A water guide laser machine tool processing efficiency real-time monitoring method

Abstract

The application discloses a water guide laser machine tool processing efficiency real-time monitoring method, relates to the water guide laser processing monitoring technical field, and comprises standard state parameters based on equipment; the application is transformed into classification based on data through equipment state judgment, makes efficiency monitoring directly aim at the ideal target of standard state, forms characteristic benchmark voiceprint data representing stable processing state, respectively establishes a benchmark for processing track sections of different geometric characteristics, improves the pertinence and accuracy of monitoring, calculates the benchmark performance index representing theoretical efficiency, realizes effective fusion and comprehensive evaluation, judges the behavior deviation of the processing process from the standard from the signal characteristic similarity angle, evaluates the comprehensive efficiency effect of the current processing from the real-time performance index angle, makes decisions by the first compliance degree reflecting the behavior and the second compliance degree reflecting the result, effectively avoids the misjudgment caused by single signal interference, and makes the water guide laser processing process have self-adaptability.

Description

Technical Field

[0001] This invention relates to the field of water-guided laser processing monitoring technology, and in particular to a method for real-time monitoring of the processing efficiency of a water-guided laser machine tool. Background Technology

[0002] Water-guided laser technology, with its characteristic of laser-water jet coupling, can achieve low heat impact and high-precision processing, and is widely used in the field of precision manufacturing. However, because its processing involves complex links such as laser-water jet coupling and dynamic operation of machine tools, traditional industrial processing monitoring and control schemes are difficult to adapt. The main method is to monitor the processing status by collecting sound signals during the processing. In order to reduce background noise interference, the threshold is calculated by collecting sound signals separately when the machine tool is stationary and not processing, so as to distinguish between effective processing signals and noise.

[0003] However, this technology has obvious defects. First, the noise acquisition method is incomplete. It only acquires the background acoustic environment when the machine tool is stationary, and fails to cover the dynamic operating noise generated by the servo drive of each axis, guide rail friction, water circulation system flow, etc. during the actual machining trajectory movement of the machine tool. This results in a large deviation between the set threshold and the actual situation, and insufficient monitoring accuracy. Secondly, the evaluation of processing efficiency or status is not related to the specific processing geometry. In fact, in water-guided laser processing, the interaction mechanism of laser-water-material, the impact angle of water jet and the stable state of water jet are different in different trajectory segments, such as linear feed, circular arc turning and drilling dwell. The resulting sound, vibration and light radiation signal characteristics are significantly different. Existing technology ignores this path-signal characteristic correlation and uses the same set of thresholds or models to evaluate all processing stages, which inevitably leads to inaccurate efficiency evaluation and misjudgment of status. To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0004] The purpose of this invention is to: acquire signals in real time during the processing, extract features, compare features and performance with a benchmark, and fuse the results to generate a confidence judgment, thereby driving the equipment to make precise adjustments, so as to realize real-time perception of the efficiency of water-guided laser processing.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for real-time monitoring of processing efficiency of a water-guided laser machine tool, comprising the following steps: Step 1: Based on the standard status parameters of the equipment, the processing status of the equipment is divided into abnormal status, early warning status and standard status, resulting in three different stages of processing status datasets; Step 2: Obtain machining parameter data under standard conditions from the machining state dataset to obtain a standard machining parameter dataset. Obtain the acoustic emission signal, vibration signal and weak light signal of machining under the standard machining parameters to form a reference acoustic pattern database corresponding to the standard machining trajectory segment. The standard machining trajectory segment includes a straight line segment, an arc segment and a hole machining segment. Step 3: Based on the benchmark voiceprint database, extract benchmark voiceprint features, benchmark vibration features, and benchmark optical signal features, and calculate the benchmark performance index; Step 4: In the actual processing, identify the real-time processing trajectory segment and match it with the corresponding path reference database. Collect the real-time acoustic emission signal, real-time vibration signal and real-time optical radiation signal during the processing, and extract the real-time features. Calculate the matching degree between the real-time features and the reference acoustic features to obtain the first matching degree. Calculate the real-time performance index based on the real-time features. Step 5: Compare the real-time performance index with the benchmark performance index to calculate the second compliance degree. Based on the first compliance degree and the second compliance degree, calculate the processing efficiency confidence factor through the preset fusion decision rule. Based on the processing efficiency confidence factor, make a processing efficiency judgment to generate a processing efficiency judgment instruction. Step 5: Based on the processing efficiency judgment instruction, perform real-time analysis of deviation characteristics and generate equipment operation adjustment instructions.

[0006] Furthermore, the processing status of the equipment is divided into abnormal status, early warning status, and standard status, resulting in three different stages of processing status datasets. The specific division process is as follows: During equipment operation, key parameters reflecting the health of equipment processing are collected by sensors as the basis for classification. When the equipment is known to be normal, time series data of key performance indicators are collected, and time domain, frequency domain and time-frequency domain features are extracted for each signal to form a feature vector. Calculate the mean and standard deviation of the feature vector under normal processing conditions, establish a standard state parameter benchmark library, set an absolute safety threshold for each key parameter, determine the benchmark value of the standard state parameter, and set a warning range interval. The parameter range that exceeds the warning interval is defined as the abnormal state interval. The raw sensor data, key processing parameters, feature vectors, and corresponding state labels are associated to form three labeled datasets: standard state dataset, early warning state dataset, and abnormal state dataset.

[0007] Furthermore, the acoustic emission signal, vibration signal, and weak light signal obtained under standard processing parameters are used to form a reference acoustic signature database corresponding to the standard processing trajectory segment. The specific process is as follows: From the processing status dataset, all processing process data segments marked as standard states are selected. For typical standard processing trajectory segments, preset straight line segments, arc segments, and hole processing segments, when processing each preset standard processing trajectory segment, the standard processing acoustic emission signal, standard processing vibration signal, and standard processing weak light signal are recorded within that time period. Each signal segment is matched with a standard processing trajectory segment. For each type of trajectory segment, representative features are extracted from multiple batches of signals. The average signal level is extracted from the acoustic emission sensor as the reference acoustic signature feature, the peak mean is extracted from the vibration signal as the reference vibration feature, and the average light intensity is extracted from the weak light signal as the reference light signal feature, thus forming the reference feature set of the trajectory segment. Create an independent database entry for each type of standard processing trajectory segment, and integrate the information from all trajectory segment entries to form a complete reference voiceprint database.

[0008] Furthermore, based on the benchmark voiceprint database, benchmark voiceprint features, benchmark vibration features, and benchmark optical signal features are extracted, and the benchmark performance index is calculated. The specific process is as follows: From the baseline acoustic signature database, the average acoustic emission level, the average peak value of vibration, and the average light intensity of optical signal are extracted as baseline features, and statistical calculations are performed to obtain the expected value and confidence zone of each feature for each type of trajectory segment. Based on the equipment operation definition, the positive and negative relationship between each feature and processing efficiency is defined, and a standardized function is designed to map the feature values ​​to [0,1] efficiency scores. For each type of trajectory segment, calculate the single-feature performance score based on the expected value of the features. Then, sum the three scores according to preset weights to obtain the baseline performance index for that trajectory segment, and store it in the database as an evaluation benchmark.

[0009] Furthermore, during the actual processing, real-time processing trajectory segments are identified and matched against the corresponding path reference database. The specific process is as follows: The system acquires the program operations executed by the device during operation, identifies the type of geometric trajectory currently being executed and its process parameters, and matches them with standard template entries in the benchmark database. When the start of the trajectory segment is detected, three raw signals, namely acoustic emission, vibration and light radiation, are collected in real time. The raw signals are preprocessed in accordance with the reference signal. Based on the actual start and end time of the trajectory segment, the corresponding real-time signal segment is extracted from the signal stream. For the captured real-time signal segments, calculate the average level of the real-time acoustic emission signal, the peak mean of the real-time vibration signal, and the average light intensity of the real-time optical signal to generate real-time features.

[0010] Furthermore, the matching degree between the real-time features and the baseline voiceprint features is calculated to obtain the first matching degree. Based on the real-time features, the real-time performance index is calculated. The specific process is as follows: The real-time features are compared with the benchmark voiceprint feature library. Based on the similarity method, the degree of conformity with the benchmark voiceprint features, benchmark vibration features and benchmark optical signal features is calculated respectively. The obtained conformity is weighted and fused to obtain the comprehensive first conformity. The obtained compliance scores are weighted and summed using the same weighting coefficients as those used when calculating the baseline performance index to obtain the real-time performance index.

[0011] Furthermore, a processing efficiency confidence factor is calculated using a pre-defined fusion decision rule. Based on this confidence factor, processing efficiency is judged to generate a processing efficiency judgment instruction. The specific process is as follows: The baseline performance index corresponding to the current trajectory segment is obtained as a comparison benchmark. Based on the relative deviation between the real-time performance index and the baseline value, the standardized second conformity is obtained. The processing efficiency confidence factor is obtained by combining the first degree of compliance with the second degree of compliance, which reflects the consistency of performance. The calculated confidence factor is compared with the preset efficiency level threshold to determine whether the current processing is in an efficient, normal, or inefficient state. Based on the determined efficiency level, a processing efficiency judgment instruction containing the specific level and confidence value is generated.

[0012] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This real-time monitoring method for the processing efficiency of water-guided laser machine tools transforms equipment status judgment into data-based classification, enabling efficiency monitoring and optimization to directly target the ideal standard state. It collects characteristic signals under optimal parameters and correlates them with specific standard processing trajectory segments to form characteristic benchmark acoustic signature data representing a stable processing state. Benchmarks are established for processing trajectory segments with different geometric features, allowing efficiency assessment to fully consider the differences in physical characteristics of different processing actions, improving the targeting and accuracy of monitoring. Through weighted fusion of features, a benchmark efficiency index representing theoretical efficiency is calculated, achieving effective fusion and comprehensive evaluation. From the perspective of signal feature similarity, it judges deviations from the standard in real time; from the perspective of real-time efficiency index, it evaluates the overall efficiency of the current processing in real time. It makes decisions based on the first degree of conformity reflecting behavior and the second degree of conformity reflecting results, integrating dual evidence of process and result to effectively avoid misjudgments caused by single signal interference. Dynamic adjustments are made based on real-time efficiency feedback, giving the water-guided laser processing process adaptive capabilities. Attached Figure Description Figure 1 A schematic diagram of the overall structure of the method of the present invention is shown. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Example 1: like Figure 1 As shown, a method for real-time monitoring of processing efficiency of a water-guided laser machine tool includes the following steps: Step 1: Based on the standard status parameters of the equipment, the processing status of the equipment is divided into abnormal status, early warning status and standard status, resulting in three different stages of processing status datasets; Step 2: Obtain machining parameter data under standard conditions from the machining state dataset to obtain a standard machining parameter dataset. Obtain the acoustic emission signal, vibration signal and weak light signal of machining under the standard machining parameters to form a reference acoustic pattern database corresponding to the standard machining trajectory segment. The standard machining trajectory segment includes a straight line segment, an arc segment and a hole machining segment. Step 3: Based on the benchmark voiceprint database, extract benchmark voiceprint features, benchmark vibration features, and benchmark optical signal features, and calculate the benchmark performance index; Step 4: In the actual processing, identify the real-time processing trajectory segment and match it with the corresponding path reference database. Collect the real-time acoustic emission signal, real-time vibration signal and real-time optical radiation signal during the processing, and extract the real-time features. Calculate the matching degree between the real-time features and the reference acoustic features to obtain the first matching degree. Calculate the real-time performance index based on the real-time features. Step 5: Compare the real-time performance index with the benchmark performance index to calculate the second compliance degree. Based on the first compliance degree and the second compliance degree, calculate the processing efficiency confidence factor through the preset fusion decision rule. Based on the processing efficiency confidence factor, make a processing efficiency judgment to generate a processing efficiency judgment instruction. Step 5: Based on the processing efficiency judgment instruction, perform real-time analysis of deviation characteristics and generate equipment operation adjustment instructions.

[0015] The processing status of the equipment is divided into abnormal status, early warning status, and standard status, resulting in three different processing status datasets. The specific division process is as follows: During equipment operation, key parameters reflecting the health of equipment processing are collected by sensors as the basis for classification. When the equipment is known to be normal, time series data of key performance indicators are collected, and time domain, frequency domain and time-frequency domain features are extracted for each signal to form a feature vector. Calculate the mean and standard deviation of the feature vector under normal processing conditions, establish a standard state parameter benchmark library, set an absolute safety threshold for each key parameter, determine the benchmark value of the standard state parameter, and set a warning range interval. The parameter range that exceeds the warning interval is defined as the abnormal state interval. The raw sensor data, key processing parameters, feature vectors, and corresponding state labels are associated to form three labeled datasets: standard state dataset, early warning state dataset, and abnormal state dataset.

[0016] Acquire acoustic emission signals, vibration signals, and weak light signals from processing under standard processing parameters to form a reference acoustic signature database corresponding to the standard processing trajectory segment. The specific process is as follows: From the processing status dataset, all processing process data segments marked as standard states are selected. For typical standard processing trajectory segments, preset straight line segments, arc segments, and hole processing segments, when processing each preset standard processing trajectory segment, the standard processing acoustic emission signal, standard processing vibration signal, and standard processing weak light signal are recorded within that time period. Each signal segment is matched with a standard processing trajectory segment. For each type of trajectory segment, representative features are extracted from multiple batches of signals. The average signal level is extracted from the acoustic emission sensor as the reference acoustic signature feature, the peak mean is extracted from the vibration signal as the reference vibration feature, and the average light intensity is extracted from the weak light signal as the reference light signal feature, thus forming the reference feature set of the trajectory segment. Create an independent database entry for each type of standard processing trajectory segment, and integrate the information from all trajectory segment entries to form a complete reference voiceprint database.

[0017] Based on the benchmark voiceprint database, benchmark voiceprint features, benchmark vibration features, and benchmark optical signal features are extracted, and the benchmark performance index is calculated. The specific process is as follows: From the baseline acoustic signature database, the average acoustic emission level, the average peak value of vibration, and the average light intensity of optical signal are extracted as baseline features, and statistical calculations are performed to obtain the expected value and confidence zone of each feature for each type of trajectory segment. Based on the equipment operation, define the positive and negative relationship between each feature and the processing efficiency, and map the feature values ​​to a standardized function of efficiency score in [0,1]. For each type of trajectory segment, the expected value of the features is used to calculate the single feature performance score. The three scores are then weighted and summed according to preset weights to obtain the baseline performance index of the trajectory segment. This index is then stored in the database as an evaluation benchmark.

[0018] In this scheme, the signal data of each type of standard machining trajectory segment, straight line, arc, and hole under the reference acoustic fingerprint database are processed, and three types of reference features are extracted from the signal data: The average signal level of the acoustic emission signal over the entire trajectory segment is calculated as the reference acoustic signature feature. , ,in Here, N represents the discrete sampled values ​​of the acoustic emission signal, and N is the total number of acoustic emission signal samples. The peak-to-average value of the vibration signal along the key sensitive axis is used as the reference vibration characteristic. First, identify the peak value of the axial vibration signal in each processing cycle, and then take the arithmetic mean of the peak values ​​for all cycles. M is the total number of peak point samples within the period, and j is the index of peak points within the period; Calculate the average light intensity of the weak light signal over the entire trajectory segment, i.e. ,in denoted as discrete sampled values ​​of the light intensity signal, where N is the total number of weak light signal samples; For each type of trajectory segment, the statistical center and dispersion measure of the multiple sets of feature values ​​are calculated, and the average number of samples used for each feature value is calculated. Confidence intervals: Based on the sample distribution, calculate the confidence interval for each feature at a specified confidence level; Based on process knowledge, a positive or negative correlation between multi-signal features and processing efficiency is established. A standardized, dimensionless single-feature efficiency scoring function is defined for each feature and mapped to the [0,1] interval, where 1 represents the optimal efficiency. Using the baseline feature expectation value of each trajectory segment calculated in step one as input, we substitute it into the corresponding single-feature performance scoring function, and perform weighted summation using predefined weights to calculate the baseline performance index of the trajectory segment of that class:

[0019] Should It is a scalar value between 0 and 1, which quantitatively represents the overall performance level that can be achieved when executing a specific processing trajectory segment under standard conditions and optimal parameters.

[0020] In the actual processing, real-time processing trajectory segments are identified and matched against the corresponding path benchmark database. The specific process is as follows: The system acquires the program operations executed by the device during operation, identifies the type of geometric trajectory currently being executed and its process parameters, and matches them with standard template entries in the benchmark database. When the start of the trajectory segment is detected, three raw signals, namely acoustic emission, vibration and light radiation, are collected in real time. The raw signals are preprocessed in accordance with the reference signal. Based on the actual start and end time of the trajectory segment, the corresponding real-time signal segment is extracted from the signal stream. For the captured real-time signal segments, calculate the average level of the real-time acoustic emission signal, the peak mean of the real-time vibration signal, and the average light intensity of the real-time optical signal to generate real-time features.

[0021] The matching degree between real-time features and baseline voiceprint features is calculated to obtain the first matching degree. Based on the real-time features, the real-time performance index is calculated. The specific process is as follows: The real-time features are compared with the benchmark voiceprint feature library. Based on the similarity method, the degree of conformity with the benchmark voiceprint features, benchmark vibration features and benchmark optical signal features is calculated respectively. The obtained conformity is weighted and fused to obtain the comprehensive first conformity. The obtained compliance scores are weighted and summed using the same weighting coefficients as those used when calculating the baseline performance index to obtain the real-time performance index.

[0022] The processing efficiency confidence factor is calculated using a preset fusion decision rule. Based on the processing efficiency confidence factor, the processing efficiency is judged to generate a processing efficiency judgment instruction. The specific process is as follows: The baseline performance index corresponding to the current trajectory segment is obtained as a comparison benchmark. Based on the relative deviation between the real-time performance index and the baseline value, the standardized second conformity is obtained. The processing efficiency confidence factor is obtained by combining the first degree of compliance with the second degree of compliance, which reflects the consistency of performance. The calculated confidence factor is compared with the preset efficiency level threshold to determine whether the current processing is in an efficient, normal, or inefficient state. Based on the determined efficiency level, a processing efficiency judgment instruction containing the specific level and confidence value is generated.

[0023] In this solution, the system reads and parses the executing code segment in real time to obtain the actual position feedback of each axis of the machine tool, determines the type and key parameters of the currently executing geometric trajectory, and matches the identified geometric trajectory with entries in the benchmark database. When the system determines that it has entered a new machining trajectory segment, it sends a trigger signal to the high-speed data acquisition card to start acquiring real-time acoustic emission signals, real-time vibration signals, and real-time optical radiation signals. For each captured real-time signal segment, the system uses the same algorithm and parameters as when constructing the benchmark database to extract the corresponding real-time features. For each type of feature, the system calculates the degree of conformity between the real-time feature value and the benchmark expected value. The conformity of the three types of features can be weighted and averaged using a standard deviation normalization method to obtain the first degree of conformity.

[0024] Read the baseline performance index of the corresponding trajectory segment from the matched baseline database entries. Calculate the real-time performance index And after normalization, we obtain ; Through a preset mapping function, The performance deviation is converted into a second compliance degree. The value range is usually [0,1]; For the first degree of conformity Second conformity The fusion decision rule is typically a weighted summation: ; The calculated and Substituting the above rules, the comprehensive processing efficiency confidence factor is calculated. ; A preset threshold is used for efficiency level determination. For high efficiency, This is normal. Inefficient The set security judgment threshold; Once the efficiency level of the current processing trajectory segment is determined, the system will generate a corresponding processing efficiency judgment instruction, which will at least include the efficiency level.

[0025] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.

[0026] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. In the two embodiments provided in this application, it should be understood that the disclosed methods can be implemented in other ways; for example, the device embodiments described above are merely illustrative, and the division of modules is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or modules may be electrical, mechanical or other forms. The above description is only a preferred embodiment of the present invention, but 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 inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for real-time monitoring of processing efficiency of a water-guided laser machine tool, characterized in that, Includes the following steps: Step 1: Based on the standard status parameters of the equipment, the processing status of the equipment is divided into abnormal status, early warning status and standard status, resulting in three different stages of processing status datasets; Step 2: Obtain machining parameter data under standard conditions from the machining state dataset to obtain a standard machining parameter dataset. Obtain the acoustic emission signal, vibration signal and weak light signal of machining under the standard machining parameters to form a reference acoustic pattern database corresponding to the standard machining trajectory segment. The standard machining trajectory segment includes a straight line segment, an arc segment and a hole machining segment. Step 3: Based on the benchmark voiceprint database, extract benchmark voiceprint features, benchmark vibration features, and benchmark optical signal features, and calculate the benchmark performance index; Step 4: In the actual processing, identify the real-time processing trajectory segment and match it with the corresponding path reference database. Collect the real-time acoustic emission signal, real-time vibration signal and real-time optical radiation signal during the processing, and extract the real-time features. Calculate the matching degree between the real-time features and the reference acoustic features to obtain the first matching degree. Calculate the real-time performance index based on the real-time features. Step 5: Compare the real-time performance index with the benchmark performance index to calculate the second compliance degree. Based on the first compliance degree and the second compliance degree, calculate the processing efficiency confidence factor through the preset fusion decision rule. Based on the processing efficiency confidence factor, make a processing efficiency judgment to generate a processing efficiency judgment instruction. Step 5: Based on the processing efficiency judgment instruction, perform real-time analysis of deviation characteristics and generate equipment operation adjustment instructions.

2. The method for real-time monitoring of processing efficiency of water-guided laser machine tools according to claim 1, characterized in that, The processing status of the equipment is divided into abnormal status, early warning status, and standard status, resulting in three different processing status datasets. The specific division process is as follows: During equipment operation, key parameters reflecting the health of equipment processing are collected by sensors as the basis for classification. When the equipment is known to be normal, time series data of key performance indicators are collected, and time domain, frequency domain and time-frequency domain features are extracted for each signal to form a feature vector. Calculate the mean and standard deviation of the feature vector under normal processing conditions, establish a standard state parameter benchmark library, set an absolute safety threshold for each key parameter, determine the benchmark value of the standard state parameter, and set a warning range interval. The parameter range that exceeds the warning interval is defined as the abnormal state interval. The raw sensor data, key processing parameters, feature vectors, and corresponding state labels are associated to form three labeled datasets: standard state dataset, early warning state dataset, and abnormal state dataset.

3. The method for real-time monitoring of processing efficiency of water-guided laser machine tools according to claim 1, characterized in that, Acquire acoustic emission signals, vibration signals, and weak light signals from processing under standard processing parameters to form a reference acoustic signature database corresponding to the standard processing trajectory segment. The specific process is as follows: From the processing status dataset, all processing process data segments marked as standard states are selected. For typical standard processing trajectory segments, preset straight line segments, arc segments, and hole processing segments, when processing each preset standard processing trajectory segment, the standard processing acoustic emission signal, standard processing vibration signal, and standard processing weak light signal are recorded within that time period. Each signal segment is matched with a standard processing trajectory segment. For each type of trajectory segment, representative features are extracted from multiple batches of signals. The average signal level is extracted from the acoustic emission sensor as the reference acoustic signature feature, the peak mean is extracted from the vibration signal as the reference vibration feature, and the average light intensity is extracted from the weak light signal as the reference light signal feature, thus forming the reference feature set of the trajectory segment. Create an independent database entry for each type of standard processing trajectory segment, and integrate the information from all trajectory segment entries to form a complete reference voiceprint database.

4. The method for real-time monitoring of processing efficiency of a water-guided laser machine tool according to claim 1, characterized in that, Based on the benchmark voiceprint database, benchmark voiceprint features, benchmark vibration features, and benchmark optical signal features are extracted, and the benchmark performance index is calculated. The specific process is as follows: From the baseline acoustic signature database, the average acoustic emission level, the average peak value of vibration, and the average light intensity of optical signal are extracted as baseline features, and statistical calculations are performed to obtain the expected value and confidence zone of each feature for each type of trajectory segment. Based on the equipment operation definition, the positive and negative relationship between each feature and processing efficiency is defined, and a standardized function is designed to map the feature values ​​to [0,1] efficiency scores. For each type of trajectory segment, calculate the single-feature performance score based on the expected value of the features. Then, sum the three scores according to preset weights to obtain the baseline performance index for that trajectory segment, and store it in the database as an evaluation benchmark.

5. The method for real-time monitoring of processing efficiency of a water-guided laser machine tool according to claim 1, characterized in that, In the actual processing, real-time processing trajectory segments are identified and matched against the corresponding path benchmark database. The specific process is as follows: The system acquires the program operations executed by the device during operation, identifies the type of geometric trajectory currently being executed and its process parameters, and matches them with standard template entries in the benchmark database. When the start of the trajectory segment is detected, three raw signals, namely acoustic emission, vibration and light radiation, are collected in real time. The raw signals are preprocessed in accordance with the reference signal. Based on the actual start and end time of the trajectory segment, the corresponding real-time signal segment is extracted from the signal stream. For the captured real-time signal segments, calculate the average level of the real-time acoustic emission signal, the peak mean of the real-time vibration signal, and the average light intensity of the real-time optical signal to generate real-time features.

6. The method for real-time monitoring of processing efficiency of a water-guided laser machine tool according to claim 1, characterized in that, The matching degree between real-time features and baseline voiceprint features is calculated to obtain the first matching degree. Based on the real-time features, the real-time performance index is calculated. The specific process is as follows: The real-time features are compared with the benchmark voiceprint feature library. Based on the similarity method, the degree of conformity with the benchmark voiceprint features, benchmark vibration features and benchmark optical signal features is calculated respectively. The obtained conformity is weighted and fused to obtain the comprehensive first conformity. The obtained compliance scores are weighted and summed using the same weighting coefficients as those used when calculating the baseline performance index to obtain the real-time performance index.

7. The method for real-time monitoring of processing efficiency of a water-guided laser machine tool according to claim 1, characterized in that, The processing efficiency confidence factor is calculated using a preset fusion decision rule. Based on the processing efficiency confidence factor, the processing efficiency is judged to generate a processing efficiency judgment instruction. The specific process is as follows: The baseline performance index corresponding to the current trajectory segment is obtained as a comparison benchmark. Based on the relative deviation between the real-time performance index and the baseline value, the standardized second conformity is obtained. The processing efficiency confidence factor is obtained by combining the first degree of compliance with the second degree of compliance, which reflects the consistency of performance. The calculated confidence factor is compared with the preset efficiency level threshold to determine whether the current processing is in an efficient, normal, or inefficient state. Based on the determined efficiency level, a processing efficiency judgment instruction containing the specific level and confidence value is generated.

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