Charging pile service life prediction system and method thereof

Abstract

The invention discloses a charging pile service life prediction system and method, and relates to the technical field of new energy equipment monitoring, and the method comprises the steps: collecting multi-dimensional data signals in the operation process of a charging pile, carrying out the time alignment according to a preset data synchronization time window, and constructing a complete service life characteristic index system; the method comprises the following steps: analyzing degradation trends of various signals by using wavelet transform, performing space-time alignment and feature fusion on acquired multi-source heterogeneous data in combination with a deep learning algorithm, establishing a degradation rate model, and realizing feature extraction and health assessment of a life state; and carrying out life feature extraction based on a result output by the degradation rate model, constructing a life state evaluation model, realizing dynamic prediction and regular correction of the residual life of the charging pile, and outputting an equipment use optimization suggestion according to a life influence factor analysis result. According to the method, the problem of prediction deviation caused by a traditional single data source is solved, and the comprehensive and multi-dimensional monitoring and evaluation of the health state of the charging pile are also realized.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy equipment monitoring, and in particular to a charging pile life prediction system and method thereof. Background Art

[0002] With the vigorous development of the new energy vehicle industry, the scale of charging infrastructure construction continues to expand, the scale of the charging network is rapidly expanding, and the service life of equipment is increasing, which puts higher requirements on the reliability and service life of charging piles. The health status monitoring and life prediction of charging facilities have become key technical issues to ensure the stable operation of the charging network. However, the existing health status monitoring of charging piles mainly relies on a single data source for evaluation, such as only collecting electrical parameters or temperature parameters. This single-dimensional monitoring method cannot fully reflect the overall health status of the equipment, and it is easy to form a monitoring blind spot, which ultimately leads to insufficient accuracy and reliability of the evaluation results.

[0003] The current evaluation methods mostly use the discrimination method based on fixed thresholds, which cannot adapt to the dynamic characteristics of the equipment under different working conditions. Especially under extreme working conditions such as high-power fast charging, the equipment degradation rate may mutate, and the solidified evaluation method is slow to respond to this mutation state and lacks the necessary dynamic adjustment and optimization mechanism. At the same time, due to the lack of effective processing methods for multi-source heterogeneous data, the existing technology is difficult to achieve the deep fusion of multi-dimensional data such as acoustics, thermal imaging, and electromagnetic fields, and cannot fully utilize the state information contained in various sensor data, affecting the accuracy of the prediction results. In addition, the current prediction model rarely considers the dynamic characteristics of equipment degradation. Once the model is established, it is rarely updated and cannot adapt to the dynamic changes of equipment status and use environment, resulting in deviations between the prediction results and the actual situation. The existing technology also focuses on fault diagnosis and emergency treatment, lacks a predictive maintenance solution for the entire life cycle, and it is difficult to achieve economical and efficient operation and maintenance of charging facilities. Summary of the invention

[0004] In view of the problems existing in the existing charging pile life prediction methods, the present invention proposes a charging pile life prediction system and method.

[0005] Therefore, the problem to be solved by the present invention is the lack of a predictive maintenance solution for the entire life cycle, making it difficult to achieve economical and efficient operation and maintenance of charging facilities.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, an embodiment of the present invention provides a charging pile life prediction method, which includes collecting multi-dimensional data signals of the charging pile operation process, performing time alignment according to a preset data synchronization time window, and constructing a complete life characteristic indicator system; using wavelet transform to analyze the degradation trends of various signals, combining deep learning algorithms to perform spatiotemporal alignment and feature fusion on the collected multi-source heterogeneous data, establishing a degradation rate model, and realizing feature extraction and health assessment of the life state; extracting life features based on the results output by the degradation rate model, constructing a life state assessment model, and outputting equipment usage optimization suggestions based on the analysis results of life influencing factors.

[0007] As a preferred solution of the charging pile life prediction method described in the present invention, the multi-dimensional data signal includes a vibration spectrum signal, a temperature distribution signal, an electromagnetic field strength signal and an electrical parameter signal, wherein the electrical parameter signal includes a power fluctuation signal and a voltage and current signal.

[0008] As a preferred solution of the charging pile life prediction method of the present invention, the vibration spectrum signal is subjected to discrete wavelet transform to obtain a vibration characteristic frequency band, and vibration amplitude data is extracted based on the vibration characteristic frequency band, which is expressed as: , ϕ j , k [ n ] = 2 − j 2 ϕ ( 2 − j n − k ) , in, To decompose the scale, is the translation parameter, x [ n ] is a discrete time series, ϕ j , k [ n ] is the discrete wavelet basis function, which is obtained by scaling and translating the mother wavelet function; is the mother wavelet function, is the index of the discrete time series; the calculation of the vibration amplitude is expressed as: , in, is the number of sampling points, is the vibration amplitude data, is the sampling point number; based on the temperature distribution signal, a temperature gradient matrix is ​​established, and temperature stress data is extracted from the temperature gradient matrix. The temperature gradient is calculated as follows: ∇ T = [ ∂ T ∂ x , ∂ T ∂ y ] , in, represents the temperature gradient vector, express Directional temperature change rate, express Directional temperature change rate; the calculation of the temperature stress is expressed as: , in, is the thermal stress value, is the elastic modulus of the material, is the linear thermal expansion coefficient, is the temperature change; based on the electromagnetic field strength signal, the electromagnetic field distortion data is obtained, and the electromagnetic feature vector is formed by combining the electromagnetic field distortion data, wherein the electromagnetic field distortion calculation is expressed as: , in, is the electromagnetic field distortion rate, No. Subharmonic magnetic field strength, is the fundamental magnetic field strength, is the highest harmonic order considered; converting the power fluctuation signal and the voltage and current signal into an electrical characteristic sequence, and extracting power factor data and insulation resistance data from the electrical characteristic sequence, wherein the calculation of the power factor is expressed as: , in, is the power factor, is the active power, is the reactive power, is the apparent power; the determination of the insulation resistance is expressed as: , in, is the insulation resistance value, For the test voltage, is the leakage current.

[0009] As a preferred solution of the charging pile life prediction method of the present invention, the time window is set to: , in, is the time window length, is the vibration signal sampling frequency, is the temperature signal sampling frequency, is the electromagnetic field sampling frequency, is the electrical parameter sampling frequency, is the window expansion coefficient; the time alignment adopts a linear interpolation algorithm, which is expressed as: , in, is the interpolated data value, Represents the data values ​​of adjacent sampling points, is the interpolation time point, are adjacent sampling time points.

[0010] As a preferred solution of the charging pile life prediction method of the present invention, the time-frequency analysis of the multi-source signals of vibration, temperature and electromagnetic field is performed based on the wavelet transform to extract the degradation characteristics of various signals. The basic formula of continuous wavelet transform is expressed as: , in, is the continuous wavelet transform coefficient matrix, is a continuous time signal, is the conjugate complex number of the continuous wavelet basis function, is the continuous wavelet basis function, is a continuous scale parameter that controls the degree of expansion and contraction of the wavelet; is the time parameter, which controls the translation position of the wavelet and represents the time position; is the energy normalization factor; based on time-frequency analysis, the time-frequency energy density is calculated and expressed as: , in, is the time-frequency energy density distribution; the degradation characteristics are extracted and the degradation trend is analyzed, which is expressed as: , in, For the The degenerate characteristic sequence of the signal-like is the number of frequency scales selected, For time point, For the The weight coefficient of each scale, For the The energy value of each scale; the spatiotemporal alignment of multi-source heterogeneous data is achieved through the attention mechanism and deep neural network. The attention mechanism includes the time attention mechanism and the space attention mechanism. The time attention mechanism is expressed as: , in, is the attention weight in the time dimension, represents the time query transformation function, represents the time key transformation function, For the The characteristics of a time point, For the The characteristics of a time point, For the The characteristics of a time point, is the length of the time series; the spatial attention mechanism is expressed as: , in, is the attention weight of the spatial dimension, represents the spatial query transformation function, represents the spatial key transformation function, For the feature dimension data, For the feature dimension data, For the feature dimension data, is the number of feature dimensions.

[0011] As a preferred solution of the charging pile life prediction method of the present invention, wherein: spatiotemporal alignment and feature fusion are performed based on the attention mechanism, and the loss function of the spatiotemporal alignment is expressed as: , , , in, is the total alignment loss, is the time alignment loss, is the spatial alignment loss, is the balance factor, For the Signal at time Features, For the The signal has a feature dimension The distribution of is the reference feature distribution, is the number of signals, is the number of feature dimensions; the formula for feature fusion is expressed as: , , in, is the initial fusion feature, is the final fusion feature, is the fusion weight, is the hidden layer representation of various features, is the feature fusion operator, is the attention enhancement function.

[0012] As a preferred solution of the charging pile life prediction method of the present invention, the degradation rate model is expressed as: , in, For charging piles in time The degradation rate of is the feature sensitivity parameter used to control the degree of feature influence. is the feature vector after multimodal fusion, For the The state quantity of the monitoring signal, is the number of state variables, is the standard normal distribution cumulative function used to normalize the output, is the comprehensive measurement value of environmental parameters, is the system state standard deviation; based on the results output by the degradation rate model, life characteristics are extracted to construct a life state assessment model, which is expressed as follows: , in, for The health status index at all times, is the degradation rate, is the change in degradation rate, , is the degradation rate threshold, The sigmoid function is used for normalization.

[0013] In the second aspect, an embodiment of the present invention provides a charging pile life prediction system, which includes: an acquisition module, which is used to collect multi-dimensional data signals during the operation process of the charging pile, perform time alignment according to a preset data synchronization time window, and build a complete life characteristic indicator system; a prediction module, which uses wavelet transform to analyze the degradation trends of various signals, combines deep learning algorithms to perform time and space alignment and feature fusion on the collected multi-source heterogeneous data, establishes a degradation rate model, and realizes feature extraction and health assessment of the life state; an optimization module, which extracts life features based on the results output by the degradation rate model, constructs a life state assessment model, and outputs equipment usage optimization suggestions based on the analysis results of life influencing factors.

[0014] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned charging pile life prediction method is implemented.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned charging pile life prediction method is implemented.

[0016] The beneficial effect of the present invention is that the present invention performs spatiotemporal alignment and feature fusion processing on the collected multi-source heterogeneous data, establishes a parameter degradation rate model, realizes feature extraction and health assessment of life status, improves the accuracy and reliability of life prediction, overcomes the prediction bias problem caused by a single data source, and realizes all-round monitoring and evaluation of the health status of charging piles. Not only does it overcome the prediction bias problem caused by a traditional single data source, but it also realizes all-round, multi-dimensional monitoring and evaluation of the health status of charging piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which: Figure 1 Scenario diagram of the charging pile life prediction method. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a charging pile life prediction method, comprising: S1: Collect multi-dimensional data during the operation of the charging pile, including vibration spectrum, temperature distribution, electromagnetic field strength, and power fluctuation parameters, to build a complete life characteristic indicator system.

[0022] By setting up acoustic sensors, infrared thermal imagers, electromagnetic field sensors and electrical parameter acquisition devices, the vibration spectrum signals, temperature distribution signals, electromagnetic field strength signals and electrical parameter signals of the charging pile are collected, among which the electrical parameter signals include power fluctuation signals and voltage and current signals.

[0023] Set the sampling frequency of the vibration spectrum signal to 1kHz, the sampling frequency of the temperature distribution signal to 10Hz, the sampling frequency of the electromagnetic field intensity signal to 500Hz, and the sampling frequency of the electrical parameter signal to 50Hz.

[0024] The vibration spectrum signal is subjected to discrete wavelet transform to obtain the vibration characteristic frequency band, and the vibration amplitude data is extracted based on the vibration characteristic frequency band. The discrete wavelet transform formula is expressed as: DWT ( j , k ) = ∑ n x [ n ] ⋅ ϕ j , k [ n ] , ϕ j , k [ n ] = 2 − j 2 ϕ ( 2 − j n − k ) , in, To decompose the scale, is the translation parameter, x [ n ] is a discrete time series, ϕ j , k [ n ] is the discrete wavelet basis function, which is obtained by scaling and translating the mother wavelet function; is the mother wavelet function, is the index of the discrete time series.

[0025] The calculation of vibration amplitude is expressed as: , in, is the number of sampling points, is the vibration amplitude data, is the sampling point number.

[0026] The temperature distribution signal is obtained through infrared thermal imaging, and the temperature gradient matrix is ​​established. The temperature stress data of key components are extracted from the temperature gradient matrix. The temperature gradient is calculated as follows: ∇ T = [ ∂ T ∂ x , ∂ T ∂ y ] , in, represents the temperature gradient vector, express Directional temperature change rate, express Directional temperature change rate.

[0027] Further calculation of temperature stress: , in, is the thermal stress value, is the elastic modulus of the material, is the linear thermal expansion coefficient, is the temperature change.

[0028] Based on the Hall sensor array, the electromagnetic field strength signal is collected to obtain the electromagnetic field distortion data, and the electromagnetic feature vector is formed by combining the electromagnetic field distortion data. The electromagnetic field distortion calculation is expressed as: , in, is the electromagnetic field distortion rate, No. Subharmonic magnetic field strength, is the fundamental magnetic field strength, is the highest harmonic order considered.

[0029] The power fluctuation signal and the voltage and current signal are converted into an electrical characteristic sequence, and the power factor data and insulation resistance data are extracted from the electrical characteristic sequence, where the calculation of the power factor is expressed as: , in, is the power factor, is the active power, is the reactive power, is the apparent power.

[0030] The determination of insulation resistance is expressed as: , in, is the insulation resistance value, For the test voltage, is the leakage current.

[0031] According to the preset data synchronization time window, the vibration amplitude data, temperature stress data, electromagnetic characteristic vector, power factor data and insulation resistance data are time aligned. The time window is set as: , in, is the time window length, is the vibration signal sampling frequency, is the temperature signal sampling frequency, is the electromagnetic field sampling frequency, is the electrical parameter sampling frequency, is the window expansion factor.

[0032] Time alignment uses a linear interpolation algorithm, expressed as: , in, is the interpolated data value, Represents the data values ​​of adjacent sampling points, is the interpolation time point, are adjacent sampling time points.

[0033] The time-aligned data are combined into a characteristic data matrix, and the outliers in the characteristic data matrix are corrected to obtain the preprocessed life characteristic indicator data.

[0034] S2: Use wavelet transform to analyze the degradation trends of various signals, combine deep learning algorithms to perform spatiotemporal alignment and feature fusion of the collected multi-source heterogeneous data, establish a parameter degradation rate model, and realize feature extraction and health assessment of life status.

[0035] Continuous wavelet transform is used to perform time-frequency analysis on multi-source signals such as vibration, temperature, and electromagnetic field, and to extract the degradation characteristics of various signals. The basic formula of continuous wavelet transform is expressed as: , in, is the continuous wavelet transform coefficient matrix, is a continuous time signal, is the conjugate complex number of the continuous wavelet basis function, is the continuous wavelet basis function, is a continuous scale parameter that controls the degree of expansion and contraction of the wavelet; is the time parameter, which controls the translation position of the wavelet and represents the time position; is the energy normalization factor.

[0036] Based on time-frequency analysis, the time-frequency energy density is calculated and expressed as: , in, is the time-frequency energy density distribution.

[0037] Extract the degradation features, expressed as: , in, For the The degenerate characteristic sequence of the signal-like is the number of frequency scales selected, For time point, For the The weight coefficient of each scale, For the The energy value of a scale.

[0038] The alignment of multi-source heterogeneous data is achieved through the attention mechanism and deep neural network. The attention mechanism includes the temporal attention mechanism and the spatial attention mechanism. The temporal attention mechanism is expressed as: , in, is the attention weight in the time dimension, represents the time query transformation function, represents the time key transformation function, For the The characteristics of a time point, For the The characteristics of a time point, For the The characteristics of a time point, is the length of the time series.

[0039] By selecting features at three different time points, calculating the change trend, capturing the dynamic response characteristics of the system, reflecting the evolution of the system state, and providing the timing context of the system behavior, sufficient timing information is ensured while avoiding excessive computational complexity.

[0040] The spatial attention mechanism is expressed as: , in, is the attention weight of the spatial dimension, represents the spatial query transformation function, represents the spatial key transformation function, For the feature dimension data, For the feature dimension data, For the feature dimension data, is the number of feature dimensions.

[0041] Then, spatiotemporal alignment is performed based on temporal and spatial features, and the spatiotemporal alignment loss function is expressed as: , , , in, is the total alignment loss, is the time alignment loss, is the spatial alignment loss, is the balance factor, For the Signal at time Features, For the The signal has a feature dimension The distribution of is the reference feature distribution, is the number of signals, is the number of feature dimensions.

[0042] The spatiotemporal alignment loss function is used to align features of different time series, reduce the differences caused by time delays, keep features consistent in the time dimension, and capture similar time series patterns. The features of different devices are mapped to the same distribution space to eliminate system errors between devices, standardize feature distribution, establish a unified metric, obtain time-synchronized feature sequences and standardized feature spaces, reduce the impact of data deviations, and improve prediction accuracy.

[0043] According to the spatiotemporal aligned data, various converted features are fused by weighted combination. Each type of feature is assigned a corresponding fusion weight, and the expression ability of the feature is further enhanced through the residual connection structure to ensure that the original information will not be lost in the fusion process.

[0044] The formula for feature fusion is expressed as: , , in, is the initial fusion feature, is the final fusion feature, is the fusion weight, is the hidden layer representation of various features, is the feature fusion operator, is the attention enhancement function.

[0045] By integrating the data features, a degradation rate model is constructed to describe the performance degradation process of the charging pile. The formula is expressed as: , in, For charging piles in time The degradation rate of is the feature sensitivity parameter used to control the degree of feature influence. is the feature vector after multimodal fusion, For the The state quantity of the monitoring signal, is the number of state variables, is the standard normal distribution cumulative function used to normalize the output, is the comprehensive measurement value of environmental parameters, is the system state standard deviation.

[0046] S3: Based on the output of the degradation rate model, life characteristics are extracted, a life status assessment model is constructed, and the results of the life influencing factors are analyzed to output equipment usage optimization suggestions.

[0047] The life characteristics are extracted based on the output of the degradation rate model to achieve accurate assessment of the health status of the charging pile. The formula of the life status assessment model is expressed as: , in, for The health status index at all times, is the degradation rate, is the change in degradation rate, , is the degradation rate threshold, The sigmoid function is used for normalization.

[0048] Through multi-source data fusion and cumulative effect analysis, the degradation process of the charging pile is characterized, the health status of the charging pile is quantitatively evaluated, the performance degradation trend of the equipment is discovered in time, and decision support is provided for predictive maintenance.

[0049] The threshold interval is set based on the output result of the life state assessment model, including the first threshold , the second threshold , the third threshold , the fourth threshold ,and > > > .

[0050] when ≥ , indicating that the equipment is in a stable operation stage; when ≤ < When , it indicates that the equipment is in the initial degradation stage; when ≤ < When , it indicates that the equipment is in the accelerated degradation stage; when ≤ < When , it indicates that the equipment is in a serious degradation stage; when < , it indicates that the equipment is at risk of failure.

[0051] If the charging pile is in a stable operation stage, continue to implement the regular monitoring and maintenance plan; If the charging pile is at risk of failure, it needs to be shut down immediately and undergo a comprehensive overhaul; If the charging pile is in the initial degradation stage or accelerated degradation stage, it will enter the secondary evaluation based on the degradation rate to determine the degree of degradation; Setting the degradation rate The threshold value is , ,and < ; If the charging pile is in the initial degradation stage and the degradation rate < , it is determined to be in a state of slight degradation, and the equipment usage conditions are recorded and analyzed, a degradation feature database is established, and a preventive maintenance plan for the equipment is formulated to prepare for subsequent maintenance; If the charging pile is in the initial degradation stage and the degradation rate < < , it is determined to be in a moderate deterioration state, and a preventive maintenance plan is initiated. Professional personnel are arranged to conduct on-site inspections, conduct comprehensive performance tests on the charging module and control system, check and clean the cooling system, connectors and other parts that are prone to deterioration, evaluate the equipment operating environment, and optimize the usage strategy; If the charging pile is in the initial degradation stage and the degradation rate < , it is determined to be in a rapid degradation state, and the equipment load is immediately reduced, the high-power charging mode is limited, the cause of the rapid degradation is analyzed, whether there are systemic problems, an emergency response mechanism is established, and alternative equipment solutions are prepared.

[0052] If the charging pile is in the accelerated degradation stage and the degradation rate < , it is determined to be a normal accelerated degradation state, and a comprehensive inspection and performance evaluation of the charging module is carried out. A real-time monitoring and early warning mechanism for the equipment status is established, necessary replacement parts and repair tools are prepared, and a detailed maintenance plan and emergency plan are formulated.

[0053] If the charging pile is in the accelerated degradation stage and the degradation rate < < , it is determined to be a rapidly accelerated degradation state, and the equipment operating power is immediately reduced to 50% of the rated power. An intensive monitoring mechanism is initiated once an hour, stress tests and life assessments are performed on key components, a complete set of spare core components are prepared, and it is assessed whether the entire equipment needs to be replaced in advance. The root cause of the accelerated degradation is analyzed and a prevention mechanism is established.

[0054] If the charging pile is in the accelerated degradation stage and the degradation rate < , it is determined to be a dangerous accelerated degradation state. The equipment must be stopped immediately, the main circuit power must be cut off, replacement equipment must be prepared to ensure uninterrupted service, and an emergency replacement plan must be developed, including equipment, personnel, and tool configuration. A comprehensive safety hazard inspection must be conducted, preventive inspections must be carried out on similar equipment to prevent chain reactions, an accident cause analysis report must be established, and prevention mechanisms must be improved.

[0055] In summary, the present invention uses multi-dimensional sensing equipment to comprehensively collect key operating parameters of charging piles during operation, builds a complete life characteristic index evaluation system, innovatively introduces deep learning algorithms to perform spatiotemporal alignment and feature fusion processing on the collected multi-source heterogeneous data, establishes a parameter degradation rate model, realizes feature extraction and health assessment of life status, improves the accuracy and reliability of life prediction, overcomes the prediction bias problem caused by a single data source, and realizes all-round monitoring and evaluation of the health status of charging piles. Not only does it overcome the prediction bias problem caused by the traditional single data source, but it also realizes all-round, multi-dimensional monitoring and evaluation of the health status of charging piles. The present invention provides reliable technical support for predictive maintenance of charging infrastructure, and has important theoretical guidance significance and practical application value for improving the intelligent operation and maintenance level of charging facilities, extending the service life of equipment, and reducing maintenance costs.

[0056] This embodiment further provides a charging pile life prediction system, including: The acquisition module is used to collect multi-dimensional data signals during the operation of the charging pile, perform time alignment according to the preset data synchronization time window, and build a complete life characteristic indicator system; The prediction module uses wavelet transform to analyze the degradation trends of various signals, combines deep learning algorithms to perform spatiotemporal alignment and feature fusion of the collected multi-source heterogeneous data, establishes a degradation rate model, and realizes feature extraction and health assessment of life status; The optimization module extracts life characteristics based on the results output by the degradation rate model, builds a life status assessment model, realizes dynamic prediction and regular correction of the remaining life of the charging pile, and outputs equipment usage optimization suggestions based on the analysis results of life influencing factors.

[0057] This embodiment also provides a computer device, which is suitable for the case of a charging pile life prediction method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the charging pile life prediction method proposed in the above embodiment.

[0058] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0059] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for predicting the life of a charging pile as proposed in the above embodiment is implemented.

[0060] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0061] Embodiment 2: This embodiment provides a method for predicting the life of a charging pile. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0062] To verify the effectiveness of the proposed method, 10 DC fast charging piles with a running time of more than 3 years in a charging station were selected for a 6-month experimental study. The rated power of these charging piles is 120kW, and the average daily usage time is about 12 hours. The experiment compares the traditional single data evaluation method, the fixed threshold evaluation method and the proposed method.

[0063] Acoustic sensors, infrared thermal imagers, electromagnetic field sensors and electrical parameter acquisition devices are installed on each charging pile, and the sampling frequency is set to: 100Hz for acoustic data, 30fps for thermal imaging, 50Hz for electromagnetic field data, and 1Hz for electrical parameters. Health status assessment and life prediction are performed every 2 hours, and the manual test results are used as the evaluation criteria.

[0064] The experimental results are shown in Table 1: Table 1 Comparative evaluation table Evaluation Metrics Traditional single data approach Fixed Threshold Method Method of the present invention Status assessment accuracy 76.3% 82.5% 94.8% Life prediction error ±25.6% ±18.4% ±8.2% Fault warning advance time 24 hours 48 hours 96 hours False Positive Rate 15.3% 12.7% 4.2% False negative rate 12.8% 8.9% 2.1% Evaluation delay 5 minutes 8 minutes 12 minutes Abnormal working condition recognition rate 65.4% 77.2% 92.6% Experimental data show that in terms of state assessment accuracy, the method of the present invention reaches 94.8%, which is a significant improvement over the 76.3% of the traditional single data method and the 82.5% of the fixed threshold method; in terms of life prediction error, the present invention controls the error within the range of ±8.2%, which is an improvement of more than 50% compared with the traditional method's ±25.6% and the fixed threshold method's ±18.4%.

[0065] In terms of fault warning capability, the present invention can issue a warning 96 hours in advance, which is four times the 24-hour warning time of the traditional method, providing more sufficient response time for equipment maintenance. At the same time, the method of the present invention reduces the false alarm rate and missed alarm rate to 4.2% and 2.1% respectively, which is a qualitative leap compared with the 15.3% and 12.8% of the prior art.

[0066] Although the evaluation delay of the present invention is slightly higher than that of the existing method, reaching 12 minutes, this time cost is completely acceptable considering its significantly improved prediction accuracy and reliability. In particular, in terms of abnormal working condition identification, the recognition rate of the present invention method is as high as 92.6%, far exceeding the 65.4% of the traditional method and the 77.2% of the fixed threshold method.

[0067] In summary, the experimental results fully verify the technical advantages and application value of the present invention in the field of charging pile health status assessment and life prediction.

[0068] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A charging pile life prediction method, characterized in that: include: Collect multi-dimensional data signals during the operation of the charging pile, perform time alignment according to the preset data synchronization time window, and build a complete life characteristic indicator system; Wavelet transform is used to analyze the degradation trends of various signals, and deep learning algorithms are used to align the time and space and fuse the features of the collected multi-source heterogeneous data, establish a degradation rate model, and realize feature extraction and health assessment of life status; Based on the output of the degradation rate model, life characteristics are extracted, and a life status assessment model is constructed. According to the analysis results of life influencing factors, equipment usage optimization suggestions are output.

2. The charging pile life prediction method according to claim 1, characterized in that: The multi-dimensional data signal includes a vibration spectrum signal, a temperature distribution signal, an electromagnetic field strength signal and an electrical parameter signal, wherein the electrical parameter signal includes a power fluctuation signal and a voltage and current signal.

3. The charging pile life prediction method according to claim 2, characterized in that: The vibration spectrum signal is subjected to discrete wavelet transform to obtain a vibration characteristic frequency band, and vibration amplitude data is extracted based on the vibration characteristic frequency band, which is expressed as: ， ， in, To decompose the scale, is the translation parameter, is a discrete time series, is the discrete wavelet basis function, which is obtained by scaling and translating the mother wavelet function; is the mother wavelet function, is the index of the discrete time series; The calculation of the vibration amplitude is expressed as: ， in, is the number of sampling points, is the vibration amplitude data, is the sampling point number; Based on the temperature distribution signal, a temperature gradient matrix is ​​established, and temperature stress data is extracted from the temperature gradient matrix. The temperature gradient is calculated as follows: ， in, represents the temperature gradient vector, express Directional temperature change rate, express Directional temperature change rate; The calculation of the temperature stress is expressed as: ， in, is the thermal stress value, is the elastic modulus of the material, is the linear thermal expansion coefficient, is the temperature change; Based on the electromagnetic field strength signal, electromagnetic field distortion data is obtained, and the electromagnetic field distortion data is combined to form an electromagnetic feature vector, wherein the electromagnetic field distortion calculation is expressed as: ， in, is the electromagnetic field distortion rate, No. Subharmonic magnetic field strength, is the fundamental magnetic field strength, is the highest harmonic order considered; The power fluctuation signal and the voltage and current signal are converted into an electrical characteristic sequence, and power factor data and insulation resistance data are extracted from the electrical characteristic sequence, wherein the calculation of the power factor is expressed as: ， in, is the power factor, is the active power, is the reactive power, is the apparent power; The determination of the insulation resistance is expressed as: ， in, is the insulation resistance value, For the test voltage, is the leakage current.

4. The charging pile life prediction method according to claim 3, characterized in that: The time window is set to: ， in, is the time window length, is the vibration signal sampling frequency, is the temperature signal sampling frequency, is the electromagnetic field sampling frequency, is the electrical parameter sampling frequency, is the window expansion factor; The time alignment adopts a linear interpolation algorithm, which is expressed as: ， in, is the interpolated data value, Represents the data values ​​of adjacent sampling points, is the interpolation time point, are adjacent sampling time points.

5. The charging pile life prediction method according to claim 4, characterized in that: Based on the wavelet transform, the time-frequency analysis of multi-source signals of vibration, temperature and electromagnetic field is performed to extract the degradation characteristics of various signals. The basic formula of continuous wavelet transform is expressed as: ， in, is the continuous wavelet transform coefficient matrix, is a continuous time signal, is the conjugate complex number of the continuous wavelet basis function, is the continuous wavelet basis function, is a continuous scale parameter that controls the degree of expansion and contraction of the wavelet; is the time parameter, which controls the translation position of the wavelet and represents the time position; is the energy normalization factor; Based on time-frequency analysis, the time-frequency energy density is calculated and expressed as: ， in, is the time-frequency energy density distribution; Extract degradation features and analyze degradation trends, expressed as: ， in, For the The degenerate characteristic sequence of the signal-like is the number of frequency scales selected, For time point, For the The weight coefficient of each scale, For the Energy value of each scale; The spatiotemporal alignment of multi-source heterogeneous data is achieved through the attention mechanism and deep neural network. The attention mechanism includes the temporal attention mechanism and the spatial attention mechanism. The temporal attention mechanism is expressed as: ， in, is the attention weight in the time dimension, represents the time query transformation function, represents the time key transformation function, For the The characteristics of a time point, For the The characteristics of a time point, For the The characteristics of a time point, is the length of the time series; The spatial attention mechanism is expressed as: ， in, is the attention weight of the spatial dimension, represents the spatial query transformation function, represents the spatial key transformation function, For the feature dimension data, For the feature dimension data, For the feature dimension data, is the number of feature dimensions.

6. The charging pile life prediction method according to claim 5, characterized in that: Based on the attention mechanism, spatiotemporal alignment and feature fusion are performed. The loss function of the spatiotemporal alignment is expressed as: ， ， ， in, is the total alignment loss, is the time alignment loss, is the spatial alignment loss, is the balance factor, For the Signal at time Features, For the The signal has a feature dimension The distribution of is the reference feature distribution, is the number of signals, is the number of feature dimensions; The formula of feature fusion is expressed as: ， ， in, is the initial fusion feature, is the final fusion feature, is the fusion weight, is the hidden layer representation of various features, is the feature fusion operator, is the attention enhancement function.

7. The charging pile life prediction method according to claim 6, characterized in that: The degradation rate model is expressed as: ， in, For charging piles in time The degradation rate of is the feature sensitivity parameter used to control the degree of feature influence. is the feature vector after multimodal fusion, For the The state quantity of the monitoring signal, is the number of state variables, is the standard normal distribution cumulative function used to normalize the output, is the comprehensive measurement value of environmental parameters, is the system state standard deviation; Based on the output of the degradation rate model, life characteristics are extracted and a life status assessment model is constructed. The formula is expressed as follows: ， in, for The health status index at all times, is the degradation rate, is the change in degradation rate, , is the degradation rate threshold, The sigmoid function is used for normalization.

8. A charging pile life prediction system, based on the charging pile life prediction method according to any one of claims 1 to 7, characterized in that: include: The acquisition module is used to collect multi-dimensional data signals during the operation of the charging pile, perform time alignment according to the preset data synchronization time window, and build a complete life characteristic indicator system; The prediction module uses wavelet transform to analyze the degradation trends of various signals, combines deep learning algorithms to perform spatiotemporal alignment and feature fusion of the collected multi-source heterogeneous data, establishes a degradation rate model, and realizes feature extraction and health assessment of life status; The optimization module extracts life characteristics based on the output of the degradation rate model, builds a life status assessment model, and outputs equipment usage optimization suggestions based on the analysis results of life influencing factors.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the charging pile life prediction method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the charging pile life prediction method described in any one of claims 1 to 7 are implemented.

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