Automatic detection and analysis method and system for electric energy meter crimping process

Abstract

The invention discloses an electric energy meter crimping process automatic detection and analysis method and system. In the prior art, when automatic detection and analysis are carried out in the electric energy meter crimping process, data processing in the crimping process is insufficient, and complex conditions in the electric energy meter crimping process cannot be comprehensively reflected due to data analysis from a single angle. The method comprises the following steps: detecting parameter data in a crimping process of the electric energy meter in real time, preprocessing the detected parameter data, and then performing intelligent enhancement processing to obtain detection data after the intelligent enhancement processing; and performing real-time analysis on the detection data after the intelligent enhancement processing to obtain an analysis result including pressure step-by-step analysis, position deviation detection and contact quality evaluation, determining an abnormal crimping condition of the electric energy meter based on the analysis result, and performing automatic alarm. According to the invention, possible abnormal pressure points, position deviation and contact quality problems can be identified, and the quality and consistency of the crimping process can be ensured from multiple angles.

Description

Technical Field

[0001] The invention belongs to the technical field of automatic detection and analysis, and in particular to an automatic detection and analysis method and system for an electric energy meter crimping process. Background Art

[0002] As an important measuring instrument in the power system, the performance and reliability of the electric energy meter directly affect the accuracy of power metering and the safe operation of the power system. In the production process of the electric energy meter, the crimping process is an important link to ensure the reliability of the internal contact and the stability of the electrical performance of the electric energy meter. However, there are many problems in the traditional electric energy meter crimping process, which seriously restricts the production efficiency and quality control level of the electric energy meter. First, the parameter detection in the crimping process of the traditional electric energy meter mainly relies on manual operation or simple instrument detection, which is difficult to achieve real-time and comprehensive monitoring. This method not only has low detection efficiency, but also easily leads to missed detection or false detection, affecting the stability and consistency of the crimping quality. Secondly, noise and random errors are common in the detection data, which directly affect the accuracy and reliability of the data. Traditional data processing methods are often difficult to effectively remove noise and errors, resulting in inaccurate analysis results, which further affects the evaluation and control of crimping quality. In addition, the data samples processed by traditional methods are limited, lack diversity and representativeness, and it is difficult to fully reflect the situation in the actual crimping process. This lack of data samples leads to the one-sidedness of the analysis results and makes it difficult to accurately evaluate the quality problems in the crimping process.

[0003] In the actual production process, the inability to timely and effectively determine abnormal conditions during the crimping process is another major problem faced by the traditional crimping process. Once an abnormality occurs during the crimping process, if it cannot be discovered and handled in time, it will lead to the accumulation of crimping quality problems, seriously affecting the performance and reliability of the energy meter.

[0004] The above technology has at least the following technical problems: when automatically detecting and analyzing the crimping process of the electric energy meter, the data processing of the crimping process is insufficient, lacking diversity and representativeness, and the data analysis from a single perspective cannot fully reflect the complex situation in the crimping process of the electric energy meter, which easily leads to inaccurate analysis results. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned prior art and provide a method and system for automatic detection and analysis of the crimping process of an electric energy meter. Through multi-level pressure distribution analysis, position deviation detection and dynamic contact quality evaluation, the method and system comprehensively evaluate various parameters in the crimping process, and can identify possible abnormal pressure points, position deviations and contact quality problems, thereby ensuring the quality and consistency of the crimping process from multiple angles.

[0006] To this end, the present invention adopts the following technical solution.

[0007] In a first aspect, the present invention provides a method for automatically detecting and analyzing an electric energy meter crimping process, which comprises the following steps: S1, real-time detection of parameter data of the electric energy meter during crimping, pre-processing of the detected parameter data, and then intelligent enhancement processing to obtain detection data after intelligent enhancement processing; S2, conducts real-time analysis on the detection data after intelligent enhanced processing to obtain analysis results including pressure step-by-step analysis, position deviation detection and contact quality assessment, determines abnormal crimping of the electric energy meter based on the analysis results, and issues an automatic alarm.

[0008] Furthermore, in S1, the preprocessed parameter data is intelligently enhanced using an adaptive multidimensional data enhancement algorithm, and the adaptive multidimensional data enhancement algorithm includes multi-stage data transformation, nonlinear feature extraction, multidimensional feature fusion and adaptive enhancement strategy performed in sequence, which significantly improves the representativeness of the data and the accuracy of analysis.

[0009] Furthermore, the multi-stage data transformation includes time domain transformation, frequency domain transformation and wavelet transformation, and each transformation step provides different feature representations for subsequent processing; nonlinear feature extraction is performed on the pre-processed detection data after the multi-stage data transformation, including singular value decomposition and adaptive filtering; and multi-dimensional feature fusion is performed on the detection data after nonlinear feature extraction to form multi-dimensional feature data.

[0010] Furthermore, the detection data in the time domain, frequency domain and after wavelet transformation are subjected to singular value decomposition respectively:

[0011]

[0012]

[0013] in, , , They are the pre-processed detection data after time domain transformation, the pre-processed detection data after frequency domain transformation and the pre-processed detection data after wavelet transformation. , , They are , , The left singular vector of ; , , They are , , The singular values ​​of , , They are , , The right singular vectors of ; The singular values ​​are adaptively filtered using an adaptive filter to obtain the adaptively adjusted singular values. , , ; The multi-dimensional feature data is:

[0014] in, It is multi-dimensional feature data; It is the detection data feature after preprocessing in the time domain. After time domain transformation and singular value decomposition, the feature representation obtained by adjusting the singular value through adaptive filtering; It is the feature of the detection data after preprocessing in the frequency domain. After frequency domain transformation and singular value decomposition, the feature representation obtained by adjusting the singular value through adaptive filtering; It is the feature of the detection data after preprocessing in the wavelet transform domain. After wavelet transform and singular value decomposition, the feature representation is obtained by adjusting the singular value through adaptive filtering; Represents the feature fusion operation.

[0015] Furthermore, the adaptive enhancement strategy is implemented using a multidimensional data enhancement formula: , in, It is the detection data after intelligent enhancement processing, which is the detection data after multi-dimensional modulation and enhancement; is the preprocessed detection data; , , , , , , All are adaptive adjustment parameters; is the time variable, indicating the time dimension; is a frequency variable, indicating the frequency dimension; It is multidimensional feature data, reflecting the multidimensional feature attributes of the data.

[0016] Furthermore, in S2, a comprehensive crimping analysis and evaluation algorithm is used to perform real-time analysis on the detection data after intelligent enhancement processing, specifically including: By analyzing the pressure data at different locations and times, we can conduct multi-level pressure distribution analysis to understand the pressure distribution and identify possible abnormal pressure points; After obtaining the pressure distribution analysis results, multi-dimensional position deviation detection is performed; by calculating the position deviation of each detection point, the position change of the sensor during the crimping process is understood, thereby evaluating the accuracy of the crimping; By comprehensively considering the pressure, contact coefficient and speed changes, the contact quality at different positions during the crimping process is evaluated, that is, dynamic contact quality evaluation is performed.

[0017] Furthermore, by comprehensively considering the results of pressure distribution, position deviation and contact quality assessment, the overall quality of the crimping process is quantified, a comprehensive quality score is given, and abnormality judgment is made through threshold setting; when the preset threshold is exceeded, an alarm will be automatically triggered to ensure that the operator can be informed and deal with the abnormal situation in time.

[0018] Going a step further, the formula for evaluating crimp accuracy is as follows:

[0019] in, Indicates time Position deviation; is the number of detection points, indicating the number of sensors used to detect position deviation; and Respectively represent Detection points at time Enhanced processed location data; and They respectively indicate the reference position, which represents the ideal crimping position; The contact quality at different positions during crimping is evaluated using the formula:

[0020] in, Indicates time The contact quality assessment value; It is the crimping cycle, which indicates the length of time for a complete crimping process; It is the pressure data obtained from the pressure distribution analysis, which is obtained by integrating the entire crimping area; is the contact coefficient, which is an evaluation index of the contact conditions at different positions; It is the crimping speed function, which indicates the change of speed during the crimping process.

[0021] Furthermore, the formula for the comprehensive quality score is:

[0022] in, represents the overall quality score; , , is a single factor judgment score, which represents the score based on pressure distribution, the score based on position deviation, and the score based on contact quality; , , are the weight coefficients of pressure, position deviation and contact quality respectively; , , is a two-factor judgment score, which represents the combined score of pressure and position deviation, the combined score of pressure and contact quality, and the combined score of position deviation and contact quality; , , They are the weight coefficients of the combination of pressure and position deviation, the combination of pressure and contact quality, and the combination of position deviation and contact quality; It is a comprehensive consideration of pressure, position deviation and contact quality to determine the score; , and represent the scores of pressure distribution, position deviation, and contact quality assessment, respectively; is a velocity function.

[0023] In a second aspect, the present invention provides an automatic detection and analysis system for an electric energy meter crimping process, which is used to implement the above-mentioned automatic detection and analysis method for an electric energy meter crimping process, and comprises: Data detection and processing unit: real-time detection of parameter data during the crimping process of the electric energy meter, pre-processing of the detected parameter data, and then intelligent enhancement processing to obtain detection data after intelligent enhancement processing; Analysis and judgment unit: The detection data after intelligent enhanced processing is analyzed in real time to obtain analysis results including pressure step-by-step analysis, position deviation detection and contact quality assessment. Based on the analysis results, the abnormal crimping situation of the electric energy meter is determined and an automatic alarm is issued.

[0024] The beneficial effects of the present invention are as follows: the present invention comprehensively evaluates various parameters in the crimping process through multi-level pressure distribution analysis, position deviation detection and dynamic contact quality evaluation; this multi-angle analysis method can identify possible abnormal pressure points, position deviations and contact quality problems, and ensure the quality and consistency of the crimping process from multiple angles. Combining the results of pressure distribution, position deviation and contact quality evaluation, the overall quality of the crimping process can be fully quantified. In addition, by setting a reasonable threshold, it can be determined in time whether there is an abnormal situation. When the scoring result exceeds the preset threshold, the alarm mechanism is automatically triggered to ensure that the operator can be informed of and handle the abnormal situation in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of a method for automatic detection and analysis of an electric energy meter crimping process according to the present invention; Figure 2 The present invention is a structural diagram of an automatic detection and analysis system for the crimping process of an electric energy meter. DETAILED DESCRIPTION

[0026] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0028] Example 1 The following is a detailed description of a specific scheme of a method for automatic detection and analysis of an electric energy meter crimping process provided by the present invention in conjunction with the accompanying drawings.

[0029] See attached Figure 1 , which shows a flow chart of a method for automatic detection and analysis of an electric energy meter crimping process provided by an embodiment of the present invention, the method comprising the following steps: S1. Real-time detection of parameter data during the crimping process of the electric energy meter, pre-processing of the detected parameter data, and then intelligent enhancement processing to obtain detection data after intelligent enhancement processing.

[0030] The data of the electric energy meter crimping process is detected in real time by sensors selected by professional technicians, such as pressure sensors, position sensors and contact condition sensors, to ensure that various parameter data in the crimping process can be detected in real time, such as pressure, position, and contact condition; the detected parameter data is preprocessed, and the preprocessing includes using a noise filtering method based on wavelet transform to remove the noise of the detection parameter data, and performing data smoothing processing on the noise filtered data through Kalman filtering to eliminate random errors in the detection parameter data; the smoothed data is normalized to ensure that the detection parameter data is subsequently processed under the same dimension to obtain the preprocessed detection data; The preprocessed detection data is intelligently enhanced using an adaptive multidimensional data enhancement algorithm.

[0031] The adaptive multidimensional data enhancement algorithm includes multi-stage data transformation, nonlinear feature extraction, multidimensional feature fusion and adaptive enhancement strategy, which significantly improves the representativeness of data and the accuracy of analysis. The specific implementation process is as follows.

[0032] Multi-stage data transformation: The pre-processed detection data is subjected to multi-stage data transformation processing, wherein the multi-stage data transformation includes time domain transformation, frequency domain transformation and wavelet transformation, and each step of the transformation provides different feature representations for subsequent processing.

[0033] For time domain transformation, the time domain transformation formula is:

[0034] in, It is the pre-processed detection data after time domain transformation, reflecting the temporal variation characteristics of the pre-processed detection data; is the pre-processed detection data after discrete processing; is an imaginary unit; is the total number of data points of the preprocessed detection data; is the data point index; Indicates time.

[0035] For frequency domain transformation, the frequency domain transformation formula is:

[0036] in, It is the pre-processed detection data after frequency domain transformation, reflecting the frequency variation characteristics of the pre-processed detection data; It is the detection data after time domain preprocessing; is the frequency; Is an imaginary unit.

[0037] For wavelet transform, the wavelet transform formula is:

[0038] in, is the pre-processed detection data after wavelet transformation; is the preprocessed detection data under continuous time variables; is the mother wavelet function; and are scale and translation parameters respectively. The purpose of wavelet transform is to extract local features of signals at different scales.

[0039] Furthermore, nonlinear feature extraction is performed on the pre-processed detection data after the above multi-stage transformation, including singular value decomposition and adaptive filtering.

[0040] Perform singular value decomposition on the detection data in time domain, frequency domain and after wavelet transformation respectively:

[0041]

[0042]

[0043] in, , , They are the pre-processed detection data after time domain transformation, the pre-processed detection data after frequency domain transformation and the pre-processed detection data after wavelet transformation. , , They are , , The left singular vector of ; , , They are , , The singular values ​​of , , They are , , The right singular vectors of ; Furthermore, the singular values ​​are processed using the existing adaptive filter to obtain the adaptively adjusted singular values. , , .

[0044] Furthermore, the data after nonlinear feature extraction is subjected to multi-dimensional feature fusion to form multi-dimensional feature data:

[0045] in, It is multidimensional feature data, reflecting the multidimensional feature attributes of the data; It is the detection data feature after preprocessing in the time domain. After time domain transformation and singular value decomposition, the feature representation obtained by adjusting the singular value through adaptive filtering; It is the feature of the detection data after preprocessing in the frequency domain. After frequency domain transformation and singular value decomposition, the feature representation obtained by adjusting the singular value through adaptive filtering; It is the feature of the detection data after preprocessing in the wavelet transform domain. After wavelet transform and singular value decomposition, the feature representation is obtained by adjusting the singular value through adaptive filtering; Represents feature fusion operations, such as feature concatenation or weighted averaging.

[0046] Furthermore, an adaptive enhancement strategy is introduced, which is implemented using a multidimensional data enhancement formula:

[0047] in, It is the detection data after intelligent enhancement processing, which is the detection data after multi-dimensional modulation and enhancement; is the preprocessed detection data; To adjust parameters adaptively; is the time variable, indicating the time dimension; is a frequency variable, indicating the frequency dimension.

[0048] Through the above steps, the present invention realizes intelligent enhancement processing of the pre-processed detection data to obtain more diversified and representative detection data, including pressure data, position data, and contact status data after enhancement processing. First, data standardization processing eliminates the dimensionality effect and ensures the comparability of the data. Then, multi-stage data transformation captures the multidimensional characteristics of the data, including time characteristics, frequency characteristics, and scale characteristics. The features are nonlinearly extracted through singular value decomposition and adaptive filtering, which enhances the expressiveness of the data. Then, the adaptive enhancement strategy generates diversified data, enhances the representativeness of the data through multi-dimensional modulation and feature domain modulation, and provides a solid data foundation for subsequent real-time analysis and abnormality judgment.

[0049] S2. Analyze the detection data after intelligent enhancement processing in real time to obtain analysis results including pressure step-by-step analysis, position deviation detection and contact quality assessment. Based on the analysis results, determine the abnormal crimping of the electric energy meter and issue an automatic alarm. The specific implementation process is as follows: First, by analyzing the pressure data at different locations and times, we can conduct a multi-level pressure distribution analysis to understand the pressure distribution and identify possible abnormal pressure points. The mathematical formula is:

[0050] in, Indicates at time and location Pressure data at is the maximum pressure, which indicates the highest pressure value that may be reached during the crimping process; is the pressure decay coefficient, which indicates how fast the pressure decays over time; Pressure over time The attenuation of the pressure simulates the phenomenon that the pressure gradually decreases as the crimping process proceeds; It is the pressure data after enhancement processing; and Respectively represent the length and width range of the crimping area; and Respectively represents the pressure in the crimping area length and width The periodic changes in direction reflect the pressure at different locations. The distribution characteristics of It is a material property function that describes the material properties at different positions, obtains material property data, determines the material properties at different positions through experiments or material manuals, and generates function; is the crimping speed function, which represents the change of crimping speed over time 𝑡; is the maximum crimping speed, used to normalize the crimping speed function; It is an environmental factor function that describes the impact of environmental conditions (such as temperature, humidity, etc.) on pressure distribution, obtains environmental factor data, monitors environmental conditions through sensors or external data sources, and generates Function; The above formula describes the distribution of pressure in the crimping area by combining time and position variables and introducing material properties (hardness, elastic modulus, etc.), the influence of crimping speed on pressure, and the influence of environmental factors on pressure.

[0051] After obtaining the pressure distribution analysis results, multi-dimensional position deviation detection is performed. By calculating the position deviation of each detection point, the position change of the sensor during the crimping process is understood, thereby evaluating the accuracy of the crimping. The mathematical formula is:

[0052] in, Indicates time Position deviation; is the number of detection points, indicating the number of sensors used to detect position deviation; and Respectively represent Detection points at time Enhanced processed location data; and The reference position and the ideal crimping position are represented respectively. By averaging the sum of squares of the position deviations of each detection point, the degree of position deviation during the crimping process is effectively quantified.

[0053] Furthermore, dynamic contact quality evaluation is performed. By comprehensively considering the pressure, contact coefficient and speed changes, the contact quality at different positions during the crimping process is evaluated. The mathematical formula is:

[0054] in, Indicates time The contact quality assessment value; It is the crimping cycle, which indicates the length of time for a complete crimping process; It is the pressure data obtained from the pressure distribution analysis, which is obtained by integrating the entire crimping area; is the contact coefficient, which is an evaluation index of the contact conditions at different positions; is a function of crimping speed, which indicates the change of speed during crimping. The above formula comprehensively evaluates the contact quality of crimping process by integrating pressure, contact coefficient and speed change.

[0055] Finally, a comprehensive quality score is performed. The overall quality of the crimping process is quantified by comprehensively considering the results of pressure distribution, position deviation and contact quality evaluation. The mathematical formula is:

[0056] in, represents the overall quality score; , , is a single factor judgment score, which represents the score based on pressure distribution, the score based on position deviation, and the score based on contact quality; , , are the weight coefficients of pressure, position deviation and contact quality respectively; , , is a two-factor judgment score, which represents the combined score of pressure and position deviation, the combined score of pressure and contact quality, and the combined score of position deviation and contact quality; , , They are the weight coefficients of the combination of pressure and position deviation, the combination of pressure and contact quality, and the combination of position deviation and contact quality; It is a comprehensive consideration of pressure, position deviation and contact quality to determine the score; , and represent the scores of pressure distribution, position deviation, and contact quality assessment, respectively; The above formula comprehensively evaluates the overall quality of the crimping process by combining the results of various analyses and introducing weight coefficients for adjustment.

[0057] Through the above detailed implementation process and complex mathematical formulas, the comprehensive crimping analysis and evaluation algorithm can effectively perform real-time analysis and quality evaluation on the crimping process of electric energy meters in practical applications, thereby improving the automation and intelligence level of the crimping process and ensuring the consistency and reliability of the crimping quality.

[0058] Further, threshold setting and abnormality determination; comprehensive quality scoring The result needs to be consistent with the preset threshold Compare to see if there is any anomaly: Single factor determination: when or or When , it is judged as abnormal; Two-factor combination judgment: when or or When , it is judged as abnormal; Three-factor combination judgment: when , it is judged as abnormal.

[0059] in, , , , , , , is the preset judgment threshold; In particular, when the comprehensive quality score result exceeds the preset threshold, the alarm mechanism will be automatically triggered; the alarm mechanism can include sound and light alarms, SMS notifications, email notifications and other methods to ensure that operators can be informed and handle abnormal situations in a timely manner.

[0060] The beneficial effects of this embodiment are: 1. Adaptive multi-dimensional data enhancement algorithm is used to significantly improve the representativeness of data and the accuracy of analysis through multi-stage processing, multi-dimensional transformation, nonlinear feature extraction and complex adaptive strategies. The specific implementation process includes time domain transformation, frequency domain transformation and wavelet transformation, which provide different feature representations. The features are further extracted nonlinearly through singular value decomposition and adaptive filtering, which enhances the expressiveness of data. Multi-dimensional feature fusion and adaptive enhancement strategies generate diversified data, which improves the representativeness and diversity of data.

[0061] 2. During the real-time analysis process, various parameters in the crimping process are comprehensively evaluated through multi-level pressure distribution analysis, position deviation detection and dynamic contact quality evaluation. This multi-angle analysis method can identify possible abnormal pressure points, position deviations and contact quality problems, ensuring the quality and consistency of the crimping process from multiple angles. Combining the results of pressure distribution, position deviation and contact quality evaluation, the overall quality of the crimping process can be fully quantified. In addition, by setting reasonable thresholds, it is possible to determine whether there are abnormal situations in a timely manner. When the scoring result exceeds the preset threshold, the alarm mechanism is automatically triggered to ensure that the operator can be informed and handle abnormal situations in a timely manner.

[0062] Example 2 This embodiment provides an automatic detection and analysis system for the crimping process of an electric energy meter, which is used to implement the automatic detection and analysis method for the crimping process of an electric energy meter described in Example 1, and includes: Data detection and processing unit: real-time detection of parameter data during the crimping process of the electric energy meter, pre-processing of the detected parameter data, and then intelligent enhancement processing to obtain detection data after intelligent enhancement processing; Analysis and judgment unit: The detection data after intelligent enhanced processing is analyzed in real time to obtain analysis results including pressure step-by-step analysis, position deviation detection and contact quality assessment. Based on the analysis results, the abnormal crimping situation of the electric energy meter is determined and an automatic alarm is issued.

[0063] It should be noted that each unit in the above-mentioned automatic detection and analysis system for the crimping process of an electric energy meter can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned units. For the specific definition of an automatic detection and analysis system for the crimping process of an electric energy meter, please refer to the definition of an automatic detection and analysis method for the crimping process of an electric energy meter above. The two have the same functions and effects, which will not be repeated here.

[0064] The sequence of the embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0066] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for automatic detection and analysis of the crimping process of an electric energy meter, characterized in that: The following steps are involved: S1, real-time detection of parameter data of the electric energy meter during crimping, pre-processing of the detected parameter data, and then intelligent enhancement processing to obtain detection data after intelligent enhancement processing; S2, conducts real-time analysis on the detection data after intelligent enhanced processing to obtain analysis results including pressure step-by-step analysis, position deviation detection and contact quality assessment, determines abnormal crimping of the electric energy meter based on the analysis results, and issues an automatic alarm.

2. The automatic detection and analysis method for the crimping process of an electric energy meter according to claim 1 is characterized in that: In S1, the preprocessed parameter data is intelligently enhanced using an adaptive multidimensional data enhancement algorithm, wherein the adaptive multidimensional data enhancement algorithm includes multi-stage data transformation, nonlinear feature extraction, multidimensional feature fusion and adaptive enhancement strategy performed in sequence.

3. The automatic detection and analysis method for the crimping process of an electric energy meter according to claim 2 is characterized in that: The multi-stage data transformation includes time domain transformation, frequency domain transformation and wavelet transformation, and each transformation step provides different feature representations for subsequent processing; nonlinear feature extraction is performed on the pre-processed detection data after the multi-stage data transformation, including singular value decomposition and adaptive filtering; multi-dimensional feature fusion is performed on the detection data after nonlinear feature extraction to form multi-dimensional feature data.

4. The automatic detection and analysis method for the crimping process of an electric energy meter according to claim 3 is characterized in that: Perform singular value decomposition on the detection data in time domain, frequency domain and after wavelet transformation respectively: ， ， ， in, , , They are the pre-processed detection data after time domain transformation, the pre-processed detection data after frequency domain transformation and the pre-processed detection data after wavelet transformation. , , They are , , The left singular vector of ; , , They are , , The singular values ​​of , , They are , , The right singular vectors of ; The singular values ​​are adaptively filtered using an adaptive filter to obtain the adaptively adjusted singular values. , , ; The multi-dimensional feature data is: ， in, It is multi-dimensional feature data; It is the detection data feature after preprocessing in the time domain. After time domain transformation and singular value decomposition, the feature representation obtained by adjusting the singular value through adaptive filtering; It is the feature of the detection data after preprocessing in the frequency domain. After frequency domain transformation and singular value decomposition, the feature representation obtained by adjusting the singular value through adaptive filtering; It is the feature of the detection data after preprocessing in the wavelet transform domain. After wavelet transform and singular value decomposition, the feature representation is obtained by adjusting the singular value through adaptive filtering; Represents the feature fusion operation.

5. The method for automatic detection and analysis of the crimping process of an electric energy meter according to claim 2, characterized in that: The adaptive enhancement strategy is implemented using a multidimensional data enhancement formula: ， in, It is the detection data after intelligent enhancement processing, which is the detection data after multi-dimensional modulation and enhancement; is the preprocessed detection data; , , , , , , All are adaptive adjustment parameters; is a time variable, indicating the time dimension; is a frequency variable, indicating the frequency dimension; It is multidimensional feature data, reflecting the multidimensional feature attributes of the data.

6. The automatic detection and analysis method for the crimping process of an electric energy meter according to claim 1 is characterized in that: In S2, a comprehensive crimping analysis and evaluation algorithm is used to perform real-time analysis on the detection data after intelligent enhancement processing, specifically including: By analyzing the pressure data at different locations and times, we can conduct multi-level pressure distribution analysis to understand the pressure distribution and identify possible abnormal pressure points; After obtaining the pressure distribution analysis results, multi-dimensional position deviation detection is performed; by calculating the position deviation of each detection point, the position change of the sensor during the crimping process is understood, thereby evaluating the accuracy of the crimping; The contact quality at different positions during the crimping process is evaluated by comprehensively considering the pressure, contact coefficient and speed changes.

7. The automatic detection and analysis method for the crimping process of an electric energy meter according to claim 6 is characterized in that: By comprehensively considering the results of pressure distribution, position deviation and contact quality evaluation, the overall quality of the crimping process is quantified, a comprehensive quality score is given, and abnormality judgment is made through threshold setting; When the preset threshold is exceeded, an alarm will be automatically triggered.

8. The automatic detection and analysis method for the crimping process of an electric energy meter according to claim 6 is characterized in that: The formula for evaluating crimping accuracy is as follows: ， in, Indicates time Position deviation; is the number of detection points, indicating the number of sensors used to detect position deviation; and Respectively represent The detection points at time Enhanced processed location data; and They respectively indicate the reference position, which represents the ideal crimping position; The contact quality at different positions during crimping is evaluated using the formula: ， in, Indicates time The contact quality assessment value; It is the crimping cycle, which indicates the length of time for a complete crimping process; It is the pressure data obtained from the pressure distribution analysis, which is obtained by integrating the entire crimping area; is the contact coefficient, which is an evaluation index of the contact conditions at different positions; It is the crimping speed function, which indicates the change of speed during the crimping process.

9. The automatic detection and analysis method for the crimping process of an electric energy meter according to claim 7, characterized in that: The formula for the comprehensive quality score is: ， in, represents the overall quality score; , , is a single factor judgment score, which represents the score based on pressure distribution, the score based on position deviation, and the score based on contact quality; , , are the weight coefficients of pressure, position deviation and contact quality respectively; , , is a two-factor judgment score, which represents the combined score of pressure and position deviation, the combined score of pressure and contact quality, and the combined score of position deviation and contact quality; , , They are the weight coefficients of the combination of pressure and position deviation, the combination of pressure and contact quality, and the combination of position deviation and contact quality; It is a comprehensive consideration of pressure, position deviation and contact quality to determine the score; , and represent the scores of pressure distribution, position deviation, and contact quality assessment, respectively; is a velocity function.

10. An automatic detection and analysis system for the crimping process of an electric energy meter, used to implement the automatic detection and analysis method for the crimping process of an electric energy meter according to any one of claims 1 to 9, characterized in that: include: Data detection and processing unit: real-time detection of parameter data during the crimping process of the electric energy meter, pre-processing of the detected parameter data, and then intelligent enhancement processing to obtain detection data after intelligent enhancement processing; Analysis and judgment unit: The detection data after intelligent enhanced processing is analyzed in real time to obtain analysis results including pressure step-by-step analysis, position deviation detection and contact quality assessment. Based on the analysis results, the abnormal crimping situation of the electric energy meter is determined and an automatic alarm is issued.

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