Intelligent charging pile cascade waste heat recovery circulation system

Abstract

The invention relates to the technical field of waste heat recovery of charging piles, and discloses an intelligent stepped waste heat recovery circulating system for a charging pile. The system comprises a cascade waste heat monitoring module, a heat grading regulation and control module, a loop dynamic adjustment module, a recovery abnormity response module and a recovery efficiency optimization module. The cascade waste heat monitoring module collects heat energy distribution and flow parameters in real time and generates distribution characteristics. The heat grading regulation and control module divides heat recovery priorities and calculates an efficiency threshold value according to the heat recovery priorities, and a grading parameter set is generated; the loop dynamic adjustment module optimizes the loop flow velocity and the medium proportion based on the parameter set; the recovery anomaly response module monitors the node temperature fluctuation rate and the pressure offset to generate an anomaly data set; and the recovery efficiency optimization module adjusts the medium circulation period and the temperature and pressure range according to the temperature and pressure, generates an optimization configuration table, and realizes efficient cascade recovery and dynamic optimization of the waste heat of the charging pile.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging pile waste heat recovery, in particular to an intelligent charging pile cascade waste heat recovery circulating system. BACKGROUND

[0002] With the rapid popularization of new energy vehicles, charging piles as important energy supply facilities, their number is increasing day by day, and the operating power is also increasing. In the process of long-term high-load operation of charging piles, a large amount of heat energy will be generated in the core components such as power units, which will not only affect the operation stability and service life of the equipment, but also cause unnecessary waste of energy if not handled in time and effectively.

[0003] The heat dissipation of charging piles mostly adopts traditional air cooling or liquid cooling methods, which only aim to maintain the normal working temperature of the equipment and do not effectively recover the generated waste heat. In some existing waste heat recovery attempts, there is a lack of accurate perception of the heat source intensity in different areas of the charging pile, and a unified recovery mode is often used, resulting in low heat recovery efficiency. Due to the differences in the heating characteristics of each power unit inside the charging pile, the operating load also changes at different times, so the fixed heat dissipation circuit configuration is difficult to adapt to the dynamic heat distribution situation, and local heat accumulation or insufficient waste heat recovery may occur.

[0004] The existing system lacks monitoring of abnormal conditions during heat exchange, and when temperature abnormal fluctuations or pressure deviations occur at the heat exchange nodes, it cannot identify and adjust in time, which may cause heat conduction efficiency to decrease, and even affect the normal operation of the charging pile. In addition, the waste heat recovery efficiency lacks a continuous optimization mechanism, and once the recovery parameters are set, they remain unchanged, making it difficult to dynamically adjust according to the actual operating state, resulting in the system being difficult to maintain efficient waste heat recovery effect in long-term operation. These problems make a large amount of waste heat generated by charging piles unable to be reasonably utilized, causing energy waste and not providing additional heat energy value for surrounding facilities or users. SUMMARY

[0005] The purpose of the present application is to provide an intelligent charging pile cascade waste heat recovery circulating system to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides an intelligent charging pile cascade waste heat recovery circulating system, which comprises:

[0007] The cascade waste heat monitoring module collects the heat energy distribution characteristics and heat dissipation circuit flow parameters of the power units of the charging pile in real time, analyzes the matching degree of the heat source intensity level and the heat dissipation path, and generates the cascade waste heat distribution characteristics;

[0008] The heat hierarchical regulation module divides the heat recovery priority of different temperature zones of the charging pile based on the hierarchical residual heat distribution characteristics, calculates the heat conduction efficiency threshold of the residual heat transmission path, and generates a hierarchical heat recovery parameter set;

[0009] The loop dynamic adjustment module calibrates the flow distribution deviation in the multi-stage heat exchange loop based on the hierarchical heat recovery parameter set, reconfigures the proportional relationship between the loop flow rate and the heat exchange medium, and generates a dynamic loop regulation result;

[0010] The recovery abnormality response module monitors the temperature fluctuation rate and pressure deviation of the heat exchange node based on the dynamic loop regulation result, identifies the influence of the abnormal fluctuation range on the heat conduction efficiency, and generates an abnormality marking data set;

[0011] The recovery efficiency optimization module analyzes the comprehensive energy efficiency ratio of the hierarchical heat recovery path based on the abnormality marking data set, adjusts the circulation period and temperature and pressure control range of the heat exchange medium, and generates a residual heat recovery optimization configuration table.

[0012] Preferably, the step of obtaining the hierarchical residual heat distribution characteristics is specifically:

[0013] Extract the thermal imaging data of the charging pile power unit and the real-time flow of the heat dissipation loop, associate the heat source position coordinates with the loop node temperature, and statistically analyze the corresponding relationship between the heat intensity and the loop flow of each temperature zone;

[0014] Based on the corresponding relationship, calculate the matching deviation value of the heat source intensity level and the heat dissipation path, correct the heat distribution weight combined with the specific heat capacity parameter of the heat dissipation medium, and generate a heat source level matching analysis result;

[0015] According to the heat source level matching analysis result, divide the heat recovery potential interval, calibrate the position distribution and heat conduction path characteristics of the high-potential heat source, and generate the hierarchical residual heat distribution characteristics.

[0016] Preferably, the step of obtaining the hierarchical heat recovery parameter set is specifically:

[0017] Analyze the heat recovery potential interval in the hierarchical residual heat distribution characteristics, arrange the heat recovery priority sequence in descending order of temperature zone, and associate the maximum heat load threshold of the heat exchange loop;

[0018] Based on the heat recovery priority sequence and the heat load threshold, calculate the heat conduction rate difference of the residual heat transmission path, and identify the transmission path below the preset efficiency threshold;

[0019] According to the efficiency defect analysis result of the transmission path, re-allocate the flow quota of the heat exchange medium, and generate a hierarchical heat recovery parameter set.

[0020] Preferably, the step of obtaining the dynamic loop regulation result is specifically:

[0021] extracting a flow distribution scheme of the multi-stage heat exchange circuit from the hierarchical heat recovery parameter set, comparing deviation values of actual flow rates and target flow rates, and calibrating flow imbalance intervals between circuit nodes;

[0022] analyzing influences of the flow imbalance intervals on temperature distributions of the heat exchange medium, and adjusting medium flow rate proportions and heat exchange durations of the high-load circuit;

[0023] based on adjustment results of the medium flow rate and the heat exchange duration, reconstructing an equilibrium parameter of the heat conduction path, and generating a dynamic circuit regulation result.

[0024] Preferably, the step of obtaining the abnormal marker dataset specifically comprises:

[0025] monitoring temperature fluctuation rates of the heat exchange nodes in the dynamic circuit regulation result, correlating real-time pressure sensor data, and eliminating abnormal fluctuation values caused by equipment failure;

[0026] calculating attenuation coefficients of effective temperature fluctuation ranges and pressure offsets on heat conduction rates, and identifying fluctuation intervals exceeding a safety threshold;

[0027] based on safety risk analysis results of the fluctuation intervals, marking heat exchange node coordinates and fluctuation parameters requiring emergency intervention, and generating an abnormal marker dataset.

[0028] Preferably, the step of obtaining the waste heat recovery optimization configuration table specifically comprises:

[0029] analyzing intervention node parameters in the abnormal marker dataset, reevaluating energy efficiency loss rates of the hierarchical heat recovery path, and correlating specific heat capacities of the heat exchange medium;

[0030] based on energy efficiency loss rates and medium characteristic analysis results, adjusting cycle period control ranges and temperature-pressure compensation parameters of the heat exchange medium;

[0031] based on adjustment schemes of the medium circulation and the temperature-pressure compensation, generating a cooperative work configuration table of the multi-stage heat recovery path, and generating a waste heat recovery optimization configuration table.

[0032] Preferably, the system further comprises a heat capacity reconstruction module:

[0033] based on the waste heat recovery optimization configuration table, extracting cycle period control ranges of the heat exchange medium, and analyzing matching difference values of medium heat capacity saturation and circuit heat load;

[0034] based on the matching difference values, recalculating medium replenishment periods and heat capacity buffer intervals, and generating a heat capacity reconstruction parameter set;

[0035] feeding the heat capacity reconstruction parameter set back to the hierarchical heat regulation module, and updating heat load threshold values of the hierarchical heat recovery parameter set.

[0036] Preferably, the system further comprises a distributed updating module:

[0037] Receiving the heat load threshold updating data of the heat capacity reconstruction parameter set feedback, rescan the temperature zone heat intensity distribution of the charging pile power unit;

[0038] Based on the updated heat load threshold and the temperature zone heat intensity distribution, calibrate the position coordinates and heat conduction path of the newly added high potential heat source;

[0039] According to the characteristics of the newly added heat source path, correct the characteristics of the cascade waste heat distribution, and input the updating result to the loop dynamic adjustment module.

[0040] Preferably, the system further comprises an efficiency verification module:

[0041] Collecting the multi-stage heat recovery path cooperative work data of the waste heat recovery optimization configuration table, and associating the medium supplement period of the heat capacity reconstruction parameter set;

[0042] Verify the deviation range of the actual heat recovery energy efficiency improvement rate and the target value, and identify the node position of the low-efficiency cooperative path;

[0043] The low-efficiency node position and deviation data are fed back to the recovery abnormal response module, and the monitoring range of the abnormal marker data set is expanded.

[0044] Preferably, the system further comprises an output integration module:

[0045] Summarize the heat exchange medium circulation parameters of the waste heat recovery optimization configuration table, the buffer interval data of the heat capacity reconstruction parameter set, and the energy efficiency improvement rate verification result of the efficiency verification module;

[0046] Integrate multi-source parameters to generate real-time operation configuration instruction set of the heat recovery system, and drive the heat exchange loop executive mechanism to complete the cascade waste heat recovery operation.

[0047] Compared with the prior art, the beneficial effects of the present application are:

[0048] The intelligent charging pile cascade waste heat recovery circulation system provides a more accurate and efficient solution for charging pile waste heat recovery through the cooperative operation of multiple modules. The cascade waste heat monitoring module can capture the heat energy distribution and heat dissipation circuit parameters of the charging pile power unit in real time, analyze the matching degree of heat source intensity level and heat dissipation path, and make the system have a clear understanding of the heat situation inside the charging pile, avoiding the problem of fuzzy heat distribution perception in traditional recovery methods, so that the subsequent waste heat recovery work can be targeted.

[0049] The heat hierarchical regulation module divides the heat recovery priority based on the gradient waste heat distribution characteristics and calculates the heat conduction efficiency threshold to generate a hierarchical heat recovery parameter set. This hierarchical processing method fully considers the heat generation differences in different temperature zones of the charging pile, changes the limitations of the previous unified recovery mode, and enables the waste heat recovery to prioritize high heat value areas, improving the relevance and effectiveness of waste heat recovery.

[0050] The loop dynamic adjustment module calibrates the flow distribution deviation of the multi-stage heat exchange loop according to the hierarchical heat recovery parameter set and reconfigures the proportional relationship between the loop flow rate and the heat exchange medium. This process can dynamically adapt to the heat changes under different operating states of the charging pile, avoiding the problem of fixed loop configuration that is difficult to cope with dynamic heat distribution, making the distribution of heat exchange medium more reasonable, helping to improve heat conduction efficiency and reduce local heat accumulation or insufficient recovery.

[0051] The recovery abnormality response module monitors the temperature fluctuation rate and pressure deviation of the heat exchange node, identifies the influence of abnormal fluctuations on heat conduction efficiency and generates an abnormality marker data set. By timely discovering abnormal conditions in the heat recovery process, the system can perceive potential problems earlier, provide a basis for subsequent adjustment and optimization, reduce the continuous impact of abnormal conditions on waste heat recovery effectiveness, and ensure the stability of system operation.

[0052] The recovery efficiency optimization module analyzes the overall energy efficiency ratio based on the abnormality marker data set, adjusts the circulation period and temperature and pressure control range of the heat exchange medium, and generates an optimized configuration table. This continuous optimization process enables the system to continuously adjust parameters according to actual operating conditions, avoiding the problem of declining system efficiency over time with fixed parameters, enabling the waste heat recovery system to maintain good working conditions in long-term operation, fully exploiting the utilization value of charging pile waste heat and reducing energy waste. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 A timing diagram of the intelligent charging pile gradient waste heat recovery circulation system described in the present invention;

[0054] Figure 2 A flowchart for obtaining the hierarchical heat recovery parameter set;

[0055] Figure 3 A flowchart for obtaining the abnormality marker data set;

[0056] Figure 4 A flowchart for the work of the heat capacity reconstruction module. DETAILED DESCRIPTION

[0057] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0058] With reference to Figure 1 The present application provides an intelligent charging pile step waste heat recovery circulating system, which comprises five core modules of step waste heat monitoring, heat hierarchical regulation, loop dynamic adjustment, recovery abnormal response and recovery efficiency optimization, and realizes fine management of the whole process of the waste heat of the power unit of the charging pile. The specific implementation is as follows:

[0059] The step waste heat monitoring module first collects the thermal imaging data and the heat dissipation loop flow parameter of the power unit, associates the heat source coordinates and the node temperature, performs matching deviation calculation and specific heat capacity correction, and generates the step waste heat distribution characteristics. The heat hierarchical regulation module analyzes the heat recovery potential interval according to the step waste heat distribution characteristics, sorts the priority in descending order of temperature zone, identifies the inefficient transmission path in combination with the heat load threshold, reallocates the medium flow quota, and generates the hierarchical heat recovery parameter set. The loop dynamic adjustment module extracts the flow scheme in the parameter set, compares the flow rate deviation and adjusts the medium flow rate and exchange time of the high-load loop, reconstructs the path balance parameter to output the dynamic regulation result. The recovery abnormal response module monitors the node temperature fluctuation rate and pressure deviation, marks the fluctuation nodes exceeding the safety threshold after excluding the equipment fault interference value, and forms an abnormal marking data set. The recovery efficiency optimization module evaluates the energy efficiency loss rate based on the data set, adjusts the cycle period and temperature and pressure compensation parameters in combination with the specific heat capacity characteristics of the medium, and finally generates the waste heat recovery optimization configuration table. At the same time, the modules of heat capacity reconstruction and distribution update are linked to continuously correct the parameters, and the waste heat recovery efficiency and system stability are comprehensively improved.

[0060] Embodiment 1: With reference to Figure 2 The core of the cooperative work process of the step waste heat monitoring module and the heat hierarchical regulation module is to collect the thermal energy distribution characteristics of the power unit of the charging pile in real time and generate an optimized heat recovery parameter set. The following will explain this implementation mode in detail through specific operation process and data processing method:

[0061] The step waste heat monitoring module first deploys an infrared thermal imager to scan the power unit of the charging pile and collects the surface temperature distribution data of the power unit. The thermal imager scans the surface of the power unit at a fixed frequency, generates a temperature distribution map every scanning period, each pixel point in the map corresponds to a temperature value, and is mapped to the actual physical position of the power unit. At the same time, the electromagnetic flowmeter in the heat dissipation loop records the flow rate of the cooling medium in real time. The flow data and the temperature data are synchronized through the time stamp, ensuring the spatio-temporal consistency of the analysis.

[0062] The determination of the heat source position coordinates is based on a three-dimensional structural model of the power unit, which divides the surface into a number of uniform grids, and the coordinates of the center point of each grid are associated with its temperature value. The loop node temperature is collected by embedded temperature sensors, which are uniformly arranged at key positions of the heat dissipation loop, such as the inlet and outlet, bends, and branch points. After combining the temperature data with the flow data, the system calculates the heat flux density of each grid unit, reflecting the heat energy release intensity of the region. The calculation of the heat flux density takes into account the thermal conductivity of the material, the ambient temperature, and the distance between the grid unit and the heat dissipation loop.

[0063] The heat source intensity level division uses a clustering algorithm to divide the heat flux density values into high, medium, and low levels. High-level heat source regions usually correspond to parts of the power unit with high current density or poor heat dissipation conditions, such as near power semiconductor devices. The system calculates the area proportion of each level of heat source region, and combines it with the actual flow data of the heat dissipation loop to analyze the matching degree of the heat source and the heat dissipation path. The matching degree is evaluated by a thermal resistance model, which calculates the thermal resistance value of each heat dissipation loop and compares it with the theoretical optimal value to obtain the matching deviation. The specific heat capacity parameter of the heat dissipation medium is used to correct the heat distribution weight, ensuring that the heat energy recovery priority of high-temperature regions is higher.

[0064] The division of the heat recovery potential interval is based on the corrected heat distribution weight and the heat flux density data, which divides the surface of the power unit into a number of evaluation units. The heat recovery potential value of each unit is obtained by weighting and summing the heat flux density of all grid units it contains. High-potential heat source regions are defined as the evaluation units with the top 20% potential values, and their coordinates are determined by centroid calculation and recorded for their spatial distribution characteristics. The topological structure of the heat conduction path is constructed by a triangulation algorithm, reflecting the heat energy transfer relationship between high-potential heat sources. The final generated hierarchical waste heat distribution characteristics include heat source coordinates, heat flux density matrix, and conduction path topology graph, providing a data basis for subsequent heat hierarchical regulation.

[0065] After receiving the hierarchical waste heat distribution characteristics, the heat hierarchical regulation module first normalizes the heat intensity of each temperature zone to eliminate dimensional differences. The normalized heat intensity value is used to generate a heat recovery priority sequence, with high-priority temperature zones corresponding to higher heat energy recovery value. The maximum heat load threshold of the heat exchange loop is calculated by a heat transfer model, considering the flow upper limit of the loop, the specific heat capacity of the medium, and the maximum allowable temperature rise. The system calculates the heat conduction rate difference of each loop according to the priority sequence and the heat load threshold, and identifies the less efficient paths.

[0066] For the efficiency defect path, the system uses an optimization algorithm to redistribute the flow quota of the heat exchange medium. The optimization goal is to maximize the total heat recovery without exceeding the pump power limit. The flow distribution scheme takes into account the actual flow rate, heat load capacity, and priority weight of each circuit to ensure that the heat energy of high-priority temperature zones is fully recovered. The final generated hierarchical heat recovery parameter set includes the target flow distribution ratio, heat exchange time constraint, and priority weight of each temperature zone, providing a basis for regulation and control for the circuit dynamic adjustment module.

[0067] In actual operation, the cascade waste heat monitoring module continuously updates temperature and flow data, and dynamically adjusts the heat source level matching analysis results. The heat hierarchical control module iteratively optimizes the hierarchical heat recovery parameter set according to the latest data to ensure that the system always adapts to the changes in the heat energy of the charging pile power unit. This closed-loop control mechanism enables the waste heat recovery system to efficiently respond to fluctuations in heat load under different working conditions of the charging pile, improving overall heat energy utilization.

[0068] Embodiment 2: see Figure 3 The core of the collaborative operation process of the circuit dynamic adjustment module and the recovery abnormal response module is to dynamically optimize the heat exchange circuit according to the hierarchical heat recovery parameter set and monitor the system operation state in real time to identify abnormal conditions. The following describes this implementation through specific operation processes and system interaction methods:

[0069] The circuit dynamic adjustment module first receives the hierarchical heat recovery parameter set from the heat hierarchical control module, which includes key parameters such as the target flow distribution ratio of each temperature zone and the heat exchange time constraint. The system monitors the actual medium flow rate of each heat exchange circuit in real time through high-precision ultrasonic flow meters, with a sampling frequency of 20 times per second to ensure real-time data. The monitoring data is compared with the target values set in the parameter set to generate a matrix containing the flow rate deviation values of each circuit. This deviation matrix reflects the difference between the current system operation state and the ideal control target.

[0070] For the identified flow imbalance interval, the system uses a multi-parameter coupling analysis method to evaluate its impact on heat exchange efficiency. The temperature distribution data of each circuit node is collected by distributed optical fiber temperature sensors with a spatial resolution of 1 centimeter. The system constructs a three-dimensional temperature field model to analyze the local temperature abnormal area caused by flow rate deviation. In a specific operation case, when the actual flow rate of a branch is detected to be 15% lower than the target value, the outlet temperature of the circuit increases by about 8°C compared to the normal state, and the system immediately marks this area as a high-load circuit that needs to be adjusted first.

[0071] The adjustment of the medium flow rate ratio is achieved by the cooperation of the variable frequency pump group and the electric regulating valve. The system calculates the required flow rate compensation according to the flow deviation degree and the temperature abnormality range. The adjustment strategy adopts a gradual approximation method, which first changes the valve opening or pump speed at a small amplitude, and then makes a second fine adjustment after observing the temperature field response. In a certain actual operation, the system detected that the flow rate of the third main circuit was insufficient, first increased the speed of the variable frequency pump, and then adjusted the speed again after the temperature sensor feedback showed that the outlet temperature was decreasing stably, finally restored the flow rate of the circuit to the target range.

[0072] The dynamic adjustment of the heat exchange time is based on the real-time heat load data of each circuit, which predicts the time required to complete the target heat exchange amount by analyzing the inlet and outlet water temperature difference and the flow rate change rate. When the heat load of a certain circuit is detected to suddenly increase, the system will automatically extend the heat exchange period of the circuit, while the operation time of the low load circuit is correspondingly shortened. This dynamic time allocation mechanism ensures that the system resources are always tilted to the high heat load area.

[0073] The recovery abnormality response module continuously monitors the state parameters of the key nodes during the circuit adjustment process. The temperature fluctuation rate is calculated by a sliding time window algorithm, which analyzes the temperature change trend of each sensor in the last 30 seconds. The pressure offset is collected by a high-response pressure sensor with a sampling frequency of up to 100Hz to capture transient fluctuations. The system establishes a temperature-pressure correlation model to distinguish between normal condition fluctuations and real abnormal situations.

[0074] The filtering process of abnormal data adopts a multi-level filtering mechanism. The original sensor data is first filtered by hardware to eliminate high-frequency noise, and then filtered by a digital filtering algorithm to eliminate occasional interference signals. In a certain operation instance, the pressure sensor detected a transient peak, but the temperature data did not show corresponding changes, and the system determined that this was a sensor interference rather than a real abnormality, and did not trigger the response process. For data confirmed as real abnormality, the system calculates its influence weight on the heat conduction efficiency, and generates an abnormality level assessment according to the preset safety threshold.

[0075] The generation of abnormality marked data set adopts a structured storage method, each abnormality record contains fields such as abnormality type, occurrence position, start time, duration, maximum deviation value, etc. The data set is organized in time sequence, and spatial index is established to quickly locate the abnormality high incidence area. The system once recorded periodic temperature fluctuations at the inlet of a certain heat exchanger, and found through analysis of historical data that the abnormality was related to a specific working mode, and optimized the control parameters of the working condition accordingly.

[0076] The system adopts a hierarchical response strategy for identified abnormal fluctuations. For slight abnormalities, only data is recorded and observation continues; for moderate abnormalities, an early warning is triggered and an operation and maintenance personnel is prompted to pay attention; and for serious abnormalities, a protection mechanism is immediately started, such as reducing the pump speed or switching to a standby loop. All response operations are recorded in the abnormality marking data set, forming a complete abnormality handling closed loop. The data set also provides an analysis basis for the recovery efficiency optimization module, realizing continuous improvement of the system running state.

[0077] Embodiment 3: The implementation of the embodiment focuses on the running mechanism of the recovery efficiency optimization module, which comprehensively optimizes the energy efficiency of the cascade heat recovery system based on the abnormality marking data set. The system re-evaluates the heat transfer performance of each heat recovery path by analyzing the abnormal fluctuation data of the heat exchange nodes, and dynamically adjusts the medium circulation parameters to improve the overall energy efficiency. The specific implementation process and technical features of the module are described in detail below.

[0078] The recovery efficiency optimization module first receives the structured abnormality marking data set from the abnormality response module, which contains temperature fluctuation rate, pressure deviation, and corresponding spatial position information. The module uses a spatiotemporal correlation analysis method to process these data, establishing a mapping relationship between abnormal events and heat exchange loop positions. Each abnormality record contains fields such as timestamp, physical coordinates, abnormality type code, and fluctuation amplitude, and the system indexes and stores these data in a distributed database, with a query response time controlled within 50 milliseconds.

[0079] The energy efficiency loss evaluation model is based on the second law of thermodynamics and calculates the exergy loss coefficient of each heat recovery path. For the pth heat recovery path, the energy efficiency loss rate is calculated by the following formula:

[0080]

[0081] wherein: and represent the medium inlet and outlet temperatures of path p, respectively, and T max represents the maximum allowable working temperature of the path. This formula quantifies the gap between the actual heat transfer efficiency of the path and the theoretical maximum value, and the closer the value is to 1, the more serious the energy efficiency loss. The system regularly calculates the value of each path, forming an energy efficiency loss trend chart for analyzing performance degradation rules.

[0082] The specific heat capacity characteristics of the heat exchange medium are obtained through laboratory calibration, and the differential scanning calorimetry method is used to measure the value of the medium at different temperatures. Among them, Cp,pis the specific heat capacity of the heat recovery path p. The measurement results are fitted as a polynomial function of temperature and input into the system database. When the medium temperature changes, the system automatically calls the function to update the current specific heat capacity value, ensuring the accuracy of thermodynamic calculations. In a certain operating instance, when the medium temperature rises from 40°C to 60°C, the system detects that its specific heat capacity has decreased, and accordingly adjusts the flow parameters of the related circuit.

[0083] The adjustment of the cycle period control parameter is based on the thermal relaxation time constant τ, which reflects the time required for the system to recover from a non-equilibrium state to an equilibrium state. The τ value is fitted by monitoring the change curve of the medium temperature over time, and the system dynamically sets the cycle period range according to the τ value. For circuits with larger τ values, the cycle period is appropriately extended to ensure sufficient heat exchange; for circuits with smaller τ values, the period is shortened to improve response speed. The control algorithm uses fuzzy logic method to seek the optimal balance between cycle adjustment amount and system stability.

[0084] The determination of temperature and pressure compensation parameters is based on the medium state equation and phase transition critical point data. The system maintains a lookup table containing the physical property parameters of the medium at different temperatures and pressures, and queries the physical property values corresponding to the current working condition in real time. When it is detected that the temperature or pressure of a certain circuit is close to the phase transition critical point, the compensation mechanism is automatically triggered, and the heat load is shared by adjusting the flow of adjacent circuits to avoid the decrease of heat transfer efficiency caused by the phase transition of the medium. The compensation coefficient matrix is dynamically generated according to the degree of thermal coupling between circuits to ensure the accuracy of compensation operation.

[0085] The cooperative optimization of multi-stage heat recovery paths is realized by using the non-dominated sorting genetic algorithm (NSGA-II). This algorithm takes the maximization of total heat recovery and the minimization of system energy consumption as double objectives, and considers the flow constraints, temperature limits and equipment operating parameters of each circuit. The optimization process generates a set of Pareto optimal solutions, and the system selects the configuration scheme that best meets the current operating requirements from among them. In each optimization iteration, the algorithm evaluates hundreds of possible parameter combinations, and the calculation time is controlled within 5 seconds to meet the real-time requirements.

[0086] The generation of the waste heat recovery optimization configuration table uses a hierarchical structure design. The top layer records system-level parameters, including the total heat recovery target, the maximum pump power allowed, etc. The middle layer contains independent parameters of each circuit, such as target temperature, flow range, cycle period, etc. The bottom layer stores node-level control instructions, such as valve opening degree, pump speed set value, etc. The configuration table is stored in JSON format, which is convenient for parsing and execution by each execution mechanism. The system updates the configuration table every 30 minutes, or triggers a recalculation immediately when a major anomaly is detected.

[0087] The execution process of the configuration table adopts a progressive adjustment strategy. When the difference between the old and new configuration table parameters is large, the system gradually approaches the target value in multiple steps, avoiding impacting the equipment. After each adjustment, the system monitors the trend of key parameters and confirms stability before proceeding to the next adjustment. All adjustment operations are recorded in the system log, including adjustment time, parameter change amount, execution result, and other information, providing data support for analysis.

[0088] The system maintains a configuration version library that saves the configuration tables generated by each optimization. The version library supports historical configuration retrieval based on time range, operating conditions, and other conditions. When the current system state is similar to a certain historical state, the corresponding optimization parameters can be quickly called as initial values to speed up the convergence speed. The version comparison function helps identify the optimal configuration rules under different operating conditions and continuously improve the optimization algorithm.

[0089] The performance optimization module interacts with other system modules in real time, receives medium thermophysical property update data from the heat capacity reconstruction module, and obtains the latest heat source distribution information from the distribution update module. At the same time, the optimization results are fed back to the actuator. This closed-loop operation mode ensures that the system always makes decisions based on the latest state data, maximizing the waste heat recovery efficiency.

[0090] Example 4: refer to Figure 4 The cooperative operation of the heat capacity reconstruction module and the distribution update module is described, focusing on how the system adjusts the heat exchange medium parameters and dynamically updates the heat source distribution information based on the optimization configuration. The following describes this implementation in detail through specific operation processes and data processing methods, and includes a key parameter example table.

[0091] The heat capacity reconstruction module first parses the waste heat recovery optimization configuration table from the recovery performance optimization module and extracts the control parameters related to the heat exchange medium circulation period. The system monitors the medium state in real time through multi-parameter sensors installed in the medium storage tank, including temperature, density, and heat capacity saturation, among other key indicators. The monitoring data is updated at a frequency of 2 times per second, forming a medium state trend chart. The built-in heat capacity saturation calculation model compares the real-time monitoring value with the theoretical maximum value to obtain the current medium energy carrying capacity.

[0092] The heat capacity buffer interval calculation is based on the medium thermodynamic property curve and historical operation data, analyzes the medium temperature fluctuation range in the past 24 hours, and dynamically determines the safe operation boundary in combination with the current heat load prediction value. When the medium temperature of a certain circuit is detected to be close to the boundary value, the buffer mechanism is automatically triggered to adjust the flow of adjacent circuits to share the heat load. The buffer interval parameters are set hierarchically according to the importance of the circuits, with wider buffer intervals for critical circuits to ensure system stability.

[0093] The determination of the medium replenishment period uses an adaptive algorithm, taking into account the following factors: the current medium heat capacity saturation rate, the future 15-minute heat load forecast value, the standby medium reserve, etc. The system maintains a medium replenishment decision matrix, as shown in Table 1, which lists the replenishment strategy selection criteria under different operating conditions. The table data is derived from long-term operation statistics and is updated regularly to reflect the latest system status.

[0094] Table 1: Medium replenishment strategy decision matrix

[0095] The generation of the heat capacity reconstruction parameter set uses a hierarchical structure, consisting of three levels: the basic parameter layer records the current physical state of the medium, the control parameter layer sets the replenishment period and buffer interval threshold, and the execution parameter layer specifies the adjustment amount of each circuit. The parameter set is shared with all system modules through a distributed database, with an update frequency synchronized with medium state monitoring.

[0096] The workflow of the distributed update module starts with receiving the heat load threshold update data of the heat capacity reconstruction parameter set. The module starts the power unit temperature zone scanning program, which uses a mobile infrared temperature measurement device to perform full-surface scanning of the charging pile power unit. The scanning path uses a serpentine trajectory to cover the entire surface, and each scanning point stays for no more than 0.1 seconds to ensure the timeliness of data collection. The scanning data is aligned with the three-dimensional model of the power unit to generate an updated temperature zone heat intensity distribution map.

[0097] The identification of new high-potential heat sources uses a change detection algorithm to compare the difference between the new and old heat intensity distribution maps. When the temperature rise amplitude of a certain area exceeds the set threshold (such as 15°C) and the duration exceeds 5 minutes, the system will mark it as a new high-potential heat source. The heat source coordinates are accurately located through a spatial interpolation algorithm, with an accuracy of ±2mm. The system automatically assigns a unique identifier to the new heat source and records its first appearance time, initial temperature, and other attributes.

[0098] The heat conduction path reconstruction process uses a finite element analysis method based on the power unit structure model and material thermal conductivity parameters to calculate the heat flow path between the new heat source and the existing heat dissipation circuit. The analysis results generate a new path topology map, labeling the thermal resistance values and recommended flow ranges of each path. The path reconstruction algorithm prioritizes the shortest heat transfer distance principle while avoiding conflicts with existing high-load paths. The updated cascade waste heat distribution characteristics include the following core elements: the heat source spatial distribution matrix records the location and intensity level of all heat sources; the heat flow path topology map describes the connection relationship of the conduction paths; and the thermal resistance parameter table lists the heat transfer characteristics data of each path. These data are transmitted to the circuit dynamic adjustment module through a standardized interface, triggering a new round of system parameter optimization.

[0099] During system operation, a new high-temperature concentration area was detected in the southeast corner of the power unit. Infrared scanning showed that the temperature in this area rose by 18°C within 10 minutes, and the system immediately marked it as a new high-potential heat source. The thermal capacity reconstruction module detected a decrease in the saturation degree of the medium thermal capacity, triggering a supplementary warning. The distribution update module analyzed the distance between the heat source and the nearest heat dissipation circuit, which was 12 cm, and generated a new heat conduction path plan. The circuit dynamic adjustment module re-allocated the flow ratio of the third circuit based on the updated distribution characteristics, controlling the temperature of the new heat source area within the safe range.

[0100] The system maintains a version control system that records the detailed parameters of each thermal capacity reconstruction and distribution update. The version record contains information such as timestamp, operation type, modified parameter list, and associated modules. When an abnormal system state is detected, the history version can be quickly traced back to analyze the correlation between parameter changes and abnormal phenomena. The version comparison tool helps identify the optimal parameter combination and provides a reference for optimization.

[0101] The data interaction between the thermal capacity reconstruction module and the distribution update module uses a publish-subscribe mode. The thermal capacity reconstruction module publishes medium state update events, and the distribution update module subscribes to these events and triggers the corresponding processing flow. This loosely coupled architecture ensures that each module can operate independently and collaboratively, improving the system's scalability and maintainability. All data exchanges are completed through a message middleware, avoiding direct module dependencies.

[0102] The system sets up a multi-level cache mechanism to ensure data processing timeliness. Real-time monitoring data is stored in an in-memory database for fast access, and historical data is archived to disk storage on a regular basis. Key indicators such as thermal capacity saturation are updated in real time using a sliding window algorithm, and the window size is dynamically adjusted based on system load. When processing a large amount of concurrent data, the data downsampling function is automatically enabled to balance the requirements of calculation accuracy and real-time performance.

[0103] Example 5: The collaborative operation of the performance verification module and the output integration module is unfolded, focusing on the process of how the system verifies the heat recovery performance and generates the final execution instruction set. The performance verification module continuously monitors the execution of the waste heat recovery optimization configuration table and collects actual operation data of each heat recovery circuit. The module obtains real-time parameters such as temperature, pressure, and flow rate through distributed data collection nodes, with a sampling frequency of 10 times per second to ensure data integrity. The collected data is compared with the target values in the optimization configuration table, and the actual heat recovery energy efficiency value of each circuit is calculated. The energy efficiency value calculation considers factors such as heat transfer quantity, medium flow loss, and equipment energy consumption, reflecting the comprehensive performance of the circuit.

[0104] The system establishes a multi-dimensional performance evaluation system to analyze the energy efficiency trend from the time dimension and compare the performance differences between different circuits from the spatial dimension. The evaluation process uses a sliding time window technique, with each window covering the last 15 minutes of operation data and a window sliding step of 1 minute. This design can capture short-term fluctuations and identify long-term trends. When the energy efficiency value of a certain circuit is lower than the target threshold for three consecutive windows, the system marks it as an inefficient cooperative path. The positioning of inefficient nodes uses a hierarchical diagnosis method to analyze the comprehensive energy efficiency index of the circuit level, determine the scope of the problem circuit, check the thermodynamic parameters of each segment of the circuit to locate the specific interval where the anomaly occurs, and check the original data of all sensor nodes in the interval to identify the root cause of the energy efficiency decline. The diagnosis results include information on the abnormal type, location, and possible causes in three dimensions, providing a clear direction for optimization.

[0105] The abnormal diagnosis data is transmitted to the recovery abnormal response module through a standardized interface, expanding its monitoring range. The data structure transmitted includes fields such as timestamp, spatial coordinates, abnormal code, and detailed description. The system maintains an abnormal knowledge base that records historical diagnosis cases and solutions, allowing for quick matching of response strategies when similar abnormal patterns are detected. The knowledge base uses a continuous learning mechanism, with new diagnosis results automatically supplemented to the knowledge base after verification, continuously improving the system's self-learning ability.

[0106] The output integration module is responsible for summarizing the optimization results of each module in the system and generating an executable instruction set. The module extracts the circulation parameters of the heat exchange medium from the waste heat recovery optimization configuration table, including key control quantities such as target temperature, flow range, and circulation period. The buffer interval data in the heat capacity reconstruction parameter set are integrated to determine the safe operating boundaries of each circuit. The control parameters are fine-tuned in combination with the energy efficiency improvement rate verification results from the efficiency verification module.

[0107] The generation of the instruction set uses a hierarchical progressive strategy, with top-level instructions defining system-level operation modes such as normal mode, energy-saving mode, or high-load mode. Middle-level instructions specify the cooperative working parameters of each circuit to ensure optimal overall heat recovery efficiency. Bottom-level instructions are specific to the operation commands of each execution mechanism, such as the speed set value of a variable frequency pump or the opening percentage of a regulating valve. The instruction set uses a binary and ASCII mixed encoding, balancing transmission efficiency and readability.

[0108] The instruction execution process uses a double verification mechanism. Before issuing control instructions, their feasibility is verified in a virtual simulation environment to predict the system state changes after execution. During actual execution, the instruction effect is monitored through real-time data feedback, and if the deviation from the expected value exceeds the allowed range, a correction process is immediately started. The execution results are recorded in the system log, including detailed information such as instruction content, issuance time, execution status, and actual effect.

[0109] When the system is running, a certain verification process finds that the energy efficiency value of the second loop is 12% lower than the target value. Diagnostic analysis shows that the heat exchange efficiency of the third section of the loop has decreased, and further inspection finds that the temperature sensor in the corresponding area has a drift phenomenon. The system supplements this anomaly to the anomaly marking data set, and adjusts the temperature compensation parameter in the instruction set. The corrected instruction makes the energy efficiency of the loop return to the normal level, and the whole process is completed within 3 minutes. The data flow between modules uses a unified time synchronization mechanism. All data records are marked with a timestamp accurate to the millisecond, ensuring the time consistency of the analysis results of different modules. The system clock is calibrated regularly through the NTP protocol, and the clock deviation of each collection node is controlled within 10 milliseconds. This strict time synchronization ensures the accurate association and analysis of distributed data.

[0110] The system sets up multiple levels of cache to improve data processing efficiency, and uses memory calculation for real-time instruction generation, with a response time controlled within 100 milliseconds. Historical data is stored in a time series database, supporting fast retrieval and analysis. When the system load is high, automatic data priority management is enabled to ensure the real-time performance of critical instructions is not affected. The cache strategy is dynamically adjusted according to the system running state, balancing processing speed and resource occupation. The version rollback function enhances system reliability, and before each major instruction update, the current system configuration snapshot is automatically saved. If an anomaly occurs after the execution of a new instruction, it can be rolled back to the previous stable version within 30 seconds. The rollback process records detailed logs, including rollback reason, operation time and system state, providing complete data chain for problem analysis.

[0111] The performance verification module and the output integration module form a closed-loop control system, and the verification results are continuously fed back to the optimization algorithm to guide the generation of instructions. Through this continuous self-correction mechanism, the system gradually improves the overall running efficiency. All optimization and adjustment processes are recorded, forming a complete running history, supporting post-analysis and algorithm improvement.

[0112] It should be noted that, in this text, relational terms such as first and second are used only to distinguish one entity or action from another, and do not necessarily require or imply any such actual relationship or order between these entities or actions. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0113] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

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5. 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6. The intelligent charging pile step waste heat recovery circulating system according to claim 5, characterized in that, The step of obtaining the waste heat recovery optimization configuration table is specifically: Analyze the intervention node parameters in the abnormal marking data set, reevaluate the energy efficiency loss rate of the step heat recovery path, and associate the specific heat capacity characteristics of the heat exchange medium; Based on the energy efficiency loss rate and medium characteristic analysis result, adjust the circulation period control range and temperature and pressure compensation parameters of the heat exchange medium; According to the adjustment scheme of medium circulation and temperature and pressure compensation, a cooperative working configuration table of the multi-stage heat recovery path is generated, and a waste heat recovery optimization configuration table is generated.

7. The smart charging pile step waste heat recovery circulating system according to claim 6, characterized in that, The system also includes a heat capacity reconstruction module: Based on the waste heat recovery optimization configuration table, extract the circulation period control range of the heat exchange medium, analyze the matching difference between the medium heat capacity saturation and the loop heat load, and generate a heat capacity reconstruction parameter set; According to the matching difference, the medium replenishment period and heat capacity buffer interval are recalculated, and a heat capacity reconstruction parameter set is generated; The heat capacity reconstruction parameter set is fed back to the heat hierarchical regulation and control module, and the heat load threshold of the hierarchical heat recovery parameter set is updated.

8. The intelligent charging pile step waste heat recovery circulating system according to claim 7, characterized in that, The system also includes a distribution update module: Receive the heat load threshold update data fed back by the heat capacity reconstruction parameter set, and rescan the temperature zone heat intensity distribution of the charging pile power unit; Based on the updated heat load threshold and temperature zone heat intensity distribution, calibrate the position coordinates and heat conduction path of the newly added high-potential heat source; According to the newly added heat source path characteristics, correct the step waste heat distribution characteristics, and input the update result to the loop dynamic adjustment module.

9. The smart charging pile step waste heat recovery circulating system according to claim 8, wherein, The system also includes an efficiency verification module: Collect the multi-stage heat recovery path cooperative working data of the waste heat recovery optimization configuration table, and associate the medium replenishment period of the heat capacity reconstruction parameter set; Verify the deviation range of the actual heat recovery energy efficiency improvement rate and the target value, identify the node position of the low-efficiency cooperative path, and feed back the low-efficiency node position and deviation data to the recovery abnormal response module to expand the monitoring range of the abnormal marking data set. The system also includes an output integration module:

10. The smart charging pile step waste heat recovery circulating system according to claim 9, wherein, Summarize the heat exchange medium circulation parameters of the waste heat recovery optimization configuration table, the buffer interval data of the heat capacity reconstruction parameter set, and the energy efficiency improvement rate verification result of the efficiency verification module; Integrate multiple sources of parameters to generate a real-time operation configuration instruction set of the heat recovery system, and drive the heat exchange loop execution mechanism to complete the step waste heat recovery operation. ​

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