Charging pile power management system based on internet of things

Abstract

The application discloses a charging pile power management system based on Internet of Things and concretely relates to the technical field of Internet of Things, and comprises a data acquisition module for collecting real-time operation data of the charging pile through a sensor to form a data set; a data processing module for processing the data according to a preset or optimized strategy to generate a processed data set; a scene analysis module for analyzing the processed data set and dividing charging pile operation scenes; a data feature extraction module for extracting and analyzing features when the optimized mechanism is triggered; a logic judgment module for formulating an optimized strategy through fuzzy logic reasoning and sending the optimized strategy to the data processing module; and a power energy efficiency management module for adaptively adjusting power distribution and improving system energy efficiency; the application can reduce data transmission delay and improve response speed, so that users can obtain a smoother experience when using the charging pile, and the transmission of redundant information is reduced and efficient power management is realized through the optimized strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things, more particularly, the present application relates to a charging pile power management system based on Internet of Things. BACKGROUND

[0002] With the emphasis on environmental protection and sustainable development, electric vehicles have gradually become the mainstream choice in the automobile market. The rapid growth of the number of electric vehicles has driven the rapid expansion of charging pile infrastructure to meet the increasing energy supply needs. However, the existing charging pile management system faces multiple technical challenges under high concurrent load and complex operating environment. The existing system is prone to response time delay, low data transmission efficiency and reduced system stability in high concurrency scenarios. When charging piles are in peak use, data transmission demand surges, network bandwidth faces great pressure, and data congestion and system response speed decline are likely to occur. In this case, the system is difficult to effectively allocate resources, resulting in some charging piles responding lag or failing to meet the charging needs of users, reducing user experience.

[0003] The problem of header information redundancy and field repetition in the data transmission process will exacerbate the burden on the system. The header information occupies a large proportion in the data packets generated by frequent communication and status updates between charging piles. Without reasonable optimization measures, these redundant information will occupy valuable bandwidth resources and reduce the transmission efficiency of effective data. At the same time, the repeated appearance of fields will increase the redundant information in data transmission, further reducing the overall transmission capacity of the network.

[0004] How to improve the overall performance of the charging pile power management system in the Internet of Things environment, optimize the data transmission strategy to reduce bandwidth consumption, improve transmission efficiency, improve system response speed, and realize intelligent power distribution and adaptive management has become an important technical problem to be solved. SUMMARY

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] The charging pile power management system based on Internet of Things comprises a data acquisition module, a data processing module, a scene analysis module, a data feature extraction module, a logic judgment module, and a power energy efficiency management module.

[0007] The data acquisition module is used to collect real-time operating data of the charging pile through sensors to obtain a set of real-time operating data of the charging pile.

[0008] The data processing module is used to process the set of real-time operating data of the charging pile according to a preset strategy or an optimized strategy to obtain a set of real-time operating data processing of the charging pile.

[0009] The scene analysis module performs a scene analysis operation based on the real-time operation data processing set of the charging pile, and performs charging pile operation scene division according to a result of the analysis operation.

[0010] The data feature extraction module is configured to perform feature extraction and feature analysis operations when the charging pile operation scene division result triggers the optimization mechanism.

[0011] The logic judgment module is configured to perform fuzzy logic reasoning based on a result of the feature analysis operation, to infer an optimized strategy to be executed, and deliver the optimized strategy to the data processing module to replace the preset strategy.

[0012] The power energy efficiency management module is configured to perform power energy efficiency management operations adaptively based on the real-time operation data processing set of the charging pile and the charging pile operation scene division result.

[0013] In a preferred embodiment, the scene analysis operation refers to:

[0014] Let the total number of currently connected charging piles be , the number of received charging requests in a unit time be , ; be a preset time interval, be the total number of newly received charging requests in the preset time interval, the time at which each charging pile that receives a request responds be recorded and averaged to obtain the average response time of the charging pile, denoted as , and then the concurrent charging demand value be calculated, with the calculation formula being:

[0015] ; represents the concurrent charging demand value.

[0016] In a preferred embodiment, the charging pile operation scene includes a low-concurrency scene, a medium-concurrency scene, and a high-concurrency scene.

[0017] In a preferred embodiment, the charging pile operation scene division according to the result of the analysis operation refers to:

[0018] The concurrent charging demand value is compared with preset division threshold value one and division threshold value two, respectively, the preset division threshold value one being less than the division threshold value two, if the concurrent charging demand value is greater than the preset division threshold value two, the charging pile operation scene is divided into a high-concurrency scene, if the concurrent charging demand value is less than the preset division threshold value one, the charging pile operation scene is divided into a low-concurrency scene, and if the concurrent charging demand value If the charging pile operation scene is greater than or equal to the preset division threshold one and less than or equal to the preset division threshold two, the charging pile operation scene is divided into a medium concurrency scene.

[0019] In a preferred embodiment, the optimization mechanism is triggered when the charging pile operation scene division result is a high concurrency scene.

[0020] In a preferred embodiment, feature extraction refers to extracting header proportion data and repeated field data according to packet information during data transmission, and then generating a header efficiency index based on the header proportion data and a field redundancy index based on the repeated field data.

[0021] In a preferred embodiment, the header efficiency index is obtained by:

[0022] In a set time window, all data packets transmitted through the charging pile are collected, the header size and the total size of each data packet are recorded, and a data packet set is formed ,

[0023] represents the header size of the i-th data packet, represents the total size of the i-th data packet, and M represents the total number of all data packets collected in the set time window;

[0024] The header proportion value of each data packet is calculated and marked as The header proportion value is obtained by calculating the ratio of the header size of the i-th data packet to the total size of the i-th data packet .

[0025] The header proportion value is processed using maximum value normalization to map it to the interval [0, 1] to obtain the corresponding standardized processing data , and then the header efficiency value is calculated:

[0026] ; , and are preset non-zero adjustment coefficients, is the header efficiency value;

[0027]

[0028] ; represents the header efficiency value calculated in the previous set time window, is a preset smoothing coefficient, represents the header efficiency index.

[0029] In a preferred embodiment, the field redundancy index acquisition logic is:

[0030] In a set time window, collect all data packets transmitted by the charging pile, extract the header field set Fi={fi1, fi2,…, fik} of each data packet, k represents the number of header fields, Fi represents the header field set of the i-th data packet, and Fij represents the j-th header field in the i-th data packet;

[0031] The number of repeated occurrences of each field in all data packets in the set time window is counted and marked as , and the number of unique occurrences of each field is counted and marked as , and the redundancy of each field is calculated, and the calculation formula is: ; represents the redundancy of the field ;

[0032] Substitute the field redundancy index calculation formula:

[0033] ; w1, w2 are preset influence coefficients, represents the total number of fields , represents the field redundancy index.

[0034] In a preferred embodiment, the logic judgment module is used for fuzzy logic reasoning based on the feature analysis operation result, and the optimized strategy to be executed refers to:

[0035] Obtain the average CPU occupancy rate of all charging piles currently performing data transmission, and compare it with the preset suitability threshold. If the average CPU occupancy rate is greater than or equal to the suitability threshold, the header efficiency index, the field redundancy index, and the average CPU occupancy rate are used as input data, the input variables are fuzzified, the values of the input variables are converted into fuzzy sets, the type of the overall charging pile processing strategy is used as the output variable, the output variable is fuzzified, the output variable is converted into a fuzzy set, a fuzzy rule is established to describe the data processing required under different data category combinations, the fuzzified input variables are inferred through the fuzzy rule, and the type of the optimized overall charging pile processing strategy is obtained.

[0036] If the CPU occupancy rate average value is less than the applicability threshold value, the head efficiency index, the field redundancy index, and the CPU occupancy rate of the charging pile unit are taken as input data, the input variables are fuzzified, the values of the input variables are converted into fuzzy sets, the type of the charging pile unit processing strategy is taken as an output variable, the output variable is fuzzified, the output variable is converted into a fuzzy set, fuzzy rules are formulated, data processing needs under different data category combinations are described, the fuzzified input variables are inferred through the fuzzy rules, and the type of the optimized charging pile unit processing strategy is obtained.

[0037] Technical effects and advantages of the present application:

[0038] Through the calculation and analysis of the head efficiency index and the field redundancy index, the present application can identify and reduce redundant header information and field repetition in data packets, optimize data transmission strategies, and reduce bandwidth consumption. This optimization improves the data transmission efficiency of the system, enabling the system to maintain fast response in a high-concurrency environment. The scene analysis module and logical judgment module introduced in the present application can adaptively adjust strategies under different load scenarios (such as high concurrency and low concurrency). Through the fuzzy logic inference mechanism, the system can dynamically select the optimal data processing and power management strategy based on real-time running data and analysis results, ensuring stable operation of the system under high load and avoiding system overload.

[0039] The present application analyzes real-time running data of charging piles through the power energy efficiency management module, adaptively adjusts power distribution and scheduling, and intelligently allocates power resources to avoid energy waste and uneven power distribution, improving overall charging efficiency and system energy efficiency, whether in peak periods or low-load situations.

[0040] Due to the ability to reduce data transmission delay and improve response speed, users can have a smoother experience when using charging piles. Through optimization strategies to reduce the transmission of redundant information and achieve efficient power management, the system can quickly adapt and adjust in various usage scenarios, reducing user waiting time and improving user satisfaction. By optimizing data transmission and power distribution management, the present application reduces the bandwidth and energy consumption of the system, thereby reducing long-term costs in charging pile operations. This is of great significance for the sustainable development of charging pile operators and electric vehicle charging networks. BRIEF DESCRIPTION OF DRAWINGS

[0041] To facilitate understanding by those skilled in the art, the present application will be further described below with reference to the accompanying drawings;

[0042] Figure 1 The schematic diagram of the charging pile power management system based on the Internet of Things in the present application. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0044] With reference to Figure 1 The following embodiments are obtained: Embodiments

[0045] The charging pile power management system based on the Internet of Things comprises a data acquisition module, a data processing module, a scene analysis module, a data feature extraction module, a logic judgment module and a power energy efficiency management module.

[0046] The data acquisition module is used for acquiring real-time operation data of the charging pile through a sensor to obtain a charging pile real-time operation data set; is responsible for collecting real-time operation data of the charging pile through a sensor to form a data set and provide basic data for subsequent module analysis and processing. The module ensures real-time monitoring of the charging pile operation and provides a timely and accurate data source for the system.

[0047] The data processing module is used for processing the charging pile real-time operation data set according to a preset strategy or an optimized strategy to obtain a charging pile real-time operation data processing set; processes the collected charging pile real-time data according to a preset standard strategy or an optimized strategy provided by the logic judgment module to generate a usable data processing set. The core function of the module is to clean, transform and preliminarily analyze the original data to ensure the integrity and consistency of the data.

[0048] The scene analysis module performs scene analysis operation based on the charging pile real-time operation data processing set and divides the charging pile operation scene according to the result of the analysis operation; analyzes the data processing set to identify and divide the charging pile operation scene, such as a low concurrency, medium concurrency or high concurrency scene. The module helps to dynamically evaluate the use state of the charging pile to trigger appropriate optimization mechanism and management strategy.

[0049] The data feature extraction module is used for performing feature extraction and feature analysis operation when the charging pile operation scene division result triggers the optimization mechanism; extracts features related to data transmission (such as a header efficiency index and a field redundancy index) and performs feature analysis to support the reasoning process of the logic judgment module when the optimization mechanism is triggered. The module ensures the extraction and processing of feature data, so that the system can adjust according to specific features in the optimization process.

[0050] The logic judgment module is configured to perform fuzzy logic reasoning based on the feature analysis operation result, to infer an optimized strategy to be executed, and to deliver the optimized strategy to the data processing module to replace the preset strategy. Through the fuzzy logic reasoning method, the analysis result of the data feature extraction module is used to infer the optimized strategy to be executed by the system. The logic judgment module outputs the optimized strategy for the overall or individual charging pile based on the input head efficiency index, field redundancy index, and CPU occupancy rate, and delivers the result to the data processing module to replace the preset strategy, thereby improving the adaptability and optimization capability of the system.

[0051] The power energy efficiency management module is configured to perform power energy efficiency management operation adaptively based on the charging pile real-time operation data processing set and the charging pile operation scene division result. According to the charging pile real-time operation data processing set and the scene analysis result, the power distribution and management strategy is automatically adjusted and optimized to realize the energy efficiency management of the charging pile system. The power scheduling and power distribution are adaptively performed by the power energy efficiency management module to ensure the maximum energy utilization and stable operation of the system under different loads and use scenarios. The adaptive characteristics of the power energy efficiency management module are based on existing technologies, such as machine learning algorithms to automatically analyze and adjust the power distribution, and the implementation details are not described herein.

[0052] The scene analysis operation refers to that the total number of currently connected charging piles is , which represents the number of charging piles actually participating in the charging operation in the system, the number of charging requests received in a unit of time is counted and recorded as , which reflects the frequency of charging demand, ; is a preset time interval, is the total number of new charging requests received in the preset time interval, the time of responding to each received request of the charging pile in the preset time interval is recorded, and the average value is processed to obtain the average response time of the charging pile and record it as , which measures the response ability of the system to the request, and the denominator structure of is introduced to reduce the change range of the index when is small, and then the concurrent charging demand value is calculated, and the calculation formula is:

[0053] ; represents the concurrent charging demand value, and the product term provides the load weight, which represents the comprehensive effect of the frequency and the number of devices participating in charging in a unit of time. By calculating the concurrent charging demand value CCD, the load condition of the current charging pile network can be comprehensively evaluated to help identify the peak period or high concurrency situation. The introduction of the average response time helps to judge the response ability of the system, and the calculated CCD value can be used to divide different types of operation scenes. The charging pile operation scene includes a low concurrency scene, a medium concurrency scene, and a high concurrency scene.

[0054] According to the results of the analysis operation, the charging pile operation scene is divided into:

[0055] The concurrent charging demand value is compared with the preset division threshold one and the preset division threshold two respectively, the preset division threshold one is less than the division threshold two, if the concurrent charging demand value is greater than the preset division threshold two, the charging pile operation scene is divided into a high concurrency scene, if the concurrent charging demand value is less than the preset division threshold one, the charging pile operation scene is divided into a low concurrency scene, if the concurrent charging demand value is greater than or equal to the preset division threshold one and less than or equal to the preset division threshold two, the charging pile operation scene is divided into a medium concurrency scene. By accurately identifying the concurrent charging demand level, the system can dynamically adjust resource allocation in different scenarios. For example, in a high concurrency scenario, the system can prioritize emergency or high-priority charging requests, limit unnecessary loads, and intelligently control to prevent system overload.

[0056] It should be noted that the optimization mechanism is triggered when the charging pile operation scene is divided into a high concurrency scene, which refers to the optimization of processing strategies, not the optimization of the power management of the charging pile. In a low concurrency scenario, the system can reduce power consumption or set some devices to standby mode to save energy and optimize overall power distribution. Scenario division helps the system take measures in advance in a high concurrency scenario to prevent response delays or system crashes. By identifying high concurrent demand in advance, the system can take measures such as flow limiting and load balancing to ensure stable system operation. In a medium concurrency scenario, the system can run smoothly and maintain high response speed and user experience. This scenario division mechanism is the basis for triggering different optimization mechanisms. According to the division result, the system can enable special optimization algorithms in a high concurrency scenario, such as data compression, field deduplication, etc., to reduce system pressure and improve bandwidth utilization. In a low concurrency scenario, the existing transmission strategy can be maintained to reduce unnecessary optimization processing and improve system efficiency. Through scenario division, the system can achieve adaptive strategy adjustment. For example, when a high concurrency scenario is identified, the system can automatically adjust the output power and transmission frequency of the charging pile to avoid waste and overload of power resources. The medium concurrency scenario allows the system to perform lightweight optimization while maintaining service quality, so that it can seamlessly transition to the processing strategy of the high concurrency scenario as the load gradually increases.

[0057] Feature extraction refers to extracting header proportion data and repeated field data according to the data packet information during data transmission, and then generating a header efficiency index based on the header proportion data and a field redundancy index based on the repeated field data. The acquisition logic of the header efficiency index is as follows:

[0058] In a set time window, all data packets that pass through the charging pile for data transmission are collected, the header size and the total size of each data packet are recorded, and a data packet set is formed ,

[0059] represents the header size of the i-th data packet, represents the total size of the i-th data packet, M represents the total number of all data packets collected in the set time window, i.e. the data sampling number, which affects the scale of calculation;

[0060] The header proportion value of each data packet is calculated and marked as , the header proportion value is obtained by calculating the ratio of the header size of the i-th data packet to the total size of the i-th data packet . By collecting all data packets in a period of time, the proportion of the header size to the total size of the data packet is calculated. The system can accurately analyze the proportion of the header information in data transmission. If the header information occupies a high proportion, it means that the actual data transmission efficiency may be reduced under the same bandwidth condition.

[0061] The maximum value normalization is used to process the header proportion value , so that it is mapped to the interval [0, 1] to obtain the corresponding standardized processing data , eliminating the influence of packet size difference, facilitating unified calculation and comparison, and then calculating the header efficiency value:

[0062] ; , are preset non-zero adjustment coefficients, is the header efficiency value; controls the exponential amplification effect of the header proportion index. A larger value will significantly increase the contribution of data packets with a large header proportion to the total, as a linear adjustment term, is used to deduct a fixed amount in the final calculation to avoid the linear growth effect caused by the number of data packets.

[0063] Substitute the header efficiency index calculation formula:

[0064] ; This represents the head efficiency value calculated within the previously set time window. It is used to smooth the calculation, provide continuity in the time series, and reduce the impact of instantaneous fluctuations. This is a preset smoothing coefficient, ranging from 0 to 1, used to balance the weights of the current head efficiency value XL and the head efficiency value XO of the previous time window. A larger value results in a smoother texture. The value indicates a high degree of dependence on the current calculation result. The HEI represents the header efficiency index. The larger the HEI, the more bandwidth the data packet header information occupies, and the greater the proportion of the header in the overall transmission. This means that when the system transmits data, the proportion of header information relative to the effective data is relatively high, which may lead to a decrease in the actual data transmission efficiency.

[0065] The field redundancy index is used to assess the degree of redundancy in header fields during data transmission. By calculating the redundancy of each field, the system can identify redundant information in data packets to optimize data transmission strategies and reduce unnecessary bandwidth consumption and transmission latency caused by redundant fields. The acquisition logic is as follows:

[0066] Within the set time window, collect all data packets that transmit data through the charging pile, and extract the header field set Fi={fi1,fi2,…,fik} of each data packet, where k represents the number of header fields, Fi represents the header field set of the i-th data packet, and Fij represents the j-th header field in the i-th data packet.

[0067] Statistical analysis of each field in all data packets within the set time window The number of times it appears and marked as And statistics for each field The unique occurrence count (counted once for the first occurrence of the field in each packet, otherwise repeated occurrences are ignored) is marked as Calculate each field The redundancy is calculated using the following formula: ; Representation field The redundancy is a measure of how often the field is repeated in data packet transmission. The closer the value is to 1, the higher the redundancy of the field; the closer the value is to 0, the lower the redundancy.

[0068] Substitute into the field redundancy index calculation formula:

[0069] ; Representation field Total quantity The field redundancy index is represented. w1 and w2 are both preset influence coefficients, w1 controls the influence of the field redundancy in the power term. A larger w1 will enhance the influence of high redundancy fields on the overall FRI. w2 controls the amplification effect of the redundancy in the exponential term, adjusts the response sensitivity of the field redundancy index to high redundancy fields. If the field redundancy index FRI is larger, it means that there is more repeated information in the data packet header field in the time window, and the redundancy is higher. This means that the system contains a large amount of repeated field information in transmission, which may increase the bandwidth consumption and reduce the data transmission efficiency.

[0070] The logical judgment module is used for fuzzy logical reasoning based on the feature analysis operation result, and the optimized strategy to be executed refers to:

[0071] The average CPU occupancy rate of all charging piles currently performing data transmission is obtained, and compared with the preset suitability threshold. If the average CPU occupancy rate is greater than or equal to the suitability threshold, the header efficiency index, the field redundancy index, and the average CPU occupancy rate are used as input data, the input variables are fuzzified, the values of the input variables are converted into fuzzy sets, the type of the overall charging pile processing strategy is used as the output variable, the output variable is fuzzified, the output variable is converted into a fuzzy set, a fuzzy rule is established, the data processing needs under different data category combinations are described, the fuzzified input variables are inferred through the fuzzy rule, and the type of the optimized overall charging pile processing strategy is obtained.

[0072] If the average CPU occupancy rate is less than the suitability threshold, the header efficiency index, the field redundancy index, and the CPU occupancy rate of the charging pile are used as input data, the input variables are fuzzified, the values of the input variables are converted into fuzzy sets, the type of the charging pile processing strategy is used as the output variable, the output variable is fuzzified, the output variable is converted into a fuzzy set, a fuzzy rule is established, the data processing needs under different data category combinations are described, the fuzzified input variables are inferred through the fuzzy rule, and the type of the optimized charging pile processing strategy is obtained.

[0073] For example, assume that there are multiple charging piles in the system that are performing data transmission, and the following data is collected: the header efficiency index is "high", the field redundancy index is "medium", the average CPU occupancy rate of all charging piles currently performing data transmission is 70%, and the preset suitability threshold is 65%.

[0074] Case 1: The average CPU occupancy rate is greater than or equal to the suitability threshold:

[0075] Judgment: Since the average CPU occupancy rate of 70% is greater than the preset threshold of 65%, the system determines that the overall load is high.

[0076] Input variable fuzzification: The header efficiency index is fuzzified as "high", the field redundancy index is fuzzified as "moderate", the average CPU utilization is fuzzified as "high". Output variable: The overall processing strategy type of the system is set to "high intensity optimization", "moderate optimization", etc.

[0077] Formulate fuzzy rules: Rule 1: If the header efficiency index is "high", the field redundancy index is "moderate", and the average CPU utilization is "high", then the overall processing strategy of the system is "high intensity optimization". Inference process: According to the above input variables and rules, the system performs fuzzy logic reasoning and determines the output as "high intensity optimization". Execute strategy: The system triggers optimization measures such as enabling header information compression and reducing field redundancy to reduce the overall load of the system.

[0078] Case two, the average CPU utilization is less than the applicability threshold:

[0079] Suppose the average CPU utilization at this time is 60%, which is lower than the preset threshold of 65%.

[0080] Judgment: The system detects that the overall load is low, so it enters the monomer analysis mode.

[0081] Input variable fuzzification: The header efficiency index is fuzzified as "high", the field redundancy index is fuzzified as "moderate", and the monomer CPU utilization of each charging pile is recorded and fuzzified respectively. For example, the utilization of a charging pile is 80%, which is fuzzified as "high"; the utilization of another charging pile is 50%, which is fuzzified as "moderate". Output variable: The type of monomer processing strategy, such as "needs optimization", "does not need optimization", etc.

[0082] Formulate fuzzy rules: Rule 1: If the header efficiency index is "high", the field redundancy index is "moderate", and the monomer CPU utilization of a charging pile is "high", then the monomer processing strategy of the charging pile is "needs optimization". Rule 2: If the header efficiency index is "high", the field redundancy index is "moderate", and the monomer CPU utilization of a charging pile is "moderate", then the monomer processing strategy of the charging pile is "not optimized for the time being".

[0083] The system determines the output strategy of each charging pile according to the input data and rules of each charging pile. For charging piles with high occupancy, the system implements optimization measures such as reducing data transmission frequency or adjusting data compression parameters. For charging piles with low occupancy, the current strategy remains unchanged. Optimization measures can include: header field compression: suitable for high HEI and low to medium FRI, which can reduce header overhead by compression. Header information caching and conditional request: suitable for high FRI, which can effectively reduce the transmission of repeated fields. Persistent connection and multiplexing: suitable for high HEI and FRI, which can reduce the transmission of header information in each request.

[0084] In the logical judgment module, although the header efficiency index and the field redundancy index are calculated based on the overall data of all charging piles, when applied, the strategy is divided into overall processing strategy and individual processing strategy, because the system needs to decide whether to adopt global optimization or fine-grained optimization for individuals according to different CPU load conditions. The following is a detailed explanation of this logic:

[0085] The header efficiency index and the field redundancy index are indeed derived from the overall data, reflecting the bandwidth occupation and redundancy of data transmission in the entire system. However, these indices are only the overall performance of data characteristics. In actual operation, the system also needs to consider the current load condition to select how to optimize the data processing strategy. If the CPU load (average value) of the entire system is high, it means that the overall system is in a high load state, so the overall optimization strategy should be applied to improve efficiency. When the average CPU occupancy of the system is low, although the overall header efficiency and field redundancy are high, the specific load of each charging pile may be different. Therefore, individual optimization strategies can be used to optimize specific charging piles with high load one by one.

[0086] Overall processing strategy: In the case of high overall system load, the optimization target should be placed in the global range to reduce the burden of the overall system. For example, uniformly compress the header or reduce the field redundancy, which can significantly reduce the overhead of overall data transmission.

[0087] Individual processing strategy: When the overall load of the system is not high, but the load of individual charging piles is high, by optimizing the strategy of these charging piles with high load individually, unnecessary global adjustment can be avoided, the impact on the normal operation of other charging piles can be reduced, and the flexibility and efficiency of system operation can be guaranteed.

[0088] Data consistency: Although the header efficiency index and the field redundancy index are calculated from the overall data, it does not mean that the load conditions of each charging pile in the system are consistent. In actual operation, due to network conditions, user demand and other factors, the load conditions of each charging pile may differ significantly.

[0089] Necessity of individual optimization: Sometimes, even if the overall head efficiency and redundancy situation needs optimization, individual charging piles may need different optimization strategies due to their special CPU load situation. For example, one charging pile may need to reduce data transmission frequency, while another may need to perform head field compression.

[0090] It should be noted that the adaptive optimization of the power energy efficiency management module is mainly used to realize intelligent management and distribution of electricity in different operating scenarios. Through adaptive optimization, the system can perform real-time power regulation during peak load periods, low load periods, and other different usage scenarios, ensuring the stability and energy efficiency maximization of the charging pile system. The main functions of adaptive optimization include:

[0091] Load balancing: Based on real-time operating data and scenario analysis results, automatically adjust the charging power distribution of each charging pile to avoid excessive load on a single charging pile.

[0092] Power management: Reduce unnecessary charging power output in high-load scenarios to ensure efficient use of the system's power resources as a whole.

[0093] Dynamic adjustment: Based on real-time analysis results, the system can adaptively adjust strategies to provide dynamic power scheduling and load management.

[0094] Data transmission optimization (optimization in processing strategy): Optimization in data processing strategy is aimed at improving the performance of data transmission, ensuring that when data communication is performed, the header redundancy is reduced, the data packet structure is optimized, and the overall system transmission efficiency and response speed are improved. The purpose of data transmission optimization is:

[0095] Reduce bandwidth occupancy: By reducing the redundancy of data packet header information, the system can reduce bandwidth consumption when transmitting the same amount of data.

[0096] Improve data transmission efficiency: Optimized data transmission strategy can ensure that the system maintains fast response in high-concurrency environments and reduces data transmission delay.

[0097] Smooth performance fluctuations: Through continuous monitoring of parameters such as the header efficiency index, when a decrease in data transmission efficiency is detected, the system will trigger optimization strategies such as header compression and field deduplication.

[0098] The adaptive optimization of the power energy efficiency management module is aimed at electricity management, and through intelligent adjustment of power resources, it ensures efficient management under different load conditions. Data transmission optimization belongs to the category of data processing strategies, aiming to improve the efficiency of data transmission by optimizing data packet structure and reducing redundancy, ensuring the stability and performance of the system under high concurrency conditions.

[0099] The above formulas are all de-dimensioned to calculate the numerical values, the formulas are obtained by collecting a large amount of data to simulate the most recent real situation, and the preset parameters in the formulas are set by a person skilled in the art according to the actual situation.

[0100] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0101] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the system, device and unit described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0103] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A charging pile power management system based on the Internet of Things, characterized in that, The system comprises a data collection module, a data processing module, a scene analysis module, a data feature extraction module, a logical judgment module, and a power energy efficiency management module. The data collection module is configured to collect real-time operation data of the charging pile through a sensor to obtain a charging pile real-time operation data set. The data processing module is configured to process the charging pile real-time operation data set according to a preset strategy or an optimized strategy to obtain a charging pile real-time operation data processing set. The scene analysis module is configured to perform a scene analysis operation based on the charging pile real-time operation data processing set and divide a charging pile operation scene according to a result of the analysis operation. The data feature extraction module is configured to perform feature extraction and feature analysis operations when the charging pile operation scene division result triggers an optimization mechanism. The logical judgment module is configured to perform fuzzy logical reasoning based on a result of the feature analysis operation, infer an optimized strategy to be executed, and deliver the optimized strategy to the data processing module to replace the preset strategy. The power energy efficiency management module is configured to adaptively perform a power energy efficiency management operation based on the charging pile real-time operation data processing set and a charging pile operation scene division result. The charging pile operation scene includes a low-concurrency scene, a medium-concurrency scene, and a high-concurrency scene. Let the total number of currently connected charging piles be , the number of charging requests received in a unit of time is counted and recorded as , ; is a preset time interval, is the total number of newly received charging requests in the preset time interval, the time of responding to each received request of the charging pile in the preset time interval is recorded, and the average value is processed to obtain the average response time of the charging pile and record it as , and then the concurrent charging demand value is calculated, the calculation formula is: ; represents a concurrent charging demand value; When the charging pile operation scene division result is a high-concurrency scene, the optimization mechanism is triggered. The feature extraction refers to extracting header proportion data and repeated field data according to data packet information during data transmission, generating a header efficiency index based on the header proportion data, and generating a field redundancy index based on the repeated field data. Concurrent charging demand value The values ​​are compared with preset threshold 1 and threshold 2 respectively. If preset threshold 1 is less than threshold 2, then the concurrent charging requirement is met. If the value exceeds the preset threshold of two, the charging pile operation scenario is classified as a high-concurrency scenario. If the concurrent charging demand value is met... If the value is less than the preset threshold, the charging pile operation scenario is classified as a low-concurrency scenario; if the concurrent charging demand value is met... If the value is greater than or equal to the preset threshold 1 and less than or equal to the preset threshold 2, then the charging pile operation scenario is classified as a medium concurrency scenario.

2. The Internet of Things based charging station power management system as claimed in claim 1, wherein, The header efficiency index is calculated as follows:

3. The IoT-based charging station power management system of claim 2, wherein, The field redundancy index is calculated as follows:

4. The Internet of Things based charging station power management system as claimed in claim 3, wherein, In a set time window, all data packets transmitted through the charging pile are collected, and the header field set Fi={fi1, fi2,…,fik} of each data packet is extracted, where k represents the number of header fields, Fi represents the header field set of the i-th data packet, and Fij represents the j-th header field in the i-th data packet. In a set time window, all data packets transmitted through the charging pile are collected, the header size and the total size of each data packet are recorded, and a data packet set is formed , a head size of the i-th data packet, a total size of the i-th data packet, and M represents a total number of all data packets collected within a set time window. The head ratio value of each data packet is calculated and marked as The head ratio value is obtained by calculating the ratio of the head size of the i-th data packet to the total size of the i-th data packet . The head proportion value is processed using maximum value normalization to map it to the interval [0, 1] to obtain the corresponding normalized processing data Then the head efficiency value is calculated: ; , are preset non-zero adjustment factors, is a head efficiency value; The field redundancy index is calculated as follows: ; denotes the head efficiency value calculated for the last set time window, is a preset smoothing coefficient, denotes the head efficiency index.

5. The IoT-based charging station power management system of claim 4, wherein, The logical judgment module is configured to perform fuzzy logical reasoning based on a result of the feature analysis operation, infer an optimized strategy to be executed, and deliver the optimized strategy to the data processing module to replace the preset strategy. The CPU occupancy rate average of all charging piles currently transmitting data is obtained and compared with a preset suitability threshold. If the CPU occupancy rate average is greater than or equal to the suitability threshold, the header efficiency index, the field redundancy index, and the CPU occupancy rate average are used as input data, the input variables are fuzzified, the values of the input variables are converted into fuzzy sets, the type of the overall charging pile processing strategy is used as an output variable, the output variable is fuzzified, the output variable is converted into a fuzzy set, fuzzy rules are established to describe the data processing needs under different data category combinations, the fuzzified input variables are inferred through the fuzzy rules, and the type of the optimized overall charging pile processing strategy is obtained. Statistical analysis of each field in all data packets within the set time window The number of times it appears and marked as And statistics for each field The unique number of occurrences and marked as Calculate each field The redundancy is calculated using the following formula: ; Representation field Redundancy; ​ ; represents the total number of fields , represents the field redundancy index, , are preset influence coefficients. 6.The IoT-based charging pile power management system of claim 5, wherein, ​ ​ If the CPU occupancy average value is less than the applicability threshold value, the head efficiency index, the field redundancy index and the CPU occupancy of the charging pile unit are taken as input data, the input variables are fuzzified, the values of the input variables are converted into fuzzy sets, the type of the charging pile unit processing strategy is taken as an output variable, the output variable is fuzzified, the output variable is converted into a fuzzy set, fuzzy rules are formulated, data processing needs under different data category combinations are described, the fuzzified input variables are inferred through the fuzzy rules, and the type of the optimized charging pile unit processing strategy is obtained.

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