Brushless gear motor based on multiple sensors and control method thereof

Abstract

The invention relates to the technical field of brushless gear motor control, and discloses a brushless gear motor based on multiple sensors and a control method thereof, and the motor comprises a motor body, a monitoring module and a control part. The monitoring module is electrically connected with the motor body, the monitoring module is provided with a temperature sensor, a rotating speed sensor, a current sensor and a load sensor, and the monitoring module is configured to monitor the motor body in real time. The control part is electrically connected with the monitoring module and the motor body, and the control part is configured to obtain structural data, historical monitoring data and real-time monitoring data of the motor body, determine the temperature state of the motor body according to the structural data, the historical monitoring data and the real-time monitoring data, and send the temperature state to the motor body. The control part is further configured to determine the energy efficiency loss amount of the motor according to the temperature state and adjust the operation data of the motor body according to the energy efficiency loss amount. According to the invention, the structure data, the historical data and the real-time data are comprehensively analyzed, so that the motor is accurately controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of brushless reduction motor control, and in particular to a brushless reduction motor based on multiple sensors and a control method thereof. Background Art

[0002] In recent years, with the rapid development of industrial automation and intelligent manufacturing, motors have been increasingly used in production equipment and new energy systems, which has put forward higher requirements for their operating efficiency and reliability. Especially under high load and long-term operation conditions, the increase in motor temperature may lead to energy efficiency loss and equipment failure, thus affecting the overall system performance. Therefore, how to monitor the motor temperature status in real time, accurately evaluate the energy efficiency loss, and realize intelligent control has become an important technical problem that needs to be solved urgently.

[0003] At present, in the existing technology, multi-sensor monitoring of motor operation status has been widely used, usually through temperature sensors, speed sensors, current sensors and load sensors to collect key motor parameters. However, most systems only rely on a single or a few data for judgment, which is difficult to fully reflect the actual operation status of the motor. In addition, traditional methods often lack the comprehensive use of historical monitoring data and motor structure data, and cannot effectively predict temperature anomalies and their impact on energy efficiency, resulting in delayed or inaccurate control measures, which restricts the further improvement of motor operation efficiency and intelligence level.

[0004] Therefore, there is an urgent need to invent a motor control technology to solve the problem in the prior art that the prediction of the temperature anomaly and energy efficiency impact of the motor is inaccurate, resulting in delayed control measures and limiting the improvement of the motor's operating efficiency and intelligence level. Summary of the invention

[0005] In view of this, the present invention proposes a brushless reduction motor based on multiple sensors and a control method thereof, aiming to solve the problem in the current technology of inaccurate prediction of the temperature anomaly and energy efficiency impact of the motor, thereby causing delayed control measures and limiting the improvement of the motor's operating efficiency and intelligence level.

[0006] The present invention proposes a brushless reduction motor based on multiple sensors, comprising: Motor body, monitoring module and control unit; The monitoring module is electrically connected to the motor body, the monitoring module is configured with a temperature sensor, a speed sensor, a current sensor and a load sensor, and the monitoring module is configured to perform real-time monitoring of the motor body; The control unit is electrically connected to the monitoring module and the motor body respectively. The control unit is configured to obtain the structural data, historical monitoring data and real-time monitoring data of the motor body, and determine the temperature state of the motor body based on the structural data, historical monitoring data and real-time monitoring data. The control unit is also configured to determine the energy efficiency loss of the motor based on the temperature state, and adjust the operating data of the motor body based on the energy efficiency loss.

[0007] Furthermore, the control unit includes: A collection module is electrically connected to each sensor, and is configured to collect monitoring data of the motor body, wherein the monitoring data includes temperature data of the motor body, motor rotation data, current data and load data; A storage module configured to store structural data and historical monitoring data of the motor body; The analysis module is electrically connected to the storage module and the acquisition module respectively. The analysis module is configured to determine the temperature threshold of the motor body according to the structural data and historical monitoring data of the motor body. The analysis module is also configured to determine the temperature data of the motor body in a preset period of time according to the real-time monitoring data, and determine the temperature state of the motor body according to the relationship between the temperature data and the temperature threshold, wherein: if the temperature data is lower than or equal to the temperature threshold, the analysis module determines that the temperature state of the motor body is a normal state; if the temperature data is higher than the temperature threshold, the analysis module determines that the temperature state of the motor body is an abnormal state; The central control module is electrically connected to the storage module and the analysis module respectively. The central control module is configured to determine the energy efficiency loss of the motor body according to the temperature data of the motor body during a preset period of time when it is determined that the temperature state of the motor body is abnormal, and control the motor body according to the energy efficiency loss.

[0008] Furthermore, when the analysis module determines the temperature threshold of the motor body according to the structural data of the motor body and the historical monitoring data, it includes: The analysis module is further configured to obtain a heat dissipation structure of the motor body, and determine a heat dissipation coefficient of the motor body based on a relationship between the heat dissipation structure and the motor body structure; The historical temperature data in the historical monitoring data are obtained, the data abnormal value and the data normal value are determined according to the historical temperature, and the temperature threshold is determined according to the relationship between the data abnormal value and the data normal value and the heat dissipation coefficient.

[0009] Furthermore, the analysis module determines the heat dissipation coefficient of the motor body based on the relationship between the heat dissipation structure and the motor body structure, including: The analysis module is further configured to determine heat flow data inside the motor body based on the fluid dynamics simulation model and the motor body structure, and determine the heat dissipation efficiency of each structure of the motor body based on the heat flow data and finite element analysis; The analysis module is further configured to establish a coupling model according to the relationship between the heat dissipation structure and the motor body structure, and determine the heat transfer efficiency between the motor body and the heat dissipation structure based on the finite element analysis and the coupling model: ; Among them, A is the heat transfer efficiency between the motor body and the heat dissipation structure, k is the thermal conductivity of the heat dissipation structure, j is the cross-sectional area of ​​the conduction path, △T is the temperature difference between the motor body and the heat dissipation structure, L is the length of the heat conduction path, h is the convection heat transfer coefficient, S is the heat dissipation surface area of ​​the heat dissipation structure, T is the motor body temperature, t is the ambient temperature, is the radiance, σ is the Stewart-Boltzmann constant; The analysis module is further configured to determine the heat dissipation coefficient of the motor body according to the heat dissipation efficiency of each structure of the motor body and the heat transfer efficiency between the motor body and the heat dissipation structure: B = α·f·A·b; Among them, B is the heat dissipation coefficient of the motor body, α is the weight coefficient, f is the heat dissipation efficiency of the motor body, and b is the heat dissipation coefficient of the heat dissipation structure.

[0010] Furthermore, the analysis module determines the data abnormal value and the data normal value according to each historical temperature, and determines the temperature threshold according to the relationship between the data abnormal value and the data normal value and the heat dissipation coefficient, including: The analysis module is further configured to determine a critical temperature value for each historical temperature based on a decision tree: ; Where Q is the critical temperature value, rmaxE is the maximum independent variable, IG(E) is the Eth information gain point, VR(E) is the Eth variance reduction, d 2 E is the second-order differential of the historical temperature, dr 2 is the time square differential of the historical temperature, W1, W2, W3 are weight coefficients, and W1, W2, W3 are not zero; The analysis module is further configured to determine data abnormal values ​​and data normal values ​​for each historical temperature based on the key temperature values: When the historical temperature is lower than the critical temperature value, the analysis module determines that the historical temperature is a normal value of the data; When the historical temperature is higher than or equal to the critical temperature value, the analysis module determines that the historical temperature is a data outlier, and determines that the historical temperature determined as the data outlier is an abnormal temperature; The analysis module is further configured to determine a temperature threshold value according to a relationship between each abnormal temperature and a heat dissipation coefficient of the motor body: ; Among them, T threshis the temperature threshold, T ambient is the reference temperature of the motor body when it is working, wi is the weight coefficient corresponding to the Fith abnormal temperature, and n is the total number of abnormal temperatures.

[0011] Furthermore, when the analysis module determines the temperature data of the motor body during a preset period of time based on the real-time monitoring data, it includes: The analysis module is also configured to obtain the operation data and temperature data of the motor body in the real-time monitoring data; The analysis module is further configured to establish a temperature characteristic vector between the operation data and the temperature data of the motor body based on the historical monitoring data; The analysis module is also configured to substitute the operation data of the motor body into the temperature characteristic vector, obtain the temperature increase of the motor body in a preset period of time, and determine the temperature data of the motor body in the preset period of time based on the relationship between the temperature increase and the temperature data.

[0012] Furthermore, when the analysis module establishes a temperature characteristic vector between the operation data and the temperature data of the motor body based on the historical monitoring data, it includes: The analysis module is configured to obtain the historical operation data and historical temperature data of each motor in the historical monitoring data, and establish a temperature correlation equation based on the historical operation data and the historical temperature data: The analysis module is further configured to obtain a distance metric between each temperature correlation equation based on the Euclidean distance, and establish a distance matrix according to the distance metric; The analysis module is also configured to iteratively cluster the temperature correlation equations according to the distance matrix, and determine the temperature feature vectors between the operating data and the temperature data in the temperature correlation equation according to the clustering results.

[0013] Furthermore, the central control module determines the energy efficiency loss of the motor body according to the temperature data of the motor body during a preset period of time, and controls the motor body according to the energy efficiency loss, including: ; Where, ΔE loss is the energy efficiency loss, γ is the weight coefficient, Ti is the temperature data at the i-th moment, T opt is the temperature threshold, max{0,Ti−T opt} is the time when the temperature data of the preset period exceeds the temperature threshold, △t is the time interval, and N is the total number of moments; The central control module is also configured to determine an adjustment coefficient according to a relationship between the energy efficiency loss amount and a preset energy efficiency loss amount configured in the central control module, and to control the real-time operation data of the motor body according to the adjustment coefficient.

[0014] Furthermore, when the central control module determines the adjustment coefficient according to the relationship between the energy efficiency loss amount and the preset energy efficiency loss amount, it includes: The central control module is also configured with a first preset energy efficiency loss amount and a second preset energy efficiency loss amount. The central control module is also configured to determine an adjustment coefficient according to a relationship between the energy efficiency loss amount and the first preset energy efficiency loss amount and the second preset energy efficiency loss amount: When the energy efficiency loss is lower than or equal to the first preset energy efficiency loss, the central control module determines the adjustment coefficient as J3; When the energy efficiency loss amount is higher than the first preset energy efficiency loss amount and the energy efficiency loss amount is lower than or equal to the second preset energy efficiency loss amount, the central control module determines the adjustment coefficient as J2; When the energy efficiency loss is higher than the second preset energy efficiency loss, the central control module determines the adjustment coefficient as J1; Among them, the first preset energy efficiency loss amount is less than the second preset energy efficiency loss amount, J1<J2<J3<1.

[0015] Compared with the prior art, the beneficial effect of the present invention is that by integrating multiple sensors such as temperature, speed, current and load, the state of the motor body can be monitored in real time, so that the temperature anomaly and changes in other operating data can be accurately captured. Combined with the structural data of the motor body and historical monitoring data, a comprehensive motor health profile can be constructed, potential abnormal conditions can be effectively identified, and early warning can be given in advance to ensure that the equipment is always in a safe and efficient working state. In addition, by using advanced data analysis and decision-making algorithms, the energy efficiency loss of the motor can be calculated according to the real-time temperature state, and the operating parameters of the motor can be dynamically adjusted to optimize energy utilization efficiency. By accurately evaluating the temperature data and energy efficiency loss during the operation of the motor, the control unit can take active control measures before the temperature approaches the critical value, thereby avoiding the occurrence of overheating, extending the service life of the equipment, and reducing energy waste and maintenance costs. Finally, through the real-time collection and comprehensive utilization of multi-sensor data, the system can not only achieve a rapid response to abnormal motor temperature, but also conduct in-depth analysis of abnormal data based on the motor structure and historical data, thereby achieving preventive maintenance. This not only reduces the risk of equipment failure, but also saves companies a lot of economic costs caused by downtime, maintenance and replacement of equipment, and ultimately promotes the application of intelligent and efficient motors.

[0016] On the other hand, the present application also provides a control method for a brushless reduction motor based on multiple sensors, comprising: Acquire real-time monitoring data, historical monitoring data and structural data of the motor, wherein the monitoring data includes temperature data, speed data, current data and load data; Determine the temperature status of the motor body based on structural data, historical monitoring data and real-time monitoring data; The energy efficiency loss of the motor is determined according to the temperature state, and the operation data of the motor body is adjusted according to the energy efficiency loss.

[0017] It can be understood that the brushless reduction motor based on multiple sensors and the control method thereof in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A functional block diagram of a brushless reduction motor based on multiple sensors provided in an embodiment of the present invention; Figure 2 A flowchart of a control method for a brushless reduction motor based on multiple sensors provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a brushless reduction motor based on multiple sensors, including: a motor body, a monitoring module and a control unit.

[0021] Specifically, the monitoring module is electrically connected to the motor body, and the monitoring module is configured with a temperature sensor, a speed sensor, a current sensor, and a load sensor, and the monitoring module is configured to monitor the motor body in real time. The control unit is electrically connected to the monitoring module and the motor body, respectively, and the control unit is configured to obtain the structural data, historical monitoring data, and real-time monitoring data of the motor body, and determine the temperature state of the motor body according to the structural data, historical monitoring data, and real-time monitoring data. The control unit is also configured to determine the motor energy efficiency loss according to the temperature state, and adjust the operation data of the motor body according to the energy efficiency loss.

[0022] It is understandable that the present invention is based on a real-time monitoring system of multiple sensors, and a monitoring module collects multiple key operating parameters of the motor body in real time, including temperature, speed, current and load, etc. The temperature sensor is used to detect the temperature state of the motor in real time, the speed sensor and the current sensor are used to detect the running speed and current change of the motor, and the load sensor monitors the load carried by the motor. These sensors jointly provide comprehensive data of the motor body to ensure accurate understanding of the running state of the motor. The control unit can evaluate the current temperature state of the motor by comprehensive analysis of the acquired real-time monitoring data, historical monitoring data and structural data of the motor body. Specifically, the control unit compares the real-time temperature data with the historical data, combined with the structural characteristics of the motor, to determine whether the motor is within a safe and stable operating temperature range. If the temperature is too high, the control unit will be able to timely identify and predict the risks that may cause energy efficiency loss, and then take appropriate measures to regulate. Finally. According to the temperature state of the motor, the control unit further calculates the energy efficiency loss of the motor, and intelligently adjusts the running data of the motor according to the energy efficiency loss. This intelligent regulation mechanism can reduce energy efficiency loss and improve the operating efficiency of the motor by optimizing the load, speed or other operating parameters of the motor. At the same time, through this control method, the system can achieve more efficient motor management, reduce energy waste and excessive wear, thereby extending the service life of the motor and ensuring that the motor operates in the best condition.

[0023] Specifically, the control unit includes: an acquisition module, a storage module, an analysis module and a central control module.

[0024] Specifically, the acquisition module is electrically connected to each sensor, and the acquisition module is configured to acquire monitoring data of the motor body, wherein the monitoring data includes temperature data of the motor body, motor rotation data, current data and load data.

[0025] Specifically, the storage module is configured to store structural data and historical monitoring data of the motor body.

[0026] Specifically, the analysis module is electrically connected to the storage module and the acquisition module respectively, and the analysis module is configured to determine the temperature threshold of the motor body according to the structural data and historical monitoring data of the motor body. The analysis module is also configured to determine the temperature data of the motor body in a preset period according to the real-time monitoring data, and determine the temperature state of the motor body according to the relationship between the temperature data and the temperature threshold, wherein: if the temperature data is lower than or equal to the temperature threshold, the analysis module determines that the temperature state of the motor body is a normal state. If the temperature data is higher than the temperature threshold, the analysis module determines that the temperature state of the motor body is an abnormal state.

[0027] Specifically, when the analysis module determines the temperature threshold of the motor body according to the structural data of the motor body and the historical monitoring data, the analysis module includes: the analysis module is also configured to obtain the heat dissipation structure of the motor body, and determine the heat dissipation coefficient of the motor body based on the relationship between the heat dissipation structure and the motor body structure. Obtain each historical temperature data in the historical monitoring data, determine the data abnormal value and the data normal value according to each historical temperature, and determine the temperature threshold according to the relationship between the data abnormal value and the data normal value and the heat dissipation coefficient.

[0028] Specifically, when the analysis module determines the heat dissipation coefficient of the motor body based on the relationship between the heat dissipation structure and the motor body structure, it includes: the analysis module is also configured to determine the heat flow data inside the motor body based on the fluid dynamics simulation model and the motor body structure, and determine the heat dissipation efficiency of each structure of the motor body based on the heat flow data and finite element analysis. The analysis module is also configured to establish a coupling model based on the relationship between the heat dissipation structure and the motor body structure, and determine the heat transfer efficiency between the motor body and the heat dissipation structure based on the finite element analysis and the coupling model: .

[0029] Among them, A is the heat transfer efficiency between the motor body and the heat dissipation structure, k is the thermal conductivity of the heat dissipation structure, j is the cross-sectional area of ​​the conduction path, △T is the temperature difference between the motor body and the heat dissipation structure, L is the length of the heat conduction path, h is the convection heat transfer coefficient, S is the heat dissipation surface area of ​​the heat dissipation structure, T is the motor body temperature, t is the ambient temperature, is the emissivity, and σ is the Stewart-Boltzmann constant. The analysis module is also configured to determine the heat dissipation coefficient of the motor body according to the heat dissipation efficiency of each structure of the motor body and the heat transfer efficiency between the motor body and the heat dissipation structure: B=α·f·A·b.

[0030] Among them, B is the heat dissipation coefficient of the motor body, α is the weight coefficient, f is the heat dissipation efficiency of the motor body, and b is the heat dissipation coefficient of the heat dissipation structure.

[0031] Specifically, the heat dissipation efficiency of the motor body is analyzed through fluid dynamics simulation and finite element analysis (FEA), and its data sources can be motor structure data, temperature field simulation data and actual operating condition data.

[0032] The heat dissipation coefficient of the heat dissipation structure reflects the ability of the heat dissipation structure to dissipate heat from itself to the environment. The specific calculation process is as follows: Where hext is the external heat transfer coefficient of the heat dissipation structure, which depends on the material, surface roughness and ambient air flow of the heat dissipation structure. 散热结构 is the effective heat dissipation area of ​​the heat dissipation structure.

[0033] Specifically, the weight coefficient α is obtained through historical monitoring data analysis or experimental calibration.

[0034] Specifically, the analysis module determines the data abnormal value and the data normal value according to each historical temperature, and determines the temperature threshold according to the relationship between the data abnormal value and the data normal value and the heat dissipation coefficient, including: the analysis module is also configured to determine the key temperature value for each historical temperature based on the decision tree: .

[0035] Where Q is the critical temperature value, rmaxE is the maximum independent variable, IG(E) is the Eth information gain point, VR(E) is the Eth variance reduction, d 2 E is the second-order differential of the historical temperature, dr 2 is the time square differential of the historical temperature, W1, W2, W3 are weight coefficients, and W1, W2, W3 are not zero. The analysis module is also configured to determine the data abnormal value and data normal value of each historical temperature according to the key temperature value: when the historical temperature is lower than the key temperature value, the analysis module determines that the historical temperature is a data normal value. When the historical temperature is higher than or equal to the key temperature value, the analysis module determines that the historical temperature is a data abnormal value, and determines that the historical temperature determined as a data abnormal value is an abnormal temperature: The analysis module is also configured to determine the temperature threshold value according to the relationship between each abnormal temperature and the heat dissipation coefficient of the motor body: .

[0036] Among them, T thresh is the temperature threshold, T ambient is the reference temperature of the motor body when it is working, wi is the weight coefficient corresponding to the Fith abnormal temperature, and n is the total number of abnormal temperatures.

[0037] Specifically, the weight coefficients W1, W2, and W3 are obtained through statistical analysis and experimental experience adjustment.

[0038] It is understandable that the analysis module obtains the structural data, historical monitoring data and real-time monitoring data of the motor body through the storage module and the acquisition module, and determines the temperature threshold of the motor based on these data. Specifically, the analysis module compares the real-time collected temperature data with the temperature threshold to determine whether the motor is in a normal working state. When the temperature is lower than or equal to the threshold, the system determines that the motor is operating normally; when the temperature exceeds the threshold, it is determined to be an abnormal state in order to trigger the corresponding control mechanism. When determining the temperature threshold of the motor, the analysis module will further consider the heat dissipation capacity of the motor. By obtaining the heat dissipation structure of the motor and analyzing the relationship between the heat dissipation structure and the motor body structure, the heat dissipation coefficient of the motor is calculated. Then, by using the fluid dynamics simulation model and the finite element analysis method, the heat flow data inside the motor is determined, and the heat dissipation efficiency is calculated in combination with the heat dissipation characteristics of different motor structures. Subsequently, by establishing a coupling model between the heat dissipation structure and the motor body, the heat transfer efficiency between the motor and the heat dissipation structure is calculated through finite element analysis, and then the overall heat dissipation capacity of the motor is derived. In the calculation process of the heat dissipation coefficient, the analysis module combines multiple parameters to evaluate the heat transfer performance of the motor. For example, the heat transfer efficiency A is determined by factors such as the thermal conductivity k of the motor material, the heat conduction path j, the temperature difference ΔT, the conduction path length L, the convection heat transfer coefficient h, and the heat dissipation surface area S. In addition, the influence of radiation heat transfer needs to be considered, which involves the Stewart-Boltzmann constant σ, the emissivity ε of the motor, and the total heat H of the motor. Finally, the heat dissipation coefficient B of the motor as a whole is calculated by a comprehensive formula B=α·f·A·b, where α is the weight coefficient, f represents the heat dissipation efficiency of the motor body, A is the heat transfer efficiency, and b is the heat dissipation coefficient of the heat dissipation structure. In the process of determining the temperature threshold, the analysis module uses a decision tree algorithm to process historical temperature data. By screening out the key temperature value Q from the historical monitoring data, the value is jointly determined by the information gain IG(E), the variance reduction VR(E) and the second-order differential d²E of the temperature data, and is optimized by the weight coefficients W1, W2, and W3. According to the critical temperature value, the historical temperature data is further divided into normal values ​​and abnormal values. The temperature data below the critical temperature is classified as normal values, while the temperature data above or equal to the critical temperature is regarded as abnormal values. Finally, the final temperature threshold T is calculated through the relationship between the abnormal temperature data and the motor heat dissipation coefficient. thresh This calculation takes into account the motor ambient temperature T ambient , each abnormal temperature Fi and its corresponding weight coefficient wi, to ensure that the threshold can accurately reflect the operating status of the motor. Based on this threshold, it is possible to effectively determine whether the motor is in an abnormal state, thereby providing intelligent temperature management and improving the stability and safety of motor operation.

[0039] It can be seen that the comprehensiveness and accuracy of temperature evaluation are ensured by combining real-time monitoring data, historical monitoring data and motor structure data. This can effectively avoid misjudgment caused by a single factor, improve the ability to identify abnormal motor temperature, and ensure that the motor can maintain stable operation under different working conditions. Secondly, by modeling the heat dissipation characteristics of the motor through the analysis module, the thermal flow characteristics of the motor can be accurately calculated based on fluid dynamics simulation and finite element analysis methods, and the heat dissipation efficiency of the motor can be further optimized. Compared with the fixed temperature threshold set by the traditional empirical method, the temperature threshold can be dynamically adjusted to adapt to different loads and environmental conditions during the operation of the motor, ensuring that the heat dissipation capacity of the motor can be more reasonably evaluated and the overall energy efficiency can be improved. In addition, when calculating the heat dissipation coefficient of the motor, a variety of influencing factors are comprehensively considered, including key parameters such as heat dissipation structure, heat transfer efficiency, convection heat transfer and radiation heat transfer, and the calculation is performed through a coupling model. This method avoids the limitation of the traditional method of relying only on a single heat conduction path to calculate the heat dissipation coefficient, making the evaluation of heat dissipation capacity more accurate, thereby optimizing the operating parameters of the motor, reducing energy consumption, and increasing service life. In terms of abnormal temperature identification, the decision tree algorithm is used to analyze historical temperature data, accurately divide the data into normal values ​​and abnormal values, and dynamically adjust the abnormal data according to the key temperature values. Compared with the traditional method of simply setting fixed abnormal temperatures, this solution can adapt to the temperature change trend under different working conditions, improve the sensitivity to temperature anomalies, help to warn of possible failures in advance, and avoid equipment damage or performance degradation caused by overheating. Finally, through intelligent temperature monitoring and dynamic threshold adjustment, real-time control of motor temperature is achieved, and targeted control measures can be taken when the temperature is abnormal, such as adjusting the operating power, optimizing the cooling system, or triggering the protection mechanism. This method not only improves the safety and reliability of the motor, but also reduces the energy waste caused by improper temperature management, improves the overall working efficiency of the motor, and provides strong support for the intelligent operation of brushless reduction motors.

[0040] Specifically, when the analysis module determines the temperature data of the motor body during a preset period of time based on the real-time monitoring data, the analysis module includes: the analysis module is also configured to obtain the operation data and temperature data of the motor body in the real-time monitoring data. The analysis module is also configured to establish a temperature feature vector between the operation data and the temperature data of the motor body based on the historical monitoring data. The analysis module is also configured to substitute the operation data of the motor body into the temperature feature vector, obtain the temperature increase of the motor body during the preset period of time, and determine the temperature data of the motor body during the preset period of time based on the relationship between the temperature increase and the temperature data.

[0041] Specifically, when the analysis module establishes the temperature characteristic vector between the operation data and the temperature data of the motor body based on the historical monitoring data, it includes: the analysis module is configured to obtain the historical operation data and the historical temperature data of each motor in the historical monitoring data, and establish a temperature correlation formula based on the historical operation data and the historical temperature data: the analysis module is also configured to obtain the distance metric between each temperature correlation formula based on the Euclidean distance, and establish a distance matrix based on the distance metric. The analysis module is also configured to iteratively cluster each temperature correlation formula according to the distance matrix, and determine the temperature characteristic vector between the operation data and the temperature data in the temperature correlation formula according to the clustering result.

[0042] It is understandable that the analysis module obtains real-time monitoring data of the motor body, including operating data (such as speed, current, load, etc.) and temperature data, to ensure that the basic data source of temperature assessment is reliable. Then, the historical monitoring data is used to establish a relationship model between the motor operating state and temperature change to provide support for subsequent temperature prediction. In the process of establishing the temperature feature vector, the analysis module constructs a temperature correlation based on the historical operating data and temperature data, that is, a mathematical model is used to describe the change law of the motor temperature under different working conditions. The purpose of this step is to summarize the trend of the motor temperature changing with the operating conditions, and to identify the temperature characteristics under different motor states. In this way, the error caused by simple linear regression can be effectively avoided and the accuracy of temperature prediction can be improved. Subsequently, the analysis module uses the Euclidean distance method to measure the similarity between different temperature correlations. The Euclidean distance can measure the degree of difference between different data points and can distinguish operating states with similar or different temperature change laws. This process establishes a distance matrix between temperature correlations, providing a basis for subsequent clustering analysis. In this way, it can be ensured that the temperature characteristics under different operating modes are reasonably classified to reduce misjudgment. Based on the calculated distance matrix, the analysis module uses an iterative clustering algorithm to classify similar temperature correlations into the same category, and then extract temperature feature vectors under different clusters. The introduction of cluster analysis enables the system to extract representative temperature change patterns from large-scale historical data, ensuring that the prediction model has wide adaptability. This not only improves the accuracy of the prediction, but also enhances the model's ability to identify abnormal situations. Finally, during the actual prediction, the analysis module substitutes the real-time operating data of the motor into the temperature feature vector, calculates the temperature increase in the preset time period, and combines the current temperature data to determine the temperature data for a certain period in the future.

[0043] Specifically, the central control module is electrically connected to the storage module and the analysis module respectively. The central control module is configured to determine the energy efficiency loss of the motor body according to the temperature data of the motor body during a preset period of time when it is determined that the temperature state of the motor body is abnormal, and control the motor body according to the energy efficiency loss.

[0044] Specifically, the central control module determines the energy efficiency loss of the motor body according to the temperature data of the motor body during a preset period of time, and controls the motor body according to the energy efficiency loss, including: .

[0045] Where, ΔE loss is the energy efficiency loss, γ is the weight coefficient, Ti is the temperature data at the i-th moment, T opt is the temperature threshold, max{0,Ti−T opt} is the moment when the temperature data of the preset time period exceeds the temperature threshold, △t is the time interval, and N is the total number of moments. The central control module is also configured to determine an adjustment coefficient according to the relationship between the energy efficiency loss amount and the preset energy efficiency loss amount configured by the central control module, and control the real-time operation data of the motor body according to the adjustment coefficient.

[0046] Specifically, the weight coefficient of γ is the energy efficiency loss coefficient, and its main purpose is to balance the impact of temperature exceeding the limit on energy efficiency, which is obtained through statistical analysis and experimental experience adjustment.

[0047] Specifically, when the central control module determines the adjustment coefficient according to the relationship between the energy efficiency loss amount and the preset energy efficiency loss amount, it includes: the central control module is also configured with a first preset energy efficiency loss amount and a second preset energy efficiency loss amount, and the central control module is also configured to determine the adjustment coefficient according to the relationship between the energy efficiency loss amount and the first preset energy efficiency loss amount and the second preset energy efficiency loss amount: when the energy efficiency loss amount is lower than or equal to the first preset energy efficiency loss amount, the central control module determines the adjustment coefficient to be J3. When the energy efficiency loss amount is higher than the first preset energy efficiency loss amount, and the energy efficiency loss amount is lower than or equal to the second preset energy efficiency loss amount, the central control module determines the adjustment coefficient to be J2. When the energy efficiency loss amount is higher than the second preset energy efficiency loss amount, the central control module determines the adjustment coefficient to be J1. Among them, the first preset energy efficiency loss amount is less than the second preset energy efficiency loss amount, and J1<J2<J3<1.

[0048] It is understandable that the central control module determines whether the motor is in an abnormal state by analyzing the motor body temperature status information provided by the module. Once the temperature exceeds the set threshold, the energy efficiency loss calculation process will be entered. In the specific calculation formula, the energy efficiency loss ΔE loss Determined by several factors, including the moment when the temperature exceeds the threshold (Ti-T opt), the time interval (△t), and the change trend of the temperature data over the total number of time instances N. Additionally, a weight coefficient γ is introduced to adjust the contribution degree of energy efficiency loss in different situations, making the calculation more accurate. Secondly, to ensure flexible adaptation to different working conditions, the central control module not only calculates the current energy efficiency loss but also compares it with a preset energy efficiency loss. By presetting the first preset energy efficiency loss and the second preset energy efficiency loss as reference values, they are used to judge the current energy efficiency state of the motor. This method introduces a hierarchical control mechanism, enabling the system to adopt different adjustment strategies according to different loss situations instead of simple binary adjustment (on or off). Thirdly, when the energy efficiency loss exceeds different preset values, the central control module uses an adjustment coefficient J to dynamically optimize the motor operating state. The value range of the adjustment coefficient is J1 < J2 < J3 < 1, where J1 corresponds to the highest energy efficiency loss situation and J3 corresponds to the lowest energy efficiency loss situation. When the loss is low (≤ the first preset energy efficiency loss), maintain a high adjustment coefficient J3 so that the motor operation is basically unaffected. When the loss is high, gradually reduce the adjustment coefficient to J1 to significantly adjust the motor operation parameters (such as reducing the speed and decreasing the load) to reduce temperature accumulation and optimize energy efficiency. In addition, the adjustment coefficient J directly affects the real-time operation data control of the motor. When the value of J decreases, the system may take measures such as reducing power output, adjusting the operation mode, or increasing heat dissipation control to avoid further temperature rise. This intelligent adjustment method is more flexible than traditional fixed-value control (such as simple high-temperature shutdown protection), can effectively reduce the energy efficiency loss caused by temperature anomalies, and extend the service life of the motor. Finally, by continuously monitoring the motor temperature, calculating the energy efficiency loss, and dynamically adjusting the operation state according to the loss situation, more optimal energy efficiency management can be achieved on the premise of ensuring the safety of the motor.

[0049] In the above embodiment, by integrating multiple sensors such as temperature, speed, current and load, the state of the motor body is monitored in real time, so that temperature anomalies and changes in other operating data can be accurately captured. Combined with the structural data of the motor body and historical monitoring data, a comprehensive motor health profile can be constructed, potential abnormal states can be effectively identified, and early warning can be given in advance to ensure that the equipment is always in a safe and efficient working state. In addition, by using advanced data analysis and decision-making algorithms, the energy efficiency loss of the motor can be calculated according to the real-time temperature state, and the operating parameters of the motor can be dynamically adjusted to optimize energy utilization efficiency. By accurately evaluating the temperature data and energy efficiency loss during the operation of the motor, the control unit can take active control measures before the temperature approaches the critical value, thereby avoiding the occurrence of overheating, extending the service life of the equipment, and reducing energy waste and maintenance costs. Finally, through the real-time collection and comprehensive utilization of multi-sensor data, the system can not only achieve a rapid response to abnormal motor temperature, but also conduct in-depth analysis of abnormal data based on the motor structure and historical data, thereby achieving preventive maintenance. In this way, not only the risk of equipment failure is reduced, but also a large amount of economic costs caused by downtime, maintenance and replacement of equipment are saved for enterprises, and ultimately promote the application of intelligent and efficient motors.

[0050] In another preferred embodiment based on the above embodiment, Figure 2 As shown, this embodiment provides a control method for a brushless reduction motor based on multiple sensors, including: Step S100: Acquire real-time monitoring data, historical monitoring data and structural data of the motor, wherein the monitoring data includes temperature data, speed data, current data and load data.

[0051] Step S200: determining the temperature state of the motor body according to the structural data, historical monitoring data and real-time monitoring data.

[0052] Step S300: determining the motor energy efficiency loss according to the temperature state, and adjusting the operation data of the motor body according to the energy efficiency loss.

[0053] It can be understood that the brushless reduction motor based on multiple sensors and the control method thereof in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail.

[0054] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0055] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0056] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A brushless reduction motor based on multiple sensors, characterized in that: include: Motor body, monitoring module and control unit; The monitoring module is electrically connected to the motor body, the monitoring module is configured with a temperature sensor, a speed sensor, a current sensor and a load sensor, and the monitoring module is configured to perform real-time monitoring of the motor body; The control unit is electrically connected to the monitoring module and the motor body respectively. The control unit is configured to obtain the structural data, historical monitoring data and real-time monitoring data of the motor body, and determine the temperature state of the motor body based on the structural data, historical monitoring data and real-time monitoring data. The control unit is also configured to determine the energy efficiency loss of the motor based on the temperature state, and adjust the operating data of the motor body based on the energy efficiency loss.

2. The multi-sensor based brushless reduction motor according to claim 1, characterized in that: The control unit includes: A collection module is electrically connected to each sensor, and is configured to collect monitoring data of the motor body, wherein the monitoring data includes temperature data of the motor body, motor rotation data, current data and load data; A storage module configured to store structural data and historical monitoring data of the motor body; The analysis module is electrically connected to the storage module and the acquisition module respectively. The analysis module is configured to determine the temperature threshold of the motor body according to the structural data and historical monitoring data of the motor body. The analysis module is also configured to determine the temperature data of the motor body in a preset period of time according to the real-time monitoring data, and determine the temperature state of the motor body according to the relationship between the temperature data and the temperature threshold, wherein: if the temperature data is lower than or equal to the temperature threshold, the analysis module determines that the temperature state of the motor body is a normal state; if the temperature data is higher than the temperature threshold, the analysis module determines that the temperature state of the motor body is an abnormal state; The central control module is electrically connected to the storage module and the analysis module respectively. The central control module is configured to determine the energy efficiency loss of the motor body according to the temperature data of the motor body in a preset time period when it is determined that the temperature state of the motor body is abnormal, and control the motor body according to the energy efficiency loss.

3. The multi-sensor based brushless reduction motor according to claim 2, characterized in that: The analysis module determines the temperature threshold of the motor body according to the structural data of the motor body and the historical monitoring data, including: The analysis module is further configured to obtain a heat dissipation structure of the motor body, and determine a heat dissipation coefficient of the motor body based on a relationship between the heat dissipation structure and the motor body structure; The historical temperature data in the historical monitoring data are obtained, the data abnormal value and the data normal value are determined according to the historical temperature, and the temperature threshold is determined according to the relationship between the data abnormal value and the data normal value and the heat dissipation coefficient.

4. The multi-sensor based brushless reduction motor according to claim 3, characterized in that: The analysis module determines the heat dissipation coefficient of the motor body based on the relationship between the heat dissipation structure and the motor body structure, including: The analysis module is further configured to determine heat flow data inside the motor body based on the fluid dynamics simulation model and the motor body structure, and determine the heat dissipation efficiency of each structure of the motor body based on the heat flow data and finite element analysis; The analysis module is further configured to establish a coupling model according to the relationship between the heat dissipation structure and the motor body structure, and determine the heat transfer efficiency between the motor body and the heat dissipation structure based on the finite element analysis and the coupling model: ； Among them, A is the heat transfer efficiency between the motor body and the heat dissipation structure, k is the thermal conductivity of the heat dissipation structure, j is the cross-sectional area of ​​the conduction path, △T is the temperature difference between the motor body and the heat dissipation structure, L is the length of the heat conduction path, h is the convection heat transfer coefficient, S is the heat dissipation surface area of ​​the heat dissipation structure, T is the motor body temperature, t is the ambient temperature, is the radiance, σ is the Stewart-Boltzmann constant; The analysis module is further configured to determine the heat dissipation coefficient of the motor body according to the heat dissipation efficiency of each structure of the motor body and the heat transfer efficiency between the motor body and the heat dissipation structure: B = α·f·A·b; Among them, B is the heat dissipation coefficient of the motor body, α is the weight coefficient, f is the heat dissipation efficiency of the motor body, and b is the heat dissipation coefficient of the heat dissipation structure.

5. The multi-sensor based brushless reduction motor according to claim 4, characterized in that: The analysis module determines the data abnormal value and the data normal value according to each historical temperature, and determines the temperature threshold according to the relationship between the data abnormal value and the data normal value and the heat dissipation coefficient, including: The analysis module is further configured to determine a critical temperature value for each historical temperature based on a decision tree: ； Where Q is the critical temperature value, rmaxE is the maximum independent variable, IG(E) is the Eth information gain point, VR(E) is the Eth variance reduction, d 2 E is the second-order differential of the historical temperature, dr 2 is the time square differential of the historical temperature, W1, W2 and W3 are weight coefficients, and W1, W2 and W3 are not zero; The analysis module is further configured to determine data abnormal values ​​and data normal values ​​for each historical temperature based on the key temperature values: When the historical temperature is lower than the critical temperature value, the analysis module determines that the historical temperature is a normal value of the data; When the historical temperature is higher than or equal to the critical temperature value, the analysis module determines that the historical temperature is a data outlier, and determines that the historical temperature determined as the data outlier is an abnormal temperature; The analysis module is further configured to determine a temperature threshold value according to a relationship between each abnormal temperature and a heat dissipation coefficient of the motor body: ； Among them, T thresh is the temperature threshold, T ambient is the reference temperature of the motor body when it is working, wi is the weight coefficient corresponding to the Fith abnormal temperature, and n is the total number of abnormal temperatures.

6. The multi-sensor based brushless reduction motor according to claim 2, characterized in that: When the analysis module determines the temperature data of the motor body during a preset period of time based on the real-time monitoring data, it includes: The analysis module is also configured to obtain the operation data and temperature data of the motor body in the real-time monitoring data; The analysis module is further configured to establish a temperature characteristic vector between the operation data and the temperature data of the motor body based on the historical monitoring data; The analysis module is also configured to substitute the operation data of the motor body into the temperature characteristic vector, obtain the temperature increase of the motor body in a preset period of time, and determine the temperature data of the motor body in the preset period of time based on the relationship between the temperature increase and the temperature data.

7. The multi-sensor based brushless reduction motor according to claim 6, characterized in that: When the analysis module establishes the temperature characteristic vector between the operation data and the temperature data of the motor body based on the historical monitoring data, it includes: The analysis module is configured to obtain the historical operation data and historical temperature data of each motor in the historical monitoring data, and establish a temperature correlation equation based on the historical operation data and the historical temperature data: The analysis module is further configured to obtain a distance metric between each temperature correlation equation based on the Euclidean distance, and establish a distance matrix according to the distance metric; The analysis module is also configured to iteratively cluster the temperature correlation equations according to the distance matrix, and determine the temperature feature vectors between the operating data and the temperature data in the temperature correlation equation according to the clustering results.

8. The multi-sensor based brushless reduction motor according to claim 1, characterized in that: The central control module determines the energy efficiency loss of the motor body according to the temperature data of the motor body during a preset period of time, and controls the motor body according to the energy efficiency loss, including: ； Where, ΔE loss is the energy efficiency loss, γ is the weight coefficient, Ti is the temperature data at the i-th moment, T opt is the temperature threshold, max{0,Ti−T opt } is the time when the temperature data of the preset period exceeds the temperature threshold, △t is the time interval, and N is the total number of moments; The central control module is also configured to determine an adjustment coefficient according to a relationship between the energy efficiency loss amount and a preset energy efficiency loss amount configured in the central control module, and to control the real-time operation data of the motor body according to the adjustment coefficient.

9. The multi-sensor based brushless reduction motor according to claim 8, characterized in that: The central control module determines the adjustment coefficient based on the relationship between the energy efficiency loss amount and the preset energy efficiency loss amount, including: The central control module is also configured with a first preset energy efficiency loss amount and a second preset energy efficiency loss amount. The central control module is also configured to determine an adjustment coefficient according to a relationship between the energy efficiency loss amount and the first preset energy efficiency loss amount and the second preset energy efficiency loss amount: When the energy efficiency loss is lower than or equal to the first preset energy efficiency loss, the central control module determines the adjustment coefficient as J3; When the energy efficiency loss amount is higher than the first preset energy efficiency loss amount and the energy efficiency loss amount is lower than or equal to the second preset energy efficiency loss amount, the central control module determines the adjustment coefficient as J2; When the energy efficiency loss is higher than the second preset energy efficiency loss, the central control module determines the adjustment coefficient as J1; Among them, the first preset energy efficiency loss amount is less than the second preset energy efficiency loss amount, J1<J2<J3<1.

10. A control method for a brushless reduction motor based on multiple sensors, applicable to a brushless reduction motor based on multiple sensors as claimed in any one of claims 1 to 9, characterized in that: include: Acquire real-time monitoring data, historical monitoring data and structural data of the motor, wherein the monitoring data includes temperature data, speed data, current data and load data; Determine the temperature status of the motor body based on structural data, historical monitoring data and real-time monitoring data; The energy efficiency loss of the motor is determined according to the temperature state, and the operation data of the motor body is adjusted according to the energy efficiency loss.

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