Charging pile power management system based on Internet of Things

Abstract

The invention discloses a charging pile power management system based on the Internet of Things, and particularly relates to the technical field of the Internet of Things, and the system comprises a data collection module which collects the real-time operation data of a charging pile through a sensor, and forms a data set; the data processing module is used for processing the data according to a preset or optimization strategy and generating a processed data set; the scene analysis module performs analysis based on the processing data set and divides the operation scene of the charging pile; the data feature extraction module is used for extracting and analyzing features when an optimization mechanism is triggered; the logic judgment module is used for formulating an optimization strategy through fuzzy logic reasoning and sending the optimization strategy to the data processing module; the power energy efficiency management module is used for adaptively adjusting power distribution and improving the energy efficiency of the system; the data transmission delay can be reduced, the response speed can be improved, a user can obtain smoother experience when using the charging pile, transmission of redundant information is reduced through an optimization strategy, and efficient power management is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and more specifically, to a charging pile power management system based on the Internet of Things. Background Art

[0002] With increasing attention to environmental protection and sustainable development, electric vehicles are becoming a mainstream choice in the automotive market. The rapid growth in the number of electric vehicles has driven a rapid expansion of charging station infrastructure to meet their increasing energy needs. However, existing charging station management systems face multiple technical challenges under high concurrent loads and complex operating environments. Existing systems are prone to response time delays, inefficient data transmission, and reduced system stability in high-concurrency scenarios. During peak usage of charging stations, data transmission demand surges, putting significant pressure on network bandwidth, easily leading to data congestion and reduced system response speed. In this situation, the system struggles to effectively allocate resources, resulting in delayed responses from some charging stations or an inability to meet user charging needs, reducing the user experience.

[0003] Redundant header information and repeated fields during data transmission can increase the system's burden. Frequent communication and status updates between charging stations generate a significant portion of data packets, often containing header information. Without proper optimization, this redundant information can consume valuable bandwidth and reduce the efficiency of valid data transmission. Furthermore, repeated fields can increase redundant information in data transmission, further reducing the network's overall transmission capacity.

[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 issue that needs to be solved urgently. Summary of the Invention

[0005] To achieve the above object, the present invention provides the following technical solutions: The charging pile power management system based on the Internet of Things includes a data acquisition module, a data processing module, a scenario analysis module, a data feature extraction module, a logic judgment module, and a power energy efficiency management module; The data acquisition module is used to collect the real-time operation data of the charging pile through sensors to obtain a real-time operation data set of the charging pile; The data processing module is used 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 scenario analysis module performs scenario analysis based on the real-time operation data processing set of the charging pile, and divides the charging pile operation scenarios according to the results of the analysis operation; The data feature extraction module is configured to perform feature extraction and feature analysis operations when the charging pile operation scenario division result triggers the optimization mechanism; The logic judgment module is configured to perform fuzzy logic reasoning based on the feature analysis operation result, infer the 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 perform power energy efficiency management operations based on the charging pile real-time operation data processing set and the charging pile operation scenario division result.

[0006] In a preferred embodiment, the scenario analysis operation refers to: 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 when each charging pile that receives a request responds is recorded and averaged to obtain the average response time of the charging pile and recorded as , and then the concurrent charging demand value is calculated, and the calculation formula is: ; represents the concurrent charging demand value.

[0007] In a preferred embodiment, the charging pile operation scenario includes a low concurrency scenario, a medium concurrency scenario, and a high concurrency scenario.

[0008] In a preferred embodiment, the charging pile operation scenario division according to the analysis operation result refers to: The concurrent charging demand value is compared with preset division threshold one and 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 scenario is divided into a high concurrency scenario, if the concurrent charging demand value is less than the preset division threshold one, the charging pile operation scenario is divided into a low concurrency scenario, 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 scenario is divided into a medium concurrency scenario.

[0009] In a preferred embodiment, the optimization mechanism is triggered when the charging pile operation scenario division result is a high concurrency scenario.

[0010] In a preferred embodiment, the feature extraction refers to extracting the header proportion data and the repeated field data according to the packet information during the 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.

[0011] In a preferred embodiment, the acquisition logic of the header efficiency index is as follows: 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 , 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; 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 ; 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: ; , are preset non-zero adjustment coefficients, is the header efficiency value; Substitute the header efficiency index calculation formula: ; represents the header efficiency value calculated in the last set time window, is a preset smoothing coefficient, represents the header efficiency index.

[0012] In a preferred embodiment, the acquisition logic of the field redundancy index is as follows: In a set time window, all data packets transmitted through the charging pile are collected, the header field set Fi={fi1, fi2,…,fik} of each data packet is extracted, k represents the number of header fields, Fi represents the header field set of the i-th data packet, and Fij is used to represent the j-th header field in the i-th data packet. The number of repeated occurrences of each field in all data packets in the set time window is counted and marked as , and statistics for each field The number of unique occurrences of , calculate each field The redundancy is calculated as follows: ; Representation field Redundancy; Substitute into the field redundancy index calculation formula: ; w1 and w2 are both preset influence coefficients, Representation field The total number of Indicates the field redundancy index.

[0013] In a preferred embodiment, the logic judgment module is used to perform fuzzy logic reasoning based on the result of the feature analysis operation, and the optimized strategy to be executed is inferred to be: The average CPU occupancy rate of all charging piles currently performing data transmission is obtained and compared with the preset applicability threshold. If the average CPU occupancy rate is greater than or equal to the applicability threshold, the head efficiency index, field redundancy index, and average CPU occupancy rate are used as input data, the input variables are fuzzified, and the values ​​of the input variables are converted into fuzzy sets. The type of the overall processing strategy of the charging pile is used as the output variable, the output variables are fuzzified, and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the data processing requirements under different data type combinations. The fuzzified input variables are reasoned through fuzzy rules to obtain the type of the optimized overall processing strategy of the charging pile. If the average CPU occupancy rate is less than the applicability threshold, the head efficiency index, field redundancy index, and CPU occupancy rate of the charging pile unit are taken as input data, the input variables are fuzzified, and the values ​​of the input variables are converted into fuzzy sets. The type of the charging pile unit processing strategy is taken as the output variable, the output variable is fuzzified, and the output variable is converted into a fuzzy set. Fuzzy rules are formulated to describe the data processing needs under different data type combinations. The fuzzified input variables are inferred through fuzzy rules to obtain the type of optimized charging pile unit processing strategy.

[0014] The technical effects and advantages of the present invention are as follows: By calculating and analyzing the header efficiency index and field redundancy index, the present invention can identify and reduce redundant header information and field duplication in data packets, optimize data transmission strategies, and reduce bandwidth consumption. This optimization improves the system's data transmission efficiency, allowing the system to maintain a rapid response even in high-concurrency environments. The scenario analysis module and logic judgment module introduced in the present invention can perform adaptive strategy adjustments under different load scenarios (such as high concurrency and low concurrency). Through a fuzzy logic reasoning mechanism, the system can dynamically select the optimal data processing and power management strategies based on real-time operating data and analysis results, ensuring stable operation of the system under high load conditions and avoiding system overload.

[0015] The present invention uses the power energy efficiency management module to analyze the real-time operating data of the charging piles and adaptively adjust power distribution and scheduling. Whether in peak period or low load conditions, the system can intelligently allocate power resources to avoid energy waste and uneven power distribution, thereby improving overall charging efficiency and system energy efficiency.

[0016] Because this invention reduces data transmission latency and improves response speed, users can enjoy a smoother experience when using charging piles. By optimizing strategies to reduce the transmission of redundant information and achieve efficient power management, the system can quickly adapt and adjust to various usage scenarios, reducing user wait time and improving user satisfaction. By optimizing data transmission and power distribution management, this invention reduces system bandwidth and energy consumption, thereby reducing the long-term costs of charging pile operations. This has important implications for the sustainable development of charging pile operators and electric vehicle charging networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a schematic diagram of the charging pile power management system based on the Internet of Things in the present invention. DETAILED DESCRIPTION

[0018] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] Reference Figure 1 The following examples were obtained: Example

[0020] The charging pile power management system based on the Internet of Things includes a data acquisition module, a data processing module, a scenario analysis module, a data feature extraction module, a logic judgment module, and a power energy efficiency management module; The data acquisition module collects real-time operating data from charging piles through sensors, generating a collection of these data. This module is responsible for collecting real-time operating data from charging piles through sensors, forming a data collection and providing basic data for analysis and processing by subsequent modules. This module ensures real-time monitoring of charging pile operation and provides a timely and accurate data source for the system.

[0021] The data processing module processes the real-time charging pile operation data according to preset or optimized strategies to generate a processed data set. It also processes the collected real-time charging pile data according to preset standard strategies or the optimized strategy provided by the logic judgment module to generate a usable processed data set. The core function of this module is to clean, transform, and perform preliminary analysis on the raw data to ensure data integrity and consistency.

[0022] The scenario analysis module performs scenario analysis based on the real-time charging pile operation data processing set and classifies the charging pile operation scenarios based on the analysis results. By analyzing the data processing set, it identifies and classifies the charging pile operation scenarios, such as low concurrency, medium concurrency, or high concurrency. This module helps to dynamically evaluate the usage status of charging piles to trigger appropriate optimization mechanisms and management strategies.

[0023] The data feature extraction module is used to perform feature extraction and analysis when the optimization mechanism is triggered by the results of the charging pile operation scenario classification. When the optimization mechanism is triggered, it extracts features related to data transmission (such as the header efficiency index and field redundancy index) and performs feature analysis to support the reasoning process of the logic judgment module. This module ensures the extraction and processing of feature data, enabling the system to adjust to specific features during the optimization process.

[0024] The logic judgment module performs fuzzy logic reasoning based on the results of feature analysis operations, inferring the optimized strategy to be implemented and sending it to the data processing module to replace the preset strategy. Using fuzzy logic reasoning methods, based on the analysis results of the data feature extraction module, the module infers the optimization strategy to be implemented by the system. Based on the input header efficiency index, field redundancy index, and CPU utilization, this module outputs an optimization strategy for the charging pile as a whole or for each individual charging pile. The result is sent to the data processing module to replace the preset strategy, improving the system's adaptability and optimization capabilities.

[0025] The power efficiency management module adaptively performs power efficiency management operations based on a processed set of real-time charging pile operation data and the results of the charging pile operation scenario classification. Based on the real-time charging pile operation data and scenario analysis results, it automatically adjusts and optimizes power distribution and management strategies to achieve energy efficiency management of the charging pile system. This module adaptively dispatches power and distributes power to ensure maximum energy utilization and stable operation under different loads and usage scenarios. This module's adaptive features build on existing technologies, such as machine learning algorithms that automatically analyze and adjust power distribution, and the implementation details are not detailed here.

[0026] The scenario analysis operation refers to: assuming the total number of currently connected charging piles is , represents the number of charging piles actually involved in charging operations in the system, and the number of charging requests received per unit time is counted and recorded as , reflects the frequency of charging demand, ; For the preset time interval, The total number of newly received charging requests within the preset time interval is recorded. The time it takes for each charging pile to respond to a request within the preset time interval is recorded, and the average value is processed to obtain the average response time of the charging pile and recorded as , measures the system's ability to respond to requests, introducing The denominator structure is to When the value is smaller, reduce the change range of the index, and then calculate the concurrent charging demand value. The calculation formula is: ; Indicates the concurrent charging demand value, the product term A load weight is provided, which represents the combined effect of the frequency and number of devices involved in charging per unit time. By calculating the concurrent charging demand value CCD, the load situation of the current charging pile network can be comprehensively evaluated to help identify peak periods or high concurrency situations. The introduction of the average response time helps to judge the responsiveness of the system. The calculated CCD value can be used to divide different types of operating scenarios. The charging pile operating scenarios include low concurrency scenarios, medium concurrency scenarios and high concurrency scenarios.

[0027] The division of charging pile operation scenarios based on the results of the analysis operation refers to: The concurrent charging demand value Compare with the preset division threshold 1 and division threshold 2 respectively. The preset division threshold 1 is less than the division threshold 2. If the concurrent charging demand value is met If the value is greater than the preset division threshold 2, the charging pile operation scenario is divided into a high concurrency scenario. If the concurrent charging demand value is met If the value is less than the preset division threshold, the charging pile operation scenario is divided into a low concurrency scenario. If the concurrent charging demand value is met, If the number of concurrent charging requests is greater than or equal to the preset threshold 1 and less than or equal to the preset threshold 2, the charging pile operation scenario is classified as a medium concurrency scenario. By accurately identifying the level of concurrent charging demand, the system can dynamically adjust resource allocation in different scenarios. For example, in a high concurrency scenario, the system can prioritize urgent or high-priority charging requests, limit non-essential loads, and perform intelligent regulation to prevent system overload.

[0028] It should be noted that when the charging pile operation scenario is classified as a high-concurrency scenario, the optimization mechanism is triggered. The optimization referred to here refers to processing strategy optimization, not charging pile power management optimization. In low-concurrency scenarios, the system can reduce power consumption or set some devices to standby mode to conserve energy and optimize overall power distribution. Scenario classification helps the system take proactive measures in high-concurrency scenarios to prevent response delays or system crashes. By proactively identifying high-concurrency demand, the system can implement measures such as current limiting and load balancing to ensure stable system operation. In medium-concurrency scenarios, the system can operate smoothly, maintaining high response speed and user experience. This scenario classification mechanism is the basis for triggering different optimization mechanisms. Based on the classification results, the system can enable specialized optimization algorithms in high-concurrency scenarios, such as data compression and field deduplication, to reduce system pressure and improve bandwidth utilization. In low-concurrency scenarios, the system can maintain the existing transmission strategy, reduce unnecessary optimization processing, and improve system efficiency. Scenario classification enables the system to achieve adaptive policy adjustments. For example, when identifying high-concurrency scenarios, the system can automatically adjust the output power and transmission frequency of the charging piles to avoid wasted power resources and overload issues. Medium-concurrency scenarios allow the system to perform lightweight optimizations while maintaining service quality, allowing for a seamless transition to high-concurrency processing strategies as the load gradually increases.

[0029] Feature extraction refers to extracting header proportion data and repeated field data based on 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 logic for obtaining the header efficiency index is as follows: Within the set time window, all data packets transmitted through the charging pile are collected, the header size and total size of each data packet are recorded, and a data packet set is formed. , Indicates 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 within the set time window, i.e., the number of data samples, which affects the scale of the calculation; Calculate the header ratio of each data packet and mark it as , head proportion By calculating the header size of the i-th packet The total size of the packets with the i-th packet By collecting all data packets within a period of time and calculating the ratio of their header size to the total size of the data packets, the system can accurately analyze the proportion of header information in data transmission. If the header information occupies a high proportion, it means that under the same bandwidth conditions, the efficiency of actual data transmission may be reduced.

[0030] Use maximum normalization to process the head ratio , so that it is mapped to the interval [0,1] and the corresponding standardized processing data is obtained , eliminating the impact of packet size differences to facilitate unified calculation and comparison, and then calculating the header efficiency value: ; 、 All are preset non-zero adjustment coefficients. is the head efficiency value; Controls the exponential magnification effect of the head ratio. The value will significantly increase the contribution of packets with a large header ratio to the total. As a linear adjustment term, a fixed amount is deducted from the final calculation to avoid the linear growth effect caused by the number of packets.

[0031] Substitute into the head efficiency index calculation formula: ; Indicates the head efficiency value calculated in the last set time window, which is used for smooth calculation, provides continuity in the time series, and reduces the impact of instantaneous fluctuations; It is a preset smoothing coefficient with a value range of 0 to 1. It is used to balance the weight of the current head efficiency value XL and the head efficiency value XO of the previous time window. The larger A value of 0 indicates a high dependency on the current calculation result. It represents the header efficiency index. When the HEI is larger, it means that the packet header information occupies more bandwidth and the proportion of the header in the overall transmission is larger. This means that when the system is transmitting data, the ratio of header information to valid data is high, which may lead to a decrease in actual data transmission efficiency.

[0032] The field redundancy index is used to assess the redundancy of header fields in data transmission. By calculating the redundancy of each field, the system can identify redundant information in the data packet to optimize data transmission strategies and reduce unnecessary bandwidth consumption and transmission delays caused by redundant fields. The acquisition logic is as follows: Within the 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. Statistics of each field in all data packets within the set time window The number of repetitions is marked as , and statistics for each field The number of unique occurrences of (the first occurrence of the field in each packet is counted once, and the remaining repetitions are not counted) and marked as , calculate each field The redundancy is calculated as follows: ; Representation field The redundancy of the field is used to measure the degree of repetition of the field in the 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.

[0033] Substitute into the field redundancy index calculation formula: ; Representation field The total number of Represents the field redundancy index. w1 and w2 are preset influence coefficients. w1 controls the impact of field redundancy in the power term. A larger w1 value increases the impact of highly redundant fields on the overall FRI. w2 controls the amplification effect of redundancy in the exponential term, adjusting the sensitivity of the field redundancy index to highly redundant fields. A larger field redundancy index (FRI) indicates a high level of redundancy in the packet header field during the time window. This indicates that the system includes a large amount of duplicate field information during transmission, which may increase bandwidth consumption and reduce data transmission efficiency.

[0034] The logic judgment module is used to perform fuzzy logic reasoning based on the results of feature analysis operations, and the optimized strategy to be executed is as follows: The average CPU utilization rate of all charging piles currently performing data transmission is obtained, and compared with a preset suitability threshold. If the average CPU utilization rate is greater than or equal to the suitability threshold, the head efficiency index, the field redundancy index, and the average CPU utilization 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 an output variable, the output variable is fuzzified, the output variable is converted into a fuzzy set, fuzzy rules are formulated 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. If the average CPU utilization rate is less than the suitability threshold, the head efficiency index, the field redundancy index, and the CPU utilization 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 an output variable, the output variable is fuzzified, the output variable is converted into a fuzzy set, fuzzy rules are formulated 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 charging pile processing strategy is obtained.

[0035] For example, assume that there are multiple charging piles in the system that are performing data transmission, and the following data is collected: the head efficiency index is "high", the field redundancy index is "medium", the average CPU utilization rate of all charging piles currently performing data transmission is 70%, and the preset suitability threshold is 65%.

[0036] Case 1: The average CPU utilization rate is greater than or equal to the suitability threshold: Judgment: Since the average CPU utilization rate of 70% is greater than the preset threshold of 65%, the system determines that the overall load is high.

[0037] Input variable fuzzification: the head efficiency index is fuzzified as "high", the field redundancy index is fuzzified as "medium", the average CPU utilization rate is fuzzified as "high", and the output variable: the type of the overall processing strategy of the system is set to "high intensity optimization", "medium optimization", etc.

[0038] Formulate fuzzy rules: Rule 1: If the head efficiency index is "high", the field redundancy index is "medium", and the average CPU utilization rate is "high", 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 inference and determines that the output is "high intensity optimization". Execution strategy: The system triggers optimization measures, such as enabling head information compression and reducing field redundancy, to reduce the overall load of the system.

[0039] Case 2: The average CPU utilization is less than the suitability threshold: Suppose the average CPU utilization is 60% at this moment, which is lower than the preset threshold of 65%.

[0040] Judgment: The system detects that the overall load is low, so it enters the single-body analysis mode.

[0041] Input variable fuzzification: The head efficiency index is fuzzified as "high", the field redundancy index is fuzzified as "medium", and the single-body 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 "medium". Output variable: the type of single-body processing strategy, such as "needs optimization", "does not need optimization", etc.

[0042] Formulate fuzzy rules: Rule 1: If the head efficiency index is "high", the field redundancy index is "medium", and the single-body CPU utilization of a charging pile is "high", then the single-body processing strategy of this charging pile is "needs optimization". Rule 2: If the head efficiency index is "high", the field redundancy index is "medium", and the single-body CPU utilization of a charging pile is "medium", then the single-body processing strategy of this charging pile is "does not need optimization".

[0043] 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 utilization, the system executes optimization measures such as reducing data transmission frequency or adjusting data compression parameters. For charging piles with low utilization, the current strategy is maintained. 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.

[0044] In the logical judgment module, although the head 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 single-body processing strategy, because the system needs to decide whether to adopt global optimization or individual fine-grained optimization according to different CPU load conditions. The following is a detailed explanation of this logic: The header efficiency index and field redundancy index are indeed indicators derived from the overall data, reflecting the bandwidth usage and redundancy of data transmission in the entire system. However, these indices are only a holistic representation of the data characteristics. In actual operation, the system also needs to consider the current load conditions to choose how to optimize the data processing strategy. If the CPU load (average value) of the entire system is high, it means that the system as a whole is under high load, so an overall optimization strategy should be applied to improve efficiency. When the average CPU occupancy rate 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, an individual optimization strategy can be used to optimize charging piles with higher specific loads one by one.

[0045] Holistic processing strategy: When the overall system load is high, optimization goals should be focused on a global scale to reduce the overall system burden. For example, unified header compression or reducing field redundancy can significantly reduce the overall data transmission overhead.

[0046] Single-processing strategy: When the overall system load is not high, but the load of individual charging piles is high, by individually optimizing the strategy of these charging piles with higher loads, unnecessary global adjustments 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 ensured.

[0047] Data consistency: Although the header efficiency index and field redundancy index are calculated from the overall data, they do not mean that the load conditions of each charging station in the system are consistent. In actual operation, the load conditions of each charging station may vary significantly due to factors such as network conditions and user needs.

[0048] Necessity of individual optimization: Sometimes, even if the overall header efficiency and redundancy need to be optimized, a single charger may require different optimization strategies due to its unique CPU load. For example, one charger may need to reduce the data transmission frequency, while another may need to perform header field compression.

[0049] It should be noted that the adaptive optimization of the power efficiency management module is mainly used to achieve intelligent management and distribution of power in different operating scenarios. Through adaptive optimization, the system can adjust power in real time during peak load periods, low load periods, and other different usage scenarios to ensure the stability and maximum energy efficiency of the charging pile system. The main functions of adaptive optimization include: Load balancing: Automatically adjust the charging power distribution of each charging pile based on real-time operation data and scenario analysis results to avoid overloading a single charging pile.

[0050] Power management: Reduces unnecessary charging power output in high-load scenarios to ensure the overall efficient use of the system's power resources.

[0051] Dynamic adjustment: Based on the results of real-time analysis, the system can adaptively adjust the strategy to provide dynamic power scheduling and load management.

[0052] Data transmission optimization (optimization in data processing strategy): Optimization in data processing strategy is aimed at improving the performance of data transmission, ensuring that when data communication is performed, header redundancy is reduced, data packet structure is optimized, and the overall system transmission efficiency and response speed are improved. The purpose of data transmission optimization is: Reducing bandwidth occupation: By reducing the redundancy of data packet header information, the system can reduce bandwidth consumption when transmitting the same amount of data.

[0053] Improving data transmission efficiency: Optimized data transmission strategy can ensure that the system maintains fast response in high-concurrency environment and reduces data transmission delay.

[0054] Smooth performance fluctuations: Through continuous monitoring of parameters such as header efficiency index, when the data transmission efficiency is detected to be reduced, the system will trigger the optimization strategy to perform header compression, field deduplication, etc.

[0055] The adaptive optimization of the power efficiency management module is for power management, which adjusts power resources intelligently to ensure efficient management under different load conditions. Data transmission optimization belongs to the category of data processing strategy, aiming to improve the efficiency of data transmission by optimizing data packet structure and reducing redundancy to ensure the stability and performance of the system under high concurrency conditions.

[0056] The above formulas are dimensionless values calculated, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the latest real situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0057] 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 by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0058] Those of ordinary skill 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 in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed 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 implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0059] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0060] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The charging pile power management system based on the Internet of Things is characterized by: Including data acquisition module, data processing module, scenario analysis module, data feature extraction module, logic judgment module, and power energy efficiency management module; The data acquisition module is used to collect the real-time operation data of the charging pile through sensors to obtain a real-time operation data set of the charging pile; The data processing module is used 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 scenario analysis module performs scenario analysis based on the real-time operation data processing set of the charging pile, and divides the charging pile operation scenarios according to the results of the analysis operation; The data feature extraction module is used to perform feature extraction and feature analysis operations when the optimization mechanism is triggered by the charging pile operation scenario division results; The logic judgment module is used to perform fuzzy logic reasoning based on the results of feature analysis operations, infer the optimized strategy to be executed, and transmit it to the data processing module to replace the preset strategy; The power energy efficiency management module is used to adaptively perform power energy efficiency management operations based on the real-time operation data processing set of the charging pile and the charging pile operation scenario division results.

2. The charging pile power management system based on the Internet of Things according to claim 1 is characterized in that: Scenario analysis operations refer to: Assume that the total number of charging piles currently connected is , count the number of charging requests received per unit time and record it as , ; For the preset time interval, The total number of newly received charging requests within the preset time interval is recorded. The time it takes for each charging pile to respond to a request within the preset time interval is recorded, and the average value is processed to obtain the average response time of the charging pile and recorded as , and then calculate the concurrent charging demand value, the calculation formula is: ; Indicates the concurrent charging demand value.

3. The charging pile power management system based on the Internet of Things according to claim 2 is characterized in that: Charging pile operation scenarios include low concurrency scenarios, medium concurrency scenarios and high concurrency scenarios.

4. The charging pile power management system based on the Internet of Things according to claim 3 is characterized in that: The division of charging pile operation scenarios based on the results of the analysis operation refers to: The concurrent charging demand value Compare with the preset division threshold 1 and division threshold 2 respectively. The preset division threshold 1 is less than the division threshold 2. If the concurrent charging demand value is met If the value is greater than the preset division threshold 2, the charging pile operation scenario is divided into a high concurrency scenario. If the concurrent charging demand value is met If the value is less than the preset division threshold, the charging pile operation scenario is divided into a low concurrency scenario. If the concurrent charging demand value is met, If the value is greater than or equal to the preset division threshold 1 and less than or equal to the preset division threshold 2, the charging pile operation scenario is divided into a medium concurrency scenario.

5. The charging pile power management system based on the Internet of Things according to claim 4 is characterized in that: The optimization mechanism is triggered when the charging pile operation scenario is divided into a high-concurrency scenario.

6. The charging pile power management system based on the Internet of Things according to claim 5 is characterized in that: Feature extraction refers to extracting header proportion data and repeated field data based on 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.

7. The charging pile power management system based on the Internet of Things according to claim 6 is characterized in that: The logic for obtaining the head efficiency index is: Within the set time window, all data packets transmitted through the charging pile are collected, the header size and total size of each data packet are recorded, and a data packet set is formed. , Indicates 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 within the set time window; Calculate the header ratio of each data packet and mark it as , head proportion By calculating the header size of the i-th packet The total size of the packets with the i-th packet The ratio is worth getting; Use maximum normalization to process the head ratio , so that it is mapped to the interval [0,1] and the corresponding standardized processing data is obtained , and then calculate the head efficiency value: ; 、 All are preset non-zero adjustment coefficients. is the head efficiency value; Substitute into the head efficiency index calculation formula: ; Indicates the head efficiency value calculated in the last set time window. is the preset smoothing coefficient, Represents the head efficiency index.

8. The charging pile power management system based on the Internet of Things according to claim 7 is characterized in that: The logic for obtaining the field redundancy index is: Within the 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. Statistics of each field in all data packets within the set time window The number of repetitions is marked as , and statistics for each field The number of unique occurrences of , calculate each field The redundancy is calculated as follows: ; Representation field Redundancy; Substitute into the field redundancy index calculation formula: ; Representation field The total number of Indicates the field redundancy index, 、 These are all preset influence coefficients.

9. The charging pile power management system based on the Internet of Things according to claim 8, characterized in that: The logic judgment module is used to perform fuzzy logic reasoning based on the results of feature analysis operations, and the optimized strategy to be executed is as follows: The average CPU occupancy rate of all charging piles currently performing data transmission is obtained and compared with the preset applicability threshold. If the average CPU occupancy rate is greater than or equal to the applicability threshold, the head efficiency index, field redundancy index, and average CPU occupancy rate are used as input data, the input variables are fuzzified, and the values ​​of the input variables are converted into fuzzy sets. The type of the overall processing strategy of the charging pile is used as the output variable, the output variables are fuzzified, and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the data processing requirements under different data type combinations. The fuzzified input variables are reasoned through fuzzy rules to obtain the type of the optimized overall processing strategy of the charging pile. If the average CPU occupancy rate is less than the applicability threshold, the head efficiency index, field redundancy index, and CPU occupancy rate of the charging pile unit are taken as input data, the input variables are fuzzified, and the values ​​of the input variables are converted into fuzzy sets. The type of the charging pile unit processing strategy is taken as the output variable, the output variable is fuzzified, and the output variable is converted into a fuzzy set. Fuzzy rules are formulated to describe the data processing needs under different data type combinations. The fuzzified input variables are inferred through fuzzy rules to obtain the type of optimized charging pile unit processing strategy.

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