A method for energy management of automobile charging stations based on the Internet of Things

Abstract

The present invention discloses an energy management method for automobile charging stations based on the Internet of Things, which relates to the technical field of energy management of charging stations. The operating data of charging piles are collected in real time through Internet of Things sensors, and these data are preprocessed and fitted to generate a systematic operating data set YW. Compared with the traditional inspection mode, the present invention can realize real-time data collection and analysis, quickly capture changes in the operating status of the equipment, and provide high-quality basic data for subsequent abnormal detection and maintenance decisions. This improvement significantly improves the timeliness and accuracy of data collection, so that the operating status of the equipment can be fully controlled. Through in-depth analysis of the operating data set YW and the extracted abnormal feature set YF, the present invention introduces two key indicators, the state abnormality score yS and the cumulative abnormality growth rate yG, to accurately evaluate the current abnormality level and long-term health status of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management of charging stations, and in particular to an energy management method for automobile charging stations based on the Internet of Things. Background Art

[0002] In the specific application of the Internet of Things, smart energy management is a rapidly developing direction, among which the operation monitoring and management of charging piles is an important part of smart energy management. As a key facility in the development of new energy, car charging piles provide charging support for electric vehicles, but their high-load operation characteristics make equipment failures frequent, resulting in reduced operating efficiency and user experience. Therefore, real-time monitoring of the operating status of charging piles through Internet of Things technology and early detection of potential problems have become a specific and key technical breakthrough in the field of charging piles.

[0003] At present, the management and maintenance of the operating status of charging piles mainly rely on regular inspections and passive fault handling. However, this traditional method has many problems: first, the inspection period is strong, but it cannot perceive equipment abnormalities in real time, resulting in faults often being discovered only after they occur; second, passive maintenance increases equipment downtime and affects the normal operation of charging stations.

[0004] The main reason for the above shortcomings is the lack of in-depth application of real-time monitoring and data analysis technology. Traditional maintenance methods cannot collect equipment operating parameters in real time, let alone provide early warning of abnormal changes in these parameters. Once a device fails, such as excessive voltage fluctuations or excessive temperatures, it may cause equipment downtime, user service interruptions, and even safety risks. This abnormal impact not only reduces the operating efficiency of charging stations, but may also lead to a significant decline in user experience, further hindering the popularization of electric vehicles and the sustainable development of the new energy industry. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an energy management method for a vehicle charging station based on the Internet of Things, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for energy management of automobile charging stations based on the Internet of Things, comprising the following steps:

[0007] S1. Collect the operation data of the charging pile in real time through the IoT sensor, including voltage U, current I, charging pile temperature T, contact resistance R and vibration frequency F, and perform preprocessing to fit the operation data set YW;

[0008] S2, extracting abnormal features from the acquired operation data set YW, including voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT and vibration offset ΔF, to form an abnormal feature set YF;

[0009] S3, analyze the acquired operation data set YW and abnormal feature set YF, and calculate the acquired state abnormal score yS and the cumulative abnormal growth rate yG;

[0010] S4. Compare the acquired status abnormality score yS and cumulative abnormality growth rate yG with the preset abnormality score threshold TyS and abnormality growth rate threshold TyG respectively to determine the status of the charging pile;

[0011] S5. Generate a maintenance plan based on the status of the charging pile, the status abnormality score yS and the cumulative abnormality growth rate yG;

[0012] S6. After the maintenance plan is executed, collect a new round of operation data and compare it with the operation data to verify the maintenance effect.

[0013] Preferably, said S1 includes S11 and S12;

[0014] S11, collecting voltage U through a voltage sensor, collecting current I through a shunt current sensor, collecting charging pile temperature T through a thermocouple sensor, collecting contact resistance R through a four-wire resistance tester, collecting vibration frequency F through a vibration sensor, and fitting them into an original data set W;

[0015] S12, cleaning and normalizing the original data set W to obtain a running data set YW;

[0016] Cleaning includes removing noise and outliers, by processing the noise in the original data set W by using a filter, and processing the outliers in the original data set W by using a median filter method;

[0017] The running data set YW is obtained by the following formula:

[0018] ;

[0019] Wherein, YWd represents the d-th data item in the running data set YW, Wd represents the d-th data item in the original data set W, Wd,min represents the valley value of the d-th data item in the original data set W, and Wd,max represents the peak value of the d-th data item in the original data set W.

[0020] Preferably, S2 includes S21 and S22;

[0021] S21, perform stationarity analysis on the data in the running data set YW, calculate the volatility index, and extract abnormal features: including voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT, and vibration offset ΔF;

[0022] The voltage fluctuation ΔU is obtained by the following formula:

[0023] ;

[0024] In the formula, N represents the total number of data samples, Ut represents the voltage value at time t, and Up represents the average value of the voltage;

[0025] The current fluctuation ΔI is obtained by the following formula:

[0026] ;

[0027] In the formula, max (It) represents the peak current value at time t, min (It) represents the valley current value at time t, and Ip represents the average current value;

[0028] The temperature change rate ΔT is obtained by the following formula:

[0029] ;

[0030] Where N represents the total number of data samples, T(t+1) represents the temperature at time t+1, and T(t) represents the temperature at time t;

[0031] The vibration offset ΔF is obtained by the following formula:

[0032] ;

[0033] Where, M represents the total number of frequencies, Fi represents the i-th frequency, P(Fi) represents the power spectrum value of the i-th frequency, and Fno represents the center value of the vibration frequency;

[0034] S22, fitting the acquired voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT and vibration offset ΔF to form an abnormal feature set YF.

[0035] Preferably, said S3 includes S31 and S32;

[0036] S31, perform feature fusion on the acquired operation data set YW and abnormal feature set YF, substitute them into the comprehensive score function, and calculate the acquired state abnormality score yS; which is used to judge the current abnormal risk of the charging pile;

[0037] The yS is obtained by the following formula:

[0038] ;

[0039] Wherein, ySt represents the abnormal state score at time t, Ut represents the voltage value at time t, Up represents the average value of voltage, It represents the current value at time t, Ip represents the average value of current, Tt represents the charging pile temperature value at time t, Tp represents the average value of charging pile temperature, Ft represents the vibration frequency value at time t, Fp represents the average value of vibration frequency, Respectively represent the voltage value Ut at time t, the current value It at time t, the charging pile temperature value Tt at time t, the vibration frequency value Ft at time t, and the preset weight values ​​of the contact resistance R; and .

[0040] Calculate the score ySt at each moment to determine whether the system is in a normal state; the higher the score, the greater the possibility of system abnormality. Determined based on historical data experience.

[0041] Preferably, S32, calculating and obtaining the cumulative abnormal growth rate yG of the charging pile according to the abnormal state score ySt at time t, and predicting the future fault trend;

[0042] The cumulative abnormal growth rate yG is obtained by the following formula:

[0043] ;

[0044] Where yGs represents the cumulative abnormal growth rate from the initial moment to time t, and dt represents the integral variable.

[0045] Preferably, said S4 includes S41 and S42;

[0046] S41, comparing the acquired status abnormality score yS and cumulative abnormality growth rate yG with the preset abnormality score threshold TyS and abnormality growth rate threshold TyG respectively, to determine the status of the charging pile;

[0047] Compare the state abnormality score yS with the preset abnormality score threshold TyS to determine whether the state abnormality score yS exceeds the preset abnormality score threshold TyS;

[0048] ;

[0049] Where TryS represents the state of the charging pile. When the state abnormality score yS> the abnormality score threshold TyS, 1 is returned, indicating that the abnormal state is triggered; when the state abnormality score yS≤ the abnormality score threshold TyS, 0 is returned, indicating a normal state.

[0050] Compare the cumulative abnormal growth rate yG with the abnormal growth rate threshold TyG to determine whether the cumulative abnormal growth rate yG exceeds the preset abnormal growth rate threshold TyG;

[0051] ;

[0052] Where TryG represents the abnormal state of the charging pile. When the cumulative abnormal growth rate yG>the abnormal growth rate threshold TyG, it returns 1, indicating that the charging pile is abnormal; when the cumulative abnormal growth rate yG≤the abnormal growth rate threshold TyG, it returns 0, indicating that the charging pile is normal.

[0053] Preferably, S42, after the abnormal state is triggered, an alarm will be triggered, and alarm information will be generated and passed to the operation and maintenance personnel; the alarm information includes abnormal parameters, abnormal time period range and fault type;

[0054] Abnormal parameters include abnormal voltage fluctuation ΔU, abnormal current fluctuation ΔI, abnormal temperature change rate ΔT and abnormal vibration deviation ΔF;

[0055] The abnormal time period includes the start and end time of the abnormality; the fault types include thermal overload, poor contact and mechanical failure.

[0056] Preferably, said S5 includes S51 and S52;

[0057] S51. Analyze the charging pile according to the abnormal state score yS and the cumulative abnormal growth rate yG, and in combination with the abnormal parameters, identify the problematic components, assign maintenance priorities, and calculate and obtain the maintenance priority score Pm;

[0058] Using the characteristics of abnormal parameters, the faulty components and fault types of the charging pile are judged;

[0059] Abnormal voltage fluctuation ΔU indicates aging of the power module and contact points;

[0060] An abnormal temperature change rate ΔT indicates a failure of the heat dissipation component and an overload of the charging pile.

[0061] Abnormal vibration deviation ΔF indicates problems with loose and worn mechanical parts;

[0062] The maintenance priority score Pm is obtained by the following formula:

[0063] ;

[0064] Where Pmk represents the maintenance priority score of the kth mechanical component of the charging pile, Δyck represents the abnormal parameter value corresponding to the kth mechanical component of the charging pile, represent the preset weight values ​​of the abnormality score yS, the cumulative abnormality growth rate yG and the abnormal parameter value Δyck corresponding to the kth mechanical component of the charging pile, respectively, and .

[0065] Preferably, S52, according to the acquired maintenance priority score Pm, a maintenance plan list is formulated, including replacement of parts and adjustment of operation mode.

[0066] Preferably, in S6, after the maintenance plan is executed, a new round of operation data is collected through the IoT sensor, and the data is compared with the historical data to verify the effect of the maintenance plan;

[0067] The operating status of the charging pile is continuously monitored through the IoT sensor, and the voltage U, current I, charging pile temperature T, contact resistance R and vibration frequency F are collected and fitted into a new operating data set YWnew, and feature extraction is performed, including the new voltage fluctuation ΔUnew, the new current fluctuation ΔInew, the new temperature change rate ΔTnew and the new vibration offset ΔFnew are fitted into a new abnormal feature set YFnew;

[0068] Compare the new abnormal feature set YFnew with the abnormal feature set YF, evaluate the difference, obtain the difference amplitude value ΔD, and compare it with the preset difference threshold TD to determine the effectiveness of the maintenance plan;

[0069] The difference amplitude value ΔD is obtained by the following formula:

[0070] ;

[0071] The effectiveness of the maintenance plan is obtained by the following formula:

[0072] When the difference amplitude value ΔD ≥ the difference threshold TD, it means that the maintenance plan is effective;

[0073] When the difference amplitude value ΔD is less than the difference threshold TD, it indicates that the maintenance plan is invalid and the charging pile is readjusted.

[0074] The present invention provides an energy management method for automobile charging stations based on the Internet of Things, which has the following beneficial effects:

[0075] (1) The operation data of the charging pile is collected in real time through the IoT sensor, and the data is preprocessed and fitted to generate a systematic operation data set YW. Compared with the traditional inspection mode, the present invention can realize real-time data collection and analysis, quickly capture the changes in the operation status of the equipment, and provide high-quality basic data for subsequent abnormal detection and maintenance decisions. This improvement significantly improves the timeliness and accuracy of data collection, so that the operation status of the equipment can be fully controlled. Through in-depth analysis of the operation data set YW and the extracted abnormal feature set YF, the present invention introduces two key indicators, the state abnormality score yS and the cumulative abnormal growth rate yG, to accurately evaluate the current abnormality level and long-term health status of the equipment. Compared with the traditional passive fault handling mode, this improvement can generate alarms in time before potential problems occur, preventing sudden equipment failures. Realizing early prediction of faults can effectively reduce unplanned downtime of equipment, ensure the continuous operation of charging stations, and improve users' charging experience and the utilization rate of charging piles.

[0076] (2) By using multiple types of sensors to collect key parameters such as voltage U, current I, charging pile temperature T, contact resistance R and vibration frequency F in real time, this embodiment realizes all-round monitoring of the charging pile operation status. By using high-precision equipment such as shunt current sensors and thermocouple sensors, the reliability and accuracy of the collected data are ensured. At the same time, the original data set W is generated by data fitting, providing a high-quality data basis for subsequent analysis. Compared with the traditional data acquisition method, this improvement significantly improves the monitoring coverage and accuracy, laying a solid foundation for the early detection of potential faults. Through cleaning and normalization processing, the original data set W is converted into an operation data set YW, which effectively eliminates the interference of noise and outliers on the analysis and improves the consistency and availability of the data. At the same time, based on the operation data set YW, a stationarity analysis is performed to calculate the key features of voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT and vibration offset ΔF to form an abnormal feature set YF. These features can accurately reflect the fluctuations and changes in the operation status of the equipment, providing high-value input for subsequent status evaluation and fault prediction.

[0077] (3) By integrating the status abnormality score yS and obtaining the cumulative abnormality growth rate yG, a quantitative assessment of the long-term health status of the charging pile is achieved. The cumulative abnormality growth rate can reflect the persistence and cumulative effect of the abnormal state of the equipment over a period of time, further improving the overall control of the health status of the equipment. Compared with the limitations of traditional methods that only focus on single-point abnormal states, the cumulative abnormality growth rate yG more comprehensively measures the stability of the long-term operation of the equipment and the potential failure trend, providing a scientific basis for the early deployment of maintenance plans and the optimization of equipment operation strategies.

[0078] By calculating the status anomaly score yS and the cumulative anomaly growth rate yG, not only can the current status of the device be evaluated in real time, but also future failures can be accurately predicted through trend analysis of yG. This capability enables the system to take targeted maintenance measures before a failure occurs, avoiding unplanned downtime or further damage to the equipment. Compared with the traditional passive maintenance mode, this embodiment supports active operation and maintenance management, significantly reducing equipment failure rate and maintenance costs, while improving the efficiency of charging piles and user satisfaction.

[0079] (4) By combining the status abnormality score yS, the cumulative abnormal growth rate yG and the abnormal parameters, the specific faulty components and types of the charging pile are analyzed and judged. For example, the abnormal voltage fluctuation ΔU indicates the aging of the power module or contact point, and the abnormal temperature change rate ΔT indicates the failure of the heat dissipation component or the overload of the equipment. By formulaically calculating the maintenance priority score Pm, the faulty components are sorted and assigned priorities to ensure that operation and maintenance resources can be focused on the most important problems. This precise component identification and priority allocation method effectively avoids the waste of resources caused by unclear problem location in traditional maintenance, and greatly improves the operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 The present invention is a schematic diagram of the steps of an energy management method for automobile charging stations based on the Internet of Things. DETAILED DESCRIPTION

[0081] 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, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0082] Example 1

[0083] The present invention provides an energy management method for automobile charging stations based on the Internet of Things. Figure 1 , including the following steps:

[0084] S1. Collect the operation data of the charging pile in real time through the IoT sensor, including voltage U, current I, charging pile temperature T, contact resistance R and vibration frequency F, and perform preprocessing to fit the operation data set YW;

[0085] S2, extracting abnormal features from the acquired operation data set YW, including voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT and vibration offset ΔF, to form an abnormal feature set YF;

[0086] S3, analyze the acquired operation data set YW and abnormal feature set YF, and calculate the acquired state abnormal score yS and the cumulative abnormal growth rate yG;

[0087] S4. Compare the acquired status abnormality score yS and cumulative abnormality growth rate yG with the preset abnormality score threshold TyS and abnormality growth rate threshold TyG respectively to determine the status of the charging pile;

[0088] S5. Generate a maintenance plan based on the status of the charging pile, the status abnormality score yS and the cumulative abnormality growth rate yG;

[0089] S6. After the maintenance plan is executed, collect a new round of operation data and compare it with the operation data to verify the maintenance effect.

[0090] In this embodiment, the operation data of the charging pile is collected in real time by the Internet of Things sensor, and these data are preprocessed and fitted to generate a systematic operation data set YW. Compared with the traditional inspection mode, the present invention can realize real-time data collection and analysis, quickly capture the changes in the operation status of the equipment, and provide high-quality basic data for subsequent abnormal detection and maintenance decisions. This improvement significantly improves the timeliness and accuracy of data collection, so that the operation status of the equipment can be fully controlled. Through in-depth analysis of the operation data set YW and the extracted abnormal feature set YF, the present invention introduces two key indicators, the state abnormality score yS and the cumulative abnormal growth rate yG, to accurately evaluate the current abnormality level and long-term health status of the equipment. Compared with the traditional passive fault handling mode, this improvement can generate alarms in time before potential problems occur to prevent sudden equipment failures. Realizing early prediction of faults can effectively reduce unplanned downtime of equipment, ensure the continuous operation of charging stations, and improve users' charging experience and the utilization rate of charging piles.

[0091] The present invention compares the status abnormality score yS and the cumulative abnormality growth rate yG with the abnormality score threshold TySTyS and the TyG abnormality growth rate threshold to intelligently judge the health status of the equipment and generate an optimized maintenance plan based on the analysis results, including specific tasks such as replacing high-wear parts and adjusting the operating mode. Traditional maintenance methods usually rely on manual experience, and there are problems of response lag and resource waste. The present invention improves operation and maintenance efficiency, optimizes the allocation of maintenance resources, reduces maintenance costs, and extends the service life of equipment through intelligent maintenance task generation.

[0092] After the maintenance plan is executed, the present invention collects a new round of data and compares it with historical data to verify the maintenance effect, and dynamically adjusts the fault prediction model so that the model can adapt to changes in the equipment status. By introducing a dynamic iteration mechanism, the present invention achieves continuous optimization of the prediction model, improves the accuracy of the prediction and the overall reliability of the system. This improvement effectively avoids the problem of prediction failure caused by model aging, and further ensures the stable operation of the charging station.

[0093] Example 2

[0094] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11 and S12;

[0095] S11, collecting voltage U through a voltage sensor, collecting current I through a shunt current sensor, collecting charging pile temperature T through a thermocouple sensor, collecting contact resistance R through a four-wire resistance tester, collecting vibration frequency F through a vibration sensor, and fitting them into an original data set W;

[0096] S12, cleaning and normalizing the original data set W to obtain a running data set YW;

[0097] Cleaning includes removing noise and outliers, by processing the noise in the original data set W by using a filter, and processing the outliers in the original data set W by using a median filter method;

[0098] The running data set YW is obtained by the following formula:

[0099] ;

[0100] Wherein, YWd represents the d-th data item in the running data set YW, Wd represents the d-th data item in the original data set W, Wd,min represents the valley value of the d-th data item in the original data set W, and Wd,max represents the peak value of the d-th data item in the original data set W.

[0101] The S2 includes S21 and S22;

[0102] S21, perform stationarity analysis on the data in the running data set YW, calculate the volatility index, and extract abnormal features: including voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT, and vibration offset ΔF;

[0103] The voltage fluctuation ΔU is obtained by the following formula:

[0104] ;

[0105] In the formula, N represents the total number of data samples, Ut represents the voltage value at time t, and Up represents the average value of the voltage;

[0106] The current fluctuation ΔI is obtained by the following formula:

[0107] ;

[0108] In the formula, max (It) represents the peak current value at time t, min (It) represents the valley current value at time t, and Ip represents the average current value;

[0109] The temperature change rate ΔT is obtained by the following formula:

[0110] ;

[0111] Where N represents the total number of data samples, T(t+1) represents the temperature at time t+1, and T(t) represents the temperature at time t;

[0112] The vibration offset ΔF is obtained by the following formula:

[0113] ;

[0114] Where, M represents the total number of frequencies, Fi represents the i-th frequency, P(Fi) represents the power spectrum value of the i-th frequency, and Fno represents the center value of the vibration frequency;

[0115] S22, fitting the acquired voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT and vibration offset ΔF to form an abnormal feature set YF.

[0116] In this embodiment, by using various types of sensors to collect key parameters such as voltage U, current I, charging pile temperature T, contact resistance R and vibration frequency F in real time, this embodiment realizes all-round monitoring of the charging pile operation status. By using high-precision equipment such as shunt current sensors and thermocouple sensors, the reliability and accuracy of the collected data are ensured, and the original data group W is generated by data fitting to provide a high-quality data basis for subsequent analysis. Compared with the traditional data acquisition method, this improvement significantly improves the monitoring coverage and accuracy, and lays a solid foundation for the early detection of potential faults. Through cleaning and normalization processing, the original data group W is converted into an operation data set YW, which effectively eliminates the interference of noise and outliers on the analysis and improves the consistency and availability of the data. At the same time, based on the operation data set YW, a stationarity analysis is performed to calculate the key features of voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT and vibration offset ΔF to form an abnormal feature set YF. These features can accurately reflect the fluctuations and changes in the operation status of the equipment, and provide high-value input for subsequent status evaluation and fault prediction. Compared with traditional methods, this embodiment greatly improves the depth and accuracy of data analysis through clear feature extraction formulas and fitting processes.

[0117] Through the volatility index calculated by formula and the abnormal feature set YF generated by fitting, this embodiment realizes an accurate description of the operating status of the equipment. The extracted voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT and vibration offset ΔF features can sensitively capture abnormal trends in equipment operation, providing a reliable basis for state abnormality scores and fault prediction models. Compared with the traditional single data dimension analysis method, this embodiment enhances the foresight of fault prediction based on multi-dimensional data analysis, which helps to perceive potential problems in advance and take corresponding measures.

[0118] Example 3

[0119] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S3 includes S31 and S32;

[0120] S31, perform feature fusion on the acquired running data set YW and the abnormal feature set YF, substitute them into the comprehensive score function, and calculate the acquired state abnormal score yS;

[0121] The yS is obtained by the following formula:

[0122] ;

[0123] Wherein, ySt represents the abnormal state score at time t, Ut represents the voltage value at time t, Up represents the average value of voltage, It represents the current value at time t, Ip represents the average value of current, Tt represents the charging pile temperature value at time t, Tp represents the average value of charging pile temperature, Ft represents the vibration frequency value at time t, Fp represents the average value of vibration frequency, They respectively represent the voltage value Ut at time t, the current value It at time t, the charging pile temperature value Tt at time t, the vibration frequency value Ft at time t, and the preset weight values ​​of the contact resistance R.

[0124] S32, calculating the cumulative abnormal growth rate yG of the charging pile according to the abnormal state score ySt at time t, and predicting the future fault trend;

[0125] The cumulative abnormal growth rate yG is obtained by the following formula:

[0126] ;

[0127] Where yGs represents the cumulative abnormal growth rate from the initial moment to time t, and dt represents the integral variable.

[0128] In this embodiment, through the feature fusion method, this embodiment integrates the multidimensional data in the operating data set YW and the abnormal feature set YF into the comprehensive score function to calculate the state abnormality score yS. The fusion process introduces the preset weight values ​​of voltage U, current I, charging pile temperature T, contact resistance R and vibration frequency F. , ensuring that the influence of each parameter on abnormal state assessment can be reasonably distributed. Compared with traditional single parameter monitoring or simple threshold judgment, this method significantly enhances the accuracy and sensitivity of state abnormality scores, can fully reflect the comprehensive abnormality degree of equipment operation status, and provides a reliable basis for subsequent fault prediction.

[0129] By integrating the status abnormality score yS and obtaining the cumulative abnormality growth rate yG, a quantitative assessment of the long-term health status of the charging pile can be achieved. The cumulative abnormality growth rate can reflect the persistence and cumulative effect of the abnormal state of the equipment over a period of time, further improving the overall control of the health status of the equipment. Compared with the limitations of traditional methods that only focus on single-point abnormal states, the cumulative abnormality growth rate yG more comprehensively measures the stability of the long-term operation of the equipment and the potential failure trend, providing a scientific basis for the early deployment of maintenance plans and the optimization of equipment operation strategies.

[0130] By calculating the status anomaly score yS and the cumulative anomaly growth rate yG, not only can the current status of the device be evaluated in real time, but also future failures can be accurately predicted through trend analysis of yG. This capability enables the system to take targeted maintenance measures before a failure occurs, avoiding unplanned downtime or further damage to the equipment. Compared with the traditional passive maintenance mode, this embodiment supports active operation and maintenance management, significantly reducing equipment failure rate and maintenance costs, while improving the efficiency of charging piles and user satisfaction.

[0131] Example 4

[0132] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: the S4 includes S41 and S42;

[0133] S41, comparing the acquired status abnormality score yS and cumulative abnormality growth rate yG with the preset abnormality score threshold TyS and abnormality growth rate threshold TyG respectively, to determine the status of the charging pile;

[0134] Compare the state abnormality score yS with the preset abnormality score threshold TyS to determine whether the state abnormality score yS exceeds the preset abnormality score threshold TyS;

[0135] ;

[0136] Where TryS represents the state of the charging pile. When the state abnormality score yS> the abnormality score threshold TyS, 1 is returned, indicating that the abnormal state is triggered; when the state abnormality score yS≤ the abnormality score threshold TyS, 0 is returned, indicating a normal state.

[0137] Compare the cumulative abnormal growth rate yG with the abnormal growth rate threshold TyG to determine whether the cumulative abnormal growth rate yG exceeds the preset abnormal growth rate threshold TyG;

[0138] ;

[0139] Where TryG represents the abnormal state of the charging pile. When the cumulative abnormal growth rate yG>the abnormal growth rate threshold TyG, it returns 1, indicating that the charging pile is abnormal; when the cumulative abnormal growth rate yG≤the abnormal growth rate threshold TyG, it returns 0, indicating that the charging pile is normal.

[0140] S42. After the abnormal state is triggered, an alarm will be triggered and alarm information will be generated and passed to the operation and maintenance personnel; the alarm information includes abnormal parameters, abnormal time period range and fault type;

[0141] Abnormal parameters include abnormal voltage fluctuation ΔU, abnormal current fluctuation ΔI, abnormal temperature change rate ΔT and abnormal vibration deviation ΔF;

[0142] The abnormal time period includes the start and end time of the abnormality; the fault types include thermal overload, poor contact and mechanical failure.

[0143] In this embodiment, a dual threshold judgment mechanism is established by comparing the state abnormality score yS and the cumulative abnormality growth rate yG with the preset abnormality score threshold TyS and abnormality growth rate threshold TyG respectively. The state abnormality score yS is used to evaluate the current abnormality level of the device in real time, while the cumulative abnormality growth rate yG quantifies the persistence and cumulative risk of the abnormality. The combination of the two can comprehensively and accurately judge the health status of the charging pile. Compared with the traditional single threshold judgment method, this method significantly improves the sensitivity and accuracy of abnormal state identification, laying the foundation for timely discovery of equipment problems.

[0144] After the abnormal state is triggered, an intelligent alarm mechanism is introduced to generate alarm content containing key information and notify the operation and maintenance personnel. The alarm information includes abnormal parameters including abnormal voltage fluctuation ΔU, abnormal current fluctuation ΔI, abnormal temperature change rate ΔT and abnormal vibration offset ΔF, the time period when the abnormality occurs including the start and end time, and possible fault types including thermal overload, poor contact and mechanical failure. This mechanism ensures that the operation and maintenance personnel can quickly understand the specific situation and possible causes of the equipment abnormality, accurately locate the source of the problem, thereby greatly shortening the response time and reducing the complexity of maintenance. Compared with the lag of relying on manual inspections to find problems in traditional methods, this embodiment significantly improves the efficiency and accuracy of abnormal response. Through systematic analysis of abnormal parameters and time period ranges, this embodiment can not only identify the current fault type of the charging pile, but also provide high-value data support for subsequent operation and maintenance optimization. For example, for thermal overload problems, the equipment heat dissipation design can be optimized in time; for poor contact problems, the installation specifications of the connection components can be adjusted. The operation and maintenance personnel can optimize the maintenance plan based on detailed alarm information, improve the utilization efficiency of maintenance resources, and avoid repetitive or ineffective operations. Compared with the traditional method, this embodiment realizes the transformation from passive maintenance to active operation and maintenance, further reducing the maintenance cost and improving the equipment operation efficiency.

[0145] By comparing the status anomaly score yS and the cumulative anomaly growth rate yG, the abnormal status of the charging pile can be captured in real time and accurately, avoiding the risk of equipment downtime due to untimely detection of anomalies. The intelligent alarm mechanism records the abnormal parameters, time range and fault type in detail, providing clear guidance information for operation and maintenance personnel, greatly improving the efficiency and accuracy of problem handling. Through the systematic analysis of abnormal data, this embodiment supports the continuous optimization of the charging pile operation and maintenance strategy, thereby achieving long-term stable operation of the equipment and effective cost control.

[0146] Example 5

[0147] This embodiment is explained in Example 4. Please refer to Figure 1 , specifically: the S5 includes S51 and S52;

[0148] S51. Analyze the charging pile according to the abnormal state score yS and the cumulative abnormal growth rate yG, and in combination with the abnormal parameters, identify the problematic components, assign maintenance priorities, and calculate and obtain the maintenance priority score Pm;

[0149] Using the characteristics of abnormal parameters, the faulty components and fault types of the charging pile are judged;

[0150] Abnormal voltage fluctuation ΔU indicates aging of the power module and contact points;

[0151] An abnormal temperature change rate ΔT indicates a failure of the heat dissipation component and an overload of the charging pile.

[0152] Abnormal vibration deviation ΔF indicates problems with loose and worn mechanical parts;

[0153] The maintenance priority score Pm is obtained by the following formula:

[0154] ;

[0155] Where Pmk represents the maintenance priority score of the kth mechanical component of the charging pile, Δyck represents the abnormal parameter value corresponding to the kth mechanical component of the charging pile, represent the preset weight values ​​of the abnormality score yS, the cumulative abnormality growth rate yG and the abnormal parameter value Δyck corresponding to the kth mechanical component of the charging pile, respectively, and .

[0156] S52. Formulate a maintenance plan list based on the obtained maintenance priority score Pm, including replacing parts and adjusting the operation mode;

[0157] Make replacing parts a high priority task and adjusting the operating mode a medium priority task.

[0158] S6: After the maintenance plan is executed, a new round of operation data is collected through the IoT sensor, and the data is compared with the historical data to verify the effect of the maintenance plan;

[0159] The operating status of the charging pile is continuously monitored through the IoT sensor, and the voltage U, current I, charging pile temperature T, contact resistance R and vibration frequency F are collected and fitted into a new operating data set YWnew, and feature extraction is performed, including the new voltage fluctuation ΔUnew, the new current fluctuation ΔInew, the new temperature change rate ΔTnew and the new vibration offset ΔFnew are fitted into a new abnormal feature set YFnew;

[0160] Compare the new abnormal feature set YFnew with the abnormal feature set YF, evaluate the difference, obtain the difference amplitude value ΔD, and compare it with the preset difference threshold TD to determine the effectiveness of the maintenance plan;

[0161] The difference amplitude value ΔD is obtained by the following formula:

[0162] ;

[0163] The effectiveness of the maintenance plan is obtained by the following formula:

[0164] When the difference amplitude value ΔD ≥ the difference threshold TD, it means that the maintenance plan is effective;

[0165] When the difference amplitude value ΔD is less than the difference threshold TD, it indicates that the maintenance plan is invalid and the charging pile is readjusted.

[0166] In this embodiment, the specific faulty components and types of the charging pile are analyzed and judged by combining the state abnormality score yS, the cumulative abnormal growth rate yG and the abnormal parameters. For example, the abnormal voltage fluctuation ΔU indicates the aging of the power module or the contact point, and the abnormal temperature change rate ΔT indicates the failure of the heat dissipation component or the overload of the equipment. By formulaically calculating the maintenance priority score Pm, the faulty components are sorted and assigned priorities to ensure that operation and maintenance resources can be focused on the most important issues. This precise component identification and priority allocation method effectively avoids the waste of resources caused by unclear problem location in traditional maintenance, and greatly improves the operation and maintenance efficiency.

[0167] According to the maintenance priority score Pm, this embodiment generates a maintenance plan list including high-priority tasks and medium-priority tasks. High-priority tasks include replacing high-wear parts, such as aging power modules and failed heat dissipation components; medium-priority tasks include checking and adjusting the operating mode of the equipment, such as reducing output power or optimizing heat dissipation management. This hierarchical maintenance task formulation method can ensure that the most critical issues are handled first, while optimizing the operating status of the equipment to extend its service life.

[0168] After the maintenance plan is executed, new operating data YWnew is collected through the IoT sensor and compared with the historical data YW. This embodiment quantifies the maintenance effect by calculating the difference amplitude value ΔD. If the difference amplitude ΔD exceeds the preset difference threshold TD, it means that the maintenance plan is effective; otherwise, the system will trigger further adjustments. The dynamic verification mechanism can ensure that the execution effect of each maintenance plan is evaluated in a timely manner, and continuously improve the stability of the equipment status through iterative optimization. Compared with the disadvantage of the lack of verification steps in traditional methods, this embodiment improves the reliability and operation and maintenance accuracy of the system through a closed-loop management mode.

[0169] This embodiment accurately identifies faulty components and assigns maintenance priorities by combining the state anomaly score yS, the cumulative anomaly growth rate yG, and the anomaly parameters, making up for the defects of low problem location efficiency and unreasonable resource allocation in traditional methods. The formulation of hierarchical maintenance tasks can clearly allocate work priorities according to priorities, thereby avoiding ineffective maintenance and significantly improving maintenance efficiency and equipment recovery speed. The dynamic verification mechanism compares the difference amplitude value ΔD between the new and old data sets to evaluate the maintenance effect in real time, ensuring that the equipment reaches the expected operating state after maintenance and avoiding repeated failures.

[0170] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for energy management of automobile charging stations based on the Internet of Things, characterized in that: The following steps are involved: S1. Collect the operation data of the charging pile in real time through the IoT sensor, including voltage U, current I, charging pile temperature T, contact resistance R and vibration frequency F, and perform preprocessing to fit the operation data set YW; S2, extracting abnormal features from the acquired operation data set YW, including voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT and vibration offset ΔF, to form an abnormal feature set YF; S3, analyze the acquired operation data set YW and abnormal feature set YF, and calculate the acquired state abnormal score yS and the cumulative abnormal growth rate yG; The S3 includes S31 and S32; S31, perform feature fusion on the acquired running data set YW and the abnormal feature set YF, substitute them into the comprehensive score function, and calculate the acquired state abnormal score yS; The yS is obtained by the following formula: ； Wherein, ySt represents the abnormal state score at time t, Ut represents the voltage value at time t, Up represents the average value of voltage, It represents the current value at time t, Ip represents the average value of current, Tt represents the charging pile temperature value at time t, Tp represents the average value of charging pile temperature, Ft represents the vibration frequency value at time t, Fp represents the average value of vibration frequency, Respectively represent the voltage value Ut at time t, the current value It at time t, the charging pile temperature value Tt at time t, the vibration frequency value Ft at time t, and the preset weight values ​​of the contact resistance R; S32, calculating the cumulative abnormal growth rate yG of the charging pile according to the abnormal state score ySt at time t, and predicting the future fault trend; The cumulative abnormal growth rate yG is obtained by the following formula: ； In the formula, dt represents the integral variable; S4. Compare the acquired status abnormality score yS and cumulative abnormality growth rate yG with the preset abnormality score threshold TyS and abnormality growth rate threshold TyG respectively to determine the status of the charging pile; S5. Generate a maintenance plan based on the status of the charging pile, the status abnormality score yS and the cumulative abnormality growth rate yG; S6. After the maintenance plan is executed, collect a new round of operation data and compare it with the operation data to verify the maintenance effect.

2. The method for energy management of automobile charging stations based on the Internet of Things according to claim 1 is characterized in that: Said S1 includes S11 and S12; S11, collecting voltage U through a voltage sensor, collecting current I through a shunt current sensor, collecting charging pile temperature T through a thermocouple sensor, collecting contact resistance R through a four-wire resistance tester, collecting vibration frequency F through a vibration sensor, and fitting them into an original data set W; S12, cleaning and normalizing the original data set W to obtain a running data set YW; Cleaning includes removing noise and outliers, by processing the noise in the original data set W by using a filter, and processing the outliers in the original data set W by using a median filter method; The running data set YW is obtained by the following formula: ； Wherein, YWd represents the d-th data item in the running data set YW, Wd represents the d-th data item in the original data set W, Wd,min represents the valley value of the d-th data item in the original data set W, and Wd,max represents the peak value of the d-th data item in the original data set W.

3. The method for energy management of automobile charging stations based on the Internet of Things according to claim 1 is characterized in that: The S2 includes S21 and S22; S21, perform stationarity analysis on the data in the running data set YW, calculate the volatility index, and extract abnormal features: including voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT, and vibration offset ΔF; The voltage fluctuation ΔU is obtained by the following formula: ； In the formula, N represents the total number of data samples, Ut represents the voltage value at time t, and Up represents the average value of the voltage; The current fluctuation ΔI is obtained by the following formula: ； In the formula, max (It) represents the peak current value at time t, min (It) represents the valley current value at time t, and Ip represents the average current value; The temperature change rate ΔT is obtained by the following formula: ； Where N represents the total number of data samples, T(t+1) represents the temperature at time t+1, and T(t) represents the temperature at time t; The vibration offset ΔF is obtained by the following formula: ； Where, M represents the total number of frequencies, Fi represents the i-th frequency, P(Fi) represents the power spectrum value of the i-th frequency, and Fno represents the center value of the vibration frequency; S22, fitting the acquired voltage fluctuation ΔU, current fluctuation ΔI, temperature change rate ΔT and vibration offset ΔF to form an abnormal feature set YF.

4. The method for energy management of automobile charging stations based on the Internet of Things according to claim 3 is characterized in that: The S4 includes S41 and S42; S41, comparing the acquired status abnormality score yS and cumulative abnormality growth rate yG with the preset abnormality score threshold TyS and abnormality growth rate threshold TyG respectively, to determine the status of the charging pile; Compare the state abnormality score yS with the preset abnormality score threshold TyS to determine whether the state abnormality score yS exceeds the preset abnormality score threshold TyS; ； Where TryS represents the state of the charging pile. When the state abnormality score yS> the abnormality score threshold TyS, 1 is returned, indicating that the abnormal state is triggered; when the state abnormality score yS≤ the abnormality score threshold TyS, 0 is returned, indicating a normal state. Compare the cumulative abnormal growth rate yG with the abnormal growth rate threshold TyG to determine whether the cumulative abnormal growth rate yG exceeds the preset abnormal growth rate threshold TyG; ； Where TryG represents the abnormal state of the charging pile. When the cumulative abnormal growth rate yG>the abnormal growth rate threshold TyG, it returns 1, indicating that the charging pile is abnormal; when the cumulative abnormal growth rate yG≤the abnormal growth rate threshold TyG, it returns 0, indicating that the charging pile is normal.

5. The method for energy management of automobile charging stations based on the Internet of Things according to claim 4 is characterized in that: S42. After the abnormal state is triggered, an alarm will be triggered and alarm information will be generated and passed to the operation and maintenance personnel; the alarm information includes abnormal parameters, abnormal time period range and fault type; Abnormal parameters include abnormal voltage fluctuation ΔU, abnormal current fluctuation ΔI, abnormal temperature change rate ΔT and abnormal vibration deviation ΔF; The abnormal time period includes the start and end time of the abnormality; the fault types include thermal overload, poor contact and mechanical failure.

6. The method for energy management of automobile charging stations based on the Internet of Things according to claim 5 is characterized in that: The S5 includes S51 and S52; S51. Analyze the charging pile according to the abnormal state score yS and the cumulative abnormal growth rate yG, and in combination with the abnormal parameters, identify the problematic components, assign maintenance priorities, and calculate and obtain the maintenance priority score Pm; Using the characteristics of abnormal parameters, the faulty components and fault types of the charging pile are judged; Abnormal voltage fluctuation ΔU indicates aging of the power module and contact points; An abnormal temperature change rate ΔT indicates a failure of the heat dissipation component and an overload of the charging pile. Abnormal vibration deviation ΔF indicates problems with loose and worn mechanical parts; The maintenance priority score Pm is obtained by the following formula: ； Where Pmk represents the maintenance priority score of the kth mechanical component of the charging pile, Δyck represents the abnormal parameter value corresponding to the kth mechanical component of the charging pile, represent the preset weight values ​​of the abnormality score yS, the cumulative abnormality growth rate yG and the abnormal parameter value Δyck corresponding to the kth mechanical component of the charging pile, respectively, and .

7. The method for energy management of automobile charging stations based on the Internet of Things according to claim 6 is characterized in that: S52. According to the obtained maintenance priority score Pm, a maintenance plan list is prepared, including replacement of parts and adjustment of operation mode.

8. The method for energy management of automobile charging stations based on the Internet of Things according to claim 2 is characterized in that: S6: After the maintenance plan is executed, a new round of operation data is collected through the IoT sensor and compared with the operation data to verify the effect of the maintenance plan; The operating status of the charging pile is continuously monitored through the IoT sensor, and the voltage U, current I, charging pile temperature T, contact resistance R and vibration frequency F are collected and fitted into a new operating data set YWnew. Feature extraction is then performed, including the new voltage fluctuation ΔUnew, the new current fluctuation ΔInew, the new temperature change rate ΔTnew and the new vibration offset ΔFnew, which are fitted into a new abnormal feature set YFnew. Compare the new abnormal feature set YFnew with the abnormal feature set YF, evaluate the difference, obtain the difference amplitude value ΔD, and compare it with the preset difference threshold TD to determine the effectiveness of the maintenance plan; The difference amplitude value ΔD is obtained by the following formula: ； The effectiveness of the maintenance plan is obtained by the following formula: When the difference amplitude value ΔD ≥ the difference threshold TD, it means that the maintenance plan is effective; When the difference amplitude value ΔD is less than the difference threshold TD, it indicates that the maintenance plan is invalid and the charging pile is readjusted.

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