Methods, systems, equipment, and storage media for monitoring the entire lifecycle of charging equipment.

Abstract

This application relates to the field of operation and maintenance technology for charging equipment, and provides a method, system, equipment, and storage medium for monitoring the entire lifecycle of charging equipment. The method includes: determining the remaining lifespan of a charging station based on its cumulative usage time; generating a correction coefficient based on the usage data of the charging station during the cumulative usage time; adjusting the remaining lifespan based on the correction coefficient to generate a first remaining lifespan; generating an environmental adjustment coefficient based on the environmental parameters of the environment in which the charging station is located; adjusting the first remaining lifespan based on the environmental adjustment coefficient to generate a second remaining lifespan; acquiring operational data of other charging stations during the cumulative usage time; calculating a mutual interference coefficient based on the operational data; and adjusting the second remaining lifespan based on the mutual interference coefficient to generate a target remaining lifespan. The technical effect of this application is to reduce the deviation between the predicted result and the actual remaining lifespan.

Description

Technical Field

[0001] This application relates to the field of monitoring, operation, and maintenance technologies for charging equipment, and specifically to a full life cycle monitoring method, system, device, and storage medium for charging equipment.

Background Art

[0002] With the rapid development of the electric vehicle industry, the large-scale deployment of charging equipment has become an important foundation to support the industrial development. In the operation process of charging equipment, accurately predicting the remaining life of the charging station is essential for equipment maintenance and renewal plans.

[0003] In the prior art, generally, based on the standard life of the charging station and combining the operating time and usage intensity of the charging station, the remaining life of the charging station is predicted. However, although this method can cope with the basic prediction of the remaining life of the charging station, it does not consider various dynamic influencing factors that occur during the use of the charging station. As a result, a large deviation exists between the prediction result and the actual remaining life.

Summary of the Invention

[0006] By employing the above technical means, a correction coefficient is generated by relating the actual usage data of the charging station to the standard lifespan of the charging station, an environmental adjustment coefficient is generated by relating environmental parameters, and a mutual interference coefficient is calculated by relating the operating data of other charging stations on the same power distribution transformer. By adjusting the remaining usage time in multiple dimensions and stages, a more accurate target remaining usage time is ultimately obtained, effectively improving the accuracy and reliability of the remaining lifespan prediction of the charging equipment, and reducing the deviation between the prediction result and the actual remaining lifespan.

[0007] Optionally, the above usage data includes the number of charges and the average charge amount in each charge, and generating a correction coefficient based on the above usage data includes: calculating the first difference between the above number of charges and the standard number of charges and generating a first correction coefficient based on the above first difference; calculating the second difference between the above average charge amount and the rated charging capacity and generating a second correction coefficient based on the above second difference; and generating a correction coefficient by weighting the above first correction coefficient and the above second correction coefficient.

[0008] By employing the above technical means, the first difference between the number of charges and the standard number of charges, and the second difference between the average charge amount and the rated charging capacity are generated as the first and second correction coefficients, respectively. A correction coefficient is obtained by weighting and adding these two correction coefficients. This allows for a comprehensive quantitative evaluation of the charging station's usage intensity, taking into account the impact of charging frequency on the device's lifespan and reflecting losses due to the charging load on the device. As a result, the correction coefficient more objectively reflects the actual usage of the charging station, improving the accuracy of predicting remaining usage time.

[0009] Optionally, adjusting the above remaining usage time based on the above correction coefficient to generate a first remaining usage time includes multiplying the above correction coefficient by the above remaining usage time to generate a first remaining usage time.

[0010] By employing the above technical means, the remaining lifespan is linearly adjusted based on usage intensity by multiplying the correction coefficient by the remaining usage time to obtain the first remaining usage time. This makes the remaining usage time adjustment process simple and intuitive, increases computational efficiency, and accurately reflects the degree to which the actual usage intensity of the charging station affects its remaining lifespan.

[0011] Optionally, generating an environmental adjustment coefficient based on the above environmental parameters includes determining, based on the above environmental parameters, the first hour during the above cumulative usage time when the temperature of the environment where the charging station is located exceeds the first preset temperature and the second hour when it falls below the second preset temperature, where the first preset temperature is higher than the second preset temperature and determining the temperature influence coefficient of the charging station based on the sum of the first and second hours; determining, based on the above environmental parameters, the third hour during the above cumulative usage time when the humidity of the environment where the charging station is located exceeds the third preset humidity and the fourth hour when it falls below the fourth preset humidity, where the third preset humidity is higher than the fourth preset humidity and determining the humidity influence coefficient of the charging station based on the sum of the third and fourth hours; and determining the level of environmental influence on the charging station from a pre-set database based on the magnitudes of the temperature influence coefficient and the humidity influence coefficient, and generating an environmental adjustment coefficient based on the influence level.

[0012] By employing the above technical means, the temperature influence coefficient and humidity influence coefficient are determined by aggregating the time during which the ambient temperature exceeds the first preset temperature but falls below the second preset temperature, and the time during which the ambient humidity exceeds the third preset humidity but falls below the fourth preset humidity. Based on these two influence coefficients, the environmental impact level is determined from a pre-set database, and an environmental adjustment coefficient is further generated. This enables precise aggregation and quantitative evaluation of charging station operating time under extreme temperature and humidity conditions, and allows for accurate reflection of the impact of environmental factors on the charging station's lifespan by calculating the remaining usage time.

[0013] Optionally, adjusting the above-mentioned first remaining usage time based on the above-mentioned environmental adjustment coefficient to generate a second remaining usage time includes multiplying the above-mentioned environmental adjustment coefficient by the above-mentioned first remaining usage time to generate a second remaining usage time.

[0014] By employing the above technical means, the environmental adjustment coefficient is multiplied by the first remaining usage time to obtain the second remaining usage time, thereby achieving a direct linear adjustment of the remaining lifespan due to environmental factors. This makes it possible to quantify the environmental impact in a simple and clear manner in the calculation of the remaining usage time, ensuring the efficiency of the calculation process and ensuring an accurate reflection of the impact of environmental factors on the lifespan of the charging station.

[0015] Optionally, calculating the mutual interference coefficient based on the above operating data involves obtaining the first charging time of the other charging station in the above cumulative usage time, the second charging time of the above charging station in the above cumulative usage time, calculating the overlap time between the first charging time and the second charging time, obtaining the power ratio of the total charging power of the other charging station and the rated power of the distribution transformer during the above overlap time, determining the power occupancy rate based on the above power ratio, generating the mutual interference coefficient based on the above power occupancy rate, and the above power occupancy rate being equal to the above mutual interference coefficient. negative This includes the fact that they are correlated with each other.

[0016] By employing the above technical means, the overlap in charging time between one charging station and other charging stations is calculated, the power occupancy rate is determined based on the ratio of the total charging power of other charging stations to the rated power of the distribution transformer during the overlapping time, and further the power occupancy rate and negative By generating mutual interference coefficients that are correlated, it is possible to accurately quantify the load superposition effect in the case of parallel operation of multiple charging stations on the same power distribution transformer, and to effectively reflect the actual impact of mutual influence between charging stations on equipment lifespan.

[0017] Optionally, adjusting the above-mentioned second remaining usage time based on the above-mentioned mutual interference coefficient to generate a target remaining usage time includes determining the decay time corresponding to the above-mentioned mutual interference coefficient and subtracting the above-mentioned decay time from the above-mentioned second remaining usage time to obtain the above-mentioned target remaining usage time.

[0018] By employing the above technical means, the decay time can be determined based on the mutual interference coefficient, and the target remaining usage time can be obtained by subtracting this decay time from the second remaining usage time. This makes it possible to quantitatively deduct the impact of mutual interference between charging stations on the equipment lifespan, and the final remaining lifespan prediction result can accurately reflect the actual loss impact on the equipment lifespan due to the load superposition effect when multiple charging stations operate in parallel.

[0019] In the second phase, the present application relates to a full-lifecycle monitoring system for charging equipment, comprising a decision module, a first adjustment module, a second adjustment module, a calculation module, and a third adjustment module, wherein,

[0020] The above determination module is used to determine the remaining usage time of the charging station based on the cumulative usage time of the charging station, taking into account the standard lifespan of the charging station. The above first adjustment module is used to obtain usage data of the charging station during the cumulative usage time, generate a correction factor based on the usage data, adjust the remaining usage time based on the correction factor, and generate a first remaining usage time. The present invention provides a full-lifecycle monitoring system for charging equipment, characterized in that the second adjustment module described above is used to obtain environmental parameters of the environment in which the charging station is located during the cumulative usage time described above, generate an environmental adjustment coefficient based on the environmental parameters described above, adjust the first remaining usage time described above based on the environmental adjustment coefficient, and generate a second remaining usage time; the calculation module described above is used to obtain operational data of other charging stations that are connected to the same distribution transformer as the charging station described above during the cumulative usage time described above, and calculate a mutual interference coefficient based on the operational data described above; and the third adjustment module described above is used to adjust the second remaining usage time described above based on the mutual interference coefficient, and generate a target remaining usage time.

[0021] In the third aspect, the present application provides an electronic device comprising a processor, a memory, a user interface, and a network interface, wherein the memory is used for storing instructions, the user interface and the network interface are used for communicating with other devices, and the processor is used for executing the instructions stored in the memory so as to cause the electronic device to execute any of the methods described above.

[0022] In the fourth aspect, the present application provides a computer-readable storage medium storing a computer program loaded by a processor and capable of executing any of the methods described above.

Advantages of the Invention

[0023] As described above, the present application has at least one of the following advantageous technical effects.

[0024] Based on the standard life of the charging stand, the actual usage data of the charging stand is associated to generate a correction factor, the environmental parameters are associated to generate an environmental adjustment factor, and the operation data of other charging stands on the same distribution transformer is associated to calculate an interference factor. By adjusting the remaining service life in multiple dimensions and multiple levels, a more accurate target remaining service life is finally obtained, effectively improving the accuracy and reliability of the remaining life prediction of the charging equipment and reducing the deviation between the prediction result and the actual remaining life.

Brief Description of the Drawings

[0025] [Figure 1] It is a flow schematic diagram of the full life cycle monitoring method of the charging equipment according to the embodiment of the present application. [Figure 2] It is a structural schematic diagram of the full life cycle monitoring system of the charging equipment according to the embodiment of the present application. [Figure 3] It is a structural schematic diagram of the electronic device according to the embodiment of the present application. [Modes for carrying out the invention]

[0026] To enable those skilled in the art to better understand the technical means described herein, the technical means in the embodiments herein will be described clearly and completely below with reference to the accompanying drawings of the embodiments herein, although it will be clear that the embodiments described are only some of the embodiments of this application and not all of them.

[0027] In the descriptions of embodiments of this application, terms such as “exemplary,” “for example,” or “as an example” are used for illustrative purposes only. No embodiment or design plan described as “exemplary,” “for example,” or “as an example” in the embodiments of this application should be construed as being preferable or more advantageous than other embodiments or design plans. Specifically, terms such as “exemplary,” “for example,” or “as an example” are used to specifically present the relevant concepts.

[0028] Figure 1 is a schematic flowchart of a method for monitoring the entire lifecycle of a charging equipment according to an embodiment of this application. As shown in Figure 1, this method includes steps S101 to S105.

[0029] S101: Based on the standard lifespan of the charging station, the remaining usage time of the charging station is determined based on the cumulative usage time of the charging station.

[0030] As a critical component of electric vehicle charging infrastructure, charging stations have a lifespan that directly impacts the reliability of charging services and operating costs. Standard lifespan refers to the period during which a charging station is expected to function normally under ideal operating conditions and standard usage stress, and is usually determined by the charging station manufacturer based on design specifications and test data. For example, the standard lifespan of a particular charging station model may be 10 years.

[0031] In this embodiment, it is possible to obtain standard lifespan data for a charging station from the equipment file information of the charging station, and simultaneously obtain the installation time of the charging station from the operation management system of the charging station, and determine the cumulative usage time as the time difference between the current time and the installation time. By subtracting the cumulative usage time from the standard lifespan, the remaining usage time of the charging station can be obtained. For example, if a charging station has a standard lifespan of 10 years (equivalent to 87,600 hours) and its current cumulative usage time is 3 years (equivalent to 26,280 hours), its remaining usage time will be 61,320 hours. Such an initial evaluation based on standard lifespan provides a reference value for lifespan adjustments based on subsequent actual usage conditions, which helps charging facility operators to rationally plan equipment maintenance and replacement and improve the operational efficiency of the charging infrastructure. However, factors such as the actual usage intensity of the charging station, the usage environment, and the effects of mutual interference differ from the standard condition, so the accuracy of remaining lifespan evaluation based solely on standard lifespan is limited, and therefore, further adjustments are necessary based on this, taking into account various influencing factors.

[0032] S102: Obtain usage data for the charging station based on the cumulative usage time, generate a correction factor based on the usage data, adjust the remaining usage time based on the correction factor, and generate a first remaining usage time.

[0033] Specifically, the first step is to calculate the first difference between the number of charges and the standard number of charges. The standard number of charges refers to the expected number of charges during the standard lifespan of the charging station, and is determined based on the charging station's design specifications. For example, if a charging station has a standard number of charges of 100 times per month, and the actual number of charges in a given month is 150 times, the first difference would be 50 times. Based on the magnitude of the first difference, a first correction factor is generated. A positive first difference indicates that the charging station is used more frequently than expected, resulting in a first correction factor of less than 1. A negative first difference indicates that the charging station is used less frequently than expected, resulting in a first correction factor greater than 1.

[0034] Simultaneously, the second difference between the average charge and the rated charge capacity is calculated. The rated charge capacity refers to the product of the charging station's design charging power and standard charging time. For example, if the actual average charge of a charging station is 80kWh and the rated charge capacity is 60kWh, the second difference will be 20kWh. Based on the magnitude of the second difference, a second correction factor is generated. A positive second difference indicates that the charging load is higher than the design value and the second correction factor is less than 1, while a negative second difference indicates that the charging load is lower than the design value and the second correction factor is greater than 1.

[0035] The first and second correction factors are weighted together to generate the final correction factor. Here, the corresponding weighting factors can be set based on the degree to which the number of charges and the average charge amount affect the lifespan of the charging station. For example, if the first correction factor is 0.9 with a weight of 0.6, and the second correction factor is 0.8 with a weight of 0.4, the final correction factor will be 0.9 × 0.6 + 0.8 × 0.4 = 0.86.

[0036] Finally, the correction factor is multiplied by the remaining usage time obtained in S101 to generate the first remaining usage time. For example, if the original remaining usage time is 84 months and the correction factor is 0.86, the first remaining usage time will be 72.24 months.

[0037] Based on the above embodiment, in one arbitrary embodiment, in S102, the usage data includes the number of charges and the average charge amount in each charge, and the generation of a correction coefficient based on the above usage data specifically includes S21 to S23.

[0038] S21: Calculate the first difference between the number of charging cycles and the standard number of charging cycles, and generate the first correction coefficient based on the first difference.

[0039] The standard number of charges refers to the average monthly number of charges for a charging station under standard usage intensity specified in the design specifications. The design parameters of the charging station are usually determined based on the equipment manufacturer. For example, if the standard number of charges for a certain charging station is 100 times per month, and the actual average monthly number of charges during the aggregation period is 130 times, the first difference will be 30 times. Based on the magnitude of the first difference, a first correction coefficient is generated according to a predetermined correction rule. If the first difference is in the range of -20 to 20 times, the first correction coefficient is set to 0.95 to 1.05; if the first difference is in the range of 21 to 50 times, the first correction coefficient is set to 0.85 to 0.95; if the first difference exceeds 50 times, the first correction coefficient is set to 0.7 to 0.85; and if the first difference is less than 20 times, the first correction coefficient is set to 1.05 to 1.15.

[0040] S22: Calculate the second difference between the average charge amount and the rated charge capacity, and generate a second correction factor based on the second difference.

[0041] The rated charging capacity refers to the design value of the single charge amount of a charging station under standard charging conditions, which is determined by the rated power and standard charging time of the charging station. For example, if the rated charging capacity of a charging station is 60kWh, and the actual average charge amount is 75kWh, the second difference will be 15kWh. Similarly, the second correction factor is generated in stages based on the magnitude of the second difference, and if the second difference is in the range of -10 to 10kWh, the second correction factor will be 0.95 to 1.05; if the second difference is in the range of 11 to 20kWh, the second correction factor will be 0.85 to 0.95; if the second difference exceeds 20kWh, the second correction factor will be 0.7 to 0.85; and if the second difference is less than -10kWh, the second correction factor will be 1.05 to 1.15.

[0042] S23: The first and second correction coefficients are weighted and added together to generate a correction coefficient.

[0043] The first and second correction factors are weighted and added together to generate the final correction factor. The weighting should be set considering the relative importance of charging frequency and charging load in affecting the lifespan of the charging station. Generally, since charging frequency has a slightly greater impact on the lifespan of a charging station than charging load, the weight of the first correction factor can be set to 0.6 and the weight of the second correction factor to 0.4, but the specific weighting factors will be determined based on the actual situation. For example, if the first correction factor is 0.9 and the second correction factor is 0.85, the final correction factor will be 0.9 × 0.6 + 0.85 × 0.4 = 0.88.

[0044] Based on the above embodiments, in one optional embodiment, in S102, adjusting the remaining usage time based on a correction factor to generate a first remaining usage time specifically includes multiplying the correction factor by the remaining usage time to generate a first remaining usage time.

[0045] S103: Obtain environmental parameters of the environment in which the charging station is located during the cumulative usage time, generate an environmental adjustment coefficient based on the environmental parameters, adjust the first remaining usage time based on the environmental adjustment coefficient, and generate the second remaining usage time.

[0046] In this embodiment, since charging stations are typically installed in outdoor environments, their operational performance and lifespan are significantly affected by environmental factors. To accurately assess the impact of the environment on the lifespan of the charging station, an environmental monitoring system is required to acquire environmental parameters, primarily temperature and humidity data, of the environment in which the charging station is located over its cumulative usage time. These environmental parameters are collected in real time by sensors installed around the charging station and are periodically recorded and stored.

[0047] In temperature impact assessment, first, the first hour during which the ambient temperature of the charging station exceeds the first preset temperature and the second hour during which the ambient temperature falls below the second preset temperature are determined. Here, the first preset temperature is typically set to 40°C and the second preset temperature to -10°C, and these two temperature values ​​are based on the heat resistance characteristics of the charging station's electronic components. For example, if a charging station is in use for 36 months, and the cumulative time during which the ambient temperature exceeds 40°C (i.e., the first hour) is 360 hours and the cumulative time during which the ambient temperature is below -10°C (i.e., the second hour) is 240 hours, the temperature impact coefficient of the charging station is determined based on the proportion of the total time of the first and second hours (600 hours in this example) to the cumulative usage time.

[0048] In humidity impact assessment, it is similarly necessary to determine the third hour during which the ambient humidity exceeds the third preset humidity and the fourth hour during which the ambient humidity falls below the fourth preset humidity. Here, the third preset humidity is typically set to 85%RH and the fourth preset humidity to 20%RH, and these two humidity values ​​are based on the protection class and component reliability requirements of the charging station. For example, if the cumulative usage time is 480 hours during which the ambient humidity exceeds 85%RH (i.e., the third hour) and 120 hours during which it is below 20%RH (i.e., the fourth hour), the humidity impact coefficient of the charging station is determined based on the proportion of the total time of the third and fourth hours (600 hours in this example) to the cumulative usage time.

[0049] The system determines the level of environmental impact on the charging station from a pre-configured database based on the magnitudes of the temperature and humidity impact coefficients. This pre-configured database stores three impact levels, such as mild, moderate, and severe, corresponding to different combinations of temperature and humidity impact coefficients. Each impact level is associated with a single pre-configured environmental adjustment coefficient, which decreases as the impact becomes more severe. For example, if the impact is determined to be moderate, the corresponding environmental adjustment coefficient would be 0.9.

[0050] The construction of the pre-configured database is primarily based on the following aspects of data sources and analysis methods. First, component reliability data provided by charging station equipment manufacturers is used as a basic reference. Key components in charging stations, such as power modules, control circuits, and communication modules, each have defined operating environment specifications. Examples include performance curves for power modules at different temperatures and failure rates for electronic components at different humidity levels. This data originates from laboratory test results and long-term usage statistics from component manufacturers.

[0051] Next, we will conduct aggregate analysis using historical data from a large number of charging stations that are actually in operation. For example, we will examine the correlation between the failure rate of the same model of charging station operating in different climate regions and environmental conditions. Specifically, we will select charging stations operating in different environments, such as typical cold northern regions, humid southern regions, and coastal regions with high salinity, as samples, collect their operational data and failure records, and analyze the correlation between environmental factors and equipment lifespan.

[0052] Furthermore, data support will be obtained through accelerated degradation testing. Environmental stress tests, including high temperature, low temperature, high humidity, and alternating temperature and humidity, will be conducted on the charging station under laboratory conditions to record the regularity of equipment performance decay and failure occurrence. For example, a quantitative relationship between environmental stress and life loss will be established by converting the time of continuous operation in a high-temperature environment of 40°C to the time of operation in a normal temperature environment.

[0053] Based on the above data, the pre-configured database has the following structure: Temperature impact assessment matrix: If the percentage of temperature anomaly time is <5%: mild impact, temperature impact coefficient range 0.95~1.0; if the percentage of temperature anomaly time is 5%~15%: moderate impact, temperature impact coefficient range 0.85~0.95; if the percentage of temperature anomaly time is >15%: severe impact, coefficient range 0.7~0.85; where, temperature anomaly time = 1st hour + 2nd hour.

[0054] Humidity Impact Assessment Matrix: If the percentage of humidity abnormality time is <8%: mild impact, humidity impact coefficient range 0.95~1.0; if the percentage of humidity abnormality time is 8%~20%: moderate impact, humidity impact coefficient range 0.85~0.95; if the percentage of humidity abnormality time is >20%: severe impact, humidity impact coefficient range 0.7~0.85; where humidity abnormality time = 3rd hour + 4th hour.

[0055] Overall Impact Level Determination Table: By combining the impact levels of temperature and humidity, nine different determination matrices are formed. For example: Temperature "Mild" + Humidity "Mild": The overall impact is mild, and the final environmental adjustment coefficient is 0.95 to 1.0. Temperature Severe + Humidity Severe: The overall impact is severe, and the final environmental adjustment coefficient is 0.6 to 0.7. For other combinations, the corresponding coefficient range is determined according to the actual impact level.

[0056] Finally, the environmental adjustment coefficient is multiplied by the first remaining usage time obtained in S102 to generate the second remaining usage time. For example, if the first remaining usage time is 72.24 months and the environmental adjustment coefficient is 0.9, the second remaining usage time will be 65.02 months.

[0057] Based on the above embodiment, in one arbitrary embodiment, generating an environmental adjustment coefficient based on environmental parameters in S103 specifically includes S31 to S35.

[0058] S31: Based on environmental parameters, determine the first hour during which the ambient temperature of the charging station is above the first preset temperature and the second hour during which it is below the second preset temperature, with the first preset temperature being higher than the second preset temperature.

[0059] The system analyzes the temperature conditions of the environment where the charging station is located over its cumulative usage time, based on environmental monitoring data from the charging station. Here, the first preset temperature is typically set to 40°C, and the second preset temperature is set to -10°C. These two temperature values ​​are critical values ​​determined based on the charging station's operating environment adaptability index. The system compiles the cumulative time when the ambient temperature is above 40°C (i.e., the first hour) and the cumulative time when it is below -10°C (i.e., the second hour). For example, if a charging station has been in use for 36 months, the cumulative time when the ambient temperature is above 40°C is 720 hours (the first hour), and the cumulative time when it is below -10°C is 480 hours (the second hour).

[0060] S32: Determine the temperature influence coefficient of the charging station based on the sum of the first and second time periods.

[0061] The system determines the temperature impact coefficient based on the proportion of the total time of the first and second hours relative to the cumulative usage time. For example, if the total proportion of the time spent under temperature anomalies is 5% or less, the temperature impact coefficient is set to 0.95 to 1.0; if the proportion is between 5% and 15%, it is set to 0.85 to 0.95; and if the proportion exceeds 15%, it is set to 0.7 to 0.85. In the example above, the total time spent under temperature anomalies is 1200 hours, which accounts for approximately 5.5% of the total time over 36 months, so the corresponding temperature impact coefficient could be 0.92.

[0062] S33: Based on the environmental parameters, determine the third hour during the cumulative usage time when the humidity of the environment where the charging station is located exceeds the third preset humidity, and the fourth hour when it falls below the fourth preset humidity, with the third preset humidity being higher than the fourth preset humidity.

[0063] The system also needs to analyze the ambient humidity conditions. The third preset humidity is typically set to 85%RH, and the fourth preset humidity is set to 20%RH. These two humidity values ​​are determined based on the operating environment adaptability index of the charging station. The system tally the cumulative time when the ambient humidity is above 85%RH (i.e., the third hour) and the cumulative time when it is below 20%RH (i.e., the fourth hour). For example, in the same time period, the cumulative time when the ambient humidity is above 85%RH is 960 hours (the third hour), and the cumulative time when it is below 20%RH is 360 hours (the fourth hour).

[0064] S34: Determine the humidity effect coefficient of the charging station based on the sum of the third and fourth hours.

[0065] The system determines the humidity influence coefficient based on the combined proportion of the third and fourth hours. The correlation between the proportion of humidity anomaly time and the influence coefficient is similar to that for temperature conditions: if the proportion is 8% or less, the humidity influence coefficient is 0.95 to 1.0; if the proportion is 8% to 20%, it is 0.85 to 0.95; and if the proportion exceeds 20%, it is 0.7 to 0.85. In the example above, the total time of humidity anomalies is 1320 hours, and the proportion is approximately 6.1%, so the corresponding humidity influence coefficient could be 0.96.

[0066] S35: Based on the magnitude of the temperature and humidity influence coefficients, the level of environmental impact on the charging station is determined from a pre-configured database, and an environmental adjustment coefficient is generated based on the impact level.

[0067] The system determines the environmental impact level by querying a pre-configured database based on the magnitudes of the temperature and humidity impact coefficients. The pre-configured database stores environmental impact levels corresponding to different combinations of temperature and humidity impact coefficients. For example, if both impact coefficients are higher than 0.95, it is determined to be a mild impact; if either impact coefficient is between 0.85 and 0.95, it is determined to be a moderate impact; and if either impact coefficient is less than 0.85, it is determined to be a severe impact. Different environmental impact levels correspond to different ranges of environmental adjustment coefficients, such as mild impact corresponding to 0.95-1.0, moderate impact corresponding to 0.85-0.95, and severe impact corresponding to 0.7-0.85. In the example above, the temperature impact coefficient is 0.92 and the humidity impact coefficient is 0.96, so it falls under the moderate impact level, and the final environmental adjustment coefficient may be 0.90.

[0068] Based on the above embodiment, in one optional embodiment, in S103, adjusting the first remaining usage time based on the environmental adjustment coefficient to generate the second remaining usage time specifically includes multiplying the environmental adjustment coefficient by the first remaining usage time to generate the second remaining usage time.

[0069] S104: Obtain operating data from other charging stations connected to the same power distribution transformer as the charging station during the cumulative usage time, and calculate the mutual interference coefficient based on the operating data.

[0070] In this embodiment, since multiple charging stations are typically connected to the same distribution transformer, when these charging stations operate simultaneously, a superposition effect of power load occurs, changing the operating conditions of each charging station and further affecting their lifespan. To accurately evaluate such mutual interference effects, it is necessary to analyze the operating data of other charging stations connected to the same distribution transformer as the target charging station. Here, the distribution transformer is a device that converts the voltage of the power system to the operating voltage of the charging stations, and its rated power determines the total charging load that it can simultaneously support.

[0071] Specifically, it is necessary to first obtain the first charging time of other charging stations in terms of cumulative usage time, and the second charging time of charging stations in terms of the target cumulative usage time. Here, charging time refers to the time period during which a charging station actually charges an electric vehicle. For example, if two charging stations A and B are connected to a distribution transformer, let's say A is the target charging station. Over the past month, the cumulative charging time of charging station B (i.e., the first charging time) is 300 hours, and the cumulative charging time of charging station A (i.e., the second charging time) is 250 hours.

[0072] The system then calculates the overlap time between the first and second charging times, that is, the period of time when multiple charging stations are charging simultaneously. By analyzing the timestamps in the operation logs of the charging stations, it is possible to accurately calculate the duration of such overlapping operation. For example, the total time that the two charging stations A and B operated simultaneously (i.e., overlap time) over a month was 150 hours.

[0073] After determining the overlap time, the system obtains the total charging power of other charging stations during the overlap time and calculates the power ratio, which is the ratio of the total charging power of other charging stations to the rated power of the distribution transformer. The power ratio refers to the proportion of the distribution transformer's rated power that the total charging power of other charging stations occupies during the overlap time. The formula for calculation is: Power Ratio = Total Charging Power of Other Charging Stations During Overlap Time ÷ Rated Power of Distribution Transformer. For example, if the rated power of the distribution transformer is 400kW and the average charging power of charging station B during the overlap time is 240kW, the power ratio would be 0.6. Based on this power ratio, the power occupancy rate is determined. The power occupancy rate reflects the extent to which other charging stations occupy the transformer capacity and directly affects the power supply quality of the target charging station.

[0074] Finally, a mutual interference coefficient is generated based on the power occupancy rate, and the two are negativeThe following correlations exist. Specifically, based on the following correlations, the range of the mutual interference coefficient is determined to be 0.95 to 1.0 when the power occupancy rate is 0.3 or less, 0.85 to 0.95 when the power occupancy rate is between 0.3 and 0.6, and 0.7 to 0.85 when the power occupancy rate is 0.6 or higher. For example, when the power occupancy rate is 0.6, the corresponding mutual interference coefficient is 0.85.

[0075] Based on the above embodiment, in one arbitrary embodiment, calculating the mutual interference coefficient based on the operating data in S104 specifically includes S41 to S44.

[0076] S41: Obtain the first charging time of other charging stations and the second charging time of charging stations based on cumulative usage time.

[0077] The system needs to obtain the first charging time of other charging stations connected to the same distribution transformer as the target charging station, and the second charging time of the target charging station during the same time period. Charging time refers to the cumulative time that a charging station has actually provided charging services to electric vehicles. For example, suppose there are three charging stations A, B, and C connected to a distribution transformer, with A being the target charging station. Over the past 36 months, the cumulative charging time (i.e., first charging time) of charging stations B and C is 8000 hours and 7500 hours, respectively, and the cumulative charging time (i.e., second charging time) of charging station A is 7800 hours.

[0078] S42: Calculate the overlap time between the first charging time and the second charging time.

[0079] The system calculates the overlap time between the first and second charging times by analyzing timestamp information in the operation logs of the charging stations. Overlap time refers to the period when multiple charging stations are charging simultaneously. The system sorts the charging records of each charging station in chronological order and determines the period of overlapping operation by cross-calculating time zones. For example, the analysis reveals that of the three charging stations mentioned above, the overlap time when all three charging stations are operating simultaneously is 3000 hours, and this overlap time is used in subsequent power utilization evaluations.

[0080] S43: Obtain the power ratio between the total charging power of other charging stations and the rated power of the distribution transformer during the overlapping time, and determine the power occupancy rate based on the power ratio.

[0081] The system obtains the total charging power of other charging stations during the overlapping time and calculates the power ratio, which is the ratio to the rated power of the distribution transformer. For example, if the rated power of the distribution transformer is 500kW and the average total charging power of charging stations B and C during the overlapping time is 350kW, the power ratio is 0.7. Based on the magnitude of the power ratio, the system determines the power occupancy rate so that if the power ratio is 0.4 or less, the power occupancy rate is 0.2 to 0.4; if the power ratio is between 0.4 and 0.7, the power occupancy rate is 0.4 to 0.7; and if the power ratio is 0.7 or greater, the power occupancy rate is 0.7 to 0.9. In the above example, if the power ratio is 0.7, the corresponding power occupancy rate could be 0.7.

[0082] S44: Based on the power occupancy rate, a mutual interference coefficient is generated, and the power occupancy rate is the mutual interference coefficient. negative They are correlated.

[0083] The system generates a mutual interference coefficient based on power occupancy, and the two are negativeThis shows the correlation. Specifically, when the power occupancy rate is in the range of 0.2 to 0.4, the mutual interference coefficient is 0.95 to 1.0, indicating that the mutual interference effect is small; when the power occupancy rate is in the range of 0.4 to 0.7, the mutual interference coefficient is 0.85 to 0.95, indicating that there is a moderate level of mutual interference; and when the power occupancy rate is in the range of 0.7 to 0.9, the mutual interference coefficient is 0.7 to 0.85, indicating that... of This indicates a significant mutual interference effect. In the example above, if the power occupancy rate is 0.7, the corresponding mutual interference coefficient could be 0.85.

[0084] S105: Based on the mutual interference coefficient, the second remaining usage time is adjusted to generate the target remaining usage time.

[0085] In this embodiment, mutual interference between charging stations causes accelerated degradation of the equipment; therefore, the second remaining lifespan must be ultimately adjusted based on the mutual interference coefficient. Such adjustments reflect the actual impact of parallel operation of multiple charging stations on the equipment's lifespan and help obtain more accurate lifespan prediction results.

[0086] Specifically, it is first necessary to determine the corresponding decay time based on the mutual interference coefficient. Decay time refers to the loss of life due to the effects of mutual interference, and its magnitude is correlated with the mutual interference coefficient. By querying a pre-created decay time reference table, the decay time corresponding to different mutual interference coefficients can be determined. For example, if the mutual interference coefficient is 0.85, the corresponding annual decay time is 1.2 months. The reference table was created based on the aggregated analysis of a large amount of actual operating data, and the regularity of the impact of different levels of mutual interference on the lifespan of the charging station has been fully considered.

[0087] In the decay time reference table, the correlation between the mutual interference coefficient and decay time can be divided into the following intervals: Stage 1: When the mutual interference coefficient is between 0.95 and 1.0, it indicates that the mutual interference effect is mild and the annual decay time is between 0.5 and 0.8 months. This situation is usually seen in scenarios where the utilization rate of charging stations is low or the peak shift effect is high. Stage 2: When the mutual interference coefficient is between 0.85 and 0.95, it indicates that the mutual interference effect is moderate and the annual decay time is between 0.8 and 1.2 months. This situation is often seen as a general condition in daily operations. Stage 3: When the mutual interference coefficient is between 0.7 and 0.85, it indicates that the mutual interference effect is large and the annual decay time is between 1.2 and 2.0 months. This situation is usually seen in scenarios where charging stations are frequently used and under high load. Stage 4: When the mutual interference coefficient is between 0.5 and 0.7, it indicates that the mutual interference effect is severe and the annual decay time is between 2.0 and 3.0 months. This situation can be seen in scenarios where distribution transformers operate under high load for extended periods. Stage 5: A mutual interference coefficient of less than 0.5 indicates that the mutual interference effect is extremely severe, with an annual decay time of 3.0 to 4.0 months. This suggests the possibility of insufficient design capacity at the charging station or operational management problems, and prompt system optimization or equipment expansion is necessary.

[0088] In practical applications, to calculate decay time more accurately, a linear interpolation method is used in each interval to determine specific decay time values. For example, if the mutual interference coefficient is 0.9, and the location is at the midpoint of the second stage interval (0.85 to 0.95), the corresponding annual decay time can be set to 1.0 month, which is the median value of that interval (0.8 to 1.2 months). This precise interval division and interpolation calculation method makes it possible to more accurately reflect the lifespan loss of charging stations due to mutual interference of varying degrees.

[0089] At the same time, considering extreme usage scenarios, if the mutual interference coefficient approaches zero (e.g., 0 to 0.1), the system will activate an alarm mechanism, prompting the operator to perform an emergency inspection and response. In such cases, normal decay time calculations are not performed, and dedicated maintenance and operational measures are required.

[0090] After determining the decay time, subtracting the decay time from the second remaining service time obtained in S103 yields the final target remaining service time. For example, if the second remaining service time is 65.02 months, the mutual interference coefficient is 0.85, the corresponding annual decay time is 1.2 months, and the cumulative service time is 36 months, the total decay time will be 3.6 months (1.2 months / year × 3 years). Therefore, the target remaining service time will be 61.42 months (65.02 - 3.6).

[0091] Based on the above embodiment, in one arbitrary embodiment, adjusting the second remaining usage time based on the mutual interference coefficient in S105 to generate a target remaining usage time specifically includes S51 to S52.

[0092] S51: Determine the decay time corresponding to the mutual interference coefficient.

[0093] The system determines the current decay time corresponding to the mutual interference coefficient by querying a pre-configured decay time reference table. The decay time reference table records in detail the annual decay times corresponding to different intervals of mutual interference coefficients. When the mutual interference coefficient is between 0.95 and 1.0, the annual decay time is 0.5 to 0.8 months, indicating a mild mutual interference effect. When the mutual interference coefficient is between 0.85 and 0.95, the annual decay time is 0.8 to 1.2 months, indicating a moderate mutual interference effect. When the mutual interference coefficient is between 0.7 and 0.85, the annual decay time is 1.2 to 2.0 months, indicating a significant mutual interference effect. When the mutual interference coefficient is between 0.5 and 0.7, the annual decay time is 2.0 to 3.0 months, indicating a severe mutual interference effect. When the mutual interference coefficient is less than 0.5, the annual decay time is 3.0 to 4.0 months, indicating an extremely severe mutual interference effect. For example, if the mutual interference coefficient is 0.85, the annual decay time obtained by the system querying a reference table is 1.2 months. To find the total decay time, multiply the annual decay time by the cumulative number of years of use. Assuming the charging station has been used for 36 months (3 years), the total decay time would be 3.6 months (1.2 months / year × 3 years).

[0094] S52: Subtract the decay time from the second remaining usage time to obtain the target remaining usage time.

[0095] The final target remaining service time is obtained by subtracting the decay time, which is calculated from the second remaining service time. For example, if the second remaining service time is 65.02 months and the total decay time is 3.6 months, the target remaining service time will be 61.42 months (65.02 - 3.6). This final result takes into account the usage intensity of the charging station, environmental impact, and mutual interference effects, and accurately reflects the actual remaining lifespan of the charging station.

[0096] This application further discloses a full lifecycle monitoring system for a charging facility, including a determination module, a first adjustment module, a second adjustment module, a calculation module, and a third adjustment module, based on the method described above, as shown in Figure 2, which is a schematic diagram of the structure of the full lifecycle monitoring system for a charging facility according to an embodiment of this application, where,

[0097] The decision module is used to determine the remaining usage time of a charging station based on the cumulative usage time of the charging station, taking into account the standard lifespan of the charging station. The first adjustment module is used to obtain usage data of the charging station during the cumulative usage time, generate a correction coefficient based on the usage data, adjust the remaining usage time based on the correction coefficient, and generate the first remaining usage time. The second adjustment module is used to obtain environmental parameters of the environment in which the charging station is located during the cumulative usage time, generate an environmental adjustment coefficient based on the environmental parameters, adjust the first remaining usage time based on the environmental adjustment coefficient, and generate the second remaining usage time. The calculation module is used to obtain operating data of other charging stations during the cumulative usage time, calculate the mutual interference coefficient based on the operating data, and the other charging stations are connected to the same distribution transformer as the charging station. The third adjustment module is used to adjust the second remaining usage time based on the mutual interference coefficient and generate the target remaining usage time.

[0098] The system according to the above embodiment is merely illustrative in its explanation of the allocation of each functional module in realizing its functions. In actual applications, the above functions can be completed by assigning them to different functional modules as needed. In other words, it is possible to achieve all or some of the functions described above by dividing the internal structure of the device into different functional modules. Furthermore, the embodiments of the system and method according to the above embodiment belong to the same concept, and their specific implementation processes should be referred to the embodiments of the method, which will not be repeated here.

[0099] Referring to Figure 3, this is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. As shown in Figure 3, the electronic device 1000 may include at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0100] Here, the communication bus 1002 is used to enable connection and communication between these components.

[0101] Here, the user interface 1003 may include a display and a camera, and optionally, the user interface 1003 may further include a standard wired interface and a wireless interface.

[0102] Here, the network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0103] Here, the processor 1001 may include one or more processing cores. The processor 1001 is connected to various parts of the entire server using various interfaces and lines, and performs various functions of the server and processes data by operating or executing instructions, programs, code sets or instruction sets stored in memory 1005, and by retrieving data stored in memory 1005. Optionally, the processor 1001 may be implemented in the form of at least one hardware component, such as a Digital Signal Processing (DSP), a Field-Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA). The processor 1001 can integrate one or more combinations of components, such as a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. Here, the CPU primarily handles the operating system, user interface, and applications, the GPU is used to render and draw content displayed on the display, and the modem is used to handle wireless communication. It is understood that the above modem could not be integrated into processor 1001, but could be implemented on a separate chip.

[0104] Here, memory 1005 may include random access memory (RAM) or read-only memory. Optionally, memory 1005 may include a non-transitory computer-readable storage medium. Memory 1005 may be used to store instructions, programs, code, code sets, or instruction sets. Memory 1005 includes a program storage area and a data storage area, where the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (e.g., touch function, audio playback function, image playback function, etc.), instructions for implementing embodiments of each of the above methods, and so on, while the data storage area may store data related to embodiments of each of the above methods. Memory 1005 may optionally also be at least one storage device located away from the processor 1001 described above. As shown in Figure 3, the memory 1005, which serves as a storage medium for one type of computer, may include an operating system, a network communication module, a user interface module, and an application for a method of monitoring the entire lifecycle of one type of charging equipment.

[0105] In the electronic device 1000 shown in Figure 3, the user interface 1003 is mainly used to provide an input interface to the user and to acquire user input data, while the processor 1001 is used to invoke an application of a full lifecycle monitoring method for a charging device stored in memory 1005, and when executed by one or more processors, causes the electronic device to perform one or more of the above methods of the above embodiment.

[0106] It is a device-readable storage medium in which instructions are stored. When executed by one or more processors, it causes electronic devices to execute one or more of the above-described methods of the above embodiments. [Industrial applicability]

[0107] While the embodiments of each method described above are presented as a combination of operations for the sake of explanation, those skilled in the art should understand that this application is not limited to the described order of operations, as some of these steps can be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should understand that each embodiment described in the specification is a preferred one, and the relevant operations and modules are not necessarily essential to this application.

[0108] In the above embodiments, the emphasis of each description differs, and for embodiments that are not described in detail, you can refer to the relevant descriptions in other embodiments.

[0109] In the various embodiments provided in this application, it should be understood that the disclosed devices can be realized in other ways. For example, the embodiments of the devices described above are merely examples, and the division of units described above is merely a logical functional division. In actual implementation, other division methods are possible, such as combining multiple units or components, integrating them into another system, or omitting or not performing some functions. Furthermore, the coupling, direct coupling, and communication connections between illustrated or discussed components may be realized through some service interface, and indirect coupling or communication connections between devices or units may be realized in electrical or other forms.

[0110] The units described as individual components may or may not be physically separated, and the components shown as units may or may not be physical units; that is, they may be located in one place or scattered across multiple network units. Depending on the actual requirements, some or all of these units may be selected to achieve the objectives of this embodiment.

[0111] Furthermore, in the various embodiments of this application, the functional units may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. These integrated units may be implemented as functional units in hardware form or software form.

[0112] The integrated unit described above may be implemented as a software functional unit and, when sold or used as an independent product, may be stored in a single computer-readable memory. Based on this understanding, the technical means of the present application may, in essence, contribute to the prior art, or all or part of the technical means may be implemented in the form of a software product, the computer software product containing several instructions stored in a single memory so that a single computer device (such as a personal computer, server, or network device) performs all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory may also include various media capable of storing program code, such as USB memory, portable hard disks, magnetic disks, or optical disks.

[0113] The foregoing describes only exemplary embodiments of the Disclosure and does not limit its scope. That is, any equivalent changes and modifications made based on the teachings of the Disclosure are included within the scope of the Disclosure. Those skilled in the art will readily conceive of other embodiments of the Disclosure, taking into account the implementation of this Specification and the Disclosure. This application is intended to encompass all variations, uses, or adaptive modifications of this Specification, including common or conventional techniques in the art not described herein, in accordance with the general principles of the Disclosure. The Specification and Examples are illustrative only, and the scope and spirit of the Disclosure are defined by the claims. [Explanation of symbols]

[0114] 1000 electronic equipment 1001 Processor 1002 Communications Bus 1003 User Interface 1004 Network Interface 1005 memory

Claims

1. A method for monitoring the entire lifecycle of charging equipment, Based on the standard lifespan of the charging station, the remaining usage time of the charging station is determined based on the cumulative usage time of the charging station. The process involves acquiring usage data for the charging station during the cumulative usage time, generating a correction coefficient based on the usage data, adjusting the remaining usage time based on the correction coefficient, and generating a first remaining usage time. The system obtains environmental parameters of the environment in which the charging station is located during the cumulative usage time, generates an environmental adjustment coefficient based on the environmental parameters, adjusts the first remaining usage time based on the environmental adjustment coefficient, and generates a second remaining usage time. The operation data of other charging stations connected to the same power distribution transformer as the aforementioned charging station is obtained during the cumulative usage time, and the mutual interference coefficient is calculated based on the operation data. Based on the aforementioned mutual interference coefficient, the second remaining usage time is adjusted to generate a target remaining usage time. A method for monitoring the entire lifecycle of a charging equipment, characterized by including the following:

2. The aforementioned usage data includes the number of charging cycles and the average charge amount in each charge, and a correction coefficient is generated based on the aforementioned usage data. The first difference between the number of charging cycles and the standard number of charging cycles is calculated, and a first correction coefficient is generated based on the first difference. The second difference between the average charge amount and the rated charge capacity is calculated, and a second correction coefficient is generated based on the second difference. The first correction coefficient and the second correction coefficient are weighted and added together to generate a correction coefficient, A method for monitoring the entire lifecycle of a charging equipment according to claim 1, characterized by including the following:

3. Adjusting the remaining usage time based on the correction coefficient and generating the first remaining usage time is, A method for monitoring the entire lifecycle of a charging equipment according to claim 1, characterized by comprising multiplying the correction coefficient by the remaining usage time to generate a first remaining usage time.

4. Generating environmental adjustment coefficients based on the aforementioned environmental parameters is, Based on the environmental parameters, the first hour during which the temperature of the environment where the charging stand is located exceeds the first preset temperature and the second hour during which it falls below the second preset temperature are determined, where the first preset temperature is higher than the second preset temperature, The temperature influence coefficient of the charging station is determined based on the sum of the first time and the second time, Based on the environmental parameters, the third hour during the cumulative usage time in which the humidity of the environment where the charging stand is located exceeds the third preset humidity, and the fourth hour in which it falls below the fourth preset humidity, wherein the third preset humidity is higher than the fourth preset humidity, The humidity influence coefficient of the charging station is determined based on the sum of the third time and the fourth time, Based on the magnitudes of the temperature influence coefficient and the humidity influence coefficient, the level of environmental influence on the charging station is determined from a pre-set database, and an environmental adjustment coefficient is generated based on the influence level. A method for monitoring the entire lifecycle of a charging equipment according to claim 1, characterized by including the following:

5. Based on the aforementioned environmental adjustment coefficient, adjusting the first remaining usage time and generating the second remaining usage time is: A method for monitoring the entire lifecycle of a charging equipment according to claim 1, characterized by multiplying the environmental adjustment coefficient by the first remaining usage time to generate a second remaining usage time.

6. Calculating the mutual interference coefficient based on the aforementioned operating data is possible. To obtain the first charging time of the other charging station in the cumulative usage time, and the second charging time of the charging station in the cumulative usage time, The overlap time between the first charging time and the second charging time is calculated, The power ratio between the total charging power of the other charging stations and the rated power of the distribution transformer during the overlapping time is obtained, and the power occupancy rate is determined based on the power ratio. Based on the aforementioned power occupancy rate, a mutual interference coefficient is generated, and the power occupancy rate is negatively correlated with the mutual interference coefficient. A method for monitoring the entire lifecycle of a charging equipment according to claim 1, characterized by including the following:

7. Based on the aforementioned mutual interference coefficient, the second remaining usage time is adjusted to generate a target remaining usage time. Determining the decay time corresponding to the aforementioned mutual interference coefficient, The target remaining usage time is obtained by subtracting the decay time from the second remaining usage time, A method for monitoring the entire lifecycle of a charging equipment according to claim 1, characterized by including the following:

8. A full-lifecycle monitoring system for charging equipment, comprising a decision module, a first adjustment module, a second adjustment module, a calculation module, and a third adjustment module, wherein, The aforementioned determination module is used to determine the remaining usage time of the charging station based on the cumulative usage time of the charging station, taking into account the standard lifespan of the charging station. The first adjustment module is used to acquire usage data of the charging station during the cumulative usage time, generate a correction coefficient based on the usage data, adjust the remaining usage time based on the correction coefficient, and generate a first remaining usage time. The second adjustment module is used to obtain environmental parameters of the environment in which the charging station is located during the cumulative usage time, generate an environmental adjustment coefficient based on the environmental parameters, adjust the first remaining usage time based on the environmental adjustment coefficient, and generate a second remaining usage time. The calculation module is used to acquire operational data from other charging stations, which are connected to the same power distribution transformer as the aforementioned charging station, during the cumulative usage time, and to calculate the mutual interference coefficient based on the operational data. A full-lifecycle monitoring system for charging equipment, characterized in that the third adjustment module is used to adjust the second remaining usage time based on the mutual interference coefficient and to generate a target remaining usage time.

9. An electronic device comprising a processor, memory, a user interface and a network interface, An electronic device characterized in that the memory is used to store instructions, the user interface and network interface are used to communicate with other devices, and the processor is used to execute instructions stored in the memory so that the electronic device performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium characterized by storing a computer program that is loaded by a processor and capable of performing the method according to any one of claims 1 to 7.

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