A charging pile charging information intelligent interaction method and system based on frequency division monitoring

Abstract

The application relates to the field of new energy vehicle charging technology, in particular to a charging pile charging information intelligent interaction method and system based on frequency division monitoring, which comprises setting a charging frequency interval, performing frequency division to obtain a basic frequency set, dynamically adjusting a target frequency section to an optimized frequency section, generating a class or two class signal groups according to abnormal charging behaviors, constructing a charging information interaction model to realize intelligent interaction. The application can improve the real-time performance, safety and intelligent level of charging information interaction, meet the efficient information interaction demand in a complex electromagnetic environment, and improve the operation efficiency of the charging pile and the user experience.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of new energy vehicle charging, and specifically relates to a charging pile charging information intelligent interaction method and system based on frequency division monitoring. BACKGROUND

[0002] With the popularization of new energy vehicles, as an important supporting facility for electric vehicles, the information interaction technology of charging piles has gradually become a research hotspot. The intelligent interaction capability of charging piles with users, vehicles and cloud systems directly affects the charging efficiency, user experience and system security. However, the existing charging pile information interaction method still has deficiencies in data monitoring accuracy, real-time performance, security and intelligent level, and it is difficult to meet the needs of increasingly complex charging scenarios. For example, the patent with publication number CN114125769B provides a charging pile information interaction method based on Wi-Fi signal grouping and sorting, which realizes low-cost information sharing. However, this technical solution lacks accurate monitoring of real-time data changes during charging, especially under high-frequency or low-frequency signal interference, which may affect the stability and reliability of data transmission. At the same time, this scheme does not involve the application of frequency division monitoring technology, and cannot effectively cope with the information interaction needs in complex electromagnetic environments, which may cause data loss or delay and affect user experience. In addition, the patent with publication number CN111098746B realizes real-time information interaction during charging by setting a hardware switch between the charging pile and the electric vehicle, and supports charging power adjustment and fault analysis. However, this technical solution mainly relies on the design of hardware switch and lacks in-depth analysis and processing of charging data frequency characteristics, making it difficult to cope with multi-band signal interference or abnormal data fluctuations. Since the intelligent processing mechanism based on frequency division monitoring is not introduced, this scheme may have low information interaction efficiency and even safety problems in complex charging environments. The deficiencies of the above existing technologies show that the current charging pile information interaction method still needs to be improved in real-time monitoring, data processing accuracy, anti-interference ability and intelligent level. Therefore, the present application proposes a charging pile charging information intelligent interaction method and system based on frequency division monitoring, aiming to realize accurate collection and processing of charging data through frequency division monitoring technology, improve the real-time performance, security and intelligent level of information interaction, and meet the needs of new energy vehicle charging scenarios for efficient and reliable information interaction. SUMMARY

[0003] The present application provides a charging pile charging information intelligent interaction method and system based on frequency division monitoring, which mainly aims to improve the real-time performance, security and intelligent level of charging information interaction, and meet the needs of new energy vehicle charging scenarios for efficient information interaction.

[0004] To achieve the above-mentioned purpose, the present application provides a charging pile charging information intelligent interaction method based on frequency division monitoring, which comprises:

[0005] based on the preset charging frequency interval, the charging data is divided into frequency, and a basic frequency set is obtained;

[0006] obtain the current charging time, extract the target frequency segment from the basic frequency set based on the current charging time, collect the to-be-tested charging signal of the target charging pile, measure the signal strength value of the to-be-tested charging signal, dynamically adjust the target frequency segment based on the signal strength value, obtain the optimized frequency segment, and record the frequency adjustment time of the target frequency segment adjustment;

[0007] determine whether a preset abnormal charging behavior occurs;

[0008] if the abnormal charging behavior occurs, a first type of signal marking is performed on the to-be-tested charging signal using a preset first type of marking to obtain a first type of marking group, an abnormal triggering time and an abnormal signal feature corresponding to the abnormal charging behavior are obtained, and a time difference between the frequency adjustment time and the abnormal triggering time is calculated;

[0009] the first type of marking group is key-value paired using the optimized frequency segment, the abnormal signal feature, the signal strength value and the time difference value to obtain a first type of signal group;

[0010] if the abnormal charging behavior does not occur, a second type of signal marking is performed on the to-be-tested charging signal using a preset second type of marking to obtain a second type of marking group, and the second type of marking group is key-value paired using the optimized frequency segment and the signal strength value to obtain a second type of signal group;

[0011] the first type of signal group or the second type of signal group is used to fill the content of the pre-constructed initial signal group set to obtain a target signal group set, and the original signal group number of the target signal group in the target signal group set is determined;

[0012] determine whether the original signal group number is less than a preset standard signal group number;

[0013] if the original signal group number is less than the standard signal group number, the initial signal group set is updated using the target signal group set, and the step of obtaining the current charging time is returned;

[0014] if the original signal group number is not less than the standard signal group number, a charging information interaction model is constructed using a preset intelligent processing technology according to the target signal group set, and intelligent interaction of charging information of the charging pile is performed based on the charging information interaction model.

[0015] Optionally, the dynamic adjustment of the target frequency segment based on the signal strength value to obtain the optimized frequency segment comprises:

[0016] based on a preset signal strength reference table, a standard signal strength value corresponding to the current charging time is queried, a frequency fluctuation coefficient and a smooth transition coefficient are set.

[0017] respectively according to the preset maximum signal strength value and minimum signal strength value, set the highest frequency band and the lowest frequency band;

[0018] According to the target frequency band, the standard signal strength value, the frequency fluctuation coefficient, the smooth transition coefficient, the highest frequency band, the lowest frequency band and the preset signal strength variable, the dynamic adjustment of the target frequency band is completed, and the optimized frequency band is obtained.

[0019] Optionally, the acquisition of the abnormal triggering moment and the abnormal signal feature corresponding to the abnormal charging behavior comprises:

[0020] In a preset standard detection time, the target charging pile is monitored for charging signal to obtain a target abnormal signal set and a target abnormal interval set;

[0021] Based on the target abnormal interval set, the average abnormal interval is calculated;

[0022] The starting monitoring moment is acquired, and the starting signal feature of the target charging pile at the starting monitoring moment is collected;

[0023] Based on the average abnormal interval and the starting monitoring moment, the current monitoring moment is calculated, and the current signal feature of the target charging pile at the current monitoring moment is collected;

[0024] Determine whether the starting signal feature is equal to the current signal feature;

[0025] If the starting signal feature is not equal to the current signal feature, the target abnormal signal set is supplemented by using the current signal feature to obtain a target abnormal feature set;

[0026] The target abnormal signal set, the starting monitoring moment and the starting signal feature are updated by using the target abnormal feature set, the current monitoring moment and the current signal feature respectively, and the step of calculating the current monitoring moment based on the average abnormal interval and the starting monitoring moment is returned;

[0027] If the starting signal feature is equal to the current signal feature, the target abnormal signal set is recorded as the target abnormal feature set;

[0028] The current monitoring moment is recorded as the abnormal triggering moment, the key feature in the target abnormal feature set is extracted, and the key feature is taken as the abnormal signal feature.

[0029] Optionally, the charging signal monitoring of the target charging pile to obtain the target abnormal signal set and the target abnormal interval set comprises:

[0030] Set the initial abnormal signal set, the initial abnormal interval set and the initial monitoring moment;

[0031] acquiring a real-time abnormal signal, recording a real-time monitoring time point;

[0032] judging whether the real-time abnormal signal appears in the initial abnormal signal set;

[0033] if the real-time abnormal signal appears in the initial abnormal signal set, returning to the step of acquiring the real-time abnormal signal;

[0034] if the real-time abnormal signal does not appear in the initial abnormal signal set, calculating an abnormal interval and a time length cumulative value according to the real-time monitoring time point and a frequency adjustment time point;

[0035] judging whether the time length cumulative value is greater than a standard detection time length;

[0036] if the time length cumulative value is not greater than the standard detection time length, supplementing the initial abnormal signal set and the initial abnormal interval set with the real-time abnormal signal and the abnormal interval respectively, obtaining a target abnormal signal set and a target abnormal interval set, updating the initial abnormal signal set, the initial abnormal interval set and the initial monitoring time point with the target abnormal signal set, the target abnormal interval set and the real-time monitoring time point respectively, and returning to the step of acquiring the real-time abnormal signal;

[0037] if the time length cumulative value is greater than the standard detection time length, recording the initial abnormal signal set and the initial abnormal interval set as the target abnormal signal set and the target abnormal interval set respectively.

[0038] Optionally, the one-type signal group is obtained by key-value pairing the one-type mark group with the optimized frequency band, the abnormal signal feature, the signal intensity value and the time difference value, and the one-type signal group includes:

[0039] the one-type signal group is obtained by key-value pairing the optimized frequency band, the abnormal signal feature, the signal intensity value, the time difference value and the one-type mark group.

[0040] Optionally, the two-type signal group is obtained by key-value pairing the two-type mark group with the optimized frequency band and the signal intensity value, and the two-type signal group includes:

[0041] the abnormal signal feature and an abnormal triggering time point are set according to the optimized frequency band and the frequency adjustment time point;

[0042] a time difference value between the abnormal triggering time point and the frequency adjustment time point is calculated;

[0043] the two-type signal group is obtained by key-value pairing the optimized frequency band, the abnormal signal feature, the signal intensity value, the time difference value and the two-type mark group.

[0044] Optionally, the charging information interaction model is constructed by using a preset intelligent processing technology according to the target signal group set, and the charging information interaction model includes:

[0045] According to a preset signal division ratio, a target signal group set is randomly divided into groups to obtain a training signal group set and a verification signal group set;

[0046] An intelligent processing model is selected, and the intelligent processing model is trained by using the training signal group set to obtain an initial interaction model;

[0047] The initial interaction model is verified by using the verification signal group set to obtain a charging information interaction model.

[0048] Optionally, the initial interaction model is verified by using the verification signal group set to obtain a charging information interaction model, including:

[0049] A verification signal group is extracted from the verification signal group set in sequence to obtain a verification abnormal signal feature and a verification time difference value of the verification signal group;

[0050] According to a preset user tolerance range, the verification abnormal signal feature and the verification time difference value are value range expanded to obtain a user tolerance feature interval and a user tolerance time difference interval;

[0051] The verification signal group is input into the initial interaction model to obtain a secondary optimized frequency segment and a secondary time difference value;

[0052] It is judged whether the secondary optimized frequency segment and the secondary time difference value are respectively within the user tolerance feature interval and the user tolerance time difference interval;

[0053] If the secondary optimized frequency segment and the secondary time difference value are respectively within the user tolerance feature interval and the user tolerance time difference interval, the verification signal group is recorded as a verification success signal group;

[0054] If the secondary optimized frequency segment and the secondary time difference value are not respectively within the user tolerance feature interval and the user tolerance time difference interval, the verification signal group is recorded as a verification failure signal group;

[0055] The number of successes and the number of failures of the verification success signal group and the verification failure signal group are recorded respectively, and a verification success rate is calculated based on the number of successes and the number of failures;

[0056] It is judged whether the verification success rate is less than a preset standard success rate;

[0057] If the verification success rate is less than the standard success rate, the initial interaction model is trained again by using the verification signal group set to obtain an optimized interaction model, and the optimized interaction model is verified by using the verification signal group set to obtain a target success rate;

[0058] The verification success rate and the initial interaction model are updated respectively by using the target success rate and the optimized interaction model, and the step of judging whether the verification success rate is less than a preset standard success rate is returned.

[0059] If the verification success rate is not less than the standard success rate, the initial interaction model is recorded as the charging information interaction model.

[0060] Optionally, the charging pile charging information intelligent interaction based on the charging information interaction model comprises:

[0061] The real-time charging signal of the target charging pile is collected, the real-time signal strength value of the real-time charging signal is measured, the charging frequency is optimized by using a frequency adjustment mechanism according to the real-time signal strength value, a real-time optimized frequency segment is obtained, and the real-time adjustment time of the frequency optimization is recorded.

[0062] The target charging pile is preliminarily adjusted in frequency according to the real-time optimized frequency segment, and a preliminarily adjusted charging pile is obtained.

[0063] The real-time signal strength value, the real-time optimized frequency segment and the real-time adjustment time are input into the charging information interaction model, and a secondary optimized frequency segment and a secondary time difference value are obtained.

[0064] The preliminarily adjustment end time is obtained, the secondary time difference value is waited for based on the preliminarily adjustment end time, the preliminarily adjusted charging pile is adjusted in frequency again according to the secondary optimized frequency segment, a completely adjusted charging pile is obtained, and the charging pile charging information intelligent interaction is completed.

[0065] To achieve the above object, the application further provides a charging pile charging information intelligent interaction system based on frequency division monitoring, comprising:

[0066] The preliminary frequency adjustment module is used for setting a charging frequency interval, dividing the charging data based on the charging frequency interval, obtaining a basic frequency set, obtaining a current charging time, extracting a target frequency segment from the basic frequency set based on the current charging time, collecting a to-be-tested charging signal of a target charging pile, measuring a signal strength value of the to-be-tested charging signal, dynamically adjusting the target frequency segment based on the signal strength value, obtaining an optimized frequency segment, and recording a frequency adjustment time of adjusting the target frequency segment.

[0067] The abnormal behavior judgment module is used for judging whether a preset abnormal charging behavior occurs, and if the abnormal charging behavior occurs, a first type of signal marking is performed on the to-be-tested charging signal by using a preset first type of marking, a first type of marking group is obtained, an abnormal triggering time and an abnormal signal feature corresponding to the abnormal charging behavior are obtained, and a time difference value between the frequency adjustment time and the abnormal triggering time is calculated.

[0068] The data set collection module is configured for key-value pairing of the first type of mark group by using the optimized frequency band, the abnormal signal feature, the signal strength value and the time difference value, to obtain a first type of signal group, if no abnormal charging behavior occurs, the second type of signal mark is used for the second type of signal mark of the to-be-tested charging signal, to obtain a second type of mark group, the optimized frequency band and the signal strength value are used for key-value pairing of the second type of mark group, to obtain a second type of signal group, the first type of signal group or the second type of signal group is used for content filling of the pre-constructed initial signal group set, to obtain a target signal group set, and the original signal group number of the target signal group in the target signal group set is determined.

[0069] The precise interaction control module is configured for judging whether the original signal group number is less than a preset standard signal group number, if the original signal group number is less than the standard signal group number, the target signal group set is used for updating the initial signal group set, and the step of obtaining the current charging time is returned, if the original signal group number is not less than the standard signal group number, a charging information interaction model is constructed by using a preset intelligent processing technology according to the target signal group set, and intelligent interaction of charging information of a charging pile is performed based on the charging information interaction model.

[0070] To solve the above problems, the present application further provides an electronic device, which comprises:

[0071] A memory for storing at least one instruction; and

[0072] A processor for executing the instructions stored in the memory to implement the above-mentioned charging pile charging information intelligent interaction method based on frequency division monitoring.

[0073] To solve the above problems, the present application further provides a computer readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned charging pile charging information intelligent interaction method based on frequency division monitoring.

[0074] The present application is to solve the problems described in the background art, first set the charging frequency interval, and based on the charging frequency interval, the charging data is divided into frequency division, and the basic frequency set is obtained, which realizes the basic control of the charging frequency, makes the charging frequency have strong adaptability, then acquires the current charging time, and extracts the target frequency segment from the basic frequency set based on the current charging time, collects the to-be-measured charging signal of the target charging pile, measures the signal strength value of the to-be-measured charging signal, adjusts the target frequency segment based on the signal strength value, obtains the optimized frequency segment, records the frequency adjustment time of the target frequency segment adjustment, which completes the preliminary optimization of the charging frequency, reduces the burden of subsequent complex interactive calculation, and can monitor the change of the charging pile operation state through the judgment of whether the abnormal charging behavior occurs, which indicates the stability of the charging pile under the current frequency segment, which improves the accuracy of the charging information interaction, if the abnormal charging behavior occurs, it represents that the current frequency segment has potential risks, then a type of signal marking is performed on the to-be-measured charging signal, and a type of marking group is obtained, which labels the abnormal charging behavior for subsequent intelligent processing, acquires the abnormal trigger time and abnormal signal characteristics corresponding to the abnormal charging behavior, calculates the time difference value between the frequency adjustment time and the abnormal trigger time, and performs key value pairing on the type of marking group by using the optimized frequency segment, the abnormal signal characteristics, the signal strength value and the time difference value, to obtain a type of signal group, the acquisition of the type of signal group can further improve the content of the type of marking group, so that the features used for intelligent processing are more abundant, if no abnormal charging behavior occurs, it represents that the current frequency segment is stable, then a type of signal marking is performed on the to-be-measured charging signal, and a type of marking group is obtained, and the type of marking group is paired with the key value by using the optimized frequency segment and the signal strength value, to obtain a type of signal group, and the representation of the type of signal group is completed, the type of signal group or the type of signal group fills the content of the initial signal group set to obtain a target signal group set, determines the original signal group number of the target signal group in the target signal group set, completes the filling of the target signal group set, and a large number of target signal groups are obtained, which also improves the accuracy of subsequent charging information interaction, by judging whether the original signal group number is less than the standard signal group number, whether enough target signal groups are obtained can be judged for subsequent intelligent interaction control of the charging pile, if the original signal group number is less than the standard signal group number, the initial signal group set is updated by using the target signal group set, and the step of acquiring the current charging time is returned, realizing the cyclic acquisition of the current signal group, if the original signal group number is not less than the standard signal group number, a charging information interaction model is constructed by using intelligent processing technology according to the target signal group set, and the charging information intelligent interaction of the charging pile is carried out based on the charging information interaction model, realizing the intelligent processing of the charging information of the charging pile, and improving the interaction efficiency and safety of the charging pile in the complex electromagnetic environment. Therefore, the present application can improve the real-time performance, safety and intelligent level of the charging information interaction. BRIEF DESCRIPTION OF DRAWINGS

[0075] Fig. 1 A flowchart of the charging pile charging information intelligent interaction method based on frequency division monitoring provided by an embodiment of the present application is shown in

[0076] Fig. 2 A functional module diagram of the charging pile charging information intelligent interaction system based on frequency division monitoring provided by an embodiment of the present application is shown in

[0077] Fig. 3 A structural diagram of an electronic device implementing the charging pile charging information intelligent interaction method based on frequency division monitoring provided by an embodiment of the present application is shown in DETAILED DESCRIPTION

[0078] The present application provides a charging pile charging information intelligent interaction method and system based on frequency division monitoring. The core of the present application is to realize dynamic adjustment of charging frequency through frequency division monitoring technology, and to construct an efficient charging information interaction model by combining abnormal behavior detection and intelligent processing technology. The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Figs. 1 to 3

[0079] First, as shown in Fig. 1 , a flowchart of the charging pile charging information intelligent interaction method based on frequency division monitoring provided by an embodiment of the present application is shown. The first step of this method is to set a predetermined charging frequency interval, and to divide the charging data based on this interval to obtain a basic frequency set. In actual application, the setting of the charging frequency interval needs to consider the working environment of the charging pile, the electromagnetic interference strength and the charging demand of new energy vehicles. For example, in a typical implementation scenario, the charging frequency interval can be set to 20kHz to 50kHz, and the interval can be divided into several subintervals, each subinterval corresponding to a basic frequency segment. These basic frequency segments constitute the basic frequency set, which provides a basis for subsequent dynamic frequency adjustment.

[0080] Next, the current charging time is obtained, and the target frequency segment is extracted from the basic frequency set based on the current charging time. This process needs to collect the to-be-tested charging signal of the target charging pile in real time, and measure its signal strength value. The measurement of the signal strength value is usually completed by a dedicated signal acquisition device, such as a high-precision current sensor or a voltage sensor. Assuming that the to-be-tested charging signal strength value of the target charging pile in a certain measurement is 4.5V, then according to the preset signal strength reference table, the standard signal strength value corresponding to the current charging time is queried. On this basis, the frequency fluctuation coefficient a and the smooth transition coefficient b are set, which are used to control the flexibility and stability of frequency adjustment, respectively. At the same time, according to the preset maximum signal strength value and the minimum signal strength value, the highest frequency segment and the lowest frequency segment​ Finally, according to the target frequency band, the standard signal strength value, the frequency fluctuation coefficient, the smooth transition coefficient, the highest frequency band, the lowest frequency band, and the preset signal strength variable, the dynamic adjustment of the target frequency band is completed, and the optimized frequency band is obtained. This dynamic adjustment process can be represented by the following formula:

[0081] ;

[0082] wherein is the optimized frequency band, is the target frequency band, is the actual signal strength value, is the standard signal strength value.

[0083] After the frequency optimization is completed, the abnormal behavior judgment link is entered. As shown in Fig. 2 , the abnormal behavior judgment module is used to judge whether the preset abnormal charging behavior occurs. The judgment standard of abnormal charging behavior can be flexibly set according to the actual application scene, for example, when the charging current exceeds 120% of the rated value or the charging voltage fluctuation amplitude exceeds ±10%, it can be determined as abnormal charging behavior. If abnormal charging behavior is detected, a preset type of marker is used to mark the test charging signal as a type of signal, and a type of marker group is obtained. At the same time, the abnormal trigger time corresponding to the abnormal charging behavior and the abnormal signal characteristics are obtained. The determination of the abnormal trigger time needs to be combined with the real-time monitoring data, and the abnormal signal characteristics are obtained by analyzing the abnormal signal set. For example, in an embodiment, it is assumed that the abnormal charging behavior occurs at the 15th second, and the abnormal signal characteristics at this time include that the instantaneous peak value of the charging current reaches 60A and the fluctuation amplitude of the charging voltage is ±15%. These characteristics will be recorded and used for subsequent key-value pairing operations.

[0084] For the case where no abnormal charging behavior occurs, a preset type of marker is used to mark the test charging signal as a type of signal, and a type of marker group is obtained. Subsequently, the optimized frequency band and the signal strength value are used to pair the type of marker group with the key value, and a type of signal group is obtained. Whether it is abnormal charging behavior or general charging behavior, the generated signal group ultimately needs to be filled into the initial signal group set to form a target signal group set. The number of original signal groups of the target signal group set needs to meet certain standard requirements to ensure the accuracy of subsequent intelligent processing. For example, in an embodiment, the standard signal group number is set to 1000 groups. If the number of original signal groups is less than 1000 groups, return to the step of obtaining the current charging time, continue to collect new charging signal data, and continue until the condition is met.

[0085] When the number of original signal groups of the target signal group set reaches or exceeds the standard signal group number, the intelligent processing stage is entered. As shown in Fig. 3As shown, the precise interaction control module uses a preset intelligent processing technology to construct a charging information interaction model according to the target signal set. The selection of the intelligent processing technology can be flexibly adjusted according to actual needs, for example, a deep learning model, a support vector machine, or a random forest algorithm can be selected. In a typical embodiment, a deep learning model is selected as the intelligent processing model, and the target signal set is randomly divided in a 7:3 ratio to obtain a training signal set and a verification signal set. The training signal set is used for model training to obtain an initial interaction model, and the verification signal set is used for result verification to evaluate the performance of the model. During the verification process, the verification signal set is extracted in sequence to obtain its verification abnormal signal features and verification time difference values, and the features are expanded in value range according to the user tolerance range to obtain the user tolerance feature interval and the user tolerance time difference interval. The verification signal set is input into the initial interaction model to obtain a secondary optimization frequency band and a secondary time difference value. If the secondary optimization frequency band and the secondary time difference value fall within the user tolerance feature interval and the user tolerance time difference interval, respectively, the verification signal set is recorded as a verification success signal set; otherwise, it is recorded as a verification failure signal set. Finally, based on the number of verification success signal sets and verification failure signal sets, the verification success rate is calculated. If the verification success rate is lower than the preset standard success rate, the initial interaction model is trained again until the verification success rate meets the standard. The final charging information interaction model will be used for intelligent interaction of charging information of charging piles.

[0086] The process of intelligent interaction of charging information of charging piles based on the charging information interaction model is as follows: First, the real-time charging signal of the target charging pile is collected, and the real-time signal strength value is measured. According to the real-time signal strength value, the charging frequency is optimized using the frequency adjustment mechanism to obtain a real-time optimization frequency band, and the real-time adjustment time of frequency optimization is recorded. Subsequently, the target charging pile is preliminarily adjusted according to the real-time optimization frequency band to obtain a preliminary adjustment charging pile. The real-time signal strength value, the real-time optimization frequency band, and the real-time adjustment time are input into the charging information interaction model to obtain a secondary optimization frequency band and a secondary time difference value. The preliminary adjustment end time is obtained, and after waiting for the secondary time difference value based on the preliminary adjustment end time, the preliminary adjustment charging pile is secondarily adjusted according to the secondary optimization frequency band to obtain a completely adjusted charging pile, and the intelligent interaction of charging information of charging piles is completed.

[0087] During the entire implementation process, the various functional modules of the system work cooperatively to ensure the efficiency and intelligence of the charging information interaction. For example, the preliminary frequency adjustment module is responsible for setting the charging frequency interval and dividing the charging data by frequency, providing a basis for subsequent frequency optimization; the abnormal behavior judgment module is responsible for detecting abnormal charging behavior and generating corresponding signal markers; the data set collection module is responsible for filling various signal groups into the initial signal group set to form the target signal group set; and the precise interaction control module is responsible for constructing the charging information interaction model and realizing intelligent interaction. In addition, the present application also provides an electronic device comprising a memory and a processor, the memory storing at least one instruction, and the processor executing the instruction in the memory to realize the above-mentioned charging pile charging information intelligent interaction method based on frequency division monitoring. This electronic device can be an embedded system, an industrial computer or other hardware devices with computing power, and can be widely applied in new energy vehicle charging scenarios.

[0088] In summary, the present application realizes dynamic adjustment of charging frequency through frequency division monitoring technology, and combines abnormal behavior detection and intelligent processing technology to construct an efficient charging information interaction model. This method not only improves the real-time, safety and intelligence level of charging information interaction, but also meets the demand of new energy vehicle charging scenarios for efficient information interaction, and has high practical value and popularization prospect.

Claims

1. A method for intelligent interaction of charging information of charging piles based on frequency division monitoring, characterized in that, The method includes: Based on the preset charging frequency range, the charging data is divided into frequency segments to obtain the basic frequency set; The current charging time is obtained, the target frequency band is extracted from the base frequency set based on the current charging time, the charging signal to be tested of the target charging pile is collected, the signal strength value of the charging signal to be tested is measured, and the target frequency band is dynamically adjusted based on the signal strength value to obtain the optimized frequency band. The frequency adjustment time of the target frequency band is recorded. Determine whether a preset abnormal charging behavior has occurred; If abnormal charging behavior occurs, a preset type of marker is used to mark the charging signal under test, resulting in a type of marker group. The abnormal trigger time and abnormal signal characteristics corresponding to the abnormal charging behavior are obtained, and the time difference between the frequency adjustment time and the abnormal trigger time is calculated. By using optimized frequency bands, abnormal signal characteristics, signal strength values, and time difference values, key-value pairing is performed on the aforementioned tag group to obtain a signal group. If no abnormal charging behavior occurs, the charging signal to be tested is marked with a preset type II marker to obtain a type II marker group. The type II marker group is then matched with a key value using an optimized frequency band and signal strength value to obtain a type II signal group. The content of the pre-constructed initial signal group set is filled by the first type of signal group or the second type of signal group to obtain the target signal group set, and the original number of target signal groups in the target signal group set is determined. Determine whether the number of original signal groups is less than the preset standard number of signal groups; If the number of original signal groups is less than the number of standard signal groups, the initial signal group set is updated using the target signal group set, and the step of obtaining the current charging time is returned. If the number of original signal groups is not less than the number of standard signal groups, then based on the target signal group set, a charging information interaction model is constructed using preset intelligent processing technology, and intelligent interaction of charging information of charging piles is carried out based on the charging information interaction model. The acquisition of the abnormal trigger time and abnormal signal characteristics corresponding to the abnormal charging behavior includes: Within a preset standard detection period, the charging signal of the target charging pile is monitored to obtain the target abnormal signal set and the target abnormal interval set. Calculate the average anomaly interval based on the target anomaly interval set; Obtain the start monitoring time and collect the start signal characteristics of the target charging pile at the start monitoring time; Based on the average anomaly interval and the start monitoring time, the current monitoring time is calculated, and the current signal characteristics of the target charging pile at the current monitoring time are collected. Determine whether the characteristics of the initial signal are equal to the characteristics of the current signal; If the initial signal features are not equal to the current signal features, then the target anomaly signal set is supplemented using the current signal features to obtain the target anomaly feature set; The target anomaly signal set, the start monitoring time, and the start signal characteristics are updated using the target anomaly feature set, the current monitoring time, and the current signal characteristics, respectively, and the step of calculating the current monitoring time based on the average anomaly interval and the start monitoring time is returned. If the initial signal characteristics are equal to the current signal characteristics, then the target abnormal signal set is denoted as the target abnormal feature set; The current monitoring time is recorded as the anomaly trigger time. Key features are extracted from the target anomaly feature set and used as anomaly signal features.

2. The intelligent interaction method for charging pile charging information based on frequency division monitoring as described in claim 1, characterized in that, The process of dynamically adjusting the target frequency band based on signal strength values ​​to obtain an optimized frequency band includes: Based on the preset signal strength reference table, query the standard signal strength value corresponding to the current charging moment, and set the frequency fluctuation coefficient and smooth transition coefficient. The highest and lowest frequency bands are set according to the preset maximum and minimum signal strength values, respectively. Based on the target frequency band, standard signal strength value, frequency fluctuation coefficient, smooth transition coefficient, highest frequency band, lowest frequency band, and preset signal strength variables, the target frequency band is dynamically adjusted to obtain an optimized frequency band.

3. The intelligent interaction method for charging pile charging information based on frequency division monitoring as described in claim 1, characterized in that, The process of monitoring the charging signals of the target charging pile to obtain a set of abnormal signals and a set of abnormal intervals includes: Set the initial abnormal signal set, the initial abnormal interval set, and the initial monitoring time; Acquire real-time anomaly signals and record the real-time monitoring time; Determine whether the real-time abnormal signal appears in the initial abnormal signal set; If the real-time abnormal signal appears in the initial abnormal signal set, then return to the step of obtaining the real-time abnormal signal; If the real-time abnormal signal does not appear in the initial abnormal signal set, the abnormal interval and duration cumulative value are calculated based on the real-time monitoring time and frequency adjustment time. Determine whether the cumulative duration is greater than the standard detection duration; If the cumulative duration is not greater than the standard detection duration, the initial abnormal signal set and the initial abnormal interval set are supplemented by the real-time abnormal signal and the abnormal interval respectively to obtain the target abnormal signal set and the target abnormal interval set. The initial abnormal signal set, the initial abnormal interval set and the initial monitoring time are updated by the target abnormal signal set, the target abnormal interval set and the real-time monitoring time respectively, and the process returns to the step of obtaining the real-time abnormal signal. If the cumulative duration is greater than the standard detection duration, the initial abnormal signal set and the initial abnormal interval set are respectively denoted as the target abnormal signal set and the target abnormal interval set.

4. The intelligent interaction method for charging pile charging information based on frequency division monitoring as described in claim 1, characterized in that, The method of using optimized frequency bands, abnormal signal characteristics, signal strength values, and time difference values ​​to perform key-value pairing on the aforementioned group of markers yields a group of signals, including: By pairing the optimized frequency band, abnormal signal characteristics, signal strength value, time difference value, and a class of markers with key values, a class of signal groups is obtained.

5. The intelligent interaction method for charging pile charging information based on frequency division monitoring as described in claim 1, characterized in that, The step of using optimized frequency bands and signal strength values ​​to perform key-value pairing on the two types of marker groups to obtain two types of signal groups includes: Based on the optimized frequency band and frequency adjustment time, set the abnormal signal characteristics and abnormal trigger time; Calculate the time difference between the abnormal triggering time and the frequency adjustment time; By pairing the optimized frequency band, abnormal signal characteristics, signal strength value, time difference value, and two types of marker groups with key values, two types of signal groups are obtained.

6. The intelligent interaction method for charging pile charging information based on frequency division monitoring as described in claim 1, characterized in that, The step of constructing a charging information interaction model based on the target signal set and using preset intelligent processing technology includes: According to the preset signal division ratio, the target signal set is randomly divided into training signal set and validation signal set. Select an intelligent processing model, and train the intelligent processing model using a training signal set to obtain an initial interaction model. The initial interaction model is validated using a set of verification signals to obtain a charging information interaction model.

7. The intelligent interaction method for charging pile charging information based on frequency division monitoring as described in claim 6, characterized in that, The step of using a set of verification signals to verify the initial interaction model and obtain a charging information interaction model includes: The verification signal groups are extracted sequentially from the verification signal group set to obtain the verification abnormal signal characteristics and verification time difference of the verification signal group. Based on the preset user tolerance range, the value range of the verification anomaly signal characteristics and verification time difference is expanded to obtain the user tolerance feature range and the user tolerance time difference range. The verification signal group is input into the initial interaction model to obtain the secondary optimized frequency band and the secondary time difference. Determine whether the secondary optimization frequency band and the secondary time difference are within the user tolerance feature range and the user tolerance time difference range, respectively. If the secondary optimization frequency band and the secondary time difference are within the user tolerance feature range and the user tolerance time difference range, respectively, then the verification signal group is recorded as the verification success signal group. If the secondary optimization frequency band and the secondary time difference are not within the user tolerance feature range and the user tolerance time difference range respectively, then the verification signal group is recorded as the verification failure signal group. Record the number of successful and failed verification signals for each group, and calculate the verification success rate based on the number of successful and failed signals. Determine whether the verification success rate is less than the preset standard success rate; If the verification success rate is less than the standard success rate, the initial interaction model is retrained using the verification signal set to obtain an optimized interaction model. The optimized interaction model is then verified using the verification signal set to obtain the target success rate. Using the target success rate and the optimized interaction model, the verification success rate and the initial interaction model are updated respectively, and the step of determining whether the verification success rate is less than the preset standard success rate is returned. If the verification success rate is not less than the standard success rate, then the initial interaction model is denoted as the charging information interaction model.

8. The intelligent interaction method for charging pile charging information based on frequency division monitoring as described in claim 1, characterized in that, The intelligent interaction of charging information based on the charging information interaction model includes: Collect the real-time charging signal of the target charging pile, measure the real-time signal strength value of the real-time charging signal, optimize the charging frequency using a frequency adjustment mechanism based on the real-time signal strength value, obtain the real-time optimized frequency band, and record the real-time adjustment time of the frequency optimization. Based on the real-time optimized frequency band, the target charging pile is initially adjusted in frequency to obtain the initially adjusted charging pile. The real-time signal strength value, real-time optimized frequency band, and real-time adjustment time are input into the charging information interaction model to obtain the secondary optimized frequency band and secondary time difference. The system obtains the end time of the initial adjustment, waits for the second time difference based on the end time of the initial adjustment, and then performs a second frequency adjustment on the initially adjusted charging pile according to the second optimized frequency band to obtain a fully adjusted charging pile, thus completing the intelligent interaction of charging pile charging information.

9. A charging pile charging information intelligent interaction system based on frequency division monitoring, used to implement the charging pile charging information intelligent interaction method based on frequency division monitoring as described in claim 1, characterized in that, The system includes: The preliminary frequency adjustment module is used to set the charging frequency range, divide the charging data into frequency segments based on the charging frequency range to obtain a basic frequency set, obtain the current charging time, extract the target frequency band from the basic frequency set based on the current charging time, collect the charging signal to be tested from the target charging pile, measure the signal strength value of the charging signal to be tested, dynamically adjust the target frequency band based on the signal strength value to obtain an optimized frequency band, and record the frequency adjustment time when the target frequency band is adjusted. The abnormal behavior judgment module is used to determine whether a preset abnormal charging behavior has occurred. If an abnormal charging behavior occurs, a preset type of marker is used to mark the charging signal under test to obtain a type of marker group, the abnormal trigger time and abnormal signal characteristics corresponding to the abnormal charging behavior are obtained, and the time difference between the frequency adjustment time and the abnormal trigger time is calculated. The data acquisition module is used to perform key-value pairing on the first type of marker group using optimized frequency bands, abnormal signal characteristics, signal strength values, and time difference values ​​to obtain a first type of signal group. If no abnormal charging behavior occurs, the module uses preset second type markers to perform second type signal marking on the charging signal to be tested to obtain a second type of marker group. The module then performs key-value pairing on the second type of marker group using optimized frequency bands and signal strength values ​​to obtain a second type of signal group. Finally, the module fills the content of the pre-constructed initial signal group set with the first type of signal group or the second type of signal group to obtain a target signal group set and determines the original number of target signal groups in the target signal group set. The precise interaction control module is used to determine whether the number of original signal groups is less than the preset standard number of signal groups. If the number of original signal groups is less than the standard number of signal groups, the initial signal group set is updated using the target signal group set, and the step of obtaining the current charging time is returned. If the number of original signal groups is not less than the standard number of signal groups, a charging information interaction model is constructed based on the target signal group set using preset intelligent processing technology, and intelligent interaction of charging information of charging pile is performed based on the charging information interaction model.

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