High-temperature-resistant direct-current coreless gear motor and test system

Abstract

The invention relates to the technical field of motors, and discloses a high-temperature-resistant direct-current coreless gear motor and a test system, and the system comprises a temperature monitoring module, a current regulation and control module, a heat dissipation optimization module, a load response module, a cloud analysis module and a virtual verification module. By arranging the temperature monitoring module, multi-dimensional temperature data during operation of the motor can be acquired in real time, and a temperature distribution diagram can be generated, so that the system can comprehensively sense the internal thermal state of the motor and accurately identify local overheating risk points, thereby improving the operation reliability and safety of the motor under a high-temperature working condition; through cooperative operation and closed-loop control of the current regulation and control module, the heat dissipation optimization module and the load response module, according to real-time temperature and load changes, driving current parameters and heat dissipation resource distribution can be dynamically adjusted, and the heat dissipation efficiency is improved. And the output performance and the torque stability of the motor under high-temperature and high-load conditions are ensured.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of electric machines, in particular to a high-temperature-resistant direct-current hollow cup reduction motor and a test system. BACKGROUND

[0002] The reduction motor refers to the integration of a reducer and a motor. The integration can also be called a gear motor or a gear motor. The reduction motor is used to reduce the speed and increase the torque to meet the power output requirements of equipment and products. The hollow cup motor belongs to a direct-current permanent magnet servo and control motor and can also be classified as a micro motor. The hollow cup reduction motor is a reduction motor manufactured by adding reduction parts to the hollow cup motor.

[0003] At present, in the operation and test process of the high-temperature-resistant direct-current hollow cup reduction motor, the winding and the shell cannot be sensed in real time by the existing temperature monitoring means when the motor runs in a high-temperature working condition. When a local overheating point cannot be identified in time, the motor insulation will be aged and burned, and the operation reliability of the motor in a high-temperature environment cannot be ensured.

[0004] Therefore, the high-temperature-resistant direct-current hollow cup reduction motor and the test system are provided to solve the above problems. SUMMARY

[0005] In view of the deficiencies of the prior art, the high-temperature-resistant direct-current hollow cup reduction motor and the test system are provided to solve the problems of motor insulation aging and burning and the inability to ensure the operation reliability of the motor in a high-temperature environment.

[0006] To achieve the above purpose, the application provides the following technical solutions: a high-temperature-resistant direct-current hollow cup reduction motor and a test system, the system comprising: A temperature monitoring module acquires real-time temperature data through thermocouple sensors arranged on the motor shell and the winding, judges the high-temperature working condition based on the real-time temperature data, and generates a temperature distribution map; A current control module receives a high-temperature working condition trigger signal of the temperature monitoring module, dynamically adjusts the driving current parameters through pulse width modulation technology, and controls the smooth change of the current amplitude based on a temperature rise rate threshold; A heat dissipation optimization module receives the temperature distribution map, adaptively adjusts the rotation speed of the heat dissipation fan and the flow direction of the heat dissipation fins by identifying the hot spot area, and triggers a standby cooling device based on the adjustment result; A load response module receives heat dissipation state data and external load signals of the heat dissipation optimization module, and solves the optimal rotation speed and torque output value in real time through a load-temperature-performance mapping model and a fuzzy logic algorithm; A cloud analysis module receives the temperature distribution map, current parameter and load data, generates an optimized control strategy through historical data analysis and issues the optimized control strategy; A virtual verification module receives the current parameter of the current regulation module and the optimized strategy of the cloud analysis module, performs virtual simulation and predictive verification of motor performance based on a digital twin model, and feeds back the simulation verification result to the cloud analysis module to form a closed-loop optimization.

[0007] Preferably, the process of acquiring real-time temperature data in the temperature monitoring module includes: A plurality of miniature thermocouple sensors are installed at key positions of the motor to collect temperature information in different areas; The collected temperature signals are filtered and processed by a signal conditioning circuit to remove noise interference; The processed temperature signals are transmitted to a central controller to generate real-time temperature data; Wherein, the formula for judging the deviation degree of high temperature working condition is: ; Wherein is the degree of current parameter deviation from the working condition warning value, is the physical quantity value collected by the sensor in real time, is the preset working condition warning threshold.

[0008] Preferably, the process of dynamically adjusting the drive current parameter in the current regulation module is: When the temperature rise rate exceeds the preset threshold, gradually reduce the drive current amplitude, specifically through pulse width modulation technology to realize linear decrease of current; When the temperature rise rate does not decrease to the safe range, further enable the current limiting algorithm to limit the peak current output.

[0009] Preferably, the process of generating a temperature distribution map in the heat dissipation optimization module includes: Based on the thermal sensor data, a three-dimensional temperature thermal map is constructed to identify hot spot areas inside the motor; When the temperature of the hot spot area exceeds the critical value, the speed of the heat dissipation fan is adjusted to increase the airflow; When the heat dissipation effect does not meet the expectation, start the standby cooling device to assist in cooling; Wherein, the similarity matching formula for heat dissipation efficiency optimization is: ; Wherein is the cosine similarity between the current temperature distribution and the ideal distribution, is the temperature value of the current state curve at the sampling point, is the temperature value of the reference state curve at the temperature values of the sampling points, total number of sampling points.

[0010] Preferably, the process of self-adaptive adjustment of the heat dissipation structure in the heat dissipation optimization module further includes: dynamically allocating heat dissipation resources according to the temperature distribution map, including increasing the density of heat dissipation fins in high-temperature areas; When the temperature of the motor housing continues to rise, switch to forced air cooling mode.

[0011] Preferably, the process of establishing a load-temperature-performance mapping model in the load response module includes: real-time collection of load change data, temperature data and performance parameters; training the model through a machine learning algorithm to predict the temperature rise trend under different loads; Using fuzzy logic algorithm to solve the optimal speed and torque output value, and applying the output value to the motor driver.

[0012] Preferably, the process of real-time adjustment of speed and torque output in the load response module further includes: When the load changes suddenly, prefer to maintain torque stability; When the temperature rise leads to performance degradation, automatically reduce the speed to ensure safe operation of the motor.

[0013] Preferably, the process of generating an optimized control strategy in the cloud analysis module includes: Upload historical temperature data, load data and test results to the cloud database; Identify high-temperature failure modes through big data analysis; Generate and issue adaptive control strategies to the current regulation module and the load response module.

[0014] Preferably, the process of virtual simulation and predictive verification in the virtual verification module includes: Simulate high-temperature conditions with temperature ≥ 150°C in a virtual environment based on a digital twin model to predict the durability indicators of the motor under continuous operation; Evaluate the reliability risk under virtual simulation in combination with the parameters of rated speed ≥ 10000 r / min and rated torque ≥ 0.27 N·m; Generate an optimization report based on the prediction results and feed back to the cloud analysis module.

[0015] Preferably, the motor includes: Hollow cup motor body made of high-temperature resistant composite material, rated speed ≥ 10000 r / min, rated current ≤ 5.8 A, locked-rotor torque ≥ 1.940 N·m; Integrated heat dissipation structure, including adjustable heat dissipation fan and heat dissipation fins, self-adaptive adjustment based on temperature distribution map; Built-in thermal sensor array, distributed in motor shell and winding area, real-time monitoring temperature data; Speed reduction mechanism, connected with motor body, combined with load change to adjust output torque in real time; Communication interface, uploading temperature data and running state to cloud analysis module.

[0016] Beneficial effects Compared with the prior art, the application provides a high-temperature-resistant direct-current hollow cup reduction motor and a test system, which has the following beneficial effects: 1. In the application, the temperature monitoring module is arranged to obtain multi-dimensional temperature data of the motor during operation in real time and generate a temperature distribution map, so that the system can comprehensively perceive the thermal state of the motor, accurately identify local overheating risk points, and thus improve the operation reliability and safety of the motor under high-temperature working conditions, and prevent insulation aging and equipment damage caused by overheating.

[0017] 2. In the application, through the cooperative operation and closed-loop control of the current control module, the heat dissipation optimization module and the load response module, the driving current parameters and the heat dissipation resource allocation can be dynamically adjusted according to the real-time temperature and load change, the accurate matching of current output and heat dissipation strategy is realized, so as to guarantee the output performance and torque stability of the motor under high-temperature and high-load conditions, optimize the heat dissipation efficiency, and avoid performance degradation.

[0018] 3. In the application, by introducing a virtual verification module and performing simulation prediction based on a digital twin model, the durability and reliability of the motor under extreme high-temperature conditions can be estimated and verified during the research and development and test stages, the excessive dependence on traditional physical tests is eliminated, the test cycle is shortened, the development cost is reduced, more risk working conditions are covered, and the product optimization efficiency and quality assurance level are comprehensively improved. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The figure is a framework diagram of the high-temperature-resistant direct-current hollow cup reduction motor test system. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0021] Specific embodiments: please refer to Figure 1 A high-temperature-resistant direct-current hollow cup reduction motor and a test system, the system comprising: A temperature monitoring module acquires real-time temperature data through thermocouple sensors arranged on the motor housing and the winding, judges high-temperature working conditions based on the real-time temperature data, and generates a temperature distribution map; A current regulation module receives the high-temperature working condition trigger signal of the temperature monitoring module, dynamically adjusts the driving current parameters through pulse width modulation technology, and controls the smooth change of the current amplitude based on the temperature rise rate threshold; A heat dissipation optimization module receives the temperature distribution map, adaptively adjusts the rotation speed of the heat dissipation fan and the flow direction of the heat dissipation fin through identifying the hot spot area, and triggers the standby cooling device based on the adjustment result; A load response module receives the heat dissipation state data of the heat dissipation optimization module and the external load signal, and solves the optimal speed and torque output value in real time through a load-temperature-performance mapping model and a fuzzy logic algorithm, and the specific embodiment includes: I. The process of constructing a load-temperature-performance mapping model: Input variables: load current , winding temperature , and ambient temperature ; Output targets: optimal speed and torque ; Training data sources: motor response records during load step changes in historical operation data, with a data sampling frequency of ≥10Hz; II. Execution process of fuzzy logic algorithm: Define input and output linguistic variables: Define load change rate and winding temperature as input linguistic variables; The domain of load change rate is [-5, 5], and its linguistic values are set to include negative large, negative small, zero, positive small, and positive large; The domain of winding temperature is [room temperature, 180], and its linguistic values are set to include low, medium, and high; Define the speed adjustment amount as the output linguistic variable, with a domain of [-200, 200] rpm, and its linguistic values are set to include rapid drop, slight drop, maintain, slight rise, and rapid rise; Establish a fuzzy rule base: The fuzzy rule base consists of at least 20 fuzzy rules, describing the mapping relationship between input linguistic variables and output linguistic variables; An example rule states: when the load change rate is "positive large" and the winding temperature is "high", the speed adjustment amount is "slight drop". The de-fuzzification calculation is performed: The fuzzy set output by the fuzzy inference is converted into an accurate control amount by using the barycentric method, and the calculation formula is: wherein ω is the calculated accurate speed adjustment amount, μ is the membership value of the first rule, ω is the center value of the output language value corresponding to the first rule, N is the total number of activated rules, and i is the index variable. The cloud analysis module receives the temperature distribution map, current parameter and load data, generates an optimized control strategy through historical data analysis, and issues the optimized control strategy; The virtual verification module receives the current parameter of the current regulation module and the optimized strategy of the cloud analysis module, performs virtual simulation and predictive verification of the motor performance based on the digital twin model, and feeds back the simulation verification result to the cloud analysis module to form a closed-loop optimization.

[0022] The process of obtaining real-time temperature data in the temperature monitoring module includes: Multiple miniature thermocouple sensors are installed at key positions of the motor to collect temperature information in different areas; The collected temperature signals are filtered and processed by the signal conditioning circuit to remove noise interference; The processed temperature signals are transmitted to the central controller to generate real-time temperature data; wherein the formula for judging the deviation degree of high temperature working condition is: wherein ω is the current parameter deviation degree from the working condition warning value, is the physical quantity value collected by the sensor in real time, is the preset working condition warning threshold. The process of dynamically adjusting the drive current parameter in the current regulation module is:

[0023] When the temperature rise rate exceeds the preset threshold, the drive current amplitude is gradually reduced, and the linear decrease of the current is realized by pulse width modulation technology; When the temperature rise rate does not decrease to the safe range, the current limiting algorithm is further enabled to limit the peak current output. The process of generating a temperature distribution map in the heat dissipation optimization module includes:

[0024] Based on the thermal sensor data, a three-dimensional temperature thermal map is constructed to identify the hot spot area inside the motor; ​​​​When the hotspot area temperature exceeds the critical value, the heat dissipation fan speed is adjusted to increase airflow; When the heat dissipation effect does not meet the expectation, the standby cooling device is started to assist in cooling; Wherein, the similarity matching formula for heat dissipation efficiency optimization is: ; Wherein is the cosine similarity of the current temperature distribution and the ideal distribution, is the temperature value of the current state curve at the sampling point, is the temperature value of the reference state curve at the sampling point, is the total number of sampling points, is the index variable.

[0025] The process of self-adaptive adjustment of the heat dissipation structure in the heat dissipation optimization module also includes: Dynamically allocate heat dissipation resources according to the temperature distribution map, including increasing the density of heat dissipation fins in high temperature areas; When the motor shell temperature continues to rise, switch to forced air cooling mode.

[0026] The process of establishing a load-temperature-performance mapping model in the load response module includes: Real-time acquisition of load change data, temperature data and performance parameters; Train the model through machine learning algorithm to predict the temperature rise trend under different loads; Use fuzzy logic algorithm to solve the optimal speed and torque output value, and apply the output value to the motor drive; Wherein, the implementation of machine learning algorithm includes: Algorithm selection and parameter configuration: adopt random forest regression algorithm, wherein the number of trees is set to 100, the maximum depth is set to 10, and the minimum leaf node sample number is set to 5; Feature processing flow: based on Gini importance for feature sorting, keep the feature variables with importance greater than 0.15; Training and verification execution: data preprocessing adopts Z-score standardization method, formula: ; Wherein is the standardized feature value, is the original feature value, is the feature mean, is the feature standard deviation; The data set is divided into 70% training set, 15% validation set and 15% test set; Model update mechanism: incremental training is performed every 24 hours to update model weights.

[0027] The process of real-time adjustment of speed and torque output in the load response module also includes: When the load changes suddenly, the torque stability is given priority; When the temperature rises and the performance deteriorates, the speed is automatically reduced to ensure the safe operation of the motor.

[0028] The process of generating an optimized control strategy in the cloud analysis module includes: Upload historical temperature data, load data and test results to the cloud database; Identify high-temperature fault patterns through big data analysis; Generate and distribute adaptive control strategies to the current regulation module and the load response module; The specific operation steps of big data analysis include: Fault pattern clustering analysis: using DBSCAN clustering algorithm, neighborhood radius parameter ε=0.5, minimum sample size=10, input data is a three-dimensional matrix of temperature-vibration-current; Fault association rule mining: execute Apriori algorithm, minimum support set to 0.1, minimum confidence set to 0.7, extract fault feature association rules from historical data; Fault mode library matching: based on the pre-defined typical fault mode library for matching, wherein: Insulation aging fault: the judgment condition is temperature>180°C for 10min and temperature rise gradient>2°C / s; Bearing overheating fault: the judgment condition is vibration speed>5mm / s and the energy ratio of 1kHz component in the frequency spectrum>30%, the calculation formula is: Energy ratio ; Wherein is the vibration energy of 1kHz band, is the total vibration energy of all frequency bands; The generation and distribution process of adaptive control strategy includes: Ⅰ、Adaptive control strategy generation: based on the high-temperature fault pattern identified by big data analysis, generate an optimized control instruction set for the current regulation module and the load response module, the instruction set includes: Current optimization strategy: when insulation aging risk is identified, generate current limiting instructions, dynamically adjust the upper limit of pulse width modulation duty cycle, the calculation formula is: Wherein is the maximum allowed duty cycle, is the basic duty cycle set value, temperature compensation coefficient, the difference between the real-time temperature and the reference temperature; Speed-torque coordination strategy: when the bearing overheating risk is identified, generate speed-torque coordination instructions, calculate the safe operation interval through the load-temperature-performance mapping model, and output the optimal speed and torque combination; Dynamic response strategy: based on the real-time load change rate and temperature gradient, dynamically adjust the rule weight of fuzzy logic algorithm to suppress excessive temperature rise; Ⅱ, Strategy issuance and execution: encapsulate the generated optimization control instruction set into an executable code package, and issue it to the local controllers of the current regulation module and the load response module through a safe communication protocol. The current regulation module adjusts the pulse width modulation parameters in real time after receiving the instructions, and the load response module updates the mapping model output value after receiving the instructions. Ⅲ, Strategy update mechanism: every 1 hour, according to the latest fault mode analysis results of the cloud database, the control strategy is iteratively optimized.

[0029] The process of virtual verification module for virtual simulation and predictive verification includes: Simulate high-temperature working conditions with temperature ≥ 150°C in a virtual environment based on a digital twin model to predict the durability indicators of the motor running continuously. The implementation of the digital twin model includes: Model architecture: based on the electromagnetic-thermal coupling equation of the motor, a physical model is constructed, and the core equation is: ; Where is the output torque, is the torque constant, is the load current; Data-driven part: an LSTM neural network is used to build a data-driven model, where the number of input layer nodes is 8, corresponding to current, temperature and speed parameters, and the number of hidden layer nodes is 32. Verification logic: Risk determination condition: when the predicted temperature rise curve at time point exceeds 150°C, a high-risk alarm is triggered; Lifetime prediction method: based on the Arrhenius equation to calculate the insulation aging rate, the formula is: ; Where is the activation energy, is the Boltzmann constant, is the winding absolute temperature, and e is the natural constant; In combination with the parameters of rated speed ≥10000r / min and rated torque ≥0.27N·m, the reliability risk under virtual simulation is evaluated; According to the prediction result, an optimization report is generated and fed back to the cloud analysis module.

[0030] The motor comprises: The hollow cup motor body is made of a high-temperature-resistant composite material, has a rated speed ≥10000r / min, a rated current ≤5.8A, and a locked-rotor torque ≥1.940N·m. The integrated heat dissipation structure comprises an adjustable heat dissipation fan and heat dissipation fins and is adaptively adjusted based on a temperature distribution map. The built-in thermal sensor array is distributed in the motor shell and the winding area and monitors temperature data in real time. The speed reduction mechanism is connected with the motor body and adjusts the output torque in real time in combination with load changes. The communication interface uploads temperature data and operating states to the cloud analysis module.

[0031] The operation steps of the high-temperature-resistant direct-current hollow cup speed reduction motor and the test system are as follows: Step one, multi-dimensional data acquisition and high-temperature working condition identification The system first acquires temperature data at different positions in real time through multiple thermocouple sensors integrated in the motor winding and shell. The original signals collected are filtered and denoised by a signal conditioning circuit, converted into digital signals, and uploaded to a central controller. The controller generates a real-time temperature distribution map of the motor interior based on these data and determines whether the motor has entered a high-temperature working condition by calculating the deviation of the current temperature from the preset safety threshold, thereby providing a decision basis for subsequent intelligent regulation and control.

[0032] Step two, dynamic current regulation and collaborative heat dissipation When the system identifies a high-temperature working condition and a too-fast temperature rise rate, the current regulation module is immediately started. The module smoothly reduces the amplitude of the driving current through pulse width modulation technology to avoid the impact of current mutation on the motor. When the temperature rise is not inhibited, the current limiting algorithm is further enabled to constrain the peak current. At the same time, the heat dissipation optimization module analyzes the temperature distribution map, locates the specific area of the overheating point, and adaptively adjusts the speed of the heat dissipation fan at the corresponding position and changes the flow direction of the heat dissipation fins. When the heat dissipation effect is poor, the standby cooling device is automatically started, thereby realizing the collaborative optimization of current output and heat dissipation management.

[0033] Step three, load-temperature adaptive response The system continuously monitors the real-time load changes of the motor, the load response module combines the load data with the current temperature data, and inputs them into the pre-trained load-temperature-performance mapping model; the model uses the built-in fuzzy logic algorithm to infer and calculate the complex operating state, and real-time solves the optimal speed and torque output value under the current load and temperature conditions, and instructs the driver to execute, ensuring that the motor can still maintain stable performance output in high temperature environment.

[0034] Step four, cloud analysis and strategy optimization The cloud analysis module of the system continuously aggregates historical temperature, current, load and operating state data, and uses big data analysis techniques to mine potential fault patterns; based on the analysis results, the module generates a new generation of optimized control strategies, including issuing new current regulation parameters and heat dissipation logic, and dynamically issuing these strategies to each control module in the motor.

[0035] Step five, digital twin virtual verification The system constructs a digital twin virtual model corresponding to the physical motor, which integrates physical laws and data-driven algorithms; before and during the actual operation of the motor, the virtual verification module injects the strategies and real-time data issued by the cloud into the digital twin model, simulates the operating state of the motor under extreme working conditions, predicts its temperature rise curve and durability indicators; when the prediction result exceeds the 150°C safety threshold, an alarm is issued in advance, and the simulation results are fed back to the cloud analysis module, forming a continuous optimization closed loop, so as to complete testing and verification in virtual space and reduce dependence on physical testing.

[0036] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "including a…" does not exclude the presence of another identical element in the process, method, article or equipment including the element.

[0037] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A high-temperature-resistant direct-current hollow-cup reduction motor test system, characterized in that: The system comprises: a temperature monitoring module that acquires real-time temperature data through thermocouple sensors arranged on the motor housing and the winding, judges high-temperature working conditions based on the real-time temperature data, and generates a temperature distribution map; a current regulation module that receives a high-temperature working condition trigger signal from the temperature monitoring module, dynamically adjusts driving current parameters through pulse width modulation technology, and controls the smooth change of current amplitude based on a temperature rise rate threshold; a heat dissipation optimization module that receives the temperature distribution map, adaptively adjusts the rotation speed of the heat dissipation fan and the flow direction of the heat dissipation fins by identifying hot spot areas, and triggers a backup cooling device based on the adjustment results; a load response module that receives heat dissipation state data from the heat dissipation optimization module and external load signals, and solves the optimal rotation speed and torque output value in real time through a load-temperature-performance mapping model and a fuzzy logic algorithm; a cloud analysis module that receives the temperature distribution map, current parameters, and load data, generates an optimized control strategy through historical data analysis, and issues the optimized control strategy; a virtual verification module that receives current parameters from the current regulation module and optimized strategies from the cloud analysis module, performs virtual simulation and predictive verification of motor performance based on a digital twin model, and feeds back the simulation verification results to the cloud analysis module to form a closed-loop optimization.

2. The high-temperature DC hollow-cup reduction motor test system according to claim 1, characterized in that: The process of acquiring real-time temperature data in the temperature monitoring module comprises: installing multiple miniature thermocouple sensors at key positions of the motor to collect temperature information in different areas; filtering the collected temperature signals through a signal conditioning circuit to remove noise interference; transmitting the processed temperature signals to a central controller to generate real-time temperature data; wherein the deviation degree formula for judging high-temperature working conditions is: ； wherein is the degree of deviation of the current parameter from the operating condition warning value, is the physical quantity value collected by the sensor in real time, is the preset operating condition warning threshold value.

3. The high-temperature DC hollow-cup motor testing system of claim 1, wherein: The process of dynamically adjusting driving current parameters in the current regulation module comprises: when the temperature rise rate exceeds the preset threshold, gradually reduce the driving current amplitude, specifically through pulse width modulation technology to achieve linear decrease of current; when the temperature rise rate does not decrease to a safe range, further enable the current limiting algorithm to limit the peak current output.

4. The high-temperature DC hollow-cup reduction motor test system of claim 1, wherein: The process of generating a temperature distribution map in the heat dissipation optimization module comprises: constructing a three-dimensional temperature thermal map based on thermal sensor data to identify hot spot areas inside the motor; when the temperature of the hot spot area exceeds a critical value, preferentially adjust the rotation speed of the heat dissipation fan to increase airflow; when the heat dissipation effect does not meet expectations, start the backup cooling device for auxiliary cooling; wherein the similarity matching formula for heat dissipation efficiency optimization is: ； wherein is a cosine similarity of the current temperature distribution to the ideal distribution, is a temperature value of the current state curve at the i-th sampling point, is a temperature value of the reference state curve at the i-th sampling point, is a temperature value of the reference state curve at the i-th sampling point, is a temperature value of the reference state curve at the i-th sampling point, is a total number of sampling points.

5. The high-temperature DC hollow-cup motor testing system of claim 1, wherein: The process of adaptively adjusting the heat dissipation structure in the heat dissipation optimization module further comprises: dynamically allocate heat dissipation resources according to the temperature distribution map, including increasing the density of heat dissipation fins in high-temperature areas; when the temperature of the motor housing continues to rise, switch to forced air cooling mode.

6. The high-temperature DC hollow-cup motor testing system of claim 1, wherein: The process of establishing a load-temperature-performance mapping model in the load response module comprises: real-time collection of load change data, temperature data, and performance parameters; training the model through machine learning algorithms to predict the temperature rise trend under different loads; use fuzzy logic algorithms to solve the optimal rotation speed and torque output value, and apply the output value to the motor driver.

7. The high-temperature DC hollow-cup reduction motor test system of claim 1, wherein: The process of real-time adjustment of rotation speed and torque output in the load response module further comprises: when the load suddenly changes, preferentially maintain torque stability; When the temperature rises, the speed is automatically reduced to ensure the safe operation of the motor.

8. The high-temperature DC hollow-cup motor testing system of claim 1, wherein: The process of generating an optimized control strategy in the cloud analysis module includes: Upload historical temperature data, load data, and test results to the cloud database; Identify high-temperature failure modes through big data analysis; Generate and issue adaptive control strategies to the current regulation module and load response module.

9. The high-temperature DC hollow-cup reluctance motor testing system of claim 1, wherein: The process of virtual simulation and predictive verification by the virtual verification module includes: Simulate high-temperature conditions (temperature ≥ 150°C) in a virtual environment based on a digital twin model to predict the durability of the motor under continuous operation; Evaluate the reliability risk under virtual simulation in combination with parameters such as rated speed ≥ 10000 r / min and rated torque ≥ 0.27 N·m; Generate an optimization report based on the prediction results and feed it back to the cloud analysis module.

10. A high temperature resistant DC hollow cup reduction motor, characterized in that: A high-temperature-resistant direct-current hollow cup reduction motor test system according to any one of claims 1-9, the motor comprising: A hollow cup motor body made of a high-temperature-resistant composite material, with a rated speed ≥ 10000 r / min, a rated current ≤ 5.8 A, and a locked-rotor torque ≥ 1.940 N·m; An integrated heat dissipation structure including an adjustable heat dissipation fan and heat dissipation fins, which are self-adaptively adjusted based on a temperature distribution map; An array of built-in thermal sensors distributed in the motor housing and winding area for real-time monitoring of temperature data; A reduction mechanism connected to the motor body, which adjusts the output torque in real time in combination with load changes; A communication interface for uploading temperature data and operating status to the cloud analysis module.

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