Fault online diagnosis system for unmanned collaborative operation of bucket wheel machine

Abstract

The invention relates to the technical field of bucket wheel machine fault detection, in particular to a bucket wheel machine unmanned collaborative operation fault on-line diagnosis system, which comprises a single machine path and instruction anomaly detection module used for performing path offset and instruction loss diagnosis on the operation state of a single bucket wheel machine; the single-machine fault root cause analysis module is used for performing root cause analysis on the detected single-machine path offset and instruction loss faults; the collaborative task fault influence evaluation module is used for evaluating the dynamic influence of the single-machine fault on the collaborative operation task of the multi-bucket wheel machine; and the cooperative task dynamic adjustment module is used for optimizing cooperative task distribution of the multiple bucket wheel machines according to the single-machine fault influence evaluation result, and redistributing operation tasks of the fault bucket wheel machine to the healthy bucket wheel machine. According to the method, the depth and accuracy of fault diagnosis are improved, whether the fault bucket wheel machine is shut down or not is dynamically judged, the task completion rate and the system efficiency are balanced, unnecessary resource waste is avoided, and meanwhile the emergency processing requirement of major faults is quickly responded.

Description

Technical Field

[0001] The invention relates to the technical field of bucket wheel machine fault detection, and in particular to an online fault diagnosis system for unmanned collaborative operation of bucket wheel machines. Background Art

[0002] Bucket wheel excavators are large-scale continuous operation equipment, widely used in bulk material loading and unloading and stockpiling operations in mines, ports, power plants and other scenarios. With the improvement of industrial automation and intelligence, the unmanned collaborative operation system of bucket wheel excavators has gradually become an important direction for improving production efficiency and reducing labor costs. However, there are still many technical bottlenecks in the unmanned collaborative operation of bucket wheel excavators, especially in online fault diagnosis and collaborative operation adjustment, facing the following problems:

[0003] In unmanned collaborative operations, bucket wheel excavators often suffer from reduced operating efficiency or even interruption due to faults such as path deviation and command loss. However, traditional systems mostly rely on single sensor data or preset rules for fault detection and are unable to accurately locate and analyze anomalies under complex working conditions. In particular, deep-seated faults involving control logic defects and path planning algorithm deviations are difficult to identify and trace.

[0004] The unmanned collaborative operation of bucket wheel excavators requires multiple devices to cooperate with each other in the same operation scenario. Once a single machine fails, its impact is not limited to its own task, but may also cause a chain reaction on the collaborative efficiency of multiple devices. In the existing technology, the evaluation of the impact of the failure often only focuses on the static changes in the task completion rate, lacks quantitative analysis of the decline in dynamic collaborative efficiency, and is difficult to effectively guide task adjustment and path optimization.

[0005] In actual applications, when a bucket wheel machine fails and its task is interrupted, how to quickly assign the unfinished tasks to healthy equipment and how to dynamically adjust the path to avoid conflicts are the core issues of the unmanned collaborative operation system. Summary of the invention

[0006] The invention provides an online fault diagnosis system for unmanned cooperative operation of a bucket wheel machine.

[0007] An online fault diagnosis system for unmanned collaborative operation of a bucket wheel machine, comprising:

[0008] Single machine path and instruction anomaly detection module: used to diagnose path deviation and instruction loss for the operating status of a single bucket wheel machine, including:

[0009] Based on the path deviation between the current path data of the bucket wheel machine and the preset path, the path deviation of the single machine is identified;

[0010] Monitor the command response time and execution result feedback time of the control units of multiple bucket wheel machines, and detect command loss failures;

[0011] Single machine fault root cause analysis module: Root cause analysis of detected single machine path deviation and instruction loss faults is performed, including:

[0012] Trace the execution records of the control logic and path planning algorithm of the control unit to determine the control logic defects that cause path deviation and command loss failures;

[0013] Collaborative task failure impact assessment module: assesses the dynamic impact of a single machine failure on the collaborative task of multiple bucket wheel machines, including:

[0014] Based on the operating area where the faulty bucket wheel excavator is located, predict the impact of its displacement or short shutdown on the completion rate of the stockpiling task;

[0015] Analyze the intersection points between the faulty bucket wheel machine and other equipment to form overlapping areas and evaluate the resulting decrease in collaborative efficiency;

[0016] Collaborative task dynamic adjustment module: optimizes the collaborative task allocation of multiple bucket wheel machines based on the single machine failure impact assessment results, and reallocates the operating tasks of the faulty bucket wheel machine to the healthy bucket wheel machine.

[0017] Optionally, the identifying of the single-machine path offset includes calculating the path offset between the current path data and the preset path through a path matching algorithm, setting an offset threshold, and when the offset exceeds the offset threshold, outputting a path offset diagnosis result and determining the offset direction;

[0018] Optionally, the detection of instruction lost failure includes real-time monitoring of the instruction input and execution results of the control unit, recording the instruction issuance time and the execution result feedback time through timestamps, calculating the instruction response time, and triggering detection when the instruction response time exceeds a preset threshold or there is no feedback. The detection includes judging whether the instruction execution is successful by comparing the task execution results with the expected effects of the instructions (such as path adjustment, stockpiling changes, etc.).

[0019] Optionally, tracing the execution record of the control logic of the control unit and the path planning algorithm to determine the control logic defects that cause the path deviation and instruction loss faults specifically include:

[0020] Control logic execution record traceability analysis: Extract the instruction log of the control unit when the fault occurs, including input instructions, algorithm call process and feedback output results, parse the instruction stream in chronological order, reconstruct the control logic execution link, including instruction distribution nodes, path planning nodes and feedback confirmation nodes, compare the current execution link with the preset logic template link, identify the logic deviation point (such as failure to call a specific node or output feedback timeout), build the control logic abnormality association matrix, and calculate the relative deviation rate R of the execution deviation between each node. logic(i, j), represents the relative deviation rate of execution time between node i and node j. When R logic When (i, j) exceeds the preset threshold, it is determined that there are abnormal nodes in the control logic and the relevant defect points are marked;

[0021] Path planning algorithm traceability analysis: Call the execution record of path planning, including the material stacking shape data, task allocation parameters and path generation results, compare the theoretical output of the path planning algorithm with the actual execution path, and calculate the path deviation rate: Among them, P deviation is the path deviation rate, which indicates the degree of deviation between the actual path and the planned path, ΔP actual is the offset distance between actual path points, ΔP expected It is the expected distance between theoretical path points, and determines whether the path deviation is caused by unresolved conflicts due to insufficient path priority processing rules in the control logic.

[0022] Optionally, the relative deviation rate R logic (i, j) is calculated as:

[0023] Among them, T actual (i, j) is the actual execution time, r expected (i, j) is the theoretical expected execution time.

[0024] Optionally, the predicting of the impact of the displacement or short shutdown of the faulty bucket wheel machine on the completion rate of the stockpiling task based on the operating area where the faulty bucket wheel machine is located includes:

[0025] The stockpile area distribution model is constructed using the stockpile 3D morphology modeling technology, and the operating task area and the unfinished task area of ​​the faulty bucket wheel excavator are mapped in the stockpile area distribution model;

[0026] Calculate the proportion of uncovered area after the faulty bucket wheel machine is offset: Among them, C coverage is the coverage rate, which indicates the proportion of the area that is not completed by the faulty bucket wheel machine in the stacking task, A uncovered is the unfinished area of ​​the stockpile area after the failure, A total is the total area of ​​the stockpile area; combined with the task priority and stockpile form (density, stability), the degree of decrease in the stockpile task completion rate is calculated:

[0027] R completion =R baseline —C coverage ·W priority , where R completion is the adjusted task completion rate, R baseline is the task completion rate in the fault-free state, W priority is the stockpile area priority weight.

[0028] Optionally, the decrease in collaborative efficiency caused by the evaluation includes:

[0029] Analyze the path intersections (i.e. path conflict points) and area overlaps between the faulty bucket wheel excavator and other bucket wheel excavators: Detect the path intersections of multiple bucket wheel excavators through bucket wheel excavator operation path planning data and stockpile area distribution model to determine the risk of path conflict:

[0030] Among them, N conflict is the number of path conflict points, P i is the i-th path point, R overlap is the overlapping area between the faulty equipment path and other equipment paths. The overlapping area is the set of path intersections. Traverse each path point Pi on the faulty bucket wheel machine path to determine whether it belongs to the overlapping area r overlap , represents the indicator function, when P i ∈R overlap When , the value is 1, otherwise it is 0, m represents the total number of path points, and i represents the path point index;

[0031] Analyze the overlapping areas and path conflict points to assess the impact of decreased coordination efficiency:

[0032] E efficiency =E baseline -α·A overlap ·N conflict , where f efficiency is the adjusted synergy efficiency, E baseline is the coordination efficiency under fault-free conditions, A overlap is the overlapping area between the faulty bucket wheel machine and other equipment, and α is the efficiency reduction weight factor of path conflict.

[0033] Optionally, the collaborative task failure impact assessment module further includes a comprehensive assessment of the decrease in task completion rate and the impact on collaborative efficiency;

[0034] Reduce the task completion rate by R completion and the synergistic efficiency decreases E efficiency Perform weighted combination to evaluate the impact of the overall collaborative task: I impact =w1·(1-R completion )+w2·(1-E efficiency ), where I impact is the total score of the dynamic impact of collaborative tasks, ranging from [0, 1]. The higher the value, the greater the impact of the fault on the collaborative task. w1 and w2 are the weight factors of task completion rate and collaborative efficiency, respectively, satisfying w1+w2=1.

[0035] Optionally, the collaborative task dynamic adjustment module includes setting a scoring threshold, which is set to 0.7, and comparing the total score of the collaborative task dynamic impact with the scoring threshold to determine whether to perform a shutdown operation on the faulty bucket wheel machine;

[0036] If the total score of the dynamic impact of the collaborative task is greater than or equal to the scoring threshold, it indicates that the fault has a great impact, and the faulty bucket wheel machine needs to be shut down to avoid further affecting the task and collaborative efficiency, and other healthy bucket wheels are called to replace the faulty bucket wheel machine;

[0037] If the total score of the dynamic impact of the collaborative task is less than the scoring threshold, it indicates that the impact of the fault is small and controllable. The faulty bucket wheel machine continues to operate and complete the remaining tasks, and other healthy bucket wheel machines are called to operate in the unoperated area caused by the offset of the faulty bucket wheel machine.

[0038] Optionally, it also includes task redistribution after the faulty bucket wheel machine stops: if the faulty bucket wheel machine stops, task allocation is performed according to the current load status of healthy bucket wheel machines, and they are sorted from high to low according to the current load status, and healthy bucket wheel machines with the lowest current load status are given priority to complete the work of the faulty bucket wheel machine.

[0039] Beneficial effects of the present invention:

[0040] The present invention, through a modular online fault diagnosis system, combines single-machine path and instruction anomaly detection, control logic tracing analysis and path planning algorithm deviation analysis, can quickly and accurately diagnose the path deviation and instruction loss faults of the bucket wheel excavator, and deeply analyze the root causes: through the path matching algorithm and timestamp analysis, it can efficiently detect path deviation and instruction loss, and combine the control logic to perform link reconstruction and abnormal association matrix calculation, and accurately identify fault points and abnormal nodes; path planning tracing analysis can effectively identify control logic defects that do not take into account dynamic morphological changes or error amplification by calculating the path deviation rate, which significantly improves the depth and accuracy of fault diagnosis.

[0041] The present invention, through the collaborative task fault impact assessment module, combines the three-dimensional stockpile morphology modeling technology and the path intersection analysis method, quantifies the comprehensive impact of the faulty bucket wheel machine on the task completion rate and collaborative efficiency, and conducts a scientific assessment through the total score of the dynamic impact of the collaborative task: using the three-dimensional stockpile morphology modeling technology, the unfinished task area of ​​the faulty bucket wheel machine is mapped to the stockpile area distribution model, and through the introduction of the non-coverage rate and priority weight, the dynamic changes of the task completion rate decline are more accurately evaluated to ensure that high-priority area tasks receive priority attention. Through the analysis of path intersections and regional overlapping areas, the degree of decline in collaborative efficiency caused by path conflicts is dynamically evaluated, and a comprehensive impact assessment report is generated in combination with the weighted combination of the total impact score, providing a global perspective for system decision-making. A scoring threshold is set to dynamically determine whether to shut down the faulty bucket wheel machine, scientifically balance the task completion rate and system efficiency, avoid unnecessary waste of resources, and quickly respond to the emergency handling needs of major faults.

[0042] In the present invention, the collaborative task dynamic adjustment module uses the task redistribution mechanism after the faulty bucket wheel machine stops, utilizes the current load status of the healthy bucket wheel machine to redistribute tasks, and combines dynamic sorting to give priority to the device with the lowest load to take over the task, thereby effectively avoiding the overload operation of the healthy bucket wheel machine and improving the overall load balance of the system. By eliminating the operating area and path intersection of the faulty bucket wheel machine, a conflict-free path plan is generated to ensure that other bucket wheel machines complete the remaining tasks without interference, thereby improving the system's collaborative efficiency and task completion rate, and enhancing the robustness and stability of the unmanned collaborative operation system in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of the functional modules of a diagnostic system according to an embodiment of the present invention;

[0045] Figure 2 Schematic diagram of calculating the total score of dynamic impact of collaborative tasks according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0047] like Figure 1-Figure 2 As shown, an online fault diagnosis system for unmanned collaborative operation of a bucket wheel machine comprises:

[0048] Single machine path and instruction anomaly detection module: used to diagnose path deviation and instruction loss for the operating status of a single bucket wheel machine, including:

[0049] Based on the path deviation between the current path data of the bucket wheel machine and the preset path, the path deviation of the single machine is identified;

[0050] Monitor the command response time and execution result feedback time of the control units of multiple bucket wheel machines, and detect command loss failures;

[0051] Single machine fault root cause analysis module: Root cause analysis of detected single machine path deviation and instruction loss faults is performed, including:

[0052] Trace the execution records of the control logic and path planning algorithm of the control unit to determine the control logic defects that cause path deviation and command loss failures;

[0053] Collaborative task failure impact assessment module: assesses the dynamic impact of a single machine failure on the collaborative task of multiple bucket wheel machines, including:

[0054] Based on the operating area where the faulty bucket wheel excavator is located, predict the impact of its displacement or short shutdown on the completion rate of the stockpiling task;

[0055] Analyze the intersection points between the faulty bucket wheel machine and other equipment to form overlapping areas and evaluate the resulting decrease in collaborative efficiency;

[0056] Collaborative task dynamic adjustment module: optimizes the collaborative task allocation of multiple bucket wheel machines based on the single machine failure impact assessment results, and reallocates the operating tasks of the faulty bucket wheel machine to the healthy bucket wheel machine.

[0057] Identifying the path deviation of a single machine includes calculating the path deviation between the current path data and the preset path through a path matching algorithm, setting a deviation threshold, and when the deviation exceeds the deviation threshold, outputting the path deviation diagnosis result and determining the deviation direction;

[0058] The path matching algorithm table is as follows: Where ΔP is the real-time path offset, which represents the Euclidean distance between the current position of the bucket wheel machine and the preset path.

[0059] (x t ,y t ) is the real-time path data, indicating the current position coordinates of the bucket wheel machine, provided by the displacement sensor or GPS, (x p ,y p ) is the preset path point coordinate, indicating the theoretical position of the bucket wheel machine in the mission planning;

[0060] Calculation logic: When ΔP>ΔP threshold (Deviation threshold), the path deviation alarm is triggered;

[0061] Calculation of path deviation direction based on the shape of the stockpile area: Among them, θ offset It is the path deviation direction, which indicates the deviation angle between the current path of the bucket wheel machine and the preset path.

[0062] Detection of command loss failure includes real-time monitoring of the command input and execution results of the control unit, recording the command issuance time and execution result feedback time through timestamps, calculating the command response time, and triggering detection when the command response time exceeds a preset threshold or there is no feedback. The detection includes judging whether the command execution is successful by comparing the task execution results with the expected effects of the command (such as path adjustment, stockpile changes, etc.).

[0063] The execution records of the control logic and the path planning algorithm of the control unit are traced to determine the control logic defects that cause path deviation and instruction loss failures, including:

[0064] Control logic execution record traceability analysis: Extract the instruction log of the control unit when the fault occurs, including input instructions, algorithm call process and feedback output results, parse the instruction stream in chronological order, reconstruct the control logic execution link, including instruction distribution nodes, path planning nodes and feedback confirmation nodes, compare the current execution link with the preset logic template link, identify the logic deviation point (such as failure to call a specific node or output feedback timeout), build the control logic abnormality association matrix, and calculate the relative deviation rate R of the execution deviation between each node. logic (i, j), represents the relative deviation rate of execution time between node i and node j. When R logic When (i, j) exceeds the preset threshold, it is determined that there are abnormal nodes in the control logic and the relevant defect points are marked;

[0065] Path planning algorithm traceability analysis: Call the execution record of path planning, including the material stacking shape data, task allocation parameters and path generation results, compare the theoretical output of the path planning algorithm with the actual execution path, and calculate the path deviation rate: Among them, Pdeviation is the path deviation rate, which indicates the degree of deviation between the actual path and the planned path, ΔP actual is the offset distance between actual path points,

[0066] ΔP expected It is the expected distance between theoretical path points. When the path deviation rate increases significantly, the sub-processes such as stockpile shape processing and regional boundary planning in the tracking algorithm identify whether there is error amplification or failure to consider dynamic shape changes; analyze the data of the path conflict area (such as path intersections and overlapping work areas) to determine whether the conflict is not resolved due to insufficient path priority processing rules in the control logic, thereby causing path deviation.

[0067] Relative deviation rate R logic (i, j) is calculated as:

[0068] Among them, T actual (i, j) is the actual execution time, T expected (i, j) is the theoretical expected execution time.

[0069] Based on the operating area where the faulty bucket wheel excavator is located, the impact of its displacement or short shutdown on the completion rate of the stockpiling task is predicted to include:

[0070] The stockpile area distribution model is constructed using the stockpile three-dimensional morphological modeling technology, and the operating task area and the unfinished task area of ​​the faulty bucket wheel excavator are mapped in the stockpile area distribution model, as follows:

[0071] 1. The three-dimensional morphological model of the stockpile area can be constructed by the following steps: use a laser radar, a millimeter-wave radar or a three-dimensional imaging device to scan the stockpile area and obtain point cloud data of the stockpile surface. The point cloud data records the three-dimensional coordinate information (x, y, z) of the stockpile area, where x and y represent the horizontal position and z represents the height of the stockpile; based on the point cloud data, a three-dimensional model of the stockpile surface is constructed using triangulation or voxelization technology. The three-dimensional morphological model can be expressed as a stockpile height distribution function h(x, y), where

[0072] h(x, y) = the height of the pile at position (x, y); Model gridding: discretize the three-dimensional pile model into gridded areas, and each grid unit records the height and density information of the pile to form a pile area distribution model G: G = {g i，j |g i，j =h(x i ,y j )}, where g i，j is the height of the pile in grid i, j.

[0073] 2. Mapping of the operating area of ​​the faulty bucket wheel machine The operating radius and boom angle range of the bucket wheel machine are used to determine the coverage area of ​​its normal operation: the operating area of ​​the bucket wheel machine is represented by the arc area R swept by the bucket wheel machine boom. operation :

[0074] in,

[0075] r min , r max is the minimum and maximum operating radius of the bucket wheel excavator, θ start ,θ end is the rotation angle range of the bucket wheel machine boom, which is the working area R of the bucket wheel machine. operation Mapped to the stockpile distribution model G, marking the grid cells g covered by the bucket wheel machine i，j :

[0076] G operation = {g i，j ∈G|(x i ,y j )∈R operation}, G operation A set of stockpile grids representing the bucket wheel machine operation area; and the unfinished task area is also mapped in the stockpile area distribution model.

[0077] Calculate the proportion of uncovered area after the faulty bucket wheel machine is offset: Among them, C coverage is the coverage rate, which indicates the proportion of the area that is not completed by the faulty bucket wheel machine in the stacking task, A uncovered is the unfinished area of ​​the stockpile area after the failure, A total is the total area of ​​the stockpile area; combined with the task priority and stockpile form (density, stability), the degree of decrease in the stockpile task completion rate is calculated:

[0078] R completion =R baseline -C coverage ·W priority , where R completion is the adjusted task completion rate, R baseline is the task completion rate in the fault-free state, W priority It is the priority weight of the stockpile area, which is used to dynamically adjust the task impact of important areas; by quantifying the impact of unfinished areas of the stockpile task on the overall task completion rate, and introducing regional priority weights, it more accurately reflects the dynamic changes in the task completion rate: CcoVerage measures the proportion of unfinished areas caused by faulty bucket wheel excavators offset or short shutdowns, which directly reflects the degree of insufficient coverage of the stockpile area. Wpriority is used as a priority weight to amplify the impact of high-priority areas on the task completion rate, ensuring that the impact of unfinished key areas is more obvious.

[0079] The evaluation results in the following synergistic efficiency reductions:

[0080] Analyze the path intersections (i.e. path conflict points) and area overlaps between the faulty bucket wheel excavator and other bucket wheel excavators: Detect the path intersections of multiple bucket wheel excavators through bucket wheel excavator operation path planning data and stockpile area distribution model to determine the risk of path conflict:

[0081] Among them, N conflict is the number of path conflict points, P i is the i-th path point, R overlap is the overlapping area between the faulty equipment path and other equipment paths. The overlapping area is the set of path intersections. Traverse each path point Pi on the faulty bucket wheel machine path to determine whether it belongs to the overlapping area r overlap , represents the indicator function, when P i ∈R overlap When , the value is 1, otherwise it is 0, m represents the total number of path points, and i represents the path point index;

[0082] Analyze the overlapping areas and path conflict points to assess the impact of decreased coordination efficiency:

[0083] E efficiency =E baseline -α·A overlap ·N conflict , where E efficiency is the adjusted synergy efficiency, E baseline is the coordination efficiency under fault-free conditions, A overlap is the overlapping area between the faulty bucket wheel machine and other equipment, and α is the efficiency reduction weight factor of path conflict.

[0084] The collaborative task failure impact assessment module also includes a comprehensive assessment of the reduction in task completion rate and the impact on collaborative efficiency;

[0085] Reduce the task completion rate by R completion and the synergistic efficiency decreases E efficiency Perform weighted combination to evaluate the impact of the overall collaborative task: I impact =w1·(1-R completion )+w2·(1-E efficiency ), where I impactis the total score of the dynamic impact of collaborative tasks, ranging from [0, 1]. The higher the value, the greater the impact of the fault on the collaborative task. w1 and w2 are the weight factors of task completion rate and collaborative efficiency, respectively, satisfying w1+w2=1, and dynamically adjusted according to task priority. This total score of the dynamic impact of collaborative tasks is represented as an overall collaborative impact for managers to view the impact level.

[0086] In the collaborative task dynamic adjustment module, a scoring threshold is set, which is set to 0.7. The total score of the collaborative task dynamic impact is compared with the scoring threshold to determine whether to perform the shutdown operation of the faulty bucket wheel machine;

[0087] If the total score of the dynamic impact of the collaborative task is greater than or equal to the scoring threshold, it indicates that the fault has a great impact, and the faulty bucket wheel machine needs to be shut down to avoid further affecting the task and collaborative efficiency, and other healthy bucket wheels are called to replace the faulty bucket wheel machine;

[0088] If the total score of the dynamic impact of the collaborative task is less than the scoring threshold, it indicates that the impact of the fault is small and controllable. The faulty bucket wheel machine continues to operate and complete the remaining tasks, and other healthy bucket wheel machines are called to operate in the unoperated area caused by the offset of the faulty bucket wheel machine.

[0089] It also includes task redistribution after a faulty bucket wheel machine stops: if a faulty bucket wheel machine stops, tasks are allocated according to the current load status of healthy bucket wheel machines, and they are sorted from high to low according to the current load status, with the healthy bucket wheel machine with the lowest current load status being given priority to complete the work of the faulty bucket wheel machine.

[0090] The current load condition is calculated as: Among them, L v is the current load condition of the vth healthy bucket wheel machine. The higher the value, the heavier the bucket wheel machine load. current is the task volume currently being performed by the vth bucket wheel machine, and the currently assigned task volume is obtained from the control unit of the bucket wheel machine. max is the maximum load-bearing capacity of the vth bucket wheel machine, which is a pre-set upper capacity limit and can be obtained based on equipment performance or historical records.

[0091] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0092] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An online fault diagnosis system for unmanned collaborative operation of bucket wheel excavators, characterized in that: include: Single machine path and instruction anomaly detection module: used to diagnose path deviation and instruction loss for the operating status of a single bucket wheel machine, including: Based on the path deviation between the current path data of the bucket wheel machine and the preset path, the path deviation of the single machine is identified; Monitor the control unit's command response time and execution result feedback time, and detect command loss failures; Single machine fault root cause analysis module: Root cause analysis of detected single machine path deviation and instruction loss faults is performed, including: Trace the execution records of the control logic and path planning algorithm of the control unit to determine the control logic defects that cause path deviation and command loss failures; Collaborative task failure impact assessment module: assesses the dynamic impact of a single machine failure on the collaborative task of multiple bucket wheel machines, including: Based on the operating area where the faulty bucket wheel excavator is located, predict the impact of its displacement or short shutdown on the completion rate of the stockpiling task; Analyze the intersection points between the faulty bucket wheel machine and other equipment to form overlapping areas and evaluate the resulting decrease in collaborative efficiency; Collaborative task dynamic adjustment module: optimizes the collaborative task allocation of multiple bucket wheel machines based on the single machine failure impact assessment results, and reallocates the operating tasks of the faulty bucket wheel machine to the healthy bucket wheel machine.

2. The online fault diagnosis system for unmanned collaborative operation of bucket wheel excavators according to claim 1 is characterized in that: The identifying of the single-machine path deviation includes calculating the path deviation between the current path data and the preset path through a path matching algorithm, setting a deviation threshold, and when the deviation exceeds the deviation threshold, outputting a path deviation diagnosis result and determining the deviation direction.

3. The online fault diagnosis system for unmanned collaborative operation of bucket wheel excavators according to claim 2 is characterized in that: The detection of instruction loss failure includes real-time monitoring of the instruction input and execution results of the control unit, recording the instruction issuance time and the execution result feedback time through timestamps, calculating the instruction response time, and triggering detection when the instruction response time exceeds a preset threshold or there is no feedback. The detection includes judging whether the instruction execution is successful by comparing the task execution result with the expected effect of the instruction.

4. The online fault diagnosis system for unmanned collaborative operation of bucket wheel excavators according to claim 1 is characterized in that: The execution record of the control logic of the control unit and the path planning algorithm are traced to determine the control logic defects that cause the path deviation and instruction loss faults, which specifically include: Control logic execution record traceability analysis: Extract the instruction log of the control unit when the fault occurs, including input instructions, algorithm call process and feedback output results, parse the instruction stream in chronological order, reconstruct the control logic execution link, including instruction distribution nodes, path planning nodes and feedback confirmation nodes, compare the current execution link with the preset logic template link, identify the logic deviation point, build the control logic abnormality association matrix, and calculate the relative deviation rate R of the execution deviation between each node logic (i, j), represents the relative deviation rate of execution time between node i and node j. When R logic When (i, j) exceeds the preset threshold, it is determined that there are abnormal nodes in the control logic and the relevant defect points are marked; Path planning algorithm traceability analysis: Call the execution record of path planning, including the material stacking shape data, task allocation parameters and path generation results, compare the theoretical output of the path planning algorithm with the actual execution path, and calculate the path deviation rate: Among them, P deviation is the path deviation rate, which indicates the degree of deviation between the actual path and the planned path, ΔP actual is the offset distance between actual path points, ΔP expected It is the expected distance between theoretical path points, and determines whether the path deviation is caused by unresolved conflicts due to insufficient path priority processing rules in the control logic.

5. The online fault diagnosis system for unmanned collaborative operation of bucket wheel excavators according to claim 4 is characterized in that: The relative deviation rate R logic (i, j) is calculated as: Among them, T actual (i, j) is the actual execution time, T expected (i, j) is the theoretical expected execution time.

6. The online fault diagnosis system for unmanned collaborative operation of bucket wheel excavators according to claim 1 is characterized in that: The prediction of the impact of the displacement or short shutdown of the faulty bucket wheel machine on the completion rate of the stockpiling task based on the operating area of ​​the faulty bucket wheel machine includes: The stockpile area distribution model is constructed using the stockpile 3D morphology modeling technology, and the operating task area and the unfinished task area of ​​the faulty bucket wheel excavator are mapped in the stockpile area distribution model; Calculate the proportion of uncovered area after the faulty bucket wheel machine is offset: Among them, C coverage is the coverage rate, which indicates the proportion of the area that is not completed by the faulty bucket wheel machine in the stacking task, A uncovered is the unfinished area of ​​the stockpile area after the failure, A total is the total area of ​​the stockpiling area; combined with the task priority and stockpiling form, the degree of decrease in the stockpiling task completion rate is calculated: R completion =R baseline -C coverage ·W priority , where R completion is the adjusted task completion rate, R baseline is the task completion rate in the fault-free state, W priority is the stockpile area priority weight.

7. The online fault diagnosis system for unmanned collaborative operation of bucket wheel excavators according to claim 6 is characterized in that: The synergistic efficiency reductions that will be triggered by the assessment include: Analyze the path intersections and area overlaps between the faulty bucket wheel excavator and other bucket wheel excavators: Detect the path intersections of multiple bucket wheel excavators through bucket wheel excavator operation path planning data and stockpile area distribution model to determine the risk of path conflict: Among them, N conflict is the number of path conflict points, P i is the i-th path point, R overlap It is the overlapping area between the faulty equipment path and other equipment paths. The overlapping area is the set of path intersections. Traverse each path point Pi on the faulty bucket wheel machine path to determine whether it belongs to the overlapping area R overlap , represents the indicator function, when P i ∈R overlap When , the value is 1, otherwise it is 0, m represents the total number of path points, i represents the path point index; Analyze the overlapping areas and path conflict points to assess the impact of decreased collaborative efficiency: E efficiency =E baseline -α·A overlap ·N conflict , where E efficiency is the adjusted synergy efficiency, E baseline is the coordination efficiency under fault-free conditions, A overlap is the overlapping area between the faulty bucket wheel machine and other equipment, and α is the efficiency reduction weight factor of path conflict.

8. The online fault diagnosis system for unmanned collaborative operation of bucket wheel excavators according to claim 7 is characterized in that: The collaborative task failure impact assessment module also includes a comprehensive assessment of the task completion rate reduction and collaborative efficiency impact; Reduce the task completion rate by R completion and the synergistic efficiency decreases E efficiency Perform weighted combination to evaluate the impact of the overall collaborative task: I impact =w1·(1-R completion )+w2·(1-E efficiency ), where I impact is the total score of the dynamic impact of collaborative tasks, ranging from [0,1]. The higher the value, the greater the impact of the fault on the collaborative task. w1 and w2 are the weight factors of task completion rate and collaborative efficiency, respectively, satisfying w1+w2=1.

9. The online fault diagnosis system for unmanned collaborative operation of bucket wheel excavators according to claim 8 is characterized in that: The collaborative task dynamic adjustment module includes setting a scoring threshold, which is set to 0.7, comparing the total score of the collaborative task dynamic impact with the scoring threshold to determine whether to perform a shutdown operation on the faulty bucket wheel machine; If the total score of the dynamic impact of the collaborative task is greater than or equal to the scoring threshold, it indicates that the fault has a great impact and the faulty bucket wheel machine needs to be shut down and other healthy bucket wheel machines are called to replace the faulty bucket wheel machine; If the total score of the dynamic impact of the collaborative task is less than the scoring threshold, it indicates that the impact of the fault is small and controllable. The faulty bucket wheel machine continues to operate and complete the remaining tasks, and other healthy bucket wheel machines are called to operate in the unoperated area caused by the offset of the faulty bucket wheel machine.

10. The online fault diagnosis system for unmanned cooperative operation of bucket wheel excavators according to claim 9, characterized in that: It also includes task redistribution after a faulty bucket wheel machine stops: if a faulty bucket wheel machine stops, tasks are allocated according to the current load status of healthy bucket wheel machines, and they are sorted from high to low according to the current load status, with the healthy bucket wheel machine with the lowest current load status being given priority to complete the work of the faulty bucket wheel machine.

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