Building construction robot dynamic scheduling method and system based on artificial intelligence

Abstract

The invention discloses a building construction robot dynamic scheduling method and system based on artificial intelligence, and particularly relates to the technical field of building construction. Potential schedulable building construction robots are screened by analyzing process requirements and resource allocation of construction tasks; based on the robot task execution record, the operation stability is evaluated; the scheduling adaptability of the robot in different construction technologies is evaluated by analyzing task switching flexibility; and the real-time performance of the scheduling demand of the robot is judged by integrating the operation stability and the scheduling adaptation degree. And the flexibility demand intensity of robot scheduling is evaluated by analyzing the frequency change and the interference range of the path crossing area. Through comprehensive analysis of operation stability, scheduling adaptability and flexibility demand intensity, a robot scheduling strategy is dynamically adjusted, the method is suitable for a complex construction environment, scheduling efficiency can be optimized, construction conflicts can be reduced, and the overall performance of multi-robot cooperation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and more specifically, to a dynamic scheduling method and system for a building construction robot based on artificial intelligence. Background Art

[0002] With the development of robotics technology, dispatching advanced robots to perform heavy and dangerous work has become an increasingly basic need in the field of construction. Traditional scheduling methods are difficult to fully consider key factors such as the stability of robot task execution, process adaptability, and path cross-interference when facing complex and changing construction environments, resulting in low scheduling efficiency and obstructed construction progress. In addition, existing methods lack a comprehensive judgment of the real-time nature of scheduling needs, and cannot flexibly respond to scenarios such as frequent switching of construction tasks, multi-robot path conflicts, and dynamic demand changes, which seriously affects the construction quality and the overall performance of robot collaboration.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a construction robot dynamic scheduling method and system based on artificial intelligence to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A dynamic scheduling method for a construction robot based on artificial intelligence comprises the following steps: By analyzing the process requirements and resource allocation of construction tasks at the construction site, potential construction robots can be screened and scheduled; Analyze the task execution records of the construction robot and evaluate the operation stability of the construction robot; Obtain the historical idle status and task switching of the construction robot, analyze the flexibility of the construction robot's task switching, and evaluate the scheduling adaptability of the construction robot in different construction processes; When the real-time performance of the construction robot scheduling requirement is high, the real-time performance of the construction robot scheduling requirement is determined based on the operation stability of the construction robot and the scheduling adaptability of the construction robot in different construction processes; Analyze the frequency changes and interference range of the path intersection areas generated by the construction robot during the construction task, and evaluate the flexibility demand intensity of the dynamic scheduling of the construction robot; A comprehensive analysis is conducted on the operating stability of the construction robot, the scheduling adaptability of the construction robot in different construction processes, and the flexibility demand intensity of the dynamic scheduling of the construction robot to adjust the scheduling strategy of the construction robot at the construction site.

[0006] In a preferred embodiment, by analyzing the process requirements and resource allocation of the construction tasks at the construction site, the construction robots that are potentially scheduled are screened, specifically: Obtain construction site task data and extract task characteristic information including construction process requirements and resource allocation; Analyze the construction process demand data and extract the process adaptability parameters of the construction tasks; Analyze resource allocation and identify construction robot resource characteristics that match construction tasks; Construct matching rules between construction task process requirements and construction robot resource characteristics; Based on the matching rules, the construction tasks are associated with the construction robots, and the potential construction robots for scheduling are screened.

[0007] In a preferred embodiment, the task execution record of the construction robot is analyzed to evaluate the operation stability of the construction robot, specifically: Collect historical task execution data of construction robots, including task execution status; Statistics on the task completion rate of construction robots, and calculation of task failure frequency and task failure time ratio; Extract the fluctuation values ​​of operating parameters during task execution, including load changes and running speed changes; According to the task completion rate and the fluctuation value of the operating parameters, the operation stability of the construction robot is quantified: Based on the task completion rate and the fluctuation value of the operating parameters, the operation stability index is defined, and the calculation formula is: ;in, is the operation stability index; represents the task completion rate of the construction robot; Indicates the frequency of task failure; Indicates the percentage of task failure time; is the load fluctuation amplitude; is the speed fluctuation amplitude.

[0008] In a preferred embodiment, the historical idle state and task switching status of the construction robot are obtained, the task switching flexibility of the construction robot is analyzed, and the scheduling adaptability of the construction robot in different construction processes is evaluated, specifically: Obtain historical idle data of construction robots, including the start and end time records of each idle period; Count the number of times the robot switches between construction process tasks and extract the task switching types; Calculate the task switching interval and the switching ratio of different process tasks; Analyze the task switching flexibility and quantify the flexibility index based on the switching interval time and switching ratio: Combined with the switching interval time and switching ratio, the flexibility index calculation formula is: ;in, represents the flexibility index; Process task type The average switching interval time; Indicates the total number of process task types; Indicates the process task type The switching ratio; Combined with the flexibility index, the scheduling adaptability of construction robots in different construction processes is evaluated: the calculation formula of the scheduling adaptability index is: ;in, represents the scheduling fitness index; represents the flexibility index; Indicates the total number of process task types; Indicates the process task type The switching ratio; Indicates the process task type demand weight.

[0009] In a preferred embodiment, based on the operation stability of the construction robot and the scheduling adaptability of the construction robot in different construction processes, the real-time nature of the scheduling demand of the construction robot is judged, specifically: A job stability index threshold is preset, and the job stability index is compared with the job stability index threshold: When the job stability index is greater than the job stability index threshold, the real-time performance of the scheduling demand is low; When the job stability index is less than or equal to the job stability index threshold, the real-time nature of the scheduling demand is high; A scheduling fitness index threshold is preset, and the scheduling fitness index is compared with the scheduling fitness index threshold: When the scheduling fitness index is greater than the scheduling fitness index threshold, the real-time performance of the scheduling demand is low; When the scheduling fitness index is less than or equal to the scheduling fitness index threshold, the real-time nature of the scheduling demand is high; When the operation stability index is greater than the operation stability index threshold, and the scheduling fitness index is greater than the scheduling fitness index threshold, the real-time nature of the scheduling demand of the construction robot is judged to be low; otherwise, the real-time nature of the scheduling demand of the construction robot is judged to be high.

[0010] In a preferred embodiment, when the real-time performance of the construction robot scheduling requirement is high, the frequency change and interference range of the path intersection area generated by the construction robot in the construction task are analyzed to evaluate the flexibility demand intensity of the dynamic scheduling of the construction robot, specifically: Collect task path data of construction robots and record the location and number of path intersection areas; Calculating the path crossing frequency in the path crossing area; Identify the scope of interference in path intersection areas, including the number of construction robots involved and the intersection area; Evaluate the flexibility demand intensity based on the path crossing frequency and interference range: Combine the path crossing frequency and interference range to define the flexibility demand index, which is calculated as follows: ;in, is the flexibility demand index; Indicates the path intersection area The path crossing frequency of Indicates the path intersection area The number of construction robots involved; Indicates the path intersection area area; Indicates the total number of path intersection areas; Represents a constant.

[0011] In a preferred embodiment, the operation stability of the construction robot, the scheduling adaptability of the construction robot in different construction processes, and the flexibility demand intensity of the dynamic scheduling of the construction robot are comprehensively analyzed to adjust the scheduling strategy of the construction robot at the construction site, specifically: The operation stability index corresponding to the operation stability of the construction robot, the scheduling fitness index corresponding to the scheduling fitness of the construction robot in different construction processes, and the flexibility demand index corresponding to the flexibility demand intensity of the dynamic scheduling of the construction robot are normalized, and the normalized operation stability index, scheduling fitness index and flexibility demand index are comprehensively analyzed to calculate the comprehensive scheduling index. The calculation formula of the comprehensive scheduling index is: ;in, represents the comprehensive dispatch index; is the flexibility demand index; represents the scheduling fitness index; is the operation stability index; are the weights of the operation stability index and the scheduling adaptability index respectively; is the adjustment factor; Preset the comprehensive dispatch index threshold, and compare the comprehensive dispatch index with the comprehensive dispatch index threshold: When the comprehensive dispatch index is greater than or equal to the comprehensive dispatch index threshold, there is no need to adjust the existing dispatch strategy; When the comprehensive scheduling index is less than the comprehensive scheduling index threshold, the existing scheduling strategy needs to be adjusted.

[0012] On the other hand, the present invention provides a dynamic scheduling system for construction robots based on artificial intelligence, including a screening module, a stability evaluation module, a fitness evaluation module, a real-time judgment module, a flexibility demand evaluation module, and a scheduling optimization module; Screening module: Screening out potential construction robots for scheduling by analyzing the process requirements and resource allocation of construction tasks at the construction site; Stability evaluation module: Analyze the task execution records of the construction robot and evaluate the operation stability of the construction robot; Adaptability evaluation module: obtains the historical idle status and task switching status of the construction robot, analyzes the flexibility of the construction robot's task switching, and evaluates the scheduling adaptability of the construction robot in different construction processes; Real-time judgment module: Based on the operation stability of the construction robot and the scheduling adaptability of the construction robot in different construction processes, the real-time nature of the construction robot scheduling requirements is judged; Flexibility demand assessment module: When the real-time nature of the construction robot scheduling requirement is high, the frequency change and interference range of the path intersection area generated by the construction robot in the construction task are analyzed to assess the flexibility demand intensity of the construction robot's dynamic scheduling; Scheduling optimization module: Comprehensively analyze the operation stability of the construction robot, the scheduling adaptability of the construction robot in different construction processes, and the flexibility demand intensity of the dynamic scheduling of the construction robot, and adjust the scheduling strategy of the construction robot at the construction site.

[0013] The technical effects and advantages of the artificial intelligence-based construction robot dynamic scheduling method and system of the present invention are as follows: By comprehensively analyzing the process requirements and resource allocation of construction tasks, the potential dispatchable construction robots are accurately screened, which improves the accuracy and efficiency of scheduling; by evaluating the robot task execution records, the operation stability is quantified to provide a scientific basis for the scheduling strategy; combined with the historical idle state and task switching situation, the scheduling adaptability is evaluated to ensure the efficient matching of robots in different construction processes; by analyzing the frequency changes and interference range of the path intersection area, the flexibility demand intensity is evaluated to optimize the real-time response capability of the scheduling system. Through the comprehensive analysis of operation stability, scheduling adaptability and flexibility demand intensity, the scheduling strategy is dynamically adjusted to reduce task conflicts and path interference, improve the efficiency of multi-robot collaboration and the overall progress of the construction project, which is suitable for complex and changeable construction scenarios, and provides an efficient solution for intelligent building construction, with wide application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of a dynamic scheduling method for a construction robot based on artificial intelligence according to the present invention; Figure 2 The present invention is a structural schematic diagram of a dynamic scheduling system for a construction robot based on artificial intelligence. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0016] Figure 1 The present invention provides a dynamic scheduling method for a construction robot based on artificial intelligence, which comprises the following steps: By analyzing the process requirements and resource allocation of construction tasks at the construction site, potential construction robots can be screened and scheduled; Analyze the task execution records of the construction robot and evaluate the operation stability of the construction robot; Obtain the historical idle status and task switching of the construction robot, analyze the flexibility of the construction robot's task switching, and evaluate the scheduling adaptability of the construction robot in different construction processes; Based on the operation stability of the construction robot and the scheduling adaptability of the construction robot in different construction processes, the real-time nature of the construction robot scheduling needs is judged; When the real-time nature of the construction robot scheduling requirement is high, the flexibility demand intensity of the construction robot dynamic scheduling is evaluated by analyzing the correlation between the priority of the construction task and the resource allocation of the construction robot; A comprehensive analysis is conducted on the operating stability of the construction robot, the scheduling adaptability of the construction robot in different construction processes, and the flexibility demand intensity of the dynamic scheduling of the construction robot to adjust the scheduling strategy of the construction robot at the construction site.

[0017] Specifically, by analyzing the process requirements and resource allocation of construction tasks at the construction site, potential construction robots for scheduling are screened, including: Obtain construction site task data and extract task feature information including construction process requirements and resource allocation: Construction site task data consists of multiple data sources, including construction plan documents, process description documents, and task status data collected by real-time monitoring equipment. The data feature extraction process includes: Parse the task content and schedule from the construction plan document and extract preliminary task process requirement information, such as process type, task priority, etc.; Through natural language processing analysis of process description documents, the process steps and detailed requirements required for each task are identified, such as welding parameters and pouring temperature range; Extract real-time status data of task resource configuration from real-time monitoring equipment, such as existing robot types, idle status, operable range, etc.; Construct a structured data table of construction task characteristic information, which contains the process requirement parameters and resource configuration attributes of each task.

[0018] Parse the construction process requirement data and extract the process adaptability parameters of the construction task: In order to refine the process requirements of the construction task, further parse the process adaptability parameters suitable for scheduling from the extracted task characteristic information, including the following steps: Standardize the process requirement parameters and unify the data from different data sources into standard numerical indicators, such as process accuracy, operation time, etc. Construct a process adaptability parameter matrix to represent the adaptability between the task and process requirement attributes. The calculation method is based on the importance and satisfaction of each process requirement and is obtained through weight allocation. The obtained process suitability parameter matrix is ​​normalized to ensure that each indicator has the same measurement standard.

[0019] Analyze resource allocation and identify construction robot resource characteristics that match construction tasks: Based on the resource allocation of construction robots at the construction site, extract the characteristics of each resource, including: Obtain the current status information of each construction robot in real time, such as load capacity, working speed, tool interface type, etc.; The various characteristics of the construction robot resources are structured to form an attribute set containing characteristic parameters, including load capacity, applicable process type, operable range, etc. Combined with the key attributes of construction task requirements, weights are assigned to the characteristics of construction robot resources to determine the importance of different resource characteristics in adapting to task requirements; Analyze the matching degree between construction robot resources and construction task characteristics, and form robot characteristic screening conditions related to task requirements.

[0020] Construct matching rules between construction task process requirements and construction robot resource characteristics: In order to achieve efficient matching between tasks and construction robots, matching rules are established. The specific steps are as follows: Calculate the matching score based on the process adaptability parameters of the construction task and the characteristic parameters of the robot resources; The matching score is based on the importance weights of the construction task requirements and the robot characteristics, and each characteristic is weighted and accumulated to obtain a comprehensive adaptation score; A scoring threshold is set. When the matching score between the construction task and the construction robot reaches the threshold, the construction robot is determined to be suitable for performing the construction task.

[0021] Based on the matching rules, the construction tasks are associated with the construction robots, and the construction robots for potential scheduling are screened: The construction tasks are associated with the construction robots through the matching rules. The specific operations are as follows: For each construction task, the matching score between it and the available robot resources is calculated in turn, and the matching result between the task and the robot is generated; Screen the matching results, remove the task and robot pairs below the matching threshold, and form a preliminary association result; An optimization algorithm is applied to the preliminary association results to adjust the association between tasks and robots according to the overall load balancing and task allocation requirements of the construction site, and finally output a set of potentially schedulable construction robots.

[0022] Specifically, the task execution records of the construction robot are analyzed to evaluate the operation stability of the construction robot, including: Collect historical task execution data of construction robots, including execution status: The task execution records of construction robots include the following data: Task execution status: records the task completion status, including success, failure, abort and other statuses.

[0023] Statistics on the task completion rate of the construction robot, and calculation of the task failure frequency and task failure time ratio: The task completion rate is defined as the proportion of tasks successfully completed by the construction robot, and the calculation formula is: ;in, represents the task completion rate of the construction robot; The number of tasks successfully completed for construction robots; Represents the total number of tasks performed by the construction robot.

[0024] The task failure frequency is defined as the number of task failures per unit time. The calculation formula is: ;in, Indicates the frequency of task failure; is the total number of task failures; The length of the time range for counting task failures.

[0025] Calculate the ratio of the time spent on failed tasks to the total task time. The calculation formula is: ;in, Indicates the percentage of task failure time; The accumulated time of failed tasks; is the total time of all tasks.

[0026] Extract the fluctuation values ​​of operating parameters during task execution, including load changes and running speed changes: During task execution, the fluctuation of operating parameters of construction robots will affect task stability. Therefore, the fluctuation values ​​of key parameters are extracted from historical records, including: Record the real-time load changes during task execution and calculate the load fluctuation amplitude: ;in, is the load fluctuation amplitude; For time Load values ​​for construction robots.

[0027] Record the real-time running speed changes during task execution and calculate the speed fluctuation range: ;in, is the speed fluctuation amplitude; For time The operating speed value of the construction robot.

[0028] Quantify the operation stability of the construction robot based on the task completion rate and the fluctuation value of the operating parameters: Based on the task completion rate and the fluctuation value of the operating parameters, define the operation stability index to quantify the operation stability of the construction robot. The calculation formula is: ;in, is the operation stability index; represents the task completion rate of the construction robot; Indicates the frequency of task failure; The percentage of task failure time is shown; is the load fluctuation amplitude; is the speed fluctuation amplitude.

[0029] The larger the operation stability index, the higher the stability of the construction robot in the process of executing the task. The larger the operation stability index, the higher the proportion of successful completion of the task, the lower the frequency and failure time of the task, and the smaller the fluctuation of the operating parameters (such as load and running speed) in the process of task execution. The larger the operation stability index, the stronger the reliability of the construction robot in dealing with complex construction tasks and the more stable the output of task performance.

[0030] Specifically, the historical idle status and task switching of the construction robot are obtained, the task switching flexibility of the construction robot is analyzed, and the scheduling adaptability of the construction robot in different construction processes is evaluated, including: Obtain the historical idle data of the construction robot, including the start and end time records of each idle state: Obtain the historical idle data of the construction robot from the construction site monitoring system and task management system. The idle data includes: environmental conditions during idleness (such as construction site temperature, humidity, etc.).

[0031] Count the number of times the robot switches between construction process tasks and extract the task switching type: Task switching refers to the process of a construction robot switching from one process task (such as welding, handling) to another process task. The switching type is classified based on task attributes, including: Process switching: for example, switching from welding to handling; Area switching: for example, switching from construction area A to construction area B; Mode switching: for example, switching from automatic mode to semi-automatic mode.

[0032] In the task execution record, the task attribute changes are detected to identify the switching events. The switching times are calculated as: ;in, Indicates the total number of all task switches; Expressed as the total number of tasks; For the A Boolean value indicating whether the task is switched (1 for switching, 0 for no switching).

[0033] Record the type of each switch (e.g., process, area, mode).

[0034] Calculate the task switching interval and the switching ratio of different process tasks: The switching interval represents the time interval between two consecutive task switches, and its calculation formula is: ;in, Indicates The interval between task switches; For the The time when the task ends; For the The time when the task starts.

[0035] Statistics on the proportion of each process task switching: ;in, Indicates the process task type The switching ratio; Indicates the process task type The number of switching times; Indicates the total number of all task switches.

[0036] Analyze the task switching flexibility and quantify the flexibility index based on the switching interval time and switching ratio: Task switching flexibility reflects the efficiency and adaptability of the construction robot in switching between tasks. The higher the flexibility, the faster the construction robot responds to switching between different task requirements. Combined with the switching interval time and switching ratio, the flexibility index calculation formula is: ;in, represents the flexibility index; Process task type The average switching interval is the process task type All task switching intervals The arithmetic mean of Indicates the total number of process task types; Indicates the process task type The switching ratio.

[0037] Combined with the flexibility index, the scheduling adaptability of the construction robot in different construction processes is evaluated: the scheduling adaptability index reflects the adaptability of the construction robot in different construction process tasks, and is quantified in combination with the task switching flexibility index. The calculation formula of the scheduling adaptability index is: ;in, represents the scheduling fitness index; represents the flexibility index; Indicates the total number of process task types; Indicates the process task type The switching ratio; Indicates the process task type demand weight.

[0038] The larger the scheduling adaptability index, the higher the scheduling adaptability of the construction robot in different construction process tasks, and the more efficient it is in completing task switching and adapting to different process requirements. A larger scheduling adaptability index indicates that the construction robot has strong task switching flexibility when performing a variety of construction process tasks, and can also meet the performance requirements of various process tasks for equipment. A larger scheduling adaptability index also indicates that the robot can adapt to the dynamic scheduling requirements of multi-task parallelism in a complex construction environment, has a higher utilization rate, and has significant value in improving construction efficiency and overall project progress.

[0039] Specifically, based on the operation stability of the construction robot and the scheduling adaptability of the construction robot in different construction processes, the real-time nature of the scheduling demand of the construction robot is judged, including: A job stability index threshold is preset, and the job stability index is compared with the job stability index threshold: When the operation stability index is greater than the operation stability index threshold, it means that the performance of the construction robot in the current task is stable, and it can complete the task normally in most scenarios, and the real-time nature of the scheduling demand is low; When the operation stability index is less than or equal to the operation stability index threshold, it means that the construction robot may have a fault or performance fluctuation during the task execution process, and the task allocation needs to be adjusted to avoid affecting the construction progress. The real-time nature of the scheduling demand is relatively high; The threshold of the operation stability index is set based on historical task data and construction site requirements, combined with statistical analysis of robot performance indicators (such as task completion rate, parameter fluctuation range, etc.).

[0040] A scheduling fitness index threshold is preset, and the scheduling fitness index is compared with the scheduling fitness index threshold: When the scheduling fitness index is greater than the scheduling fitness index threshold, it means that the construction robot is highly matched with the current construction process, and the real-time performance of the scheduling demand is low; When the scheduling fitness index is less than or equal to the scheduling fitness index threshold, it means that the construction robot has low adaptability in a specific process, which may lead to a decrease in task completion efficiency and a high real-time nature of scheduling requirements; The threshold of the scheduling adaptability index is determined comprehensively based on the construction process requirement parameters, task completion efficiency, and flexibility demand intensity, and is usually set to the minimum adaptation requirement to meet the efficiency of on-site construction task switching.

[0041] When the operation stability index is greater than the operation stability index threshold, and the scheduling fitness index is greater than the scheduling fitness index threshold, the real-time nature of the scheduling demand of the construction robot is judged to be low; otherwise, the real-time nature of the scheduling demand of the construction robot is judged to be high.

[0042] Specifically, when the real-time nature of the construction robot scheduling requirement is high, the frequency change and interference range of the path intersection area generated by the construction robot in the construction task are analyzed to evaluate the flexibility demand intensity of the dynamic scheduling of the construction robot, including: Collect task path data of construction robots and record the location and number of path intersection areas: obtain the operation path data of construction robots when performing tasks through sensors at the construction site. Each path is represented by a continuous sequence of space-time coordinates, and the data includes: path number; two-dimensional coordinates of each position point on the path. According to the collected path data, identify the path intersection area. The specific judgment rule is: if two or more paths overlap in the spatial coordinate system and the time interval is less than the threshold, it is considered a path intersection, and the total number of path intersection areas is counted. .

[0043] Calculate the path intersection frequency of the path intersection area: extract the start time and end time of each path intersection area, and calculate the duration of each path intersection area In each path intersection area, count the total number of path intersection events .

[0044] Based on the duration of each path intersection area The total number of path intersection events , calculate the path crossing frequency in the path crossing area .

[0045] Identify the interference scope of the path intersection area, including the number of construction robots involved and the intersection area: For each path intersection area, count the number of robots involved in the construction intersection ;in, Indicates the path intersection area The number of construction robots involved; Indicates that in the path intersection area A numbered collection of construction robots involved in construction tasks; Representing a collection The number of unique elements in .

[0046] Calculate the coverage area of ​​the path intersection area through the coordinate data of the path intersection point ;in, Indicates the path intersection area area; Respectively represent the maximum and minimum values ​​of the path intersection coordinates.

[0047] Evaluate the flexibility demand intensity based on the path crossing frequency and interference range: The flexibility demand intensity reflects the response requirements of robot scheduling to the path crossing frequency and interference range. Combined with the path crossing frequency and interference range, the flexibility demand index is defined. The calculation formula is: ;in, is the flexibility demand index; Indicates the path intersection area The path crossing frequency of Indicates the path intersection area The number of construction robots involved; Indicates the path intersection area area; Indicates the total number of path intersection areas; Represents a constant.

[0048] The larger the flexibility demand index, the greater the flexibility demand intensity of the dynamic scheduling of construction robots. This shows that the task environment on the construction site is more complex and changes frequently, such as the increase in path intersection areas, the increase in the frequency of task switching, and the increase in the number of robots involved in the task, which requires the scheduling system to have higher real-time and adaptability to cope with these dynamic changes. The increase in the intensity of flexibility demand means that robot scheduling needs to respond to task adjustments more quickly, optimize path planning, and coordinate task allocation among multiple robots to avoid affecting construction efficiency due to task conflicts or path interference. The increase in the index may also reflect the diversification of construction processes and the high utilization rate of resource allocation, which puts higher requirements on the flexibility of the scheduling system.

[0049] Specifically, a comprehensive analysis is conducted on the operation stability of the construction robot, the scheduling adaptability of the construction robot in different construction processes, and the flexibility demand intensity of the dynamic scheduling of the construction robot, and the scheduling strategy of the construction robot at the construction site is adjusted, including: The operation stability index corresponding to the operation stability of the construction robot, the scheduling fitness index corresponding to the scheduling fitness of the construction robot in different construction processes, and the flexibility demand index corresponding to the flexibility demand intensity of the dynamic scheduling of the construction robot are normalized, and a comprehensive analysis is performed on the normalized operation stability index, scheduling fitness index and flexibility demand index to calculate the comprehensive scheduling index. The scheduling strategy of the construction robot at the construction site is adjusted according to the comprehensive scheduling index.

[0050] The calculation formula of the comprehensive dispatch index is: ;in, represents the comprehensive dispatch index; is the flexibility demand index; represents the scheduling fitness index; is the operation stability index; are the weights of the operation stability index and the scheduling adaptability index respectively; It is an adjustment factor used to control the nonlinear impact of the flexibility demand index on the comprehensive dispatch index.

[0051] Preset the comprehensive dispatch index threshold, and compare the comprehensive dispatch index with the comprehensive dispatch index threshold: When the comprehensive scheduling index is greater than or equal to the comprehensive scheduling index threshold, it means that the current scheduling strategy of the construction robot at the construction site can effectively meet the operation requirements. At this time, there is no need to adjust the existing scheduling strategy, only regular monitoring is required to ensure the normal completion of the construction task.

[0052] When the comprehensive scheduling index is less than the comprehensive scheduling index threshold, it means that the scheduling strategy of the existing construction site construction robot cannot fully adapt to the dynamic needs of the construction site. There may be insufficient operation stability, low process scheduling adaptability, or unmet flexibility requirements. At this time, it is necessary to adjust the existing scheduling strategy, such as optimizing task allocation, improving path planning, or improving collaboration efficiency, to improve scheduling performance and avoid negative impacts on construction progress and quality.

[0053] Example 2 The difference between Example 2 of the present invention and Example 1 is that, based on Example 1, this example introduces a dynamic scheduling system for a construction robot based on artificial intelligence.

[0054] Figure 2 A structural schematic diagram of a dynamic scheduling system for a construction robot based on artificial intelligence is given in the present invention. A dynamic scheduling system for a construction robot based on artificial intelligence includes a screening module, a stability evaluation module, a fitness evaluation module, a real-time judgment module, a flexibility demand evaluation module and a scheduling optimization module; Screening module: Screening out potential construction robots for scheduling by analyzing the process requirements and resource allocation of construction tasks at the construction site; Stability evaluation module: Analyze the task execution records of the construction robot and evaluate the operation stability of the construction robot; Adaptability evaluation module: obtains the historical idle status and task switching status of the construction robot, analyzes the flexibility of the construction robot's task switching, and evaluates the scheduling adaptability of the construction robot in different construction processes; Real-time judgment module: Based on the operation stability of the construction robot and the scheduling adaptability of the construction robot in different construction processes, the real-time nature of the construction robot scheduling requirements is judged; Flexibility demand assessment module: When the real-time nature of the construction robot scheduling requirement is high, the frequency change and interference range of the path intersection area generated by the construction robot in the construction task are analyzed to assess the flexibility demand intensity of the construction robot's dynamic scheduling; Scheduling optimization module: Comprehensively analyze the operation stability of the construction robot, the scheduling adaptability of the construction robot in different construction processes, and the flexibility demand intensity of the dynamic scheduling of the construction robot, and adjust the scheduling strategy of the construction robot at the construction site.

[0055] The present embodiment is an artificial intelligence-based dynamic scheduling system for construction robots. In the process of implementing the above-mentioned artificial intelligence-based dynamic scheduling method for construction robots, the technical details implemented are the same or equivalent to those in Example 1 and will not be repeated here.

[0056] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0057] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0058] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0059] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0060] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0061] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0062] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0063] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0064] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0065] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dynamic scheduling method for construction robots based on artificial intelligence, characterized in that: The steps include: By analyzing the process requirements and resource allocation of construction tasks at the construction site, potential construction robots can be screened and scheduled; Analyze the task execution records of the construction robot and evaluate the operation stability of the construction robot; Obtain the historical idle status and task switching status of the construction robot, analyze the flexibility of the construction robot's task switching, and evaluate the scheduling adaptability of the construction robot in different construction processes; Based on the operation stability of the construction robot and the scheduling adaptability of the construction robot in different construction processes, the real-time nature of the scheduling demand of the construction robot is judged; When the real-time nature of the construction robot scheduling requirement is high, the frequency change and interference range of the path intersection area generated by the construction robot in the construction task are analyzed to evaluate the flexibility demand intensity of the construction robot dynamic scheduling; A comprehensive analysis is conducted on the operating stability of the construction robot, the scheduling adaptability of the construction robot in different construction processes, and the flexibility demand intensity of the dynamic scheduling of the construction robot to adjust the scheduling strategy of the construction robot at the construction site.

2. According to the artificial intelligence-based dynamic scheduling method for construction robots according to claim 1, it is characterized in that: By analyzing the process requirements and resource allocation of construction tasks at the construction site, potential construction robots for scheduling are screened, specifically: Obtain construction site task data and extract task characteristic information including construction process requirements and resource allocation; Analyze the construction process demand data and extract the process adaptability parameters of the construction tasks; Analyze resource allocation and identify construction robot resource characteristics that match construction tasks; Construct matching rules between construction task process requirements and construction robot resource characteristics; Based on the matching rules, the construction tasks are associated with the construction robots, and the potential construction robots for scheduling are screened.

3. The method for dynamic scheduling of construction robots based on artificial intelligence according to claim 2 is characterized in that: Analyze the task execution records of the construction robot and evaluate the operation stability of the construction robot, specifically: Collect historical task execution data of construction robots, including task execution status; Statistics on the task completion rate of construction robots, and calculation of task failure frequency and task failure time ratio; Extract the fluctuation values ​​of operating parameters during task execution, including load changes and running speed changes; According to the task completion rate and the fluctuation value of the operating parameters, the operation stability of the construction robot is quantified: Based on the task completion rate and the fluctuation value of the operating parameters, the operation stability index is defined, and the calculation formula is: ;in, is the operation stability index; represents the task completion rate of the construction robot; Indicates the frequency of task failure; Indicates the percentage of task failure time; is the load fluctuation amplitude; is the speed fluctuation amplitude.

4. The method for dynamic scheduling of construction robots based on artificial intelligence according to claim 3 is characterized in that: The historical idle status and task switching of the construction robot are obtained, the task switching flexibility of the construction robot is analyzed, and the scheduling adaptability of the construction robot in different construction processes is evaluated. Specifically: Obtain historical idle data of construction robots, including the start and end time records of each idle period; Count the number of times the robot switches between construction process tasks and extract the task switching types; Calculate the task switching interval and the switching ratio of different process tasks; Analyze the task switching flexibility and quantify the flexibility index based on the switching interval time and switching ratio: Combined with the switching interval time and switching ratio, the flexibility index calculation formula is: ;in, represents the flexibility index; Process task type The average switching interval time; Indicates the total number of process task types; Indicates the process task type The switching ratio; Combined with the flexibility index, the scheduling adaptability of construction robots in different construction processes is evaluated.

5. The method for dynamic scheduling of construction robots based on artificial intelligence according to claim 4 is characterized in that: Combined with the flexibility index, the scheduling adaptability of construction robots in different construction processes is evaluated, specifically: The calculation formula of the scheduling fitness index is: ;in, represents the scheduling fitness index; represents the flexibility index; Indicates the total number of process task types; Indicates the process task type The switching ratio; Indicates the process task type demand weight.

6. The method for dynamic scheduling of construction robots based on artificial intelligence according to claim 5 is characterized in that: Based on the operation stability of the construction robot and the scheduling adaptability of the construction robot in different construction processes, the real-time nature of the scheduling demand of the construction robot is judged, specifically: A job stability index threshold is preset, and the job stability index is compared with the job stability index threshold: When the job stability index is greater than the job stability index threshold, the real-time performance of the scheduling demand is low; When the job stability index is less than or equal to the job stability index threshold, the real-time nature of the scheduling demand is high; A scheduling fitness index threshold is preset, and the scheduling fitness index is compared with the scheduling fitness index threshold: When the scheduling fitness index is greater than the scheduling fitness index threshold, the real-time performance of the scheduling demand is low; When the scheduling fitness index is less than or equal to the scheduling fitness index threshold, the real-time nature of the scheduling demand is high; When the operation stability index is greater than the operation stability index threshold, and the scheduling fitness index is greater than the scheduling fitness index threshold, the real-time nature of the scheduling demand of the construction robot is judged to be low; otherwise, the real-time nature of the scheduling demand of the construction robot is judged to be high.

7. The method for dynamic scheduling of construction robots based on artificial intelligence according to claim 6 is characterized in that: When the real-time nature of the construction robot scheduling requirement is high, the frequency change and interference range of the path intersection area generated by the construction robot in the construction task are analyzed to evaluate the flexibility demand intensity of the construction robot dynamic scheduling, specifically: Collect task path data of construction robots and record the location and number of path intersection areas; Calculating the path crossing frequency in the path crossing area; Identify the scope of interference in path intersection areas, including the number of construction robots involved and the intersection area; Evaluate the flexibility demand intensity based on the path crossing frequency and interference range: Combine the path crossing frequency and interference range to define the flexibility demand index, which is calculated as follows: ;in, is the flexibility demand index; Indicates the path intersection area The path crossing frequency of Indicates the path intersection area The number of construction robots involved; Indicates the path intersection area area; Indicates the total number of path intersection areas; Represents a constant.

8. The method for dynamic scheduling of construction robots based on artificial intelligence according to claim 7 is characterized in that: A comprehensive analysis is conducted on the operation stability of the construction robot, the scheduling adaptability of the construction robot in different construction processes, and the flexibility demand intensity of the dynamic scheduling of the construction robot, specifically: The operation stability index corresponding to the operation stability of the construction robot, the scheduling fitness index corresponding to the scheduling fitness of the construction robot in different construction processes, and the flexibility demand index corresponding to the flexibility demand intensity of the dynamic scheduling of the construction robot are normalized, and the normalized operation stability index, scheduling fitness index and flexibility demand index are comprehensively analyzed to calculate the comprehensive scheduling index. The calculation formula of the comprehensive scheduling index is: ;in, represents the comprehensive dispatch index; is the flexibility demand index; represents the scheduling fitness index; is the operation stability index; are the weights of the operation stability index and the scheduling adaptability index respectively; is the adjustment factor.

9. The method for dynamic scheduling of construction robots based on artificial intelligence according to claim 8 is characterized in that: According to the comprehensive analysis results, the scheduling strategy of the construction robot at the construction site is adjusted, specifically: Preset the comprehensive dispatch index threshold, and compare the comprehensive dispatch index with the comprehensive dispatch index threshold: When the comprehensive dispatch index is greater than or equal to the comprehensive dispatch index threshold, there is no need to adjust the existing dispatch strategy; When the comprehensive scheduling index is less than the comprehensive scheduling index threshold, the existing scheduling strategy needs to be adjusted.

10. A dynamic scheduling system for a construction robot based on artificial intelligence, used to implement a dynamic scheduling method for a construction robot based on artificial intelligence as described in any one of claims 1 to 9, characterized in that: It includes screening module, stability assessment module, adaptability assessment module, real-time judgment module, flexibility demand assessment module and scheduling optimization module; Screening module: Screening out potential construction robots for scheduling by analyzing the process requirements and resource allocation of construction tasks at the construction site; Stability evaluation module: Analyze the task execution records of the construction robot and evaluate the operation stability of the construction robot; Adaptability evaluation module: obtains the historical idle status and task switching status of the construction robot, analyzes the flexibility of the construction robot's task switching, and evaluates the scheduling adaptability of the construction robot in different construction processes; Real-time judgment module: Based on the operation stability of the construction robot and the scheduling adaptability of the construction robot in different construction processes, the real-time nature of the construction robot scheduling requirements is judged; Flexibility demand assessment module: When the real-time nature of the construction robot scheduling requirement is high, the frequency change and interference range of the path intersection area generated by the construction robot in the construction task are analyzed to assess the flexibility demand intensity of the construction robot's dynamic scheduling; Scheduling optimization module: Comprehensively analyze the operation stability of the construction robot, the scheduling adaptability of the construction robot in different construction processes, and the flexibility demand intensity of the dynamic scheduling of the construction robot, and adjust the scheduling strategy of the construction robot at the construction site.

Images