Method, system, device and medium for analyzing humanoid robot cooling capacity

Abstract

The application discloses a kind of humanoid robot cooling capacity analysis method, system, equipment and medium, wherein, the method comprises: obtaining ambient temperature, the first temperature and working current of heat-generating component, and the second temperature of cooling structure;According to the ambient temperature and the working current, the first temperature and the second temperature are temperature rise mixed calibration, and calibration temperature rise data is obtained;Actual temperature rise data of the heat-generating component is obtained, and the time window of the actual temperature rise data is same with the time window of the calibration temperature rise data;According to the calibration temperature rise data, the actual temperature rise data is cooled and analyzed, and the cooling capacity analysis result of the humanoid robot is obtained.The method can effectively improve the accuracy of the cooling capacity analyzed, and is beneficial to improve the reliability and stability of humanoid robot in working process.The application relates to the technical field of intelligent robot.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent robots, and in particular to a method, system, device and medium for analyzing the cooling capacity of a humanoid robot. Background Art

[0002] When a humanoid robot performs complex operations, engages in large amounts of motion, or works in high-temperature conditions, the driving motors, controllers, computing chips, and other heat-generating components within the humanoid robot will generate a large amount of heat. This generated heat will affect the working efficiency and service life of the heat-generating components. Therefore, the cooling capacity of the humanoid robot has become one of the key areas of concern.

[0003] At present, the existing technology mainly determines the cooling capacity of the humanoid robot directly based on the rated heat dissipation of the humanoid robot (such as the rated heat dissipation of air cooling, the rated heat dissipation of natural cooling, etc.), and dissipates the heat generated by the heat-generating components based on the determined cooling capacity. The cooling capacity determined by this method is not accurate, which makes the reliability and stability of the humanoid robot during operation unsatisfactory.

[0004] Therefore, the problems existing in the existing technology still need to be solved and optimized. Summary of the Invention

[0005] The purpose of the present invention is to solve one of the technical problems existing in the related art to at least a certain extent.

[0006] To this end, an object of an embodiment of the present invention is to provide a method, system, device and medium for analyzing the cooling capacity of a humanoid robot, wherein the method can effectively improve the accuracy of the cooling capacity obtained by analysis, which is conducive to improving the reliability and stability of the humanoid robot during operation.

[0007] In order to achieve the above technical objectives, the technical solutions adopted in the embodiments of the present application include:

[0008] In a first aspect, embodiments of the present application provide a method for analyzing the cooling capacity of a humanoid robot, which is applied to a humanoid robot including a heat-generating component and a cooling structure. The method includes:

[0009] Acquiring an ambient temperature, a first temperature and an operating current of the heat-generating component, and a second temperature of the cooling structure;

[0010] performing a temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data;

[0011] Acquiring actual temperature rise data of the heat-generating component, wherein a time window of the actual temperature rise data is the same as a time window of the calibrated temperature rise data;

[0012] A cooling analysis is performed on the actual temperature rise data according to the calibrated temperature rise data to obtain a cooling capacity analysis result of the humanoid robot.

[0013] In addition, the method according to the above embodiment of the present application may also have the following additional technical features:

[0014] Furthermore, in one embodiment of the present application, performing temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data includes:

[0015] Calculating the temperature difference between the second temperature and the first temperature to obtain intermediate temperature difference data;

[0016] Performing temperature difference correction on the intermediate temperature difference data according to the ambient temperature to obtain target temperature difference data;

[0017] According to the working current, a temperature rise mixed analysis is performed on the target temperature difference data to obtain the calibrated temperature rise data.

[0018] Furthermore, in one embodiment of the present application, performing a temperature rise hybrid analysis on the target temperature difference data according to the operating current to obtain the calibrated temperature rise data includes:

[0019] Obtain theoretical temperature rise model, mapping relationship data table and quantitative neural network;

[0020] Inputting the operating current and the target temperature difference data into the theoretical temperature rise model to perform theoretical temperature rise calculation to obtain first temperature rise data;

[0021] Performing empirical temperature rise mapping on the operating current and the target temperature difference data according to the mapping relationship data table to obtain second temperature rise data;

[0022] Inputting the operating current and the target temperature difference data into the quantized neural network to perform quantized temperature rise prediction to obtain third temperature rise data;

[0023] Adaptively fusing the first temperature rise data, the second temperature rise data, and the third temperature rise data to obtain the calibrated temperature rise data.

[0024] Furthermore, in one embodiment of the present application, the adaptively fusing the first temperature rise data, the second temperature rise data, and the third temperature rise data to obtain the calibrated temperature rise data includes:

[0025] obtaining a variance of adjacent data and a maximum variance threshold of a plurality of target sensors, each of the target sensors being any one of a temperature sensor collecting the first temperature, a temperature sensor collecting the second temperature, a temperature sensor collecting the ambient temperature, or a current sensor collecting the working current;

[0026] obtaining an adaptive weight coefficient corresponding to each of the target sensors according to the variance of adjacent data and the maximum variance threshold of all the target sensors;

[0027] performing weight distribution on the first temperature rise data, the second temperature rise data and the third temperature rise data according to all the adaptive weight coefficients to obtain a first weight of the first temperature rise data, a second weight of the second temperature rise data and a third weight of the third temperature rise data;

[0028] performing weighted calculation on the first temperature rise data, the second temperature rise data and the third temperature rise data according to the first weight, the second weight and the third weight to obtain the calibration temperature rise data.

[0029] Further, in an embodiment of the present application, the cooling analysis on the actual temperature rise data according to the calibration temperature rise data to obtain the cooling capability analysis result of the humanoid robot includes:

[0030] obtaining a cooling data table of the humanoid robot, the cooling data table recording a plurality of cooling states of the humanoid robot, each of the cooling states corresponding to a cooling efficiency coefficient range;

[0031] performing efficiency coefficient calculation on the actual temperature rise data according to the calibration temperature rise data to obtain a target efficiency coefficient;

[0032] performing matching analysis on the cooling efficiency coefficient range in the cooling data table according to the target efficiency coefficient to obtain the cooling capability analysis result of the humanoid robot.

[0033] Further, in an embodiment of the present application, the method further includes:

[0034] performing matching mapping on the cooling efficiency coefficient range in the cooling data table according to the target efficiency coefficient to obtain a target cooling state;

[0035] obtaining a plurality of adjacent cooling states, the adjacent cooling states being historical cooling states of the humanoid robot in adjacent time windows, the adjacent time windows being time windows adjacent to a time window of the target cooling state;

[0036] A cooling capacity analysis is performed on the target cooling state according to all the adjacent cooling states to obtain a cooling capacity analysis result of the humanoid robot.

[0037] Furthermore, in one embodiment of the present application, the theoretical temperature rise model is expressed as:

[0038]

[0039] Wherein, ΔT is the first temperature rise data; I is the operating current; R is the equivalent resistance value of the heating component; h is the heat transfer coefficient; A is the heat dissipation area; T s is the first temperature of the heating component; T c is the second temperature of the cooling structure; T env is the ambient temperature; m is the mass of the heat-generating component; c is the specific heat capacity of the heat-generating component; Δt is the time window.

[0040] In a second aspect, embodiments of the present application provide a system for analyzing the cooling capacity of a humanoid robot, which is applied to a humanoid robot including a heat-generating component and a cooling structure. The system includes:

[0041] a first processing unit, configured to obtain an ambient temperature, a first temperature and an operating current of the heat-generating component, and a second temperature of the cooling structure;

[0042] a second processing unit, configured to perform a temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data;

[0043] a third processing unit, configured to obtain actual temperature rise data of the heat-generating component, wherein a time window of the actual temperature rise data is the same as a time window of the calibrated temperature rise data;

[0044] The fourth processing unit is configured to perform a cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain a cooling capacity analysis result of the humanoid robot.

[0045] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0046] at least one processor;

[0047] at least one memory for storing at least one program;

[0048] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0049] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a program executable by a processor, and the program executable by the processor is used to implement the above method when executed by the processor.

[0050] The advantages and benefits of this application will be partially given in the following description, and partially become apparent from the following description, or learned through practice of this application:

[0051] The embodiments of the present application disclose a method, system, device and medium for analyzing the cooling capacity of a humanoid robot, wherein the method is applied to a humanoid robot, the humanoid robot including a heat-generating component and a cooling structure, the method comprising: obtaining an ambient temperature, a first temperature and an operating current of the heat-generating component, and a second temperature of the cooling structure; performing a temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data; obtaining actual temperature rise data of the heat-generating component, the time window of the actual temperature rise data being the same as the time window of the calibrated temperature rise data; performing a cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain a cooling capacity analysis result of the humanoid robot. The method performs a cooling analysis on the actual temperature rise data using the calibrated temperature rise data obtained through multi-angle mixed calibration, which can effectively improve the accuracy of the cooling capacity obtained through analysis, and is conducive to improving the reliability and stability of the humanoid robot during operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present application or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly expressing some embodiments of the technical solutions of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 A schematic flow chart of a method for analyzing the cooling capacity of a humanoid robot provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the structural framework of a system for analyzing the cooling capacity of a humanoid robot provided in an embodiment of the present application;

[0055] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application. For the step numbers in the following embodiments, they are provided only for the convenience of explanation and are not intended to limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0058] At present, the existing technology mainly determines the cooling capacity of the humanoid robot directly based on the rated heat dissipation of the humanoid robot (such as the rated heat dissipation of air cooling, the rated heat dissipation of natural cooling, etc.), and dissipates the heat generated by the heat-generating components based on the determined cooling capacity. The cooling capacity determined by this method is not accurate, which makes the reliability and stability of the humanoid robot during operation unsatisfactory.

[0059] In addition, some existing technologies set up a thermal management system inside the humanoid robot, and evaluate and analyze the cooling capacity of the humanoid robot through the thermal management system; however, as the functions of the humanoid robot become increasingly complex, the complexity of the thermal management system has increased dramatically, making it difficult for the thermal management system to accurately grasp the heat dissipation requirements of each heat-generating component and its actual cooling effect, and thus the cooling capacity analyzed by the thermal management system is not accurate, and it is unable to perceive the situation of insufficient cooling capacity of the humanoid robot in a timely manner, and its adaptability to environmental changes is not high.

[0060] In view of this, an embodiment of the present invention provides a method, system, device and medium for analyzing the cooling capacity of a humanoid robot, wherein the method performs multi-angle mixed calibration on the ambient temperature, working current, first temperature and second temperature, and performs cooling analysis on the actual temperature rise data based on the obtained calibration temperature rise data, so that the cooling capacity obtained by analysis has high adaptability to environmental changes, which is conducive to improving the timeliness of perceiving that the cooling capacity of the humanoid robot is not high, and improving the accuracy of the cooling capacity obtained by analysis.

[0061] Reference Figure 1 In an embodiment of the present application, a method for analyzing the cooling capacity of a humanoid robot is applied to the humanoid robot, wherein the humanoid robot includes a heat-generating component and a cooling structure. The method includes:

[0062] Step 110: Acquire the ambient temperature, the first temperature and the operating current of the heat-generating component, and the second temperature of the cooling structure;

[0063] In an embodiment of the present application, the ambient temperature may be the temperature of the environment in which the humanoid robot is located, or the ambient temperature may be the temperature inside the humanoid robot; the heat-generating components may be key components of the drive motor, controller, and computing chip inside the humanoid robot; and the cooling structure may be a component for cooling the heat-generating components, which may specifically be a natural cooling structure such as a heat sink and a temperature equalizing plate, an air-cooled cooling structure such as a heat dissipation fan, or a liquid-cooled cooling structure, etc.

[0064] It can be understood that the ambient temperature, the first temperature and the second temperature may be temperature data collected by corresponding sensors, and the operating current may be the current collected by the current sensor during the operation of the heating component.

[0065] Step 120: Perform temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data;

[0066] In an embodiment of the present application, the first temperature and the second temperature can be calibrated at multiple angles based on the ambient temperature and the operating current, and then the temperature rise obtained at each angle can be integrated to obtain the final calibrated temperature rise data.

[0067] In some embodiments, step 120 of performing temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data includes:

[0068] A1. Calculate the temperature difference between the second temperature and the first temperature to obtain intermediate temperature difference data;

[0069] A2. performing temperature difference correction on the intermediate temperature difference data according to the ambient temperature to obtain target temperature difference data;

[0070] In an embodiment of the present application, step A1 may be to calculate the difference between the first temperature and the second temperature, which difference is used to indicate the temperature difference between the heating structure and the cooling structure, and is recorded as the intermediate temperature difference data; step A2 may be to compensate and correct the intermediate temperature difference data based on the ambient temperature. There are many specific correction methods, for example, the intermediate temperature difference data and the ambient temperature can be summed or differed to obtain the target temperature difference data.

[0071] A3. Perform a temperature rise mixed analysis on the target temperature difference data according to the operating current to obtain the calibrated temperature rise data.

[0072] Furthermore, the step A3 of performing a temperature rise mixed analysis on the target temperature difference data according to the operating current to obtain the calibrated temperature rise data includes:

[0073] A31. Obtain theoretical temperature rise model, mapping relationship data table and quantitative neural network;

[0074] A32. Inputting the operating current and the target temperature difference data into the theoretical temperature rise model to perform theoretical temperature rise calculation to obtain first temperature rise data;

[0075] A33. Performing empirical temperature rise mapping on the operating current and the target temperature difference data according to the mapping relationship data table to obtain second temperature rise data;

[0076] A34. Inputting the operating current and the target temperature difference data into the quantized neural network to perform quantized temperature rise prediction to obtain third temperature rise data;

[0077] In the embodiment of the present application, the theoretical temperature rise model is used to obtain the theoretical temperature rise corresponding to the operating current and the target temperature difference data, which is recorded as the first temperature rise data. Specifically, the expression form of the theoretical temperature rise model in the embodiment of the present application is:

[0078]

[0079] Wherein, ΔT is the first temperature rise data; I is the operating current; R is the equivalent resistance value of the heating component; h is the heat transfer coefficient; A is the heat dissipation area; T s is the first temperature of the heating component; T c is the second temperature of the cooling structure; T env is the ambient temperature; m is the mass of the heat-generating component; c is the specific heat capacity of the heat-generating component; Δt is the time window.

[0080] It is understandable that the equivalent resistance value R, heat transfer coefficient h, heat dissipation area A, mass m of the heating component, specific heat capacity c of the heating component and time window Δt in the theoretical temperature rise model are parameters that can be obtained based on actual conditions, and this application will not elaborate on them here. s -T c -T env Used to characterize target temperature difference data.

[0081] It should be noted that the mapping relationship data table can be an experience mapping table based on historical working condition data of the humanoid robot, and the mapping relationship data table records the temperature difference between the heat generating component and the cooling structure of the humanoid robot, and the relationship between the working current of the heat generating component and the calibration temperature rise. Specifically, step A33 can be based on the working current and the target temperature difference data to determine the calibration temperature rise corresponding to the working current and the target temperature difference data from the mapping relationship data table, and the calibration temperature rise is determined as the second temperature rise data obtained from the experience angle.

[0082] It is worth mentioning that the quantization neural network can be a long short-term memory (LSTM) network trained based on historical working condition data of the humanoid robot. Step A34 can specifically be inputting the working current and the target temperature difference data into the quantization neural network, and predicting the predicted temperature rise corresponding to the working current and the target temperature difference data through the quantization neural network, which is recorded as the third temperature rise data.

[0083] A35, adaptively fusing the first temperature rise data, the second temperature rise data and the third temperature rise data to obtain the calibration temperature rise data.

[0084] Further, the step A35, adaptively fusing the first temperature rise data, the second temperature rise data and the third temperature rise data to obtain the calibration temperature rise data, comprises:

[0085] A351, obtaining the adjacent data variance of a plurality of target sensors and the maximum variance threshold, each of the target sensors being any one of a temperature sensor collecting the first temperature, a temperature sensor collecting the second temperature, a temperature sensor collecting the ambient temperature or a current sensor collecting the working current;

[0086] A352, obtaining an adaptive weight coefficient corresponding to each of the target sensors according to the adjacent data variance of all the target sensors and the maximum variance threshold;

[0087] A353, according to all the adaptive weight coefficients, performing weight distribution on the first temperature rise data, the second temperature rise data and the third temperature rise data to obtain a first weight of the first temperature rise data, a second weight of the second temperature rise data and a third weight of the third temperature rise data;

[0088] A354, according to the first weight, the second weight and the third weight, performing weighted calculation on the first temperature rise data, the second temperature rise data and the third temperature rise data to obtain the calibration temperature rise data.

[0089] In the embodiments of the present application, the adjacent data variance of the target sensor can be the variance of the data collected by the target sensor in the recent period. Specifically, if the target sensor is a temperature sensor for collecting the first temperature, the adjacent data variance of the target sensor can be the variance of all the first temperatures in the recent period, for example, the variance of the first temperatures in the last several time windows, and the maximum variance threshold can be the variance threshold corresponding to the first temperature. For other target sensors, the content can be simply deduced by analogy with the foregoing content of the target sensor being the temperature sensor for collecting the first temperature, which will not be described herein again.

[0090] It can be understood that for a target sensor, step A352 can be to determine the adaptive weight coefficient of the target sensor based on the adjacent data variance of the target sensor and the maximum variance threshold. The adaptive weight coefficient of the target sensor can dynamically change with the change of the current time window, and the adaptive weight coefficients of the remaining target sensors are the same.

[0091]

[0092] wherein w i is the adaptive weight coefficient of the i-th target sensor; σ i is the adjacent data variance of the i-th target sensor; σ max is the maximum variance threshold of the i-th target sensor.

[0093] It should be noted that step A353 can be to distribute a plurality of adaptive weight coefficients among all the adaptive weight coefficients to the first temperature rise data, the second temperature rise data and the third temperature rise data. The specific distribution manner can be to distribute the adaptive weight coefficients to the corresponding temperature rise data based on a fixed distribution relationship, for example, the fixed distribution relationship records that the adaptive weight coefficient of the temperature sensor for collecting the first temperature is distributed to the first temperature rise data, and the second temperature rise data and the third temperature rise data are similar to the foregoing first temperature rise data, which can be simply deduced by analogy. In addition, the specific distribution manner can also have a plurality of manners, for example, the size relationship of each adaptive weight coefficient can be judged, and a plurality of larger adaptive weight coefficients are sequentially distributed to the first temperature rise data, the second temperature rise data and the third temperature rise data, which will not be described herein again.

[0094] It is worth mentioning that step A354 can be to perform weighted calculation on the corresponding first temperature rise data, second temperature rise data and third temperature rise data based on the first weight, second weight and third weight. The specific weighted calculation manner can be weighted summation, weighted average or the like, so as to obtain the final temperature rise data, which is the calibration temperature rise data of the heat generating component in a certain time window.

[0095] Step 130: Acquire actual temperature rise data of the heat-generating component, where the time window of the actual temperature rise data is the same as the time window of the calibrated temperature rise data;

[0096] In an embodiment of the present application, actual temperature rise data that is the same as the time window of the calibrated temperature rise data can be obtained based on the time window of the calibrated temperature rise data. Specifically, the starting temperature of the heating component can be obtained based on the starting time point of the calibrated temperature rise data time window, and the ending temperature of the heating component can be obtained based on the ending time point of the calibrated temperature rise data time window. The actual temperature rise data can be determined based on the difference between the ending temperature and the starting temperature.

[0097] It can be understood that in another feasible embodiment, the temperature of the heating component at each time point can be obtained based on each time point within the time window of the calibrated temperature rise data, and the actual temperature rise data can be constructed based on the difference between the temperature at each time point and the starting temperature at the starting time point.

[0098] Step 140: Perform cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain a cooling capacity analysis result of the humanoid robot.

[0099] In an embodiment of the present application, the calibrated temperature rise data and the actual temperature rise data may include the difference (i.e., temperature rise) at a plurality of time points within a time window. For a certain time point within the time window, step 140 may be to perform a ratio analysis of the temperature rise at that time point in the calibrated temperature rise data with the temperature rise at that time point in the actual temperature rise data, thereby obtaining the cooling state at that time point within the time window. The same process is repeated for the remaining time points within the time window, and the cooling capacity analysis result of the humanoid robot is constructed based on the cooling states at all time points.

[0100] In some embodiments, step 140 of performing a cooling analysis on the actual temperature rise data based on the calibrated temperature rise data to obtain a cooling capacity analysis result of the humanoid robot includes:

[0101] B1. Obtain a cooling data table of the humanoid robot, wherein the cooling data table records a plurality of cooling states of the humanoid robot, each cooling state corresponding to a cooling efficiency coefficient range;

[0102] B2. Calculating the efficiency coefficient of the actual temperature rise data based on the calibrated temperature rise data to obtain a target efficiency coefficient;

[0103] B3. According to the target efficiency coefficient, a matching analysis is performed on the cooling efficiency coefficient range in the cooling data table to obtain a cooling capacity analysis result of the humanoid robot.

[0104] In the embodiment of the present application, for the calibrated temperature rise data at a certain time point, step B2 may be based on the temperature rise, and a ratio calculation is performed on the actual temperature rise at the corresponding time point to obtain a target efficiency coefficient at the time point. The target efficiency coefficient is used to indicate the cooling efficiency of the humanoid robot at the time point, which can be expressed as:

[0105]

[0106] Where η is the target efficiency coefficient; ΔT c is the calibration temperature rise data; ΔT a The actual temperature rise data.

[0107] It is understood that after obtaining the target efficiency coefficients for all time points within the time window, the cooling capacity analysis results of the humanoid robot can be directly constructed based on all target efficiency coefficients. For example, based on each target efficiency coefficient, all cooling efficiency coefficient ranges in the cooling data table can be matched to obtain the cooling state corresponding to each target efficiency coefficient at the time point. Then, by counting the number of different cooling state types, the type with the largest number of cooling states can be determined as the cooling capacity analysis result of the humanoid robot.

[0108] It should be noted that in actual applications, mathematical statistics can also be performed on all target efficiency coefficients. For example, by performing average value statistical analysis, cluster analysis, etc. on all cooling states, the final mathematical statistical value can be obtained, and the final mathematical statistical value can be matched with the cooling efficiency coefficient range in the cooling data table. The matched cooling state is determined as the cooling capacity analysis result of the humanoid robot.

[0109] It is worth mentioning that the embodiment of the present application takes the cooling data table recording the three cooling states of the humanoid robot as an example. The specific cooling states can be divided into normal state, warning state and fault state. Among them, the cooling efficiency coefficient range corresponding to the normal state is η≥80%, the cooling efficiency coefficient range corresponding to the warning state can be 50%≤η<80%, and the cooling efficiency coefficient range corresponding to the fault state can be η<50%. The examples of this application are for illustration only and do not limit this application.

[0110] In some embodiments, the method further comprises:

[0111] C1. Matching and mapping the cooling efficiency coefficient range in the cooling data table according to the target efficiency coefficient to obtain a target cooling state;

[0112] C2. Acquire several adjacent cooling states, where the adjacent cooling states are historical cooling states of the humanoid robot in adjacent time windows, where the adjacent time windows are time windows adjacent to the time window of the target cooling state;

[0113] C3. Perform a cooling capacity analysis on the target cooling state based on all the adjacent cooling states to obtain a cooling capacity analysis result of the humanoid robot.

[0114] In this embodiment of the present application, step C1 is similar to step B3 and can be simply deduced by analogy. Step C2 may first obtain several time windows adjacent to the time window of the target cooling state and obtain the target efficiency coefficient within each adjacent time window; then, based on the target efficiency coefficient for each adjacent time window and the cooling efficiency coefficient range in the cooling data table, determine the adjacent cooling state for each adjacent time window.

[0115] It can be understood that step C3 can be based on the adjacent cooling states of all adjacent time windows to analyze the target cooling state of the current time window. If there are multiple cooling states in warning state or fault state in the continuous period from the adjacent time window to the current time window, then a cooling capacity analysis result can be generated to indicate that the cooling capacity of the humanoid robot is insufficient; or, if there are not multiple cooling states in warning state or fault state, then a cooling capacity analysis result can be generated to indicate that the cooling capacity of the humanoid robot is normal.

[0116] A system for analyzing the cooling capacity of a humanoid robot according to an embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0117] Reference Figure 2 In an embodiment of the present application, a system for analyzing the cooling capacity of a humanoid robot is proposed. The system is applied to a humanoid robot including a heat-generating component and a cooling structure. The system includes:

[0118] A first processing unit 101 is configured to obtain an ambient temperature, a first temperature and an operating current of the heat-generating component, and a second temperature of the cooling structure;

[0119] A second processing unit 102 is configured to perform a temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data;

[0120] The third processing unit 103 is configured to obtain actual temperature rise data of the heat-generating component, where the time window of the actual temperature rise data is the same as the time window of the calibrated temperature rise data;

[0121] The fourth processing unit 104 is configured to perform a cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain a cooling capacity analysis result of the humanoid robot.

[0122] Reference Figure 3 , an embodiment of the present application further provides an electronic device, including:

[0123] at least one processor 201;

[0124] At least one memory 202, configured to store at least one program;

[0125] When the at least one program is executed by the at least one processor 201 , the at least one processor 201 implements the above method embodiment.

[0126] Similarly, it can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0127] An embodiment of the present application further provides a computer-readable storage medium, in which a program executable by the processor 201 is stored. The program executable by the processor 201 is used to implement the above-mentioned method embodiment when executed by the processor 201.

[0128] Similarly, the contents of the above method embodiments are applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0129] In some optional embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, the two boxes shown in succession may actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flow chart of the present application are provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logic flows presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0130] Furthermore, although the present application is described in the context of functional modules, it is understood that one or more of the functions and / or features can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary to an understanding of the present application. Rather, the actual implementation is within the routine skill of engineers familiar with the property, function and internal relationships of the various functional modules disclosed herein. Accordingly, the present application is not limited to the specific details of the functional modules described herein. Rather, it is understood that one of ordinary skill in the art is able to practice the application as claimed without undue experimentation having regard to the property, function and internal relationships of the various functional modules disclosed herein. It is also understood that the specific concepts disclosed are merely illustrative and that the scope of the present application is determined by the appended claims and their equivalents.

[0131] If the functions are implemented in software, the functions can be stored in or implemented as one or more computer program products. The computer program product can be stored in a computer readable medium, which can include, but is not limited to, RAM, ROM, electrically programmable read only memory (EPROM or EEPROM), flash memory, or a tangible computer diskette, such as a compact disc (CD) or DVD, etc. When the computer program product is implemented as one or more computer program products, the computer program product can be executed by one or more processors.

[0132] The logic and / or steps represented in the flowcharts and / or otherwise described herein, for example, can be embodied in non-transitory computer-readable media, which can be executed by an instruction execution system, apparatus, or device such as a computer-based system, processor, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0133] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0134] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), and / or the like.

[0135] In the above description of the present specification, reference to the description of the terms "one embodiment / one example", "another embodiment / another example" or "certain embodiments / certain examples" and the like means that a particular feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. The illustrative representations of the above terms in the present specification do not necessarily refer to the same embodiment or example. Also, the described particular features, structures, materials or characteristics can be combined in any appropriate manner in one or more embodiments or examples.

[0136] Although embodiments of the present application have been shown and described, it would be recognized by those of ordinary skill in the art that various changes, modifications, alternatives, and variations can be made thereto without departing from the spirit and scope of the application, which are defined by the claims and their equivalents.

[0137] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the embodiments, and those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present application, and these equivalent modifications or replacements are included in the scope defined by the claims of the present application.

Claims

1. A method for analyzing the cooling capacity of a humanoid robot, characterized in that: Applied to a humanoid robot, the humanoid robot including a heat-generating component and a cooling structure, the method comprising: Acquiring an ambient temperature, a first temperature and an operating current of the heat-generating component, and a second temperature of the cooling structure; performing a temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data; Acquiring actual temperature rise data of the heat-generating component, wherein a time window of the actual temperature rise data is the same as a time window of the calibrated temperature rise data; performing a cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain a cooling capacity analysis result of the humanoid robot; The performing temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data includes: Calculating the temperature difference between the second temperature and the first temperature to obtain intermediate temperature difference data; Performing temperature difference correction on the intermediate temperature difference data according to the ambient temperature to obtain target temperature difference data; Performing a temperature rise mixed analysis on the target temperature difference data according to the working current to obtain the calibrated temperature rise data; The step of performing a temperature rise mixed analysis on the target temperature difference data according to the operating current to obtain the calibrated temperature rise data includes: Obtain theoretical temperature rise model, mapping relationship data table and quantitative neural network; Inputting the operating current and the target temperature difference data into the theoretical temperature rise model to perform theoretical temperature rise calculation to obtain first temperature rise data; Performing empirical temperature rise mapping on the operating current and the target temperature difference data according to the mapping relationship data table to obtain second temperature rise data; Inputting the operating current and the target temperature difference data into the quantized neural network to perform quantized temperature rise prediction to obtain third temperature rise data; Adaptively fusing the first temperature rise data, the second temperature rise data, and the third temperature rise data to obtain the calibrated temperature rise data.

2. The method according to claim 1, characterized in that The adaptively fusing the first temperature rise data, the second temperature rise data, and the third temperature rise data to obtain the calibrated temperature rise data includes: Obtaining variances and maximum variance thresholds of neighboring data of a plurality of target sensors, where each target sensor is any one of a temperature sensor for collecting the first temperature, a temperature sensor for collecting the second temperature, a temperature sensor for collecting the ambient temperature, or a current sensor for collecting the operating current; Obtaining an adaptive weight coefficient corresponding to each target sensor according to the neighborhood data variance and the maximum variance threshold of all the target sensors; performing weight allocation on the first temperature rise data, the second temperature rise data, and the third temperature rise data according to all the adaptive weight coefficients to obtain a first weight of the first temperature rise data, a second weight of the second temperature rise data, and a third weight of the third temperature rise data; The first temperature rise data, the second temperature rise data, and the third temperature rise data are weightedly calculated according to the first weight, the second weight, and the third weight to obtain the calibrated temperature rise data.

3. The method according to claim 1, characterized in that The step of performing a cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain a cooling capacity analysis result of the humanoid robot includes: Obtaining a cooling data table of the humanoid robot, wherein the cooling data table records a plurality of cooling states of the humanoid robot, each cooling state corresponding to a cooling efficiency coefficient range; Calculating the efficiency coefficient of the actual temperature rise data according to the calibrated temperature rise data to obtain a target efficiency coefficient; According to the target efficiency coefficient, a matching analysis is performed on the cooling efficiency coefficient range in the cooling data table to obtain a cooling capacity analysis result of the humanoid robot.

4. The method according to claim 3, characterized in that The method further comprises: According to the target efficiency coefficient, matching and mapping the cooling efficiency coefficient range in the cooling data table to obtain a target cooling state; Acquire a plurality of adjacent cooling states, where the adjacent cooling states are historical cooling states of the humanoid robot in adjacent time windows, where the adjacent time windows are time windows adjacent to a time window of the target cooling state; A cooling capacity analysis is performed on the target cooling state according to all the adjacent cooling states to obtain a cooling capacity analysis result of the humanoid robot.

5. The method according to claim 1, wherein The theoretical temperature rise model is expressed as: in, is the first temperature rise data; is the working current; is the equivalent resistance value of the heating component; is the heat transfer coefficient; is the heat dissipation area; is the first temperature of the heating component; is a second temperature of the cooling structure; is the ambient temperature; is the mass of the heating component; is the specific heat capacity of the heat generating component; is the time window.

6. A system for analyzing the cooling capacity of a humanoid robot, characterized in that: Applied to a humanoid robot, the humanoid robot includes a heat-generating component and a cooling structure, and the system includes: a first processing unit, configured to obtain an ambient temperature, a first temperature and an operating current of the heat-generating component, and a second temperature of the cooling structure; a second processing unit, configured to perform a temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data; a third processing unit, configured to obtain actual temperature rise data of the heat-generating component, wherein a time window of the actual temperature rise data is the same as a time window of the calibrated temperature rise data; a fourth processing unit, configured to perform a cooling analysis on the actual temperature rise data according to the calibrated temperature rise data, to obtain a cooling capacity analysis result of the humanoid robot; The performing temperature rise mixed calibration on the first temperature and the second temperature according to the ambient temperature and the operating current to obtain calibrated temperature rise data includes: Calculating the temperature difference between the second temperature and the first temperature to obtain intermediate temperature difference data; Performing temperature difference correction on the intermediate temperature difference data according to the ambient temperature to obtain target temperature difference data; Performing a temperature rise mixed analysis on the target temperature difference data according to the working current to obtain the calibrated temperature rise data; The step of performing a temperature rise mixed analysis on the target temperature difference data according to the operating current to obtain the calibrated temperature rise data includes: Obtain theoretical temperature rise model, mapping relationship data table and quantitative neural network; Inputting the operating current and the target temperature difference data into the theoretical temperature rise model to perform theoretical temperature rise calculation to obtain first temperature rise data; Performing empirical temperature rise mapping on the operating current and the target temperature difference data according to the mapping relationship data table to obtain second temperature rise data; Inputting the operating current and the target temperature difference data into the quantized neural network to perform quantized temperature rise prediction to obtain third temperature rise data; Adaptively fusing the first temperature rise data, the second temperature rise data, and the third temperature rise data to obtain the calibrated temperature rise data.

7. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 5 when executed by the processor.

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