Intelligent power distribution cabinet data acquisition control system and method

Abstract

The invention relates to the technical field of intelligent power distribution and electrical equipment state monitoring, in particular to an intelligent power distribution cabinet data acquisition control system and method, and the method comprises the steps: presetting a temperature sensor and a Hall sensor at the lap joint of a circuit breaker contact and a copper bar of an intelligent power distribution cabinet; calibrating inherent parameters of the system; in response to starting operation of the intelligent power distribution cabinet, the following steps are triggered: obtaining real-time load current, real-time environment temperature and actual temperature through filtering processing; generating a theoretical healthy temperature through a thermoelectric coupling evolution equation; calculating aging resistance increment through dynamic impedance differential evolution processing; generating an aging resistance estimated value through contact state multi-dimensional evaluation; generating a dynamic current limit value through an intelligent current limiting control model; outputting a control signal to adjust the operation state of the intelligent power distribution cabinet; according to the invention, the problem that a traditional single threshold scheme cannot accurately identify a real health state is solved, and the sensitivity and accuracy of contact aging detection are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent power distribution and electrical equipment condition monitoring technology, specifically to an intelligent power distribution cabinet data acquisition and control system and method. Background Technology

[0002] In the long-term operation and maintenance of intelligent power distribution cabinets, the increased contact resistance caused by oxidation or loosening of key nodes such as circuit breaker contacts and copper busbar connections is the main cause of equipment overheating failure. Existing monitoring solutions generally employ single temperature threshold alarms or periodic offline detection modes, lacking in-depth analysis of the coupling relationship between real-time load current, ambient temperature, and node temperature rise. Although such solutions can provide basic over-temperature protection, the lack of a thermoelectric coupling model reflecting the physical characteristics of the nodes makes it difficult to effectively decouple the thermal effects caused by normal load fluctuations or ambient temperature increases from the abnormal temperature rise caused by contact aging. This results in the system being unable to accurately identify the true health status of the equipment, leading to delayed response when facing potential overheating risks due to increased contact resistance, or false triggering of current limiting and load shedding operations within safe limits due to improper static threshold settings. Therefore, how to achieve real-time and accurate quantification of contact aging status and dynamic control of operating boundaries through multi-dimensional data fusion and physical model construction has become an urgent technical problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide a data acquisition and control method for intelligent power distribution cabinets, which can effectively decouple the thermal effects caused by normal load fluctuations or changes in ambient temperature from abnormal temperature rises caused by contact aging, accurately identify the true health status of the equipment, and generate dynamic current limit values ​​based on the current aging degree and environmental conditions to achieve dynamic current limiting control based on physical boundaries. Specifically, the technical solution of this invention is as follows: A data acquisition and control method for an intelligent power distribution cabinet includes: Temperature sensors and Hall effect sensors are pre-installed at the circuit breaker contacts and copper busbar connections of the intelligent power distribution cabinet. Calibrate the inherent parameters of the system; The inherent parameters of the system include virtual heat capacity, reference contact resistance, and virtual convection thermal resistance. In response to the startup and operation of the intelligent power distribution cabinet, the following steps are triggered: Step 1: Based on the monitoring data collected by the temperature sensor and Hall sensor, the real-time load current, real-time ambient temperature and actual temperature are obtained through filtering. Step 2: Based on the real-time load current and real-time ambient temperature, and combined with the inherent parameters of the system, the theoretical healthy temperature is generated through the thermoelectric coupling evolution equation. Step 3: Combining the actual temperature and the theoretical healthy temperature, calculate the aging resistance increment through dynamic impedance differential evolution processing; Step 4: Based on the aging resistance increment, real-time load current and inherent system parameters, generate an estimated aging resistance value through multi-dimensional evaluation of contact status. Step 5: Based on the estimated aging resistance, real-time ambient temperature, and inherent system parameters, a dynamic current limit value is generated using an intelligent current limiting control model. Step 6: Based on the comparison results between the real-time load current and the dynamic current limit value, output a control signal to adjust the operating status of the intelligent power distribution cabinet.

[0004] Preferably, the calibration system's inherent parameters include: Obtain initial health data of the system; Based on health operation data, the magnitude of the inertia of node heat absorption and temperature rise is identified, and virtual heat capacity is generated; Identify the resistance value of the node at the reference temperature and generate a reference contact resistance; Identify the heat dissipation capacity of nodes and generate virtual convection thermal resistance.

[0005] Preferably, in step 2, the theoretical healthy temperature is generated through the thermoelectric coupling evolution equation, including: The input thermal power is generated by multiplying the square of the real-time load current by the reference contact resistance. Calculate the product of virtual heat capacity and temperature change rate to generate storage heat power; The difference between the theoretical healthy temperature and the real-time ambient temperature is calculated, and this difference is divided by the virtual convection thermal resistance to generate the heat loss power. Based on the law of conservation of energy, a dynamic differential equation is constructed in which the input thermal power is equal to the sum of the stored thermal power and the dissipated thermal power. Solve the dynamic differential equation to generate the theoretical healthy temperature.

[0006] Preferably, in step 3, the aging resistance increment is calculated through dynamic impedance differential evolution processing, including: Calculate the difference between the actual temperature and the theoretical healthy temperature, and generate the temperature residual at the current moment; Retrieve the temperature residual from the previous sampling moment stored in system memory; Calculate the difference between the temperature residual at the current moment and the temperature residual at the previous sampling moment, and divide it by the preset sampling period to generate the residual change rate; Based on the residual change rate, the phase lag caused by thermal inertia is compensated, and the aging resistance increment is calculated by combining the virtual convection thermal resistance and the real-time load current.

[0007] Preferably, in step 4, an estimated aging resistance value is generated through multi-dimensional assessment of contact conditions, including: Create a sliding window with a preset time window length; Remove sampling points within the sliding window whose real-time load current is less than the preset effective current threshold, and retain the effective sampling points; The aging resistance increments corresponding to the valid sampling points are processed by discrete weighted moving average to generate statistical values; The output statistics are used as an estimate of the aging resistance.

[0008] Preferably, in step 5, a dynamic current limit value is generated using an intelligent current limiting control model, including: Obtain the preset maximum allowable safe temperature of the insulation material; Calculate the difference between the maximum allowable safe temperature and the real-time ambient temperature, and divide it by the virtual convection thermal resistance to generate the allowable heat dissipation power; Calculate the sum of the baseline contact resistance and the estimated aging resistance to generate the total impedance; Calculate the ratio of allowable heat dissipation power to total impedance, and then perform a square root operation on this ratio to generate a dynamic current limit value.

[0009] Preferably, in step 6, outputting a control signal to adjust the operating status of the intelligent power distribution cabinet includes: If the real-time load current is greater than the dynamic current limit value, a current limiting control signal is output to the intelligent circuit breaker to perform the current limiting operation. If the real-time load current is less than or equal to the dynamic current limit value, a sustain signal is output to maintain the current operating state.

[0010] A smart power distribution cabinet data acquisition and control system includes: The parameter initialization module is used to calibrate the inherent parameters of the system. The data acquisition module is used to acquire real-time load current, real-time ambient temperature and actual temperature through temperature sensors and Hall sensors; The core computing module includes: Theoretical evolution unit is used to generate theoretical healthy temperature based on real-time load current and inherent system parameters; Impedance decoupling unit is used to calculate the aging resistance increment by combining actual temperature with theoretical healthy temperature; The condition assessment unit is used to generate an estimated value of aging resistance based on the aging resistance increment; The closed-loop control module is used to generate dynamic current limit values ​​based on the estimated values ​​of aging resistors, and output control signals based on the comparison results between the real-time load current and the dynamic current limit values.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention establishes a thermoelectric coupling evolution equation based on the law of energy conservation, and utilizes calibrated virtual heat capacity and reference contact resistance, as well as other inherent system parameters, to dynamically generate a theoretical healthy temperature by combining real-time load current and ambient temperature. This overcomes the shortcomings of existing technologies that ignore the physical coupling relationship between load fluctuations and temperature rise, ensuring that the monitoring model can accurately reflect the inertia of node heat absorption and temperature rise and the response speed to changes in input power, providing a precise physical reference for subsequent state identification. 2. This invention employs dynamic impedance differential evolution processing technology. By comparing the actual temperature with the theoretical healthy temperature and compensating for the phase lag caused by thermal inertia, it can back-calculate the resistance increment caused only by physical aging. This method effectively decouples ambient temperature fluctuations, normal current temperature rise, and abnormal temperature rise caused by faults, solving the problem that traditional single threshold schemes cannot accurately identify the true health status, and significantly improving the sensitivity and accuracy of contact aging detection. 3. This invention introduces a multi-dimensional assessment mechanism for contact status. By establishing a sliding window to eliminate invalid small current sampling points, and by performing discrete weighted moving average processing on the aging resistance increment, this statistical method effectively shields the calculation singularities and numerical noise caused by electromagnetic interference, generating a highly robust aging resistance estimate, ensuring that the system's assessment of the deterioration of equipment contact status remains stable and reliable in complex industrial environments. 4. This invention constructs an intelligent current limiting control model, which can calculate the dynamic current limit value to ensure that the insulation material does not exceed the temperature based on the current estimated aging resistance value and the real-time ambient temperature. This changes the traditional passive protection mode of fixed threshold and realizes dynamic control based on physical boundaries. When contact aging or harsh environment is detected, the system automatically performs current limiting or load shedding operations, which maintains the continuity of power supply to the greatest extent while preventing equipment overheating failure. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a structural diagram of the system of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0014] Example 1: Please see Figure 1 A data acquisition and control method for an intelligent power distribution cabinet, comprising: Temperature sensors and Hall effect sensors are pre-installed at the circuit breaker contacts and copper busbar connections of the intelligent power distribution cabinet. Calibrate the inherent parameters of the system; The inherent parameters of the system include virtual heat capacity, reference contact resistance, and virtual convection thermal resistance. In response to the startup and operation of the intelligent power distribution cabinet, the following steps are triggered: Step 1: Based on the monitoring data collected by the temperature sensor and Hall sensor, the real-time load current, real-time ambient temperature and actual temperature are obtained through filtering. Step 2: Based on the real-time load current and real-time ambient temperature, and combined with the inherent parameters of the system, the theoretical healthy temperature is generated through the thermoelectric coupling evolution equation. Step 3: Combining the actual temperature and the theoretical healthy temperature, calculate the aging resistance increment through dynamic impedance differential evolution processing; Step 4: Based on the aging resistance increment, real-time load current and inherent system parameters, generate an estimated aging resistance value through multi-dimensional evaluation of contact status. Step 5: Based on the estimated aging resistance, real-time ambient temperature, and inherent system parameters, a dynamic current limit value is generated using an intelligent current limiting control model. Step 6: Based on the comparison results between the real-time load current and the dynamic current limit value, output a control signal to adjust the operating status of the intelligent power distribution cabinet.

[0015] This embodiment provides a data acquisition and control method for an intelligent distribution cabinet. Temperature sensors and Hall effect sensors are pre-installed at the circuit breaker contacts and copper busbar connections of the intelligent distribution cabinet. The temperature sensors are used to collect the actual surface temperature of the contact points, and the Hall effect sensors are used for non-contact acquisition of the load current flowing through the node. The system's inherent parameters are calibrated before monitoring starts, covering virtual heat capacity. Reference contact resistance and virtual convection thermal resistance Virtual heat capacity Defined as the magnitude of inertia caused by heat absorption and temperature rise at a node, in units of . This is used to characterize the response speed of node temperature to changes in input power; reference contact resistance. Defined as the initial resistance of a node at a reference temperature, in units of . As a zero-point benchmark for assessing subsequent aging; virtual convection thermal resistance Defined as the heat dissipation capacity of a node, in units of . This characterizes the resistance to heat dissipation from the node to the surrounding environment; In response to the startup and operation of the intelligent power distribution cabinet, the real-time load current is obtained based on the monitoring data collected by temperature sensors and Hall sensors, processed by Kalman filtering or low-pass filtering. Real-time ambient temperature and actual temperature Based on real-time load current With real-time ambient temperature By combining the system's inherent parameters and using the thermoelectric coupling evolution equation, the theoretical healthy temperature is generated. This step uses the law of conservation of energy to build a physical model and calculate the temperature that the node should exhibit when it is in a fully healthy state under the current and environment. Combined with actual temperature Compared with theoretical healthy temperature The aging resistance increment is calculated through dynamic impedance differential evolution processing. This process utilizes the deviation between measured and theoretical temperatures, combined with a thermal inertia compensation algorithm, to deduce the increase in resistance caused solely by physical aging, effectively decoupling ambient temperature fluctuations, normal current temperature rise, and fault temperature rise; based on the aging resistance increment... Real-time load current Based on the system's inherent parameters and through multi-dimensional assessment of contact conditions, an estimated value of aging resistance is generated. This estimate is a robust index characterizing the degree of contact condition deterioration, generated by evaluating the increase in aging resistance over a period of time using statistical methods. Based on aging resistance estimation value Real-time ambient temperature Based on the system's inherent parameters, a dynamic current limit value is generated using an intelligent current limiting control model. This value represents the maximum safe current allowed through the system to prevent the insulation material from overheating under the current aging level and ambient temperature; based on real-time load current. With dynamic current limit value The comparison results are used to output control signals to adjust the operating status of the intelligent power distribution cabinet; if the real-time load current is greater than the dynamic current limit value, the system performs current limiting or load shedding operations; otherwise, the current state is maintained, thereby realizing dynamic current limiting control based on physical boundaries.

[0016] Example 2: Calibrate the system's inherent parameters, including: Obtain initial health data of the system; Based on health operation data, the magnitude of the inertia of node heat absorption and temperature rise is identified, and virtual heat capacity is generated; Identify the resistance value of the node at the reference temperature and generate a reference contact resistance; Identify the heat dissipation capacity of nodes and generate virtual convection thermal resistance.

[0017] When calibrating the system's inherent parameters, obtain the initial healthy operating dataset of the system. This dataset contains calibration current sequences. Calibration node temperature sequence and calibration ambient temperature sequence Based on health operation dataset Identify the magnitude of the inertia of heat absorption and temperature rise at nodes to generate virtual heat capacity. By fitting the parameters of the heat balance equation using the least squares method, the parameters in the calibration current sequence were identified. When a step change occurs, the calibration node temperature sequence The rising slope characteristic of the response curve is used to determine the virtual heat capacity. ; Identify nodes at reference temperature The resistance value below is used to generate a reference contact resistance. Select a healthy operating dataset The steady-state operation segment in the middle, according to Joule's law Backwards, at the reference temperature Reference contact resistance Identify the heat dissipation capacity of nodes and generate virtual convection thermal resistance. ;Analyze the temperature sequence of the calibration node The descent phase or steady-state temperature rise and the calibration ambient temperature sequence The difference relationship is used to determine the virtual convective thermal resistance of the system based on Newton's law of cooling. This calibration process establishes a digital twin benchmark for each specific distribution cabinet node, eliminating individual parameter deviations caused by differences in manufacturing processes. It is worth noting that virtual convection thermal resistance... This characterizes the current overall heat dissipation conditions. It is recommended to re-trigger the above calibration process during system maintenance cycles, or when sensors detect changes in the status of the power distribution cabinet's cooling fans, to update the system's inherent parameters and ensure model accuracy.

[0018] Example 3: In step 2, the theoretical healthy temperature is generated using the thermoelectric coupling evolution equation, including: The input thermal power is generated by multiplying the square of the real-time load current by the reference contact resistance. Calculate the product of virtual heat capacity and temperature change rate to generate storage heat power; The difference between the theoretical healthy temperature and the real-time ambient temperature is calculated, and this difference is divided by the virtual convection thermal resistance to generate the heat loss power. Based on the law of conservation of energy, a dynamic differential equation is constructed in which the input thermal power is equal to the sum of the stored thermal power and the dissipated thermal power. Solve the dynamic differential equation to generate the theoretical healthy temperature.

[0019] The theoretical healthy temperature is generated using the thermoelectric coupling evolution equation. Calculate the real-time load current. The square of the reference contact resistance The product of these factors generates the input heat power. The calculation follows the formula. ,in The temperature coefficient of resistance for conductor materials is derived from the Material Physical Properties Handbook. Set the preset reference temperature for resistor calibration; Calculate virtual heat capacity The product of the temperature change rate and the stored heat power is generated. Its expression is ; Calculate the theoretical healthy temperature With real-time ambient temperature The difference is then divided by the virtual convection thermal resistance. Generating heat loss power Its expression is ; Based on the law of conservation of energy, the input heat power is constructed. Equal to stored heat power With heat loss power The dynamic differential equation of the sum: , Solve the dynamic differential equation using the Runge-Kutta method or the Euler method to generate the theoretical healthy temperature at the current moment. This equation introduces a differential term. It describes thermal dynamic processes and can accurately predict the temperature trajectory during load fluctuations.

[0020] Example 4: In step 3, the aging resistance increment is calculated through dynamic impedance differential evolution processing, including: Calculate the difference between the actual temperature and the theoretical healthy temperature, and generate the temperature residual at the current moment; Retrieve the temperature residual from the previous sampling moment stored in system memory; Calculate the difference between the temperature residual at the current moment and the temperature residual at the previous sampling moment, and divide it by the preset sampling period to generate the residual change rate; Based on the residual change rate, the phase lag caused by thermal inertia is compensated, and the aging resistance increment is calculated by combining the virtual convection thermal resistance and the real-time load current.

[0021] The aging resistance increment is calculated by dynamic impedance differential evolution processing. Calculate the actual temperature. Compared with theoretical healthy temperature The difference is used to generate the temperature residual at the current moment. ,Right now ; Retrieves the temperature residual from the previous sampling time stored in system memory. ; Calculate the temperature residual at the current moment Temperature residual with the previous sampling time The difference is divided by the preset sampling period. Generate residual change rate ; Based on the residual rate of change, phase lag caused by thermal inertia is compensated; to prevent divergence due to numerical instability under small current, it is only performed when the real-time load current is... Greater than the preset effective current threshold At the same time, combined with virtual convection thermal resistance With real-time load current The aging resistance increment is calculated based on the following discretized decoupling model. ,otherwise Keep the values ​​from the previous moment: , This model compensates for the phase lag of the temperature signal on the time axis by introducing a differential term, and can calculate the aging resistance increment in real time during the dynamic process when the temperature has not yet stabilized.

[0022] Example 5: In step 4, an estimated aging resistance value is generated through a multi-dimensional assessment of the contact condition, including: Create a sliding window with a preset time window length; Remove sampling points within the sliding window whose real-time load current is less than the preset effective current threshold, and retain the effective sampling points; The aging resistance increments corresponding to the valid sampling points are processed by discrete weighted moving average to generate statistical values; The output statistics are used as an estimate of the aging resistance.

[0023] Aging resistance estimates are generated through multi-dimensional assessment of contact conditions. At that time, establish a time window with a preset length. The sliding window corresponds to the number of sampling points. Remove real-time load current within the sliding window. Less than the preset effective current threshold Sampling points are selected, and valid sampling points are retained; a preset valid current threshold is used. It is set to 10% of the rated current to eliminate numerical noise caused by the small denominator of the formula under low current conditions. The aging resistance increment corresponding to the valid sampling point Perform discrete weighted moving average processing to generate statistical values; the calculation formula is: , in This is a unit step function, taking the value 1 when the input is greater than 0, and 0 otherwise; the output of this statistical value is used as an estimate of the aging resistance. By statistically smoothing out computational singularities generated by electromagnetic interference, the robustness of fault diagnosis is improved.

[0024] Example 6: In step 5, a dynamic current limit value is generated using an intelligent current limiting control model, including: Obtain the preset maximum allowable safe temperature of the insulation material; Calculate the difference between the maximum allowable safe temperature and the real-time ambient temperature, and divide it by the virtual convection thermal resistance to generate the allowable heat dissipation power; Calculate the sum of the baseline contact resistance and the estimated aging resistance to generate the total impedance; Calculate the ratio of allowable heat dissipation power to total impedance, and then perform a square root operation on this ratio to generate a dynamic current limit value.

[0025] Dynamic current limit values ​​are generated using an intelligent current limiting control model. At that time, the preset maximum allowable safe temperature of the insulation material is obtained. This parameter is determined by the heat resistance rating standard of the insulation material inside the distribution cabinet; the maximum allowable safe temperature is calculated. With real-time ambient temperature The difference, divided by the virtual convection thermal resistance. Generates allowable heat dissipation power ,Right now ; Calculate the reference contact resistance Estimated aging resistance The sum of these factors generates the total impedance. ,Right now Calculate the allowable heat dissipation power. With total impedance The ratio is calculated, and the square root of this ratio is taken to generate the dynamic current limit value. : , This step enables condition-based maintenance through inversion calculations, automatically reducing the maximum allowable current limit when contact aging or excessively high ambient temperature is detected.

[0026] Example 7: In step 6, output control signals to adjust the operating status of the intelligent power distribution cabinet, including: If the real-time load current is greater than the dynamic current limit value, a current limiting control signal is output to the intelligent circuit breaker to perform the current limiting operation. If the real-time load current is less than or equal to the dynamic current limit value, a sustain signal is output to maintain the current operating state.

[0027] When outputting control signals to adjust the operating status of the intelligent power distribution cabinet, if the real-time load current... Greater than the dynamic current limit value It outputs a current-limiting control signal to the intelligent circuit breaker to perform current-limiting operations or disconnect some non-critical loads to prevent equipment overheating; if the real-time load current... Less than or equal to the dynamic current limit value It outputs a sustain signal to maintain the current operating state, at which point the system is within the thermal safety domain.

[0028] Example 8: Please see Figure 2 A smart power distribution cabinet data acquisition and control system, comprising: The parameter initialization module is used to calibrate the inherent parameters of the system. The data acquisition module is used to acquire real-time load current, real-time ambient temperature and actual temperature through temperature sensors and Hall sensors; The core computing module includes: Theoretical evolution unit is used to generate theoretical healthy temperature based on real-time load current and inherent system parameters; Impedance decoupling unit is used to calculate the aging resistance increment by combining actual temperature with theoretical healthy temperature; The condition assessment unit is used to generate an estimated value of aging resistance based on the aging resistance increment; The closed-loop control module is used to generate dynamic current limit values ​​based on the estimated values ​​of aging resistors, and output control signals based on the comparison results between the real-time load current and the dynamic current limit values.

[0029] A smart distribution cabinet data acquisition and control system includes a parameter initialization module, used to execute parameter identification logic, calibrate and store the system's inherent parameters. The data acquisition module includes an A / D conversion circuit and a digital filtering algorithm, used to obtain real-time load current through temperature and Hall effect sensors. Real-time ambient temperature and actual temperature ; The core calculation module includes a theoretical evolution unit, an impedance decoupling unit, and a state evaluation unit; the theoretical evolution unit is based on real-time load current. Using the system's inherent parameters, the differential equations are solved in real time to generate the theoretical healthy temperature. Impedance decoupling unit combined with actual temperature Compared with theoretical healthy temperature The aging resistance increment is calculated using the differential evolution algorithm. The condition assessment unit generates an estimated aging resistance value based on sliding window statistics. The closed-loop control module is based on the estimated value of aging resistance. Generate dynamic current limit value And based on the real-time load current With dynamic current limit value The comparison result is used to output a control signal to the circuit breaker actuator.

[0030] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent power distribution cabinet data acquisition control method, characterized in that, The application relates to a method for estimating and controlling the contact resistance of a circuit breaker in a smart power distribution cabinet. The method comprises the following steps:

1. Pre-installing temperature sensors and Hall sensors at the joint of the circuit breaker contact and the copper bar of the smart power distribution cabinet; 2. Calibrating the system inherent parameters, including virtual thermal capacity, reference contact resistance and virtual convective thermal resistance; 3. In response to the start of the smart power distribution cabinet, triggering the following steps: Step 1: Based on the monitoring data collected by the temperature sensors and Hall sensors, through filtering processing, obtaining the real-time load current, real-time environmental temperature and actual temperature; Step 2: Based on the real-time load current and real-time environmental temperature, combining the system inherent parameters, through the thermoelectric coupling evolution equation, generating the theoretical healthy temperature; Step 3: Combining the actual temperature and the theoretical healthy temperature, through dynamic impedance differential evolution processing, solving the aging resistance increment; Step 4: Based on the aging resistance increment, real-time load current and system inherent parameters, through multi-dimensional evaluation of the contact state, generating the aging resistance estimation value; Step 5: Based on the aging resistance estimation value, real-time environmental temperature and system inherent parameters, through the intelligent current limiting control model, generating the dynamic current limiting value; Step 6: Based on the comparison result of the real-time load current and the dynamic current limiting value, outputting the control signal to adjust the running state of the smart power distribution cabinet.

2. The intelligent power distribution cabinet data acquisition control method according to claim 1, characterized in that, Calibrating the system inherent parameters, including:

1. Obtaining the initial healthy running data of the system; 2. Based on the healthy running data, identifying the inertia size of the node heat absorption temperature rise, generating the virtual thermal capacity; 3. Identifying the resistance value of the node at the reference temperature, generating the reference contact resistance; 4. Identifying the heat dissipation capacity of the node, generating the virtual convective thermal resistance.

3. The intelligent power distribution cabinet data acquisition control method according to claim 1, characterized in that, In step 2, through the thermoelectric coupling evolution equation, the theoretical healthy temperature is generated, including:

1. Calculating the product of the square of the real-time load current and the reference contact resistance, generating the input heat power; 2. Calculating the product of the virtual thermal capacity and the temperature change rate, generating the storage heat power; 3. Calculating the difference between the theoretical healthy temperature and the real-time environmental temperature, and dividing the difference by the virtual convective thermal resistance, generating the dissipated heat power; 4. Based on the law of conservation of energy, constructing a dynamic differential equation that the input heat power is equal to the sum of the storage heat power and the dissipated heat power; 5. Solving the dynamic differential equation to generate the theoretical healthy temperature.

4. The intelligent power distribution cabinet data acquisition control method according to claim 1, characterized in that, In step 3, through dynamic impedance differential evolution processing, the aging resistance increment is solved, including:

1. Calculating the difference between the actual temperature and the theoretical healthy temperature, generating the current time temperature residual; 2. Calling the last sampling time temperature residual stored in the system memory; 3. Calculating the difference between the current time temperature residual and the last sampling time temperature residual, and dividing by the preset sampling period, generating the residual change rate; 4. Based on the residual change rate, compensating the phase lag caused by thermal inertia, and combining the virtual convective thermal resistance and the real-time load current, solving the aging resistance increment.

5. The intelligent power distribution cabinet data acquisition control method according to claim 1, characterized in that, In step 4, through multi-dimensional evaluation of the contact state, the aging resistance estimation value is generated, including:

1. Establishing a sliding window with a preset time window length; 2. Eliminating the sampling points in the sliding window whose real-time load current is less than the preset effective current threshold, and retaining the effective sampling points; 3. Discrete weighted moving average processing is performed on the aging resistance increments corresponding to the effective sampling points to generate a statistical value; 4. Outputting the statistical value as the aging resistance estimation value.

6. The intelligent power distribution cabinet data acquisition control method according to claim 1, characterized in that, In step 5, through the intelligent current limiting control model, the dynamic current limiting value is generated, including: Obtaining a preset maximum allowable safe temperature of an insulation material; Calculating a difference between the maximum allowable safe temperature and a real-time ambient temperature, and dividing the difference by a virtual convection heat resistance to generate an allowable heat dissipation power; Calculating a sum of a reference contact resistance and an aging resistance estimation value to generate a total impedance; Calculating a ratio of the allowable heat dissipation power to the total impedance, and performing a square root operation on the ratio to generate a dynamic current limit value.

7. The intelligent power distribution cabinet data acquisition control method according to claim 1, characterized in that, In step 6, an output control signal is outputted to adjust the operating state of the intelligent power distribution cabinet, including: If the real-time load current is greater than the dynamic current limit value, a current limiting control signal is outputted to the intelligent circuit breaker to perform a current limiting operation; If the real-time load current is less than or equal to the dynamic current limit value, a maintenance signal is outputted to maintain the current operating state.

8. An intelligent power distribution cabinet data acquisition control system applied to the intelligent power distribution cabinet data acquisition control method of any one of claims 1-7, characterized in that, Including: a parameter initialization module for calibrating system inherent parameters; a data acquisition module for obtaining a real-time load current, a real-time ambient temperature and an actual temperature through temperature sensors and Hall sensors; a core calculation module, including: a theoretical evolution unit for generating a theoretical health temperature based on the real-time load current and the system inherent parameters; an impedance decoupling unit for calculating an aging resistance increment in combination with the actual temperature and the theoretical health temperature; a state evaluation unit for generating an aging resistance estimation value based on the aging resistance increment; a closed-loop control module for generating a dynamic current limit value based on the aging resistance estimation value, and outputting a control signal according to a comparison result of the real-time load current and the dynamic current limit value.

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