0070] Reference Figure 3 The steps for generating multidimensional reward parameters by analyzing the standard local observation vector, the global cooperative objective function, and the initial pile driver operating parameters include: Step S300: Substitute the standard local observation vector and the initial pile driver operation parameters into the preset construction efficiency reward function for calculation to generate construction efficiency reward parameters.

[0071] The construction efficiency reward parameter refers to data used to quantify the quality of construction work performed by the piling machine in the current cycle. The processing terminal retrieves standardized piling machine current position data, standardized point cloud data, and standardized task status data from the standard local observation vector. It also retrieves the piling machine's travel speed and target piling position from the initial piling machine operation parameters. After determining that the piling machine is driving at the target piling position according to its travel speed, the processing terminal monitors the piling machine's construction actions in real time. During piling, the verticality is measured using an inclination sensor to obtain the vertical error. When the remaining number of piles in the standardized task status data decreases, the number of piles completed increases, and the verticality is less than 1°, it indicates that the piling machine has completed one piling operation. The processing terminal then collects the time taken from start to finish for this piling operation to obtain the time consumption for completing that pile position. Finally, the processing terminal substitutes the time consumption and verticality error for completing that pile position into the construction efficiency reward function. By performing calculations, the construction efficiency bonus parameters can be obtained, where, This refers to the construction efficiency incentive parameter. This refers to the time consumed to complete the work at that pile location. This refers to standard time. Taking 50s as an example, This refers to verticality error. The construction efficiency reward function adopts a piecewise reward function to encourage the pile driver to quickly complete the pile position and ensure construction quality.

[0072] Step S301: Substitute the standard local observation vector and the initial pile driver operation parameters into the preset collision avoidance penalty function for calculation to generate collision avoidance penalty parameters.

[0073] The collision avoidance penalty parameter refers to the parameter used to assess the risk of collisions during collaborative piling machine operations. The processing terminal retrieves the standardized current position data of the piling machines from the standard local observation vector. Each piling machine actively sends its standard local observation vector to other piling machines via the communication network. The processing terminal then aggregates this data to obtain the global construction status information. From this global status information, the current position data of other piling machines is retrieved. The piling machine's travel speed and target piling position data are then retrieved from the initial piling machine operating parameters. Finally, the standardized current position data of the piling machines, the current position data of other piling machines, the piling machine's travel speed, and the target piling position are substituted into the collision avoidance penalty function. By performing calculations, the collision avoidance penalty parameters can be obtained, where, This refers to the collision avoidance penalty parameters. This refers to the horizontal distance between two pile drivers. , This refers to the current position data of a standardized pile driver. This refers to the current position data of other pile drivers. This refers to the safe distance, in order to For example, this function is a penalty function designed based on the inverse of the distance, used to highlight the risk of close-range collisions.

[0074] Step S302: Substitute the standard local observation vector and the initial pile driver operation parameters into the preset path optimization penalty function for calculation to generate path waste penalty parameters.

[0075] The path waste penalty parameter is a parameter used to measure the cost of the piling machine's construction path. It is obtained by processing the terminal to find the standardized current position data of the piling machine in the standard local observation vector and the target piling position data in the initial piling machine operation parameters. The standardized current position data and the target piling position data are then substituted into the path optimization penalty function. By performing calculations, the path waste penalty parameter can be obtained, where, This refers to the path waste penalty parameter. This refers to the area of ​​the overlapping path region. This refers to the total area of ​​the current path region. , This refers to the path width of the pile driver during construction. Taking 3m as an example, This refers to the current path length. , This refers to the current position data of a standardized pile driver. This refers to the target piling location data. The formula quantifies the path waste during the piling machine construction process by calculating the overlap area between the current path and the historical paths of the machine group.

[0076] The path overlap area refers to the area where the construction paths of the piling machine overlap with those of all other piling machines. In one embodiment, the construction area is divided into several fixed-size grid units by a processing terminal, and each grid unit is assigned a unique index identifier. Based on the target piling position data in the initial piling machine operation parameters and the standardized current position data of the piling machine, the path from the current position to the target position is obtained, and the grids traversed by this path are marked as currently occupied grids. All grids traversed by other piling machines during construction are found in historical data and recorded as the historical total occupied grids. The intersection of the historical total occupied grids and the currently occupied grids is recorded as the path overlap grid. The number of path overlap grids is statistically counted, and the product of the number of path overlap grids and the area of ​​each grid is calculated. The area of ​​each grid is 0.25m². 2 For example, the area of ​​the overlapping path region can be obtained.

[0077] Step S303: Input the construction efficiency reward parameters, collision avoidance penalty parameters, path waste penalty parameters and global collaborative objective function into the preset global reward fusion and gradient propagation model for analysis to generate multi-dimensional reward parameters.

[0078] In this step, the multidimensional reward parameters are the same as those in step S103 above. By inputting the construction efficiency reward parameters, collision avoidance penalty parameters, path waste penalty parameters, and global collaborative objective function into the global reward fusion and gradient propagation model through the processing terminal, the multidimensional reward parameters can be obtained.

[0079] The global reward fusion and gradient propagation model refers to a model that integrates construction efficiency reward parameters, collision avoidance penalty parameters, and path waste penalty parameters according to dynamic weights. This model employs dynamic weight fusion, with weight coefficients adaptively adjusted according to the construction stage, and outputs multi-dimensional reward parameters. It consists of an input normalization layer, a weight adaptive layer, and a comprehensive output layer. The processing terminal retrieves standardized task state data from the standard local observation vector, and then finds the remaining number of piles to be driven and the number of piles already completed by the pile driver within this standardized task state data. The sum of the remaining number of piles to be driven and the number of piles already completed is calculated, and then the number of piles already completed is divided by the sum to determine the completion rate of the pile driving operation. The piling machine construction completion rate, construction efficiency reward parameters, collision avoidance penalty parameters, path waste penalty parameters, and the global collaborative objective function are input to the input normalization layer to unify the dimensions of the construction efficiency reward parameters, collision avoidance penalty parameters, and path waste penalty parameters, resulting in normalized output parameters. The global collaborative objective function is input to the weight adaptive layer for gradient updates. Weight coefficients for the three dimensions are dynamically generated based on the piling machine construction completion rate. When the piling machine construction completion rate is <30%, it indicates the early stage of construction, prioritizing construction safety and path planning; the corresponding weight coefficients are then calculated. When 30% ≤ piling machine completion rate ≤ 70%, it indicates that the project is in the middle stage. Therefore, efficiency should be prioritized, and the corresponding weighting coefficient is: When the pile driver's construction completion rate is >70%, it indicates that the construction is in its later stages. Therefore, the focus should be on optimizing the remaining paths, and the corresponding weighting coefficient is [value missing]. After determining the weight coefficients, substitute them into the weight update formula: Perform the calculation, where, These refer to the weighting coefficients corresponding to the construction efficiency reward parameter, collision avoidance penalty parameter, and path waste penalty parameter, respectively. This refers to the weight learning rate. For example, This refers to the gradient of the global collaborative objective function with respect to the weights, which is input into the weighted summation formula of the integrated output layer along with the data output from the normalization layer and the weight coefficients output from the integrated output layer. By performing calculations and outputting the results, the multidimensional reward parameters can be obtained, where, This refers to multidimensional reward parameters. These refer to the weighting coefficients corresponding to the construction efficiency reward parameter, collision avoidance penalty parameter, and path waste penalty parameter, respectively. This refers to the normalized construction efficiency reward parameter output from the input normalization layer. It refers to the normalized collision avoidance penalty parameters output by the input normalization layer. This refers to the normalized path waste penalty parameter output from the input normalization layer.

[0080] Reference Figure 4 The steps for generating collaborative operation parameters for the pile driver include analyzing the pile driver observation space, pile driver action space, global construction state transition rules, global collaborative objective function, and initial pile driver operation parameters: Step S400: Input the initial piling machine operation parameters, piling machine observation space, global construction state transition rules, global collaborative objective function, and piling machine motion space into the preset task allocation model for analysis to generate the piling machine task allocation matrix.

[0081] The pile driver task allocation matrix is ​​a task matching matrix used to enable each pile driver to carry out construction according to the pile position task to be constructed. The construction cost can be minimized by the pile driver carrying out construction according to the pile position task allocated by the pile driver task allocation matrix. The pile driver task allocation matrix can be obtained by inputting the initial pile driver operation parameters, pile driver observation space, global construction state transition rules, global collaborative objective function and pile driver action space into the task allocation model through the processing terminal for analysis.

[0082] The task allocation model is a model that can assign pile positions to each pile driver to minimize the total construction cost. It consists of a cost matrix optimization and standardization layer, row and column reduction layers, an optimal matching search layer, and a constraint verification layer. The cost matrix optimization and standardization layer performs weighted fusion calculations on the initial pile driver operating parameters, pile driver observation space, global construction state transition rules, global collaborative objective function, and pile driver action space to obtain the cost matrix. Each element in the cost matrix represents the comprehensive cost incurred by each pile driver when traveling to different pile positions. The smaller the value of each element, the better the allocation scheme for that pile driver to the corresponding pile position. The generated cost matrix is ​​normalized to obtain a standardized cost matrix, so that each element in the standardized cost matrix maps to the range of 0-1, eliminating the influence of different physical quantities' dimensions and making subsequent calculations more fair and reasonable. The row and column reduction layers optimize the minimum value of each row in the standardized cost matrix and the subsequent... After row reduction, the minimum value of each column is subtracted row by row and column by column to obtain the reduced cost matrix. The position of the zero element in the reduced cost matrix is ​​used as the candidate point. The optimal matching search layer is used to match the position of the zero element in each row and column of the reduced cost matrix according to the heuristic rule of prioritizing the matching of the zero element position and the rule that each pile driver is assigned at most one pile position and each pile position is assigned at most one pile driver, thus obtaining the preliminary matching matrix. Finally, the constraint verification layer calculates the remaining number of piles in the pile driver observation space to obtain the average remaining number of piles for all pile drivers. The preliminary matching matrix, the remaining number of piles, and the average remaining number of piles are then constrained, verified, and optimized to obtain the pile driver task allocation matrix.

[0083] Step S401: Input the pile driver task allocation matrix and pile driver observation space into the preset load balancing model for analysis to generate pile driver task allocation instructions.

[0084] Among them, the pile driver task allocation instruction refers to the data set used to clarify the construction sequence and target construction location of each pile driver. By inputting the pile driver task allocation matrix and the pile driver observation space into the load balancing model through the processing terminal for analysis, the pile driver task allocation instruction can be obtained. The pile driver task allocation instruction further optimized by the load balancing model can avoid the situation where the pile driver task allocation is unbalanced, resulting in some pile drivers being overloaded while others are idle.

[0085] The load balancing model is a model that optimizes the load balancing degree based on the pile driver task allocation matrix to achieve a balanced distribution of pile driver tasks, while dynamically responding to load changes during construction. It consists of a load balancing quantification layer, a dual-objective optimization layer, a real-time assurance layer, and a data output layer. The load balancing quantification layer calculates the quantified load balancing degree based on the pile driver task allocation matrix and the pile driver observation space. The dual-objective optimization layer, with the objectives of "minimizing total travel distance" and "optimizing load balancing degree," uses a weighted summation method to transform the load balancing degree into a single objective. A greedy algorithm, prioritizing minimum cost, assigns the optimal pile position to each pile driver while ensuring the allocated load is less than 1.1 times the average load, thus obtaining the optimal pile position selection. The real-time assurance layer scans surrounding pile positions to be allocated in advance when the remaining tasks of a pile driver are few, pre-calculating the allocation cost to ensure real-time real-time redistribution. Finally, the data output layer summarizes the calculation results from the above layers and converts them into a task list executable by each pile driver; this task list is the pile driver task allocation instruction.

[0086] Step S402: Analyze the pile driver task allocation instructions and the pile driver observation space to generate a pile driver collision avoidance scheme.

[0087] The pile driver collision avoidance scheme refers to a set of control parameters used to ensure that the pile driver maintains a safe distance from other pile drivers during construction and adjusts the pile driver's path when necessary to avoid collisions. The collision avoidance scheme can be obtained by analyzing the pile driver task allocation instructions and the pile driver's observation space through a processing terminal. Specific methods are described in [reference needed]. Figure 5 These steps prevent collisions between pile drivers during collaborative operations, thereby improving safety during pile driver construction.

[0088] Step S403: Analyze the global construction state transition rules and pile driver collision avoidance scheme to generate pile driver collaborative operation parameters.

[0089] The collaborative operation parameters of the piling machine in this step are the same as those in step S103 above. The collaborative operation parameters are obtained by analyzing the global construction state transition rules and the piling machine collision avoidance scheme through the processing terminal. The specific method is described in [reference needed]. Figure 6 These steps enable pile drivers to avoid collisions with other pile drivers during collaborative operations and minimize the cost of collaborative operations.

[0090] Reference Figure 5 The steps for analyzing the pile driver task allocation instructions and the pile driver observation space to generate a pile driver collision avoidance scheme include: Step S500: Substitute the observation space of the pile driver into the preset dynamic safety distance derivation formula for calculation to generate the dynamic safety distance of the pile driver.

[0091] The dynamic safety distance of a pile driver refers to the evaluation standard used during pile driver construction to determine whether the distance between one pile driver and other pile drivers is safe. This is achieved by processing the terminal to find the pile driver's travel speed in the pile driver's observation space and substituting that speed into the dynamic safety distance derivation formula. By performing calculations, the dynamic safety distance of the pile driver can be obtained, where, This refers to the dynamic safety distance of a pile driver. This refers to the reaction distance. This refers to braking distance. This refers to the buffer distance. This refers to reaction time, in Taking 0.2s as an example, This refers to the speed at which the pile driver travels. This refers to the coefficient of friction, under sunny conditions. =0.6, in rainy weather =0.3, This refers to gravitational acceleration, in =9.8m / s 2 For example, in one embodiment, when When =1.2m / s, =1.2×0.2+1.22 / 2×0.6×9.8+0.5≈0.85m. Considering the redundancy of the construction scenario, the dynamic safety distance of the pile driver is set to 3m to ensure construction safety.

[0092] Step S501: Substitute the dynamic safety distance of the pile driver and the observation space of the pile driver into the preset speed adjustment derivation formula for calculation to generate the speed adjustment value of the pile driver.

[0093] The pile driver speed adjustment value refers to the adjustment value used to prevent collisions between pile drivers when a collision risk is detected. Each pile driver actively sends its observation space data to other pile drivers via a communication network. The processing terminal retrieves the current position data of each pile driver in its observation space and also retrieves the current position data of other pile drivers in their respective observation spaces. The dynamic safety distance, the current position data of each pile driver, and the current position data of other pile drivers are then substituted into the speed adjustment derivation formula. By performing calculations, the adjustment value for the pile driver speed can be obtained, where, This refers to the speed adjustment value of the pile driver. , and All adjustment parameters were obtained using the Ziegler-Nichols method. =5、 =0.1 and For example, =0.5 This refers to the safety distance deviation. , This refers to the dynamic safety distance of a pile driver. This refers to the horizontal distance between two pile drivers. , This refers to the current position data of the pile driver. This refers to the current position data of other pile drivers. The speed adjustment derivation formula uses the safety distance deviation as the control quantity. The parameter tuning adopts the Ziegler-Nichols method. After determining the speed adjustment value of the pile driver, the stability is verified: the real part of the pole of the closed-loop transfer function is less than 0, ensuring that there is no overshoot in the speed adjustment.

[0094] Step S502: Collect the real-time spacing between pile drivers.

[0095] The real-time spacing between pile drivers refers to the real-time horizontal distance between two pile drivers during construction. Each pile driver actively transmits its observation space to other pile drivers via a communication network. A processing terminal then locates the current position of each pile driver within its own observation space and locates the current positions of the other pile drivers within their respective observation spaces. Finally, the current positions of the pile drivers and other pile drivers are substituted into a formula. The real-time spacing between pile drivers can be obtained by calculation, where, This refers to the real-time spacing between pile drivers. This refers to the current position of the pile driver. This refers to the current position of other pile drivers.

[0096] Step S503: Analyze the task allocation instructions for pile drivers, the real-time spacing between pile drivers, the dynamic safety distance of pile drivers, and the movement space of pile drivers to generate the corrected target pile position.

[0097] The "corrected target pile position" refers to the adjusted position coordinates of the target pile position when speed adjustment fails to meet safety requirements, in order to avoid collisions between pile drivers. The processing terminal allocates tasks to the pile drivers according to the task allocation instructions and determines whether the real-time distance between pile drivers is less than the dynamic safety distance. If it is greater, it means the speed adjustment meets safety requirements, and no correction of the target pile position is needed; in this case, the target pile position coordinates in the pile driver's action space are determined as the corrected target pile position. If it is less, it means the speed adjustment fails to meet safety requirements, and the target pile position needs correction. Therefore, the processing terminal locates the current position of the pile driver in its observation space and the current positions of other pile drivers in their respective observation spaces. The current positions of the pile drivers, the current positions of other pile drivers, the dynamic safety distance, and the target pile position coordinates are then substituted into the formula... By performing calculations, the corrected target pile position can be obtained, where, This refers to correcting the target pile position. This refers to the coordinates of the target pile position for the pile driver. This refers to the unit vector connecting the two pile drivers. This unit vector ensures that the angle between the obstacle avoidance path and the original path is less than 30°, thus reducing efficiency loss. This refers to the dynamic safety distance of a pile driver. This refers to the current position of the pile driver. This refers to the current position of other pile drivers.

[0098] Step S504: Summarize the dynamic safety distance of the pile driver, the speed adjustment value of the pile driver, and the corrected target pile position to generate a collision avoidance scheme for the pile driver.

[0099] The collision avoidance scheme for the pile driver in this step is the same as that in step S402 above. By summarizing the dynamic safety distance of the pile driver, the speed adjustment value of the pile driver, and the corrected target pile position through the processing terminal, the collision avoidance scheme for the pile driver can be obtained, thereby providing data support for the subsequent determination of the collaborative operation parameters of the pile driver.

[0100] Reference Figure 6 The steps for analyzing the global construction state transition rules and pile driver collision avoidance schemes to generate pile driver collaborative operation parameters include: Step S600: Input the pile driver collision avoidance scheme, pile driver observation space and global construction state transition rules into the preset decision total delay definition model for analysis, so as to generate the pile driver decision total delay time.

[0101] The total decision delay time of the pile driver refers to the time required for the pile driver to perceive the surrounding environment and make and execute construction actions. By inputting the pile driver collision avoidance scheme, the pile driver observation space and the global construction state transition rules into the total decision delay definition model through the processing terminal for analysis, the implementation parameters of the pile driver path can be obtained.

[0102] The total decision delay definition model refers to a model that quantifies and verifies the real-time performance of the entire process by adopting a closed-loop localized "observation-decision-execution" process. It mainly consists of a perception delay layer, an inference delay layer, an execution delay layer, and a data output layer. The perception delay layer fuses the data collected from the pile driver's observation space using Kalman filtering and calculates the time required from acquisition to fusion completion to obtain the perception delay time. The inference delay layer calculates the time required for the edge computing device to operate the strategy network based on the complexity of the pile driver collision avoidance scheme and the computing power of the edge devices to obtain the inference delay time. The execution delay layer analyzes the inherent delay of the pile driver based on the global construction state transition rules to obtain the execution delay time. Finally, the data output layer adds the perception delay time, the inference delay time, and the execution delay time to obtain the total decision delay time of the pile driver.

[0103] Step S601: Analyze the total decision delay time of the pile driver and the pile driver collision avoidance scheme to generate pile driver collaborative operation parameters.

[0104] In this step, the collaborative operation parameters of the piling machine are consistent with those in step S103 above. The processing terminal determines whether the total decision delay time of the piling machine is less than 0.05s. If the total decision delay time is less than 0.05s, the piling machine collision avoidance scheme is determined as the collaborative operation parameter; otherwise, the process returns to... Figure 5 The steps involve regenerating the pile driver collision avoidance scheme to redetermine the total decision delay time of the pile driver, and then comparing the total decision delay time of the pile driver with the set threshold of 0.05s again until the total decision delay time of the pile driver is less than 0.05s, at which point the loop stops, and the pile driver collision avoidance scheme determined at this time is determined as the pile driver collaborative operation parameter.

[0105] Based on the same inventive concept, embodiments of this application provide a photovoltaic-based collaborative piling machine system, including: The data acquisition module is used to collect basic parameters of construction operations, real-time sampling parameters, construction process parameters, construction progress, and real-time spacing between pile drivers; A memory for storing the program of a photovoltaic-based collaborative operation method for piling machines; The processor and memory can load and execute programs to realize a photovoltaic-based collaborative operation method for piling machines.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0107] This application provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor for a photovoltaic-based collaborative piling machine operation method.

[0108] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0109] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to perform a photovoltaic-based collaborative piling machine operation method.

[0110] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0111] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A photovoltaic-based collaborative operation method for pile drivers, characterized in that, include: Collect the pre-set construction operation parameters of the pile driver; The basic parameters of the construction operation are input into the preset multi-machine system modeling and problem definition model for analysis to generate construction constraint parameters; Collect real-time sampling parameters; Analyze construction constraint parameters and real-time sampling parameters to generate pile driver collaborative operation parameters and multi-dimensional reward parameters; The collaborative operation of the pile drivers is controlled based on the collaborative operation parameters, and construction process parameters and construction progress are collected. Determine whether the work progress is consistent with the preset construction completion schedule; If they match, then control the pile driver to stop working; If there is a discrepancy, the construction constraint parameters, pile driver collaborative operation parameters, multi-dimensional reward parameters, and construction process parameters will be analyzed to generate collaborative operation parameters for the next cycle. The piling machine is controlled to operate collaboratively based on the parameters of the next cycle of collaborative operation, and the construction process parameters and construction progress are collected and cyclically judged.

2. The photovoltaic-based collaborative operation method for piling machines according to claim 1, characterized in that, The steps for analyzing construction constraint parameters and real-time sampling parameters to generate pile driver collaborative operation parameters and multi-dimensional reward parameters include: Real-time sampling parameters and construction limitation parameters are input into a pre-set photovoltaic construction-specific observation model for analysis to generate standard local observation vectors; The standard local observation vectors are input into a preset strategy value network for analysis to generate initial piling machine operation parameters; Find the pile driver observation space, pile driver action space, global construction state transition rules, and global collaborative objective function from the construction constraint parameters; The standard local observation vector, global collaborative objective function, and initial pile driver operation parameters are analyzed to generate multidimensional reward parameters; The observation space, action space, global construction state transition rules, global collaborative objective function, and initial piling machine operation parameters of the piling machine are analyzed to generate collaborative operation parameters of the piling machine.

3. The photovoltaic-based collaborative operation method for piling machines according to claim 2, characterized in that, The steps for generating multidimensional reward parameters by analyzing the standard local observation vector, the global cooperative objective function, and the initial pile driver operating parameters include: The standard local observation vector and the initial pile driver operation parameters are substituted into the preset construction efficiency reward function for calculation to generate construction efficiency reward parameters. The standard local observation vector and the initial pile driver operation parameters are substituted into the preset collision avoidance penalty function for calculation to generate collision avoidance penalty parameters; The standard local observation vector and the initial pile driver operation parameters are substituted into the preset path optimization penalty function for calculation to generate path waste penalty parameters; The construction efficiency reward parameters, collision avoidance penalty parameters, path waste penalty parameters, and global collaborative objective function are input into a preset global reward fusion and gradient propagation model for analysis to generate multidimensional reward parameters.

4. The photovoltaic-based collaborative operation method for piling machines according to claim 2, characterized in that, The steps for generating collaborative operation parameters for the pile driver include analyzing the pile driver observation space, pile driver action space, global construction state transition rules, global collaborative objective function, and initial pile driver operation parameters: The initial piling machine operation parameters, piling machine observation space, global construction state transition rules, global collaborative objective function, and piling machine motion space are input into a preset task allocation model for analysis to generate a piling machine task allocation matrix. The pile driver task allocation matrix and pile driver observation space are input into a preset load balancing model for analysis to generate pile driver task allocation instructions. The task allocation instructions and observation space of the pile driver are analyzed to generate a collision avoidance scheme for the pile driver. The global construction state transition rules and pile driver collision avoidance schemes are analyzed to generate pile driver collaborative operation parameters.

5. The photovoltaic-based collaborative operation method for piling machines according to claim 4, characterized in that, The steps for analyzing the pile driver task allocation instructions and the pile driver observation space to generate a pile driver collision avoidance scheme include: The observation space of the pile driver is substituted into the preset dynamic safety distance derivation formula for calculation to generate the dynamic safety distance of the pile driver. The dynamic safety distance of the pile driver and the observation space of the pile driver are substituted into the preset speed adjustment derivation formula for calculation to generate the speed adjustment value of the pile driver; Collect real-time spacing between pile drivers; The task allocation instructions for pile drivers, the real-time spacing between pile drivers, the dynamic safety distance of pile drivers, and the movement space of pile drivers are analyzed to generate corrected target pile positions. The dynamic safety distance of the pile driver, the speed adjustment value of the pile driver, and the corrected target pile position are summarized to generate a collision avoidance scheme for the pile driver.

6. The photovoltaic-based collaborative operation method for piling machines according to claim 4, characterized in that, The steps for analyzing the global construction state transition rules and pile driver collision avoidance schemes to generate pile driver collaborative operation parameters include: The pile driver collision avoidance scheme, pile driver observation space and global construction state transition rules are input into the preset decision total delay definition model for analysis to generate the pile driver decision total delay time; The total decision delay time of the pile driver and the pile driver collision avoidance scheme are analyzed to generate pile driver collaborative operation parameters.

7. A photovoltaic-based collaborative piling machine system, characterized in that, include: The data acquisition module is used to collect basic construction operation parameters, real-time sampling parameters, construction process parameters, and construction progress. A memory for storing the program of the photovoltaic-based piling machine collaborative operation method as described in any one of claims 1 to 6; The processor and the program in the memory can be loaded and executed by the processor to implement the photovoltaic-based piling machine collaborative operation method as described in any one of claims 1 to 6.

8. A terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 6, which is a photovoltaic-based piling machine collaborative operation method.

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