A new energy bus charging monitoring method and system

Abstract

The present application relates to the technical field of charging monitoring, and in particular to a method and system for monitoring charging of new energy buses. The method comprises: respectively organizing vehicle operation information and vehicle charging information according to time nodes, determining the correspondence between initial operation power and non-initial operation power and initial charging data and non-initial charging data, determining the initial battery operation power corresponding to the initial charging data and the non-initial battery operation power corresponding to the non-initial charging data based on the correspondence, performing fatigue analysis on the initial battery operation power and the non-initial battery operation power to obtain battery fatigue information, matching the battery fatigue information with a preset battery fatigue charging standard to obtain a battery charging plan, and controlling the time nodes for switching between strong and weak currents at the charging pile when charging the new energy bus battery according to the battery charging plan. The present application improves the application efficiency of new energy bus batteries.

Description

Technical Field

[0001] The present application relates to the technical field of charging monitoring, and in particular to a charging monitoring method and system for new energy buses. Background Art

[0002] With the global emphasis on environmental protection and sustainable development, new energy buses, as a key component of urban public transportation, are becoming increasingly popular. New energy buses, particularly battery-powered electric buses, offer significant advantages in reducing exhaust emissions and noise pollution.

[0003] Currently, a staged charging strategy is widely used when charging new energy buses to protect batteries and optimize charging efficiency. This involves switching from a weak current to a strong current and then back to a weak current. This charging method fully considers the characteristics and requirements of the battery at different charging stages, aiming to reduce internal stress, extend battery life, and ensure a safe and efficient charging process.

[0004] During the initial charging phase, a weak current is used, also known as the pre-charge stage. This stage aims to gradually activate the chemical reactions within the battery, stabilizing parameters like temperature and voltage, and preparing for subsequent fast charging. Weak current charging helps reduce the increase in internal resistance and capacity decay that can occur after prolonged periods of disuse, while also avoiding damage that could result from a direct impact of high current. Once the battery reaches a predetermined condition (such as voltage stabilization within a certain range), the charging process switches to a high current stage. During this stage, the charger outputs a high current, replenishing energy to the battery at a faster rate. High current charging significantly shortens charging time and improves efficiency, meeting the rapid charging needs of new energy buses. However, high current charging also increases heat generation and stress accumulation within the battery, requiring strict control of charging time and current to avoid irreversible damage. As the battery charge continues to increase, the charging process switches back to a weak current stage, also known as trickle charge or maintenance charge. During this stage, the charger outputs a lower current to continue replenishing the remaining energy until the battery is fully charged. Weak current charging helps balance the charge distribution within the battery, reducing polarization within the battery, further improving the battery's charging efficiency and capacity utilization. At the same time, trickle charging ensures that the battery maintains stable voltage and current output after being fully charged, extending the battery's service life.

[0005] However, with the adoption of new energy buses, battery performance will change with age, charge and discharge cycles, and other factors. If the charging station cannot sense and respond to these changes in real time and continues to charge according to a fixed charging strategy, it may not be able to adjust charging parameters to suit the current battery state, thus affecting charging performance and battery life. Summary of the Invention

[0006] In order to solve at least one of the above technical problems, the present application provides a new energy bus charging monitoring method and system.

[0007] In the first aspect, the present application provides a new energy bus charging monitoring method, which adopts the following technical solutions:

[0008] Obtain vehicle operation information and vehicle charging information, wherein the vehicle operation information includes the initial operating power of the battery during the first operation of different new energy buses and the non-initial operating power of the battery during non-first operation, and the vehicle charging information includes the initial charging data of the charging pile when charging different new energy buses for the first time and the non-initial charging data when charging for non-first time;

[0009] The vehicle operation information and the vehicle charging information are respectively sorted according to time nodes, and a correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus is determined;

[0010] Determine, according to the corresponding relationship, the initial battery operating power corresponding to the initial charging data when each new energy bus is charged for the first time and the non-initial battery operating power corresponding to the non-initial charging data when charging is not the first time;

[0011] Performing fatigue analysis on the initial battery operating power and the non-initial battery operating power to obtain battery fatigue information corresponding to each new energy bus battery;

[0012] Matching the battery fatigue information with a preset battery fatigue charging standard to obtain a battery charging solution based on the battery fatigue information;

[0013] Generate charging control instructions according to the battery charging scheme to control the strong and weak current conversion time nodes of the charging pile when charging the battery of the new energy bus.

[0014] By implementing this technical solution, comprehensive and accurate data on both the operation and charging dimensions of new energy buses can be captured. This not only covers the battery's power performance at different stages of use but also records detailed charging data from charging stations, laying a solid foundation for subsequent, precise analysis. This information, organized by time, seamlessly correlates the operating power and charging data for each new energy bus, providing a visual basis for how battery performance changes over the lifespan. This correspondence significantly simplifies data analysis, improving efficiency and accuracy. Furthermore, the specific operating status of each new energy bus's battery during both initial and non-initial charging can be precisely identified. This provides key input for subsequent battery fatigue analysis, ensuring that the results are more closely aligned with actual usage, which is crucial for optimizing battery management strategies and extending battery life. The introduced fatigue analysis method provides in-depth analysis of how battery performance degrades with usage. By carefully comparing the initial and non-initial battery operating power, the system can accurately calculate battery fatigue information, match this information with preset standards, and automatically generate a personalized battery charging plan. This not only enables intelligent control of the charging process, but also dynamically adjusts the charging strategy based on the actual battery condition, effectively avoiding damage to the battery from overcharging or over-discharging, further extending the battery life, and improving charging efficiency and safety. The resulting charging control instructions can precisely control the time nodes for the strong and weak current conversion of the charging pile during the charging process. This precise control strategy not only optimizes the charging process and improves charging efficiency, but also reduces unnecessary energy waste and lowers operating costs, thereby improving the battery application efficiency of new energy buses.

[0015] In one possible implementation, the vehicle operation information and the vehicle charging information are sorted according to time nodes, and the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus is determined, including:

[0016] Create multiple data monitoring coordinate systems, where the X-axes of the multiple data monitoring coordinate systems are all different time nodes, and the Y-axes of the multiple data monitoring coordinate systems are operating power in different units and battery charging data in different units. The number of the multiple data coordinate systems is consistent with the number of new energy buses to be monitored;

[0017] Bind the multiple data monitoring coordinate systems to each new energy bus in a one-to-one correspondence, and map the initial operating power, the non-initial operating power, the initial charging data, and the non-initial charging data corresponding to each new energy bus to the corresponding data monitoring coordinate system according to the time node, to obtain the operating data coordinate system corresponding to each new energy bus;

[0018] Determining, according to the operating data coordinate system, an initial operating waveform segment corresponding to the initial operating power, a non-initial operating waveform segment of the non-initial operating power, an initial charging waveform segment corresponding to the initial charging data, and a non-initial charging waveform segment of the non-initial charging data;

[0019] A distribution correlation binding analysis is performed on the initial operation waveform segment, the non-initial operation waveform segment, the initial charging waveform segment, and the non-initial charging waveform segment to determine the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus.

[0020] In one possible implementation, the distribution correlation binding analysis is performed on the initial operation waveform segment, the non-initial operation waveform segment, the initial charging waveform segment, and the non-initial charging waveform segment to determine the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus, including:

[0021] determining an operating waveform segment existing before an initial time node of the initial charging waveform segment, and binding the existing operating waveform segment with the initial charging waveform segment to obtain a first corresponding relationship between the initial charging data and the initial operating power and / or the non-initial operating power;

[0022] determining a first non-initial charging waveform segment adjacent to the initial charging waveform segment, and binding a non-initial operation waveform segment between the initial charging waveform segment and the first non-initial charging waveform segment with the first non-initial charging waveform segment to obtain a second correspondence between non-initial charging data corresponding to the first non-initial charging waveform segment and the non-initial operation power;

[0023] determining a second non-initial charging waveform segment adjacent to the first non-initial charging waveform segment, and binding a non-initial operating waveform segment between the first non-initial charging waveform segment and the second non-initial charging waveform segment with the second non-initial charging waveform segment to obtain a third corresponding relationship between the non-initial charging corresponding to the second non-initial charging waveform segment and the non-initial operating power;

[0024] determining a second non-initial charging waveform segment set that is not adjacent to the initial charging waveform segment, and binding a non-initial operating waveform segment between every two adjacent non-initial charging waveform segments in the second non-initial charging waveform segment set with a non-initial charging waveform segment that is temporally shifted after the adjacent non-initial charging waveform segment, to obtain a fourth correspondence between non-initial charging data corresponding to each non-initial charging waveform segment in the second non-initial charging waveform segment set and non-initial operating power;

[0025] The correspondence between the initial operating power and the non-initial operating power in the vehicle operating information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus is determined according to the first correspondence, the second correspondence, the third correspondence and the fourth correspondence.

[0026] In one possible implementation, fatigue analysis is performed on the initial battery operating power and the non-initial battery operating power to obtain battery fatigue information corresponding to each new energy bus battery, including:

[0027] performing feature analysis on a first operating waveform segment corresponding to the initial battery operating power and a plurality of second operating waveform segments corresponding to the non-initial battery operating power to obtain first operating feature data corresponding to the first operating waveform segment and a set of second operating feature data corresponding to the plurality of second operating waveform segments;

[0028] Generating a first running hash object by combining the first running feature data and the second running feature data based on a fingerprint algorithm, and converting the data format of the first running hash object into a byte stream format to obtain a second running hash object;

[0029] According to the fingerprint algorithm, the second operation hash object is used to generate a reference operation data fingerprint and a fatigue monitoring operation data fingerprint set, and the fatigue monitoring operation data fingerprint set is compared with the reference operation data fingerprint one by one to obtain the operation change data of each new energy bus battery in different time periods after each charge;

[0030] Identify the output current, voltage, and power of each new energy bus battery based on the operation change data to obtain parameter change data of different attribute parameters;

[0031] Draw a data dynamic waveform diagram of parameter change data of different attribute parameters, and perform a steady-state analysis on the corresponding new energy bus battery based on the data dynamic waveform diagram to obtain battery stability data;

[0032] The battery stability data is matched with a preset battery fatigue condition evaluation standard to obtain battery fatigue information corresponding to each new energy bus battery.

[0033] In one possible implementation, the matching the battery fatigue information with a preset battery fatigue charging standard to obtain a battery charging solution based on the battery fatigue information further includes:

[0034] Determining abnormal fatigue information of a battery having fatigue abnormality according to the preset battery fatigue charging standard;

[0035] Determining abnormal fatigue limit data corresponding to the abnormal battery fatigue information in the preset battery fatigue condition assessment standard;

[0036] Determine whether the battery stability data is consistent with the abnormal fatigue limit data. If consistent, generate battery maintenance information based on the new energy bus battery in the battery stability data and the consistent abnormal data, and send the battery maintenance information to the target terminal.

[0037] In one possible implementation, determining whether the battery stability data is consistent with the abnormal fatigue limit data includes:

[0038] If the battery stability data does not match the abnormal fatigue limit data, performing unsupervised time series data sorting on the battery stability data based on the time series length in the data dynamic waveform diagram to obtain battery matrix data;

[0039] Performing data periodicity analysis on the battery matrix data to generate future battery matrix data;

[0040] Processing the data contained in the future battery matrix data to obtain deduction matrix data, and inputting the obtained deduction matrix data into a preset algorithm model for data calculation to obtain future battery stability data of different new energy bus batteries within a preset time in the future;

[0041] Determine a future abnormal time node in the future battery stability data that meets the abnormal fatigue limit data, and generate remaining maintenance duration information according to the current time node and the future abnormal time node.

[0042] In one possible implementation, performing data periodicity analysis on the battery matrix data to generate future battery matrix data includes:

[0043] Performing basic data distribution exploration on the battery matrix data to determine a relative periodicity pattern when changes occur in the battery stability data, and determining a time period length based on the relative periodicity pattern;

[0044] Performing supervised time series data sorting on the battery matrix data based on the time period length to obtain data period regularity data;

[0045] Based on the data periodic regularity data, the battery stability data change trend within a future preset time period is predicted to generate future battery matrix data.

[0046] In the second aspect, the present application provides a new energy bus charging monitoring system, which adopts the following technical solutions:

[0047] A new energy bus charging monitoring system, comprising:

[0048] An information acquisition module is used to acquire vehicle operation information and vehicle charging information. The vehicle operation information includes the initial operating power of the battery during the first operation of different new energy buses and the non-initial operating power of the battery during non-first operation. The vehicle charging information includes the initial charging data of the charging pile when charging different new energy buses for the first time and the non-initial charging data when charging for non-first time;

[0049] An information collating module is used to sort the vehicle operation information and the vehicle charging information according to time nodes, and determine the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus;

[0050] A power determination module is used to determine, according to the corresponding relationship, the initial battery operating power corresponding to the initial charging data when each new energy bus is charged for the first time and the non-initial battery operating power corresponding to the non-initial charging data when charging is not the first time;

[0051] A fatigue analysis module is used to perform fatigue analysis on the initial battery operating power and the non-initial battery operating power to obtain battery fatigue information corresponding to each new energy bus battery;

[0052] a scheme determination module, configured to match the battery fatigue information with a preset battery fatigue charging standard to obtain a battery charging scheme based on the battery fatigue information;

[0053] The charging control module is used to generate charging control instructions according to the battery charging scheme, and control the strong and weak current conversion time nodes of the charging pile when charging the battery of the new energy bus.

[0054] In one possible implementation, the information collating module, when collating the vehicle operation information and the vehicle charging information according to time nodes, and determining the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information of the same new energy bus and the initial charging data and the non-initial charging data in the vehicle charging information, is specifically used to:

[0055] Create multiple data monitoring coordinate systems, where the X-axes of the multiple data monitoring coordinate systems are all different time nodes, and the Y-axes of the multiple data monitoring coordinate systems are operating power in different units and battery charging data in different units. The number of the multiple data coordinate systems is consistent with the number of new energy buses to be monitored;

[0056] Bind the multiple data monitoring coordinate systems to each new energy bus in a one-to-one correspondence, and map the initial operating power, the non-initial operating power, the initial charging data, and the non-initial charging data corresponding to each new energy bus to the corresponding data monitoring coordinate system according to the time node, to obtain the operating data coordinate system corresponding to each new energy bus;

[0057] Determining, according to the operating data coordinate system, an initial operating waveform segment corresponding to the initial operating power, a non-initial operating waveform segment of the non-initial operating power, an initial charging waveform segment corresponding to the initial charging data, and a non-initial charging waveform segment of the non-initial charging data;

[0058] A distribution correlation binding analysis is performed on the initial operation waveform segment, the non-initial operation waveform segment, the initial charging waveform segment, and the non-initial charging waveform segment to determine the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus.

[0059] In another possible implementation, the information collating module performs distribution correlation binding analysis on the initial operation waveform segment, the non-initial operation waveform segment, the initial charging waveform segment, and the non-initial charging waveform segment to determine the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information of the same new energy bus and the initial charging data and the non-initial charging data in the vehicle charging information, specifically for:

[0060] determining an operating waveform segment existing before an initial time node of the initial charging waveform segment, and binding the existing operating waveform segment with the initial charging waveform segment to obtain a first corresponding relationship between the initial charging data and the initial operating power and / or the non-initial operating power;

[0061] determining a first non-initial charging waveform segment adjacent to the initial charging waveform segment, and binding a non-initial operation waveform segment between the initial charging waveform segment and the first non-initial charging waveform segment with the first non-initial charging waveform segment to obtain a second correspondence between non-initial charging data corresponding to the first non-initial charging waveform segment and the non-initial operation power;

[0062] determining a second non-initial charging waveform segment adjacent to the first non-initial charging waveform segment, and binding a non-initial operating waveform segment between the first non-initial charging waveform segment and the second non-initial charging waveform segment with the second non-initial charging waveform segment to obtain a third corresponding relationship between the non-initial charging corresponding to the second non-initial charging waveform segment and the non-initial operating power;

[0063] determining a second non-initial charging waveform segment set that is not adjacent to the initial charging waveform segment, and binding a non-initial operating waveform segment between every two adjacent non-initial charging waveform segments in the second non-initial charging waveform segment set with a non-initial charging waveform segment that is temporally shifted after the adjacent non-initial charging waveform segment, to obtain a fourth correspondence between non-initial charging data corresponding to each non-initial charging waveform segment in the second non-initial charging waveform segment set and non-initial operating power;

[0064] The correspondence between the initial operating power and the non-initial operating power in the vehicle operating information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus is determined according to the first correspondence, the second correspondence, the third correspondence and the fourth correspondence.

[0065] In another possible implementation, when the fatigue analysis module performs fatigue analysis on the initial battery operating power and the non-initial battery operating power to obtain battery fatigue information corresponding to each new energy bus battery, it is specifically used to:

[0066] performing feature analysis on a first operating waveform segment corresponding to the initial battery operating power and a plurality of second operating waveform segments corresponding to the non-initial battery operating power to obtain first operating feature data corresponding to the first operating waveform segment and a set of second operating feature data corresponding to the plurality of second operating waveform segments;

[0067] Generating a first running hash object by combining the first running feature data and the second running feature data based on a fingerprint algorithm, and converting the data format of the first running hash object into a byte stream format to obtain a second running hash object;

[0068] According to the fingerprint algorithm, the second operation hash object is used to generate a reference operation data fingerprint and a fatigue monitoring operation data fingerprint set, and the fatigue monitoring operation data fingerprint set is compared with the reference operation data fingerprint one by one to obtain the operation change data of each new energy bus battery in different time periods after each charge;

[0069] Identify the output current, voltage, and power of each new energy bus battery based on the operation change data to obtain parameter change data of different attribute parameters;

[0070] Draw a data dynamic waveform diagram of parameter change data of different attribute parameters, and perform a steady-state analysis on the corresponding new energy bus battery based on the data dynamic waveform diagram to obtain battery stability data;

[0071] The battery stability data is matched with a preset battery fatigue condition evaluation standard to obtain battery fatigue information corresponding to each new energy bus battery.

[0072] In another possible implementation, the system further includes: a first determination module, a second determination module, and a maintenance generation module, wherein:

[0073] The first determining module is configured to determine abnormal fatigue information of a battery having abnormal fatigue according to the preset battery fatigue charging standard;

[0074] The second determining module is used to determine abnormal fatigue limit data corresponding to the abnormal battery fatigue information in the preset battery fatigue condition assessment standard;

[0075] The maintenance generation module is used to determine whether the battery stability data is consistent with the abnormal fatigue limit data. If consistent, it generates battery maintenance information based on the new energy bus battery in the battery stability data and the consistent abnormal data, and sends the battery maintenance information to the target terminal.

[0076] In another possible implementation, when determining whether the battery stability data is consistent with the abnormal fatigue limit data, the maintenance generation module is specifically configured to:

[0077] If the battery stability data does not match the abnormal fatigue limit data, performing unsupervised time series data sorting on the battery stability data based on the time series length in the data dynamic waveform diagram to obtain battery matrix data;

[0078] Performing data periodicity analysis on the battery matrix data to generate future battery matrix data;

[0079] Processing the data contained in the future battery matrix data to obtain deduction matrix data, and inputting the obtained deduction matrix data into a preset algorithm model for data calculation to obtain future battery stability data of different new energy bus batteries within a preset time in the future;

[0080] Determine a future abnormal time node in the future battery stability data that meets the abnormal fatigue limit data, and generate remaining maintenance duration information according to the current time node and the future abnormal time node.

[0081] In another possible implementation, when the maintenance generation module performs data periodicity analysis on the battery matrix data and generates future battery matrix data, it is specifically configured to:

[0082] Performing basic data distribution exploration on the battery matrix data to determine a relative periodicity pattern when changes occur in the battery stability data, and determining a time period length based on the relative periodicity pattern;

[0083] Performing supervised time series data sorting on the battery matrix data based on the time period length to obtain data period regularity data;

[0084] Based on the data periodic regularity data, the battery stability data change trend within a future preset time period is predicted to generate future battery matrix data.

[0085] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0086] at least one processor;

[0087] Memory;

[0088] At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a new energy bus charging monitoring method as described in any one of the first aspects.

[0089] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0090] A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute any new energy bus charging monitoring method according to the first aspect.

[0091] In summary, this application includes at least one of the following beneficial technical effects:

[0092] When charging new energy buses, comprehensive and accurate data from both operational and charging dimensions is captured. This data not only covers the battery's power performance at different stages of use but also details charging data from charging stations, laying a solid foundation for subsequent, precise analysis. This information, organized by time, seamlessly correlates the operating power and charging data for each new energy bus, providing a visual basis for battery performance changes over the lifespan. This correspondence significantly simplifies data analysis, improving efficiency and accuracy. Furthermore, the data precisely identifies the specific operating status of each new energy bus's battery during both initial and non-initial charging. This provides key input for subsequent battery fatigue analysis, ensuring that the results are more closely aligned with actual usage, which is crucial for optimizing battery management strategies and extending battery life. The introduced fatigue analysis method provides in-depth analysis of how battery performance degrades with usage. By carefully comparing the initial and non-initial battery operating power, the system can accurately calculate battery fatigue information, match this information with preset standards, and automatically generate a personalized battery charging plan. This not only enables intelligent control of the charging process, but also dynamically adjusts the charging strategy based on the actual battery condition, effectively avoiding damage to the battery from overcharging or over-discharging, further extending the battery life, and improving charging efficiency and safety. The resulting charging control instructions can precisely control the time nodes for the strong and weak current conversion of the charging pile during the charging process. This precise control strategy not only optimizes the charging process and improves charging efficiency, but also reduces unnecessary energy waste and lowers operating costs, thereby improving the battery application efficiency of new energy buses. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 A flow chart of a new energy bus charging monitoring method provided in an embodiment of the present application.

[0094] Figure 2 This is a structural diagram of a new energy bus charging monitoring system provided in an embodiment of the present application.

[0095] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0096] The following is combined with Figure 1-3 This application is described in further detail.

[0097] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the present application, they are protected by patent law.

[0098] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0099] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0100] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0101] The embodiment of the present application provides a method for monitoring the charging of a new energy bus, which is executed by an electronic device, wherein the electronic device can be an independent physical electronic device, or an electronic device cluster or distributed system composed of multiple physical electronic devices, or a cloud electronic device that provides cloud computing services. The embodiment of the present application is not limited here, such as Figure 1 As shown, the method includes:

[0102] Step S10: Acquire vehicle operation information and vehicle charging information.

[0103] Among them, the vehicle operation information includes the initial operating power of the battery during the first operation of different new energy buses and the non-initial operating power of the battery during non-first operation. The vehicle charging information includes the initial charging data of the charging pile when charging different new energy buses for the first time and the non-initial charging data when charging for non-first time.

[0104] For the purposes of this application, vehicle operation information refers to various data records regarding the operation of a new energy bus, used to indicate the vehicle's performance under different conditions. Specifically, the initial operating power of the battery during initial operation represents the output power of the battery during the initial phase of a new energy bus's initial use, a key indicator for evaluating the battery's initial performance. The non-initial operating power of the battery during non-initial operation refers to the power variation exhibited by the battery at different stages of use during subsequent operation, reflecting the degradation of battery performance over time. Vehicle charging information refers to data recorded when a charging station provides charging services for a new energy bus, reflecting various parameters during the charging process. Initial charging data for each new energy bus during its initial charging represents key data recorded by the charging station during the first charging of each new energy bus, including charging starting conditions, charging power, and charging capacity. Non-initial charging data for non-initial charging refers to charging data recorded by the charging station during subsequent charging processes that differs from the initial charge. This data can reflect changes in the battery's charging efficiency and charging characteristics over different charging cycles.

[0105] In this embodiment of the present application, the onboard sensor monitors and records the vehicle's battery power data in real time during operation, including the initial operating power during the first run and subsequent non-initial operating power. Furthermore, when the vehicle is charging, the charging pile control system records detailed data for each charge, including the initial charging data for the first charge and subsequent non-initial charging data. This data is then transmitted to the backend data center for unified storage and analysis.

[0106] Step S11: sort the vehicle operation information and the vehicle charging information according to the time nodes, and determine the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus.

[0107] Specifically, multiple data monitoring coordinate systems are created, the X-axes of the multiple data monitoring coordinate systems are all different time nodes, the Y-axes of the multiple data monitoring coordinate systems are operating power in different units and battery charging data in different units, the number of the multiple data coordinate systems is consistent with the number of new energy buses to be monitored, the multiple data monitoring coordinate systems are bound to each new energy bus in a one-to-one correspondence, and the initial operating power, non-initial operating power, initial charging data, and non-initial charging data corresponding to each new energy bus are mapped to the corresponding data monitoring coordinate system according to the time node, so as to obtain the operating data coordinate system corresponding to each new energy bus, and determine the initial operating waveform segment corresponding to the initial operating power, the non-initial operating waveform segment of the non-initial operating power, the initial charging waveform segment corresponding to the initial charging data, and the non-initial charging waveform segment of the non-initial charging data according to the operating data coordinate system, and perform distribution correlation binding analysis on the initial operating waveform segment, the non-initial operating waveform segment, the initial charging waveform segment, and the non-initial charging waveform segment, so as to determine the corresponding relationship between the initial operating power and the non-initial operating power in the vehicle operating information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus.

[0108] Specifically, the operating waveform segment existing before the initial time node of the initial charging waveform segment is determined, and the existing operating waveform segment is bound to the initial charging waveform segment to obtain a first corresponding relationship between the initial charging data and the initial operating power and / or the non-initial operating power, a first non-initial charging waveform segment adjacent to the initial charging waveform segment is determined, and the non-initial operating waveform segment between the initial charging waveform segment and the first non-initial charging waveform segment is bound to the first non-initial charging waveform segment to obtain a second corresponding relationship between the non-initial charging data corresponding to the first non-initial charging waveform segment and the non-initial operating power, a second non-initial charging waveform segment adjacent to the first non-initial charging waveform segment is determined, and the non-initial operating waveform segment between the first non-initial charging waveform segment and the second non-initial charging waveform segment is bound to the second non-initial charging waveform segment to obtain a first corresponding relationship between the non-initial charging data corresponding to the first non-initial charging waveform segment and the non-initial operating power. A third correspondence between the non-initial charging and the non-initial operating power corresponding to the second non-initial charging waveform segment is determined, and a second non-initial charging waveform segment set that is not adjacent to the initial charging waveform segment is determined, and the non-initial operating waveform segment between every two adjacent non-initial charging waveform segments in the second non-initial charging waveform segment set is bound to the non-initial charging waveform segment that is time-postponed with the adjacent non-initial charging waveform segment, to obtain a fourth correspondence between the non-initial charging data corresponding to each non-initial charging waveform segment in the second non-initial charging waveform segment set and the non-initial operating power, and the correspondence between the initial operating power and the non-initial operating power in the vehicle operating information of the same new energy bus and the initial charging data and the non-initial charging data in the vehicle charging information is determined based on the first correspondence, the second correspondence, the third correspondence, and the fourth correspondence.

[0109] Step S12: determining the initial battery operating power corresponding to the initial charging data when each new energy bus is charged for the first time and the non-initial battery operating power corresponding to the non-initial charging data when charging is not the first time according to the corresponding relationship.

[0110] Step S13: Perform fatigue analysis on the initial battery operating power and the non-initial battery operating power to obtain battery fatigue information corresponding to each new energy bus battery.

[0111] Specifically, a feature analysis is performed on a first operating waveform segment corresponding to the initial battery operating power and multiple second operating waveform segments corresponding to the non-initial battery operating power to obtain first operating feature data corresponding to the first operating waveform segment and a set of second operating feature data corresponding to the multiple second operating waveform segments. Then, based on a fingerprint algorithm, a first operating hash object is generated from the first operating feature data and the second operating feature data set. The data format of the first operating hash object is converted into a byte stream format to obtain a second operating hash object. Then, based on the fingerprint algorithm, a reference operating data fingerprint and a set of fatigue monitoring operating data fingerprints are generated from the second operating hash object. The fatigue monitoring operating data fingerprint set is compared one-to-one with the reference operating data fingerprint to obtain operating change data of each new energy bus battery in different time periods after each charge. Then, based on the operating change data, the output current, voltage, and power of each new energy bus battery are identified to obtain parameter change data of different attribute parameters. Then, a data dynamic waveform diagram of the parameter change data of different attribute parameters is plotted. Based on the data dynamic waveform diagram, a steady-state analysis is performed on the corresponding new energy bus battery to obtain battery stability data. The battery stability data is then matched with a preset battery fatigue condition assessment standard to obtain battery fatigue information corresponding to each new energy bus battery.

[0112] In the present application, steady-state analysis refers to the process of evaluating and analyzing the battery's performance in a stable output state. Because the battery cannot immediately reach a stable output state during initial operation, it is necessary to determine the corresponding output data for the battery in a stable output state based on the dynamic waveform of the data, i.e., the battery stability data.

[0113] For the embodiment of the present application, the preset battery fatigue condition assessment standards for new energy bus batteries include the following aspects:

[0114] Battery capacity decay: Battery capacity is one of the primary indicators for assessing battery health and fatigue. Generally, a battery capacity above 80% of its initial capacity is considered healthy. However, battery capacity gradually decays over time, and the degree of decay directly reflects the battery's fatigue.

[0115] Battery internal resistance changes: Battery internal resistance increases as the battery ages, leading to a decrease in operating power and cycle life. Therefore, battery internal resistance is also an important parameter for evaluating battery fatigue. The specific threshold for internal resistance increase may vary for different battery types, but generally speaking, a significant increase in internal resistance indicates a significant decline in battery health.

[0116] Cyclic charge and discharge performance: As the number of cycles increases, the battery's charge and discharge performance gradually decreases. By testing parameters such as charge and discharge rate, discharge time, and discharge voltage curve, the battery's cyclic charge and discharge performance can be evaluated, thereby reflecting the battery's fatigue condition.

[0117] Vibration fatigue life: Vibration fatigue life is a key evaluation metric for battery packs used in applications such as new energy vehicles. By simulating the vibration environment of actual operating conditions and evaluating the fatigue damage and life of the battery pack under long-term vibration loading, the reliability and durability of the battery pack in actual use can be predicted.

[0118] Based on the following aspects, this application collects operating data of the battery during actual use, such as changes in parameters such as voltage, current, and temperature, and uses data analysis tools to perform data mining and pattern recognition to discover the patterns and trends of battery performance degradation, thereby assessing the battery fatigue condition. The preset battery fatigue condition assessment standards include four levels: excellent, good, fair, and poor. Each level covers the dynamic change range of different battery parameter data. The current battery stability data is matched with the dynamic change range of the parameter data to obtain the battery fatigue information of the current battery.

[0119] Step S14: Match the battery fatigue information with a preset battery fatigue charging standard to obtain a battery charging plan based on the battery fatigue information.

[0120] In an embodiment of the present application, by adopting the technical means of big data analysis, the optimal charging scheme corresponding to different levels of battery fatigue is determined, and the optimal charging scheme is bound to the corresponding battery fatigue level to form a preset battery fatigue charging standard. The specific big data analysis method records the standard charging method corresponding to the first charging of a new battery. And as the battery stability data changes, the standard charging method is adjusted to different schemes to determine the optimal charging scheme that meets the stable output of the battery. Among them, the optimal charging scheme includes: early charging power and early charging period, mid-term charging power and mid-term charging period, late charging power and late charging period.

[0121] Step S15: Generate a charging control instruction according to the battery charging plan to control the strong and weak current conversion time nodes of the charging pile when charging the new energy bus battery.

[0122] For the embodiment of the present application, when charging the new energy buses, the dual-dimensional data of the operation and charging of the new energy buses are captured comprehensively and accurately. It not only covers the power performance of the battery in different stages of use, but also records the charging data of the charging pile in detail, laying a solid foundation for subsequent accurate analysis. The information organized by time nodes enables the operating power and charging data of the same new energy bus to correspond seamlessly, providing an intuitive basis for the changes in battery performance over the usage cycle. The establishment of this correspondence greatly simplifies the complexity of data analysis and improves the efficiency and accuracy of the analysis. At the same time, it can accurately identify the specific operating status of the battery of each new energy bus during the first and non-first charging. It provides key input for subsequent battery fatigue analysis, making the analysis results closer to actual usage, which is of great significance for optimizing battery management strategies and extending battery life. The introduced fatigue analysis method can deeply analyze the attenuation of battery performance with the number of uses. By carefully comparing the initial and non-initial battery operating power, the system can accurately calculate battery fatigue information, match this information with preset standards, and automatically generate a personalized battery charging plan. This not only enables intelligent control of the charging process, but also dynamically adjusts the charging strategy based on the actual battery condition, effectively avoiding damage to the battery from overcharging or over-discharging, further extending the battery life, and improving charging efficiency and safety. The resulting charging control instructions can precisely control the time nodes for the strong and weak current conversion of the charging pile during the charging process. This precise control strategy not only optimizes the charging process and improves charging efficiency, but also reduces unnecessary energy waste and lowers operating costs, thereby improving the battery application efficiency of new energy buses.

[0123] A possible implementation method of an embodiment of the present application is to match the battery fatigue information with a preset battery fatigue charging standard to obtain a battery charging plan for the battery fatigue information, which also includes: determining the abnormal fatigue information of the battery with fatigue abnormality according to the preset battery fatigue charging standard, and then determining the abnormal fatigue limit data corresponding to the abnormal fatigue information of the battery in the preset battery fatigue condition assessment standard, and then judging whether the battery stability data is consistent with the abnormal fatigue limit data. If consistent, battery maintenance information is generated based on the new energy bus battery in the battery stability data and the consistent abnormal data, and the battery maintenance information is sent to the target terminal.

[0124] Specifically, if the battery stability data does not match the abnormal fatigue limit data, the battery stability data is sorted in an unsupervised time series based on the time series length in the data dynamic waveform diagram to obtain battery matrix data, and then the battery matrix data is analyzed for data periodicity to generate future battery matrix data. The data contained in the future battery matrix data is then processed to obtain deduction matrix data, and the obtained deduction matrix data is input into a preset algorithm model for data extrapolation to obtain future battery stability data of different new energy bus batteries within a preset future time, determine the future abnormal time node in the future battery stability data that meets the abnormal fatigue limit data, and generate remaining maintenance time information based on the current time node and the future abnormal time node.

[0125] In the embodiment of the present application, the preset algorithm model includes a bidirectional LSTM prediction model.

[0126] Specifically, the basic data distribution of the battery matrix data is explored to determine the relative periodicity of the battery stability data when changes occur, and the length of the time period is determined based on the relative periodicity. Then, the battery matrix data is supervised and time series data is sorted based on the length of the time period to obtain data periodicity data. Then, based on the data periodicity data, the change trend of the battery stability data in the future preset time period is predicted to generate future battery matrix data.

[0127] The following is an introduction to a new energy bus charging monitoring system provided by an embodiment of the present application. The new energy bus charging monitoring system described below and the new energy bus charging monitoring method described above can be referred to each other. Please refer to Figure 2 , Figure 2 : is a structural diagram of a new energy bus charging monitoring system 20 provided in an embodiment of the present application, including:

[0128] An information acquisition module 21 is configured to acquire vehicle operation information and vehicle charging information. The vehicle operation information includes the initial operating power of the battery during the first operation of different new energy buses and the non-initial operating power of the battery during non-first operation. The vehicle charging information includes the initial charging data of the charging pile when charging different new energy buses for the first time and the non-initial charging data when charging for non-first time.

[0129] An information collating module 22 is configured to collate the vehicle operation information and the vehicle charging information according to time nodes, and determine the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus;

[0130] The power determination module 23 is used to determine the initial battery operating power corresponding to the initial charging data when each new energy bus is charged for the first time and the non-initial battery operating power corresponding to the non-initial charging data when charging is not the first time according to the corresponding relationship;

[0131] The fatigue analysis module 24 is used to perform fatigue analysis on the initial battery operating power and the non-initial battery operating power to obtain the battery fatigue information corresponding to each new energy bus battery;

[0132] a solution determination module 25, configured to match the battery fatigue information with a preset battery fatigue charging standard to obtain a battery charging solution based on the battery fatigue information;

[0133] The charging control module 26 is used to generate charging control instructions according to the battery charging plan, and control the strong and weak current conversion time nodes of the charging pile when charging the battery of the new energy bus.

[0134] In one possible implementation of the embodiment of the present application, the information collating module 22, when collating the vehicle operation information and the vehicle charging information according to time nodes, determines the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information of the same new energy bus and the initial charging data and the non-initial charging data in the vehicle charging information, is specifically used to:

[0135] Create multiple data monitoring coordinate systems. The X-axes of the multiple data monitoring coordinate systems are all different time nodes. The Y-axes of the multiple data monitoring coordinate systems are different units of operating power and different units of battery charging data. The number of multiple data coordinate systems is consistent with the number of new energy buses to be monitored.

[0136] Bind multiple data monitoring coordinate systems to each new energy bus in a one-to-one correspondence, and map the initial operating power, non-initial operating power, initial charging data, and non-initial charging data of each new energy bus to the corresponding data monitoring coordinate system according to the time node, to obtain the operating data coordinate system corresponding to each new energy bus;

[0137] Determine, according to the operating data coordinate system, an initial operating waveform segment corresponding to the initial operating power, a non-initial operating waveform segment of the non-initial operating power, an initial charging waveform segment corresponding to the initial charging data, and a non-initial charging waveform segment of the non-initial charging data;

[0138] A distribution correlation binding analysis is performed on the initial operation waveform segment, non-initial operation waveform segment, initial charging waveform segment and non-initial charging waveform segment to determine the correspondence between the initial operation power and non-initial operation power in the vehicle operation information and the initial charging data and non-initial charging data in the vehicle charging information of the same new energy bus.

[0139] In another possible implementation of the embodiment of the present application, the information sorting module 22 performs distribution correlation binding analysis on the initial operation waveform segment, the non-initial operation waveform segment, the initial charging waveform segment, and the non-initial charging waveform segment to determine the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information of the same new energy bus and the initial charging data and the non-initial charging data in the vehicle charging information. Specifically, it is used to:

[0140] Determining an operating waveform segment existing before an initial time node of the initial charging waveform segment, and binding the existing operating waveform segment with the initial charging waveform segment to obtain a first correspondence between initial charging data and initial operating power and / or non-initial operating power;

[0141] determining a first non-initial charging waveform segment adjacent to the initial charging waveform segment, and binding a non-initial operation waveform segment between the initial charging waveform segment and the first non-initial charging waveform segment with the first non-initial charging waveform segment to obtain a second correspondence between non-initial charging data corresponding to the first non-initial charging waveform segment and non-initial operation power;

[0142] Determining a second non-initial charging waveform segment adjacent to the first non-initial charging waveform segment, and binding the non-initial operation waveform segment between the first non-initial charging waveform segment and the second non-initial charging waveform segment with the second non-initial charging waveform segment to obtain a third correspondence between the non-initial charging power and the non-initial operation power corresponding to the second non-initial charging waveform segment;

[0143] Determining a second non-initial charging waveform segment set that is not adjacent to the initial charging waveform segment, and binding a non-initial operating waveform segment between every two adjacent non-initial charging waveform segments in the second non-initial charging waveform segment set with a non-initial charging waveform segment that is temporally shifted after the adjacent non-initial charging waveform segment, to obtain a fourth correspondence between non-initial charging data corresponding to each non-initial charging waveform segment in the second non-initial charging waveform segment set and non-initial operating power;

[0144] The correspondence between the initial operating power and non-initial operating power in the vehicle operating information and the initial charging data and non-initial charging data in the vehicle charging information of the same new energy bus is determined according to the first correspondence, the second correspondence, the third correspondence and the fourth correspondence.

[0145] In another possible implementation of the embodiment of the present application, the fatigue analysis module 24 performs fatigue analysis on the initial battery operating power and the non-initial battery operating power to obtain the battery fatigue information corresponding to each new energy bus battery, specifically for:

[0146] Performing feature analysis on a first operating waveform segment corresponding to the initial battery operating power and a plurality of second operating waveform segments corresponding to the non-initial battery operating power to obtain first operating feature data corresponding to the first operating waveform segment and a set of second operating feature data corresponding to the plurality of second operating waveform segments;

[0147] Generating a first running hash object by combining the first running feature data and the second running feature data based on a fingerprint algorithm, and converting the data format of the first running hash object into a byte stream format to obtain a second running hash object;

[0148] According to the fingerprint algorithm, the second operation hash object is used to generate a reference operation data fingerprint and a fatigue monitoring operation data fingerprint set, and the fatigue monitoring operation data fingerprint set is compared with the reference operation data fingerprint one by one to obtain the operation change data of each new energy bus battery in different time periods after each charge;

[0149] Identify the output current, voltage, and power of each new energy bus battery based on operational change data, and obtain parameter change data for different attribute parameters;

[0150] Draw a data dynamic waveform diagram of the parameter change data of different attribute parameters, and perform steady-state analysis on the corresponding new energy bus battery based on the data dynamic waveform diagram to obtain battery stability data;

[0151] The battery stability data is matched with the preset battery fatigue condition assessment standard to obtain the battery fatigue information corresponding to each new energy bus battery.

[0152] In another possible implementation of the embodiment of the present application, the system 20 further includes: a first determination module, a second determination module, and a maintenance generation module, wherein:

[0153] A first determining module is used to determine abnormal fatigue information of a battery having fatigue abnormality according to a preset battery fatigue charging standard;

[0154] The second determining module is used to determine abnormal fatigue limit data corresponding to the abnormal fatigue information of the battery in the preset battery fatigue condition assessment standard;

[0155] The maintenance generation module is used to determine whether the battery stability data is consistent with the abnormal fatigue limit data. If it is consistent, the battery maintenance information is generated based on the new energy bus battery in the battery stability data and the consistent abnormal data, and the battery maintenance information is sent to the target terminal.

[0156] In another possible implementation of the embodiment of the present application, the maintenance generation module is specifically configured to:

[0157] If the battery stability data does not match the abnormal fatigue limit data, the battery stability data is sorted in an unsupervised time series based on the time series length in the data dynamic waveform diagram to obtain the battery matrix data;

[0158] Analyze the periodicity of battery matrix data and generate future battery matrix data;

[0159] Process the data contained in the future battery matrix data to obtain deduction matrix data, and input the obtained deduction matrix data into a preset algorithm model for data calculation to obtain future battery stability data of different new energy bus batteries within a preset time in the future;

[0160] Determine the future abnormal time nodes in the future battery stability data that meet the abnormal fatigue limit data, and generate the remaining maintenance time information based on the current time node and the future abnormal time node.

[0161] In another possible implementation of the embodiment of the present application, the maintenance generation module is specifically configured to:

[0162] Conduct basic data distribution exploration on battery matrix data to determine the relative periodicity of battery stability data when changes occur, and determine the length of the time period based on the relative periodicity;

[0163] The battery matrix data is supervised time series data sorted based on the time period length to obtain data period regularity data;

[0164] Based on the data cycle regularity data, the battery stability data change trend within the future preset time period is predicted to generate future battery matrix data.

[0165] The present application embodiment provides an electronic device, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 The electronic device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0166] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0167] The bus 302 may include a path for transmitting information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0168] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0169] The memory 303 is used to store application code for executing the solution of the embodiment of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0170] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0171] A computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the method described above can be referenced to each other.

[0172] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned new energy bus charging monitoring method are implemented.

[0173] Since the embodiments of the computer-readable storage medium part and the embodiments of the method part correspond to each other, the embodiments of the computer-readable storage medium part refer to the description of the embodiments of the method part.

[0174] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0175] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A new energy bus charging monitoring method, characterized in that: include: Obtain vehicle operation information and vehicle charging information, wherein the vehicle operation information includes the initial operating power of the battery during the first operation of different new energy buses and the non-initial operating power of the battery during non-first operation, and the vehicle charging information includes the initial charging data of the charging pile when charging different new energy buses for the first time and the non-initial charging data when charging for non-first time; The vehicle operation information and the vehicle charging information are respectively sorted according to time nodes, and a correspondence between the initial operation power and the non-initial operation power in the vehicle operation information, and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus and the time nodes is determined; Determine, according to the corresponding relationship, the initial battery operating power corresponding to the initial charging data when each new energy bus is charged for the first time and the non-initial battery operating power corresponding to the non-initial charging data when charging is not the first time; Performing fatigue analysis on the initial battery operating power and the non-initial battery operating power to obtain battery fatigue information corresponding to each new energy bus battery; Matching the battery fatigue information with a preset battery fatigue charging standard to obtain an optimal charging plan for the battery fatigue information, the optimal charging plan including: an early charging power and an early charging period, a mid-term charging power and a mid-term charging period, and a late charging power and a late charging period; A charging control instruction is generated according to the optimal charging solution to control the time nodes of strong and weak current conversion of the charging pile when charging the battery of the new energy bus.

2. A new energy bus charging monitoring method according to claim 1, characterized in that: The method of arranging the vehicle operation information and the vehicle charging information according to time nodes and determining the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus includes: Create multiple data monitoring coordinate systems, where the X-axes of the multiple data monitoring coordinate systems are all different time nodes, and the Y-axes of the multiple data monitoring coordinate systems are operating power in different units and battery charging data in different units. The number of the multiple data monitoring coordinate systems is consistent with the number of new energy buses to be monitored; Bind the multiple data monitoring coordinate systems to each new energy bus in a one-to-one correspondence, and map the initial operating power, the non-initial operating power, the initial charging data, and the non-initial charging data corresponding to each new energy bus to the corresponding data monitoring coordinate system according to the time node, to obtain the operating data coordinate system corresponding to each new energy bus; Determining, according to the operating data coordinate system, an initial operating waveform segment corresponding to the initial operating power, a non-initial operating waveform segment of the non-initial operating power, an initial charging waveform segment corresponding to the initial charging data, and a non-initial charging waveform segment of the non-initial charging data; A distribution correlation binding analysis is performed on the initial operation waveform segment, the non-initial operation waveform segment, the initial charging waveform segment, and the non-initial charging waveform segment to determine the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus.

3. A new energy bus charging monitoring method according to claim 2, characterized in that: The performing distribution correlation binding analysis on the initial operation waveform segment, the non-initial operation waveform segment, the initial charging waveform segment, and the non-initial charging waveform segment to determine the correspondence between the initial operation power and the non-initial operation power in the vehicle operation information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus includes: determining an operating waveform segment existing before an initial time node of the initial charging waveform segment, and binding the existing operating waveform segment with the initial charging waveform segment to obtain a first corresponding relationship between the initial charging data and the initial operating power and / or the non-initial operating power; determining a first non-initial charging waveform segment adjacent to the initial charging waveform segment, and binding a non-initial operation waveform segment between the initial charging waveform segment and the first non-initial charging waveform segment with the first non-initial charging waveform segment to obtain a second correspondence between non-initial charging data corresponding to the first non-initial charging waveform segment and the non-initial operation power; determining a second non-initial charging waveform segment adjacent to the first non-initial charging waveform segment, and binding a non-initial operation waveform segment between the first non-initial charging waveform segment and the second non-initial charging waveform segment with the second non-initial charging waveform segment to obtain a third correspondence between the non-initial charging power corresponding to the second non-initial charging waveform segment and the non-initial operation power; determining a second non-initial charging waveform segment set that is not adjacent to the initial charging waveform segment, and binding a non-initial operating waveform segment between every two adjacent non-initial charging waveform segments in the second non-initial charging waveform segment set with a non-initial charging waveform segment that is temporally shifted after the adjacent non-initial charging waveform segment, to obtain a fourth correspondence between non-initial charging data corresponding to each non-initial charging waveform segment in the second non-initial charging waveform segment set and non-initial operating power; The correspondence between the initial operating power and the non-initial operating power in the vehicle operating information and the initial charging data and the non-initial charging data in the vehicle charging information of the same new energy bus is determined according to the first correspondence, the second correspondence, the third correspondence and the fourth correspondence.

4. A new energy bus charging monitoring method according to claim 1, characterized in that: The fatigue analysis of the initial battery operating power and the non-initial battery operating power is performed to obtain battery fatigue information corresponding to each new energy bus battery, including: performing feature analysis on a first operating waveform segment corresponding to the initial battery operating power and a plurality of second operating waveform segments corresponding to the non-initial battery operating power to obtain first operating feature data corresponding to the first operating waveform segment and a set of second operating feature data corresponding to the plurality of second operating waveform segments; Generating a first running hash object by combining the first running feature data and the second running feature data based on a fingerprint algorithm, and converting the data format of the first running hash object into a byte stream format to obtain a second running hash object; According to the fingerprint algorithm, the second operation hash object is used to generate a reference operation data fingerprint and a fatigue monitoring operation data fingerprint set, and the fatigue monitoring operation data fingerprint set is compared with the reference operation data fingerprint one by one to obtain the operation change data of each new energy bus battery in different time periods after each charge; Identify the output current, voltage, and power of each new energy bus battery based on the operation change data to obtain parameter change data of different attribute parameters; Draw a data dynamic waveform diagram of parameter change data of different attribute parameters, and perform a steady-state analysis on the corresponding new energy bus battery based on the data dynamic waveform diagram to obtain battery stability data; The battery stability data is matched with a preset battery fatigue condition evaluation standard to obtain battery fatigue information corresponding to each new energy bus battery.

5. A new energy bus charging monitoring method according to claim 4, characterized in that: The matching of the battery fatigue information with a preset battery fatigue charging standard to obtain an optimal charging solution for the battery fatigue information also includes: Determining abnormal fatigue information of a battery having fatigue abnormality according to the preset battery fatigue charging standard; Determining abnormal fatigue limit data corresponding to the abnormal battery fatigue information in the preset battery fatigue condition assessment standard; Determine whether the battery stability data is consistent with the abnormal fatigue limit data. If consistent, generate battery maintenance information based on the new energy bus battery in the battery stability data and the consistent abnormal data, and send the battery maintenance information to the target terminal.

6. A new energy bus charging monitoring method according to claim 5, characterized in that: The determining whether the battery stability data is consistent with the abnormal fatigue limit data includes: If the battery stability data does not match the abnormal fatigue limit data, performing unsupervised time series data sorting on the battery stability data based on the time series length in the data dynamic waveform diagram to obtain battery matrix data; Performing data periodicity analysis on the battery matrix data to generate future battery matrix data; Processing the data contained in the future battery matrix data to obtain deduction matrix data, and inputting the obtained deduction matrix data into a preset algorithm model for data calculation to obtain future battery stability data of different new energy bus batteries within a preset time in the future; Determine a future abnormal time node in the future battery stability data that meets the abnormal fatigue limit data, and generate remaining maintenance time information according to the current time node and the future abnormal time node.

7. A new energy bus charging monitoring method according to claim 6, characterized in that: The performing periodic regularity analysis on the battery matrix data to generate future battery matrix data includes: Performing basic data distribution exploration on the battery matrix data to determine a relative periodicity pattern when changes occur in the battery stability data, and determining a time period length based on the relative periodicity pattern; Performing supervised time series data sorting on the battery matrix data based on the time period length to obtain data period regularity data; Based on the data periodic regularity data, the battery stability data change trend within a future preset time period is predicted to generate future battery matrix data.

8. A new energy bus charging monitoring system, characterized in that: A new energy bus charging monitoring method applied to any one of claims 1 to 7, comprising: An information acquisition module is used to acquire vehicle operation information and vehicle charging information. The vehicle operation information includes the initial operating power of the battery during the first operation of different new energy buses and the non-initial operating power of the battery during non-first operation. The vehicle charging information includes the initial charging data of the charging pile when charging different new energy buses for the first time and the non-initial charging data when charging for non-first time; An information collating module is used to sort the vehicle operation information and the vehicle charging information according to time nodes, and determine the correspondence between the initial operation power and non-initial operation power in the vehicle operation information, the initial charging data and non-initial charging data in the vehicle charging information of the same new energy bus, and the time nodes; A power determination module is used to determine, according to the corresponding relationship, the initial battery operating power corresponding to the initial charging data when each new energy bus is charged for the first time and the non-initial battery operating power corresponding to the non-initial charging data when charging is not the first time; A fatigue analysis module is used to perform fatigue analysis on the initial battery operating power and the non-initial battery operating power to obtain battery fatigue information corresponding to each new energy bus battery; a scheme determination module, configured to match the battery fatigue information with a preset battery fatigue charging standard to obtain an optimal charging scheme for the battery fatigue information, the optimal charging scheme comprising: an early charging power and an early charging period, a mid-term charging power and a mid-term charging period, and a late charging power and a late charging period; The charging control module is used to generate charging control instructions according to the optimal charging solution and control the time nodes of strong and weak current conversion when the charging pile is charging the battery of the new energy bus.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a new energy bus charging monitoring method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that include: A computer program is stored which can be loaded by a processor and executes a new energy bus charging monitoring method as described in any one of claims 1 to 7.

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