Charging pile liquid cooling module intelligent cooperative control system based on industrial Ethernet

Abstract

The invention discloses a charging pile liquid cooling module intelligent cooperative control system based on an industrial Ethernet, and relates to the technical field of liquid cooling control. The charging pile liquid cooling module intelligent cooperative control system based on the industrial Ethernet comprises a liquid cooling data acquisition and preprocessing unit used for acquiring and preprocessing liquid cooling cooperative operation data; the thermal load sensing and intelligent liquid cooling regulation and control unit is used for dividing independent cooling branches, evaluating the thermal load degree of each cooling branch, judging the real-time thermal load state and generating and executing a control instruction; the anomaly detection and cooling redundancy control unit is used for evaluating the thermal response deviation condition of a cooling branch and judging whether a double-ring redundancy pipeline is started or not; and the fault root cause judgment and recovery enhancement unit is used for evaluating the component fault risk degree in the abnormal branch and executing fault-tolerant control and branch recovery strategies. The problem that a whole set of liquid cooling system is shut down due to the fact that local fault tolerance cannot be achieved when a local branch breaks down in a traditional pipeline structure is solved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of liquid cooling control, in particular to an intelligent collaborative control system for a liquid cooling module of a charging pile based on an industrial Ethernet. BACKGROUND

[0002] With the rapid development of new energy vehicles and their supporting infrastructure, direct current fast charging piles gradually evolve towards high power and high density, which significantly increases the demand for system-level thermal management. Especially during high-power charging, the charging gun line, power module, whole machine control unit and other core components continuously generate heat. As the main thermal management means, the operation efficiency of the liquid cooling system directly affects the reliability and energy consumption performance of the charging pile. In order to meet the dynamic heat dissipation requirements under different load conditions, the liquid cooling system needs to have comprehensive control capabilities such as multi-parameter perception, load state evaluation, regulation and control path optimization and abnormal response.

[0003] For example, the invention with the publication number CN119717491A discloses a heat dissipation control method, system, device and medium based on a genetic algorithm adjusting a PID controller. The parameter value of the PID controller, the value selection parameter range and the search space are composed. The search space is formed into an initial population. The population individual fitness function value is calculated based on the initial population to realize the setting of the PID controller parameters by the genetic algorithm. The output of the PID controller is calculated based on the PID controller after parameter setting and the detected temperature difference at the gun line, so as to adjust the opening degree of the engine electromagnetic valve to control the output of the liquid cooling oil. The invention continuously outputs the optimal liquid cooling oil flow rate by genetic iteration to stabilize the temperature of the charging pile gun line, and adjusts the liquid cooling oil flow rate in real time according to the gun line temperature detection value to reduce the power consumption of the liquid cooling machine while meeting the gun line heat dissipation requirement.

[0004] For example, the invention with the publication number CN120578065A discloses a rotating speed prediction and control method for a charging gun liquid cooling system, which comprises the following steps: acquiring the performance parameters of key components in the liquid cooling system, including the radiator, water pump, fan, gun line and liquid tank; inputting the performance parameters into a one-dimensional simulation model to obtain the maximum temperature Tcoolant of the cooling liquid under different working conditions; based on Tcoolant under different working conditions, fitting the relationship between different working conditions and Tcoolant to obtain a rotating speed prediction model; inputting the environmental temperature under the target working condition and the gun line resistance heating power into the rotating speed prediction model, and under the constraint of Tcoolant, predicting a plurality of rotating speed combinations that meet the constraint, and selecting the rotating speed combination with the lowest total energy consumption of the liquid cooling system from the plurality of rotating speed combinations as the optimal rotating speed combination of the target working condition; during charging, the fan rotating speed and the water pump rotating speed are adjusted in real time based on the optimal rotating speed combination.

[0005] However, the existing liquid cooling control scheme focuses on single-link parameter optimization or model prediction, lacks precise perception and intelligent control of the coordinated operation state of multiple branches in the liquid cooling system, and is difficult to balance energy efficiency, response speed and fault tolerance ability under varying conditions. Especially in practical applications, traditional liquid cooling pipelines are mostly single-loop or series structure, once local branch leakage or key element failure occurs, it may cause the shutdown of the entire liquid cooling system, and the reliability is low, which is difficult to meet the technical requirements of high frequency, long time continuous operation and high safety of charging piles.

[0006] Therefore, in view of the above problems, there is an urgent need for an intelligent collaborative control system for a liquid cooling module of a charging pile based on an industrial Ethernet. SUMMARY

[0007] Technical problems solved In view of the deficiencies of the prior art, the present application provides an intelligent collaborative control system for a liquid cooling module of a charging pile based on an industrial Ethernet, which solves the problem that the traditional pipeline structure cannot realize local fault tolerance when a local branch fails, thereby causing the shutdown of the entire liquid cooling system.

[0008] Technical scheme

[0009] To achieve the above purpose, the present application realizes the following technical scheme: an intelligent collaborative control system for a liquid cooling module of a charging pile based on an industrial Ethernet, comprising: a liquid cooling data acquisition and preprocessing unit, a thermal load perception and intelligent liquid cooling regulation unit, an abnormality detection and cooling redundancy control unit, and a fault root cause judgment and recovery enhancement unit, wherein: the liquid cooling data acquisition and preprocessing unit is used to acquire liquid cooling collaborative operation data, and automatically calibrate, exclude abnormalities, standardize and normalize the liquid cooling collaborative operation data to obtain preprocessed liquid cooling collaborative operation data; the thermal load perception and intelligent liquid cooling regulation unit is used to divide the liquid outlet ring pipeline and the liquid inlet ring pipeline into independent cooling branches, and evaluate the thermal load degree of each cooling branch based on the preprocessed liquid cooling collaborative operation data, determine the real-time thermal load state and generate and execute corresponding control instructions; the abnormality detection and cooling redundancy control unit is used to extract the liquid cooling collaborative operation data within N sampling periods after the execution of the control instructions, calculate the response characteristics of temperature difference, pressure difference and flow, evaluate the thermal response deviation of the cooling branch, and determine whether to enable the double-ring redundant pipeline; the fault root cause judgment and recovery enhancement unit is used to evaluate the element fault risk degree in the abnormal branch based on the regulation and control execution feedback data of the abnormal branch, determine whether the abnormal branch is a fault type fluctuation, execute fault tolerance control and branch recovery strategy, and realize branch state update and control closed loop.

[0010] Furthermore, the specific steps for collecting liquid-cooled collaborative operation data are as follows: Set a fixed-width sliding time window as a sampling period, and periodically collect the liquid-cooled collaborative operation data of the entire charging pile. The liquid-cooled collaborative operation data includes inlet temperature, outlet temperature, coolant flow rate, loop inlet pressure, loop outlet pressure, charging module power, electromagnetic pump speed, coolant density, and coolant specific heat capacity.

[0011] Furthermore, the specific steps for obtaining preprocessed liquid-cooled collaborative operation data are as follows: The collected liquid-cooled collaborative operation data is calibrated using multi-source comparison and thermal parameter calibration methods to eliminate baseline shifts caused by hardware differences, installation conditions, and environmental changes; a mutation detection mechanism based on heat flow evolution characteristics and dynamic threshold ranges is used to identify abrupt changes and remove data points inconsistent with operating conditions; a standardized mapping method using a unified physical unit system is used to convert the format of the liquid-cooled collaborative operation data, generating a data structure with numerical consistency; and an operating interval index and normalized mapping function are constructed to perform interval transformation and numerical compression on the liquid-cooled collaborative operation data.

[0012] Furthermore, the liquid outlet annular pipe and the liquid inlet annular pipe are divided into independent cooling branches. The specific steps for evaluating the heat load of each cooling branch based on the pre-processed liquid-cooled collaborative operation data are as follows: Based on the physical connection relationship between the liquid outlet annular pipe and the liquid inlet annular pipe, combined with the spatial distribution structure of the liquid cooling module, the double ring is divided into multiple independent cooling branches; the pre-processed liquid-cooled collaborative operation data is extracted, and the coolant density, coolant specific heat capacity, and coolant flow rate are multiplied together, and then multiplied by the difference between the outlet temperature and the inlet temperature to obtain the liquid cooling heat absorption per unit time; the real-time speed of the electromagnetic pump is divided by the rated speed of the electromagnetic pump to obtain the pump speed normalization factor; the liquid cooling heat absorption is multiplied by the pump speed normalization factor and used as the numerator of the heat load calculation; the charging module power is added to the minimum term and used as the denominator of the heat load calculation; the numerator is divided by the denominator to obtain the liquid cooling heat load evaluation value.

[0013] Further, the specific steps of determining the real-time thermal load state and generating and executing the corresponding control instructions are as follows: comparing the liquid cooling thermal load evaluation value S with the thermal load threshold values S1 and S2 in real time, determining the thermal load state, and issuing the corresponding control instructions through the industrial Ethernet: when S≤S1, it is determined that the cooling branch is in a thermal stable state, and the current pump speed and valve opening degree are maintained unchanged; when S1<S<S2, it is determined that the cooling branch is in a thermal transition state, the target speed of the electromagnetic pump is calculated according to the difference between the liquid cooling thermal load evaluation value S and the secondary thermal load threshold value S2 by using a linear regulation algorithm, and the electromagnetic pump speed is increased to the target speed; when S≥S2, it is determined that the cooling branch is in a thermal overload state, and the electromagnetic pump speed is increased to the rated speed of the electromagnetic pump, and the electric isolation valve is adjusted to a fully open state; the central controller receives the control instructions and drives the electromagnetic pump and the electric isolation valve to perform corresponding adjustment operations.

[0014] Further, the specific steps of extracting the liquid cooling cooperative operation data in the N sampling periods after the execution of the control instructions and calculating the response characteristics of the temperature difference, the pressure difference and the flow are as follows: after the execution of the control instructions, the liquid cooling cooperative operation data in the N sampling periods are extracted, the difference between the inlet temperature and the outlet temperature of the cooling liquid is calculated to obtain the cooling liquid heat exchange temperature difference, the difference between the inlet pressure and the outlet pressure of the cooling loop is calculated to obtain the cooling loop pressure difference, and based on the exponential weighted average method, the cooling liquid heat exchange temperature difference average, the cooling liquid pressure difference average and the cooling liquid flow average are calculated.

[0015] Further, the specific steps of evaluating the thermal response deviation of the cooling branch are as follows: the absolute value of the difference between the cooling liquid heat exchange temperature difference and the cooling liquid heat exchange temperature difference average is taken, and then divided by the sum of the cooling liquid heat exchange temperature difference average and the minimum term to obtain a temperature difference deviation factor; the absolute value of the difference between the cooling liquid loop pressure difference and the cooling liquid loop pressure difference average is taken, and then divided by the sum of the cooling liquid loop pressure difference average and the minimum term to obtain a pressure difference deviation factor; the cooling liquid flow average is divided by the sum of the minimum term and the cooling liquid flow, and the natural logarithm of the obtained ratio is taken to obtain a flow response difference factor; the square of the temperature difference deviation factor, the square of the pressure difference deviation factor and the square of the flow response difference factor are added to obtain an abnormal change amplitude factor; the liquid cooling thermal load evaluation value is subtracted from the first thermal load threshold value, multiplied by a thermal sensitivity gain coefficient, and the negative value of the obtained product is taken as the exponential part to perform a power function operation with the natural logarithm base e to obtain a thermal load weighting factor; the reciprocal of the sum of the abnormal change amplitude factor and the thermal load weighting factor is multiplied to obtain a branch abnormality evaluation value.

[0016] Further, the specific steps of judging whether to enable the double-loop redundant pipeline are as follows: comparing the branch abnormality evaluation value and the liquid cooling abnormality threshold value in real time, when the branch abnormality evaluation value is less than or equal to the liquid cooling abnormality threshold value, maintaining the current control state; when the branch abnormality evaluation value is greater than the liquid cooling abnormality threshold value, marking the cooling branch as an abnormal branch, controlling the electric isolation valve to adjust to an open state, and enabling the double-loop redundant pipeline to switch the cooling path.

[0017] Further, the specific steps of executing feedback data based on the abnormal branch regulation to evaluate the element failure risk degree in the abnormal branch are as follows: after the cooling branch is marked as an abnormal branch, combining the abnormal branch control instruction execution process, extracting the electromagnetic pump real-time rotating speed and the electric isolation valve response delay time, and calculating the cooling liquid flow standard deviation within N sampling periods after the control instruction execution is completed; taking the absolute value of the difference between the electromagnetic pump real-time rotating speed and the electromagnetic pump target rotating speed, and dividing the sum of the electromagnetic pump target rotating speed and the minimum term to obtain the pump speed deviation ratio factor; dividing the electric isolation valve response delay time by the sum of the electric isolation valve reference response time and the minimum term to obtain the response time ratio factor; squaring the product of the pump speed deviation ratio factor and the response time ratio factor to obtain the operation deviation factor; dividing the cooling liquid flow standard deviation by the sum of the cooling liquid flow mean value and the minimum term, adding one to the obtained ratio to obtain the flow fluctuation factor; dividing the electromagnetic pump real-time rotating speed by the sum of the electromagnetic pump rated rotating speed and the minimum term, multiplying the failure response coefficient and taking the negative value to obtain the pump speed normalized response index; taking the exponential power of the pump speed normalized response index with the natural logarithm base e to obtain the pump speed abnormal response factor; multiplying the operation deviation factor, the flow fluctuation factor and the pump speed abnormal response factor by one in turn, and taking the natural logarithm of the obtained product to obtain the element failure risk evaluation value.

[0018] Further, the specific steps of judging whether the abnormal branch is a failure type fluctuation, executing the fault-tolerant control and branch recovery strategy, and realizing the branch state update and control closed loop are as follows: comparing the element failure risk evaluation value and the failure judgment threshold value in real time, when the element failure risk evaluation value is greater than the failure judgment threshold value, judging that the abnormal branch is a failure type fluctuation, controlling the electric isolation valve to adjust to a fully open state, and sending a failure warning to the central controller; when the element failure risk evaluation value is less than or equal to the failure judgment threshold value, judging that the abnormal branch is a non-failure type fluctuation, and activating the path recovery strategy: re-performing the liquid cooling branch abnormality evaluation, if the branch abnormality evaluation value is lower than the liquid cooling abnormality threshold value for a continuous fixed number of times, removing the flow limitation and isolation control, and restoring the branch to normal operation; otherwise, maintaining the current control state.

[0019] Beneficial effects

[0020] The present application has the following beneficial effects: (1) The intelligent collaborative control system of the charging pile liquid cooling module based on industrial Ethernet can dynamically identify the current thermal stability, thermal transition or thermal overload state by constructing a liquid cooling thermal load evaluation model and comparing the liquid cooling thermal load evaluation value with the thermal load threshold in real time, and automatically generates and issues control instructions according to the state level to adjust the electromagnetic pump speed and the electric isolation valve opening degree. It can effectively avoid over-regulation or delayed response phenomenon, and ensure the rapid response and thermal balance control of the liquid cooling system under complex working conditions.

[0021] (2) The intelligent collaborative control system of the charging pile liquid cooling module based on industrial Ethernet uses a deviation aggregation algorithm and a weight adjustment mechanism to comprehensively evaluate the response deviation of the cooling branch through the deviation between the exponentially weighted average value and the real-time value. If the branch abnormal evaluation value exceeds the set threshold, it is automatically identified as an abnormal branch, and a double-loop redundant switching operation is performed to control the electric isolation valve to open the standby cooling passage, realizing automatic path switching. It greatly improves the anti-interference ability and emergency capability of the liquid cooling system, and avoids the risk of whole machine thermal runaway caused by cooling failure.

[0022] (3) The intelligent collaborative control system of the charging pile liquid cooling module based on industrial Ethernet constructs a component fault risk evaluation model by combining the real-time speed deviation of the electromagnetic pump, the response delay time of the electric isolation valve and the flow fluctuation standard deviation, obtains the component fault risk evaluation value, and when the evaluation value exceeds the fault judgment threshold, it is determined that there is a structural fault in the branch, and a fault warning is automatically sent and the valve control strategy is adjusted. If it is a non-fault type fluctuation, the path recovery mechanism is started, realizing the closed-loop control process from local abnormal identification to component-level fault judgment.

[0023] (4) The intelligent collaborative control system of the charging pile liquid cooling module based on industrial Ethernet divides the liquid outlet ring pipeline and the liquid inlet ring pipeline into multiple physically independent cooling branches, and establishes a more fine-grained thermal management unit combined with the spatial distribution structure of the liquid cooling module, so that each branch can be independently perceived, evaluated and regulated. Compared with the traditional single-loop or series system, this structure has stronger response flexibility and control accuracy when the thermal load changes, local faults or environmental fluctuations, can allocate cooling resources as needed, effectively avoid the performance decline or failure of the whole system caused by local abnormalities, and thus improve the stability, energy efficiency and operation safety of the system. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The structure diagram of the intelligent collaborative control system of the charging pile liquid cooling module based on industrial Ethernet;

[0025] Figure 2 The cooling branch liquid cooling thermal load evaluation value and thermal state distribution diagram;

[0026] Figure 3The liquid cooling cooperative control system structural principle diagram.

[0027] In the figure, 1, charging module; 2, liquid outlet annular pipeline; 3, electric isolation valve; 4, cold plate; 5, liquid inlet annular pipeline; 6, central controller; 7, flow sensor; 8, temperature sensor; 9, electromagnetic pump; 10, liquid storage tank; 11, cooling tower. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0029] Please refer to Figures 1-3 The embodiment of the present application provides a technical solution: an intelligent cooperative control system of a liquid cooling module of a charging pile based on an industrial Ethernet, comprising: a liquid cooling data acquisition and preprocessing unit, a thermal load sensing and intelligent liquid cooling regulation unit, an abnormality detection and cooling redundancy control unit, and a fault root cause judgment and recovery enhancement unit, wherein: the liquid cooling data acquisition and preprocessing unit is used to acquire liquid cooling cooperative operation data, and automatically calibrate, exclude abnormalities, standardize and normalize the liquid cooling cooperative operation data to obtain preprocessed liquid cooling cooperative operation data; the thermal load sensing and intelligent liquid cooling regulation unit is used to divide the liquid outlet annular pipeline 2 and the liquid inlet annular pipeline 5 into independent cooling branches, and evaluate the thermal load degree of each cooling branch based on the preprocessed liquid cooling cooperative operation data, determine the real-time thermal load state and generate and execute corresponding control instructions; the abnormality detection and cooling redundancy control unit is used to extract the liquid cooling cooperative operation data within N sampling periods after execution of the control instructions, calculate the response characteristics of temperature difference, pressure difference and flow, evaluate the thermal response deviation of the cooling branch, and determine whether to enable double-ring redundant pipelines; the fault root cause judgment and recovery enhancement unit is used to evaluate the element fault risk degree in the abnormal branch based on the regulation and control execution feedback data of the abnormal branch, determine whether the abnormal branch is a fault type fluctuation, execute fault-tolerant control and branch recovery strategies, and realize branch state updating and control closed loop.

[0030] Specifically, the specific steps of collecting liquid cooling cooperative operation data are as follows: a fixed-width sliding time window is set as a sampling period, and the liquid cooling cooperative operation data of the charging pile complete machine are periodically collected, the liquid cooling cooperative operation data including inlet temperature, outlet temperature, cooling liquid flow, loop inlet pressure, loop outlet pressure, charging module power, electromagnetic pump speed, cooling liquid density and cooling liquid specific heat capacity; wherein, the temperature data are collected in real time by the temperature sensor 8 arranged on the inlet ring pipeline 5 and the outlet ring pipeline 2, and the inlet temperature and the outlet temperature are obtained respectively; the cooling liquid flow is continuously measured by the flow sensor 7 installed in the liquid cooling circulation pipeline, and the cooling liquid volume change in a unit of time is obtained; the pressure sensor arranged at the front end of the inlet ring pipeline 5 senses the liquid pressure state at the loop inlet in real time, and the pressure sensor at the end of the outlet ring pipeline 2 is combined to obtain the loop inlet pressure and the outlet pressure respectively; the voltage and current product of the charging module 1 is calculated in real time by the central controller 6 connected to the charging module 1 to obtain the charging module power; the actual speed of the electromagnetic pump 9 is obtained by the speed detection unit in the electromagnetic pump 9; the standard physical property parameter database is consulted according to the type of cooling liquid used in the liquid tank 10, and the temperature is corrected combined with the measurement data of the temperature sensor 8 to determine the current cooling liquid density; at the same time, based on the cooling liquid physical property database and combined with the actual running temperature interval, the corresponding cooling liquid specific heat capacity is obtained.

[0031] In the embodiment, by setting a fixed-width sliding time window in the liquid cooling system, the inlet temperature, the outlet temperature, the cooling liquid flow, the loop inlet pressure, the loop outlet pressure, the charging module power, the electromagnetic pump speed, the cooling liquid density and the cooling liquid specific heat capacity are systematically collected, ensuring the complete coverage of the liquid cooling cooperative operation data in space position and time scale. By combining the standard physical property parameter database and the real-time running state, the accuracy and consistency of the thermal parameter acquisition are effectively improved, which provides data basis and algorithm support for subsequent cooling branch thermal load modeling and dynamic control, and enhances the sensing accuracy and control reliability of the system under multiple working conditions.

[0032] Specifically, the specific steps of automatically calibrating, removing outliers, standardizing and normalizing the liquid cooling cooperative operation data are as follows: by comparing multiple sources, the inlet temperature, outlet temperature, cooling liquid flow rate, loop inlet pressure, loop outlet pressure, charging module power, electromagnetic pump speed, cooling liquid density and specific heat capacity of the cooling liquid are compared, and a thermal parameter calibration method based on the actual structure parameters of the liquid cooling system is combined to automatically calibrate the liquid cooling cooperative operation data, eliminate baseline shifts caused by temperature sensor 8 measurement drift, flow sensor 7 response delay, pressure sensor accuracy deviation, charging module 1 power estimation error, electromagnetic pump speed feedback jitter and cooling liquid physical property parameter fluctuation; by combining the evolution law of heat flow along the cooling branch, a dynamic anomaly detection mechanism based on gradient change rate and historical window fluctuation range is constructed to identify the collected liquid cooling cooperative operation data one by one, and the temperature, pressure, flow rate and power data points that do not match the actual working condition thermal load characteristics are removed; by constructing a unified physical unit system based on joule, pascal, second, kilogram and degree Celsius, a one-to-one mapping standardization conversion formula is established to convert the physical quantity format of the original liquid cooling cooperative operation data, and the numerical consistency expression of the liquid cooling cooperative operation data is realized; by constructing an operation interval index function based on the historical maximum and minimum boundaries of the liquid cooling cooperative operation data, and combining a compression mapping rule established by a normalization mapping function, the liquid cooling cooperative operation data after standardization is further subjected to interval transformation and numerical compression to improve the operation efficiency and generalization ability of subsequent thermal load evaluation.

[0033] In the embodiment, by introducing multi-source comparison calibration, thermal evolution feature recognition, unified physical unit standardization conversion and running interval normalization compression, the precision, consistency and usability of the liquid cooling cooperative operation data are significantly improved, so that the liquid cooling cooperative operation data can still realize stable and efficient feature expression under the condition of multiple source collection errors, fluctuation interference and dimension inconsistency, effectively supporting accurate calculation of liquid cooling thermal load evaluation value and reliable execution of intelligent control strategy.

[0034] Specifically, the specific steps of evaluating the heat load degree of each cooling branch based on the pretreated liquid cooling collaborative operation data are as follows: first, based on the physical connection relationship between the liquid outlet annular pipeline 2 and the liquid inlet annular pipeline 5, combined with the spatial distribution structure of the charging module 1 and the cold plate 4 in the liquid cooling module, a topological mapping model of the cooling circuit is constructed, and the double-ring liquid cooling structure is divided into multiple independent cooling branches to meet the needs of targeted thermal management control; then, the liquid cooling collaborative operation data after automatic calibration, abnormality rejection, standardization and normalization processing are extracted, and the cooling liquid density, the cooling liquid specific heat capacity, the cooling liquid flow, the liquid inlet temperature, the liquid outlet temperature, the electromagnetic pump real-time speed, the electromagnetic pump rated speed and the charging module power are obtained in turn; wherein, the cooling liquid density, the cooling liquid specific heat capacity and the cooling liquid flow are multiplied and multiplied by the difference between the liquid outlet temperature and the liquid inlet temperature to obtain the liquid cooling heat absorption amount per unit time, which is used to represent the heat exchange capacity of the cooling branch under the current working condition; the pump speed normalization factor is obtained by dividing the electromagnetic pump real-time speed by the electromagnetic pump rated speed, which represents the influence of the liquid cooling flow state on the heat transfer efficiency; the product of the liquid cooling heat absorption amount and the pump speed normalization factor is used as the numerator part of the heat load evaluation formula; the charging module power is added to the minimum term to form the denominator; wherein, the minimum term is a positive real number with a very small value but not zero, which is used to avoid the numerical instability problem caused by the zero denominator in the operation process, and the value range is to ; the minimum term involved later is defined and valued as above unless otherwise stated; finally, the numerator is divided by the denominator to obtain the liquid cooling heat load evaluation value, which is used to represent the heat load intensity of each cooling branch under different working conditions, and provides a quantitative basis for subsequent intelligent control.

[0035] The specific calculation formula of the liquid cooling heat load evaluation value is as follows:

[0036] ;

[0037] In the formula, represents the liquid cooling heat load evaluation value, represents the cooling liquid density, represents the cooling liquid specific heat capacity, represents the cooling liquid flow, represents the liquid outlet temperature, represents the liquid inlet temperature, represents the electromagnetic pump real-time speed, represents the electromagnetic pump rated speed, represents the charging module power, represents the minimum term.

[0038] In the embodiment, Table 1 is a liquid cooling heat load evaluation value data table, which lists key operating parameters of five cooling branches under the same working condition and corresponding liquid cooling heat load evaluation values. The key operating parameters include cooling liquid density, cooling liquid specific heat capacity, cooling liquid flow rate, outlet liquid temperature, inlet liquid temperature, real-time speed of the electromagnetic pump, rated speed of the electromagnetic pump, and charging module power. The specific data are described as follows: in the cooling branch 1, the cooling liquid density is 997, the cooling liquid specific heat capacity is 4186, the cooling liquid flow rate is 30, the outlet liquid temperature is 45, the inlet liquid temperature is 35, the real-time speed of the electromagnetic pump is 2800, the rated speed of the electromagnetic pump is 3600, the module power is 1200, and the corresponding liquid cooling heat load evaluation value is 13.53; in the cooling branch 2, the cooling liquid density is 998, the cooling liquid specific heat capacity is 4184, the cooling liquid flow rate is 45, the outlet liquid temperature is 50, the inlet liquid temperature is 36, the real-time speed of the electromagnetic pump is 3000, the rated speed of the electromagnetic pump is 3600, the module power is 1300, and the corresponding liquid cooling heat load evaluation value is 28.11; in the cooling branch 3, the cooling liquid density is 999, the cooling liquid specific heat capacity is 4185, the cooling liquid flow rate is 60, the outlet liquid temperature is 51, the inlet liquid temperature is 37, the real-time speed of the electromagnetic pump is 2900, the rated speed of the electromagnetic pump is 3600, the module power is 1250, and the corresponding liquid cooling heat load evaluation value is 37.72; in the cooling branch 4, the cooling liquid density is 997, the cooling liquid specific heat capacity is 4186, the cooling liquid flow rate is 50, the outlet liquid temperature is 47, the inlet liquid temperature is 34, the real-time speed of the electromagnetic pump is 3100, the rated speed of the electromagnetic pump is 3600, the module power is 1350, and the corresponding liquid cooling heat load evaluation value is 28.84; and in the cooling branch 5, the cooling liquid density is 998, the cooling liquid specific heat capacity is 4184, the cooling liquid flow rate is 40, the outlet liquid temperature is 49, the inlet liquid temperature is 36, the real-time speed of the electromagnetic pump is 2950, the rated speed of the electromagnetic pump is 3600, the module power is 1280, and the corresponding liquid cooling heat load evaluation value is 23.17.

[0039] Table 1 Liquid cooling heat load evaluation value data table

[0040]

[0041] As Figure 2As shown, the liquid cooling heat load evaluation value changes of five cooling branches are displayed, which reflect the heat load state differences of the liquid cooling system in each branch. The heat states of different branches are represented by color column charts in the figure: green represents thermal stability, orange represents thermal transition, and red represents thermal overload, which visually distinguishes the heat risk levels between branches. At the same time, two dashed threshold lines are set in the figure as a reference for judgment: the gray dashed line is the first heat load threshold, which is used to identify the initial heat load increase interval; the black dashed line is the second heat load threshold, which is used to mark the limit of the system entering an overload state. As can be seen from the figure, the liquid cooling heat load evaluation values of cooling branch 1 and cooling branch 5 are 13.53 and 23.17 respectively, both of which are lower than the first heat load threshold, and are in a thermal stable state; the evaluation values of cooling branch 2 and cooling branch 4 are 28.11 and 28.84 respectively, which are between the two heat load thresholds, indicating that there is a certain heat load pressure, and they are in a thermal transition state; the evaluation value of cooling branch 3 reaches 37.72, which exceeds the second threshold, indicating that it has entered a thermal overload state, and cooling reinforcement or path switching and other control strategies should be taken immediately to reduce the thermal risk. Figure 2 The heat distribution characteristics of the liquid cooling system under multi-branch operation are effectively reflected, providing quantitative decision basis for intelligent cooling strategies.

[0042] In this embodiment, by constructing a cooling branch division and heat load calculation mechanism based on liquid cooling cooperative operation data, combined with cooling liquid density, cooling liquid specific heat capacity, cooling liquid flow rate, liquid outlet temperature, liquid inlet temperature, electromagnetic pump real-time speed, electromagnetic pump rated speed, and charging module power, the liquid cooling heat load evaluation value obtained in real time, the important basis for branch thermal state discrimination and temperature control strategy issuing is obtained, which significantly enhances the thermal management accuracy and control robustness of the present application under multi-branch working conditions.

[0043] Specifically, the specific steps of determining the real-time thermal load state and generating and executing the corresponding control instructions are as follows: comparing the liquid cooling thermal load evaluation value S with the thermal load threshold value in real time, the thermal load threshold value including the first-level thermal load threshold value S1 and the second-level thermal load threshold value S2; determining the thermal load state based on the current thermal response degree of the cooling branch, and sending the corresponding control instruction to the central controller 6 through the industrial Ethernet; when the liquid cooling thermal load evaluation value S is less than or equal to the first-level thermal load threshold value S1, it is determined that the cooling branch is in a thermal stable state, and the actual speed of the electromagnetic pump and the opening degree of the electric isolation valve 3 are not adjusted, and the current control state is maintained; when the liquid cooling thermal load evaluation value S is greater than the first-level thermal load threshold value S1 and less than the second-level thermal load threshold value S2, it is determined that the cooling branch is in a thermal transition state, and the target speed of the electromagnetic pump 9 is calculated according to the difference between the liquid cooling thermal load evaluation value S and the second-level thermal load threshold value S2, and the actual speed of the electromagnetic pump is gradually adjusted to the calculated target speed to enhance the cooling capacity response; when the liquid cooling thermal load evaluation value S is greater than or equal to the second-level thermal load threshold value S2, it is determined that the cooling branch is in a thermal overload state, and the actual speed of the electromagnetic pump is directly increased to the rated speed of the electromagnetic pump, and the electric isolation valve 3 is controlled to the fully open state to realize the maximum flow cooling path and quickly reduce the branch thermal load; after the central controller 6 receives the control instruction, the electromagnetic pump 9 and the electric isolation valve 3 complete the corresponding adjustment operation, and a closed-loop regulation and control mechanism of thermal state response and control instruction execution is constructed.

[0044] As shown in Figure 3 , the core component layout and logical connection relationship under the double-ring liquid cooling structure are shown. The system includes a closed-loop cooling circuit composed of the liquid outlet ring pipeline 2 and the liquid inlet ring pipeline 5, a plurality of cold plates 4 connected between the double-ring pipelines through parallel branches, and an electromagnetic pump 9, a flow sensor 7, a temperature sensor 8, and an electric isolation valve 3 arranged in each cold plate 4 branch for realizing independent regulation and control and state sensing of the cooling branch. Among them, the charging module 1 is the main heat source of the liquid cooling module, and the heat released during work is taken away by the cooling liquid through the cold plate 4, thereby maintaining the stability of the internal temperature of the module. In the liquid cooling circuit, the liquid tank 10 provides circulating cooling liquid, and the cooling tower 11 is used for cooling high-temperature cooling liquid; the central controller 6 collects various liquid cooling cooperative operation data in real time through the industrial Ethernet, and executes intelligent control strategies such as thermal load evaluation, abnormality identification, fault-tolerant control, and path switching of each cooling branch, thereby ensuring the thermal safety and operation stability of the system under variable working conditions.

[0045] In this embodiment, by real-time comparison between the liquid cooling heat load evaluation value S and the first and second heat load threshold values S1 and S2, a hierarchical heat response judgment mechanism is constructed, which can effectively identify the cooling branch in a thermal stable state, a thermal transition state or a thermal overload state, and dynamically adjust the actual speed of the electromagnetic pump 9 and the opening of the electric isolation valve 3 accordingly. This method uses a linear regulation algorithm to realize continuous calculation and execution of the target speed, effectively enhancing the response flexibility and cooling efficiency under different heat load conditions, improving the adaptability and control accuracy of the thermal management system to variable working conditions, and providing technical support for closed-loop dynamic adjustment of the cooling branch state.

[0046] Specifically, the specific steps of extracting the liquid cooling cooperative operation data within N sampling periods after the execution of the control instruction are as follows: after the control instruction is issued through the industrial Ethernet and executed by the central controller 6, a fixed-length data extraction mechanism is started to extract the liquid cooling cooperative operation data within N sampling periods; wherein N is a positive integer greater than three; then, the cooling liquid heat exchange temperature difference is obtained by calculating the difference between the inlet and outlet temperatures of the cooling liquid, reflecting the temperature change intensity of the cooling branch in the response stage; the cooling loop pressure difference is obtained by calculating the difference between the inlet and outlet pressures of the cooling loop, used to characterize the flow resistance change of the cooling circuit; at the same time, the exponential weighted average algorithm is constructed to perform weighted smoothing processing on the heat exchange temperature difference, the loop pressure difference and the cooling liquid flow rate, respectively, and the cooling liquid heat exchange temperature difference average, the cooling liquid pressure difference average and the cooling liquid flow rate average are calculated in turn, which are used to establish the dynamic reference value of the branch response characteristics, further supporting the subsequent evaluation and calculation of the branch response deviation degree.

[0047] In this embodiment, by extracting the liquid cooling cooperative operation data within a fixed number of sampling periods after the execution of the control instruction, the dynamic changes of the temperature difference between the inlet and outlet temperatures, the pressure difference between the inlet and outlet pressures of the cooling loop and the cooling liquid flow rate are systematically calculated, and the exponential weighted average method is introduced to obtain the cooling liquid heat exchange temperature difference average, the cooling liquid pressure difference average and the cooling liquid flow rate average, thereby establishing a complete dynamic reference model of the cooling branch response state, improving the accuracy and timeliness of the cooling branch response characteristic extraction, and providing a highly reliable data foundation support for subsequent abnormal detection and intelligent control.

[0048] Specifically, the specific steps of evaluating the thermal response deviation of the cooling branch are as follows: taking the absolute value of the difference between the cooling liquid heat exchange temperature difference obtained in N sampling periods and the corresponding cooling liquid heat exchange temperature difference average, and dividing the sum of the cooling liquid heat exchange temperature difference average and the minimum term, to obtain a cooling liquid heat exchange temperature difference deviation factor, which is used to measure the temperature response deviation degree of the current branch in the heat exchange capacity compared with the average working condition; taking the absolute value of the difference between the cooling liquid loop pressure difference and the corresponding cooling liquid loop pressure difference average, and dividing the sum of the cooling liquid loop pressure difference average and the minimum term, to obtain a cooling liquid loop pressure difference deviation factor, which is used to reflect the change degree of the internal hydraulic transmission efficiency of the current branch; taking the sum of the cooling liquid flow average and the minimum term as the numerator, and the sum of the cooling liquid flow and the minimum term as the denominator, and taking the natural logarithm of the obtained ratio, to obtain a cooling liquid flow response difference factor, which is used to capture the dynamic fluctuation characteristics of the cooling liquid flow relative to the long-term flow level; adding the square of the cooling liquid heat exchange temperature difference deviation factor, the cooling liquid loop pressure difference deviation factor and the cooling liquid flow response difference factor, to obtain a cooling branch abnormal change amplitude factor, which is used to comprehensively describe the overall deviation intensity of the cooling branch in multiple physical quantity dimensions; subtracting the first level heat load threshold value from the liquid cooling heat load evaluation value, and multiplying the heat sensitive gain coefficient, taking the negative value of the obtained product as the exponential part, and performing power function operation with the natural logarithm base e as the base, to obtain a heat load weighting factor, which is used to dynamically adjust the influence weight of the deviation degree in the overall evaluation according to the heat load level; wherein the heat sensitive gain coefficient is obtained by fitting and training the relationship between the branch thermal response deviation degree and the cooling system failure probability based on the correlation sample data composed of the historical liquid cooling heat load evaluation value and the actual cooling abnormal event, and the value range is 0.1 to 10; finally, the product of the cooling branch abnormal change amplitude factor and the reciprocal of the heat load weighting factor plus one is calculated, and the branch abnormal evaluation value of the cooling branch is output as an important quantitative index for identifying the response deviation and the operation risk level of the cooling branch, and providing accurate basis for subsequent control decision.

[0049] wherein the specific calculation formula of the branch abnormal evaluation value is:

[0050] ;

[0051] In the formula, represents the branch abnormal evaluation value, represents the cooling liquid heat exchange temperature difference, represents the cooling liquid heat exchange temperature difference average, represents the cooling loop pressure difference, represents the cooling loop pressure difference average, represents the cooling liquid flow, represents the cooling liquid flow average, represents the liquid cooling heat load evaluation value, represents a primary heat load threshold value, represents a minimum term, represents a heat-sensitive gain coefficient.

[0052] In the embodiment, by introducing a cooling liquid heat exchange temperature difference offset factor, a cooling liquid loop pressure difference offset factor and a cooling liquid flow response difference factor, a multi-dimensional deviation feature measurement system is constructed, and on this basis, a heat load weighting factor is fused to form a comprehensive branch abnormality evaluation mechanism. The high-precision quantitative evaluation of the response state of the cooling branch is realized, and the accuracy and reliability of the cooling state identification are significantly improved, providing quantifiable basis for subsequent state regulation and fault response of the cooling branch.

[0053] Specifically, the specific steps of determining whether to enable the double-loop redundant pipeline are as follows: comparing the branch abnormality evaluation value of the cooling branch with the liquid cooling abnormality threshold value in real time, dynamically adjusting the operation strategy according to the response deviation degree of the current cooling branch; when the branch abnormality evaluation value is less than or equal to the liquid cooling abnormality threshold value, it is determined that the cooling branch is in a normal working state, the current electromagnetic pump speed and electric isolation valve 3 opening degree are maintained unchanged, and the original cooling path is kept unchanged; when the branch abnormality evaluation value is greater than the liquid cooling abnormality threshold value, it is determined that the operating state of the cooling branch deviates significantly, and there is a potential thermal instability risk, the corresponding cooling branch is marked as an abnormal branch, a control command is issued to the electric isolation valve 3, the electric isolation valve 3 is adjusted to the open state, the standby cooling path is enabled, the double-loop redundant pipeline is activated, the switching of the main cooling path to the standby path is realized, and the heat exchange process of the abnormal branch is ensured to continue.

[0054] In the embodiment, by comparing the branch abnormality evaluation value of the cooling branch with the liquid cooling abnormality threshold value in real time, and combining the precise control of the electric isolation valve 3, an automatic decision mechanism is realized for quickly enabling the double-loop redundant pipeline and switching the cooling path when the operating state of the cooling branch is abnormal, which significantly improves the operation continuity, response flexibility and structural fault tolerance of the liquid cooling system under complex working conditions, and ensures the heat dissipation reliability and system stability of the charging pile under varying thermal load conditions.

[0055] Specifically, the specific steps of evaluating the element failure risk degree in the abnormal branch based on the feedback data of the abnormal branch regulation are as follows: when the cooling branch is marked as an abnormal branch, the real-time speed of the electromagnetic pump and the response delay time of the electric isolation valve are extracted in combination with the running performance of the abnormal branch in the control instruction execution process, and the data recorded by the flow sensor 7 in the liquid cooling circulation pipeline within N sampling periods after the completion of the control instruction execution is used to calculate the standard deviation of the cooling liquid flow; the absolute value of the difference between the real-time speed of the electromagnetic pump and the target speed of the electromagnetic pump is taken, and then divided by the sum of the target speed of the electromagnetic pump and the minimum term, to obtain a pump speed deviation ratio factor for evaluating the actual running deviation degree of the pump body; the response delay time of the electric isolation valve is divided by the sum of the reference response time of the electric isolation valve and the minimum term, to obtain a response time ratio factor reflecting the time response consistency of the valve action; the pump speed deviation ratio factor and the response time ratio factor are multiplied and then squared to obtain an operation deviation factor for representing the linkage stability change of the electromagnetic pump 9 and the electric isolation valve 3 in the control instruction execution process; the standard deviation of the cooling liquid flow is divided by the sum of the average value of the cooling liquid flow and the minimum term, and the obtained ratio is added by one to obtain a flow fluctuation factor for reflecting the instability degree of the cooling liquid flow in the liquid cooling circuit; the real-time speed of the electromagnetic pump is divided by the sum of the rated speed of the electromagnetic pump and the minimum term, multiplied by a failure response coefficient and taken as a negative value to obtain a pump speed normalized response index representing the deviation risk of the running state of the electromagnetic pump 9; wherein the failure response coefficient is obtained by constructing a feature set through historical real-time speeds of the electromagnetic pump, standard deviations of the cooling liquid flow and corresponding failure marked samples, modeling and fitting the probability distribution relationship between the pump speed normalized response index and the cooling system failure occurrence by using a logistic regression algorithm, and taking the value range as 0.1 to 10. The pump speed normalized response index is subjected to exponential power function transformation with the natural logarithm base e as the base to obtain a pump speed abnormal response factor, which further reflects the amplification effect of the pump speed deviation on the system risk in a high load scenario; the operation deviation factor, the flow fluctuation factor and the pump speed abnormal response factor are added by one and multiplied in sequence, and the obtained product is added by one and taken as a natural logarithm to finally obtain an element failure risk evaluation value as an important basis for judging whether there is a potential structural failure in the abnormal branch.

[0056] wherein the specific calculation formula of the element failure risk evaluation value is:

[0057] ;

[0058] In the formula, represents the element failure risk evaluation value, represents the real-time speed of the electromagnetic pump, represents the target speed of the electromagnetic pump, represents the rated speed of the electromagnetic pump, represents the response delay time of the electric isolation valve, represents the reference response time of the electric isolation valve, represents a coolant flow rate standard deviation, represents a coolant flow rate mean value, represents a fault response coefficient, represents a minimum term.

[0059] In this embodiment, by the actual performance of the cooling branch in the control instruction execution process, a component fault risk assessment method is constructed with the real-time speed of the electromagnetic pump, the response delay time of the electric isolation valve, the key feedback parameters such as the standard deviation of the coolant flow rate as inputs, effectively quantifying the performance deviation characteristics of the electromagnetic pump 9 and the electric isolation valve 3 in the high-frequency regulation state, and fusing the operation deviation factor, the flow fluctuation factor and the pump speed abnormal response factor to form a component fault risk assessment value that comprehensively reflects the abnormal branch operation stability and fault trend, thereby improving the accuracy of fault judgment and the foresight of response, and providing a high-reliability decision basis for the hierarchical diagnosis and control strategy selection of the abnormal branch.

[0060] Specifically, whether the abnormal branch is a fault type fluctuation is determined, fault-tolerant control and branch recovery strategies are executed, and the specific steps of branch state updating and control closed loop are as follows: the component fault risk assessment value is compared with the fault judgment threshold in real time, when the component fault risk assessment value is greater than the fault judgment threshold, it is determined that the current cooling branch belongs to the fault type fluctuation state dominated by structural fault, the central controller 6 controls the corresponding electric isolation valve 3 to adjust to the full open state according to the fault judgment result, ensures the smooth flow of the coolant, and immediately sends a fault warning signal corresponding to the cooling branch number to trigger the upper protection mechanism of the cooling system; at the same time, the system enters the component hot plug preparation state, allowing the on-site maintenance personnel to perform modular replacement operation on the cold plate 4 and the electromagnetic pump 9 contained in the current abnormal branch without interrupting the operation of the remaining cooling branches; after the replacement is completed, the control node automatically detects the online signal of the new component, loads the initialization parameters of the corresponding cooling branch based on the unique identification information of the component, and after completing the rapid matching with the current liquid cooling cooperative operation data, seamlessly integrates the new component into the liquid cooling cooperative control network, ensuring the continuity and maintainability of the overall stable operation of the system. When the component fault risk assessment value is less than or equal to the fault judgment threshold, it is determined that the current cooling branch is in a non-fault type fluctuation state caused by parameter disturbance, the path recovery strategy is automatically activated, and the cooling branch abnormality assessment is re-performed, when the branch abnormality assessment value is less than the liquid cooling abnormality threshold for N consecutive times, it is determined that the cooling branch state has returned to the normal interval, and the central controller 6 issues a command to release the current cooling liquid flow rate restriction and the electric isolation valve 3 control instruction, so that the cooling branch returns to the normal operation mode; if the re-calculated branch abnormality assessment value still exceeds the liquid cooling abnormality threshold, the existing control strategy remains unchanged, and the branch state continues to be monitored and waits for subsequent evaluation update.

[0061] In the embodiment, by comparing the component failure risk evaluation value with the failure judgment threshold value in real time, combining the state control of the electric isolation valve 3, the dynamic reevaluation of the liquid cooling cooperative operation data, and the component hot plug preparation mechanism, an integrated closed-loop control process of cooling branch abnormality identification, failure response, path recovery, and component replacement is constructed, the adaptive fault-tolerant capability and the maintenance convenience of the cooling branch in the abnormal fluctuation scenario are improved, and the thermal stability, the structural reliability, and the system sustainability cooperative control level of the cooling branch in the multi-working condition operation are ensured.

[0062] 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 these entities or operations have any such actual relationship or order. Also, the terms "comprises", "comprising", or any other variations thereof are intended to cover a non-exclusive inclusion, so that a process, method, article, or apparatus that includes a list of elements does not only include those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0063] 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, and the application is not limited 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. The intelligent collaborative control system of the liquid cooling module of the charging pile based on industrial Ethernet, characterized in that, include: The liquid cooling data acquisition and preprocessing unit is used to acquire liquid cooling collaborative operation data, and to automatically calibrate, remove anomalies, standardize and normalize the liquid cooling collaborative operation data to obtain preprocessed liquid cooling collaborative operation data. The heat load sensing and intelligent liquid cooling control unit is used to divide the liquid outlet ring pipe (2) and the liquid inlet ring pipe (5) into independent cooling branches, and evaluate the heat load of each cooling branch based on the pre-processed liquid cooling collaborative operation data, determine the real-time heat load status, and generate and execute corresponding control commands. The anomaly detection and cooling redundancy control unit is used to extract liquid cooling coordinated operation data within N sampling cycles after the control command is executed, calculate the response characteristics of temperature difference, pressure difference and flow rate, evaluate the thermal response deviation of the cooling branch, and determine whether to enable dual-loop redundant piping. The fault root cause judgment and recovery enhancement unit is used to assess the degree of component failure risk in abnormal branches based on the control execution feedback data of abnormal branches, determine whether the abnormal branch is a fault-type fluctuation, execute fault-tolerant control and branch recovery strategies, and realize branch status update and control closed loop. 2.The industrial Ethernet based intelligent collaborative control system for charging pile liquid cooling module according to claim 1, characterized in that: The specific steps for collecting liquid cooling collaborative operation data are as follows: A fixed-width sliding time window is set as a sampling period to periodically collect the liquid-cooled collaborative operation data of the entire charging pile. The liquid-cooled collaborative operation data includes inlet temperature, outlet temperature, coolant flow rate, loop inlet pressure, loop outlet pressure, charging module power, electromagnetic pump speed, coolant density, and coolant specific heat capacity. 3.The industrial Ethernet based intelligent collaborative control system for charging pile liquid cooling module according to claim 1, characterized in that: The specific steps for automatically calibrating, removing anomalies, standardizing, and normalizing the liquid-cooled collaborative operation data to obtain preprocessed liquid-cooled collaborative operation data are as follows: The collected liquid-cooled collaborative operation data were calibrated using multi-source comparison and thermal parameter calibration methods to eliminate baseline offsets caused by hardware differences, installation conditions, and environmental changes. An anomaly detection mechanism based on heat flow evolution characteristics and dynamic threshold ranges was used to identify sudden changes and eliminate data points that did not conform to the operating conditions. A standardized mapping method with a unified physical unit system was used to convert the format of the liquid-cooled collaborative operation data to generate a data structure with numerical consistency. An operating interval index and a normalized mapping function were constructed to perform interval transformation and numerical compression on the liquid-cooled collaborative operation data.

4. The industrial Ethernet-based intelligent collaborative control system for charging pile liquid cooling modules according to claim 1, characterized in that: The specific steps for dividing the liquid outlet annular pipe (2) and the liquid inlet annular pipe (5) into independent cooling branches, and evaluating the heat load of each cooling branch based on the pre-processed liquid cooling synergistic operation data, are as follows: Based on the physical connection relationship between the liquid outlet annular pipeline (2) and the liquid inlet annular pipeline (5), combined with the spatial distribution structure of the liquid cooling module, the double ring is divided into multiple independent cooling branches; the pre-processed liquid cooling cooperative operation data is extracted, the cooling liquid density, the cooling liquid specific heat capacity and the cooling liquid flow rate are multiplied, and then the difference between the liquid outlet temperature and the liquid inlet temperature is multiplied to obtain the liquid cooling heat absorption amount per unit time; the real-time speed of the electromagnetic pump is divided by the rated speed of the electromagnetic pump to obtain the pump speed normalization factor; the liquid cooling heat absorption amount and the pump speed normalization factor are multiplied to obtain the numerator of the heat load calculation; the charging module power and the minimum term are added to obtain the denominator of the heat load calculation; the numerator is divided by the denominator to obtain the liquid cooling heat load evaluation value. 5.The industrial Ethernet based intelligent collaborative control system for charging pile liquid cooling module according to claim 4, characterized in that: The specific steps of determining the real-time heat load state and generating and executing the corresponding control instruction are as follows: Real-time comparison of liquid cooling heat load evaluation value S and heat load threshold S1 and S2 to determine the heat load state and issue corresponding control instructions through industrial Ethernet: When S≤S1, it is determined that the cooling branch is in a heat stable state, and the current pump speed and valve opening degree remain unchanged; When S1<S<S2, it is determined that the cooling branch is in a heat transition state, and the target speed of the electromagnetic pump (9) is calculated according to the difference between the liquid cooling heat load evaluation value S and the second heat load threshold S2 using a linear regulation algorithm, and the electromagnetic pump speed is increased to the target speed; When S≥S2, it is determined that the cooling branch is in a heat overload state, and the electromagnetic pump speed is increased to the rated speed of the electromagnetic pump, and the electric isolation valve (3) is adjusted to the fully open state; The central controller (6) receives the control instruction and drives the electromagnetic pump (9) and the electric isolation valve (3) to perform corresponding adjustment operations. 6.The industrial Ethernet based intelligent collaborative control system for charging pile liquid cooling module according to claim 1, wherein: The specific steps of calculating the response characteristics of temperature difference, pressure difference and flow rate based on the liquid cooling cooperative operation data within N sampling periods after the execution of the extracted control instruction are as follows: After the execution of the control instruction, the liquid cooling cooperative operation data within N sampling periods is extracted, the difference between the liquid inlet temperature and the liquid outlet temperature is calculated to obtain the cooling liquid heat exchange temperature difference, the difference between the loop liquid inlet pressure and the loop liquid outlet pressure is calculated to obtain the cooling loop pressure difference, and based on the exponential weighted average method, the cooling liquid heat exchange temperature difference average, the cooling liquid pressure difference average and the cooling liquid flow rate average are calculated.

7. The industrial Ethernet-based intelligent collaborative control system for charging pile liquid cooling modules according to claim 6, characterized in that: The specific steps of evaluating the thermal response deviation of the cooling branch are as follows: The absolute value of the difference between the cooling liquid heat exchange temperature difference and the average cooling liquid heat exchange temperature difference is divided by the sum of the average cooling liquid heat exchange temperature difference and the minimum term to obtain a temperature difference deviation factor; the absolute value of the difference between the cooling liquid loop pressure difference and the average cooling liquid loop pressure difference is divided by the sum of the average cooling liquid loop pressure difference and the minimum term to obtain a pressure difference deviation factor; the average cooling liquid flow rate is divided by the sum of the average cooling liquid flow rate and the minimum term, and the natural logarithm of the obtained ratio is taken to obtain a flow rate response difference factor; the temperature difference deviation factor, the pressure difference deviation factor and the flow rate response difference factor are squared respectively, and then added to obtain an abnormal change amplitude factor; the liquid cooling heat load evaluation value is subtracted from the first level heat load threshold value, multiplied by the heat sensitivity gain coefficient, and the negative value of the obtained product is taken as an exponential part to perform power function operation with the natural logarithm base e as the base to obtain a heat load weighting factor; the reciprocal of the sum of the abnormal change amplitude factor and the heat load weighting factor plus one is multiplied to obtain a branch abnormality evaluation value. 8.The industrial Ethernet based intelligent collaborative control system for charging pile liquid cooling module according to claim 7, characterized in that: The specific steps of determining whether to enable the double-loop redundant pipeline are as follows: The branch abnormality evaluation value and the liquid cooling abnormality threshold value are compared in real time, and when the branch abnormality evaluation value is less than or equal to the liquid cooling abnormality threshold value, the current control state is maintained; when the branch abnormality evaluation value is greater than the liquid cooling abnormality threshold value, the cooling branch is marked as an abnormal branch, the electric isolation valve (3) is controlled to adjust to an open state, the double-loop redundant pipeline is enabled, and the cooling path is switched. 9.The industrial Ethernet based intelligent collaborative control system for charging pile liquid cooling module according to claim 1, wherein: The specific steps of executing feedback data based on the abnormal branch to evaluate the element failure risk degree in the abnormal branch are as follows: After the cooling branch is marked as an abnormal branch, the electromagnetic pump real-time speed and the electric isolation valve response delay time are extracted in combination with the abnormal branch control instruction execution process, and the cooling liquid flow rate standard deviation in N sampling periods after the control instruction execution is completed is calculated; the absolute value of the difference between the electromagnetic pump real-time speed and the electromagnetic pump target speed is divided by the sum of the electromagnetic pump target speed and the minimum term to obtain a pump speed deviation ratio factor; the electric isolation valve response delay time is divided by the sum of the electric isolation valve reference response time and the minimum term to obtain a response time ratio factor; The pump speed deviation ratio factor is multiplied by the response time ratio factor and then squared to obtain an operation deviation factor; the cooling liquid flow rate standard deviation is divided by the sum of the average cooling liquid flow rate and the minimum term, and the obtained ratio is added by one to obtain a flow rate fluctuation factor; the electromagnetic pump real-time speed is divided by the sum of the electromagnetic pump rated speed and the minimum term, multiplied by the failure response coefficient and taken as a negative value to obtain a pump speed normalized response index; the pump speed normalized response index is taken as an exponential power with the natural logarithm base e as the base to obtain a pump speed abnormal response factor; the operation deviation factor, the flow rate fluctuation factor and the pump speed abnormal response factor are multiplied in sequence by one, and the obtained product is added by one and then taken as a natural logarithm to obtain an element failure risk evaluation value.

10. The industrial Ethernet-based intelligent collaborative control system for charging pile liquid cooling modules according to claim 9, characterized in that: The specific steps of determining whether the abnormal branch is a failure type fluctuation, executing fault tolerance control and branch recovery strategy, and realizing branch state updating and control closed loop are as follows: The element failure risk assessment value is compared with the failure judgment threshold in real time. When the element failure risk assessment value is greater than the failure judgment threshold, it is determined that the abnormal branch is a failure type fluctuation, the electric isolation valve (3) is controlled to be fully open, and a failure warning is sent to the central controller (6); When the element failure risk assessment value is less than or equal to the failure judgment threshold, it is determined that the abnormal branch is a non-failure type fluctuation, and the path recovery strategy is activated: the liquid cooling branch abnormality assessment is performed again, if the branch abnormality assessment value is lower than the liquid cooling abnormality threshold for a fixed number of times in succession, the flow limitation and isolation control are released, and the branch is restored to normal operation; otherwise, the current control state is maintained.

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