Industrial ethernet-based intelligent collaborative control system for liquid cooling module of charging pile

Abstract

The application discloses a charging pile liquid cooling module intelligent collaborative control system based on an industrial Ethernet, and relates to the technical field of liquid cooling control.The charging pile liquid cooling module intelligent collaborative control system based on the industrial Ethernet comprises 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.The liquid cooling data acquisition and preprocessing unit is used for acquiring and preprocessing liquid cooling collaborative operation data.The thermal load sensing and intelligent liquid cooling regulation unit is used for dividing independent cooling branches, evaluating the thermal load degree of each cooling branch, determining a real-time thermal load state, generating and executing control instructions.The abnormality detection and cooling redundancy control unit is used for evaluating the thermal response deviation of the cooling branch and determining whether to enable a double-loop redundant pipeline.The fault root cause judgment and recovery enhancement unit is used for evaluating the element fault risk degree in the abnormal branch and executing fault-tolerant control and branch recovery strategies.The application solves the problem that the traditional pipeline structure cannot realize local fault tolerance when a local branch fails, thereby causing the shutdown of the whole liquid cooling system.

Description

Technical Field

[0001] This invention relates to the field of liquid cooling control technology, specifically to an intelligent collaborative control system for liquid cooling modules of charging piles based on industrial Ethernet. Background Technology

[0002] With the rapid development of new energy vehicles and their supporting infrastructure, DC fast charging piles are gradually evolving towards higher power and higher density, leading to a significant increase in the demand for system-level thermal management. Especially during high-power charging, core components such as charging gun cables, power modules, and overall control units continuously generate heat. As the primary means of thermal management, the operating efficiency of the liquid cooling system directly affects the reliability and energy consumption of the charging pile. To meet the dynamic heat dissipation requirements under different load conditions, the liquid cooling system needs to possess comprehensive control capabilities, including multi-parameter sensing, load status assessment, control path optimization, and anomaly response.

[0003] For example, the invention disclosed in CN119717491A discloses a heat dissipation control method, system, device, and medium based on a genetic algorithm to adjust a PID controller. The invention uses the parameter values ​​of the PID controller, selecting a range of values ​​to form a search space; this search space forms an initial population; the fitness function value of each individual population is calculated based on the initial population to achieve the tuning of the PID controller parameters using a genetic algorithm; based on the temperature difference between the tuned PID controller and the charging cable, the output of the PID controller is calculated, thereby adjusting the opening of the engine solenoid valve to control the output of the liquid cooling oil. This invention continuously outputs the optimal liquid cooling oil flow rate through genetic iteration to stabilize the charging cable temperature, and adjusts the liquid cooling oil flow rate in real time according to the detected cable temperature, reducing the power consumption of the liquid cooler while meeting the cooling requirements of the charging cable.

[0004] For example, the invention disclosed in CN120578065A provides a method for predicting and controlling the rotational speed of a liquid cooling system for a charging gun. The method includes: acquiring performance parameters of key components in the liquid cooling system, including a radiator, water pump, fan, charging gun wire, and liquid storage tank; inputting the performance parameters into a one-dimensional simulation model to obtain the maximum coolant temperature (Tcoolant) under different operating conditions; fitting the relationship between different operating conditions and Tcoolant based on the Tcoolant under different operating conditions to obtain a rotational speed prediction model; inputting the ambient temperature and the heating power of the charging gun wire resistance under the target operating condition into the rotational speed prediction model, predicting multiple rotational speed combinations that satisfy the constraints under the Tcoolant, and selecting the rotational speed combination that minimizes the total energy consumption of the liquid cooling system from the multiple rotational speed combinations as the optimal rotational speed combination for the target operating condition; and real-time regulating the fan speed and water pump speed based on the optimal rotational speed combination during charging.

[0005] However, existing liquid cooling control solutions mostly focus on parameter optimization or model prediction for single components, lacking precise perception and intelligent control of the coordinated operation of multiple branches in the liquid cooling system. This makes it difficult to balance energy efficiency, response speed, and fault tolerance under varying operating conditions. Especially in practical applications, traditional liquid cooling pipelines are mostly single-loop or series structures. Once a leak occurs in a local branch or a key component fails, the entire liquid cooling system may shut down, resulting in low reliability and making it difficult to meet the technical requirements of high-frequency, long-term continuous operation and high safety of charging piles.

[0006] Therefore, in order to address the above issues, there is an urgent need for an intelligent collaborative control system for the liquid cooling module of charging piles based on industrial Ethernet. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides an intelligent collaborative control system for charging pile liquid cooling modules based on industrial Ethernet, which solves the problem that traditional pipeline structures cannot achieve local fault tolerance when a fault occurs in a local branch, thus causing the entire liquid cooling system to shut down.

[0009] Technical solution

[0010] To achieve the above objectives, this invention provides the following technical solution: an intelligent collaborative control system for liquid-cooled charging pile modules based on industrial Ethernet, comprising: a liquid-cooled data acquisition and preprocessing unit, a heat load sensing and intelligent liquid-cooled regulation unit, an anomaly detection and cooling redundancy control unit, and a fault root cause judgment and recovery enhancement unit. The liquid-cooled data acquisition and preprocessing unit is used to acquire liquid-cooled collaborative operation data and perform automatic calibration, anomaly removal, standardization, and normalization on the liquid-cooled collaborative operation data to obtain preprocessed liquid-cooled collaborative operation data. The heat load sensing and intelligent liquid-cooled regulation unit is used to divide the outlet annular pipe and the inlet annular pipe into independent cooling branches, and based on… The pre-processed liquid-cooled synergistic operation data is used to assess the thermal load of each cooling branch, determine the real-time thermal load status, and generate and execute corresponding control commands. The anomaly detection and cooling redundancy control unit is used to extract the liquid-cooled synergistic operation data within N sampling periods after the control command is executed, calculate the response characteristics of temperature difference, pressure difference, and flow rate, assess 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 component failure risk level in the abnormal branch based on the 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 status update and control closed loop.

[0011] 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.

[0012] 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.

[0013] 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.

[0014] Further, the specific steps for determining the real-time thermal load status and generating and executing corresponding control instructions are as follows: Compare the liquid-cooled thermal load evaluation value S with the thermal load thresholds S1 and S2 in real time, determine the thermal load status, and issue 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 are maintained unchanged; when S1 < S < S2, it is determined that the cooling branch is in a thermal transition state, and according to the difference between the liquid-cooled thermal load evaluation value S and the secondary thermal load threshold S2, the target speed of the electromagnetic pump is calculated using a linear adjustment algorithm, and the speed of the electromagnetic pump is increased to the target speed; when S ≥ S2, it is determined that the cooling branch is in a thermal overload state, the speed of the electromagnetic pump is increased to the rated speed of the electromagnetic pump, and at the same time, the electric isolation valve is adjusted to the 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.

[0015] Further, the specific steps for extracting the liquid-cooled collaborative operation data within N sampling cycles after the execution of the control instructions and calculating the response characteristics of the temperature difference, pressure difference, and flow rate are as follows: After the execution of the control instructions, extract the liquid-cooled collaborative operation data within N sampling cycles, calculate the difference between the inlet temperature and the outlet temperature of the liquid, and obtain the heat exchange temperature difference of the coolant; calculate the difference between the inlet pressure of the loop and the outlet pressure of the loop, and obtain the pressure difference of the cooling loop; at the same time, based on the exponentially weighted moving average method, calculate the mean value of the heat exchange temperature difference of the coolant, the mean value of the cooling liquid pressure difference, and the mean value of the coolant flow rate.

[0016] Further, the specific steps for evaluating the thermal response deviation of the cooling branch are as follows: Take the absolute value of the difference between the heat exchange temperature difference of the coolant and the mean value of the heat exchange temperature difference of the coolant, and divide it by the sum of the mean value of the heat exchange temperature difference of the coolant and the minimum term to obtain the temperature difference deviation factor; take the absolute value of the difference between the coolant loop pressure difference and the mean value of the coolant loop pressure difference, and divide it by the sum of the mean value of the coolant loop pressure difference and the minimum term to obtain the pressure difference deviation factor; divide the sum of the mean value of the coolant flow rate and the minimum term by the sum of the coolant flow rate and the minimum term, take the natural logarithm of the obtained ratio to obtain the flow rate response difference factor; square the temperature difference deviation factor, the pressure difference deviation factor, and the flow rate response difference factor respectively and then add them together to obtain the abnormal change amplitude factor; subtract the primary thermal load threshold from the liquid-cooled thermal load evaluation value, multiply the obtained product by the thermal sensitivity gain coefficient, take the negative value of the obtained product as the exponent part, and perform a power function operation with the base e of the natural logarithm to obtain the thermal load weighting factor; multiply the abnormal change amplitude factor by the reciprocal of the sum of the thermal load weighting factor and one to obtain the branch abnormal evaluation value.

[0017] Furthermore, the specific steps for determining whether to enable the dual-loop redundant pipeline are as follows: compare the branch abnormality assessment value and the liquid cooling abnormality threshold in real time. When the branch abnormality assessment value is less than or equal to the liquid cooling abnormality threshold, maintain the current control state; when the branch abnormality assessment value is greater than the liquid cooling abnormality threshold, mark the cooling branch as an abnormal branch, control the electric isolation valve to adjust to the open state, enable the dual-loop redundant pipeline, and switch the cooling path.

[0018] Furthermore, based on the control execution feedback data of the abnormal branch, the specific steps for assessing the component failure risk level in the abnormal branch 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 conjunction with the control command execution process of the abnormal branch, and the standard deviation of the coolant flow rate within N sampling periods after the completion of the control command execution are calculated; the absolute value of the difference between the real-time speed of the electromagnetic pump and the target speed of the electromagnetic pump is divided by the sum of the target speed of the electromagnetic pump and the minimum term to obtain the pump speed deviation ratio factor; 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 the response time ratio factor. The following steps are performed: 1. Multiply the pump speed deviation ratio factor by the response time ratio factor and square the result to obtain the operation deviation factor. 2. Divide the coolant flow rate standard deviation by the sum of the coolant flow rate mean and minimum terms, and add one to the resulting ratio to obtain the flow fluctuation factor. 3. Divide the electromagnetic pump real-time speed by the sum of the electromagnetic pump rated speed and minimum terms, multiply by the fault response coefficient, and take the negative value to obtain the pump speed normalized response index. 4. Power the pump speed normalized response index with the natural logarithm base e to obtain the pump speed abnormal response factor. 5. Multiply the operation deviation factor, flow fluctuation factor, and pump speed abnormal response factor by one in sequence, and then add one to the product and take the natural logarithm to obtain the component failure risk assessment value.

[0019] Furthermore, the specific steps for determining whether an abnormal branch is a fault-type fluctuation, implementing fault-tolerant control and branch recovery strategies, and realizing branch status updates and control closed loops are as follows: Real-time comparison of component fault risk assessment values ​​with fault judgment thresholds. When the component fault risk assessment value is greater than the fault judgment threshold, the abnormal branch is determined to be a fault-type fluctuation. The electric isolation valve is controlled to be adjusted to the fully open state, and a fault warning is sent to the central controller. When the component fault risk assessment value is less than or equal to the fault judgment threshold, the abnormal branch is determined to be a non-fault-type fluctuation. The path recovery strategy is activated: the liquid-cooled branch anomaly assessment is re-performed. If the branch anomaly assessment value is lower than the liquid-cooled anomaly threshold for a fixed number of consecutive times, the flow restriction and isolation control are lifted, and the branch is restored to normal operation; otherwise, the current control state is maintained.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) This intelligent collaborative control system for the liquid cooling module of the charging pile based on industrial Ethernet constructs a liquid cooling heat load assessment model and compares the liquid cooling heat load assessment value with the heat load threshold in real time. The system can dynamically identify whether it is in a thermally stable, thermally transitional, or thermally overloaded state, and automatically generate and issue control commands according to the state level to adjust the speed of the electromagnetic pump and the opening of the electric isolation valve. It can effectively avoid over-adjustment or delayed response, and ensure the rapid response and thermal balance control of the liquid cooling system under complex working conditions.

[0023] (2) This intelligent collaborative control system for the liquid cooling module of the charging pile based on industrial Ethernet comprehensively evaluates the response deviation of the cooling branch by using a deviation aggregation algorithm and a weight adjustment mechanism based on the deviation between the exponentially weighted average value and the real-time value. If the abnormal evaluation value of the branch exceeds the set threshold, it is automatically identified as an abnormal branch and a dual-loop redundancy switching operation is performed, controlling the electric isolation valve to open the backup cooling path to achieve automatic path switching. This greatly improves the anti-interference capability and emergency response capability of the liquid cooling system and avoids the risk of thermal runaway of the whole machine caused by cooling failure.

[0024] (3) The intelligent collaborative control system for the liquid cooling module of the charging pile based on industrial Ethernet constructs a component failure risk assessment model by combining the real-time speed deviation of the electromagnetic pump, the response delay time of the electric isolation valve, and the standard deviation of the flow fluctuation. It obtains the component failure risk assessment value. When the assessment value exceeds the fault judgment threshold, it determines that there is a structural fault in the branch, automatically issues a fault warning, and adjusts the valve control strategy. If it is a non-fault fluctuation, the path recovery mechanism is activated, realizing a closed-loop control process from local anomaly identification to component-level fault judgment.

[0025] (4) This intelligent collaborative control system for the liquid cooling module of the charging pile based on industrial Ethernet divides the liquid outlet ring pipe and the liquid inlet ring pipe into multiple physically independent cooling branches. Combined with the spatial distribution structure of the liquid cooling module, it establishes a more granular thermal management unit, enabling each branch to be independently sensed, evaluated, and controlled. Compared with traditional single-ring or series systems, this structure has stronger response flexibility and control precision when there are changes in heat load, local faults, or environmental fluctuations. It can allocate cooling resources as needed, effectively avoiding the performance degradation or failure of the entire system due to local anomalies, thereby improving the stability, energy efficiency, and operational safety of the system. Attached Figure Description

[0026] Figure 1 This is a structural diagram of an intelligent collaborative control system for liquid-cooled charging pile modules based on industrial Ethernet.

[0027] Figure 2 The cooling branch liquid cooling heat load assessment value and thermal state distribution diagram are shown.

[0028] Figure 3This is a schematic diagram of the liquid-cooled collaborative control system.

[0029] In the diagram, 1. Charging module; 2. Outlet ring pipe; 3. Electric isolation valve; 4. Cold plate; 5. Inlet ring pipe; 6. Central controller; 7. Flow sensor; 8. Temperature sensor; 9. Electromagnetic pump; 10. Liquid storage tank; 11. Cooling tower. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Please see Figures 1-3 This invention provides a technical solution: an intelligent collaborative control system for liquid cooling modules of charging piles based on industrial Ethernet, comprising: a liquid cooling data acquisition and preprocessing unit, a heat load sensing and intelligent liquid cooling regulation unit, an anomaly detection and cooling redundancy control unit, and a fault root cause judgment and recovery enhancement unit. The liquid cooling data acquisition and preprocessing unit is used to acquire liquid cooling collaborative operation data and perform automatic calibration, anomaly removal, standardization, and normalization processing on the liquid cooling collaborative operation data to obtain preprocessed liquid cooling collaborative operation data. The heat load sensing and intelligent liquid cooling regulation unit is used to divide the outlet annular pipe 2 and the inlet annular pipe 5 into independent cooling branches and, based on the preprocessed data... The system evaluates the thermal load of each cooling branch based on the liquid-cooled coordinated operation data, determines the real-time thermal load status, and generates and executes corresponding control commands. The anomaly detection and cooling redundancy control unit extracts liquid-cooled coordinated operation data within N sampling periods after the control commands are executed, calculates the response characteristics of temperature difference, pressure difference, and flow rate, evaluates the thermal response deviation of the cooling branch, and determines whether to enable dual-loop redundant piping. The fault root cause judgment and recovery enhancement unit evaluates the component failure risk level in the abnormal branch based on the control execution feedback data, determines whether the abnormal branch is a fault-type fluctuation, executes fault-tolerant control and branch recovery strategies, and realizes branch status update and control closed loop.

[0032] Specifically, the steps for collecting liquid-cooled collaborative operation data are as follows: A fixed-width sliding time window is set as one sampling period, and liquid-cooled collaborative operation data of the entire charging pile is collected periodically. This 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. Temperature data is collected in real time by temperature sensors 8 arranged on the inlet annular pipe 5 and the outlet annular pipe 2, obtaining the inlet and outlet temperatures respectively. The flow rate is obtained by continuously measuring the volume change of the coolant passing through the pipe per unit time using a flow sensor 7 installed in the liquid-cooled circulation pipeline. The pressure sensor at the front end of the inlet annular pipe 5 senses the hydraulic state at the loop inlet in real time, and together with the pressure sensor at the end of the outlet annular pipe 2, obtains the inlet and outlet pressures of the loop respectively; the charging module 1 connected to the central controller 6 calculates the product of the voltage and current of the charging module 1 in real time to obtain the power of the charging module; the speed detection unit in the electromagnetic pump 9 provides real-time feedback signals to obtain the actual speed of the electromagnetic pump; the standard physical property parameter database is consulted according to the type of coolant used in the storage tank 10, and temperature correction is performed in combination with the measurement data of the temperature sensor 8 to determine the current coolant density; at the same time, based on the coolant physical property database and combined with the actual operating temperature range, the specific heat capacity of the corresponding coolant is obtained.

[0033] In this implementation scheme, by setting a fixed-width sliding time window in the liquid cooling system, the system systematically collects 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, ensuring complete coverage of liquid cooling collaborative operation data in both spatial location and time scale. By combining a standard physical property parameter database with real-time operating status, the accuracy and consistency of thermal parameter acquisition are effectively improved, providing a data foundation and algorithmic support for subsequent cooling branch heat load modeling and dynamic control, and enhancing the system's sensing accuracy and control reliability under multiple operating conditions.

[0034] Specifically, the preprocessed liquid-cooled collaborative operation data is obtained through automatic calibration, anomaly removal, standardization, and normalization. The steps are as follows: Multi-source comparisons are performed on inlet and outlet temperatures, coolant flow rate, loop inlet pressure, loop outlet pressure, charging module power, electromagnetic pump speed, coolant density, and coolant specific heat capacity. Combined with a thermal parameter calibration method based on the actual structural parameters of the liquid-cooling system, the liquid-cooled collaborative operation data is automatically calibrated to 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 coolant property fluctuations. Furthermore, the evolution of heat flow along the cooling branch is considered. A dynamic anomaly detection mechanism based on gradient change rate and historical window fluctuation range is constructed to identify abrupt changes in the collected liquid-cooled co-operation data one by one, eliminating temperature, pressure, flow rate, and power data points that do not conform to the actual heat load characteristics. By constructing a unified physical unit system based on joules, pascals, seconds, kilograms, and degrees Celsius, a standardized conversion formula with one-to-one mapping is established to convert the physical quantity format of the original liquid-cooled co-operation data, achieving consistent numerical expression of the liquid-cooled co-operation data. An operating interval index function is constructed based on the historical maximum and minimum boundaries of the liquid-cooled co-operation data, and a compression mapping rule is established in combination with the normalized mapping function. The standardized liquid-cooled co-operation data is further subjected to interval transformation and numerical compression to improve the operating efficiency and generalization ability of subsequent heat load assessment.

[0035] In this implementation plan, by introducing multi-source comparison calibration, thermal evolution feature identification, unified physical unit standardization conversion, and normalization compression of operating range, the accuracy, consistency, and availability of liquid cooling collaborative operation data are significantly improved. This enables stable and efficient feature expression of liquid cooling collaborative operation data even in the presence of multi-source acquisition errors, fluctuation interference, and inconsistencies in dimensions, effectively supporting the accurate calculation of liquid cooling heat load assessment values ​​and the reliable execution of intelligent control strategies.

[0036] Specifically, the liquid outlet annular pipe 2 and the liquid inlet annular pipe 5 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: First, based on the physical connection relationship between the liquid outlet annular pipe 2 and the liquid inlet annular pipe 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 dual-ring liquid-cooling structure is divided into multiple independent cooling branches to meet the targeted thermal management and control requirements. Subsequently, the liquid-cooled collaborative operation data after automatic calibration, anomaly removal, standardization and normalization are extracted, and the coolant density, coolant specific heat capacity, coolant flow rate, inlet temperature, outlet temperature, electromagnetic pump real-time speed, and electric... The formula considers the rated speed of the magnetic pump and the power of the charging module. Specifically, it calculates the heat exchange capacity of the cooling branch under current conditions by multiplying the coolant density, specific heat capacity, and flow rate by the difference between the outlet and inlet temperatures. The real-time speed of the electromagnetic pump is then divided by its rated speed to obtain a pump speed normalization factor, representing the impact of the liquid cooling flow state on heat transfer efficiency. The product of the liquid cooling heat absorption and the pump speed normalization factor is used as the numerator of the heat load assessment formula. The charging module power is added to the minimum term to form the denominator. The minimum term is a small but non-zero positive real number used to avoid numerical instability caused by division by zero during calculation; its value range is... arrive Unless otherwise specified, all subsequent minterms shall adopt the definition and range of this definition. Finally, the numerator shall be divided by the denominator to obtain the liquid cooling heat load assessment value, which is used to characterize the heat load intensity of each cooling branch under different operating conditions, and to provide a quantitative basis for subsequent intelligent control.

[0037] The specific formula for calculating the liquid cooling heat load assessment value is as follows:

[0038] ;

[0039] In the formula, This represents the liquid cooling heat load assessment value. Indicates the density of the coolant. Indicates the specific heat capacity of the coolant. Indicates coolant flow rate. Indicates the outlet temperature. Indicates the inlet temperature. Indicates the real-time speed of the electromagnetic pump. Indicates the rated speed of the electromagnetic pump. Indicates the power of the charging module. Indicates a minus term.

[0040] In this embodiment, Table 1 is a data table of liquid cooling heat load assessment values, listing the key operating parameters and corresponding liquid cooling heat load assessment values ​​for the five cooling branches under the same operating conditions. The key operating parameters include coolant density, coolant specific heat capacity, coolant flow rate, outlet temperature, inlet temperature, real-time speed of the electromagnetic pump, rated speed of the electromagnetic pump, and charging module power. Specific data is as follows: In cooling branch 1, the coolant density is 997, the coolant specific heat capacity is 4186, the coolant flow rate is 30, the outlet temperature is 45°C, the inlet temperature is 35°C, the electromagnetic pump real-time speed is 2800 rpm, the electromagnetic pump rated speed is 3600 rpm, the module power is 1200 kW, and the corresponding liquid cooling heat load assessment value is 13.53; In cooling branch 2, the coolant density is 998, the coolant specific heat capacity is 4184, the coolant flow rate is 45, the outlet temperature is 50°C, the inlet temperature is 36°C, the electromagnetic pump real-time speed is 3000 rpm, the electromagnetic pump rated speed is 3600 rpm, the module power is 1300 kW, and the corresponding liquid cooling heat load assessment value is 28.11; In cooling branch 3, the coolant density is 999, the coolant specific heat capacity is 4185, the coolant flow rate is 60, the outlet temperature is 51°C, and the inlet temperature is... With a temperature of 37°C, a real-time electromagnetic pump speed of 2900 rpm, a rated electromagnetic pump speed of 3600 rpm, and a module power of 1250 kW, the corresponding liquid cooling heat load assessment value is 37.72. In cooling branch 4, the coolant density is 997 g / L, the coolant specific heat capacity is 4186 g / L, the coolant flow rate is 50 kW, the outlet temperature is 47°C, the inlet temperature is 34°C, the real-time electromagnetic pump speed is 3100 rpm, the rated electromagnetic pump speed is 3600 rpm, and the module power is 1350 kW, the corresponding liquid cooling heat load assessment value is 28.84. In cooling branch 5, the coolant density is 998 g / L, the coolant specific heat capacity is 4184 g / L, the coolant flow rate is 40 kW, the outlet temperature is 49°C, the inlet temperature is 36°C, the real-time electromagnetic pump speed is 2950 rpm, the rated electromagnetic pump speed is 3600 rpm, and the module power is 1280 kW, the corresponding liquid cooling heat load assessment value is 23.17.

[0041] Table 1. Liquid Cooling Heat Load Assessment Data Table

[0042]

[0043] like Figure 2As shown, the graph illustrates the changes in liquid cooling heat load assessment values ​​for five cooling branches, reflecting the differences in heat load status of the liquid cooling system across each branch. The graph uses color-coded bar charts to represent the thermal state of different branches: green indicates thermal stability, orange indicates thermal transition, and red indicates thermal overload, visually clearly distinguishing the thermal risk levels between branches. Additionally, two dashed threshold lines are included as references: the gray dashed line represents the primary heat load threshold, used to identify the initial increase in heat load; the black dashed line represents the secondary heat load threshold, used to mark the boundary where the system may enter an overload state. As shown in the figure, the liquid cooling heat load assessment values ​​of cooling branch 1 and cooling branch 5 are 13.53 and 23.17, respectively, both below the first-level heat load threshold, indicating a thermally stable state. The assessment values ​​of cooling branch 2 and cooling branch 4 are 28.11 and 28.84, respectively, between the two-level heat load thresholds, indicating a certain heat load pressure and a thermal transition state. The assessment value of cooling branch 3 reaches 37.72, exceeding the second-level threshold, indicating a thermal overload state. Cooling enhancement or path switching control strategies should be implemented immediately to reduce thermal risks. Figure 2 It effectively reflects the heat distribution characteristics of the liquid cooling system under multi-branch operation, providing a quantitative basis for intelligent cooling strategy decisions.

[0044] In this implementation scheme, by constructing a cooling branch division and heat load calculation mechanism based on liquid cooling collaborative operation data, and combining the real-time liquid cooling heat load assessment value obtained by combining the coolant density, coolant specific heat capacity, coolant flow rate, outlet temperature, inlet temperature, real-time speed of electromagnetic pump, rated speed of electromagnetic pump, and charging module power, an important basis for branch thermal state judgment and temperature control strategy issuance is obtained, which significantly enhances the thermal management accuracy and control robustness of the present invention under multi-branch operating conditions.

[0045] Specifically, the steps for determining the real-time thermal load status and generating and executing corresponding control commands are as follows: Real-time comparison of the liquid cooling thermal load assessment value S and the thermal load threshold, which includes a primary thermal load threshold S1 and a secondary thermal load threshold S2; Determining the thermal load status based on the current thermal response of the cooling branch, and sending corresponding control commands to the central controller 6 via industrial Ethernet; When the liquid cooling thermal load assessment value S is less than or equal to the primary thermal load threshold S1, the cooling branch is determined to be in a thermally stable state, and the actual speed of the electromagnetic pump and the opening of the electric isolation valve 3 are not adjusted, maintaining the current control state; When the liquid cooling thermal load assessment value S is greater than the primary thermal load threshold S1 and less than the secondary thermal load threshold S2, the cooling branch is determined to be in a thermal transition state. Based on the difference between the liquid cooling heat load assessment value S and the secondary heat load threshold S2, the linear adjustment algorithm is invoked to calculate the target speed of the electromagnetic pump 9, and the actual speed of the electromagnetic pump is gradually increased to the calculated target speed to enhance the cooling capacity response. When the liquid cooling heat load assessment value S is greater than or equal to the secondary heat load threshold 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. At the same time, the electric isolation valve 3 is controlled to the fully open state to achieve the maximum flow cooling path and quickly reduce the branch heat load. After receiving the control command, the central controller 6 drives the electromagnetic pump 9 and the electric isolation valve 3 to complete the corresponding adjustment operations, thus constructing a closed-loop control mechanism for thermal state response and control command execution.

[0046] like Figure 3 The diagram illustrates the layout and logical connections of the core components within the dual-ring liquid cooling structure. The system comprises a closed-loop cooling circuit consisting of an outlet annular pipe 2 and an inlet annular pipe 5. Multiple cold plates 4 are connected between the two ring pipes via parallel branches. Each branch of the cold plate 4 is equipped with an electromagnetic pump 9, a flow sensor 7, a temperature sensor 8, and an electric isolation valve 3, enabling independent control and status awareness of the cooling branch. The charging module 1 serves as the primary heat source for the liquid cooling module; the heat released during operation is carried away by the coolant through the cold plates 4, thus maintaining the stability of the module's internal temperature. In the liquid cooling circuit, a storage tank 10 provides circulating coolant, and a cooling tower 11 cools the high-temperature coolant. The central controller 6 collects various liquid cooling collaborative operation data in real time via industrial Ethernet and executes intelligent control strategies such as thermal load assessment, anomaly identification, fault-tolerant control, and path switching for each cooling branch, ensuring the system's thermal safety and operational stability under varying operating conditions.

[0047] In this implementation scheme, a graded thermal response judgment mechanism is constructed by comparing the liquid cooling heat load assessment value S with the primary heat load threshold S1 and the secondary heat load threshold S2 in real time. This mechanism can effectively identify whether the cooling branch is in a thermally stable state, a thermally transitional state, or a thermally overloaded state, and dynamically adjust the actual speed of the electromagnetic pump 9 and the opening degree of the electric isolation valve 3 accordingly. This method uses a linear adjustment algorithm to achieve 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 changing conditions, and providing technical support for realizing closed-loop dynamic adjustment of the cooling branch state.

[0048] Specifically, the steps for extracting liquid-cooled collaborative operation data within N sampling periods after the control command is executed, and calculating the response characteristics of temperature difference, pressure difference, and flow rate are as follows: After the control command is issued via industrial Ethernet and executed by the central controller 6, a fixed-length data extraction mechanism is initiated to extract liquid-cooled collaborative operation data within N sampling periods; where N is a positive integer greater than three; subsequently, the heat exchange temperature difference of the coolant is obtained by calculating the difference between the inlet temperature and the outlet temperature, reflecting the intensity of temperature change in the cooling branch during the response phase; the cooling loop pressure difference is obtained by calculating the difference between the loop inlet pressure and the loop outlet pressure, used to characterize the change in flow resistance of the cooling loop; simultaneously, by constructing an exponential weighted average algorithm, the heat exchange temperature difference, loop pressure difference, and coolant flow rate are weighted and smoothed respectively, and the average values ​​of the coolant heat exchange temperature difference, the average value of the coolant pressure difference, and the average value of the coolant flow rate are calculated sequentially to establish dynamic benchmark values ​​for the branch response characteristics, further supporting the subsequent evaluation and calculation of the deviation degree of the branch response.

[0049] In this implementation scheme, by extracting liquid cooling coordinated operation data within a fixed number of sampling periods after the control command is executed, the system systematically calculates the temperature difference between the inlet and outlet temperatures, the pressure difference between the inlet and outlet pressures of the loop, and the dynamic changes in coolant flow rate. An exponential weighted average method is introduced to obtain the average values ​​of coolant heat exchange temperature difference, coolant pressure difference, and coolant flow rate, respectively. This establishes a complete dynamic benchmark model of the cooling branch response state, improves the accuracy and timeliness of cooling branch response feature extraction, and provides a highly reliable data foundation for subsequent anomaly detection and intelligent control.

[0050] Specifically, the steps for evaluating the thermal response deviation of the cooling branch are as follows: Take the absolute value of the difference between the coolant heat exchange temperature difference obtained over N sampling periods and the corresponding mean coolant heat exchange temperature difference, then divide it by the sum of the mean and minimum terms of the coolant heat exchange temperature difference to obtain the coolant heat exchange temperature difference deviation factor, which measures the degree of temperature response deviation of the current branch in terms of heat exchange capacity compared to the average operating condition; Take the absolute value of the difference between the coolant loop pressure difference and the corresponding mean coolant loop pressure difference, then divide it by the sum of the mean and minimum terms of the coolant loop pressure difference to obtain the coolant loop pressure difference deviation factor, which reflects the degree of change in the hydraulic transmission efficiency within the current branch; Take the sum of the mean and minimum terms of the coolant flow rate as the numerator and the sum of the mean and minimum terms of the coolant flow rate as the denominator, and take the natural logarithm of the resulting ratio to obtain the coolant flow rate response difference factor, which captures the dynamic fluctuation characteristics of the coolant flow rate relative to the long-term flow rate level; Separate the coolant heat exchange temperature difference deviation factor, the coolant loop pressure difference deviation factor, and the coolant flow rate response difference factor... After squaring and summing the results, a cooling branch anomaly variation amplitude factor is obtained, which comprehensively describes the overall deviation intensity of the cooling branch under multiple physical quantity dimensions. The liquid cooling heat load assessment value is subtracted from the first-level heat load threshold, multiplied by the thermistor gain coefficient, and the negative value of the product is used as the exponent. This product is then subjected to a power function operation with the natural logarithm base e to obtain the heat load weighting factor, which dynamically adjusts the influence weight of the deviation degree in the overall assessment based on the heat load level. The thermistor gain coefficient is obtained by fitting and training the relationship between the branch thermal response deviation degree and the cooling system failure probability using a least squares regression algorithm based on correlated sample data consisting of historical liquid cooling heat load assessment values ​​and actual cooling anomaly events. Its value ranges from 0.1 to 10. Finally, the cooling branch anomaly variation amplitude factor is multiplied by the reciprocal of the sum of one and the heat load weighting factor to calculate the branch anomaly assessment value, which serves as an important quantitative indicator for identifying cooling branch response deviation and operational risk level, providing accurate basis for subsequent control decisions.

[0051] The specific formula for calculating the branch abnormality assessment value is as follows:

[0052] ;

[0053] In the formula, This indicates the branch abnormality assessment value. Indicates the temperature difference of the coolant heat exchange. This represents the average temperature difference for heat exchange in the coolant. Indicates the pressure difference in the cooling loop. This represents the average pressure difference in the cooling loop. Indicates coolant flow rate. This represents the average coolant flow rate. This represents the liquid cooling heat load assessment value. This indicates the first-level heat load threshold. Indicates minterms, This represents the thermistor gain coefficient.

[0054] In this implementation scheme, a multi-dimensional deviation characteristic measurement system is constructed by introducing a coolant heat transfer temperature difference deviation factor, a coolant loop pressure difference deviation factor, and a coolant flow response difference factor. Based on this, a heat load weighting factor is integrated to form a comprehensive branch anomaly assessment mechanism. This achieves high-precision quantitative assessment of the cooling branch response status, significantly improving the accuracy and reliability of cooling status identification and providing a quantifiable basis for subsequent cooling branch status control and fault response.

[0055] Specifically, the steps for determining whether to activate the dual-loop redundant pipeline are as follows: The abnormal assessment value of the cooling branch is compared with the liquid cooling abnormal threshold in real time, and the operating strategy is dynamically adjusted based on the current deviation of the cooling branch's response. When the abnormal assessment value is less than or equal to the liquid cooling abnormal threshold, the cooling branch is determined to be in normal working condition, maintaining the current electromagnetic pump speed and the opening of the electric isolation valve 3 unchanged, and keeping the original cooling path unchanged. When the abnormal assessment value is greater than the liquid cooling abnormal threshold, the operating state of the cooling branch is determined to deviate significantly, posing a potential risk of thermal instability. The corresponding cooling branch is marked as an abnormal branch, a control command is sent to the electric isolation valve 3 to adjust it to the open state, and the backup cooling path is activated, thus activating the dual-loop redundant pipeline and achieving the switching from the main cooling path to the backup path, ensuring the continuous heat exchange process of the abnormal branch.

[0056] In this implementation plan, by comparing the branch anomaly assessment value of the cooling branch with the liquid cooling anomaly threshold in real time, and combining the precise control of the electric isolation valve 3, an automated decision-making mechanism is realized to quickly activate the dual-ring redundant pipeline and switch the cooling path when the cooling branch is in an abnormal operating state. This significantly improves the operational 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 variable heat load conditions.

[0057] Specifically, based on the control execution feedback data of the abnormal branch, the specific steps for assessing the component failure risk level in the abnormal branch 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 by combining the operating performance of the abnormal branch during the control command execution process. The standard deviation of the coolant flow rate is calculated using data recorded by the flow sensor 7 in the liquid cooling circulation pipeline over N consecutive sampling periods after the control command execution. The absolute value of the difference between the real-time speed of the electromagnetic pump and the target speed of the electromagnetic pump is divided by the sum of the target speed and the minimum term to obtain the pump speed deviation ratio factor, which is used to assess the degree of actual operating deviation of the pump body. The response delay time of the electric isolation valve is divided by the sum of the reference response time and the minimum term to obtain the response time ratio factor, reflecting the consistency of the valve's time response. The pump speed deviation ratio factor is then compared with the response time... The time ratio factor is multiplied and then squared to obtain the operation deviation factor, which is used to characterize the linkage stability change of electromagnetic pump 9 and electric isolation valve 3 during the execution of control commands; the standard deviation of coolant flow rate is divided by the sum of the mean and minimum terms of coolant flow rate, and the resulting ratio is incremented by one to obtain the flow fluctuation factor, which is used to reflect the degree of instability of coolant flow rate in the liquid cooling circuit; the real-time speed of electromagnetic pump is divided by the sum of the rated speed and minimum terms of electromagnetic pump, multiplied by the fault response coefficient and negative, to obtain the pump speed normalized response index, which reflects the deviation risk of electromagnetic pump 9's operating state; the fault response coefficient is obtained by constructing a feature set using historical electromagnetic pump real-time speed, coolant flow rate standard deviation and corresponding fault marker samples, and using a logistic regression algorithm to model and fit the relationship between the pump speed normalized response index and the probability distribution of cooling system faults, with a value range of 0.1 to 10. The pump speed normalized response index is transformed by an exponential power function with the natural logarithm base e to obtain the pump speed abnormal response factor, which further reflects the amplification effect of pump speed deviation on system risk under high load scenarios. The operation deviation factor, flow fluctuation factor and pump speed abnormal response factor are multiplied by one in sequence, and the product is multiplied by one and the natural logarithm is taken to obtain the component failure risk assessment value, which serves as an important basis for judging whether there is a potential structural failure in the abnormal branch.

[0058] The specific formula for calculating the component failure risk assessment value is as follows:

[0059] ;

[0060] In the formula, This indicates the component failure risk assessment value. Indicates the real-time speed of the electromagnetic pump. Indicates the target speed of the electromagnetic pump. Indicates the rated speed of the electromagnetic pump. Indicates the response delay time of the electric isolation valve. This indicates the reference response time of the electric isolation valve. Indicates the standard deviation of coolant flow rate. This represents the average coolant flow rate. Indicates the fault response coefficient. Indicates a minus term.

[0061] In this implementation scheme, by analyzing the actual operating performance of the cooling branch during the execution of control commands, a component failure risk assessment method is constructed, using key feedback parameters such as the real-time speed of the electromagnetic pump, the response delay time of the electric isolation valve, and the standard deviation of the coolant flow rate as inputs. This method effectively quantifies the performance deviation characteristics of the electromagnetic pump 9 and the electric isolation valve 3 under high-frequency control conditions. By integrating the operation deviation factor, flow fluctuation factor, and pump speed abnormality response factor, a component failure risk assessment value that comprehensively reflects the operational stability and failure trend of the abnormal branch is formed. This improves the accuracy of fault judgment and the foresight of response, providing a highly reliable decision-making basis for the hierarchical diagnosis and control strategy selection of the abnormal branch.

[0062] Specifically, the steps for determining whether an abnormal branch is a fault-type fluctuation, implementing fault-tolerant control and branch recovery strategies, and achieving branch status updates and control loops are as follows: Real-time comparison of component fault risk assessment values ​​with fault judgment thresholds. When the component fault risk assessment value is greater than the fault judgment threshold, the current cooling branch is determined to be a fault-type fluctuation state dominated by structural faults. Based on the fault judgment result, the central controller 6 controls the corresponding electric isolation valve 3 to be fully open to ensure smooth coolant flow and immediately sends a fault warning signal corresponding to the cooling branch number to trigger the upper-level protection mechanism of the cooling system. Simultaneously, the system enters a component hot-swap preparation state, allowing on-site maintenance personnel to perform modular replacement operations on the cold plate 4 and electromagnetic pump 9 included in the current abnormal branch without interrupting the operation of other cooling branches. After replacement, the control node automatically detects the new component's online signal, loads the initialization parameters of the corresponding cooling branch based on the component's unique identification information, and seamlessly integrates the new component into the liquid cooling collaborative control network after completing rapid matching with the current liquid cooling collaborative operation data, ensuring the continuity and maintainability of the overall stable operation of the system. When the component failure risk assessment value is less than or equal to the failure judgment threshold, the current cooling branch is determined to be in a non-fault-type fluctuation state caused by parameter disturbance. The path recovery strategy is automatically activated, and the cooling branch anomaly assessment is re-performed. When the branch anomaly assessment value is lower than the liquid cooling anomaly threshold for N consecutive times, it is determined that the cooling branch status has been restored to the normal range. 6 issues a control command to release the current coolant flow limit and the electric isolation valve 3, so that the cooling branch returns to the normal operation mode. If the recalculated branch anomaly assessment value still exceeds the liquid cooling anomaly threshold, the existing control strategy remains unchanged, the branch status is monitored, and the subsequent assessment update is awaited.

[0063] In this implementation plan, by comparing the component failure risk assessment value with the failure judgment threshold in real time, and combining the state control of the electric isolation valve 3, the dynamic reassessment of liquid cooling collaborative operation data, and the component hot-swap preparation mechanism, an integrated closed-loop control process for cooling branch anomaly identification, fault response, path restoration, and component replacement is constructed. This improves the adaptive fault tolerance capability and maintenance convenience of the cooling branch under abnormal fluctuation scenarios, and ensures the thermal stability, structural reliability, and continuous collaborative control level of the cooling branch in multi-condition operation.

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

[0065] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention 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, The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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.

2. The industrial Ethernet-based intelligent collaborative control system for charging pile liquid cooling modules according to claim 1, characterized in that: The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. 3.The industrial Ethernet based intelligent collaborative control system for charging pile liquid cooling module according to claim 1, characterized in that: The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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.

4. The industrial Ethernet-based intelligent collaborative control system for charging pile liquid cooling modules according to claim 1, characterized in that: The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat 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. The application relates to a liquid cooling cooperative operation data acquisition and preprocessing unit, a heat load sensing and intelligent liquid cooling regulation 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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