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

Abstract

The invention relates to the technical field of new energy automobile charging, in particular to a charging pile charging information intelligent interaction method and system based on frequency division monitoring, and the method comprises the steps: setting a charging frequency interval, carrying out the frequency division to obtain a basic frequency set, dynamically adjusting a target frequency band into an optimized frequency band, and carrying out the frequency division. And a first-class or second-class signal group is generated according to the abnormal charging behavior, and a charging information interaction model is constructed to realize intelligent interaction. The real-time performance, the safety and the intelligent level of charging information interaction can be improved, the efficient information interaction requirement under the complex electromagnetic environment is met, and therefore the operation efficiency of the charging pile and the user experience are improved.

Description

Technical Field

[0001] This invention belongs to the field of new energy vehicle charging technology, specifically a method and system for intelligent interaction of charging information of charging piles based on frequency division monitoring. Background Technology

[0002] With the popularization of new energy vehicles, charging piles, as an important supporting facility for electric vehicles, have seen their information interaction technology gradually become a research hotspot. The intelligent interaction capabilities of charging piles with users, vehicles, and cloud systems directly affect charging efficiency, user experience, and system safety. However, existing charging pile information interaction methods still have shortcomings in terms of data monitoring accuracy, real-time performance, security, and intelligence level, making it difficult to meet the increasingly complex charging scenario requirements. For example, patent CN114125769B provides a charging pile information interaction method based on Wi-Fi signal grouping and sorting, achieving 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. Furthermore, this solution does not involve the application of frequency division monitoring technology, failing to effectively address information interaction needs in complex electromagnetic environments, potentially leading to data loss or delays and impacting user experience. In addition, patent CN111098746B achieves real-time information interaction during charging by setting a hardware switch between the charging pile and the electric vehicle, supporting charging power adjustment and fault analysis. However, this technical solution mainly relies on the design of hardware switches and lacks in-depth analysis and processing of the frequency characteristics of charging data, making it difficult to cope with multi-band signal interference or abnormal data fluctuations. Due to the lack of an intelligent processing mechanism based on frequency division monitoring, this solution may suffer from low information interaction efficiency or even safety hazards in complex charging environments. The shortcomings of the existing technology indicate that current charging pile information interaction methods still need improvement in terms of real-time monitoring, data processing accuracy, anti-interference capability, and intelligence level. Therefore, this invention proposes a charging pile charging information intelligent interaction method and system based on frequency division monitoring, aiming to achieve accurate collection and processing of charging data through frequency division monitoring technology, improving the real-time performance, security, and intelligence level of information interaction, thereby meeting the needs of new energy vehicle charging scenarios for efficient and reliable information interaction. Summary of the Invention

[0003] This invention provides a method and system for intelligent interaction of charging information in charging piles based on frequency division monitoring. Its main purpose is to improve the real-time performance, security and intelligence of charging information interaction, and to meet the demand for efficient information interaction in new energy vehicle charging scenarios.

[0004] To achieve the above objectives, the present invention provides an intelligent interaction method for charging pile charging information based on frequency division monitoring, comprising: 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.

[0005] Optionally, the step of dynamically adjusting the target frequency band based on the signal strength value 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.

[0006] Optionally, obtaining 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.

[0007] Optionally, the step of monitoring the charging signal of the target charging pile to obtain the target abnormal signal set and the target abnormal interval set 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.

[0008] Optionally, the step of using optimized frequency bands, abnormal signal characteristics, signal strength values, and time difference values ​​to perform key-value pairing on the first type of marker group to obtain a first type of signal group includes: 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.

[0009] Optionally, 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.

[0010] Optionally, the step of constructing a charging information interaction model based on the target signal set 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.

[0011] Optionally, 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.

[0012] Optionally, 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.

[0013] To achieve the above objectives, the present invention also provides an intelligent interactive system for charging pile charging information based on frequency division monitoring, comprising: 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.

[0014] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: Memory, storing at least one instruction; and The processor executes the instructions stored in the memory to implement the above-described intelligent interaction method for charging pile charging information based on frequency division monitoring.

[0015] To address the aforementioned issues, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned intelligent interaction method for charging pile charging information based on frequency division monitoring.

[0016] To address the problems described in the background section, this invention first establishes a charging frequency range and then divides the charging data into frequency segments based on this range to obtain a base frequency set. This achieves basic control over the charging frequency, giving it strong adaptability. Next, it acquires the current charging time and extracts a target frequency band from the base frequency set based on this time. It then collects the target charging pile's charging signal and measures its signal strength. Based on the signal strength, it dynamically adjusts the target frequency band to obtain an optimized frequency band and records the frequency adjustment time. This step completes the initial optimization of the charging frequency, reducing the burden of subsequent complex interactive calculations. By determining whether abnormal charging behavior occurs, changes in the charging pile's operating status can be monitored, indicating the charging pile's stability within the current frequency band. This improves the accuracy of charging information interaction. If abnormal charging behavior occurs, it indicates a potential risk in the current frequency band. The tested charging signal is then labeled with a signal type, resulting in a label group. This tags the abnormal charging behavior for subsequent intelligent processing. The system obtains the abnormal trigger time and abnormal signal characteristics corresponding to the abnormal charging behavior, calculates the time difference between the frequency adjustment time and the abnormal trigger time, and uses the optimized frequency band, abnormal signal characteristics, signal strength value, and time difference to perform key-value pairing on the label group, resulting in a signal group. The process involves acquiring content to further refine the first-class marker group, enriching the features used for subsequent intelligent processing. If no abnormal charging behavior occurs, it indicates that the current frequency band is stable. The charging signal under test is then labeled with a second-class signal, resulting in a second-class marker group. Key-value pairing of the second-class marker group is performed using optimized frequency bands and signal strength values ​​to obtain a second-class signal group, completing the representation of the second-class signal group. The first-class or second-class signal group is then used to fill the content of the initial signal group set, resulting in the target signal group set. The original number of target signal groups in the target signal group set is determined, completing the filling of the target signal group set. Simultaneously, acquiring a large number of target signal groups improves the accuracy of subsequent charging information interaction. By judging the original... Whether the number of signal groups is less than the standard number of signal groups determines whether enough target signal groups have been acquired for subsequent intelligent interactive control of the charging pile. If the original number of 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 process returns to the step of acquiring the current charging time, thus realizing the cyclic acquisition of the current signal groups. If the original number of 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 intelligent processing technology. Intelligent interaction of charging information is then performed based on this model, achieving intelligent processing of charging pile charging information and improving the interaction efficiency and safety of the charging pile in complex electromagnetic environments. Therefore, this invention can improve the real-time performance, security, and intelligence level of charging information interaction. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the intelligent interaction method for charging pile charging information based on frequency division monitoring provided by the present invention. Figure 2 This is a functional block diagram of a charging pile charging information intelligent interaction system based on frequency division monitoring provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device that implements the intelligent interaction method for charging pile charging information based on frequency division monitoring, according to an embodiment of the present invention. Detailed Implementation

[0018] This invention provides a method and system for intelligent interaction of charging information in charging piles based on frequency division monitoring. Its core lies in dynamically adjusting the charging frequency through frequency division monitoring technology, and combining it with abnormal behavior detection and intelligent processing technologies to construct an efficient charging information interaction model. The following description will be based on the accompanying drawings. Figures 1 to 3 The invention will be described in detail with reference to specific embodiments and implementation details.

[0019] First, such as Figure 1 The diagram shows a flowchart of an intelligent interaction method for charging pile charging information based on frequency division monitoring, according to an embodiment of the present invention. The first step of this method is to set a preset charging frequency range and then divide the charging data into frequency segments based on this range to obtain a base frequency set. In practical applications, the setting of the charging frequency range needs to comprehensively consider the working environment of the charging pile, the intensity of electromagnetic interference, and the charging needs of new energy vehicles. For example, in a typical implementation scenario, the charging frequency range can be set to 20kHz to 50kHz, and this range can be divided into several sub-ranges, each corresponding to a base frequency segment. These base frequency segments constitute the base frequency set, providing a basis for subsequent dynamic frequency adjustments.

[0020] Next, the current charging time is acquired, and the target frequency band is extracted from the base frequency set based on this time. This process requires real-time acquisition of the target charging pile's charging signal and measurement of its signal strength. Signal strength measurement is typically performed using dedicated signal acquisition equipment, such as high-precision current or voltage sensors. Assuming the target charging pile's signal strength is 4.5V in a given measurement, the standard signal strength value corresponding to the current charging time is retrieved from a preset signal strength reference table. Based on this, a frequency fluctuation coefficient α and a smooth transition coefficient β are set to regulate the flexibility and stability of frequency adjustment, respectively. Simultaneously, the highest frequency band is set based on preset maximum and minimum signal strength values. and lowest frequency band Finally, 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 the optimized frequency band. This dynamic adjustment process can be expressed by the following formula: ; in For the optimized frequency band, For the target frequency band, This represents the actual signal strength value. This is the standard signal strength value.

[0021] After frequency optimization is completed, the abnormal behavior judgment stage begins. For example... Figure 2 As shown, the abnormal behavior judgment module is used to determine whether a preset abnormal charging behavior has occurred. The judgment criteria for abnormal charging behavior can be flexibly set according to the actual application scenario. For example, when the charging current exceeds 120% of the rated value or the charging voltage fluctuation exceeds ±10%, it can be judged as abnormal charging behavior. If abnormal charging behavior is detected, a preset type of marker is used to mark the charging signal under test, resulting in a marker group. At the same time, the abnormal trigger time and abnormal signal characteristics corresponding to the abnormal charging behavior are obtained. The determination of the abnormal trigger time needs to be combined with real-time monitoring data, while the abnormal signal characteristics are obtained by analyzing and extracting the abnormal signal set. For example, in one embodiment, assuming that the abnormal charging behavior occurs at the 15th second, the abnormal signal characteristics at this time include the instantaneous peak value of the charging current reaching 60A and the fluctuation range of the charging voltage being ±15%. These characteristics will be recorded and used for subsequent key-value pairing operations.

[0022] For cases where no abnormal charging behavior occurs, the tested charging signal is labeled using preset Class II markers to obtain Class II marker groups. Subsequently, key-value pairing is performed on these Class II marker groups using optimized frequency bands and signal strength values ​​to obtain Class II signal groups. Regardless of whether the charging behavior is abnormal or normal, the generated signal groups ultimately need to be added to the initial signal group set to form the target signal group set. The original number of signal groups in the target signal group set needs to meet certain standard requirements to ensure the accuracy of subsequent intelligent processing. For example, in one embodiment, the standard number of signal groups is set to 1000. If the original number of signal groups is less than 1000, the process returns to the step of obtaining the current charging time and continues to collect new charging signal data until the conditions are met.

[0023] Once the number of original signal groups in the target signal set reaches or exceeds the standard number of signal groups, the intelligent processing stage begins. For example... Figure 3As shown, the precise interaction control module constructs a charging information interaction model based on the target signal set using preset intelligent processing technology. The selection of intelligent processing technology can be flexibly adjusted according to actual needs; for example, algorithms such as deep learning models, support vector machines, or random forests 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 validation signal set. The training signal set is used for model training to obtain the initial interaction model; the validation signal set is used for result validation to evaluate the model's performance. During the validation process, the validation signal set is extracted sequentially, and its validation anomaly signal characteristics and validation time difference are obtained. The value range of these characteristics is expanded according to the user's tolerance range to obtain the user-tolerance feature interval and the user-tolerance time difference interval. The validation signal set is input into the initial interaction model to obtain the secondary optimization frequency segment and the secondary time difference. If the secondary optimization frequency segment and the secondary time difference fall within the user-tolerance feature interval and the user-tolerance time difference interval, respectively, the validation signal set is recorded as a successful validation signal set; otherwise, it is recorded as a failed validation signal set. Finally, the verification success rate is calculated based on the number of successful and failed verification signal groups. If the verification success rate is lower than the preset standard success rate, the initial interaction model is retrained until the verification success rate reaches the standard. The final charging information interaction model will be used for intelligent interaction of charging pile charging information.

[0024] The process of intelligent interaction of charging information for 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 its real-time signal strength value is measured. Based on the real-time signal strength value, the charging frequency is optimized using a frequency adjustment mechanism to obtain a real-time optimized frequency band, and the real-time adjustment time of the frequency optimization is recorded. Subsequently, the target charging pile is initially adjusted according to the real-time optimized frequency band to obtain an initially adjusted charging pile. The real-time signal strength value, the real-time optimized frequency band, and the real-time adjustment time are input into the charging information interaction model to obtain a secondary optimized frequency band and a secondary time difference value. The end time of the initial adjustment is obtained, and after waiting for the secondary time difference value based on the end time of the initial adjustment, the initially adjusted charging pile is adjusted a second time according to the secondary optimized frequency band to finally obtain a fully adjusted charging pile, thus completing the intelligent interaction of charging information for the charging pile.

[0025] Throughout the implementation process, the various functional modules of the system work collaboratively to ensure the efficiency and intelligence of charging information interaction. For example, the initial frequency adjustment module is responsible for setting the charging frequency range and dividing the charging data into frequency groups, providing a foundation for subsequent frequency optimization; the abnormal behavior judgment module is responsible for detecting abnormal charging behavior and generating corresponding signal markers; the data set acquisition module is responsible for filling various signal groups into the initial signal set to form the target signal set; and the precise interaction control module is responsible for constructing the charging information interaction model and realizing intelligent interaction. Furthermore, this invention also provides an electronic device, including a memory and a processor. The memory stores at least one instruction, and the processor executes the instructions in the memory to implement the above-mentioned intelligent interaction method for charging pile charging information based on frequency division monitoring. This electronic device can be an embedded system, an industrial computer, or other hardware device with computing capabilities, and can be widely applied in new energy vehicle charging scenarios.

[0026] In summary, this invention achieves dynamic adjustment of charging frequency through frequency division monitoring technology, and constructs an efficient charging information interaction model by combining abnormal behavior detection and intelligent processing technologies. This method not only improves the real-time performance, security, and intelligence of charging information interaction, but also meets the demand for efficient information interaction in new energy vehicle charging scenarios, demonstrating high practical value and promising prospects for widespread adoption.

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.

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 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.

4. The intelligent interaction method for charging pile charging information based on frequency division monitoring as described in claim 3, 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.

5. 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 tag group yields a signal group, 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.

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 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.

7. 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.

8. The intelligent interaction method for charging pile charging information based on frequency division monitoring as described in claim 7, 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.

9. 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.

10. A charging pile charging information intelligent interaction system based on frequency division monitoring, 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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