Method and system for monitoring and evaluating running state of pre-modulation adjustable intensity source applicator

Abstract

The invention relates to the field of pre-modulation intensity-adjustable source applicators, in particular to a method and a system for monitoring and evaluating the running state of a pre-modulation intensity-adjustable source applicator. The method for monitoring and evaluating the running state of the pre-modulation adjustable intensity source applicator comprises the following steps: establishing a multi-dimensional data feature model of the pre-modulation adjustable intensity source applicator by using real-time data of the pre-modulation adjustable intensity source applicator; acquiring a real-time data trend of a pre-modulation adjustable intensity source applicator by using the multi-dimensional data feature model; according to the real-time data trend of the pre-modulation adjustable intensity source applicator, an operation state monitoring and evaluation result is obtained, the operation state of equipment can be reflected more comprehensively by fusing a control data feature model and an operation data feature model, the operation state of the equipment can be accurately judged by comparing key trend features with a threshold value, and the accuracy of the operation state of the equipment is improved. And corresponding processing suggestions are provided, so that the reliability and the safety of the equipment are improved.

Description

Technical Field

[0001] The present invention relates to the field of pre-modulated adjustable intensity applicators, and in particular to a method and system for monitoring and evaluating the operating status of pre-modulated adjustable intensity applicators. Background Art

[0002] The pre-modulated adjustable intensity applicator is a key component widely used in medical detection equipment. It is mainly used to generate and adjust high-intensity, high-precision energy output to support medical applications such as medical imaging, radiotherapy, and ultrasound detection. The stability of its operating state directly affects the detection accuracy, treatment effect and patient safety of medical equipment. Traditional operating status monitoring methods usually rely on simple threshold judgments from a single data source, which makes it difficult to fully reflect the actual operating status of the equipment and is easily affected by noise and data redundancy, resulting in inaccurate monitoring results. In addition, existing technologies lack deep fusion and dynamic trend analysis of multi-dimensional data features, and cannot effectively predict potential failures or abnormal conditions of equipment.

[0003] In medical testing equipment, abnormal operating conditions of pre-modulated adjustable-intensity applicators can lead to distorted test results, inaccurate treatment doses, and even unnecessary harm to patients. Therefore, a method for monitoring and evaluating the operating conditions of pre-modulated adjustable-intensity applicators based on a multi-dimensional data feature model, combined with real-time data trend analysis and feedback verification, is urgently needed to improve monitoring accuracy and evaluation reliability, thereby ensuring the safety and effectiveness of medical equipment. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method and system for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator. By establishing a multi-dimensional data feature model and combining real-time data trend analysis and feedback verification, comprehensive monitoring and evaluation of the operating status of the pre-modulated adjustable intensity applicator can be achieved.

[0005] To achieve the above objectives, the present invention provides a method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator, comprising:

[0006] S1. Using the real-time data of the pre-modulated adjustable intensity applicator, a multi-dimensional data feature model of the pre-modulated adjustable intensity applicator is established;

[0007] S2. Using the multi-dimensional data feature model to obtain real-time data trends of the pre-modulated adjustable intensity applicator;

[0008] S3. Obtaining an operation status monitoring and evaluation result according to the real-time data trend of the pre-modulated adjustable intensity applicator.

[0009] Preferably, using the real-time data of the pre-modulation adjustable intensity applicator to establish a multi-dimensional data feature model of the pre-modulation adjustable intensity applicator includes:

[0010] respectively collecting real-time control data and real-time operation data of the pre-modulation adjustable intensity applicator as real-time data of the pre-modulation adjustable intensity applicator;

[0011] Using the real-time data of the pre-modulation adjustable intensity applicator, a control data characteristic model and an operation data characteristic model of the pre-modulation adjustable intensity applicator are respectively established;

[0012] The control data feature model and the operation data feature model of the pre-modulation adjustable intensity applicator are used as the multi-dimensional data feature model of the pre-modulation adjustable intensity applicator.

[0013] Furthermore, respectively collecting the real-time control data and the real-time operation data of the pre-modulation adjustable intensity applicator as the real-time data of the pre-modulation adjustable intensity applicator includes:

[0014] Collecting the real-time control voltage, real-time control current and real-time control frequency of the pre-modulation adjustable power applicator as the real-time control data of the pre-modulation adjustable power applicator;

[0015] Collecting the real-time operating temperature, real-time operating vibration and real-time operating displacement of the pre-modulated adjustable intensity applicator as the real-time operating data of the pre-modulated adjustable intensity applicator;

[0016] The real-time control data and real-time operation data of the pre-modulation adjustable intensity applicator are used as the real-time data of the pre-modulation adjustable intensity applicator.

[0017] Furthermore, using the real-time data of the pre-modulation adjustable intensity applicator to respectively establish a control data characteristic model and an operation data characteristic model of the pre-modulation adjustable intensity applicator includes:

[0018] Establishing a control data characteristic model of the pre-modulation adjustable intensity applicator by using the real-time data corresponding to the real-time control data of the pre-modulation adjustable intensity applicator;

[0019] An operation data characteristic model of the pre-modulation adjustable intensity applicator is established by using the real-time data corresponding to the real-time control data of the pre-modulation adjustable intensity applicator.

[0020] Furthermore, establishing a control data feature model of the pre-modulation adjustable intensity applicator by using the real-time data of the pre-modulation adjustable intensity applicator corresponding to the real-time control data includes:

[0021] Preprocessing the real-time control data of the premodulated adjustable intensity applicator includes smoothing the control voltage, control current, and control frequency based on a sliding average filter algorithm, and converting the data into a standard distribution with a mean of 0 and a variance of 1 based on a Z-score normalization method to obtain standardized control data;

[0022] Based on the standardized control data, a support vector machine algorithm is used to establish a control data characteristic model of the pre-modulated adjustable intensity applicator, wherein the control data characteristic model is used to describe the dynamic relationship between the control voltage, control current and control frequency and their changing rules;

[0023] Preprocessing the real-time operating data of the premodulated adjustable intensity applicator includes denoising the operating temperature, operating vibration, and operating displacement based on wavelet transform, and mapping the data to the [0, 1] interval based on a Min-Max normalization method to obtain standardized operating data;

[0024] Based on the standardized operating data, a Gaussian process regression algorithm is used to establish an operating data characteristic model of the pre-modulated adjustable intensity applicator, wherein the operating data characteristic model is used to describe the dynamic relationship between operating temperature, operating vibration and operating displacement and their changing laws;

[0025] The control data feature model is fused with the operation data feature model, and the output results of the two types of models are integrated based on the weighted average method to form a multi-dimensional data feature model of the pre-modulated adjustable intensity applicator.

[0026] Furthermore, establishing an operation data characteristic model of the pre-modulation adjustable intensity applicator using the real-time data of the pre-modulation adjustable intensity applicator corresponding to the real-time control data includes:

[0027] Time-aligning the real-time control data and real-time operation data of the pre-modulated adjustable intensity applicator to ensure consistency of the control data and the operation data in the time dimension;

[0028] Based on the time-aligned data, the real-time control data and real-time operation data are subjected to dimensionality reduction processing using the principal component analysis method to extract key features;

[0029] Using the extracted key features, an operating data feature model of the pre-modulated adjustable intensity applicator is established based on the long short-term memory neural network algorithm.

[0030] Furthermore, using the multi-dimensional data feature model to obtain real-time data trends of the pre-modulated adjustable intensity applicator includes:

[0031] S2-1. Utilizing the multi-dimensional data feature model to obtain multi-dimensional staggered intersecting period data features according to the staggered intersecting period;

[0032] S2-1-1. Divide the data in the multi-dimensional data feature model into several time periods according to time series;

[0033] S2-1-2. Use the data of adjacent time periods to perform staggered cross processing. The second half of the previous time period is overlapped with the first half of the next time period to create staggered cross time period data.

[0034] S2-1-3. Extract features using the staggered and intersecting period data, and extract time-frequency domain features based on a discrete wavelet transform method to obtain multi-dimensional staggered and intersecting period data features;

[0035] S2-2. Perform feedback verification processing based on the multi-dimensional staggered cross-period data characteristics to obtain real-time data trends of the pre-modulated adjustable intensity applicator.

[0036] Furthermore, the real-time data trend of the pre-modulated adjustable intensity applicator obtained by performing feedback verification processing based on the multi-dimensional staggered cross-period data characteristics includes:

[0037] The multi-dimensional staggered cross-period data features are input into a pre-trained feedback verification model, wherein the feedback verification model is constructed using a recursive neural network algorithm;

[0038] Error analysis is performed based on the prediction results. Evaluation indicators are established based on the root mean square error to verify the accuracy of the prediction results.

[0039] The feedback verification model is used to dynamically predict the data characteristics of the multi-dimensional staggered cross-period, and obtain the real-time data trend of the pre-modulated adjustable intensity applicator.

[0040] Furthermore, the operation status monitoring and evaluation results obtained based on the real-time data trend of the pre-modulated adjustable intensity applicator include:

[0041] Based on the real-time data trend, key trend characteristics are obtained, including trend slope, fluctuation amplitude and periodic changes;

[0042] The key trend features are compared with predefined operating status thresholds to determine whether the operating status of the pre-modulated adjustable intensity applicator is normal, and an operating status monitoring and evaluation result is obtained.

[0043] A system for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator, comprising:

[0044] A model building module is used to build a multi-dimensional data feature model of the pre-modulated adjustable intensity applicator using real-time data of the pre-modulated adjustable intensity applicator;

[0045] A trend acquisition module, configured to acquire real-time data trends of the pre-modulated adjustable intensity applicator using the multi-dimensional data feature model;

[0046] The monitoring and evaluation module is used to obtain the operation status monitoring and evaluation results according to the real-time data trend of the pre-modulated adjustable intensity applicator.

[0047] Compared with the closest prior art, the present invention has the following beneficial effects:

[0048] By integrating the control data feature model with the operation data feature model, the operating status of the equipment can be more comprehensively reflected; real-time trend analysis: based on the data characteristics of the staggered cross-period and the feedback verification model, the real-time data trends of the equipment can be dynamically predicted and potential faults can be discovered in advance; by comparing key trend features with thresholds, the operating status of the equipment can be accurately judged and corresponding processing suggestions can be provided to improve the reliability and safety of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator provided by the present invention. DETAILED DESCRIPTION

[0050] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0052] Example 1:

[0053] The present invention provides a method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator, such as Figure 1 Shown, including:

[0054] S1. Using the real-time data of the pre-modulated adjustable intensity applicator, a multi-dimensional data feature model of the pre-modulated adjustable intensity applicator is established;

[0055] S2. Using the multi-dimensional data feature model to obtain real-time data trends of the pre-modulated adjustable intensity applicator;

[0056] S3. Obtaining an operation status monitoring and evaluation result according to the real-time data trend of the pre-modulated adjustable intensity applicator.

[0057] S1 specifically includes:

[0058] S1-1, respectively collecting real-time control data and real-time operation data of the pre-modulation adjustable intensity applicator as real-time data of the pre-modulation adjustable intensity applicator;

[0059] S1-2, using the real-time data of the pre-modulation adjustable intensity applicator to respectively establish a control data characteristic model and an operation data characteristic model of the pre-modulation adjustable intensity applicator;

[0060] S1-3. Utilize the control data feature model and the operation data feature model of the pre-modulation adjustable intensity applicator as a multi-dimensional data feature model of the pre-modulation adjustable intensity applicator.

[0061] S1-1 specifically includes:

[0062] S1-1-1. Collect the real-time control voltage, real-time control current and real-time control frequency of the pre-modulation adjustable power applicator as the real-time control data of the pre-modulation adjustable power applicator;

[0063] S1-1-2. Collect the real-time operating temperature, real-time operating vibration and real-time operating displacement of the pre-modulated adjustable intensity applicator as the real-time operating data of the pre-modulated adjustable intensity applicator;

[0064] S1-1-3. Utilize the real-time control data and real-time operation data of the pre-modulation adjustable intensity applicator as the real-time data of the pre-modulation adjustable intensity applicator.

[0065] S1-2 specifically includes:

[0066] S1-2-1. Establishing a control data characteristic model of the pre-modulation adjustable intensity applicator by using the real-time data corresponding to the real-time control data of the pre-modulation adjustable intensity applicator;

[0067] S1-2-2. Use the real-time data of the pre-modulation adjustable intensity applicator to correspond to the real-time control data to establish an operation data characteristic model of the pre-modulation adjustable intensity applicator.

[0068] S1-2-1 specifically includes:

[0069] S1-2-1-1. Preprocessing the real-time control data of the pre-modulated adjustable intensity applicator includes smoothing the control voltage, control current, and control frequency based on a sliding average filtering algorithm, and converting the data into a standard distribution with a mean of 0 and a variance of 1 based on a Z-score normalization method to obtain standardized control data;

[0070] S1-2-1-2. Based on the standardized control data, a support vector machine algorithm is used to establish a control data characteristic model of the pre-modulated adjustable intensity applicator, wherein the control data characteristic model is used to describe the dynamic relationship between the control voltage, control current and control frequency and their changing rules;

[0071] S1-2-1-3. Preprocessing the real-time operating data of the pre-modulated adjustable intensity applicator includes denoising the operating temperature, operating vibration, and operating displacement based on wavelet transform, and mapping the data to the [0, 1] interval based on the Min-Max normalization method to obtain standardized operating data;

[0072] S1-2-1-4. Based on the standardized operating data, a Gaussian process regression algorithm is used to establish an operating data characteristic model of the pre-modulated adjustable intensity applicator, wherein the operating data characteristic model is used to describe the dynamic relationship between operating temperature, operating vibration, and operating displacement, and their changing patterns;

[0073] S1-2-1-5. Use the control data feature model and the operation data feature model to merge, and integrate the output results of the two types of models based on the weighted average method to form a multi-dimensional data feature model of the pre-modulated adjustable intensity applicator.

[0074] S1-2-2 specifically includes:

[0075] S1-2-2-1. Time-align the real-time control data and real-time operation data of the pre-modulated adjustable intensity applicator to ensure consistency between the control data and the operation data in the time dimension;

[0076] S1-2-2-2. Based on the time-aligned data, perform dimensionality reduction on the real-time control data and real-time operation data using the principal component analysis method to extract key features.

[0077] S1-2-2-3. Using the extracted key features, based on the long short-term memory neural network algorithm, establish the operating data feature model of the pre-modulated adjustable intensity applicator.

[0078] S2 specifically includes:

[0079] S2-1. Utilizing the multi-dimensional data feature model to obtain multi-dimensional staggered intersecting period data features according to the staggered intersecting period;

[0080] S2-1-1. Divide the data in the multi-dimensional data feature model into several time periods according to time series;

[0081] S2-1-2. Use the data of adjacent time periods to perform staggered cross processing. The second half of the previous time period is overlapped with the first half of the next time period to create staggered cross time period data.

[0082] S2-1-3. Extract features using the staggered and intersecting period data, and extract time-frequency domain features based on a discrete wavelet transform method to obtain multi-dimensional staggered and intersecting period data features;

[0083] S2-2. Perform feedback verification processing based on the multi-dimensional staggered cross-period data characteristics to obtain real-time data trends of the pre-modulated adjustable intensity applicator.

[0084] S2-2 specifically includes:

[0085] S2-2-1. Utilize the multi-dimensional dislocation cross-period data features as input into a pre-trained feedback verification model, wherein the feedback verification model is constructed using a recursive neural network algorithm;

[0086] S2-2-2. Perform error analysis based on the prediction results and establish evaluation indicators based on the root mean square error to verify the accuracy of the prediction results;

[0087] S2-2-3. Use the feedback verification model to dynamically predict the data characteristics of the multi-dimensional staggered cross-period, and obtain the real-time data trend of the pre-modulated adjustable intensity applicator.

[0088] S3 specifically includes:

[0089] S3-1. Based on the real-time data trend, obtain key trend characteristics, including trend slope, fluctuation amplitude, and periodic changes;

[0090] S3-2. Compare the key trend features with a predefined operating status threshold to determine whether the operating status of the pre-modulated adjustable intensity applicator is normal, and obtain an operating status monitoring and evaluation result.

[0091] Example 2:

[0092] The present invention provides an operating status monitoring and evaluation system for a pre-modulated adjustable intensity applicator, comprising:

[0093] A model building module is used to build a multi-dimensional data feature model of the pre-modulated adjustable intensity applicator using real-time data of the pre-modulated adjustable intensity applicator;

[0094] A trend acquisition module, configured to acquire real-time data trends of the pre-modulated adjustable intensity applicator using the multi-dimensional data feature model;

[0095] The monitoring and evaluation module is used to obtain the operation status monitoring and evaluation results according to the real-time data trend of the pre-modulated adjustable intensity applicator.

[0096] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator, characterized in that: include: S1. Using the real-time data of the pre-modulated adjustable intensity applicator, a multi-dimensional data feature model of the pre-modulated adjustable intensity applicator is established; S2. Using the multi-dimensional data feature model to obtain real-time data trends of the pre-modulated adjustable intensity applicator; S3. Obtaining an operation status monitoring and evaluation result according to the real-time data trend of the pre-modulated adjustable intensity applicator.

2. The method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator according to claim 1, wherein: The multi-dimensional data feature model of the pre-modulated adjustable intensity applicator is established by using the real-time data of the pre-modulated adjustable intensity applicator, including: respectively collecting real-time control data and real-time operation data of the pre-modulation adjustable intensity applicator as real-time data of the pre-modulation adjustable intensity applicator; Using the real-time data of the pre-modulation adjustable intensity applicator, a control data characteristic model and an operation data characteristic model of the pre-modulation adjustable intensity applicator are respectively established; The control data feature model and the operation data feature model of the pre-modulation adjustable intensity applicator are used as the multi-dimensional data feature model of the pre-modulation adjustable intensity applicator.

3. The method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator according to claim 2, wherein: The real-time control data and real-time operation data of the pre-modulation adjustable intensity applicator are collected separately as the real-time data of the pre-modulation adjustable intensity applicator including: Collecting the real-time control voltage, real-time control current and real-time control frequency of the pre-modulation adjustable power applicator as the real-time control data of the pre-modulation adjustable power applicator; Collecting the real-time operating temperature, real-time operating vibration and real-time operating displacement of the pre-modulated adjustable intensity applicator as the real-time operating data of the pre-modulated adjustable intensity applicator; The real-time control data and real-time operation data of the pre-modulation adjustable intensity applicator are used as the real-time data of the pre-modulation adjustable intensity applicator.

4. The method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator according to claim 3, wherein: Using the real-time data of the pre-modulation adjustable intensity applicator to establish a control data characteristic model and an operation data characteristic model of the pre-modulation adjustable intensity applicator includes: Establishing a control data characteristic model of the pre-modulation adjustable intensity applicator by using the real-time data corresponding to the real-time control data of the pre-modulation adjustable intensity applicator; An operation data characteristic model of the pre-modulation adjustable intensity applicator is established by using the real-time data corresponding to the real-time control data of the pre-modulation adjustable intensity applicator.

5. The method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator according to claim 4, characterized in that: Establishing a control data feature model of the pre-modulation adjustable intensity applicator by using the real-time data of the pre-modulation adjustable intensity applicator corresponding to the real-time control data includes: Preprocessing the real-time control data of the premodulated adjustable intensity applicator includes smoothing the control voltage, control current, and control frequency based on a sliding average filter algorithm, and converting the data into a standard distribution with a mean of 0 and a variance of 1 based on a Z-score normalization method to obtain standardized control data; Based on the standardized control data, a support vector machine algorithm is used to establish a control data characteristic model of the pre-modulated adjustable intensity applicator, wherein the control data characteristic model is used to describe the dynamic relationship between the control voltage, control current and control frequency and their changing rules; Preprocessing the real-time operating data of the premodulated adjustable intensity applicator includes denoising the operating temperature, operating vibration, and operating displacement based on wavelet transform, and mapping the data to the [0, 1] interval based on a Min-Max normalization method to obtain standardized operating data; Based on the standardized operating data, a Gaussian process regression algorithm is used to establish an operating data characteristic model of the pre-modulated adjustable intensity applicator, wherein the operating data characteristic model is used to describe the dynamic relationship between operating temperature, operating vibration and operating displacement and their changing laws; The control data feature model is fused with the operation data feature model, and the output results of the two types of models are integrated based on the weighted average method to form a multi-dimensional data feature model of the pre-modulated adjustable intensity applicator.

6. The method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator according to claim 5, characterized in that: Establishing an operation data characteristic model of the pre-modulation adjustable intensity applicator using the real-time data corresponding to the real-time control data of the pre-modulation adjustable intensity applicator includes: Time-aligning the real-time control data and real-time operation data of the pre-modulated adjustable intensity applicator to ensure consistency of the control data and the operation data in the time dimension; Based on the time-aligned data, the real-time control data and real-time operation data are subjected to dimensionality reduction processing using the principal component analysis method to extract key features; Using the extracted key features, an operating data feature model of the pre-modulated adjustable intensity applicator is established based on the long short-term memory neural network algorithm.

7. The method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator according to claim 2, wherein: The method of obtaining the real-time data trend of the pre-modulated adjustable intensity applicator by using the multi-dimensional data feature model includes: S2-1. Utilizing the multi-dimensional data feature model to obtain multi-dimensional staggered intersecting period data features according to the staggered intersecting period; S2-1-1. Divide the data in the multi-dimensional data feature model into several time periods according to time series; S2-1-2. Use the data of adjacent time periods to perform staggered cross processing. The second half of the previous time period is overlapped with the first half of the next time period to create staggered cross time period data. S2-1-3. Extract features using the staggered and intersecting period data, and extract time-frequency domain features based on a discrete wavelet transform method to obtain multi-dimensional staggered and intersecting period data features; S2-2. Perform feedback verification processing based on the multi-dimensional staggered cross-period data characteristics to obtain real-time data trends of the pre-modulated adjustable intensity applicator.

8. The method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator according to claim 6, wherein: The real-time data trend of the pre-modulated adjustable intensity applicator obtained by performing feedback verification processing based on the multi-dimensional staggered cross-period data characteristics includes: The multi-dimensional staggered cross-period data features are input into a pre-trained feedback verification model, wherein the feedback verification model is constructed using a recursive neural network algorithm; Error analysis is performed based on the prediction results. Evaluation indicators are established based on the root mean square error to verify the accuracy of the prediction results. The feedback verification model is used to dynamically predict the data characteristics of the multi-dimensional staggered cross-period, and obtain the real-time data trend of the pre-modulated adjustable intensity applicator.

9. The method for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator according to claim 3, wherein: The operating status monitoring and evaluation results obtained based on the real-time data trend of the pre-modulated adjustable intensity applicator include: Based on the real-time data trend, key trend characteristics are obtained, including trend slope, fluctuation amplitude and periodic changes; The key trend features are compared with predefined operating status thresholds to determine whether the operating status of the pre-modulated adjustable intensity applicator is normal, and an operating status monitoring and evaluation result is obtained.

10. A system for monitoring and evaluating the operating status of a pre-modulated adjustable intensity applicator according to any one of claims 1 to 9, characterized in that: include: A model building module is used to build a multi-dimensional data feature model of the pre-modulated adjustable intensity applicator using real-time data of the pre-modulated adjustable intensity applicator; A trend acquisition module, configured to acquire real-time data trends of the pre-modulated adjustable intensity applicator using the multi-dimensional data feature model; The monitoring and evaluation module is used to obtain the operation status monitoring and evaluation results according to the real-time data trend of the pre-modulated adjustable intensity applicator.

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