Numerical control machine tool production resource collaborative management method and system

Abstract

The invention discloses a numerical control machine tool production resource collaborative management method and system, and belongs to the technical field of production resource scheduling, and the method comprises the steps: 1, decomposing a production order into atomic manufacturing units which have standardized sizes determined based on the machining capability of a numerical control machine tool and carry priority labels and resource dependence identifiers; 2, constructing a dynamic state vector of the numerical control machine tool, wherein the dynamic state vector comprises an availability index, a load factor and an expected idle timestamp; step 3, when the availability index is lower than a preset threshold value, generating a resource redistribution event signal; and step 4, in response to the resource redistribution event signal, calculating a compatibility matrix. By constructing the dynamic state vector of the numerical control machine tool and establishing an event-driven resource redistribution mechanism, the availability change of the machine tool can be sensed in real time, scheduling adjustment can be quickly triggered, a traditional manual intervention mode is replaced, the abnormal response time is effectively shortened, and the problems of production line blockage, order delivery delay and the like are solved.

Description

Technical Field

[0001] This invention relates to the field of production resource scheduling technology, and more specifically, to a method and system for collaborative management of CNC machine tool production resources. Background Technology

[0002] In current collaborative management systems for CNC machine tool production resources, many production planning systems rely on static scheduling models. Although these systems can comprehensively consider basic factors such as order delivery time and equipment capacity when formulating initial production plans, their operating logic is usually based on fixed rules and parameters. Once the plan is issued to the workshop execution level, the system lacks an effective mechanism to perceive and process the dynamic changes that occur in real time on the production site.

[0003] The fundamental problem lies in the fact that the upper-level system responsible for planning is often disconnected from the lower-level system that monitors the status of workshop resources. The resource status data used by the planning system is usually a lagging or periodic snapshot, rather than real-time feedback. This information gap leads to contradictions. When critical CNC machine tools malfunction, machining tools break unexpectedly, or high-priority orders that require urgent handling are encountered, changes in resource status cannot be immediately and structurally transmitted to the planning decision-making level. As a result, the decision-making system cannot quickly and automatically adjust the matching relationship between tasks and resources according to new constraints, resulting in a lack of effective linkage between dynamic changes in resource status and task allocation optimization.

[0004] This lack of a mechanism directly leads to the system's passive response to emergencies. Planners can only rely on manual intervention, from perceiving the problem to analyzing its impact and then manually adjusting the schedule. The whole process is time-consuming and cumbersome. Manual decision-making is difficult to optimize multiple objectives and the overall situation in a short period of time. The resulting adjustment plan is often suboptimal and may even trigger new resource conflicts. This not only leads to production line blockage and production rhythm disorder, but may also cause a series of problems such as order delivery delays, reduced equipment utilization, and insufficient production flexibility and responsiveness. Most existing technical solutions focus on local optimization of static algorithms and fail to establish an event-driven dynamic collaboration and real-time re-optimization mechanism at the system architecture level. Summary of the Invention

[0005] To address the problems existing in the prior art, the purpose of this invention is to provide a collaborative management method and system for CNC machine tool production resources. This system can realize the construction of dynamic state vectors for CNC machine tools and the establishment of an event-driven resource reallocation mechanism. It can detect changes in machine tool availability in real time and quickly trigger scheduling adjustments, replacing the traditional manual intervention mode. This effectively shortens the abnormal response time and reduces problems such as production line blockage and order delivery delays.

[0006] To solve the above problems, the present invention adopts the following technical solution:

[0007] The first aspect is the collaborative management method for CNC machine tool production resources, including:

[0008] Step 1: Decompose the production order into atomic manufacturing units. Each atomic manufacturing unit has a standardized size determined based on the machining capabilities of the CNC machine tool and carries a priority tag and a resource dependency identifier.

[0009] Step 2: Construct the dynamic state vector of the CNC machine tool. The dynamic state vector includes the availability index, load factor, and expected idle timestamp.

[0010] Step 3: When the availability index is lower than a preset threshold, a resource reallocation event signal is generated;

[0011] Step 4: In response to the resource reallocation event signal, calculate the compatibility matrix. The elements of the compatibility matrix are based on the weighted result of the load factor and priority label, representing the expected execution efficiency of the atomic manufacturing cell on the CNC machine tool.

[0012] Step 5: Based on the compatibility matrix, use a greedy selection strategy to allocate atomic manufacturing units and update the task scheduling table;

[0013] Step 6: Monitor the execution effect after the atomic manufacturing unit is allocated, measure the task completion time and resource utilization rate, and dynamically adjust the preset threshold based on the measurement results;

[0014] Step 7: Store adjustment instance and compatibility matrix data to build an experience base. The experience base stores event patterns and allocation preferences. When a new resource reallocation event signal is generated, similar event patterns are retrieved from the experience base through pattern matching, and atomic manufacturing units are pre-allocated based on allocation preferences.

[0015] Furthermore, production orders are broken down into atomic manufacturing units, each with standardized dimensions determined by the machining capabilities of CNC machine tools, and carrying priority tags and resource dependency identifiers, including:

[0016] Step 11: Extract the geometric feature vector and process parameter set of the production order to generate the order feature process association diagram;

[0017] Step 12: Construct a processing capability matrix, calculate the processability index of feature blocks in the order feature process association graph, and generate a processability score list;

[0018] Step 13: Apply a dynamic clustering algorithm to group the feature blocks in the manufacturability score list to generate a set of atomic manufacturing units. Each atomic manufacturing unit is accompanied by a geometric bounding box and a process parameter package.

[0019] Step 14: Perform critical path analysis on the set of atomic manufacturing units, assign priority labels, and parse the process parameter package to generate resource dependency identifiers, thus obtaining the complete set of atomic manufacturing units with labels and identifiers.

[0020] Furthermore, a dynamic state vector for the CNC machine tool is constructed. This dynamic state vector includes an availability index, a load factor, and an expected idle timestamp, including:

[0021] Step 21: Collect real-time operation sequences, compare real-time operation sequences with historical baseline sequences, identify abnormal operation intervals and mark the abnormality type and occurrence timestamp, and generate operation sequences marked with abnormalities;

[0022] Step 22: Extract the frequency and duration of anomalies from the marked anomaly operation sequences, calculate the performance deviation index, parse the resource requirement descriptors of tasks in the current task queue, and aggregate to generate load factors;

[0023] Step 23: Based on the performance deviation index and load factor, simulate the execution timeline of the task queue, analyze the resource gaps in the timeline, predict the expected idle timestamp, and generate a dynamic state vector containing the availability index, load factor, and expected idle timestamp.

[0024] Furthermore, when the availability index falls below a preset threshold, a resource reallocation event signal is generated, including:

[0025] Step 31: Analyze the historical trajectory of the availability index, calculate the second derivative of the availability index change, and dynamically adjust the preset threshold based on the predicted trend.

[0026] Step 32: When the availability index is lower than the dynamically adjusted trigger threshold, assess the rate of decline, duration and scope of impact of the index, classify the event and label its urgency value, and generate an event descriptor with type label and urgency value.

[0027] Step 33: Encode the event type, urgency value and trigger timestamp in the event descriptor into a structured event signal, assign processing priority to the event signal according to the urgency value, and generate a resource reallocation event signal with priority tag.

[0028] Furthermore, in response to a resource reallocation event signal, a compatibility matrix is ​​calculated. The elements of the compatibility matrix, weighted based on load factors and priority labels, represent the expected execution efficiency of the atomic manufacturing cell on the CNC machine tool, including:

[0029] Step 41: Analyze the priority marker and event type in the resource reallocation event signal, filter CNC machine tools according to the event type to generate a candidate machine tool set, and determine the screening strictness based on the priority marker;

[0030] Step 42: Extract the process parameter package and geometric bounding box of the atomic manufacturing unit, match them with the processing capabilities of the candidate machine tool set, verify the process constraints and real-time state constraints, filter out machine tool unit combinations that do not meet the constraints, and generate a feasible allocation combination set.

[0031] Step 43: Based on the feasible allocation combination set, dynamically calculate the weighting coefficients of the load factor and priority label, adjust the weighting coefficients according to the urgency of the event, and construct a compatibility matrix based on the dynamically weighted efficiency value.

[0032] Furthermore, based on the compatibility matrix, a greedy selection strategy is used to allocate atomic manufacturing units and update the task scheduler, including:

[0033] Step 51: Traverse each row of the compatibility matrix, select the CNC machine tool with the highest efficiency value for each atomic manufacturing unit, establish an initial allocation mapping table, and record the number of atomic manufacturing units allocated to each CNC machine tool and its cumulative efficiency value.

[0034] Step 52: Based on the initial allocation mapping table, detect time conflicts in multiple atomic manufacturing units being assigned to the same CNC machine tool, reallocate conflicting units according to the priority of atomic manufacturing units, dynamically correct the efficiency values ​​at the corresponding positions in the compatibility matrix, and generate an optimized allocation scheme.

[0035] Step 53: Based on the optimized allocation scheme, generate an execution sequence for each atomic manufacturing unit set on the CNC machine tool, and update the task scheduling table according to the generated sequence.

[0036] Furthermore, the execution effect of the atomic manufacturing units after allocation is monitored, task completion time and resource utilization are measured, and preset thresholds are dynamically adjusted based on the measurement results, including:

[0037] Step 61: Collect the actual start time, end time and resource consumption records of the atomic manufacturing unit, identify abnormal intervals in the execution records and mark and clean them to generate a cleaned set of execution records;

[0038] Step 62: Calculate the deviation between the actual completion time and the expected completion time, calculate the deviation rate between the actual resource consumption and the expected consumption, and combine the time deviation and the resource deviation rate into an effect evaluation vector.

[0039] Step 63: Establish a threshold adjustment calculation model, calculate the threshold adjustment amount, verify the impact of the adjusted threshold on the event triggering frequency, and generate the verified threshold adjustment amount.

[0040] Step 64: Update the preset threshold using the verified threshold adjustment amount, monitor the event triggering stability under the new threshold, and activate the damping adjustment mechanism when threshold oscillation is detected to generate a stable threshold set.

[0041] Furthermore, the system stores adjustment instance and compatibility matrix data to construct an experience base. This experience base stores event patterns and optimal allocation preferences. When a new resource reallocation event signal is generated, similar event patterns are retrieved from the experience base through pattern matching, and atomic manufacturing units are pre-allocated based on the optimal allocation preferences, including:

[0042] Step 71: Standardize the threshold adjustment amount and compatibility matrix data, extract event type feature vectors, resource allocation feature vectors and execution effect evaluation vectors, and generate a standardized empirical data feature set;

[0043] Step 72: Analyze the correlation between event type feature vector and resource allocation feature vector, mine event allocation patterns, assign confidence weights to patterns based on frequency of occurrence and execution effect, and build an event allocation pattern library.

[0044] Furthermore, it also includes:

[0045] Step 73: When a new resource reallocation event signal is generated, extract the real-time event feature vector, calculate the similarity with the historical patterns in the event allocation pattern library, and screen the set of matching historical patterns based on a comprehensive evaluation of confidence weight and feature similarity.

[0046] Step 74: Generate a pre-allocation scheme for atomic manufacturing units based on allocation preferences in the matching historical pattern set, predict resource conflicts and efficiency bottlenecks, and generate a pre-allocation scheme.

[0047] Secondly, the CNC machine tool production resource collaborative management system includes:

[0048] The order decomposition module is used to decompose production orders into atomic manufacturing units. Each atomic manufacturing unit has standardized dimensions determined based on the machining capabilities of CNC machine tools and carries priority tags and resource dependency identifiers.

[0049] The state vector module is used to construct the dynamic state vector of the CNC machine tool. The dynamic state vector includes the availability index, load factor and expected idle timestamp.

[0050] The signal generation module generates a resource reallocation event signal when the availability index falls below a preset threshold.

[0051] The matrix calculation module is used to calculate the compatibility matrix in response to resource reallocation event signals. The elements of the compatibility matrix are based on the weighted result of load factor and priority label, representing the expected execution efficiency of the atomic manufacturing cell on the CNC machine tool.

[0052] The task allocation module, based on the compatibility matrix, uses a greedy selection strategy to allocate atomic manufacturing units and update the task scheduling table;

[0053] The effect adjustment module is used to monitor the execution effect after the atomic manufacturing unit is allocated, measure the task completion time and resource utilization, and dynamically adjust the preset threshold based on the measurement results.

[0054] The experience building module stores adjustment instances and compatibility matrix data to build an experience library. The experience library stores event patterns and allocation preferences. When a new resource reallocation event signal is generated, similar event patterns are retrieved from the experience library through pattern matching, and atomic manufacturing units are pre-allocated based on allocation preferences.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] (1) This solution breaks down the information gap between the production planning layer and the workshop execution layer by constructing a dynamic state vector of CNC machine tools and establishing an event-driven resource reallocation mechanism. It can perceive changes in machine tool availability in real time and quickly trigger scheduling adjustments, replacing the traditional manual intervention mode, effectively shortening the abnormal response time, and reducing problems such as production line blockage and order delivery delays.

[0057] (2) This solution breaks down production orders into standardized atomic manufacturing units, combines load factors and priority weighting to generate a compatibility matrix, optimizes allocation through a greedy strategy, accurately matches units and machine tool resources, greatly improves equipment utilization, avoids resource conflicts, and achieves efficient matching between production tasks and machine tool processing capabilities.

[0058] (3) This scheme constructs an experience base containing event patterns and allocation preferences. When a new resource reallocation event is triggered, similar historical cases are retrieved through pattern matching to quickly generate a pre-allocation scheme, reducing redundant calculations and decision-making time. At the same time, resource conflicts are predicted, reducing the probability of suboptimal decisions and improving scheduling efficiency.

[0059] (4) This scheme dynamically adjusts the preset threshold based on the task execution effect, and uses the damping adjustment mechanism to avoid threshold oscillation, so that the triggering frequency of resource redistribution events is kept within a reasonable range, which not only ensures that equipment abnormalities are responded to in a timely manner, but also prevents meaningless frequent scheduling, stabilizes the production rhythm, and improves the reliability of system operation. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0061] Figure 1 This is a flowchart of the CNC machine tool production resource collaborative management method of the present invention;

[0062] Figure 2 This is a flowchart illustrating the relationships between the various modules in the CNC machine tool production resource collaborative management system of the present invention. Detailed Implementation

[0063] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0064] Example 1:

[0065] Please see Figure 1 The collaborative management method for CNC machine tool production resources includes the following operational steps:

[0066] Step 1: Decompose the production order into atomic manufacturing units. Each atomic manufacturing unit has standardized dimensions determined based on the machining capabilities of the CNC machine tool, carries priority tags and resource dependency identifiers, and also performs the following operations:

[0067] Step 11: Extract the geometric feature vector and process parameter set of the production order to generate the order feature process association diagram. The specific operations are as follows:

[0068] First, geometric features are extracted from the workpieces to be processed for each production order, covering the geometric attributes of the workpieces, including but not limited to the dimensional accuracy of the machined surface, contour shape, surface curvature, and hole coordinates. These discrete geometric parameters are mapped into geometric feature vectors in a high-dimensional Euclidean space. Each dimension of this vector uniquely corresponds to a geometric feature parameter, which can fully characterize the geometric machining requirements of the workpiece. Simultaneously, all process execution parameters required for the production order are identified, covering key process indicators such as cutting speed, feed rate, depth of machining, tool type and life requirements, and cooling method, forming a structured set of process parameters. Based on the geometric feature vectors and the set of process parameters, an order feature process association graph is constructed. The order feature process association graph uses the nodes of each dimension of the geometric feature vectors and the nodes of each parameter in the set of process parameters as its topological foundation. The edge weights between nodes represent the correlation strength between geometric features and process parameters. The weight values ​​are determined based on the statistical analysis results of historical machining data in the process knowledge base. For example, the correlation weight between the machining requirements of a certain high-precision plane and specific cutting speed and feed rate parameters will be higher than that of other parameter combinations. The final generated association graph can intuitively and quantitatively reflect the inherent coupling relationship between the geometric machining requirements of the production order and the process execution conditions.

[0069] Step 12: Construct a processing capability matrix, calculate the processing index of feature blocks in the order feature process association graph, and generate a processing score list. The specific operations are as follows:

[0070] First, a machining capability matrix is ​​constructed. The rows of this matrix correspond to the geometric feature blocks to be evaluated, while the columns cover the machining capability indicators of the CNC machine tool, including key dimensions such as spindle speed range, machining accuracy level, tool compatibility type, feed system response speed, and machining stroke range. The element values ​​within the matrix represent the matching degree of the CNC machine tool's machining capability for that geometric feature block, with values ​​ranging from 0 to 1; higher values ​​indicate better matching. Based on the machining capability matrix, the machinability index for each geometric feature block is calculated using the following formula: ;

[0071] This formula originates from the mathematical concept of weighted summation of multi-dimensional indicators. Combined with the actual needs of process manufacturing, it quantifies the influence of each processing capability indicator on the processing realization of feature blocks through weight coefficients. These coefficients are then multiplied by the matching degree of each indicator and summed to obtain a quantitative value that comprehensively reflects the processability of the feature blocks. This represents the quantitative evaluation value of the manufacturability of the i-th geometric feature block. This represents the weight of the j-th CNC machine tool machining capability index in the machinability assessment. represents the matching degree value between the i-th geometric feature block and the j-th machining capability index, and n represents the total number of CNC machine tool machining capability indices participating in the evaluation. By calculating the machinability index for each feature block in the order feature process association diagram, the unique identifier of each feature block is associated with the corresponding index value to generate an ordered list of machinability scores.

[0072] Step 13: Apply a dynamic clustering algorithm to group the feature blocks in the manufacturability score list to generate a set of atomic manufacturing units. Each atomic manufacturing unit is accompanied by a geometric bounding box and a process parameter package. The specific operations are as follows:

[0073] A dynamic clustering algorithm is introduced based on the index values ​​in the manufacturability score list to group all feature blocks. This algorithm differs from traditional static clustering methods in that its cluster centers adaptively adjust according to the real-time changes in the feature block score distribution. The cluster radius is determined based on the machining capability threshold of the CNC machine tool; that is, feature blocks within the same cluster can be completed in the same type of machining operation on the same CNC machine tool, and the adjustment range of machining parameters is within the allowable process range of the machine tool. During the clustering process, the algorithm uses the manufacturability index as the clustering dimension, while also considering the geometric similarity of feature blocks, such as size range and shape type, as well as the similarity of process parameters, such as cutting parameter range and tool type requirements. This ensures that feature blocks within the same cluster have homogeneity in machining technology. After clustering, each cluster result constitutes an atomic manufacturing unit. Standardized dimensions based on the CNC machine tool's machining capabilities are determined for this unit. These dimensions comprehensively consider hardware constraints such as the machine tool's machining stroke, fixture adaptation range, and worktable load-bearing capacity to ensure that the unit's machining range does not exceed the machine tool's physical machining boundaries. Simultaneously, a geometric boundary box and a process parameter package are encapsulated for each atomic manufacturing unit. The geometric boundary box is the minimum spatial boundary obtained by enveloping the geometric dimensions of all feature blocks within the unit, which can completely cover all geometric machining areas of the unit. The process parameter package integrates the process parameter sets corresponding to all feature blocks within the unit and optimizes and integrates them based on process compatibility rules to eliminate parameter conflicts, forming unified process execution parameters for the atomic manufacturing unit. The final generated set of atomic manufacturing units has standardized and directly schedulable characteristics, which can support subsequent resource allocation processes.

[0074] Step 14: Perform critical path analysis on the set of atomic manufacturing units, assign priority labels, and parse the process parameter package to generate resource dependency identifiers, obtaining the complete set of atomic manufacturing units with labels and identifiers. The specific operations are as follows:

[0075] First, critical path analysis is performed on the set of atomic manufacturing units. This analysis is based on the process sequence constraints between the atomic manufacturing units. For example, the completion of one unit is a prerequisite for starting another unit. By drawing a process network diagram at the unit level, the critical path that determines the entire production order delivery cycle is identified. Atomic manufacturing units located on the critical path are assigned higher basic priority scores. Non-critical path units are assigned corresponding scores based on their relevance to the critical path, such as whether they are immediate predecessors or successors of the critical path. On this basis, the basic priority scores are adjusted in conjunction with external constraints such as the overall order delivery time requirement, customer priority, and urgency. Finally, a unique priority label is assigned to each atomic manufacturing unit. This label represents the unit's priority in the scheduling process in numerical form. Prioritize execution levels, then parse the process parameter package of each atomic manufacturing unit to extract the resource requirement information involved, including the model and specifications of the required CNC machine tools, the type and quantity of cutting tools, the model of the fixture, the type of machining auxiliary materials, and the spindle power requirements. Encode this information into structured resource dependency identifiers. These identifiers can accurately characterize the dependencies of the atomic manufacturing unit on various production resources during execution. For example, the identifiers will clearly indicate key resource attributes such as the spindle speed range and tool material requirements required for the unit's machining. By completing priority label assignment and resource dependency identifier parsing for all atomic manufacturing units, a complete set of atomic manufacturing units with complete labels and identifiers is finally obtained. This complete set has all the attributes required to participate in subsequent dynamic resource allocation and can directly support compatibility matching and scheduling optimization in resource reallocation events.

[0076] In a preferred embodiment of the present invention, step 2 is further included: constructing a dynamic state vector of the CNC machine tool, wherein the dynamic state vector includes an availability index, a load factor, and an expected idle timestamp, and the following operations are also performed:

[0077] Step 21: Collect real-time operation sequences, compare real-time operation sequences with historical baseline sequences, identify abnormal operation intervals and mark the abnormality type and occurrence timestamp, and generate operation sequences marked with abnormalities. The specific operations are as follows:

[0078] Specifically, real-time operation sequences of various functional modules of the machine tool are continuously collected at a frequency synchronized with the sampling frequency of the CNC machine tool control system. These sequences encompass timing data of key operating parameters such as spindle speed, feed rate, spindle current, tool position, cooling system flow rate, and hydraulic system pressure, providing a complete and continuous reflection of the machine tool's real-time operating status. Subsequently, historical baseline sequences of the CNC machine tool under similar machining tasks and without abnormal operating conditions are retrieved. These baseline sequences are obtained by statistical filtering, trend fitting, and noise reduction of a large amount of normal operating data, serving as a reference standard for determining whether operations are abnormal. The real-time operation sequence is compared with the historical baseline sequence dimension by dimension. The dynamic time warping algorithm is used to eliminate the differences between the two in terms of sampling point offset and time length, and accurately identify the operation intervals in the real-time sequence that deviate from the reasonable range of the baseline. For these abnormal operation intervals, the anomaly type is determined by combining the process knowledge base and the fault diagnosis rule base, such as abnormal spindle speed fluctuation, abnormal feed resistance caused by tool wear, and abnormal cooling system flow. The precise timestamp of the anomaly is recorded. Finally, the anomaly type, occurrence timestamp and original real-time operation sequence are associated and marked to generate an operation sequence marked with complete anomaly information.

[0079] Step 22: Extract the frequency and duration of anomalies from the marked anomaly operation sequences, calculate the performance deviation index, parse the resource requirement descriptors of tasks in the current task queue, and aggregate to generate the load factor. The specific operations are as follows:

[0080] First, core anomaly features are extracted from the marked abnormal operation sequences, including anomaly frequency and anomaly duration. Anomaly frequency is the number of times an abnormal operation interval occurs per unit time, and anomaly duration is the sum of the time lengths from the start to the end of each abnormal interval. Based on these features, a performance deviation index is calculated using the following formula: ;

[0081] This formula is based on the linear coupling relationship between machine tool performance deviation and the frequency and duration of anomalies. The more frequent the anomalies and the longer the duration of each anomaly, the more significant the deviation between the actual and nominal performance of the machine tool. By introducing weighting coefficients to balance the influence of the two types of anomaly characteristics on performance deviation, it is ultimately abstracted into a linear weighted sum mathematical form. Represents the performance deviation index; is the weighting coefficient for the abnormal frequency, representing the degree of influence of the abnormal frequency on performance deviation; f is the frequency of occurrence of abnormal operation intervals per unit time; The weighting coefficient for the duration of the anomaly represents the degree of influence of the anomaly duration on the performance deviation. The cumulative duration of abnormal operation intervals within a unit of time is used. Simultaneously, the system parses the resource requirement descriptors of all pending tasks in the current task queue. These descriptors cover the specific resource requirements of each task for machine tool spindle load, tool occupancy time, machining energy consumption, and travel range. After normalizing the requirements of each dimension, the system aggregates them by resource type to generate a load factor that can comprehensively characterize the current task load status of the machine tool. The value of this factor ranges from 0 to 1, with a higher value indicating a higher current resource occupancy rate of the machine tool. The performance deviation index and the load factor together constitute the quantitative basis for evaluating the availability of the machine tool.

[0082] Step 23: Based on the performance deviation index and load factor, simulate the execution timeline of the task queue, analyze the resource gaps in the timeline, predict the expected idle timestamp, and generate a dynamic state vector containing the availability index, load factor, and expected idle timestamp. The specific operations are as follows:

[0083] First, based on the calculated performance deviation index and load factor, a simulation model of machine tool task queue execution is constructed. This model takes the current task queue's task order, the theoretical processing time of each task, and resource requirements as inputs. It incorporates the performance deviation index's correction effect on the actual task execution time; the higher the performance deviation index, the greater the extension of the actual task execution time compared to the theoretical value. This simulates a task queue execution timeline that closely reflects the actual operating state of the machine tool. A refined analysis of this timeline identifies resource gaps where the machine tool has no tasks executing. The starting time of these gaps represents the machine tool's potential idle time. The system statistically analyzes these resource gaps. After excluding brief and unstable gaps, the expected idle timestamp with practical scheduling value is predicted by considering the duration and probability of occurrence of the idle timestamp. This timestamp is presented in an absolute time format and can accurately indicate the time node when the machine tool can take on a new task next. Based on this, the availability index is calculated by integrating the performance deviation index and the load factor. The availability index comprehensively reflects the machine tool's operational reliability and resource idleness. The higher the value, the more capable the machine tool is of taking on new tasks. Finally, the availability index, load factor, and expected idle timestamp are integrated into a structured dynamic state vector. This vector can characterize the operating status of the CNC machine tool in real time and comprehensively.

[0084] In a preferred embodiment of the present invention, step 3 is further included: when the availability index is lower than a preset threshold, a resource reallocation event signal is generated, wherein the following operations are also performed:

[0085] Step 31: Analyze the historical trajectory of the availability index, calculate the second derivative of the availability index change, and dynamically adjust the preset threshold based on the predicted trend. The specific operations are as follows:

[0086] First, the full historical time-series data of the availability index of the target CNC machine tool is retrieved. The data is preprocessed, including outlier removal, linear interpolation of missing values, and sliding window smoothing, to eliminate random noise and data acquisition errors, forming a continuous and smooth historical trajectory of the availability index. Based on this trajectory, the second derivative of the availability index over time is calculated using the following formula: ;

[0087] This formula originates from the mathematical quantification of the changing trend of the availability index. The first derivative only reflects the rate of change of the index, while the second derivative characterizes the increasing or decreasing trend of the rate of change. By calculating the ratio of the difference of the first derivative within adjacent time steps to the time step size, the curvature of the index change can be accurately quantified, thereby predicting whether the index is accelerating, decelerating, or trending towards stabilization. In the formula, The second time derivative of the availability index I; It is a time variable; The time step for calculating the difference; for The first derivative of the time availability exponent; for The first derivative at time step is used to determine the trend of the exponential change based on the sign and absolute value of the second derivative: if the second derivative is negative and the absolute value is large, it indicates that the exponential is decreasing rapidly, and the preset threshold needs to be lowered to trigger the resource reallocation event in advance; if the second derivative is positive, it indicates that the rate of decrease of the exponential is slowing down, and the threshold can be appropriately raised to avoid meaningless frequent triggering. Combined with the threshold adaptation data of machine tool historical fault handling, the preset threshold is dynamically adjusted based on the above predicted trend to ensure that the threshold always fits the actual change law of the availability index, ensuring the timeliness and rationality of event triggering.

[0088] Step 32: When the availability index falls below the dynamically adjusted trigger threshold, assess the rate of decline, duration, and scope of impact of the index, classify the events and assign urgency values, and generate event descriptors with type labels and urgency values. The specific operations are as follows:

[0089] When the availability index value is detected to be lower than the trigger threshold dynamically adjusted in step 31, the following characteristics of the index change are extracted: the rate of index decline is the ratio of the difference in the index within the preset time window before the threshold is triggered to the window duration, which intuitively reflects the speed of index deterioration; the duration is the cumulative time from the first time the index falls below the threshold to the current moment, reflecting the continuity of the abnormal state; the scope of impact is determined by comprehensively analyzing the number of atomic manufacturing units currently being carried by the machine tool, the delivery date requirements of related production orders, and the scheduling margin of alternative machine tools, quantifying the impact of the anomaly on the overall production plan. Based on these characteristics and referring to the preset event classification rule base, the following are classified: Events are categorized into different types, such as sudden drops in availability due to equipment failure, gradual declines due to task overload, and localized declines due to auxiliary system anomalies. Each type of event is assigned a unique type label. Simultaneously, an urgency value calculation model is constructed, which normalizes and weights the feature values ​​of the three dimensions of decline rate, duration, and impact range to generate an urgency value from 0 to 10. The higher the value, the more urgent the impact of the event on production and the higher the priority it needs to be addressed. Finally, the event type label, urgency value, trigger timestamp when the index first falls below the threshold, and the machine tool's unique identifier are integrated to generate a structured event descriptor containing complete event attributes.

[0090] Step 33: Encode the event type, urgency value, and trigger timestamp in the event descriptor into a structured event signal. Assign processing priorities to the event signals based on the urgency value, and generate resource reallocation event signals with priority tags. The specific operations are as follows:

[0091] First, the information in the event descriptor is encoded and converted according to the preset standardized encoding rules: the event type label is mapped to a fixed-length character code to ensure the unique identification of different types of events; the urgency value is converted into a numerical code according to a preset range to retain its quantitative characteristics; the trigger timestamp is converted into a high-precision time code, accurate to the millisecond level, and the machine tool identification code is added to locate abnormal equipment. This encoding process transforms unstructured event descriptors into structured event signals containing type, urgency, time, and machine tool identification codes. This ensures efficient transmission and parsing of signals between various functional modules of the system. Subsequently, based on the urgency code in the event signal, processing priorities are assigned to the event signal according to a preset priority mapping rule: urgency values ​​of 8 to 10 correspond to the highest priority, triggering the resource reallocation process first; 5 to 7 correspond to medium priority, processed according to the regular scheduling queue; and 0 to 4 correspond to low priority, included in the delayed processing queue. A priority flag field is added to the structured event signal, and the assigned priority level code is written into this field. Finally, a resource reallocation event signal with a priority flag is generated. This signal has both a clear processing priority and complete event attribute information, enabling precise triggering of subsequent compatibility matrix calculations and atomic manufacturing unit reallocation processes, ensuring that high-urgency events are processed first.

[0092] In a preferred embodiment of the present invention, step 4 is further included: in response to a resource reallocation event signal, a compatibility matrix is ​​calculated, wherein the elements of the compatibility matrix are weighted based on the load factor and priority label, representing the expected execution efficiency of the atomic manufacturing unit on the CNC machine tool, wherein the following operations are also performed:

[0093] Step 41: Parse the priority marker and event type in the resource reallocation event signal, filter CNC machine tools according to the event type to generate a candidate machine tool set, and determine the screening strictness based on the priority marker. The specific operations are as follows:

[0094] First, the resource reallocation event signals are structured and analyzed to extract two core pieces of information: priority markers and event types. Priority markers directly map the urgency level of the event, while event types clearly identify the root cause of the CNC machine tool availability index falling below the threshold, such as hardware failure, task overload, or auxiliary system malfunctions. Based on the analyzed event types, the processing capacity files and real-time status data of all CNC machine tools are retrieved to filter out machine tools capable of handling the current reassigned atomic manufacturing unit and matching the event cause. If the event type is a decrease in availability due to spindle failure, machine tools with spindle malfunctions of the same model are eliminated, and only those with spindles of the same type are retained. Machine tools with normal axis status and matching machining capabilities are selected. If the event type is task overload, the focus is on screening machine tools whose load margin meets the basic threshold. At the same time, the screening strictness is determined based on the priority marker in the event signal: high priority markers correspond to high screening strictness, and in addition to basic machining capability matching, additional indicators such as the expected idle timestamp and alternative scheduling margin of the machine tool need to be verified; medium and low priority markers appropriately reduce the strictness and relax some non-core constraints, such as moderately lowering the load margin threshold. Through the above screening logic, a candidate machine tool set is finally generated. This set not only meets the machine tool adaptation requirements corresponding to the event type, but also matches the screening criteria corresponding to the event priority.

[0095] Step 42: Extract the process parameter package and geometric bounding box of the atomic manufacturing unit, match them with the processing capabilities of the candidate machine tool set, verify the process constraints and real-time state constraints, filter out machine tool unit combinations that do not meet the constraints, and generate a feasible allocation combination set. The specific operations are as follows:

[0096] First, the core process and geometric attributes of the atomic manufacturing units to be reassigned are extracted: the process parameter package covers key process execution parameters such as cutting speed, feed rate, tool type and specifications, and cooling method required for unit machining; the geometric boundary box defines the spatial range required for unit machining through envelope calculation, including geometric constraints such as machining stroke range, fixture adaptation size, and table bearing boundary. For each machine tool in the candidate machine tool set, its machining capability matrix and real-time dynamic state vector are retrieved, and constraint verification is carried out in two layers: the first layer is process constraint verification, which compares the process parameter package of the atomic manufacturing unit with the process adaptation range of the machine tool one by one to ensure that the cutting speed required by the unit is within the machine tool's range. The first layer considers factors such as the allowable range of the machine spindle speed, compatibility of tool type with the machine tool magazine configuration, and machining accuracy requirements not exceeding the machine tool's nominal accuracy level. The second layer involves real-time state constraint verification, which combines the machine tool's load factor to determine whether the current resource occupancy status can accommodate the unit. Simultaneously, it analyzes whether the machine tool's time resources match the unit's processing time based on the expected idle timestamp to avoid time conflicts. For any machine tool unit combination that does not meet any constraint, it is directly filtered out. Only combinations that simultaneously satisfy both process constraints and real-time state constraints are retained, ultimately generating a feasible allocation combination set. Each combination in this set ensures that the atomic manufacturing unit is processable at the process level and the machine tool is available at the real-time state level.

[0097] Step 43: Based on the feasible allocation combination set, dynamically calculate the weighting coefficients of the load factor and priority label, adjust the weighting coefficients according to the urgency of the events, and construct a compatibility matrix based on the dynamically weighted efficiency values. The specific operations are as follows:

[0098] For each combination in the feasible allocation set, the weighting coefficients of the load factor and priority label are first dynamically determined, and then the weighted result of the two is calculated as the expected execution efficiency value. The calculation formula is as follows: ;

[0099] The formula originates from the fact that the execution efficiency of an atomic manufacturing unit on a machine tool is jointly determined by the machine tool's load status and the unit's priority. A lower load factor means a stronger ability of the machine tool to accept new units, resulting in higher execution efficiency. A higher priority label means the unit must prioritize execution efficiency. A linear weighted sum can comprehensively quantify the coupled influence of these two factors. Simultaneously, to adapt to the urgency of events, higher urgency results in a greater weight for the priority label, while the load factor weight decreases accordingly, ensuring that high-priority units are preferentially matched with low-load machine tools. In the formula... Representing the The atomic manufacturing unit in the first The projected execution efficiency value for the candidate machine tools in Taiwan. For the first Taiwanese machine tool load factor The weighting coefficients, For the first Real-time load factor of the machine tool. For the first Priority tag for individual atomic manufacturing units The weighting coefficients, For the first The priority tag value of each atomic manufacturing unit is dynamically adjusted based on the urgency value of the resource reallocation event signal. and The higher the urgency value, The larger the value, The smaller the value, the lower the urgency value, and the more balanced the weights become. Based on the adjusted weighting coefficients, the feasible allocation combination for each group is calculated. The matrix is ​​constructed with atomic manufacturing units as the row dimension and candidate machine tools as the column dimension. The matrix positions with no feasible combinations are assigned a value of 0. Finally, a compatibility matrix is ​​generated, which presents the expected execution efficiency of each atomic manufacturing unit on different candidate machine tools in numerical form.

[0100] In a preferred embodiment of the present invention, step 5 is further included: allocating atomic manufacturing units and updating the task scheduling table based on the compatibility matrix using a greedy selection strategy, wherein the following operations are also performed:

[0101] Step 51: Traverse each row of the compatibility matrix, select the CNC machine tool with the highest efficiency value for each atomic manufacturing unit, establish an initial allocation mapping table, and record the number of atomic manufacturing units allocated to each CNC machine tool and its cumulative efficiency value. The specific operations are as follows:

[0102] Firstly, using the compatibility matrix as the core data basis, each row of the matrix uniquely corresponds to an atomic manufacturing unit to be assigned, and each column corresponds to a candidate CNC machine tool. The matrix elements represent the expected execution efficiency value of each unit on its corresponding machine tool. During execution, the system traverses the compatibility matrix row by row, performing a global comparison of all column element values ​​for each row, i.e., a single atomic manufacturing unit, and selecting the element with the largest value. The CNC machine tool in the column dimension corresponding to this element is the initial optimal allocation object for that atomic manufacturing unit. This selection logic conforms to the idea of ​​a greedy algorithm that guides from local optima to global optima, prioritizing the maximization of the execution efficiency of each unit. Based on the above selection results, a one-to-one mapping relationship between atomic manufacturing unit identifiers and CNC machine tool identifiers is established. All mapping relationships are integrated to form a structured initial allocation mapping table. This table clearly records the initial allocation machine tool information for each unit. At the same time, an independent statistical dimension is established for each candidate CNC machine tool, accumulating and recording in real time the number of atomic manufacturing units assigned to that machine tool, as well as the sum of the expected execution efficiency values ​​of these units on that machine tool, i.e., the cumulative efficiency value. The cumulative efficiency value can quantitatively characterize the overall execution efficiency level of a single machine tool after undertaking the initial allocation task.

[0103] Step 52: Based on the initial allocation mapping table, detect time conflicts where multiple atomic manufacturing units are assigned to the same CNC machine tool, reallocate conflicting units according to their priority, dynamically adjust the efficiency values ​​at the corresponding positions in the compatibility matrix, and generate an optimized allocation scheme. The specific operations are as follows:

[0104] The initial allocation mapping table only focuses on the optimal execution efficiency of a single unit and does not consider the time resource constraints of CNC machine tools. Therefore, time conflict detection is performed first: Time-series data such as the expected idle timestamps of each CNC machine tool, the theoretical processing time of the allocated atomic manufacturing units, and the process switching buffer time are retrieved to simulate the complete execution timeline of each machine tool after receiving the initially allocated units. Atomic manufacturing units with overlapping time intervals are identified in the timeline; these units are judged to have time conflicts. For the detected conflicting units, they are sorted in descending order according to their priority labels. Units with higher priority labels retain their original assigned machine tools, while units with lower priority labels need to initiate a reassignment process. During reassignment, the efficiency values ​​of other candidate machine tools corresponding to the conflicting unit are retrieved from the compatibility matrix. After excluding machine tools that have triggered time conflicts, the machine tool with the second highest efficiency value is selected as the new allocation target. Simultaneously, since an increase in the number of units allocated to a machine tool will lead to load superposition and actual execution efficiency will decrease, the system dynamically corrects the efficiency values ​​at the corresponding positions in the compatibility matrix. The correction formula is: ;

[0105] This formula is based on the negative correlation between machine tool load and execution efficiency. The more units a machine tool handles and the higher the load, the more significant the decrease in execution efficiency. By introducing a decay coefficient calibrated from the machine tool's processing capacity and historical data, the original efficiency value is multiplied by a load decay factor, allowing for precise quantification of the impact of increased load on execution efficiency. In the formula, For the revised first The atomic manufacturing unit in the first The expected execution efficiency value of the CNC machine tool; The original efficiency value before correction; The efficiency attenuation coefficient per unit number of units; For the first The number of newly allocated atomic manufacturing units for the machine tool is reassessed based on the revised efficiency value to ensure that the new allocation scheme not only resolves the time conflict problem but also maximizes the overall execution efficiency, ultimately generating an optimized allocation scheme.

[0106] Step 53: Based on the optimized allocation scheme, generate execution timing for each atomic manufacturing unit set on the CNC machine tool, and update the task scheduling table according to the generated timing. The specific operations are as follows:

[0107] First, for each CNC machine tool's corresponding atomic manufacturing unit set in the optimized allocation scheme, timing planning is carried out based on the attributes of each unit: the theoretical processing time and process sequence constraints of each unit are extracted. For example, some units require the completion of a previous processing step before starting. Combined with the machine tool's expected idle timestamp, a timing planning algorithm is used to determine the precise start time of each unit, which must meet the machine tool's idle time requirements and conform to the process sequence logic. Then, the end time is determined based on the processing time, while reserving buffer time for process switching such as tool changes and tooling adjustments to ensure the timing is actually executable. During the timing generation process, the priority principle is followed, with units with high priority tags occupying the machine tool's effective time window first to ensure that they are executed on time according to order delivery requirements. For units without process sequence constraints, according to... Efficiency values ​​are sorted from highest to lowest for execution to maximize the overall execution efficiency of a single machine tool. After generating the unit execution sequence for all CNC machine tools, the global task scheduling table is updated: old records related to the atomic manufacturing units in this reallocation are deleted, and optimized unit execution sequence information is added, including fields such as unique unit identifier, corresponding CNC machine tool identifier, precise start time, end time, and associated process parameter packages. At the same time, the derived statistical fields in the scheduling table are updated synchronously, such as the load rate of each machine tool and the estimated delivery date of each production order. The updated task scheduling table can reflect the production task execution plan after resource reallocation in real time and accurately, providing a clear and implementable scheduling basis for CNC machine tool operation in the workshop, ensuring that production tasks proceed in an orderly manner according to the optimized plan.

[0108] In a preferred embodiment of the present invention, step 6 is further included: monitoring the execution effect after the atomic manufacturing unit is allocated, measuring the task completion time and resource utilization rate, and dynamically adjusting the preset threshold based on the measurement results. The following operations are also performed:

[0109] Step 61: Collect the actual start time, end time, and resource consumption records of the atomic manufacturing unit; identify and mark abnormal intervals in the execution records; and generate a cleaned set of execution records. The specific operations are as follows:

[0110] Through multi-source data interfaces such as the CNC machine tool control system and workshop data acquisition terminals, execution data of the allocated atomic manufacturing units is collected in real time: the actual start and end times are accurately recorded to mark the start and stop times of the unit's machining operations, covering the entire time period from tooling preparation to the end of the machining operation; resource consumption records cover the actual consumption values ​​of key resource dimensions such as tool wear, spindle energy consumption, coolant consumption, and man-hours during the machining process, forming multi-dimensional raw execution records. For the collected raw records, the first step is to identify abnormal intervals: by comparing the numerical range and temporal patterns of historical normal execution records, the system identifies time record gaps caused by data acquisition interruptions, abnormal resource consumption values ​​caused by sensor errors, and human error. The system identifies abnormal intervals, such as timestamp errors caused by operational mistakes, and marks each interval with its corresponding anomaly type, such as missing data, numerical out-of-limit, or time-series disorder, along with the time range in which the anomaly occurred. After marking the anomalies, the original records are cleaned: for missing data anomalies, linear interpolation is used in conjunction with the execution timing patterns of similar units to fill in the missing data; for numerical out-of-limit anomalies, invalid data that deviates significantly from the reasonable range is removed while retaining the anomaly marker; for time-series disorder anomalies, erroneous timestamps are corrected based on the machine tool operation log. Finally, a cleaned set of execution records is generated, which removes invalid data, fills in key missing values, and retains the anomaly marker. This set can accurately reflect the actual execution status of the atomic manufacturing unit.

[0111] Step 62: Calculate the deviation between the actual completion time and the expected completion time, calculate the deviation rate between the actual resource consumption and the expected consumption, and combine the time deviation and the resource deviation rate into an effect evaluation vector. The specific operation is as follows:

[0112] First, based on the cleaned execution record set, the actual completion time of each atomic manufacturing unit is extracted, calculated using the difference between the end time and the start time. The estimated completion time is calculated using the theoretical processing time from the task schedule table. The time deviation is then calculated using the following formula: ;

[0113] Based on the standardized quantification requirement for execution time deviation, directly using absolute differences cannot eliminate the impact of variations in processing time between different units. By comparing this deviation with the expected completion time, it is transformed into a dimensionless relative value, making the deviations of units with different durations comparable. This logic starts from the quantification requirement of the time deviation between actual and planned execution, abstracting it into the form of a ratio of absolute deviation to the expected value. In the formula, Represents the degree of time deviation. The actual completion time of an atomic manufacturing unit. The estimated completion time of the unit is calculated. Simultaneously, the actual resource consumption value of the unit is extracted and compared with the preset standard resource consumption value. Using the same relative deviation calculation logic, the deviation rate between the actual and estimated resource consumption is obtained. ,in This represents the actual resource consumption value. To predict resource consumption, after calculating the time deviation and resource deviation rate, these two quantitative indicators are combined according to a fixed dimension to generate a two-dimensional performance evaluation vector. This vector can comprehensively and structurally reflect the deviation level of the unit in two dimensions: time execution efficiency and resource utilization efficiency.

[0114] Step 63: Establish a threshold adjustment calculation model, calculate the threshold adjustment amount, verify the impact of the adjusted threshold on the event triggering frequency, and generate the verified threshold adjustment amount. The specific operations are as follows:

[0115] First, based on historical threshold adjustment feedback data, a threshold adjustment calculation model is established, and the calculation formula is as follows: ;

[0116] This stems from the positive correlation between threshold adjustment and execution performance deviation. Larger time and resource deviation rates indicate a lower fit between the current preset threshold and the actual execution state, requiring a more significant threshold adjustment. By introducing weighted coefficients to balance the impact of these two deviation dimensions on threshold adjustment, the weighting coefficients are iteratively optimized based on feedback data of historically adjusted event-triggered effects. Ultimately, this is abstracted into a linear weighted summation mathematical form. In the formula... The adjustment amount represents the preset threshold. Time deviation The weighting coefficient represents the degree of influence of time deviation on threshold adjustment; Resource Deviation Rate The weighting coefficient represents the degree of influence of resource deviation on threshold adjustment. After calculating the initial threshold adjustment amount based on this model, it is necessary to verify the impact of the adjusted threshold on the event triggering frequency: by simulating the number of event triggers of the new threshold under different production scenarios, such as low equipment load, high load, and sudden failure, the preset reasonable triggering frequency range is compared. If the triggering frequency is too high, the adjustment amount is reduced; if the triggering frequency is too low, the adjustment amount is increased. This ensures that the adjusted threshold can respond to production anomalies in a timely manner without causing meaningless frequent triggering. After verification through multi-scenario simulation, a verified threshold adjustment amount is generated. This adjustment amount takes into account both the quantitative characteristics of the execution effect deviation and the stability requirements of system event triggering.

[0117] Step 64: Update the preset threshold using the verified threshold adjustment amount, monitor the event triggering stability under the new threshold, and activate the damping adjustment mechanism when threshold oscillation is detected to generate a stable threshold set. The specific operations are as follows:

[0118] First, the validated threshold adjustment is applied to the original preset threshold to update the global preset threshold: if the adjustment is positive, the threshold is raised to reduce the probability of event triggering; if the adjustment is negative, the threshold is lowered to increase the sensitivity of event triggering. After the threshold is updated, a continuous monitoring phase begins, collecting in real time indicators such as the triggering frequency, triggering interval, and adjustment magnitude of resource reallocation events under the new threshold. By calculating the sliding window variance of the triggering frequency, threshold oscillation is identified, i.e., the threshold is repeatedly raised and lowered in a short period of time, causing drastic fluctuations in the event triggering frequency. This phenomenon can disrupt the stability of production scheduling and increase the cost of manual intervention. When monitoring... When the threshold oscillates, that is, when the sliding window variance exceeds the preset stable threshold, the damping adjustment mechanism is immediately activated. This mechanism weakens the immediate impact of execution effect deviation on the threshold by reducing the amplitude of a single threshold adjustment and extending the time interval between two threshold adjustments, thus avoiding the threshold from over-responding to small deviations. At the same time, it combines historical stable operation threshold data to smooth the oscillating threshold and gradually converge to the stable range. After damping adjustment and continuous monitoring and verification, a stable threshold set is finally generated. Each threshold in this set is adapted to the actual execution state of the corresponding CNC machine tool and can ensure the stability and timeliness of resource reallocation events.

[0119] In a preferred embodiment of the present invention, step 7 is further included: storing adjustment instances and compatibility matrix data to construct an experience base, the experience base storing event patterns and optimal allocation preferences; when a new resource reallocation event signal is generated, retrieving similar event patterns from the experience base through pattern matching, and pre-allocating atomic manufacturing units based on the optimal allocation preferences; wherein the following operations are also performed:

[0120] Step 71: Standardize the threshold adjustment amount and compatibility matrix data, extract event type feature vectors, resource allocation feature vectors, and execution effect evaluation vectors to generate a standardized empirical data feature set. The specific operations are as follows:

[0121] First, we collected the threshold adjustment amounts and full compatibility matrix data corresponding to each resource reallocation event. We then standardized the data to address the dimensional differences between the two types: For threshold adjustment amounts, we used min-max normalization to map them to the range of 0 to 1, eliminating the impact of differences in threshold adjustment magnitudes across different machine tools and event types. For the compatibility matrix data, we first extracted the eigenvalues, such as the maximum value of each row, the matrix mean, and the variance. Then, we standardized these eigenvalues ​​to ensure that data across different dimensions have the same numerical scale. After standardization, we extracted three types of feature vectors from the processed data: the event type feature vector, which includes the event type label, urgency value, and trigger time. The core attributes of an event are comprehensively represented by dimensions such as the production period characteristics corresponding to the time stamp and the types of CNC machine tools involved. The resource allocation feature vector integrates the standardized feature values ​​of the compatibility matrix, the allocation mapping relationship between atomic manufacturing units and CNC machine tools, and the load factors of each machine tool, reflecting the rules of resource allocation. The execution effect evaluation vector reuses the standardized values ​​such as the previously calculated time deviation and resource deviation rate to reflect the actual execution effect of the allocation scheme. These three types of feature vectors are combined according to fixed dimensions to generate a complete standardized experience data feature record for each resource reallocation event. All records are integrated to form a standardized experience data feature set, which eliminates the interference caused by data heterogeneity.

[0122] Step 72: Analyze the correlation between event type feature vectors and resource allocation feature vectors, mine event allocation patterns, assign confidence weights to patterns based on frequency of occurrence and execution effect, and construct an event allocation pattern library. The specific operations are as follows:

[0123] First, correlation analysis is conducted on the event type feature vector and resource allocation feature vector in the standardized experience data feature set. Association rule mining algorithms are used to identify the correlation between the two types of vectors. For example, specific types of resource reallocation events, such as spindle failure events, always correspond to specific resource allocation strategies, such as prioritizing allocation to standby spindle machines. These stable correlations constitute the initial event allocation pattern. To quantify the reliability of the pattern, the confidence weight of each pattern is calculated using the following formula: ;

[0124] This formula originates from the dual dimensions of pattern reliability: frequency of occurrence reflects the universality of the pattern in historical data, and execution effect reflects the practical application value of the pattern. A linear weighted sum can comprehensively quantify the contribution of both to pattern reliability; in the formula, The confidence weights represent the event assignment patterns. The weighting coefficient for the frequency F is the proportion of the number of times the pattern appears in the empirical dataset relative to the total number of events. The weight coefficient for the execution effect E is the mean of the execution effect evaluation vector corresponding to the pattern. The higher the value, the better the execution effect. The confidence weight is assigned to each mined event allocation pattern. The feature vector of the pattern, the confidence weight and the corresponding optimal allocation preference, such as machine tool selection priority and unit allocation rules, are integrated and stored to finally build a structured event allocation pattern library.

[0125] Step 73: When a new resource reallocation event signal is generated, extract the real-time event feature vector and calculate its similarity with historical patterns in the event allocation pattern library. Based on the confidence weight and feature similarity, comprehensively evaluate and select the set of matching historical patterns. The specific operation is as follows:

[0126] When a new resource reallocation event signal is generated, the real-time event feature vector is first extracted from the event signal according to the feature extraction rules in step 71. Its dimension is completely consistent with the event type feature vectors of historical patterns in the event allocation pattern library, ensuring comparability. The cosine similarity algorithm is used to calculate the similarity between the real-time event feature vector and the feature vectors of each historical pattern in the pattern library. This algorithm quantifies the degree of feature matching by calculating the cosine value of the angle between vectors; the closer the value is to 1, the higher the feature similarity. Based on the similarity calculation results, the system combines the confidence weights of historical patterns to conduct a comprehensive evaluation. The comprehensive evaluation score formula is: ;

[0127] Where Sim is the similarity value between real-time events and historical patterns. The formula combines pattern similarity and reliability to determine the confidence weight of historical patterns, avoiding the problem of selecting low-reliability patterns based solely on similarity. It sets a comprehensive evaluation score threshold and filters out historical patterns with scores higher than the threshold, forming a set of matching historical patterns. The patterns in this set not only have high feature similarity to new events but also have high execution reliability verified by history.

[0128] Step 74: Generate a pre-allocation scheme for atomic manufacturing units based on allocation preferences in the matched historical pattern set, predict resource conflicts and efficiency bottlenecks, and generate the pre-allocation scheme. The specific operations are as follows:

[0129] First, the optimal allocation preference is extracted from the historical pattern set, including the selection priority of CNC machine tools under this type of event; the allocation rules of atomic manufacturing units, such as prioritizing high-priority units to match low-load machine tools; and the planning logic of execution timing. Combining the atomic manufacturing unit attributes corresponding to the new event with the real-time status of candidate machine tools, an initial pre-allocation scheme is generated. To ensure the feasibility of the scheme, simulation verification is conducted on the initial pre-allocation scheme, simulating the load changes of each CNC machine tool and the task execution timing during the scheme execution process. Potential resource conflicts are predicted, such as overlapping time intervals caused by multiple units being allocated to the same machine tool, machine tool load factors exceeding safety thresholds, and efficiency bottlenecks, such as a certain machine tool... Overloading machine tools with too many units leads to a decrease in overall execution efficiency, and assigning critical path units to inefficient machine tools causes delivery delays. To address the anticipated conflicts and bottlenecks, adjustments are made based on optimization strategies for similar problems in the pattern library: if there are time conflicts, the execution sequence of units is adjusted or they are reassigned to standby machine tools; if there are efficiency bottlenecks, the matching relationship between units and machine tools is optimized, and the number of units assigned to high-load machine tools is reduced. After conflict prediction and optimization, a final pre-allocation scheme is generated. This scheme reuses historically validated optimal allocation experience and adapts to the real-time status of new events, which can significantly shorten the decision cycle of resource reallocation and improve the rationality and execution efficiency of the allocation scheme.

[0130] Example 2:

[0131] Please see Figure 2 Based on Example 1, this example provides a collaborative management system for CNC machine tool production resources, including:

[0132] The order decomposition module is used to decompose production orders into atomic manufacturing units. Each atomic manufacturing unit has standardized dimensions determined based on the machining capabilities of CNC machine tools and carries priority tags and resource dependency identifiers.

[0133] The state vector module is used to construct the dynamic state vector of the CNC machine tool. The dynamic state vector includes the availability index, load factor and expected idle timestamp.

[0134] The signal generation module generates a resource reallocation event signal when the availability index falls below a preset threshold.

[0135] The matrix calculation module is used to calculate the compatibility matrix in response to resource reallocation event signals. The elements of the compatibility matrix are based on the weighted result of load factor and priority label, representing the expected execution efficiency of the atomic manufacturing cell on the CNC machine tool.

[0136] The task allocation module, based on the compatibility matrix, uses a greedy selection strategy to allocate atomic manufacturing units and update the task scheduling table;

[0137] The effect adjustment module is used to monitor the execution effect after the atomic manufacturing unit is allocated, measure the task completion time and resource utilization, and dynamically adjust the preset threshold based on the measurement results.

[0138] The experience building module stores adjustment instances and compatibility matrix data to build an experience library. The experience library stores event patterns and allocation preferences. When a new resource reallocation event signal is generated, similar event patterns are retrieved from the experience library through pattern matching, and atomic manufacturing units are pre-allocated based on allocation preferences.

[0139] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concepts, should be covered within the scope of protection of the present invention.

Claims

1. A collaborative management method for CNC machine tool production resources, characterized in that, include: Step 1: Decompose the production order into atomic manufacturing units. Each atomic manufacturing unit has a standardized size determined based on the machining capabilities of the CNC machine tool and carries a priority tag and a resource dependency identifier. Step 2: Construct the dynamic state vector of the CNC machine tool. The dynamic state vector includes the availability index, load factor, and expected idle timestamp. Step 3: When the availability index is lower than a preset threshold, a resource reallocation event signal is generated; Step 4: In response to the resource reallocation event signal, calculate the compatibility matrix. The elements of the compatibility matrix are based on the weighted result of the load factor and priority label, representing the expected execution efficiency of the atomic manufacturing cell on the CNC machine tool. Step 5: Based on the compatibility matrix, use a greedy selection strategy to allocate atomic manufacturing units and update the task scheduling table; Step 6: Monitor the execution effect after the atomic manufacturing unit is allocated, measure the task completion time and resource utilization rate, and dynamically adjust the preset threshold based on the measurement results; Step 7: Store adjustment instance and compatibility matrix data to build an experience base. The experience base stores event patterns and allocation preferences. When a new resource reallocation event signal is generated, similar event patterns are retrieved from the experience base through pattern matching, and atomic manufacturing units are pre-allocated based on allocation preferences.

2. The method for collaborative management of CNC machine tool production resources according to claim 1, characterized in that, Production orders are broken down into atomic manufacturing units, each with standardized dimensions determined by the machining capabilities of CNC machine tools, and carrying priority tags and resource dependency identifiers, including: Step 11: Extract the geometric feature vector and process parameter set of the production order to generate the order feature process association diagram; Step 12: Construct a processing capability matrix, calculate the processability index of feature blocks in the order feature process association graph, and generate a processability score list; Step 13: Apply a dynamic clustering algorithm to group the feature blocks in the manufacturability score list to generate a set of atomic manufacturing units. Each atomic manufacturing unit is accompanied by a geometric bounding box and a process parameter package. Step 14: Perform critical path analysis on the set of atomic manufacturing units, assign priority labels, and parse the process parameter package to generate resource dependency identifiers, thus obtaining the complete set of atomic manufacturing units with labels and identifiers.

3. The method for collaborative management of CNC machine tool production resources according to claim 2, characterized in that, Construct the dynamic state vector of the CNC machine tool. The dynamic state vector includes the availability index, load factor, and expected idle timestamp, including: Step 21: Collect real-time operation sequences, compare real-time operation sequences with historical baseline sequences, identify abnormal operation intervals and mark the abnormality type and occurrence timestamp, and generate operation sequences marked with abnormalities; Step 22: Extract the frequency and duration of anomalies from the marked anomaly operation sequences, calculate the performance deviation index, parse the resource requirement descriptors of tasks in the current task queue, and aggregate to generate load factors; Step 23: Based on the performance deviation index and load factor, simulate the execution timeline of the task queue, analyze the resource gaps in the timeline, predict the expected idle timestamp, and generate a dynamic state vector containing the availability index, load factor, and expected idle timestamp.

4. The method for collaborative management of CNC machine tool production resources according to claim 3, characterized in that, When the availability index falls below a preset threshold, a resource reallocation event signal is generated, including: Step 31: Analyze the historical trajectory of the availability index, calculate the second derivative of the availability index change, and dynamically adjust the preset threshold based on the predicted trend. Step 32: When the availability index is lower than the dynamically adjusted trigger threshold, assess the rate of decline, duration and scope of impact of the index, classify the event and label its urgency value, and generate an event descriptor with type label and urgency value. Step 33: Encode the event type, urgency value and trigger timestamp in the event descriptor into a structured event signal, assign processing priority to the event signal according to the urgency value, and generate a resource reallocation event signal with priority tag.

5. The method for collaborative management of CNC machine tool production resources according to claim 4, characterized in that, In response to resource reallocation event signals, a compatibility matrix is ​​calculated. The elements of the compatibility matrix, weighted based on load factors and priority labels, represent the expected execution efficiency of the atomic manufacturing cell on the CNC machine tool, including: Step 41: Analyze the priority marker and event type in the resource reallocation event signal, filter CNC machine tools according to the event type to generate a candidate machine tool set, and determine the screening strictness based on the priority marker; Step 42: Extract the process parameter package and geometric bounding box of the atomic manufacturing unit, match them with the processing capabilities of the candidate machine tool set, verify the process constraints and real-time state constraints, filter out machine tool unit combinations that do not meet the constraints, and generate a feasible allocation combination set. Step 43: Based on the feasible allocation combination set, dynamically calculate the weighting coefficients of the load factor and priority label, adjust the weighting coefficients according to the urgency of the event, and construct a compatibility matrix based on the dynamically weighted efficiency value.

6. The method for collaborative management of CNC machine tool production resources according to claim 5, characterized in that, Based on the compatibility matrix, a greedy selection strategy is used to allocate atomic manufacturing units and update the task scheduling table, including: Step 51: Traverse each row of the compatibility matrix, select the CNC machine tool with the highest efficiency value for each atomic manufacturing unit, establish an initial allocation mapping table, and record the number of atomic manufacturing units allocated to each CNC machine tool and its cumulative efficiency value. Step 52: Based on the initial allocation mapping table, detect time conflicts in multiple atomic manufacturing units being assigned to the same CNC machine tool, reallocate conflicting units according to the priority of atomic manufacturing units, dynamically correct the efficiency values ​​at the corresponding positions in the compatibility matrix, and generate an optimized allocation scheme. Step 53: Based on the optimized allocation scheme, generate an execution sequence for each atomic manufacturing unit set on the CNC machine tool, and update the task scheduling table according to the generated sequence.

7. The method for collaborative management of CNC machine tool production resources according to claim 6, characterized in that, Monitor the execution effect after the atomic manufacturing unit is allocated, measure the task completion time and resource utilization, and dynamically adjust the preset thresholds based on the measurement results, including: Step 61: Collect the actual start time, end time and resource consumption records of the atomic manufacturing unit, identify abnormal intervals in the execution records and mark and clean them to generate a cleaned set of execution records; Step 62: Calculate the deviation between the actual completion time and the expected completion time, calculate the deviation rate between the actual resource consumption and the expected consumption, and combine the time deviation and the resource deviation rate into an effect evaluation vector. Step 63: Establish a threshold adjustment calculation model, calculate the threshold adjustment amount, verify the impact of the adjusted threshold on the event triggering frequency, and generate the verified threshold adjustment amount. Step 64: Update the preset threshold using the verified threshold adjustment amount, monitor the event triggering stability under the new threshold, and activate the damping adjustment mechanism when threshold oscillation is detected to generate a stable threshold set.

8. The method for collaborative management of CNC machine tool production resources according to claim 7, characterized in that, The system stores adjustment instances and compatibility matrix data to construct an experience base. This experience base stores event patterns and optimal allocation preferences. When a new resource reallocation event signal is generated, similar event patterns are retrieved from the experience base through pattern matching, and atomic manufacturing units are pre-allocated based on the optimal allocation preferences, including: Step 71: Standardize the threshold adjustment amount and compatibility matrix data, extract event type feature vectors, resource allocation feature vectors and execution effect evaluation vectors, and generate a standardized empirical data feature set; Step 72: Analyze the correlation between event type feature vector and resource allocation feature vector, mine event allocation patterns, assign confidence weights to patterns based on frequency of occurrence and execution effect, and build an event allocation pattern library.

9. The method for collaborative management of CNC machine tool production resources according to claim 8, characterized in that, Also includes: Step 73: When a new resource reallocation event signal is generated, extract the real-time event feature vector, calculate the similarity with the historical patterns in the event allocation pattern library, and screen the set of matching historical patterns based on a comprehensive evaluation of confidence weight and feature similarity. Step 74: Generate a pre-allocation scheme for atomic manufacturing units based on allocation preferences in the matching historical pattern set, predict resource conflicts and efficiency bottlenecks, and generate a pre-allocation scheme.

10. A CNC machine tool production resource collaborative management system, applied to the CNC machine tool production resource collaborative management method of any one of claims 1-9, characterized in that, include: The order decomposition module is used to decompose production orders into atomic manufacturing units. Each atomic manufacturing unit has standardized dimensions determined based on the machining capabilities of CNC machine tools and carries priority tags and resource dependency identifiers. The state vector module is used to construct the dynamic state vector of the CNC machine tool. The dynamic state vector includes the availability index, load factor and expected idle timestamp. The signal generation module generates a resource reallocation event signal when the availability index falls below a preset threshold. The matrix calculation module is used to calculate the compatibility matrix in response to resource reallocation event signals. The elements of the compatibility matrix are based on the weighted result of load factor and priority label, representing the expected execution efficiency of the atomic manufacturing cell on the CNC machine tool. The task allocation module, based on the compatibility matrix, uses a greedy selection strategy to allocate atomic manufacturing units and update the task scheduling table; The effect adjustment module is used to monitor the execution effect after the atomic manufacturing unit is allocated, measure the task completion time and resource utilization, and dynamically adjust the preset threshold based on the measurement results. The experience building module stores adjustment instances and compatibility matrix data to build an experience library. The experience library stores event patterns and allocation preferences. When a new resource reallocation event signal is generated, similar event patterns are retrieved from the experience library through pattern matching, and atomic manufacturing units are pre-allocated based on allocation preferences.

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