A method and system for automatic detection and analysis of electric energy meter crimping process

Abstract

The present invention discloses a method and system for automatic detection and analysis of the crimping process of an electric energy meter. In the prior art, when automatically detecting and analyzing the crimping process of an electric energy meter, the data processing during the crimping process is insufficient, and the data analysis from a single angle cannot fully reflect the complex situation during the crimping process of the electric energy meter. The method of the present invention comprises: 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; real-time analysis of the detection data after intelligent enhancement processing to obtain analysis results including step-by-step pressure analysis, position deviation detection, and contact quality evaluation, and based on the analysis results, determine the abnormality of the crimping of the electric energy meter and automatically alarm. The present invention 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.

Description

Technical Field

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

[0002] As a critical metering instrument in power systems, the performance and reliability of electricity meters directly impact the accuracy of electricity metering and the safe operation of power systems. During meter production, the crimping process is crucial for ensuring reliable internal contact and stable electrical performance. However, traditional meter crimping processes present numerous challenges, severely hindering meter production efficiency and quality control. First, parameter detection during the traditional meter crimping process relies primarily on manual operation or simple instrumentation, making it difficult to achieve real-time, comprehensive monitoring. This approach is not only inefficient but also prone to missed or false detections, compromising the stability and consistency of crimping quality. Second, noise and random errors are prevalent in the test data, directly impacting its accuracy and reliability. Traditional data processing methods often struggle to effectively remove these noise and errors, resulting in inaccurate analysis results and further compromising the assessment and control of crimping quality. Furthermore, traditional methods process limited data samples, lacking diversity and representativeness, making it difficult to fully reflect the actual crimping process. This limited data sample size leads to biased analysis results, making it difficult to accurately assess quality issues during the crimping process.

[0003] Another major challenge facing traditional crimping processes is the inability to effectively and timely identify anomalies during the crimping process. If these anomalies are not promptly identified and addressed, they can lead to a buildup of quality issues, severely impacting the performance and reliability of the meter.

[0004] The above technology has at least the following technical problems: when performing automatic detection and analysis of the electricity meter crimping process, there is insufficient data processing during the crimping process, lacking diversity and representativeness, and the data analysis from a single perspective cannot fully reflect the complex situation during the electricity meter crimping process, 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 existing technologies 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 assessment, it comprehensively evaluates various parameters in the crimping process, 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.

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

[0007] In a first aspect, the present invention provides a method for automatically detecting and analyzing the crimping process of an electric energy meter, which comprises the following steps:

[0008] 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 performing intelligent enhancement processing to obtain detection data after intelligent enhancement processing;

[0009] S2 performs real-time analysis on the detection data after intelligent enhanced processing to obtain analysis results including step-by-step pressure analysis, position deviation detection and contact quality assessment. Based on the analysis results, the abnormality of the electric energy meter crimping is determined and an automatic alarm is issued.

[0010] Furthermore, in S1, the preprocessed parameter data is intelligently enhanced using an adaptive multidimensional data enhancement algorithm. 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.

[0011] Furthermore, 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; 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.

[0012] Furthermore, the detection data in the time domain, frequency domain and after wavelet transformation are subjected to singular value decomposition respectively:

[0013]

[0014]

[0015]

[0016] in, 、 、 They are respectively 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 ;

[0017] The adaptive filter is used to perform adaptive filtering on the singular value to obtain the adaptively adjusted singular value. 、 、 ;

[0018] The multidimensional feature data is:

[0019]

[0020] in, It is multidimensional feature data; It is the feature representation of the detection data after preprocessing in the time domain. After time domain transformation and singular value decomposition, the singular value is adjusted by adaptive filtering; It is the feature representation of the detection data after preprocessing in the frequency domain. After frequency domain transformation and singular value decomposition, the singular value is adjusted by 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.

[0021] Furthermore, the adaptive enhancement strategy is implemented using a multi-dimensional data enhancement formula:

[0022] ,

[0023] in, It is the detection data after intelligent enhancement processing, which is the detection data after multi-dimensional modulation and enhancement; is the pre-processed 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.

[0024] 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:

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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 judgments are 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 handle the abnormal situation in a timely manner.

[0029] Going further, the formula for evaluating crimp accuracy is as follows:

[0030]

[0031] in, Indicates time Position deviation; is the number of detection points, which indicates the number of sensors used to detect position deviation; and Respectively represent Detection points at time Enhanced processed location data; and They represent the reference position and the ideal crimping position respectively;

[0032] The contact quality at different positions during crimping is evaluated using the following formula:

[0033]

[0034] 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.

[0035] Furthermore, the formula for the comprehensive quality score is:

[0036]

[0037] 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; 、 、 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 evaluation respectively; is a velocity function.

[0038] 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:

[0039] 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 the detection data after intelligent enhancement processing;

[0040] Analysis and judgment unit: The intelligent enhanced processing detection data 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 abnormality of the electric energy meter crimping is determined and an automatic alarm is issued.

[0041] The present invention has the following beneficial effects: It comprehensively evaluates various parameters during the crimping process through multi-level pressure distribution analysis, position deviation detection, and dynamic contact quality assessment. This multi-angle analysis method can identify possible abnormal pressure points, position deviations, and contact quality issues, ensuring the quality and consistency of the crimping process from multiple perspectives. Combining the results of pressure distribution, position deviation, and contact quality assessments can comprehensively quantify the overall quality of the crimping process. Furthermore, by setting reasonable thresholds, it is possible to promptly determine whether anomalies exist. When the scoring result exceeds the preset threshold, an alarm mechanism is automatically triggered, ensuring that the operator is promptly notified of and can address any anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of a method for automatically detecting and analyzing the crimping process of an electric energy meter according to the present invention;

[0043] 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

[0044] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in 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.

[0045] 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.

[0046] Example 1

[0047] The following describes in detail a specific solution of a method for automatically detecting and analyzing the crimping process of an electric energy meter provided by the present invention in conjunction with the accompanying drawings.

[0048] Refer to the attached Figure 1 , which shows a flow chart of a method for automatically detecting and analyzing the crimping process of an electric energy meter provided by one embodiment of the present invention, the method comprising the following steps:

[0049] 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.

[0050] 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 of the crimping process, such as pressure, position, and contact condition, can be detected in real time; the detected parameter data is preprocessed, and the preprocessing includes removing noise from the detection parameter data by using a noise filtering method based on wavelet transform, and smoothing the noise-filtered data by using Kalman filtering to eliminate random errors in the detection parameter data; the smoothed data is normalized to ensure that the detection parameter data are subsequently processed under the same dimension, thereby obtaining preprocessed detection data;

[0051] The pre-processed detection data is intelligently enhanced using an adaptive multi-dimensional data enhancement algorithm.

[0052] 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.

[0053] Multi-stage data transformation:

[0054] 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.

[0055] For time domain transformation, the time domain transformation formula is:

[0056]

[0057] 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.

[0058] For frequency domain transformation, the frequency domain transformation formula is:

[0059]

[0060] 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.

[0061] For wavelet transform, the wavelet transform formula is:

[0062]

[0063] in, is the pre-processed detection data after wavelet transformation; is the preprocessed detection data under continuous time variable; 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.

[0064] Furthermore, nonlinear feature extraction is performed on the pre-processed detection data after the multi-stage transformation, including singular value decomposition and adaptive filtering.

[0065] Perform singular value decomposition on the detection data in time domain, frequency domain and after wavelet transformation respectively:

[0066]

[0067]

[0068]

[0069] in, 、 、 They are respectively 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 ;

[0070] Furthermore, the existing adaptive filter is used to process the singular value to obtain the adaptively adjusted singular value 、 、 .

[0071] Furthermore, the data after nonlinear feature extraction is subjected to multi-dimensional feature fusion to form multi-dimensional feature data:

[0072]

[0073] in, It is multidimensional feature data, reflecting the multidimensional feature attributes of the data; It is the feature representation of the detection data after preprocessing in the time domain. After time domain transformation and singular value decomposition, the singular value is adjusted by adaptive filtering; It is the feature representation of the detection data after preprocessing in the frequency domain. After frequency domain transformation and singular value decomposition, the singular value is adjusted by 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 a feature fusion operation, such as feature concatenation or weighted averaging.

[0074] Furthermore, an adaptive enhancement strategy is introduced, which is implemented using the multidimensional data enhancement formula:

[0075]

[0076] in, It is the detection data after intelligent enhancement processing, which is the detection data after multi-dimensional modulation and enhancement; is the pre-processed detection data; To adjust parameters adaptively; is the time variable, indicating the time dimension; is a frequency variable, indicating the frequency dimension.

[0077] Through the above steps, the present invention realizes the 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 nonlinear extraction of features is performed through singular value decomposition and adaptive filtering, which enhances the expressive power 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.

[0078] S2. Analyze the intelligently enhanced detection data 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 abnormality of the electric energy meter crimping and automatically issue an alarm. The specific implementation process is as follows:

[0079] First, by analyzing the pressure data at different locations and times, we conduct a multi-level pressure distribution analysis to understand the pressure distribution and identify possible abnormal pressure points. The mathematical formula is:

[0080]

[0081] in, Indicates 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 the rate at which pressure decays over time; Pressure over time The attenuation of the pressure simulates the phenomenon that the pressure gradually decreases as the crimping process progresses; 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 change in direction reflects the pressure at different locations distribution characteristics; 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.

[0082] 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:

[0083]

[0084] in, Indicates time Position deviation; is the number of detection points, which indicates 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 the squares of the position deviations of each detection point, the degree of position deviation during the crimping process is effectively quantified.

[0085] 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:

[0086]

[0087] 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, representing the change in speed during the crimping process. The above formula comprehensively evaluates the contact quality of the crimping process by integrating pressure, contact coefficient, and speed changes.

[0088] Finally, a comprehensive quality score is performed. By comprehensively considering the results of pressure distribution, position deviation and contact quality evaluation, the overall quality of the crimping process is quantified. Its mathematical formula is:

[0089]

[0090] 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; 、 、 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 evaluation 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.

[0091] 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 of the electricity meter crimping process in practical applications, thereby improving the automation and intelligence level of the crimping process and ensuring the consistency and reliability of the crimping quality.

[0092] Further, threshold setting and abnormality determination; comprehensive quality scoring The result needs to be consistent with the preset threshold Compare to see if there are any anomalies:

[0093] Single factor judgment:

[0094] when or or When , it is judged as abnormal;

[0095] Two-factor combination judgment:

[0096] when or or When , it is judged as abnormal;

[0097] Three-factor combination judgment:

[0098] when , it is judged as abnormal.

[0099] in, 、 、 、 、 、 、 is the preset judgment threshold;

[0100] 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.

[0101] The beneficial effects of this embodiment are:

[0102] 1. Using an adaptive multidimensional data enhancement algorithm, this approach significantly improves data representativeness and analytical accuracy through multi-stage processing, multidimensional transformations, nonlinear feature extraction, and complex adaptive strategies. The specific implementation process includes time domain transformation, frequency domain transformation, and wavelet transform, which provide different feature representations. Singular value decomposition and adaptive filtering are then used to perform nonlinear feature extraction, enhancing the data's expressive power. Multidimensional feature fusion and adaptive enhancement strategies generate diverse data, improving both its representativeness and diversity.

[0103] 2. During real-time analysis, all parameters of the crimping process are comprehensively evaluated through multi-level pressure distribution analysis, position deviation detection, and dynamic contact quality assessment. This multi-faceted analysis method identifies possible abnormal pressure points, position deviations, and contact quality issues, ensuring the quality and consistency of the crimping process from multiple perspectives. Combining the results of pressure distribution, position deviation, and contact quality assessment, the overall quality of the crimping process can be comprehensively quantified. Furthermore, by setting appropriate thresholds, the presence of anomalies can be promptly determined. When the score exceeds the preset threshold, an alarm mechanism is automatically triggered, ensuring that the operator is promptly notified and able to address any anomalies.

[0104] Example 2

[0105] This embodiment provides a system for automatically detecting and analyzing the crimping process of an electric energy meter, which is used to implement the method for automatically detecting and analyzing the crimping process of an electric energy meter described in Example 1, and includes:

[0106] 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 the detection data after intelligent enhancement processing;

[0107] Analysis and judgment unit: The intelligent enhanced processing detection data 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 abnormality of the electric energy meter crimping is determined and an automatic alarm is issued.

[0108] It should be noted that the various units in the above-mentioned automatic detection and analysis system for the crimping process of an electric energy meter can be implemented in whole or in part through 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 operations corresponding to 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 and will not be repeated here.

[0109] The order in which the embodiments of the present invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0110] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0111] 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 they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These 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 all be included in the scope of protection 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 during the crimping process of the electric energy meter, pre-processing of the detected parameter data, and then intelligent enhancement processing of the pre-processed parameter data using an adaptive multi-dimensional data enhancement algorithm to obtain detection data after intelligent enhancement processing; S2: Analyze the intelligently enhanced detection data in real time to obtain analysis results including pressure distribution analysis, position deviation detection, and contact quality assessment. Based on the analysis results, determine the abnormality of the electric energy meter crimping and issue an automatic alarm. In S1, the adaptive multidimensional data enhancement algorithm includes multi-stage data transformation, nonlinear feature extraction, multidimensional feature fusion and adaptive enhancement strategy performed in sequence; the multi-stage data transformation includes time domain transformation, frequency domain transformation and wavelet transformation, and each step of transformation 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; the detection data after nonlinear feature extraction is subjected to multidimensional feature fusion to form multidimensional feature data; The adaptive enhancement strategy is implemented using a multi-dimensional 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 pre-processed 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; 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 formula for evaluating crimping accuracy is as follows: ， in, Indicates time Position deviation; is the number of detection points, which indicates the number of sensors used to detect position deviation; and Respectively represent Detection points at time Enhanced processed location data; and They represent the reference position and the ideal crimping position respectively; Evaluate the contact quality at different positions during the crimping process by comprehensively considering pressure, contact coefficient and speed changes; The contact quality at different positions during crimping is evaluated using the following 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; is the crimping speed function, which indicates the change of speed during the crimping process; 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 judgments are made through threshold setting; when the preset threshold is exceeded, an alarm is automatically triggered.

2. The method for automatic detection and analysis of the electric energy meter crimping process according to claim 1, characterized in that: Perform singular value decomposition on the detection data in time domain, frequency domain and after wavelet transformation respectively: ， ， ， in, 、 、 They are respectively 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 feature representation of the detection data after preprocessing in the time domain. After time domain transformation and singular value decomposition, the singular value is adjusted by adaptive filtering; It is the feature representation of the detection data after preprocessing in the frequency domain. After frequency domain transformation and singular value decomposition, the singular value is adjusted by 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.

3. The automatic detection and analysis method for the electric energy meter crimping process according to claim 1 is 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; 、 、 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 evaluation respectively; is a velocity function.

4. An automatic detection and analysis system for an electric energy meter crimping process, used to implement the automatic detection and analysis method for an electric energy meter crimping process according to any one of claims 1 to 3, 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 the detection data after intelligent enhancement processing; Analysis and judgment unit: The intelligent enhanced processing detection data is analyzed in real time to obtain analysis results including pressure distribution 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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