Multi-source data-based numerical control machine tool multi-stage maintenance strategy optimization system

Abstract

The invention relates to a numerical control machine tool multistage maintenance strategy optimization system based on multi-source data, which belongs to the technical field of numerical control machine tool maintenance and comprises a data acquisition and preprocessing unit used for acquiring operation data of a numerical control machine tool in real time and preprocessing the operation data to extract a quantitative feature set; the health state quantification unit is used for performing operation processing based on the quantification feature set to generate a health stability index; the deterioration gradient evaluation unit is used for performing operation processing based on the health stability index to generate a health deterioration gradient; the potential risk assessment unit is used for performing operation processing in combination with the health stability index and the health deterioration gradient so as to generate a potential risk level; and the maintenance strategy mapping unit is used for carrying out comparative analysis on the potential risk level and a preset risk threshold sequence to generate a multi-level maintenance strategy instruction, and the risk identification sensitivity and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of maintenance of numerically controlled machine tools, and in particular to a multi-level maintenance strategy optimization system for numerically controlled machine tools based on multi-source data. Background Art

[0002] As core equipment in the high-end manufacturing industry, the operational stability and reliability of CNC machine tools directly impact product quality and production efficiency. Traditional maintenance models are mainly divided into post-fault maintenance and preventive maintenance based on fixed time periods. The former suffers from delayed response and huge economic losses; the latter fails to consider the actual working conditions and individual health status of the machine tool, often leading to insufficient or excessive maintenance. Existing predictive maintenance technologies are mostly focused on improving the prediction accuracy of single fault points, but lack a quantitative description of the system health deterioration process, especially the deterioration rate, a key dynamic indicator. Maintenance decisions are usually based on rigid thresholds, which cannot match the intensity of maintenance intervention with the gradient of the system's deviation from normal state. There is also a lack of comprehensive optimization of maintenance costs and potential risks. Therefore, how to go beyond single-point fault prediction and build a system that can quantify the gradient of machine tool health deterioration and continuously optimize multi-level maintenance strategies is a technical problem that needs to be solved in the current field.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data to solve the problems raised in the above background technology.

[0005] The technical solution of the present invention comprises: a data acquisition and preprocessing unit for acquiring the operation data of the CNC machine tool in real time and preprocessing the operation data to extract a quantitative feature set;

[0006] A health status quantification unit is used to perform calculations based on the quantitative feature set to obtain a system unhealthiness; the health stability index is generated by subtracting the system unhealthiness from a preset ideal health status value;

[0007] a deterioration gradient evaluation unit, configured to perform calculations based on the health stability index to obtain a health state difference; and divide the health state difference by a preset calculation time window to generate a health deterioration gradient;

[0008] The potential risk assessment unit is used to combine the health stability index and the health deterioration gradient to perform calculations to determine the system deviation, deterioration rate value, and bottleneck risk value, and to perform a weighted summation operation on the system deviation, deterioration rate value, and bottleneck risk value to generate a potential risk level;

[0009] A maintenance strategy mapping unit is used to compare and analyze the potential risk level with a preset risk threshold sequence to generate a multi-level maintenance strategy instruction;

[0010] The feedback correction unit is used to record the changes in the health stability index caused by maintenance activities after the multi-level maintenance strategy instructions are executed, and iteratively optimize the internal parameters of the system based on the changes.

[0011] Preferably, the process of generating the health stability index by the health status quantification unit includes:

[0012] Normalizing the feature data in the quantitative feature set extracted by the data acquisition and preprocessing unit to obtain normalized feature values; retrieving the preset feature weight coefficients corresponding to each normalized feature value; multiplying each normalized feature value by the corresponding feature weight coefficient to obtain each individual unhealthy degree; summing each individual unhealthy degree to obtain the system unhealthy degree; and calculating the health stability index by subtracting the system unhealthy degree from the preset ideal health state value.

[0013] Preferably, the process of generating the health deterioration gradient by the deterioration gradient assessment unit includes:

[0014] Obtain the health stability index at the current moment and the historical health stability index of the previous calculation cycle; subtract the historical health stability index from the health stability index at the current moment to obtain the health status difference; divide the health status difference by the preset calculation time window to obtain the health deterioration gradient.

[0015] Preferably, the system deviation is determined by subtracting a health stability index from a preset ideal health state value.

[0016] Preferably, the process of determining the deterioration rate value includes:

[0017] Determine the gradient of health deterioration;

[0018] When the health deterioration gradient is negative, the negative value is extracted as the deterioration rate value;

[0019] When the health deterioration gradient is non-negative, the deterioration rate is set to zero.

[0020] Preferably, the process of determining the bottleneck risk value includes:

[0021] Retrieve the normalized eigenvalues ​​and preset eigenweight coefficients generated by the health status quantification unit; multiply each normalized eigenvalue by the corresponding eigenweight coefficient to obtain each individual risk contribution value; extract the maximum value from each individual risk contribution value as the bottleneck risk value.

[0022] Preferably, the process of the maintenance strategy mapping unit generating the multi-level maintenance strategy instructions includes:

[0023] Retrieve a preset risk threshold sequence and its corresponding multi-level maintenance strategy set; compare the potential risk level with the risk threshold sequence to determine the risk interval to which it belongs; and select the corresponding maintenance strategy from the multi-level maintenance strategy set based on the determined risk interval to generate a multi-level maintenance strategy instruction.

[0024] Preferably, the process of iterative optimization performed by the feedback correction unit includes:

[0025] After the maintenance activity is performed, the health and stability index after maintenance is collected and compared with the health and stability index before maintenance to quantify the maintenance effect; the data records containing the pre-maintenance status, maintenance activities and quantified maintenance effects are used as knowledge samples; and the knowledge samples are used to feedback and update the feature weight coefficients in the health status quantification unit or the risk threshold sequence in the maintenance strategy mapping unit.

[0026] The present invention provides a multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data through improvements. Compared with the prior art, it has the following improvements and advantages:

[0027] 1. By constructing a health status quantification unit and a deterioration gradient assessment unit, this invention quantifies the health level of a machine tool at a given moment and calculates the rate of change of this index. This expands the focus of maintenance from isolated, static health point information to continuous, dynamic health evolution information. The system can thus distinguish between two distinct risk scenarios: one in which the health index is low but stable, and the other in which the health index is acceptable but rapidly declining. Existing technologies often only identify the former, ignoring the risk of sudden failure indicated by the latter.

[0028] 2. This invention establishes a comprehensive potential risk level assessment model that integrates three key risk dimensions: the system deviation, determined by the health stability index, which reflects the overall stock risk of the system; the deterioration rate, determined by the health deterioration gradient, which reflects the dynamic incremental risk of the system health; and the bottleneck risk, determined by the maximum value of each individual unhealthy degree, which aims to capture the extreme risks of local components. This design is advanced in that it effectively avoids the averaging effect in traditional risk assessments, ensuring that the rapid deterioration of a single component or the rapid decline in the overall health trend are promptly and accurately reflected in the final risk level, thereby greatly improving the sensitivity and accuracy of risk identification.

[0029] 3. Achieve precise matching of maintenance interventions with risk levels: The maintenance strategy mapping unit matches intervals within a preset risk threshold sequence based on the calculated potential risk level to generate multi-level maintenance strategy instructions. The advanced nature of this mechanism lies in its elimination of the one-size-fits-all maintenance decision-making based on rigid thresholds found in existing technologies. Instead, it adopts a refined, hierarchical intervention model that dynamically matches the risk level. From enhanced monitoring and online compensation to planned maintenance and component replacement, this ensures that maintenance resource investment is precisely commensurate with actual risk, achieving an optimal balance between operation and maintenance costs and equipment reliability.

[0030] 4. The system is endowed with the ability to self-learn and adapt: ​​The original feedback correction unit of the present invention constitutes a closed-loop learning circuit for the system. After the multi-level maintenance strategy instructions are executed, the unit quantifies the maintenance effects to form knowledge samples containing states, activities and effects. These samples are used to continuously iteratively optimize the system's internal parameters, such as feature weight coefficients or risk threshold sequences. This function enables the system to learn from its own maintenance history, and its model will become more and more adapted to the operating characteristics and failure modes of specific machine tools over time. This is a significant adaptive evolutionary advantage that existing static maintenance models do not have. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further explained below in conjunction with the accompanying drawings and examples:

[0032] Figure 1 It is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0033] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0034] Example 1:

[0035] See also Figure 1,The present invention provides a multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data, comprising: a data acquisition and preprocessing unit for acquiring the operation data of the CNC machine tools in real time and preprocessing the operation data to extract a quantitative feature set;

[0036] A health status quantification unit is used to perform calculations based on the quantitative feature set to obtain a system unhealthiness; the health stability index is generated by subtracting the system unhealthiness from a preset ideal health status value;

[0037] a deterioration gradient evaluation unit, configured to perform calculations based on the health stability index to obtain a health state difference; and divide the health state difference by a preset calculation time window to generate a health deterioration gradient;

[0038] The potential risk assessment unit is used to combine the health stability index and the health deterioration gradient to perform calculations to determine the system deviation, deterioration rate value, and bottleneck risk value, and to perform a weighted summation operation on the system deviation, deterioration rate value, and bottleneck risk value to generate a potential risk level;

[0039] A maintenance strategy mapping unit is used to compare and analyze the potential risk level with a preset risk threshold sequence to generate a multi-level maintenance strategy instruction;

[0040] The feedback correction unit is used to record the changes in the health stability index caused by maintenance activities after the multi-level maintenance strategy instructions are executed, and iteratively optimize the internal parameters of the system based on the changes.

[0041] In this implementation scenario, a five-axis machining center is monitored. The system dynamically optimizes maintenance strategies through the collaborative work of its internal units. The system's data acquisition and preprocessing unit continuously acquires operational information from key machine tool components, such as the spindle vibration spectrum, drive motor current fluctuations, and tool soundprint characteristics, and processes this information into a quantifiable feature set.

[0042] The processing process includes: For the spindle vibration spectrum, it can be converted to the frequency domain through fast Fourier transform, and the preset fault characteristic frequencies, such as the fault frequencies of the bearing inner and outer rings, rolling elements, and cages, the energy integral value within the corresponding frequency band, and the time domain root mean square value, kurtosis, and margin of the vibration signal can be extracted as quantitative features; for the drive motor current, the mean and standard deviation of its fluctuation signal can be extracted; for the tool soundprint feature, its sound pressure level energy in a specific high-frequency band can be extracted. These extracted values ​​with clear physical meanings together constitute the quantitative feature set.

[0043] Based on this set, the health status quantification unit converts multidimensional data into a single health stability index, and then the deterioration gradient assessment unit calculates the rate of change of this index to provide dynamic input; the potential risk assessment unit integrates the static health level and the dynamic deterioration trend to generate a comprehensive potential risk level; the maintenance strategy mapping unit matches the optimal maintenance intervention measures based on this risk level; after the maintenance activity is executed, the feedback correction unit uses the maintenance effect data to reversely optimize the system model parameters, thereby forming a complete, adaptive closed-loop control system; the inherent advantage of this architecture is that it elevates maintenance decisions from isolated fault prediction to continuous control of the overall health evolution process of the equipment, realizing global optimization of machine tool operation and maintenance.

[0044] Example 2:

[0045] The process of generating the health stability index by the health status quantification unit includes:

[0046] Normalizing the feature data in the quantitative feature set extracted by the data acquisition and preprocessing unit to obtain normalized feature values; retrieving preset feature weight coefficients corresponding to each normalized feature value; multiplying each normalized feature value by the corresponding feature weight coefficient to obtain each individual unhealthy degree; summing each individual unhealthy degree to obtain the system unhealthy degree; and calculating the health stability index by subtracting the system unhealthy degree from the preset ideal health state value.

[0047] The process of generating the health deterioration gradient by the deterioration gradient assessment unit includes:

[0048] Obtain the health stability index at the current moment and the historical health stability index of the previous calculation cycle; subtract the historical health stability index from the health stability index at the current moment to obtain the health status difference; divide the health status difference by the preset calculation time window to obtain the health deterioration gradient.

[0049] In order to transform the discrete multi-dimensional performance characteristics of CNC machine tools into a health status that can be uniformly evaluated, the health status quantification unit introduces the health stability index model, while the deterioration gradient assessment unit introduces the health deterioration gradient Model, the two together form the basis for a complete description of the machine tool status;

[0050] Health Stability Index The technical motivation of the model is to integrate multiple performance indicators of different dimensions into a single dimensionless comprehensive evaluation index to quantify the macroscopic health status of the machine tool; the composition is as follows: ;

[0051] in, Representative Moment The health stability index is , 1 represents an ideal health state, and 0 represents complete failure; It is the first Original features Normalized eigenvalues ​​after min-max normalization; is the preset feature weight coefficient corresponding to each normalized eigenvalue. The determination method can use the hierarchical analysis method combined with expert experience, or objectively assign values ​​based on data-driven methods such as principal component analysis, and meet the requirements. ; : refers to the moment; : represents the total number of quantitative features; : Index used for summation, representing the Features

[0052] The ideal health status value is usually preset to 1, indicating no unhealthy state; the HSI value range is [0,1], and the closer the value is to 1, the healthier the state;

[0053] When using the hierarchical analysis method, a three-level hierarchical structure can be constructed, with the machine tool health status as the target layer, the vibration status, electrical status, etc. as the criterion layer, and each specific quantitative feature as the solution layer; multiple field experts are invited to compare the importance of each feature under the same criterion, build a judgment matrix, and calculate the normalized eigenvector corresponding to the maximum eigenvalue of the matrix as the weight of each feature under the criterion. Finally, a consistency test is performed, such as the consistency ratio , to ensure the validity of expert judgment. When principal component analysis is used, the normalized characteristic matrix composed of a large amount of historical normal operation data can be analyzed. Usually, the first principal component explains the largest part of the data variability, and its load vector It reflects the contribution of each original feature to PC1; therefore, the absolute value of each load coefficient of PC1 can be normalized to calculate the weight of each feature, where : represents the transpose of a matrix or vector, and the calculation formula is: ;

[0054] in, : No. The loading coefficient of the original features on the first principal component; : Index used for summation, representing the Features : No. The loading coefficient of the original features on the first principal component; : The total number of features; the effectiveness of this formula is that it integrates complex multi-source data streams into an intuitive curve of the health status evolution over time, providing a quantitative foundation for subsequent decision-making;

[0055] health deterioration gradient The model is logically The extension of the model is is considered as a position function of the system health state, then It is the speed function of the state change. The technical motivation is to accurately quantify the rate of change of the health state and capture its dynamic evolution trend. Its form in the discrete time system is: ;

[0056] in, It's time gradient of health deterioration; and They are the historical health stability indexes of the current moment and the previous calculation period; is the preset calculation time window. This parameter is set according to the failure mode of the monitored object. It takes a smaller value for the fast failure mode and a larger value for the slow degradation process. The application of this model is to The dynamic supplement of static indicators and the combination of the two enable the system to simultaneously grasp the current health level of the machine tool and its deterioration trend, realizing the expansion from static indicators to dynamic evaluation.

[0057] The potential risk assessment unit is used to combine the health stability index and the health deterioration gradient to perform calculations to determine the system deviation, deterioration rate value, and bottleneck risk value, and to perform a weighted summation operation on the system deviation, deterioration rate value, and bottleneck risk value to generate a potential risk level;

[0058] The system deviation is determined by subtracting the health stability index from the preset ideal health state value;

[0059] The process of determining the deterioration rate value includes:

[0060] Determine the gradient of health deterioration;

[0061] When the health deterioration gradient is negative, the negative value is extracted as the deterioration rate value;

[0062] When the health deterioration gradient is non-negative, the deterioration rate is set to zero;

[0063] The process of determining the bottleneck risk value includes:

[0064] Retrieve the normalized eigenvalues ​​and preset eigenweight coefficients generated by the health status quantification unit; multiply each normalized eigenvalue by the corresponding eigenweight coefficient to obtain each individual risk contribution value; extract the maximum value from each individual risk contribution value as the bottleneck risk value.

[0065] The core function of the potential risk assessment unit is to generate potential risk levels The technical motivation behind the adopted model is to overcome the flaw in traditional risk assessment that the averaging of indicators masks local extreme risks. It deconstructs total risk into a weighted sum of three dimensions: stock risk represented by system deviation, incremental risk represented by the deterioration rate value, and sudden risk dominated by a single weakest link represented by the bottleneck risk value.

[0066] The potential risk level The evaluation model is composed as follows: ;

[0067] The structure and parameters of this formula are as follows: The first item is the overall state risk item, the core of which is the system deviation , measuring the gap between the system and the ideal health state; the second item is the overall gradient risk item, the core of which is the deterioration rate value , ensuring that only The risk is only included when it is a negative value; the third item is the single point bottleneck risk item, the core of which is the bottleneck risk value ,pass The operator directly identifies and quantifies the single risk source that currently contributes most to the system's unhealthiness. The parameters in the formula all have a clear source: , , , All inherited from the above modules; is the weight coefficient of the three risks, satisfying , configured according to the focus of the maintenance strategy; is the risk magnification factor, which is greater than 1 and is set according to the risk aversion of the application scenario; The reference deterioration gradient is a significant deterioration rate benchmark value based on historical data statistics or expert definition, which is used to perform dimensionless processing on the deterioration rate value. Its physical dimension is the same as Stay consistent; :time potential risk level; :time Health stability index; : Reference deterioration gradient, a benchmark value used to perform dimensionless processing on the deterioration rate value; :time No. Normalized eigenvalues;

[0068] Risk weight factor The weights can be determined by inviting experts in the field to use the hierarchical analysis method to compare the relative importance of system deviation risk, deterioration rate risk and bottleneck risk; the risk amplification factor The value of can be set according to the machining accuracy and safety level requirements of different machine tools or workpieces. For example, for aerospace parts processing scenarios that require high reliability and low failure risk, a higher risk magnification factor value, such as 1.5 or 2, can be set;

[0069] An objective determination method based on historical data statistics is to collect all health deterioration gradients of the machine tool during multiple complete life cycles or long-term operation. Data, filter out all negative values, representing the gradient value of health decline, and calculate the absolute value of these negative gradients. These absolute values ​​are counted and the 95th percentile is taken as the reference deterioration gradient. ,Right now ;

[0070] This method ensures Represents the most severe 5% rate of deterioration ever experienced, thus providing a data-backed, reproducible benchmark for significant deterioration; : 95th percentile in statistics; : health deterioration gradient;

[0071] In an application example, suppose that at a certain moment: and ; Set model parameters , and the reference deterioration gradient ; The potential risk assessment unit determines the system deviation as The deterioration rate is ; Then perform a weighted sum operation: ;

[0072] The result of this calculation is the potential risk level at the current moment; this model produces a highly sensitive and informative risk indicator that not only reflects the current health of the machine tool but also takes into account the rate of deterioration, thereby providing earlier and more accurate warnings of potential problems.

[0073] Example 3:

[0074] The process of the maintenance strategy mapping unit generating multi-level maintenance strategy instructions includes:

[0075] Retrieve a preset risk threshold sequence and its corresponding multi-level maintenance strategy set; compare the potential risk level with the risk threshold sequence to determine the risk interval to which it belongs; and select the corresponding maintenance strategy from the multi-level maintenance strategy set based on the determined risk interval to generate a multi-level maintenance strategy instruction.

[0076] The maintenance strategy mapping unit is designed to convert the quantified risk into specific operational instructions; the core operating logic is to convert the calculated potential risk level Value, and the preset risk threshold sequence The method for determining the risk threshold sequence does not rely on subjective experience, but is based on statistical modeling of historical data and finding the optimal split point through cost-benefit analysis;

[0077] This cost-benefit analysis can be performed by constructing the total expected cost function This function takes into account both the preventive maintenance cost and the failure loss cost. For example, for a two-level strategy L0-no maintenance, L1-maintenance, the total expected cost can be defined as: ;

[0078] in, is the risk threshold to be optimized; is the average cost of performing L1 repairs; is the average loss caused by an unexpected failure; It is the probability density function of the potential risk level obtained through historical data statistics; is the conditional probability of a machine tool failure in the next cycle under a specific PRL value. This function can be fitted by historical failure data and the corresponding PRL value through methods such as logistic regression. Get the minimum value The optimal split point can be determined by ;For multi-level thresholds, this optimization can be performed level by level; :by is the total expected cost function of the threshold; : potential risk level; : Average loss caused by unexpected failure; A differential or infinitesimal increment representing the potential level of risk;

[0079] The analysis process comprehensively considers various levels of maintenance strategies, such as - Enhanced data monitoring frequency, -Online parameter compensation, - Cost of executing planned downtime inspections , as well as the probability of major failures and corresponding loss costs at different risk levels; Continuing with the above example, assuming that the maintenance strategy threshold is set to ; Due to the calculated potential risk level ,satisfy The system determines that the risk belongs to the first level interval and selects and generates a multi-level maintenance strategy from the set of multi-level maintenance strategies. The technical effect of this mapping mechanism is to achieve on-demand allocation and intensity matching of maintenance interventions, ensuring that the investment of maintenance resources is dynamically and accurately adapted to the actual risk level, thereby achieving lean management of maintenance activities.

[0080] Example 4:

[0081] The iterative optimization process of the feedback correction unit includes:

[0082] After the maintenance activity is performed, the health and stability index after maintenance is collected and compared with the health and stability index before maintenance to quantify the maintenance effect; the data records containing the pre-maintenance status, maintenance activities and quantified maintenance effects are used as knowledge samples; and the knowledge samples are used to feedback and update the feature weight coefficients in the health status quantification unit or the risk threshold sequence in the maintenance strategy mapping unit.

[0083] For feature weight coefficient When a maintenance activity, such as replacing component i, is confirmed to be effective, the maintenance If the value is significantly higher than before maintenance, the system can regard this as a positive feedback. Make incremental adjustments, for example, using the following update rules: ;

[0084] in is a smaller learning rate, and the other weights are scaled down proportionally to ensure that the sum of all weights is still 1; for the risk threshold sequence The update is to add new knowledge samples to the historical database after accumulating a certain number of new knowledge samples, for example, 100, and re-execute the cost-benefit analysis model in Example 4 to calculate the optimal threshold value that is more suitable for the current working conditions; : updated new feature weights; : old feature weights before adjustment; : Health stability index after maintenance activities are performed; : Health stability index before maintenance activities are performed;

[0085] The feedback correction unit is the core component for achieving system adaptive optimization. When a multi-level maintenance strategy instruction is executed, such as replacing a spindle bearing during a planned shutdown, the function of this unit is activated. The health stability index of the machine tool after the maintenance activity is executed is collected and recorded, and compared with the index before maintenance, so as to quantify the actual effect of the maintenance activity, such as recording the health stability index before maintenance. for After maintenance, it returns to A complete data record including the pre-maintenance status, maintenance activity content and quantified maintenance effect will be structured and stored as a new knowledge sample. The system uses the continuously accumulated knowledge samples to feedback update and iteratively optimize the key internal parameters in the upstream model, including reverse updating the weight coefficients of each feature in the health status quantification unit. , or modify the risk threshold sequence in the maintenance strategy mapping unit The technical effect of this closed-loop feedback correction mechanism is that it gives the entire optimization system the ability to self-learn and adapt, enabling it to learn from every maintenance practice. The model parameters become more and more in line with the specific machine tools and specific working conditions being monitored over time, ultimately achieving continuous and automated improvement in prediction accuracy and decision-making level.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data, characterized in that: include: A data acquisition and preprocessing unit is used to obtain the operating data of the CNC machine tool in real time and preprocess the operating data to extract a quantitative feature set; A health status quantification unit is used to perform calculations based on the quantitative feature set to obtain a system unhealthiness; the health stability index is generated by subtracting the system unhealthiness from a preset ideal health status value; A deterioration gradient evaluation unit is used to perform calculations based on the health stability index to obtain a health status difference; Dividing the health state difference by a preset calculation time window to generate a health deterioration gradient; The potential risk assessment unit is used to combine the health stability index and the health deterioration gradient to perform calculations to determine the system deviation, deterioration rate value, and bottleneck risk value, and to perform a weighted summation operation on the system deviation, deterioration rate value, and bottleneck risk value to generate a potential risk level; A maintenance strategy mapping unit is used to compare and analyze the potential risk level with a preset risk threshold sequence to generate a multi-level maintenance strategy instruction; The feedback correction unit is used to record the changes in the health stability index caused by maintenance activities after the multi-level maintenance strategy instructions are executed, and iteratively optimize the internal parameters of the system based on the changes.

2. The multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data according to claim 1 is characterized in that: The process of generating the health stability index by the health status quantification unit includes: Normalizing the feature data in the quantitative feature set extracted by the data acquisition and preprocessing unit to obtain normalized feature values; retrieving the preset feature weight coefficients corresponding to each normalized feature value; multiplying each normalized feature value by the corresponding feature weight coefficient to obtain each individual unhealthy degree; summing each individual unhealthy degree to obtain the system unhealthy degree; and calculating the health stability index by subtracting the system unhealthy degree from the preset ideal health state value.

3. The multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data according to claim 1 is characterized in that: The process of generating the health deterioration gradient by the deterioration gradient evaluation unit includes: Obtain the health stability index at the current moment and the historical health stability index of the previous calculation cycle; subtract the historical health stability index from the health stability index at the current moment to obtain the health status difference; divide the health status difference by the preset calculation time window to obtain the health deterioration gradient.

4. The multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data according to claim 1 is characterized in that: The system deviation is determined by subtracting the health stability index from a preset ideal health state value.

5. The multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data according to claim 1 is characterized in that: The process of determining the deterioration rate value includes: Determine the gradient of health deterioration; When the health deterioration gradient is negative, the negative value is extracted as the deterioration rate value; When the health deterioration gradient is non-negative, the deterioration rate is set to zero.

6. The multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data according to claim 2 is characterized in that: The process of determining the bottleneck risk value includes: Retrieve the normalized eigenvalues ​​and preset eigenweight coefficients generated by the health status quantification unit; multiply each normalized eigenvalue by the corresponding eigenweight coefficient to obtain each individual risk contribution value; extract the maximum value from each individual risk contribution value as the bottleneck risk value.

7. The multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data according to claim 1 is characterized in that: The process of the maintenance strategy mapping unit generating a multi-level maintenance strategy instruction includes: Retrieve a preset risk threshold sequence and its corresponding multi-level maintenance strategy set; compare the potential risk level with the risk threshold sequence to determine the risk interval to which it belongs; and select the corresponding maintenance strategy from the multi-level maintenance strategy set based on the determined risk interval to generate a multi-level maintenance strategy instruction.

8. The multi-level maintenance strategy optimization system for CNC machine tools based on multi-source data according to claim 1 is characterized in that: The process of iterative optimization performed by the feedback correction unit includes: After the maintenance activity is performed, the health and stability index after maintenance is collected and compared with the health and stability index before maintenance to quantify the maintenance effect; the data records containing the pre-maintenance status, maintenance activities and quantified maintenance effects are used as knowledge samples; and the knowledge samples are used to feedback and update the feature weight coefficients in the health status quantification unit or the risk threshold sequence in the maintenance strategy mapping unit.

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