Wireless communication system and communication method of charging pile

Abstract

The invention relates to the technical field of charging piles, and provides a wireless communication system and communication method for a charging pile, and the method comprises the steps: obtaining charging request information, carrying out the signal analysis of the charging request information, obtaining load balance data, carrying out the matching analysis of the current available power data of the charging pile based on the load balance data, and obtaining a wireless communication scheduling parameter; and generating signal interference information and signal intensity distribution data based on the wireless communication scheduling parameters, and constructing a wireless signal scheduling scheme by using the signal interference information and the signal intensity distribution data. According to a wireless signal scheduling scheme constructed through signal interference information and signal intensity distribution data, interference in the communication process is reduced, the stability and efficiency of signal transmission are improved, and the problem that the communication efficiency is greatly improved under the conditions of multi-device concurrent connection, real-time transmission of a large amount of data and complex charging is solved. The problems of limited data transmission efficiency, network congestion and difficult elimination of communication interference exist in the prior art.

Description

Technical Field

[0001] The present application relates to the technical field of charging piles, and in particular to a wireless communication system and a communication method for charging piles. Background Art

[0002] In recent years, with the rapid development of the new energy vehicle industry, the demand for charging infrastructure has also grown rapidly. As the core facility for electric vehicle energy replenishment, the intelligence, efficiency and reliability of charging piles have become an important direction for the development of industry technology. In this context, advanced wireless communication technology has gradually been introduced into the charging pile system to cope with the increasingly complex electric vehicle charging scenarios and user needs.

[0003] Among the relevant technical means, wireless communication technology has become one of the more mature solutions. Common methods include using Wi-Fi, Bluetooth or cellular communication technology. These technical means are mainly used to realize the identity authentication between users and charging piles, feedback of charging status information and preliminary remote management functions. For example, Wi-Fi communication can monitor the charging process in real time, Bluetooth communication is suitable for short-distance fast pairing and data transmission, and cellular communication can support a wide range of remote charging pile network management. The application of these technical means has improved the convenience and intelligence of charging operations to a certain extent, and provided users with a relatively stable basic service experience.

[0004] Regarding the above technical solutions, although the existing wireless communication technology can realize basic data interaction between users and charging piles and meet the charging scheduling needs to a certain extent, the existing technology still has problems such as limited data transmission efficiency, network congestion and communication interference that are difficult to eliminate in the case of multiple devices connected concurrently, large amounts of data transmitted in real time, and complex charging scenarios. These problems are more prominent during the peak period of charging demand, resulting in reduced charging efficiency or impaired user experience. Summary of the invention

[0005] In order to improve the problems of limited data transmission efficiency, network congestion and communication interference that are difficult to eliminate in scenarios where multiple devices are connected concurrently, large amounts of data are transmitted in real time, and complex charging, the present application provides a wireless communication system and a communication method for a charging pile.

[0006] The present application provides a wireless communication method for a charging pile, including: obtaining charging request information, performing signal analysis on the charging request information, and obtaining user identity data and vehicle charging demand data; performing pattern analysis on the user identity data to obtain a user charging behavior pattern, and performing pattern prediction analysis on the vehicle charging demand data using the user charging behavior pattern to obtain a charging power demand sequence; decomposing the charging power demand sequence to obtain load balancing data, and performing matching analysis on the current available power data of the charging pile based on the load balancing data to obtain wireless communication scheduling parameters; generating signal interference information and signal strength distribution data based on the wireless communication scheduling parameters, and constructing a wireless signal scheduling plan using the signal interference information and the signal strength distribution data.

[0007] As a preferred solution, the steps of obtaining charging request information, performing signal analysis on the charging request information, and obtaining user identity data and vehicle charging demand data include: obtaining current operating status information of the charging pile, screening the operating status information for environmental factors, and obtaining device status data and communication environment parameters; performing status identification on the device status data to obtain historical operating data, and correcting the communication environment parameters based on the historical operating data to obtain charging status evaluation data; collecting real-time load information of the charging pile to obtain charging device response capability data, and correcting the charging status evaluation data based on the charging device response capability data to obtain optimized charging status evaluation data; performing real-time signal enhancement processing on the obtained charging request information using the optimized charging status evaluation data to obtain enhanced charging request data, and performing signal analysis on the enhanced charging request data to obtain preliminary charging request data and request signal strength data; performing signal enhancement processing on the preliminary charging request data based on the request signal strength data to obtain optimized charging request information, and performing data analysis on the optimized charging request information to obtain user identity data and vehicle charging demand data.

[0008] As a preferred scheme, the steps of performing pattern analysis on the user identity data to obtain the user charging behavior pattern, performing pattern prediction analysis on the vehicle charging demand data using the user charging behavior pattern, and obtaining a charging power demand sequence include: classifying the user identity data to obtain an account type label, and screening historical transaction records based on the account type label to obtain historical charging transaction data and charging habit parameters; performing time series modeling on the charging habit parameters to obtain charging interval data, and performing pattern analysis on the historical charging transaction data using the charging interval data to obtain the user charging behavior pattern and charging time period preference; performing feature mapping on the vehicle charging demand data based on the user charging behavior pattern to obtain charging time deviation data, and adjusting the charging time deviation data using the charging time period preference to obtain charging power prediction data and charging load control parameters; performing fluctuation analysis on the charging power prediction data to obtain a long-term power demand trend, and correcting the charging load control parameters based on the long-term power demand trend to obtain a charging power demand sequence.

[0009] As a preferred scheme, the step of decomposing the charging power demand sequence to obtain load balancing data, matching and analyzing the current available power data of the charging pile based on the load balancing data, and obtaining wireless communication scheduling parameters includes: decomposing the charging power demand sequence to obtain dynamic power adjustment parameters, and calculating the load balancing data based on the dynamic power adjustment parameters; matching and analyzing the current available power data of the charging pile according to the load balancing data to obtain a charging power allocation scheme and a power regulation factor; dynamically adjusting the charging power allocation scheme using the power regulation factor to obtain a power stability parameter, analyzing the power stability parameter to obtain a communication adjustment strategy; calculating the wireless signal transmission adjustment parameter based on the communication adjustment strategy to obtain the wireless communication scheduling parameters.

[0010] As a preferred scheme, the step of matching and analyzing the current available power data of the charging pile according to the load balancing data to obtain a charging power allocation scheme and a power control factor includes: acquiring historical charging data of the charging pile, analyzing the historical charging data to obtain power allocation trend data and charging time distribution information, and using the power allocation trend data to perform correlation calculation on the load balancing data to obtain an adjustable power range; optimizing and analyzing the adjustable power range to obtain optimized power allocation data, and matching and analyzing the charging time distribution information using the optimized power allocation data to obtain a charging power allocation scheme; matching and analyzing the current available power data of the charging pile based on the charging power allocation scheme to obtain a power control factor.

[0011] As a preferred solution, the steps of generating signal interference information and signal strength distribution data based on the wireless communication scheduling parameters, and constructing a wireless signal scheduling scheme using the signal interference information and the signal strength distribution data include: inputting the wireless communication scheduling parameters into a preset wireless signal optimization model to obtain real-time wireless signal status data, performing feature extraction on the wireless signal status data to obtain signal interference information and signal strength distribution data; performing noise filtering on the response data of the charging request information using the signal interference information to obtain optimized response data and signal reliability parameters; classifying the optimized response data using the signal strength distribution data to obtain high priority response data and low priority response data, and performing transmission optimization on the high priority response data based on the signal reliability parameters to obtain a wireless signal transmission strategy; performing scheduling calculation on the low priority response data using the wireless signal transmission strategy to obtain dynamic scheduling parameters, and redistributing the high priority response data based on the dynamic scheduling parameters to obtain a wireless signal scheduling scheme.

[0012] As a preferred scheme, after the steps of generating signal interference information and signal strength distribution data based on the wireless communication scheduling parameters and constructing a wireless signal scheduling scheme using the signal interference information and the signal strength distribution data, it includes: performing channel state analysis on the wireless signal scheduling scheme to obtain channel interference information, optimizing the allocation of channel resources based on the channel interference information to obtain channel switching threshold data; adjusting the wireless signal transmission strategy using the channel switching threshold data to obtain a dynamic channel allocation strategy, integrating the dynamic channel allocation strategy with the wireless signal scheduling scheme to obtain a wireless communication data stream.

[0013] The present application also provides a wireless communication system for a charging pile, including: an acquisition module, used to acquire charging request information, perform signal analysis on the charging request information, and obtain user identity data and vehicle charging demand data; an analysis module, used to perform pattern analysis on the user identity data, obtain a user charging behavior pattern, and use the user charging behavior pattern to perform pattern prediction analysis on the vehicle charging demand data to obtain a charging power demand sequence; a decomposition module, used to decompose the charging power demand sequence to obtain load balancing data, and perform matching analysis on the current available power data of the charging pile based on the load balancing data to obtain wireless communication scheduling parameters; a construction module, used to generate signal interference information and signal strength distribution data based on the wireless communication scheduling parameters, and use the signal interference information and the signal strength distribution data to construct a wireless signal scheduling plan.

[0014] Compared with the prior art, the present application has the following beneficial effects: high flexibility and strong stability. By analyzing the user identity data and vehicle charging demand data, the charging power demand is predicted. By decomposing the charging power demand sequence and generating load balancing data, the power distribution efficiency of the charging pile is optimized. Then, through matching analysis and the generation of wireless communication scheduling parameters, the load balance of the large-scale charging pile network is ensured. The wireless signal scheduling scheme constructed based on signal interference information and signal strength distribution data reduces interference during communication, improves the stability and efficiency of signal transmission, and improves the problems of limited data transmission efficiency, network congestion and communication interference that are difficult to eliminate in the case of multiple devices connected concurrently, large amounts of data transmitted in real time, and complex charging scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0017] Figure 1 It is a flow chart of a wireless communication method for a charging pile provided by an embodiment of the present invention; Figure 2 It is a schematic block diagram of the structure of the wireless communication system of the charging pile provided in an embodiment of the present invention.

[0018] Description of reference numerals: 10. Wireless communication system of charging pile; 11. Acquisition module; 12. Analysis module; 13. Decomposition module; 14. Construction module. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0021] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0022] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0024] Embodiment 1: like Figure 1 As shown, the present application provides a wireless communication method for a charging pile, including steps S100 to S400.

[0025] Step S100: Acquire charging request information, perform signal analysis on the charging request information, and obtain user identity data and vehicle charging demand data.

[0026] In this step, the charging request information is received through the wireless communication module, which contains the original signal of the user identity data and the vehicle charging demand data. Specifically, the received original signal is processed using the signal parsing algorithm to extract the user identity data (such as user ID, account information) and the vehicle charging demand data (such as the required current, voltage and estimated charging time). In addition, the integrity and accuracy of the parsed data are ensured through the data verification mechanism.

[0027] For example, when a user initiates a charging request using a smartphone or vehicle-mounted device, the wireless communication module will receive an encrypted signal containing user identity data and vehicle charging demand data, and decrypt the specific information through analysis for use in subsequent steps.

[0028] Step S200: Perform pattern analysis on user identity data to obtain user charging behavior patterns, and use the user charging behavior patterns to perform pattern prediction analysis on vehicle charging demand data to obtain a charging power demand sequence.

[0029] In this step, the user's historical charging data is used to perform statistical analysis on the user's identity data to mine their charging behavior patterns. Specifically, the user's charging behavior characteristics (such as charging frequency and time preference) are established through pattern recognition algorithms, and combined with the historical change trend of vehicle charging demand data, their future charging power demand is predicted to obtain the charging power demand sequence.

[0030] For example, users usually charge their vehicles between 10 pm and 2 am, and the historical charging power demand data of their vehicles shows a certain periodicity. Therefore, the charging power demand sequence obtained through analysis and prediction can more accurately reflect the actual needs of users.

[0031] Step S300: Decompose the charging power demand sequence to obtain load balancing data, and perform matching analysis on the current available power data of the charging pile based on the load balancing data to obtain wireless communication scheduling parameters.

[0032] In this step, the charging power demand sequence is decomposed, the charging power distribution in each time period is determined, and load balancing data is generated to optimize power scheduling. Specifically, combined with the available power data of the current charging pile, the charging scheduling strategy is dynamically adjusted through the matching analysis algorithm to generate efficient wireless communication scheduling parameters to guide the subsequent signal transmission process.

[0033] For example, during the peak charging period, the current available power of a charging pile is 50kW. Through matching analysis, the charging power demand can be decomposed into two parts, 30kW and 20kW, and allocated to different vehicles respectively to ensure load balance and efficient utilization.

[0034] Step S400: Generate signal interference information and signal strength distribution data based on wireless communication scheduling parameters, and construct a wireless signal scheduling solution using the signal interference information and signal strength distribution data.

[0035] In this step, the signal interference distribution in the area where the charging pile is located is calculated according to the wireless communication scheduling parameters, the signal interference information is generated, and the signal strength distribution is analyzed to optimize the wireless communication coverage. Specifically, by combining the signal interference information and the signal strength distribution data, a wireless signal scheduling scheme suitable for the current environment is designed to minimize communication interference and improve signal transmission quality.

[0036] For example, in an area with dense charging networks, by measuring signal strength and interference, higher priority communication resources can be allocated to users with weaker signals to ensure communication stability.

[0037] In this embodiment, by acquiring charging request information and performing signal analysis on the charging request information, user identity data and vehicle charging demand data are extracted therefrom. Subsequently, the user identity data is subjected to pattern analysis to form a user charging behavior pattern, and the user charging behavior pattern is used to perform pattern prediction analysis on the vehicle charging demand data to obtain a charging power demand sequence. Next, the charging power demand sequence is decomposed to obtain load balancing data, and matching analysis is performed on the current available power data of the charging pile based on the load balancing data to generate wireless communication scheduling parameters. Finally, signal interference information and signal strength distribution data are generated through wireless communication scheduling parameters to construct a wireless signal scheduling scheme, thereby realizing efficient wireless communication scheduling of charging piles. In-depth analysis and prediction of user identity data and vehicle charging demand data are realized, and then the available power data of the charging pile is efficiently matched to improve the accuracy and real-time performance of communication scheduling, and improve the problems of limited data transmission efficiency, network congestion and communication interference that are difficult to eliminate in the case of concurrent connection of multiple devices, real-time transmission of a large amount of data and complex charging scenarios. At the same time, the wireless signal scheduling scheme constructed using signal interference information and signal strength distribution data can effectively reduce signal interference problems and improve the stability of wireless communication. It can not only meet the concurrent needs of multiple devices in complex scenarios, but also optimize the load balancing of the charging pile network and greatly improve the service quality during user peak hours.

[0038] Embodiment 2: In step S100, the current operating status information of the charging pile is obtained, and the operating status information is screened for environmental factors to obtain device status data and communication environment parameters.

[0039] The current operating status information is obtained through the multiple sensors (such as temperature sensors, current sensors and voltage sensors) built into the charging pile and its communication module, including temperature information, power consumption data, real-time voltage and current parameters, etc. Specifically, the environmental data screening algorithm is used to filter the acquired operating status information, remove the secondary information not related to communication, and extract the device status data and communication environment parameters that have a greater impact on communication performance. The device status data includes the current status of the hardware components of the charging pile, such as device temperature, power load conditions and hardware fault marks, and the communication environment parameters include key data such as wireless signal strength, surrounding interference source strength and network available bandwidth.

[0040] For example, when the temperature of a charging pile exceeds the safe operation threshold (such as 60°C), the information will be marked as abnormal data in the device status data; at the same time, if the strength of the wireless signal is lower than the set signal quality standard (such as 30dBm), it will be extracted as an interference factor in the communication environment parameters for subsequent optimization processing.

[0041] The device status data is identified to obtain historical operation data, and the communication environment parameters are corrected based on the historical operation data to obtain charging status evaluation data.

[0042] By inputting the device status data into the status identification module, the device status data is comprehensively identified in combination with the pre-stored historical data, key operating indicators are extracted, and the level or risk level of the current device status is determined. Specifically, using the historical operating data as a comparison benchmark, the communication environment parameters are dynamically corrected through feature matching and trend analysis methods to minimize the impact of random factors in the environment on the data, and the charging status evaluation data is generated. The evaluation data presents in detail in a structured form whether the device is currently suitable for continued efficient operation or requires additional maintenance operations.

[0043] For example, if it is identified that the temperature value in the device status data is significantly higher than the historical average, and the environmental parameters indicate that the wireless signal interference has increased, the revised charging status assessment data will indicate that the device needs to reduce the current power output to avoid the risk of overheating, and recommend switching to a more suitable communication frequency band to reduce interference.

[0044] The real-time load information of the charging pile is collected to obtain the charging equipment response capability data, and the charging state evaluation data is corrected based on the charging equipment response capability data to obtain the optimized charging state evaluation data.

[0045] By connecting to the real-time load monitoring module of the charging pile, the real-time data of the current processing load is collected, including the number of connected electric vehicles, the charging power transmission per unit time, and the efficiency of charging task execution. Specifically, according to the actual response capability of the charging equipment, the relevant content in the charging status evaluation data is dynamically adjusted to ensure more accurate prediction of the equipment operation status and evaluation of the communication environment, and generate optimized charging status evaluation data.

[0046] For example, during peak charging hours, a charging pile is connected to five electric vehicles for charging at the same time. Its real-time load information shows that the current response time is 15% higher than usual. The system corrects the charging status assessment data accordingly and classifies its load handling capacity as "high". It also recommends queuing or directing new charging requests that will be connected in the future to other charging piles with lighter loads.

[0047] The acquired charging request information is subjected to real-time signal enhancement processing using the optimized charging state assessment data to obtain enhanced charging request data, and the enhanced charging request data is subjected to signal analysis to obtain preliminary charging request data and request signal strength data.

[0048] By combining the optimized charging status assessment data with the acquired charging request information, the dynamic signal enhancement algorithm is used to perform real-time enhancement processing on the signal characteristics (such as signal strength, frequency stability, etc.) in the charging request information, ensuring that the signal has higher anti-interference ability and transmission efficiency during transmission. Specifically, the real-time signal enhancement processing includes filtering compensation for the interference frequency appearing in the signal packet, multi-dimensional gain optimization of the signal strength, and generating enhanced charging request data after the enhancement processing. Then, the enhanced charging request data is decoded and semantically disassembled through the built-in parsing module to extract key information such as preliminary charging request data (including user charging intention and estimated charging time) and request signal strength data (a quantitative indicator of signal transmission quality).

[0049] For example, when a user sends a charging request message through the wireless communication module, the signal strength is weak due to the long distance. The optimized charging status assessment data is used to compensate for the signal attenuation, and the interference frequency part is calibrated and enhanced in real time. The generated enhanced charging request data obtains clear preliminary charging request data (such as the required power is 30kW, the charging time is 2 hours) and request signal strength data (such as the signal strength is 40dBm) after signal analysis.

[0050] Based on the request signal strength data, signal enhancement processing is performed on the preliminary charging request data to obtain optimized charging request information, and data analysis is performed on the optimized charging request information to obtain user identity data and vehicle charging demand data.

[0051] By using the request signal strength data to re-optimize the specific communication parameters (such as data packet integrity, data transmission speed, etc.) in the preliminary charging request data, the stability and accuracy of data transmission are enhanced, and the optimized charging request information is generated. Specifically, in this process, the missing or incomplete fields in the charging request information are supplemented and corrected, and the optimized charging request information is secondary parsed using an efficient data parsing algorithm, and finally the user identity data (such as user ID, account information) and vehicle charging demand data (such as target battery power, charging mode) are completely extracted.

[0052] For example, when parsing the preliminary data of the charging request, it was found that some data fields were missing due to insufficient signal quality (such as an account identification code that was not fully transmitted). These fields were supplemented after requesting signal strength data to optimize the transmission, and finally an optimized request information containing complete user identity data (user ID is "12345", account is "standard account") and vehicle charging demand data (target charging power is 20kW, mode is fast charging) was generated.

[0053] In step S200, the user identity data is classified to obtain an account type label, and the historical transaction records are screened based on the account type label to obtain historical charging transaction data and charging habit parameters.

[0054] The user identity data parsing module performs structured processing on the acquired user identity data, classifies it according to predefined classification standards (such as account type, user priority, and frequency of use), and generates clear account type labels. Specifically, the account type label is used to associate the user's historical transaction records, from which historical charging transaction data related to charging is screened out. At the same time, charging habit parameters are extracted based on behavioral characteristics such as charging time intervals and charging location selection reflected in the transaction records for subsequent analysis.

[0055] For example, a user is classified as a "VIP account type", and his historical transaction records show that he usually charges between 8 and 10 p.m., and his preferred charging location is "home charging station". The historical charging transaction data selected based on this includes specific charging time periods, power requirements, etc., and the charging habit parameters include structured information such as common time periods and common locations.

[0056] The charging habit parameters are modeled in time series to obtain the charging interval data. The charging interval data is used to perform pattern analysis on the historical charging transaction data to obtain the user's charging behavior pattern and charging period preference.

[0057] The charging habit parameters are modeled through time series analysis tools to explore the regularity of user charging behaviors, such as the average charging time interval, the most common charging duration, etc., and generate charging interval data. Specifically, deep pattern analysis is performed in combination with historical charging transaction data to identify the charging behavior characteristics of users in different time periods, such as the commonly used charging time range and charging frequency, and thus construct the user's charging behavior pattern and summarize the user's charging time preferences.

[0058] For example, the charging interval data of a user shows that he charges once every three days on average, and each charging lasts for 1.5 hours. Through pattern analysis, it can be found that the charging behavior pattern of this user is "frequent night charging" and the preferred time period is "8pm to 11pm".

[0059] Based on the user's charging behavior pattern, the vehicle charging demand data is feature mapped to obtain the charging time deviation data. The charging time deviation data is adjusted using the charging period preference to obtain the charging power prediction data and charging load control parameters.

[0060] By mapping the analysis results of the user's charging behavior pattern with the vehicle charging demand data, the time and power requirements in the actual vehicle demand are matched with the user's preferences to generate charging time deviation data (reflecting the difference between the user's planned charging time and the optimal charging time). Specifically, these deviation data are optimized and adjusted in combination with the user's charging period preferences to ensure that the generated charging power prediction data is more in line with the user's actual needs; at the same time, by evaluating the power distribution, the charging load control parameters are extracted to optimize the overall charging scheduling.

[0061] For example, if a user's charging behavior pattern is charging at night and the vehicle charging demand data indicates charging in the afternoon, the charging time deviation data will indicate a mismatch. After adjusting the charging period preference, the generated charging power forecast data recommends that the user postpone charging to night time, and generates charging load control parameters to allocate appropriate power output.

[0062] Fluctuation analysis is performed on the charging power forecast data to obtain the long-term power demand trend, and the charging load control parameters are corrected based on the long-term power demand trend to obtain the charging power demand sequence.

[0063] By performing statistical fluctuation analysis on charging power forecast data, the law and abnormal values ​​of power demand changes over time are identified, and long-term power demand trends are generated to describe the changes in power demand of users in the future. Specifically, the long-term trend data is used to further correct the charging load control parameters to ensure that the power allocation plan takes into account the balance of long-term and short-term demand, and finally generate a charging power demand sequence to guide the power scheduling strategy of charging piles.

[0064] For example, after analyzing the charging power forecast data of a certain user, it was found that the user's power demand was higher in the first week of each month, and its long-term power demand trend indicated that similar peak demand would occur in the future. By predicting the trend, the charging load control parameter was adjusted to "peak time priority allocation" to ensure the service quality during the high power demand period, and the generated charging power demand sequence accurately reflected the power allocation plan.

[0065] In step S300, the charging power demand sequence is decomposed to obtain dynamic power adjustment parameters, and load balancing data is calculated based on the dynamic power adjustment parameters.

[0066] By inputting the charging power demand sequence into the power decomposition module, the power demand in the sequence is refined using the decomposition algorithm to determine the specific power demand of each time segment, thereby generating dynamic power adjustment parameters. Specifically, combined with the real-time operating parameters and historical load data of the charging pile, the load distribution in different time periods is balanced and calculated using dynamic power adjustment parameters to generate load balancing data. The load balancing data includes the power allocation priority and power adjustment strategy for each time period, which is used to guide the dynamic allocation of power resources.

[0067] For example, during peak charging periods for users, the charging power demand sequence shows a surge in demand over a period of time. The system generates dynamic power adjustment parameters by decomposition, indicating that key user needs should be met first, and sets the load balancing data to "high power priority allocation" to ensure efficient resource utilization.

[0068] According to the load balancing data, the current available power data of the charging pile is matched and analyzed to obtain the charging power allocation plan and power regulation factor.

[0069] The matching analysis algorithm integrates and analyzes the load balancing data and the current available power data of the charging pile to find the power supply and demand matching point and generate the optimal charging power allocation plan. Specifically, the available power and workload of each charging pile are evaluated during the analysis process, and the power allocation ratio is dynamically adjusted according to the power regulation factor to maximize charging efficiency and load balance. The power regulation factor is mainly set according to the power dynamic adjustment parameters and device status, including power allocation priority, adjustment sensitivity, etc.

[0070] For example, there are 5 charging piles in a certain area, and the system detects that the current available power is 50kW, 40kW, 30kW, etc. Through matching analysis, high-priority users are assigned to charging piles with sufficient power, and power control factors are generated to adjust the allocation flexibility of low-power charging piles.

[0071] The power regulation factor is used to dynamically adjust the charging power allocation scheme to obtain the power stability parameter. The power stability parameter is analyzed to obtain the communication adjustment strategy.

[0072] By dynamically adjusting the power control factor under real-time monitoring, the execution effect of the power allocation scheme is optimized, and the power stability parameter is generated to reflect the stability of the current power allocation. Specifically, the fluctuation and deviation of the power stability parameter are analyzed to identify the factors affecting the balance of power allocation, and a communication adjustment strategy is formed based on the analysis results to optimize the wireless communication configuration related to power allocation, such as signal priority scheduling, communication frequency band adjustment, etc.

[0073] For example, if a charging pile has an increased response delay due to network interference, the power stability parameter will show a decrease in stability. The communication adjustment strategy will recommend switching to a low-interference communication frequency band and optimize the user connection signal to ensure the effective implementation of overall power distribution.

[0074] The wireless signal transmission adjustment parameters are calculated based on the communication adjustment strategy to obtain the wireless communication scheduling parameters.

[0075] By using the communication adjustment strategy to evaluate the current wireless signal status and network resource allocation, the signal optimization algorithm is used to calculate the wireless signal transmission adjustment parameters to ensure low latency and high stability of signal transmission. Specifically, the wireless signal transmission adjustment parameters mainly include frequency allocation priority, interference elimination strategy and transmission rate adjustment value. Through the comprehensive optimization of these parameters, the final wireless communication scheduling parameters are generated to guide the efficient scheduling of the charging pile communication network.

[0076] For example, when the signal strength in a certain area is uneven and the communication quality of some users decreases, the system will prioritize the signals of low-quality users by calculating the wireless signal transmission adjustment parameters, and the generated wireless communication scheduling parameters can optimize the signal allocation strategy in real time to improve the overall service quality.

[0077] Among them, the steps of matching and analyzing the current available power data of the charging pile according to the load balancing data to obtain the charging power distribution plan and the power control factor include: obtaining the historical charging data of the charging pile, analyzing the historical charging data to obtain the power distribution trend data and the charging time distribution information, and using the power distribution trend data to correlate the load balancing data to obtain the power adjustable range.

[0078] By comprehensively sorting out the historical charging data of the charging piles, using the trend analysis algorithm to extract the time series characteristics of power distribution, and combining the dynamic needs of the load balancing data to perform correlation calculations on the power distribution capacity, the current power adjustable range is determined. Specifically, the power adjustable range reflects the maximum and minimum power adjustment values ​​in different time periods to balance load distribution and resource utilization.

[0079] For example, historical data of a charging pile shows that its maximum power allocation capacity during peak hours is 70kW. Calculation shows that the adjustable range is 50kW to 70kW, and the system will dynamically adjust the power according to this range.

[0080] The power adjustable range is optimized and analyzed to obtain optimized power allocation data, and the charging time distribution information is matched and analyzed using the optimized power allocation data to obtain a charging power allocation plan.

[0081] By further optimizing and analyzing the adjustable power range in combination with the current load conditions, a more efficient power allocation strategy can be extracted. Specifically, the optimized power allocation data is combined with the charging time distribution information to generate an accurate charging power allocation plan to ensure maximum power efficiency and optimized user experience.

[0082] For example, through optimization analysis, it was found that the utilization rate of a certain charging pile during the morning peak period was insufficient. The system recommended adjusting the power distribution to improve efficiency. The final distribution plan was "increase power by 20% during peak hours and maintain the status quo during other periods."

[0083] Based on the charging power allocation scheme, the current available power data of the charging pile is matched and analyzed to obtain the power regulation factor.

[0084] By matching the optimized power allocation scheme with real-time power data, the key parameters guiding dynamic power adjustment, namely the power regulation factor, are extracted. Specifically, the power regulation factor is used to adjust the power output strategy in real time and dynamically balance the priority and sensitivity of different load demands.

[0085] For example, when the available power of the charging pile is lower than a certain critical value (such as 30kW), the power regulation factor will trigger the reduction of the allocated power for low-priority users, thereby ensuring that the needs of high-priority users are met first.

[0086] In step S400, the wireless communication scheduling parameters are input into a preset wireless signal optimization model to obtain real-time wireless signal status data, and feature extraction is performed on the wireless signal status data to obtain signal interference information and signal strength distribution data.

[0087] By loading the wireless communication scheduling parameters into the preset wireless signal optimization model, the model simulates and analyzes the wireless signal status of the current area according to the environmental parameters and input scheduling parameters, and generates wireless signal status data in real time. Specifically, the model is used to extract multi-dimensional features of signal parameters, from which signal interference information (such as interference frequency, channel occupancy) and signal strength distribution data (such as signal coverage and strong and weak distinction) are separated. These data provide key basis for the subsequent signal tuning process.

[0088] For example, within the range of a charging station, the wireless signal optimization model detected that the interference signal strength in the 2.4GHz band was 60dBm, affecting the communication stability of this band. At the same time, the signal strength distribution data showed that the weak coverage area of ​​the signal was mainly concentrated on the south side of the station. These results can guide further signal optimization processing.

[0089] The signal interference information is used to filter the noise of the response data of the charging request information to obtain the optimized response data and signal reliability parameters.

[0090] By using signal interference information to perform digital noise processing on the response data, an adaptive filtering algorithm is used to eliminate high-frequency interference and pseudo-signal components in the data, thereby improving the integrity and accuracy of the data. Specifically, during the noise filtering process, the signal attenuation and distortion during the transmission process are measured and corrected in real time, and signal reliability parameters are generated as a basis for evaluating the quality of response data transmission after optimization.

[0091] For example, the charging request data sent by a user was partially damaged due to interference from the surrounding Wi-Fi signals. By filtering the noise of the response data, the lost data fields were repaired. The optimized response data was complete and correct, and the signal reliability parameter was evaluated as 98%.

[0092] The optimized response data is classified using the signal strength distribution data to obtain high priority response data and low priority response data, and the transmission of the high priority response data is optimized based on the signal reliability parameter to obtain a wireless signal transmission strategy.

[0093] The optimized response data is classified item by item through the signal strength distribution data, and the data is divided into high-priority and low-priority response data using priority classification rules (such as user identity type, request urgency, etc.). Specifically, for high-priority response data, its transmission path, frequency band and rate are adjusted in combination with signal reliability parameters, and a wireless signal transmission strategy is generated to ensure that high-priority data can be transmitted quickly and without errors.

[0094] For example, the charging request data of VIP users is classified as high-priority response data. During the transmission optimization process, the system allocates the 5GHz frequency band with the least interference to the data, and adjusts the transmission rate to 300Mbps to ensure stable communication quality.

[0095] The low priority response data is scheduled and calculated using the wireless signal transmission strategy to obtain dynamic scheduling parameters, and the high priority response data is reallocated based on the dynamic scheduling parameters to obtain a wireless signal scheduling solution.

[0096] By applying the wireless signal transmission strategy to the scheduling of low-priority response data, network resources are dynamically allocated to balance the transmission requirements of low-priority data and the timeliness of high-priority data. Specifically, the transmission resources of high-priority response data are reallocated using dynamic scheduling parameters to generate the final wireless signal scheduling solution, ensuring the reasonable scheduling and efficient transmission of data of different priorities.

[0097] For example, during peak hours, the total bandwidth of a charging station is limited to 500Mbps. The system allocates 100Mbps of remaining bandwidth to low-priority data through dynamic scheduling parameters, and prioritizes high-priority data to occupy 400Mbps bandwidth, thereby maximizing overall transmission efficiency.

[0098] After step S400, the method further includes performing a channel state analysis on the wireless signal scheduling scheme to obtain channel interference information, optimizing the allocation of channel resources based on the channel interference information, and obtaining channel switching threshold data.

[0099] The channel analysis module is used to conduct an in-depth channel status analysis of the wireless signal scheduling scheme, and identify the signal characteristics of the currently used channel, including key indicators such as channel utilization, signal interference strength, and signal transmission quality. Specifically, the channel interference strength is calculated through the interference elimination model based on the historical channel usage and real-time signal status data, and converted into quantifiable channel interference information. This information is used to optimize the allocation of current channel resources and generate channel switching threshold data to determine whether dynamic channel switching is required to improve communication efficiency and reliability.

[0100] For example, in a charging network in a certain area, channel status analysis shows that the interference intensity of the 2.4GHz band reaches 70%, while the interference intensity of the 5GHz band is 30%. Based on this, the system generates channel switching threshold data and determines that when the interference intensity exceeds 50%, it should prioritize switching to the low-interference frequency band to optimize communication resource allocation.

[0101] The wireless signal transmission strategy is adjusted by using the channel switching threshold data to obtain a dynamic channel allocation strategy, which is then integrated with the wireless signal scheduling scheme to obtain a wireless communication data stream.

[0102] By combining the channel switching threshold data with the current wireless signal transmission strategy, the signal transmission path and channel allocation priority are dynamically adjusted to generate a dynamic channel allocation strategy. Specifically, the dynamic channel allocation strategy optimizes the signal transmission frequency band and bandwidth allocation according to the real-time channel status and interference level, and integrates the optimized strategy with the wireless signal scheduling scheme to form an efficient wireless communication data stream. This data stream can achieve stable multi-user concurrent communication in a complex environment and improve the overall communication quality.

[0103] For example, during the channel allocation process, the system detected that a large number of users were connected to a charging pile, causing congestion in the original channel frequency band. The dynamic channel allocation strategy released the main channel resources by switching some low-priority users to the backup channel, and applied the optimized wireless signal scheduling scheme to high-priority users. The wireless communication data stream finally generated ensured the stable operation of the charging network.

[0104] In this embodiment, by comprehensively acquiring the operation status information of the charging pile and screening the environmental factors, the accurate extraction of the device status data and the communication environment parameters is realized, and the charging status evaluation data is optimized by state identification and historical operation data correction. The evaluation data is further adjusted using the real-time load information of the charging pile to accurately reflect the device response capability and operation status. Through the optimized charging status evaluation data, the acquired charging request information is subjected to signal enhancement processing to generate enhanced charging request data, and the charging request preliminary data and request signal strength data are obtained through multi-layer analysis to ensure the reliability of data transmission. Combined with the priority of the charging request, the channel scheduling and resource allocation are dynamically optimized to improve the transmission efficiency of high-priority response data and balance the resource usage of low-priority data. Based on the analysis and modeling of the user's historical charging transaction data and charging habit parameters, the charging power prediction data and charging load control parameters are generated, and the power demand sequence is further calculated. Through the decomposition of the power demand sequence and the dynamic adjustment of the load balancing data, a reasonable power allocation scheme and power control factor are formulated, and the stability of the power allocation is deeply analyzed to generate a communication adjustment strategy. Combined with the wireless signal optimization model, it can identify real-time channel interference and calculate the channel switching threshold, dynamically adjust the wireless signal transmission strategy, and finally generate efficient wireless communication data streams through the integration of channel resource optimization and signal scheduling strategy. It effectively improves the communication stability and load balancing capabilities of the charging pile network and optimizes the user experience in high-concurrency scenarios.

[0105] Embodiment 3: like Figure 2 As shown, the present application also provides a wireless communication system 10 for a charging pile, including an acquisition module 11, an analysis module 12, a decomposition module 13 and a construction module 14.

[0106] The acquisition module 11 is mainly used to acquire charging request information, perform signal analysis on the charging request information, and obtain user identity data and vehicle charging demand data.

[0107] The analysis module 12 is mainly used to perform pattern analysis on user identity data to obtain user charging behavior patterns, and use the user charging behavior patterns to perform pattern prediction analysis on vehicle charging demand data to obtain a charging power demand sequence.

[0108] The decomposition module 13 is mainly used to decompose the charging power demand sequence to obtain load balancing data, and to match and analyze the current available power data of the charging pile based on the load balancing data to obtain wireless communication scheduling parameters.

[0109] The construction module 14 is mainly used to generate signal interference information and signal strength distribution data based on wireless communication scheduling parameters, and to construct a wireless signal scheduling plan using the signal interference information and signal strength distribution data.

[0110] In this embodiment, the acquisition module 11 receives and analyzes the charging request information, accurately extracts the user identity data and the vehicle charging demand data, and lays the foundation for subsequent analysis and scheduling. The analysis module 12 is used to conduct in-depth pattern analysis on the user identity data to form a user charging behavior pattern, and applies it to the prediction analysis of the vehicle charging demand data to generate a charging power demand sequence, thereby realizing the dynamic prediction of the charging demand. The decomposition module 13 refines and decomposes the charging power demand sequence to generate load balancing data, and combines the current available power data of the charging pile for intelligent matching analysis, outputs the wireless communication scheduling parameters, and provides a scientific basis for power allocation. With the help of the construction module 14, signal interference information and signal strength distribution data are generated based on the wireless communication scheduling parameters, and the stability and transmission quality of the wireless signal are effectively improved by optimizing the signal scheduling scheme. Through the synergy of the four modules of acquisition, analysis, decomposition and construction, this system can not only meet the complex needs of multi-user concurrent scenarios, but also dynamically optimize the communication and power scheduling efficiency of the charging pile, thereby significantly improving the service quality and user experience of the overall charging network.

[0111] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned embodiment 1, and will not be repeated here.

[0112] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0113] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wireless communication method for a charging pile, characterized in that: include: Acquire charging request information, perform signal analysis on the charging request information, and obtain user identity data and vehicle charging demand data; Performing pattern analysis on the user identity data to obtain a user charging behavior pattern, and performing pattern prediction analysis on the vehicle charging demand data using the user charging behavior pattern to obtain a charging power demand sequence; Decomposing the charging power demand sequence to obtain load balancing data, and performing matching analysis on the current available power data of the charging pile based on the load balancing data to obtain wireless communication scheduling parameters; Signal interference information and signal strength distribution data are generated based on the wireless communication scheduling parameters, and a wireless signal scheduling scheme is constructed using the signal interference information and the signal strength distribution data.

2. The wireless communication method for a charging pile according to claim 1, characterized in that: The step of acquiring charging request information, performing signal analysis on the charging request information, and obtaining user identity data and vehicle charging demand data includes: Obtain the current operating status information of the charging pile, filter the operating status information by environmental factors, and obtain device status data and communication environment parameters; Performing status identification on the device status data to obtain historical operation data, and correcting the communication environment parameters based on the historical operation data to obtain charging status evaluation data; Collecting real-time load information of the charging pile to obtain charging device response capability data, and correcting the charging state evaluation data based on the charging device response capability data to obtain optimized charging state evaluation data; Using the optimized charging state evaluation data, the acquired charging request information is subjected to real-time signal enhancement processing to obtain enhanced charging request data, and the enhanced charging request data is subjected to signal analysis to obtain preliminary charging request data and request signal strength data; The preliminary charging request data is subjected to signal enhancement processing based on the request signal strength data to obtain optimized charging request information, and the optimized charging request information is subjected to data analysis to obtain user identity data and vehicle charging demand data.

3. The wireless communication method for a charging pile according to claim 1, characterized in that: The step of performing pattern analysis on the user identity data to obtain a user charging behavior pattern, and performing pattern prediction analysis on the vehicle charging demand data using the user charging behavior pattern to obtain a charging power demand sequence includes: Classify the user identity data to obtain an account type label, and filter historical transaction records based on the account type label to obtain historical charging transaction data and charging habit parameters; Performing time series modeling on the charging habit parameters to obtain charging interval data, and using the charging interval data to perform pattern analysis on the historical charging transaction data to obtain the user's charging behavior pattern and charging time period preference; Based on the user charging behavior pattern, feature mapping is performed on the vehicle charging demand data to obtain charging time deviation data, and the charging time deviation data is adjusted using the charging period preference to obtain charging power prediction data and charging load control parameters; Fluctuation analysis is performed on the charging power prediction data to obtain a long-term power demand trend, and the charging load control parameters are corrected based on the long-term power demand trend to obtain a charging power demand sequence.

4. The wireless communication method for a charging pile according to claim 1, characterized in that: The step of decomposing the charging power demand sequence to obtain load balancing data, and performing matching analysis on the current available power data of the charging pile based on the load balancing data to obtain the wireless communication scheduling parameters includes: Decomposing the charging power demand sequence to obtain dynamic power adjustment parameters, and calculating load balancing data based on the dynamic power adjustment parameters; According to the load balancing data, the current available power data of the charging pile is matched and analyzed to obtain a charging power allocation scheme and a power regulation factor; Dynamically adjusting the charging power allocation scheme by using the power regulation factor to obtain a power stability parameter, and analyzing the power stability parameter to obtain a communication adjustment strategy; The wireless signal transmission adjustment parameters are calculated based on the communication adjustment strategy to obtain the wireless communication scheduling parameters.

5. The wireless communication method for a charging pile according to claim 4, characterized in that: The step of matching and analyzing the current available power data of the charging pile according to the load balancing data to obtain a charging power allocation scheme and a power regulation factor includes: Acquire historical charging data of the charging pile, analyze the historical charging data to obtain power distribution trend data and charging time distribution information, and use the power distribution trend data to perform correlation calculation on the load balance data to obtain a power adjustable range; Optimizing and analyzing the power adjustable range to obtain optimized power allocation data, and using the optimized power allocation data to match and analyze the charging time distribution information to obtain a charging power allocation plan; Based on the charging power allocation scheme, a matching analysis is performed on the current available power data of the charging pile to obtain a power regulation factor.

6. The wireless communication method for a charging pile according to claim 1, characterized in that: The step of generating signal interference information and signal strength distribution data based on the wireless communication scheduling parameters, and constructing a wireless signal scheduling solution using the signal interference information and the signal strength distribution data, comprises: Inputting the wireless communication scheduling parameters into a preset wireless signal optimization model to obtain real-time wireless signal status data, performing feature extraction on the wireless signal status data to obtain signal interference information and signal strength distribution data; Using the signal interference information, noise filtering is performed on the response data of the charging request information to obtain optimized response data and signal reliability parameters; Using the signal strength distribution data to classify the optimized response data to obtain high priority response data and low priority response data, and optimizing the transmission of the high priority response data based on the signal reliability parameter to obtain a wireless signal transmission strategy; The low priority response data is scheduled and calculated using the wireless signal transmission strategy to obtain dynamic scheduling parameters, and the high priority response data is reallocated based on the dynamic scheduling parameters to obtain a wireless signal scheduling solution.

7. The wireless communication method for a charging pile according to claim 6, characterized in that: After the step of generating signal interference information and signal strength distribution data based on the wireless communication scheduling parameters, and constructing a wireless signal scheduling solution using the signal interference information and the signal strength distribution data, the method further comprises: Performing a channel state analysis on the wireless signal scheduling scheme to obtain channel interference information, and optimizing the allocation of channel resources based on the channel interference information to obtain channel switching threshold data; The wireless signal transmission strategy is adjusted by using the channel switching threshold data to obtain a dynamic channel allocation strategy, and the dynamic channel allocation strategy is integrated with the wireless signal scheduling scheme to obtain a wireless communication data stream.

8. A wireless communication system for a charging pile, characterized in that: include: An acquisition module is used to acquire charging request information, perform signal analysis on the charging request information, and obtain user identity data and vehicle charging demand data; An analysis module, configured to perform pattern analysis on the user identity data to obtain a user charging behavior pattern, and perform pattern prediction analysis on the vehicle charging demand data using the user charging behavior pattern to obtain a charging power demand sequence; A decomposition module, used to decompose the charging power demand sequence to obtain load balancing data, and to match and analyze the current available power data of the charging pile based on the load balancing data to obtain wireless communication scheduling parameters; A construction module is used to generate signal interference information and signal strength distribution data based on the wireless communication scheduling parameters, and use the signal interference information and the signal strength distribution data to construct a wireless signal scheduling plan.

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