Intelligent control method and system for liquid cooling three-level leakage prevention and pump barrier prevention of charging pile

Abstract

The application discloses a charging pile liquid cooling three-level leakage prevention and pump barrier prevention intelligent control method and system, and relates to the technical field of liquid cooling control. The charging pile liquid cooling three-level leakage prevention and pump barrier prevention intelligent control method and system comprises the following steps: S1, periodically collecting liquid cooling monitoring data, and performing pretreatment on the liquid cooling monitoring data; S2, determining whether an abnormal period exists, and evaluating whether a cooling liquid leakage trend exists in combination with real-time liquid cooling monitoring data; S3, performing cooling liquid leakage risk level evaluation, determining a leakage risk level in the current abnormal period, and executing a corresponding leakage prevention strategy; and S4, during the execution of the leakage prevention strategy, evaluating a circulating pump operation state, and determining whether a pump barrier prevention control strategy is triggered. The problems that micro cooling liquid leakage in an existing liquid cooling system is difficult to identify in time, pump barrier prevention is difficult to accurately predict, and cooling abnormalities are not intervened in time, thereby causing heat runaway, are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of liquid cooling control, in particular to a charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method and system. BACKGROUND

[0002] In application scenarios such as high-power charging piles, data center servers, and high-performance computing devices, which have strong heat dissipation requirements, liquid cooling technology is widely used to replace traditional air cooling methods due to its high heat exchange capacity and good thermal control performance. During the operation of the liquid cooling system, it usually involves liquid pump driving, cooling liquid circulation, heat exchanger heat dissipation, and other links. Once cooling liquid leakage, liquid pump failure, or circulation abnormalities occur, not only will the heat dissipation performance be affected, but also equipment short circuits, performance degradation, and even serious safety accidents may be caused. Therefore, the research on leakage monitoring, leakage prevention control, and pump obstacle early warning mechanisms around the operating state of the liquid cooling system has gradually become a key technical direction for the intelligent control of liquid cooling systems.

[0003] For example, the invention with publication number CN119414936A discloses a liquid cooling heat dissipation leakage prevention device and a liquid cooling system. The liquid cooling heat dissipation leakage prevention device includes a leakage collection assembly and a leakage flow guide pipe. The liquid cooling device includes a first plate and a second plate. The bottom edge of the second plate is connected to the edge of the first plate. A liquid cooling channel for cooling liquid flow is provided between the second plate and the first plate. The leakage collection assembly is arranged around the edge where the second plate and the first plate are connected. When leakage occurs at the connecting edge of the second plate and the first plate, the cooling liquid can flow into the leakage collection assembly. The leakage flow guide pipe is in communication with the leakage collection assembly and can guide the cooling liquid collected by the leakage collection assembly out to prevent the cooling liquid from flowing onto the electronic devices inside the server.

[0004] For example, the invention with publication number CN106527637B discloses a leakage prevention device for a liquid cooling system in a server. The device includes a high-heat device arranged inside the server, a liquid cooling mechanism arranged on top of the high-heat device, and a leakage prevention container and a leakage monitoring system arranged outside the liquid cooling mechanism. The liquid cooling mechanism is a solid-liquid heat exchanger. The solid-liquid heat exchanger and its liquid circulation pipeline are arranged in the inner cavity of the leakage prevention container. The leakage prevention container is provided with a water collection groove and a leakage detection sensor. The probe of the leakage detection sensor is arranged at the bottom of the water collection groove. The leakage monitoring system timely feeds back a leakage alarm signal, performs server load transfer, and closes the liquid cooling circulation pipeline valve.

[0005] However, existing technologies mainly focus on structural leakage prevention and hardware-level monitoring methods, and less on leakage trend judgment driven by dynamic monitoring data, leakage risk grading response, and pump obstacle joint control strategies. Especially in scenarios such as charging piles, which have very high requirements for the stability of the liquid cooling system, there is a lack of an overall solution that combines operation data trend analysis, real-time state identification, and intelligent prevention and control closed loop.

[0006] Therefore, in order to address the above problems, there is an urgent need for intelligent control methods and systems for liquid cooling three-level leak prevention and pump failure prevention in charging piles. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a three-level leak prevention and pump failure prevention intelligent control method and system for liquid cooling in charging piles. This solves the problem that it is difficult to identify trace coolant leaks in time and to accurately predict pump failures in existing liquid cooling systems, which leads to the failure to intervene in cooling anomalies in time and thus causes thermal runaway.

[0009] Technical solution

[0010] To achieve the above objectives, the present invention provides the following technical solution: a three-level anti-leakage and pump failure prevention intelligent control method and system for liquid cooling in charging piles, comprising: S1, periodically collecting liquid cooling monitoring data and performing synchronous alignment, anomaly removal, trend fitting, and cross-dimensional normalization processing on the liquid cooling monitoring data to obtain pre-processed liquid cooling monitoring data; S2, extracting the pre-processed liquid cooling monitoring data, analyzing the dynamic fluctuation characteristics of coolant flow rate, liquid level, and pressure difference, determining whether there is an abnormal cycle, and assessing whether there is a coolant leakage trend in conjunction with real-time liquid cooling monitoring data; S3, extracting liquid cooling monitoring data within the abnormal cycle, assessing the coolant leakage risk level, determining the leakage risk level within the current abnormal cycle, and executing the corresponding anti-leakage strategy; S4, during the execution of the anti-leakage strategy, continuously tracking the characteristic changes of the circulating pump drive power and liquid cooling load response, assessing the operating status of the circulating pump, determining whether the pump failure prevention control strategy is triggered, and completing the state closed-loop judgment after the prevention strategy is executed.

[0011] Furthermore, liquid cooling monitoring data is periodically collected, and synchronization alignment, anomaly removal, trend fitting, and cross-dimensional normalization are performed on the liquid cooling monitoring data. The specific steps to obtain the preprocessed liquid cooling monitoring data are as follows: A fixed-width sliding window is set as one sampling period, and liquid cooling monitoring data for charging pile liquid cooling leak prevention is collected. The liquid cooling monitoring data includes coolant inlet pressure, coolant outlet pressure, coolant flow rate, coolant level, coolant temperature, coolant density, coolant specific heat capacity, ambient temperature, circulating pump current, and circulating pump speed. By constructing a data synchronization method based on timestamp hash matching, the liquid cooling monitoring data is indexed and fused according to a unified time window to achieve synchronization alignment of the liquid cooling monitoring data. The liquid cooling monitoring data is processed by a median filtering algorithm to eliminate outliers caused by instantaneous fluctuations. The liquid cooling monitoring data is trend-fitted and smoothed by a local weighted regression algorithm. A robust scaling algorithm based on the median and quartile interval is used to perform cross-dimensional normalization processing on the liquid cooling monitoring data and unify the dimensions.

[0012] Further, the specific steps of extracting the pre-processed liquid cooling monitoring data, analyzing the dynamic fluctuation characteristics of the cooling liquid flow, liquid level and pressure difference, and determining whether there is an abnormal period are as follows: extracting the pre-processed liquid cooling monitoring data, calculating the difference between the cooling liquid inlet pressure and the cooling liquid outlet pressure to obtain the cooling liquid pressure difference; setting a continuous sliding window as an evaluation period, calculating the average change rate of the cooling liquid flow, the cooling liquid level, the cooling liquid pressure difference, the circulating pump current and the circulating pump speed in each evaluation period; comparing the average change rate of each parameter with the corresponding change rate threshold value, and marking the corresponding parameter as abnormal when the average change rate is greater than the change rate threshold value; determining whether there are two or more parameters marked as abnormal in the same evaluation period, and if the condition is met, marking the evaluation period as an abnormal period.

[0013] Further, the specific steps of combining real-time liquid cooling monitoring data to evaluate whether there is a cooling liquid leakage trend are as follows: calculating the cooling liquid level change amount of each sampling period relative to the previous sampling period in each abnormal period, taking the absolute value of the cooling liquid level change amount in the jth sampling period, and dividing it by the sum of the cooling liquid flow and the minimum item in the corresponding sampling period to obtain the normalized liquid level fluctuation item; taking the absolute value of the first-order time derivative of the cooling liquid pressure difference in the jth sampling period, taking the natural logarithm of the result plus one to obtain the pressure fluctuation adjustment item; adding the normalized liquid level fluctuation item to the pressure fluctuation adjustment item to obtain the liquid leakage trend contribution item; summing and averaging the liquid leakage trend contribution items of all k sampling periods in the current abnormal period to obtain the liquid leakage trend evaluation value; comparing the liquid leakage trend evaluation value with the liquid leakage trend threshold value in real time, and maintaining the current leakage prevention strategy when the liquid leakage trend evaluation value is less than or equal to the liquid leakage trend threshold value; when the liquid leakage trend evaluation value is greater than the liquid leakage trend threshold value, triggering the cooling liquid leakage risk level evaluation.

[0014] Further, the specific steps of extracting the liquid cooling monitoring data in the abnormal period and performing the cooling liquid leakage risk level evaluation are as follows: extracting the liquid cooling monitoring data corresponding to the abnormal period, calculating the first-order time derivative of the cooling liquid level to obtain the liquid level change rate; dividing the liquid level change rate by the product of the cooling liquid flow and the cooling liquid pressure difference to obtain the leakage trend factor; taking the difference between the ambient temperature and the cooling liquid temperature as a temperature difference item, and dividing it by the product of the specific heat capacity of the cooling liquid, the density of the cooling liquid and the cooling liquid flow, and taking the natural logarithm of the result plus one to obtain the temperature difference influence factor; multiplying the leakage trend factor and the temperature difference influence factor to obtain the leakage risk evaluation value.

[0015] Further, the specific steps of determining the leakage risk level in the current abnormal period and executing the corresponding anti-leakage strategy are as follows: comparing the leakage risk evaluation value and the leakage risk threshold value in real time, determining the leakage risk level in the current abnormal period and executing the corresponding anti-leakage strategy: when S≤S1, it is determined that the risk is level one, and the level one anti-leakage structure continues to act: relying on the elastic deformation compensation of the composite sealing element and the mechanism of extending the leakage path of the labyrinth structure, without controller intervention; when S1<S<S2, it is determined that the risk is level two, and the level two anti-leakage mechanism is called: combining the cooling liquid pressure and the cooling liquid flow to identify the local leakage trend and locate the leakage position, generate warning information, and at the same time reduce the circulating pump speed; when S≥S2, it is determined that the risk is level three, and the level three anti-leakage measure is immediately started: the controller is linked to close the blocking valve of the cooling circuit corresponding to the leakage position, the anti-leakage tank is started to collect the cooling liquid, and the audible and light alarms and remote fault reporting are triggered.

[0016] Further, during the execution of the anti-leakage strategy, the characteristic changes of the circulating pump driving power and the liquid cooling load response are continuously tracked, and the specific steps of evaluating the circulating pump operating state are as follows: during the execution of the anti-leakage strategy, the circulating pump operating state is dynamically trend tracked, and the circulating pump current, circulating pump speed, cooling liquid flow and cooling liquid pressure difference are extracted, the natural logarithm of the circulating pump current and circulating pump speed plus one is multiplied to obtain a driving power factor; the driving power factor is divided by the sum of the cooling liquid flow and the minimum term, and the obtained ratio is squared to obtain a load response factor; the absolute value of the first order time derivative of the cooling liquid pressure difference is taken, and then divided by the sum of the cooling liquid pressure difference and the minimum term to obtain a disturbance enhancement factor; the power function value of the mathematical constant e with the disturbance enhancement factor as the exponent is taken, and the power function value is multiplied by the load response factor to obtain a pump obstacle identification evaluation value.

[0017] Further, the specific steps of determining whether to trigger the pump obstacle prevention control strategy are as follows: comparing the pump obstacle identification evaluation value and the pump obstacle threshold value in real time, when the pump obstacle identification evaluation value is less than or equal to the pump obstacle threshold value, it is determined that the circulating pump is operating normally; when the pump obstacle identification evaluation value is greater than the pump obstacle threshold value, it is determined that the circulating pump is operating abnormally, and the pump obstacle prevention control strategy is triggered: reducing the circulating pump speed and enabling the intermittent on-off control channel to periodically reset the circulating period; monitoring the real-time environmental temperature, if the environmental temperature is lower than the environmental temperature threshold value, the heating device in the cooling liquid passage is enabled, and the on-off period of the heating device is set according to the current cooling liquid flow rate.

[0018] Further, the specific steps of completing the state closed loop judgment after the implementation of the prevention strategy are as follows: extracting the liquid cooling monitoring data of the Mth sampling period after the implementation of the pump barrier prevention control strategy, calculating the pump barrier identification evaluation value again, if the pump barrier identification evaluation value is still greater than the pump barrier threshold value, closing the circulating pump driving output, switching the current cooling circuit to the standby channel, and synchronously triggering the charging pile audible and visual alarm; otherwise, closing the heat tracing device, and restoring the normal driving control logic of the circulating pump.

[0019] The second aspect of the present application provides a charging pile liquid cooling three-level leakage prevention and pump barrier prevention intelligent control system, comprising: a data acquisition and preprocessing module, a state recognition and leakage judgment module, a leakage risk assessment and response module, and a pump barrier identification and prevention control module, wherein: the data acquisition and preprocessing module is used for periodically acquiring liquid cooling monitoring data, and performing synchronous alignment, abnormality rejection, trend fitting and cross-dimensional normalization processing on the liquid cooling monitoring data to obtain preprocessed liquid cooling monitoring data; the state recognition and leakage judgment module is used for extracting the preprocessed liquid cooling monitoring data, analyzing the dynamic fluctuation characteristics of the cooling liquid flow, liquid level and pressure difference, determining whether there is an abnormal period, and combining the real-time liquid cooling monitoring data to assess whether there is a cooling liquid leakage trend; the leakage risk assessment and response module is used for extracting the liquid cooling monitoring data in the abnormal period, performing cooling liquid leakage risk level assessment, determining the leakage risk level in the current abnormal period and executing the corresponding leakage prevention strategy; the pump barrier identification and prevention control module is used for continuously tracking the characteristic changes of the circulating pump driving power and the liquid cooling load response during the execution of the leakage prevention strategy, assessing the circulating pump operating state, determining whether to trigger the pump barrier prevention control strategy, and completing the state closed loop judgment after the implementation of the prevention strategy.

[0020] Advantages

[0021] The present application has the following advantages:

[0022] (1) The charging pile liquid cooling three-level leakage prevention and pump barrier prevention intelligent control method and system can effectively suppress the interference caused by single-point fluctuation by constructing the normalized liquid level fluctuation term and the pressure fluctuation adjustment term and performing average processing on the contribution values of multiple items in the sampling period, and can highlight the persistent change characteristics of the liquid cooling system in the abnormal period. The method realizes the early capture of the potential leakage trend through the coupling characteristics of the liquid level slight fluctuation and the pressure difference disturbance, and makes up for the deficiency that the slight cooling liquid loss is difficult to be identified in time in the prior art.

[0023] (2) The charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method and system, by adopting the combination of leakage trend factor and temperature difference influence factor, a more comprehensive leakage risk assessment value is obtained, and the risk level is subdivided into three levels of protection. The first level relies on structural compensation to cope with slight leakage, the second level locates the leakage and adjusts the pump speed, and the third level quickly isolates and alarms through the block valve and the leakage prevention groove. This grading strategy not only ensures the continuous operation of the system in the case of slight leakage, but also ensures the rapid disposal in the case of serious leakage, improving the overall resilience of the liquid cooling system.

[0024] (3) The charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method and system, by constructing driving power factor, load response factor and disturbance enhancement factor, and combining exponential function relationship to obtain pump obstacle identification evaluation value. Compared with the traditional judgment method relying on single current or speed threshold, the multi-parameter coupling model can more comprehensively reflect the running health status of the circulating pump. The accurate prediction of slight pump obstacle is realized, and the pump obstacle prevention strategy triggered based on the result can reduce the pump speed in advance, reset the running period, avoid the pump body entering the dry friction or overheating state, reduce the component loss and prolong the service life of the circulating pump.

[0025] (4) The charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method and system, after the execution of the leakage prevention and pump obstacle prevention strategy, instead of determining the abnormal state at one time, the liquid cooling monitoring data of the subsequent sampling period is re-extracted, the pump obstacle identification evaluation value is dynamically calculated, and the state is confirmed twice. When the abnormality continues, the standby cooling circuit is automatically switched and the audible and light alarm is triggered to ensure that the main circuit failure does not affect the operation of the entire charging pile; when the state recovers, the heat tracing device is closed and the normal driving logic of the circulating pump is restored, avoiding the energy waste caused by long-term additional operation. Not only the robustness of the control logic is improved, but also the adaptability and stability of the system under different operating environments are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 The charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method flow chart;

[0027] Figure 2 The charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control system structure diagram;

[0028] Figure 3 The abnormal period leakage risk assessment value and grade division schematic diagram;

[0029] Figure 4 The liquid cooling three-level leakage prevention and pump obstacle prevention control flow chart. DETAILED DESCRIPTION

[0030] 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. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0031] Please refer to Figures 1-4 The embodiments of the present application provide a technical solution: a charging pile liquid cooling three-level leakage prevention and pump barrier prevention intelligent control method and system, comprising: S1, periodically collecting liquid cooling monitoring data, and performing synchronous alignment, abnormality elimination, trend fitting and cross-dimension normalization processing on the liquid cooling monitoring data to obtain pre-processed liquid cooling monitoring data; S2, extracting the pre-processed liquid cooling monitoring data, analyzing the dynamic fluctuation characteristics of the cooling liquid flow, liquid level and pressure difference, determining whether there is an abnormal period, and combining the real-time liquid cooling monitoring data to evaluate whether there is a cooling liquid leakage trend; S3, extracting the liquid cooling monitoring data in the abnormal period, performing cooling liquid leakage risk level evaluation, determining the leakage risk level in the current abnormal period and executing the corresponding leakage prevention strategy; S4, during the execution of the leakage prevention strategy, continuously tracking the characteristic changes of the circulating pump driving power and the liquid cooling load response, evaluating the circulating pump operation state, determining whether to trigger the pump barrier prevention control strategy, and completing the state closed loop judgment after the prevention strategy is executed.

[0032] Specifically, the liquid cooling monitoring data is periodically collected, and the liquid cooling monitoring data is subjected to synchronous alignment, abnormality elimination, trend fitting and cross-dimension normalization processing. The specific steps of obtaining the preprocessed liquid cooling monitoring data are as follows: a fixed-width sliding window is set as one sampling period, the liquid cooling monitoring data for leakage prevention of the charging pile is collected, the liquid cooling monitoring data includes cooling liquid inlet pressure, cooling liquid outlet pressure, cooling liquid flow, cooling liquid level, cooling liquid temperature, cooling liquid density, cooling liquid specific heat capacity, ambient temperature, circulating pump current and circulating pump speed; the cooling liquid inlet pressure is collected in real time by a distributed pressure sensor arranged at the inlet of the cooling circuit; wherein the cooling liquid outlet pressure and the cooling liquid inlet pressure are continuously recorded by pressure sensors arranged at the inlet and outlet pipe sections; the cooling liquid flow is obtained in real time by an electromagnetic flowmeter embedded in the main circulating pipe; the cooling liquid level is obtained by a high-precision liquid level meter installed in the liquid storage tank; the cooling liquid temperature is obtained by an NTC temperature sensor close to the pipe wall; the cooling liquid density is obtained by periodic sampling detection; the cooling liquid specific heat capacity is determined according to the type of cooling liquid used according to the thermophysical property parameter table provided by the manufacturer; the ambient temperature is obtained in real time by a temperature sensor installed outside the equipment cabin; the circulating pump current is obtained by a current transformer on the control panel; the circulating pump speed is fed back in real time by an internal speed detector, and all raw monitoring data have a unified time stamp; the liquid cooling monitoring data is indexed and fused according to a unified time window by constructing a data synchronization method based on time stamp hash matching, to ensure that the liquid cooling monitoring data from different sources is strictly aligned within the same sampling period, and to realize the synchronous alignment of the liquid cooling monitoring data; the liquid cooling monitoring data is processed by a median filtering algorithm, and the instantaneous abnormal values caused by electromagnetic interference, communication jitter, flow fluctuation, etc. within the sampling period are eliminated point by point, to improve the overall stability of the liquid cooling monitoring data; the liquid cooling monitoring data is subjected to trend fitting and smoothing correction by a local weighted regression algorithm, to capture the underlying change trend of the liquid cooling monitoring data and weaken the interference of high-frequency disturbance on subsequent identification calculation; a robust scaling algorithm based on median and interquartile range is used to normalize the liquid cooling monitoring data across dimensions and unify the dimension, to ensure that each variable has comparability under different physical units, and to facilitate the input fusion of subsequent leakage trend identification, risk assessment and control strategy calculation, etc.

[0033] In this embodiment, by constructing a multi-source liquid cooling monitoring data collection system including cooling liquid inlet pressure, cooling liquid outlet pressure, cooling liquid flow rate, cooling liquid level, cooling liquid temperature, cooling liquid density, cooling liquid specific heat capacity, ambient temperature, circulating pump current and circulating pump rotating speed, and by using a time stamp hash matching synchronous alignment method, a median filtering abnormality rejection method, a local weighted regression trend fitting method and a robust scaling cross-dimensional normalization method, the integrity, stability, continuity and comparability of the liquid cooling monitoring data are effectively improved, and the accuracy and timeliness of the liquid cooling system operation state perception are significantly enhanced, providing a high-quality data basis for subsequent liquid cooling leakage trend identification, leakage risk level assessment and multi-level control strategy execution.

[0034] Specifically, the specific steps of extracting the pre-processed liquid cooling monitoring data, analyzing the dynamic fluctuation characteristics of the cooling liquid flow rate, liquid level and pressure difference, and determining whether there is an abnormal period are as follows: extracting the pre-processed liquid cooling monitoring data, calculating the difference between the cooling liquid inlet pressure and the cooling liquid outlet pressure, obtaining the cooling liquid pressure difference, which is used to reflect the change of the flow resistance of the cooling liquid in the circuit; setting a continuous sliding window as an evaluation period, and keeping the sliding window width consistent with the sampling period, which is used to enhance the continuity and timeliness of the identification; calculating the average change rate of the cooling liquid flow rate, the cooling liquid level, the cooling liquid pressure difference, the circulating pump current and the circulating pump rotating speed in each evaluation period, the change rate being derived from the numerical difference and time interval ratio of adjacent sampling points, which is used to quantify the dynamic response trend of the parameters over time; comparing the average change rate of each parameter with the corresponding change rate threshold; when the average change rate is greater than the corresponding change rate threshold, the corresponding parameter is marked as abnormal; determining whether there are two or more parameters marked as abnormal in the same evaluation period, and excluding the interference of single variable accidental disturbance on the identification result, if the condition is met, the evaluation period is marked as an abnormal period, which is used as the basis for subsequent leakage trend analysis and risk assessment.

[0035] In this embodiment, by analyzing the dynamic change rates of the cooling liquid flow rate, the cooling liquid level, the cooling liquid pressure difference, the circulating pump current and the circulating pump rotating speed, and introducing the change rate threshold for fine comparison, the misjudgment problem caused by single parameter fluctuation is effectively avoided. Using a continuous sliding window as an evaluation period improves the timeliness and stability of abnormal period identification. Through the joint determination mechanism of the number of abnormal parameters, the sensitivity to multi-dimensional parameter collaborative abnormality is strengthened, the accuracy and robustness of abnormal period identification are improved, and a high-reliability trigger basis is provided for subsequent cooling liquid leakage trend analysis and leakage risk level assessment, enhancing the early risk identification capability of the liquid cooling system.

[0036] Specifically, the specific steps of evaluating whether there is a cooling liquid leakage trend in combination with real-time liquid cooling monitoring data are as follows: calculating the cooling liquid level change amount of each sampling period relative to the previous sampling period in each abnormal period, wherein the cooling liquid level change amount is derived from the continuous output of the liquid tank level gauge; taking the absolute value of the cooling liquid level change amount in the jth sampling period, and dividing it by the sum of the cooling liquid flow obtained by the main circulation pipeline electromagnetic flowmeter in the corresponding sampling period and the minimum term, as the normalized level fluctuation term; wherein the minimum term is a minimum positive real number not equal to zero, to avoid the problem of denominator tending to zero due to low flow; taking the absolute value of the first-order time derivative of the cooling liquid pressure difference in the jth sampling period, and then taking the natural logarithm of the result after adding one, to obtain the pressure fluctuation adjustment term, which is used to reflect the change in the pressure stability of the cooling system; adding the normalized level fluctuation term to the pressure fluctuation adjustment term to obtain the leakage trend contribution term, which is used to measure the potential leakage characteristics of a single sampling period in the dual dimensions of level fluctuation and pressure disturbance; summing and averaging the leakage trend contribution terms of all k sampling periods in the current abnormal period to obtain the final leakage trend evaluation value, which is used as a criterion for determining whether to enter the leakage risk level evaluation process; comparing the leakage trend evaluation value and the leakage trend threshold value in real time, and when the leakage trend evaluation value is less than or equal to the leakage trend threshold value, maintaining the current leakage prevention strategy unchanged to ensure stable operation of the system; when the leakage trend evaluation value is greater than the leakage trend threshold value, triggering the cooling liquid leakage risk level evaluation, and entering the active prevention stage to prevent the initial micro-leakage from evolving into a high-risk leakage event.

[0037] wherein the specific calculation formula of the leakage trend evaluation value is:

[0038] ;

[0039] In the formula, represents the leakage trend evaluation value, represents the number of sampling periods, represents the cooling liquid level change amount in the jth sampling period, represents the cooling liquid flow in the jth sampling period, represents the cooling liquid pressure difference in the jth sampling period, represents the minimum term.

[0040] In this embodiment, by introducing a combined computer mechanism of normalized liquid level fluctuation term and pressure fluctuation adjustment term, a double-factor leakage trend contribution model based on the change amount of cooling liquid level and the change rate of cooling liquid pressure difference is constructed, which comprehensively reflects the potential leakage characteristics of the liquid cooling system in the abnormal period. The model can effectively eliminate the denominator amplification effect interference caused by extremely low cooling liquid flow, and enhance the response sensitivity of the system to pressure fluctuation anomalies by logarithmic adjustment of the cooling liquid pressure difference change. Further, by combining the average processing of the trend contribution term at multiple points in the sampling period, the final generated leakage trend evaluation value is more stable and reliable, realizing the quantitative identification of the trace cooling liquid leakage trend. Under the premise of ensuring the time consistency and physical quantity comparability of the liquid cooling monitoring data, the dynamic adaptability of the leakage prevention strategy is enhanced, and the identification ability and response level of the liquid cooling system to early hidden dangers are effectively improved.

[0041] Specifically, the specific steps of extracting the liquid cooling monitoring data in the abnormal period and evaluating the cooling liquid leakage risk level are as follows: extracting the liquid cooling monitoring data corresponding to the abnormal period, calculating the first time derivative of the cooling liquid level to obtain the liquid level change rate, which is used to depict the trend of the cooling liquid volume change per unit time, thereby capturing the signs of possible small leakage in the cooling system; the liquid level change rate is divided by the product of the cooling liquid flow and the cooling liquid pressure difference to obtain the leakage trend factor, which is used to comprehensively reflect the correlation between the system flow efficiency and the cooling liquid loss, and improve the sensitivity of the leakage trend identification; the difference between the ambient temperature and the cooling liquid temperature is taken as the temperature difference term, and then divided by the product of the specific heat capacity of the cooling liquid, the density of the cooling liquid and the cooling liquid flow, and the result is added by one and then taken the natural logarithm to obtain the temperature difference influence factor, which is used to quantify the adjustment effect of the cold-hot end temperature difference on the system thermal load change, and enhance the robustness of the leakage trend judgment to the working condition disturbance; the leakage risk evaluation value is obtained by multiplying the leakage trend factor and the temperature difference influence factor, thereby realizing the accurate measurement of the cooling liquid leakage severity in the current abnormal period, and providing a quantitative basis for the response classification of the subsequent leakage prevention strategy.

[0042] wherein the specific calculation formula of the leakage risk evaluation value is:

[0043] ;

[0044] In the formula, represents the leakage risk evaluation value, represents the cooling liquid level, represents the cooling liquid flow, represents the cooling liquid pressure difference, represents the ambient temperature, represents the cooling liquid temperature, represents the cooling liquid density, represents the specific heat capacity of the cooling liquid.

[0045] In the present embodiment, Table 1 is a leakage risk assessment value data table listing key parameters and corresponding leakage risk assessment values for five abnormal periods. The key parameters used include: coolant level change rate, coolant flow rate, coolant pressure difference, ambient temperature, coolant temperature, coolant specific heat capacity, and coolant density. The specific data are as follows: in abnormal period 1, the level change rate is 0.018, the coolant flow rate is 0.50, the coolant pressure difference is 0.21, the ambient temperature is 45, the coolant temperature is 35, the coolant specific heat capacity is 4.2, the coolant density is 1.03, and the calculated leakage risk assessment value is 0.518; in abnormal period 2, the level change rate is 0.022, the coolant flow rate is 0.60, the coolant pressure difference is 0.13, the ambient temperature is 46, the coolant temperature is 35, the coolant specific heat capacity is 4.2, the coolant density is 1.03, and the calculated leakage risk assessment value is 0.467; in abnormal period 3, the level change rate is 0.027, the coolant flow rate is 0.65, the coolant pressure difference is 0.14, the ambient temperature is 49, the coolant temperature is 35, the coolant specific heat capacity is 4.2, the coolant density is 1.03, and the calculated leakage risk assessment value is 0.531; in abnormal period 4, the level change rate is 0.035, the coolant flow rate is 0.70, the coolant pressure difference is 0.16, the ambient temperature is 48, the coolant temperature is 35, the coolant specific heat capacity is 4.2, the coolant density is 1.03, and the calculated leakage risk assessment value is 0.521; and in abnormal period 5, the level change rate is 0.042, the coolant flow rate is 0.75, the coolant pressure difference is 0.17, the ambient temperature is 49, the coolant temperature is 35, the coolant specific heat capacity is 4.2, the coolant density is 1.03, and the calculated leakage risk assessment value is 0.550.

[0046] Table 1 Leakage risk assessment value data table

[0047]

[0048] As Figure 3As shown, the liquid cooling leakage risk assessment values and risk level determination results corresponding to five abnormal periods are displayed, which are used to identify the leakage risk distribution of the liquid cooling system during operation. In the figure, the leakage risk assessment values S of each period are represented in the form of a color column chart, in which green represents a first-level risk, blue represents a second-level risk, and red represents a third-level risk, directly reflecting the leakage risk level of the cooling liquid in each abnormal period. Two dashed lines are set as risk level division reference lines in the figure: an orange dashed line represents a first-level risk threshold S1=0.50, and a purple dashed line represents a second-level risk threshold S2=0.55. As can be seen from the figure, the leakage risk assessment value of abnormal period 2 is 0.467, which is lower than S1, and is determined to be a first-level risk; the leakage risk assessment values of abnormal periods 1, 3, and 4 are 0.518, 0.531, and 0.521, respectively, which are between S1 and S2, corresponding to a second-level risk; and the leakage risk assessment value of period 5 is 0.550, which is equal to S2, and is determined to be a third-level risk. Figure 3 The leakage risk evolution trend of the liquid cooling system under different abnormal periods is effectively described, providing data support and decision basis for subsequent implementation of hierarchical leakage prevention control strategies and pump barrier early warning mechanisms.

[0049] In the present embodiment, through in-depth analysis of the liquid cooling monitoring data in the abnormal period, a composite evaluation mechanism of leakage trend factor and temperature difference influence factor is constructed, which can realize multi-dimensional modeling of the cooling liquid leakage risk on the basis of ensuring the uniformity of data names and terms. This method effectively improves the pre-perception ability of the liquid cooling system to potential leakage hazards under abnormal conditions, provides quantitative basis for the active hierarchical response of the leakage prevention strategy, and enhances the intrinsic safety level of the liquid cooling system.

[0050] Specifically, the specific steps of determining the leakage risk level in the current abnormal period and executing the corresponding anti-leakage strategy are as follows: comparing the leakage risk assessment value and the leakage risk threshold value in real time, determining the leakage risk level in the current abnormal period and executing the corresponding anti-leakage strategy: when the leakage risk assessment value S is less than or equal to the first risk threshold value S1, it is determined as the first risk, and the first-level anti-leakage structure continues to act: relying on the composite sealing element arranged at the liquid cooling connection part, automatically coping with low-intensity leakage through the elastic deformation compensation capacity of the composite sealing element, and combining the multi-stage labyrinth structure to form a long leakage path at the sealing interface, significantly inhibiting the diffusion of cooling liquid along the structural gap without controller intervention; when the leakage risk assessment value S is greater than the first risk threshold value S1 and less than the second risk threshold value S2, it is determined as the second risk, and the second-level anti-leakage mechanism is called: based on the combined fluctuation characteristics of cooling liquid pressure and cooling liquid flow, local leakage trend identification is performed, and the potential leakage position is accurately locked through the flow and pressure difference cooperative anomaly positioning method, while the controller outputs a risk prompt signal and reduces the circulating pump speed to slow down the internal pressure accumulation and delay the leakage expansion speed; when the leakage risk assessment value S is greater than or equal to the second risk threshold value S2, it is determined as the third risk, and the third-level anti-leakage measure is immediately started: the controller controls the blocking valve corresponding to the leakage position in the cooling liquid circuit to execute the closing operation, quickly isolates the fault circuit, and then activates the anti-leakage groove structure arranged at the bottom of the device to receive and temporarily store the cooling liquid, and triggers the audible and visual alarm device to issue a significant alarm signal, ensuring that the response closed loop is established in time under the fault condition.

[0051] In the present embodiment, through real-time comparison of the leakage risk assessment value and the multi-stage leakage risk threshold value, a hierarchical response mechanism is established to realize accurate determination of the leakage risk level in the abnormal period, and the first-level anti-leakage structure, the second-level anti-leakage mechanism and the third-level anti-leakage measure are triggered in turn according to the risk level. The response measures at each level are adjusted in linkage around the dynamic change of the leakage risk assessment value, taking into account the structure self-adaptive compensation capacity, the joint diagnosis capacity of flow and pressure difference and the rapid response capacity of the controller, effectively improving the undisturbed running capacity in the early stage of trace cooling liquid leakage, the active intervention capacity in the medium risk stage and the isolation control capacity in the high risk stage, thereby ensuring that the liquid cooling system has closed-loop safety protection capacity and strategy hierarchical regulation capacity under different risk levels.

[0052] Specifically, during the execution of the leakage prevention strategy, the characteristic changes of the circulating pump driving power and the liquid cooling load response are continuously tracked, and the specific steps for evaluating the circulating pump operating state are as follows: during the execution of the leakage prevention strategy, the dynamic trend of the circulating pump operating state is tracked, the circulating pump operating behavior in different time periods is extracted through a sliding evaluation window, and the potential pump obstacle is identified in advance; the circulating pump current, the circulating pump speed, the cooling liquid flow and the cooling liquid pressure difference are extracted as inputs for pump obstacle identification calculation; the natural logarithm of the circulating pump current plus one is multiplied by the circulating pump speed to obtain a driving power factor, which is used to represent the coupling relationship between the motor load level and the mechanical output state of the circulating pump; the driving power factor is divided by the sum of the cooling liquid flow and the minimum term, and the square of the obtained ratio is taken to obtain a load response factor to evaluate the response degree of the liquid cooling load to the circulating pump power output; the absolute value of the first-order time derivative of the cooling liquid pressure difference is taken to reflect the dynamic change amplitude of the cooling loop impedance, and then divided by the sum of the cooling liquid pressure difference and the minimum term to obtain a disturbance enhancement factor; the power function value of the mathematical constant e with the disturbance enhancement factor as the exponent is taken to amplify the contribution degree of the disturbance in a short time to the pump obstacle risk; the power function value is multiplied by the load response factor to fuse the combined effects of the driving power factor and the disturbance enhancement factor, and finally the pump obstacle identification evaluation value is calculated.

[0053] wherein the specific calculation formula of the pump obstacle identification evaluation value is:

[0054] ;

[0055] In the formula, represents the pump obstacle identification evaluation value, represents the circulating pump current, represents the circulating pump speed, represents the current cooling liquid flow, represents the cooling liquid pressure difference, represents the minimum term.

[0056] In the present embodiment, by introducing the circulating pump current, the circulating pump speed, the cooling liquid flow and the cooling liquid pressure difference, a multi-dimensional fusion calculation system of the driving power factor, the load response factor and the disturbance enhancement factor is constructed, which effectively captures the electrical response, fluid response and pressure difference fluctuation characteristics of the circulating pump under abnormal operating conditions, and realizes a high sensitivity judgment mechanism of the pump obstacle identification evaluation value. This method improves the feedforward identification ability of potential pump obstacle signs, provides accurate data support for the timely triggering of pump obstacle prevention control strategy, and enhances the intervention initiative and response accuracy of the liquid cooling system in the fault initiation stage.

[0057] Specifically, the specific steps of determining whether to trigger the pump barrier prevention control strategy are as follows: comparing the pump barrier identification evaluation value and the pump barrier threshold value in real time, ensuring the stability and reliability of the judgment by continuously evaluating the trend of the pump barrier identification evaluation value in multiple sampling periods; when the pump barrier identification evaluation value is less than or equal to the pump barrier threshold value, further confirming that the current circulating pump operating state has no obvious abnormality in combination with the historical fluctuation range of the circulating pump current and the circulating pump speed, and determining that the circulating pump is operating normally; when the pump barrier identification evaluation value is greater than the pump barrier threshold value, determining that the circulating pump is operating abnormally, and immediately triggering the pump barrier prevention control strategy: calling specific execution logic in the controller kernel, reducing the circulating pump speed, reducing the electrical load and heat dissipation, and simultaneously enabling the intermittent on-off control channel, periodically resetting the liquid cooling cycle under the built-in beat parameter constraint in the control algorithm, avoiding the risk of risk spreading caused by the continuous accumulation of abnormal pump working conditions; and monitoring the environmental temperature sampling data installed outside the equipment cabin in real time, if the environmental temperature is lower than the environmental temperature threshold value, the heating device in the cooling liquid passage is linked to be started to inhibit the additional resistance caused by the increase of the cooling liquid viscosity on the pump start under low temperature conditions; and further setting the on-off period of the heating device according to the current cooling liquid flow rate, wherein: when the cooling liquid flow rate is less than or equal to the first flow rate threshold value, the heating device works according to the first on-off period; when the cooling liquid flow rate is greater than the first flow rate threshold value and less than the second flow rate threshold value, the heating device works according to the second on-off period; when the cooling liquid flow rate is greater than or equal to the second flow rate threshold value, the heating device works according to the third on-off period. The on-off period parameters are set according to the cooling liquid type, the cooling circuit structure and the environmental temperature to adapt to the dynamic heat compensation demand under different cooling loads, thereby effectively reducing the occurrence probability of pump barrier inducing factors such as waxing and solidification in the cooling circuit.

[0058] In the embodiment, on the basis of comparing the pump barrier identification evaluation value and the pump barrier threshold value in real time, the dynamic changes of the circulating pump current, the circulating pump speed, the cooling liquid flow rate and the cooling liquid pressure difference are combined to realize accurate determination of the circulating pump operating state. By introducing the linkage control mechanism of the circulating pump speed regulation, the intermittent on-off control channel and the heating device, not only the pump barrier risk caused by the change of the cooling liquid viscosity or the circulation abnormality is effectively alleviated, but also the heat compensation ability of the liquid cooling system in the low temperature scene is enhanced. The method improves the robustness of the liquid cooling system under extreme operating conditions, and provides technical support for realizing high-reliability pump barrier prevention control.

[0059] Specifically, the specific steps of completing the state closed-loop judgment after the implementation of the prevention strategy are as follows: extracting liquid cooling monitoring data in M charging pile liquid cooling three leakage prevention and pump barrier prevention intelligent control system sampling periods after the implementation of the pump barrier prevention control strategy, the extracted liquid cooling monitoring data including circulating pump current, circulating pump speed, cooling liquid flow and cooling liquid pressure difference, and keeping consistent with the previous evaluation period data structure; wherein M is a positive integer, determined comprehensively according to the sampling period length, the circulating pump response lag characteristic and the fault convergence speed; based on the extracted liquid cooling monitoring data, the pump barrier identification evaluation value is calculated again to ensure the continuity of the evaluation logic and the consistency of the parameter processing; if the pump barrier identification evaluation value is still greater than the pump barrier threshold value, it is judged that the circulating pump is in a continuous abnormal state, and the fault isolation measures are immediately executed: the circulating pump drive output is closed to ensure that the pump body stops running to prevent fault propagation, the current cooling circuit is switched to the configured perfect standby channel to ensure the continuous closure of the cooling passage, and the audible and light alarm function of the charging pile is triggered synchronously, the abnormal state is reported to the remote system synchronously, the alarm prompt and operation and maintenance linkage are realized; if the pump barrier identification evaluation value is no longer greater than the pump barrier threshold value, it is judged that the prevention strategy has played a role and the pump body operation has returned to normal, the heat tracing device is immediately closed to prevent unnecessary energy consumption, and the normal driving control logic of the circulating pump is restored to maintain the continuous and stable operation of the liquid cooling system.

[0060] In the embodiment, by continuously extracting liquid cooling monitoring data in M charging pile liquid cooling three leakage prevention and pump barrier prevention intelligent control system sampling periods after the implementation of the pump barrier prevention control strategy, and recalculating the pump barrier identification evaluation value, the running state of the circulating pump is reconfirmed, and the risk of false triggering caused by short-time disturbance or abnormal judgment is effectively avoided. By comparing the newly calculated pump barrier identification evaluation value with the pump barrier threshold value in real time, it is ensured that the control decision has data closed-loop support. The closed-loop judgment mechanism establishes a feedback channel between the liquid cooling monitoring data, the pump barrier identification evaluation value and the control action, ensuring that the control system has continuous and stable fault identification and response ability.

[0061] As Figure 2As shown, the second aspect of this invention provides an intelligent control system for three-level liquid cooling leakage prevention and pump failure prevention in charging piles, including: a data acquisition and preprocessing module, a status identification and leakage judgment module, a leakage risk assessment and response module, and a pump failure identification and prevention control module. The data acquisition and preprocessing module is used to periodically acquire liquid cooling monitoring data and perform synchronization alignment, anomaly removal, trend fitting, and cross-dimensional normalization processing on the liquid cooling monitoring data to obtain preprocessed liquid cooling monitoring data. The status identification and leakage judgment module is used to extract the preprocessed liquid cooling monitoring data and analyze the coolant flow rate, liquid level, and pressure difference. The system features dynamic fluctuation characteristics to determine the existence of abnormal cycles and, in conjunction with real-time liquid cooling monitoring data, assesses the potential for coolant leakage. A leakage risk assessment and response module extracts liquid cooling monitoring data within abnormal cycles, assesses the level of coolant leakage risk, determines the leakage risk level within the current abnormal cycle, and executes corresponding leak prevention strategies. A pump failure identification and prevention control module continuously tracks the characteristic changes in the circulating pump drive power and liquid cooling load response during the execution of leak prevention strategies, assesses the circulating pump's operating status, determines whether a pump failure prevention control strategy is triggered, and completes a closed-loop status assessment after the prevention strategy is executed.

[0062] like Figure 4 The diagram illustrates the overall operation flow of the intelligent control system for three-level leakage prevention and pump failure prevention in charging piles provided by this invention. This includes a closed-loop judgment mechanism for multi-level leakage risk response control, circulation pump operation status identification, and pump failure prevention control. The system constructs a multi-dimensional liquid cooling monitoring data system using intelligent sensors deployed at the coolant inlet pressure, coolant outlet pressure, coolant flow rate, and coolant level. The intelligent controller continuously collects and analyzes the liquid cooling monitoring data in real time across each sampling period. Upon detecting a coolant leakage trend warning signal, the controller determines whether a multi-level leakage risk threshold has been reached based on a preset leakage risk assessment model. If leakage exceeds the threshold, the system automatically invokes the corresponding leakage prevention strategy and executes response measures. If the assessment result is a level three risk, the system immediately triggers a level three blocking mechanism, performing coolant loop isolation and leakage collection operations. Simultaneously, it links with a remote reporting platform and a local audible and visual alarm device to prevent further coolant leakage into the circuit structure, ensuring system electrical safety. The controller can also perform device status analysis based on historical trend frequency characteristics, prompting checks on the operating status of aging components and assisting in remote maintenance and strategy optimization. The above process forms a complete intelligent control link from data acquisition, risk assessment, strategy execution to state closure, providing the charging pile liquid cooling system with robust and timely leak prevention and pump failure prevention and control capabilities under abnormal operating conditions.

[0063] In the embodiment, a full-process closed-loop control architecture driven by liquid cooling monitoring data is established through a modular design. In the system, the data acquisition and preprocessing module ensures high-frequency synchronous acquisition and standardized processing of the liquid cooling monitoring data; the state identification and liquid leakage judgment module identifies abnormal periods through dynamic fluctuation characteristics, and determines the cooling liquid leakage trend in combination with real-time liquid cooling monitoring data, to build an early risk identification channel; the leakage risk assessment and response module builds a leakage risk assessment value based on the liquid level change rate, cooling liquid flow, cooling liquid pressure difference and temperature difference influencing factors, to drive the accurate response of the graded leakage prevention strategy; the pump obstacle identification and prevention control module further identifies the change trend of the circulating pump driving power and the liquid cooling load response, forms a pump obstacle identification assessment value, and triggers the prevention control mechanism based on the pump obstacle identification assessment value, to realize the active intervention on the potential fault of the circulating pump. Through the module cooperation, the overall system strengthens the adaptive identification ability, graded response ability and state closed-loop ability of the liquid cooling system in the running process, and improves the cooling safety and operation reliability of the charging pile under multiple working conditions.

[0064] It should be noted that, in this document, the terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or inherent to such a process, method, article, or apparatus.

[0065] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details of the application, nor limit the application to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of the specification. The specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A method for intelligent control of liquid cooling three-level leakage prevention and pump barrier prevention of charging piles, characterized in that, The method comprises the following steps: S1, periodically collecting liquid cooling monitoring data, and performing synchronous alignment, abnormality elimination, trend fitting and cross-dimension normalization processing on the liquid cooling monitoring data to obtain preprocessed liquid cooling monitoring data; S2, extracting the preprocessed liquid cooling monitoring data, analyzing the dynamic fluctuation characteristics of the cooling liquid flow, liquid level and pressure difference, determining whether there is an abnormal period, and evaluating whether there is a cooling liquid leakage trend in combination with real-time liquid cooling monitoring data; The specific steps of evaluating whether there is a cooling liquid leakage trend in combination with real-time liquid cooling monitoring data are as follows: Calculate the cooling liquid level change amount of each sampling period in each abnormal period relative to the previous sampling period, take the absolute value of the cooling liquid level change amount in the jth sampling period, and divide it by the sum of the cooling liquid flow and the minimum item in the corresponding sampling period, as the normalized liquid level fluctuation item; take the absolute value of the first-order time derivative of the cooling liquid pressure difference in the jth sampling period, add one to the result, and take the natural logarithm to obtain the pressure fluctuation adjustment item; Add the normalized liquid level fluctuation item to the pressure fluctuation adjustment item to obtain the liquid leakage trend contribution item; Sum and average the liquid leakage trend contribution items of all k sampling periods in the current abnormal period to obtain the liquid leakage trend evaluation value; Real-time comparison of the liquid leakage trend evaluation value and the liquid leakage trend threshold value, when the liquid leakage trend evaluation value is less than or equal to the liquid leakage trend threshold value, the current leakage prevention strategy is maintained unchanged; When the liquid leakage trend evaluation value is greater than the liquid leakage trend threshold value, trigger the cooling liquid leakage risk level evaluation; S3, extract the liquid cooling monitoring data in the abnormal period, perform cooling liquid leakage risk level evaluation, determine the leakage risk level in the current abnormal period and execute the corresponding leakage prevention strategy; S4, during the execution of the leakage prevention strategy, continuously track the characteristic changes of the circulating pump driving power and the liquid cooling load response, evaluate the circulating pump operating state, determine whether to trigger the pump obstacle prevention control strategy, and complete the state closed-loop judgment after the prevention strategy is executed.

2. The charging pile liquid cooling three-level leakage prevention and pump barrier prevention intelligent control method according to claim 1, characterized in that: The specific steps of periodically collecting liquid cooling monitoring data, and performing synchronous alignment, abnormality elimination, trend fitting and cross-dimension normalization processing on the liquid cooling monitoring data to obtain preprocessed liquid cooling monitoring data are as follows: Set a fixed-width sliding window to be a sampling period, collect liquid cooling monitoring data for charging pile liquid cooling leakage prevention, and the liquid cooling monitoring data includes cooling liquid inlet pressure, cooling liquid outlet pressure, cooling liquid flow, cooling liquid level, cooling liquid temperature, cooling liquid density, cooling liquid specific heat capacity, ambient temperature, circulating pump current and circulating pump speed; Through constructing a data synchronization method based on timestamp hash matching, the liquid cooling monitoring data is indexed and fused according to a unified time window to realize synchronous alignment of the liquid cooling monitoring data; the liquid cooling monitoring data is processed by a median filtering algorithm to eliminate abnormal values in the liquid cooling monitoring data due to instantaneous fluctuations; the liquid cooling monitoring data is trend fitted and smoothed by a local weighted regression algorithm; and a robust scaling algorithm based on median and interquartile range is used to perform cross-dimension normalization processing on the liquid cooling monitoring data and unify the dimension.

3. The intelligent control method for preventing liquid cooling, three-level leakage and pump barrier of the charging pile according to claim 1, characterized in that: The specific steps of the liquid cooling monitoring data after the pretreatment are as follows: The liquid cooling monitoring data in the abnormal period is extracted, and the specific steps of the cooling liquid leakage risk level evaluation are as follows: The liquid cooling monitoring data corresponding to the abnormal period is extracted, the first-order time derivative of the cooling liquid level is calculated, and the liquid level change rate is obtained; the liquid level change rate is divided by the product of the cooling liquid flow and the cooling liquid pressure difference, and the leakage trend factor is obtained; the difference between the ambient temperature and the cooling liquid temperature is taken as the temperature difference term, and then divided by the product of the specific heat capacity of the cooling liquid, the density of the cooling liquid and the cooling liquid flow, and the result is added to one and then the natural logarithm is taken, and the temperature difference influence factor is obtained; the leakage trend factor and the temperature difference influence factor are multiplied to obtain the leakage risk evaluation value.

4. The charging pile liquid cooling three-level leakage prevention and pump barrier prevention intelligent control method according to claim 1, characterized in that: The specific steps of the leakage risk level in the current abnormal period are as follows: Real-time comparison of the leakage risk evaluation value and the leakage risk threshold value, determination of the leakage risk level in the current abnormal period and execution of the corresponding leakage prevention strategy:

5. The intelligent control method for liquid cooling, three-level leakage prevention and pump barrier prevention of the charging pile according to claim 1, characterized in that: When S≤S1, it is determined to be a first-level risk, and the first-level leakage prevention structure continues to act: relying on the elastic deformation compensation of the composite sealing element and the labyrinth structure to prolong the leakage path mechanism, without the intervention of the controller; When S1<S<S2, it is determined to be a second-level risk, and the second-level leakage prevention mechanism is called: combining the cooling liquid pressure and the cooling liquid flow to identify the local leakage trend and locate the leakage position, generate warning information, and reduce the circulating pump speed at the same time; When S≥S2, it is determined to be a third-level risk, and the third-level leakage prevention measure is immediately started: the controller is linked to close the blocking valve of the cooling circuit corresponding to the leakage position, the leakage tank is started to collect the cooling liquid, and the audible and light alarms are triggered and the remote fault is reported. The specific steps of the circulating pump running state evaluation during the execution of the leakage prevention strategy are as follows: ​ 6. The charging pile liquid cooling three-level leakage prevention and pump barrier prevention intelligent control method according to claim 1, characterized in that: ​ In the process of executing the leakage prevention strategy, the running state of the circulating pump is dynamically trend tracked, and the circulating pump current, circulating pump speed, cooling liquid flow and cooling liquid pressure difference are extracted. The natural logarithm of the circulating pump current plus one is multiplied by the circulating pump speed to obtain a driving power factor. The driving power factor is divided by the sum of the cooling liquid flow and the minimum term to obtain a load response factor. The absolute value of the first order time derivative of the cooling liquid pressure difference is divided by the sum of the cooling liquid pressure difference and the minimum term to obtain a disturbance enhancement factor. The power function value of the mathematical constant e with the disturbance enhancement factor as the exponent is multiplied by the load response factor to obtain a pump obstacle identification evaluation value.

7. The charging pile liquid cooling three-level leakage prevention and pump barrier prevention intelligent control method according to claim 1, characterized in that: The specific steps of determining whether to trigger the pump obstacle prevention control strategy are as follows: Real-time comparison of the pump obstacle identification evaluation value and the pump obstacle threshold value is performed. When the pump obstacle identification evaluation value is less than or equal to the pump obstacle threshold value, it is determined that the circulating pump is running normally. When the pump obstacle identification evaluation value is greater than the pump obstacle threshold value, it is determined that the circulating pump is running abnormally, and the pump obstacle prevention control strategy is triggered: the circulating pump speed is reduced, and the intermittent on-off control channel is enabled to periodically reset the circulating period; the real-time environmental temperature is monitored, and if the environmental temperature is lower than the environmental temperature threshold value, the heating device in the cooling liquid passage is enabled, and the on-off period of the heating device is set according to the current cooling liquid flow rate.

8. The charging pile liquid cooling three-level leakage prevention and pump barrier prevention intelligent control method according to claim 1, characterized in that: The specific steps of completing the state closed loop judgment after the execution of the prevention strategy are as follows: The liquid cooling monitoring data of the Mth sampling period after the implementation of the pump obstacle prevention control strategy is extracted, and the pump obstacle identification evaluation value is calculated again. If the pump obstacle identification evaluation value is still greater than the pump obstacle threshold value, the circulating pump driving output is closed, the current cooling circuit is switched to the standby channel, and the charging pile audible and visual alarm is triggered synchronously; otherwise, the heating device is closed, and the normal driving control logic of the circulating pump is restored.

9. The intelligent control system for preventing liquid cooling three-level leakage and pump barrier of charging pile, characterized in that: It includes: A data acquisition and preprocessing module, a state recognition and leakage judgment module, a leakage risk assessment and response module, and a pump obstacle recognition and prevention control module, wherein: The data acquisition and preprocessing module is used to periodically acquire liquid cooling monitoring data and perform synchronous alignment, abnormality rejection, trend fitting and cross-dimensional normalization processing on the liquid cooling monitoring data to obtain preprocessed liquid cooling monitoring data; The state recognition and leakage judgment module is used to extract the preprocessed liquid cooling monitoring data, analyze the dynamic fluctuation characteristics of the cooling liquid flow, liquid level and pressure difference, determine whether there is an abnormal period, and evaluate whether there is a cooling liquid leakage trend in combination with real-time liquid cooling monitoring data; The specific steps of evaluating whether there is a cooling liquid leakage trend in combination with real-time liquid cooling monitoring data are as follows: The change amount of the liquid level of the coolant in each sampling period relative to the previous sampling period is calculated, the absolute value of the change amount of the liquid level of the coolant in the jth sampling period is taken, and then divided by the sum of the coolant flow and the minimum item in the corresponding sampling period, as a normalized liquid level fluctuation item; the first-order time derivative of the pressure difference of the coolant in the jth sampling period is taken, the result is added to one, and then the natural logarithm is taken, to obtain a pressure fluctuation adjustment item; the normalized liquid level fluctuation item is added to the pressure fluctuation adjustment item, to obtain a liquid leakage trend contribution item; the liquid leakage trend contribution items of all k sampling periods in the current abnormal period are summed and averaged, to obtain a liquid leakage trend evaluation value; The liquid leakage trend evaluation value and the liquid leakage trend threshold value are compared in real time, when the liquid leakage trend evaluation value is less than or equal to the liquid leakage trend threshold value, the current anti-leakage strategy is maintained unchanged; when the liquid leakage trend evaluation value is greater than the liquid leakage trend threshold value, the coolant leakage risk level evaluation is triggered; The leakage risk assessment and response module is used to extract the liquid cooling monitoring data in the abnormal period, to perform the coolant leakage risk level evaluation, to determine the leakage risk level in the current abnormal period and to execute the corresponding anti-leakage strategy; The pump barrier identification and prevention control module is used to continuously track the characteristic changes of the circulating pump driving power and the liquid cooling load response in the anti-leakage strategy execution process, to evaluate the circulating pump operation state, to determine whether to trigger the pump barrier prevention control strategy, and to complete the state closed-loop judgment after the prevention strategy is executed.

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