Analysis method, system and equipment for cooling capacity of humanoid robot and medium

Abstract

The invention discloses a humanoid robot cooling capacity analysis method, system and device and a medium, and the method comprises the steps: obtaining an environment temperature, a first temperature and a working current of a heating part, and a second temperature of a cooling structure; performing temperature rise hybrid calibration on the first temperature and the second temperature according to the environment temperature and the working current to obtain calibrated temperature rise data; acquiring actual temperature rise data of the heating component, wherein the time window of the actual temperature rise data is the same as the time window of the calibrated temperature rise data; and according to the calibrated temperature rise data, cooling analysis is carried out on the actual temperature rise data, and a cooling capacity analysis result of the humanoid robot is obtained. According to the method, the accuracy of the cooling capacity obtained through analysis can be effectively improved, and the reliability and stability of the humanoid robot in the working process can be improved. The invention relates to the technical field of intelligent robots.

Description

Technical Field

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

[0002] When a humanoid robot performs complex operations, has a large amount of movement, or works in a high-temperature working condition, heat-generating components such as drive motors, controllers, and computing chips inside the humanoid robot will generate a large amount of heat, and the generated heat will affect the working efficiency, working life, etc. of the heat-generating components. Therefore, the cooling capacity of the humanoid robot has become one of the key concerns.

[0003] Currently, 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-cooled heat dissipation, natural cooling, etc.), and dissipates the heat generated by the heat-generating components based on the determined cooling capacity. The accuracy of the cooling capacity determined by this method is not high, making 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 urgently. Summary of the Invention

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

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

[0007] To achieve the above technical purpose, the technical solutions adopted in the embodiments of the present application include:

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

[0009] Obtain the ambient temperature, the first temperature and working current of the heat-generating component, and the second temperature of the cooling structure;

[0010] Perform temperature rise mixing calibration on the first temperature and the second temperature according to the ambient temperature and the working current to obtain calibrated temperature rise data;

[0011] Obtain the actual temperature rise data of the heat-generating component, and the time window of the actual temperature rise data is the same as that of the calibrated temperature rise data;

[0012] Based on the calibrated temperature rise data, perform cooling analysis on the actual temperature rise data to obtain the cooling capacity analysis result of the humanoid robot.

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

[0014] Further, in an embodiment of the present application, the step of performing temperature rise mixing calibration on the first temperature and the second temperature according to the environmental temperature and the working current to obtain calibrated temperature rise data includes:

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

[0016] Perform temperature difference correction on the intermediate temperature difference data according to the environmental temperature to obtain target temperature difference data;

[0017] Perform temperature rise mixing analysis on the target temperature difference data according to the working current to obtain the calibrated temperature rise data.

[0018] Further, in an embodiment of the present application, the step of performing temperature rise mixing analysis on the target temperature difference data according to the working current to obtain the calibrated temperature rise data includes:

[0019] Obtain a theoretical temperature rise model, a mapping relation data table, and a quantization neural network;

[0020] Input the working current and the target temperature difference data into the theoretical temperature rise model for theoretical temperature rise calculation to obtain first temperature rise data;

[0021] Perform empirical temperature rise mapping on the working current and the target temperature difference data according to the mapping relation data table to obtain second temperature rise data;

[0022] Input the working current and the target temperature difference data into the quantization neural network for quantization temperature rise prediction to obtain third temperature rise data;

[0023] Perform adaptive fusion on the first temperature rise data, the second temperature rise data, and the third temperature rise data to obtain the calibrated temperature rise data.

[0024] Further, in an embodiment of the present application, the step of performing adaptive fusion on 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] Obtain the variance of adjacent data and the maximum variance threshold of a number of target sensors, where each of the target sensors is any one of the temperature sensor for collecting the first temperature, the temperature sensor for collecting the second temperature, the temperature sensor for collecting the ambient temperature, or the current sensor for collecting the working current;

[0026] According to the variance of adjacent data and the maximum variance threshold of all the target sensors, obtain the adaptive weight coefficients corresponding to each of the target sensors;

[0027] According to all the adaptive weight coefficients, perform weight allocation on the first temperature rise data, the second temperature rise data, and the third temperature rise data to obtain the first weight of the first temperature rise data, the second weight of the second temperature rise data, and the third weight of the third temperature rise data;

[0028] According to the first weight, the second weight, and the third weight, perform weighted calculation on the first temperature rise data, the second temperature rise data, and the third temperature rise data to obtain the calibrated temperature rise data.

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

[0030] Obtain the cooling data table of the humanoid robot, where the cooling data table records a number of cooling states of the humanoid robot, and each cooling state corresponds to a range of cooling efficiency coefficients;

[0031] According to the calibrated temperature rise data, perform efficiency coefficient calculation on the actual temperature rise data to obtain the target efficiency coefficient;

[0032] According to the target efficiency coefficient, perform matching analysis on the range of cooling efficiency coefficients in the cooling data table to obtain the cooling capacity analysis result of the humanoid robot.

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

[0034] According to the target efficiency coefficient, perform matching mapping on the range of cooling efficiency coefficients in the cooling data table to obtain the target cooling state;

[0035] Obtain a number of adjacent cooling states, where the adjacent cooling states are the historical cooling states of the humanoid robot in an adjacent time window, and the adjacent time window is a time window adjacent to the time window of the target cooling state;

[0036] According to all the adjacent cooling states, perform a cooling capacity analysis on the target cooling state to obtain the cooling capacity analysis result of the humanoid robot.

[0037] Further, in an embodiment of the present application, the expression form of the theoretical temperature rise model is:

[0038]

[0039] where ΔT is the first temperature rise data; I is the working 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 heating component; c is the specific heat capacity of the heating component; Δt is the time window.

[0040] In a second aspect, an embodiment of the present application provides an analysis system for the cooling capacity of a humanoid robot, which is applied to a humanoid robot. The humanoid robot includes a heating component and a cooling structure. The system includes:

[0041] A first processing unit for obtaining the ambient temperature, the first temperature and the working current of the heating component, and the second temperature of the cooling structure;

[0042] A second processing unit for performing a temperature rise mixing calibration on the first temperature and the second temperature according to the ambient temperature and the working current to obtain calibrated temperature rise data;

[0043] A third processing unit for obtaining the actual temperature rise data of the heating component, and the time window of the actual temperature rise data is the same as that of the calibrated temperature rise data;

[0044] A fourth processing unit for performing a cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain the 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] Fourthly, an embodiment of the present application further provides a computer-readable storage medium, which stores a program executable by a processor. The program executable by the processor is used to implement the above method when executed by the processor.

[0050] The advantages and beneficial effects of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application:

[0051] An analysis method, system, device and medium for the cooling capacity of a humanoid robot disclosed in an embodiment of the present application. The method is applied to a humanoid robot, and the humanoid robot includes a heat-generating component and a cooling structure. The method includes: obtaining the ambient temperature, the first temperature and working current of the heat-generating component, and the second temperature of the cooling structure; performing temperature rise mixing calibration on the first temperature and the second temperature according to the ambient temperature and the working current to obtain calibrated temperature rise data; obtaining the actual temperature rise data of the heat-generating component, and the time window of the actual temperature rise data is the same as that of the calibrated temperature rise data; performing cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain the cooling capacity analysis result of the humanoid robot. By performing cooling analysis on the actual temperature rise data with the calibrated temperature rise data obtained through multi-angle mixing calibration, the method can effectively improve the accuracy of the analyzed cooling capacity, which is beneficial to improving the reliability and stability of the humanoid robot during operation. Description of the Drawings

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

[0053] Figure 1 It is a schematic flowchart of an analysis method for the cooling capacity of a humanoid robot provided by an embodiment of the present application;

[0054] Figure 2 It is a schematic structural framework diagram of an analysis system for the cooling capacity of a humanoid robot provided by an embodiment of the present application;

[0055] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0056] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application and should not be construed as a limitation of the present application. For the step numbers in the following embodiments, they are only set for the convenience of explanation and illustration, and no limitation is imposed on the order between the steps. The execution order of each step 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 of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0058] Currently, the prior art mainly determines the cooling capacity of a humanoid robot directly based on the rated heat dissipation of the humanoid robot (such as the rated heat dissipation of air cooling, natural cooling, etc.), and dissipates the heat generated by the heat-generating components based on the determined cooling capacity. The accuracy of the cooling capacity determined by this method is not high, resulting in unsatisfactory reliability and stability of the humanoid robot during operation.

[0059] In addition, there is some prior art that sets up a thermal management system inside the humanoid robot and evaluates and analyzes 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 increases sharply, 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. As a result, the accuracy of the cooling capacity analyzed by the thermal management system is not high, and it is unable to promptly detect the situation where the cooling capacity of the humanoid robot is insufficient, and its adaptability to environmental changes is not high.

[0060] In view of this, embodiments of the present invention provide a method, system, device, and medium for analyzing the cooling capacity of a humanoid robot. Among them, this 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 calibrated temperature rise data obtained, so that the analyzed cooling capacity has a high adaptability to environmental changes, which is beneficial to improving the timeliness of detecting that the cooling capacity of the humanoid robot is not high, and improving the accuracy of the analyzed cooling capacity.

[0061] Referring to Figure 1 , in the embodiments of the present application, a method for analyzing the cooling capacity of a humanoid robot, a control decision method for a humanoid robot, is applied to a humanoid robot. The humanoid robot includes heat-generating components and a cooling structure. The method includes:

[0062] Step 110: Obtain the ambient temperature, the first temperature and the working current of the heating component, and the second temperature of the cooling structure;

[0063] In the embodiments of the present application, the ambient temperature may be the temperature of the environment where the humanoid robot is located, or the ambient temperature may also be the temperature inside the humanoid robot; the heating component may be key components such as a driving motor, a controller, and a computing chip inside the humanoid robot; and the cooling structure may be a component for cooling the heating component, which may specifically be a natural cooling structure such as a heat sink or a vapor chamber, an air-cooling structure such as a cooling fan, or a liquid-cooling structure.

[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 working current may be the current collected by a current sensor during the operation of the heating component.

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

[0066] In the embodiments of the present application, temperature rise calibration of the first temperature and the second temperature can be performed from multiple angles based on the ambient temperature and the working current, and then the temperature rises obtained at each angle are integrated to obtain the final calibrated temperature rise data.

[0067] In some embodiments, step 120: Perform temperature rise mixing calibration on the first temperature and the second temperature according to the ambient temperature and the working current to obtain calibrated temperature rise data, includes:

[0068] A1: Calculate the temperature difference between the second temperature based on the first temperature to obtain intermediate temperature difference data;

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

[0070] In the embodiments of the present application, step A1 may be to calculate the difference between the first temperature and the second temperature, and this difference is used to indicate the temperature difference between the heating structure and the cooling structure, and is denoted as intermediate temperature difference data; step A2 may be to perform compensation correction on the intermediate temperature difference data based on the ambient temperature. There are various specific correction methods. For example, the intermediate temperature difference data and the ambient temperature can be added or subtracted to obtain the target temperature difference data.

[0071] A3: Perform temperature rise mixing analysis on the target temperature difference data according to the working current to obtain the calibrated temperature rise data.

[0072] Further, step A3, performing temperature rise mixing analysis on the target temperature difference data according to the working current to obtain the calibrated temperature rise data, includes:

[0073] A31. Obtaining a theoretical temperature rise model, a mapping relation data table, and a quantization neural network;

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

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

[0076] A34. Inputting the working current and the target temperature difference data into the quantization neural network for quantization 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 working current and the target temperature difference data, denoted 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] where, ΔT is the first temperature rise data; I is the working 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 heating component; c is the specific heat capacity of the heating component; Δt is the time window.

[0080] It can be understood that for 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, they are parameters that can be obtained based on the actual situation, and the present application will not elaborate on this here. Also, T s -T c -T env is used to characterize the target temperature difference data.

[0081] It should be noted that the mapping relationship data table can be an empirical mapping table based on the historical working condition data of the humanoid robot. This mapping relationship data table records the temperature difference between the heat-generating components and the cooling structure of the humanoid robot, as well as the relationship between the working current of the heat-generating components and the calibrated temperature rise. Specifically, step A33 can be to determine the calibrated temperature rise corresponding to the working current and the target temperature difference data from the mapping relationship data table based on the working current and the target temperature difference data, and determine this calibrated temperature rise as the second temperature rise data obtained from the empirical perspective.

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

[0083] A35. Perform adaptive fusion on the first temperature rise data, the second temperature rise data, and the third temperature rise data to obtain the calibrated temperature rise data.

[0084] Furthermore, the step A35, performing adaptive fusion on the first temperature rise data, the second temperature rise data, and the third temperature rise data to obtain the calibrated temperature rise data, includes:

[0085] A351. Obtain the adjacent data variance and the maximum variance threshold of several target sensors. Each of the target sensors is any one of the temperature sensor for collecting the first temperature, the temperature sensor for collecting the second temperature, the temperature sensor for collecting the ambient temperature, or the current sensor for collecting the working current.

[0086] A352. Obtain the adaptive weight coefficient corresponding to each target sensor according to the adjacent data variance and the maximum variance threshold of all the target sensors.

[0087] A353. Perform 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 the first weight of the first temperature rise data, the second weight of the second temperature rise data, and the third weight of the third temperature rise data.

[0088] A354. Perform 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 calibrated temperature rise data.

[0089] In the embodiments of the present application, the variance of adjacent data may be the variance of the data recently collected by the target sensor. Specifically, if the target sensor is a temperature sensor that collects the first temperature, its variance of adjacent data may be the variance of all the first temperatures in the recent period. For example, it may be the variance of the first temperatures in several recent time windows, and the maximum variance threshold is the variance threshold corresponding to the first temperature. For other sensors as the target sensor, it can be simply analogized by referring to the content where the target sensor is a temperature sensor that collects the first temperature, and the present application will not elaborate here.

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

[0091]

[0092] where, w i is the adaptive weight coefficient of the i-th target sensor; σ i is the variance of adjacent data 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 may be to allocate several of all the adaptive weight coefficients to the first temperature rise data, the second temperature rise data, and the third temperature rise data. The specific allocation method may be to allocate the adaptive weight coefficients to the corresponding temperature rise data based on a fixed allocation relationship. For example, the fixed allocation relationship records that the adaptive weight coefficient of the temperature sensor that collects the first temperature is allocated to the first temperature rise data. The second temperature rise data and the third temperature rise data are similar to the aforementioned first temperature rise data and can be simply analogized. Moreover, there can be various specific allocation methods. For example, it can be determined the magnitude relationship of each adaptive weight coefficient, and the larger several adaptive weight coefficients are sequentially allocated to the first temperature rise data, the second temperature rise data, and the third temperature rise data. The present application will not elaborate here.

[0094] It is worth mentioning that step A354 may 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, the second weight, and the third weight. The specific weighted calculation method may be methods such as weighted summation and weighted average, so as to obtain the final temperature rise data. The final temperature rise data is the calibrated temperature rise data of the heating component at a certain time window.

[0095] Step 130: Obtain the actual temperature rise data of the heating component, where the time window of the actual temperature rise data is the same as that of the calibrated temperature rise data;

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

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

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

[0099] In the embodiment of the present application, the calibrated temperature rise data and the actual temperature rise data may include the differences (i.e., temperature rises) at several time points within the time window. For a certain time point within the time window, step 140 may be to perform ratio analysis on the temperature rise at this time point in the calibrated temperature rise data and the temperature rise at this time point in the actual temperature rise data, so as to obtain the cooling state at this time point within the time window. The same applies to the remaining time points within the time window. 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, performing cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain the cooling capacity analysis result of the humanoid robot, includes:

[0101] B1: Obtain the cooling data table of the humanoid robot, where the cooling data table records several cooling states of the humanoid robot, and each cooling state corresponds to a range of cooling efficiency coefficients;

[0102] B2: Calculate the target efficiency coefficient according to the calibrated temperature rise data for the actual temperature rise data;

[0103] B3: Perform matching analysis on the range of cooling efficiency coefficients in the cooling data table according to the target efficiency coefficient to obtain the cooling capacity analysis result of the humanoid robot.

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

[0105]

[0106] where η is the target efficiency coefficient; ΔT c is the calibrated temperature rise data; ΔT a is the actual temperature rise data.

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

[0108] It should be noted that in practical applications, mathematical statistics can also be performed on all the target efficiency coefficients. For example, by performing average statistical analysis, clustering analysis, etc. on all the cooling states, the final mathematical statistical value can be obtained, and based on this final mathematical statistical value and 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 in the embodiments of the present application, taking the cooling data table recording three cooling states of the humanoid robot as an example, the specific cooling states can be specifically divided into a normal state, a warning state, and a 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 the present application are only for illustration and do not limit the present application.

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

[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. Obtain a plurality of adjacent cooling states, where the adjacent cooling states are the historical cooling states of the humanoid robot in an adjacent time window, and the adjacent time window is a time window adjacent to the time window of the target cooling state;

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

[0114] In the embodiments of the present application, step C1 is similar to the foregoing step B3 and can be simply analogized. Step C2 can first be to obtain a plurality of 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 of each adjacent time window and the cooling efficiency coefficient range in the cooling data table, determine the adjacent cooling states corresponding to each adjacent time window.

[0115] It can be understood that step C3 can be to analyze the target cooling state of the current time window based on the adjacent cooling states of all adjacent time windows. If there are multiple cooling states in the continuous period from the adjacent time window to the current time window that are in the warning state or the failure state, a cooling capacity analysis result indicating insufficient cooling capacity of the humanoid robot can be generated; or, if there are no multiple cooling states in the warning state or the failure state, a cooling capacity analysis result indicating normal cooling capacity of the humanoid robot can be generated.

[0116] Next, a system for analyzing the cooling capacity of a humanoid robot according to an embodiment of the present application will be described in detail with reference to the accompanying drawings.

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

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

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

[0120] A third processing unit 103, configured to obtain the 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, so as to obtain a cooling capacity analysis result of the humanoid robot.

[0122] Referring to 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 content in the above method embodiment is applicable to the device embodiment of the present application. The functions specifically implemented by the device embodiment of the present application are the same as those in the above method embodiment, and the beneficial effects achieved are also the same as those in the above method embodiment.

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

[0128] Similarly, the content in the above method embodiment is applicable to the computer-readable storage medium embodiment of the present application. The functions specifically implemented by the computer-readable storage medium embodiment of the present application are the same as those in the above method embodiment, and the beneficial effects achieved are also the same as those in the above method embodiment.

[0129] In some alternative 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, two consecutive blocks shown may actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present application are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are expected, in which the order of various operations is changed and the sub-operations described as part of a larger operation are executed independently.

[0130] In addition, although the present application has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. Rather, given the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Thus, those skilled in the art can implement the present application as set forth in the claims without undue experimentation. It should also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0131] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0132] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a predefined sequence of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0133] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0134] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0135] In the above description of the present specification, the description with reference to the terms "one embodiment / example", "another embodiment / example", or "certain embodiments / examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0136] Although the embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the claims and their equivalents.

[0137] The above has specifically described the preferred embodiments of the present application, but the present application is not limited to the embodiments. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the present application, and these equivalent deformations or substitutions are all included within 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 includes a heating component and a cooling structure, and the method includes: Obtain the ambient temperature, the first temperature and the working current of the heating component, and the second temperature of the cooling structure; According to the ambient temperature and the working current, perform temperature rise mixing calibration on the first temperature and the second temperature to obtain calibrated temperature rise data; Obtain the actual temperature rise data of the heating component, and the time window of the actual temperature rise data is the same as that of the calibrated temperature rise data; According to the calibrated temperature rise data, perform cooling analysis on the actual temperature rise data to obtain the cooling capacity analysis result of the humanoid robot.

2. The method according to claim 1, wherein The step of performing temperature rise mixing calibration on the first temperature and the second temperature according to the ambient temperature and the working current to obtain calibrated temperature rise data includes: Perform temperature difference calculation on the second temperature according to the first temperature to obtain intermediate temperature difference data; Perform temperature difference correction on the intermediate temperature difference data according to the ambient temperature to obtain target temperature difference data; Perform temperature rise mixing analysis on the target temperature difference data according to the working current to obtain the calibrated temperature rise data.

3. The method according to claim 2, wherein The step of performing temperature rise mixing analysis on the target temperature difference data according to the working current to obtain the calibrated temperature rise data includes: Obtain a theoretical temperature rise model, a mapping relation data table and a quantization neural network; Input the working current and the target temperature difference data into the theoretical temperature rise model for theoretical temperature rise calculation to obtain first temperature rise data; According to the mapping relation data table, perform empirical temperature rise mapping on the working current and the target temperature difference data to obtain second temperature rise data; Input the working current and the target temperature difference data into the quantization neural network for quantization temperature rise prediction to obtain third temperature rise data; Perform adaptive fusion on the first temperature rise data, the second temperature rise data and the third temperature rise data to obtain the calibrated temperature rise data.

4. The method according to claim 3, characterized in that, The step of performing adaptive fusion on the first temperature rise data, the second temperature rise data and the third temperature rise data to obtain the calibrated temperature rise data includes: Obtain the adjacent data variance and the maximum variance threshold of a plurality of target sensors, and 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 working current; According to the adjacent data variance and the maximum variance threshold of all the target sensors, obtain an adaptive weight coefficient corresponding to each target sensor; According to all the adaptive weight coefficients, perform 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; According to the first weight, the second weight and the third weight, perform weighted calculation on the first temperature rise data, the second temperature rise data and the third temperature rise data to obtain the calibrated temperature rise data.

5. The method according to claim 1, characterized in that, Performing cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain the cooling capacity analysis result of the humanoid robot, including: Obtaining a cooling data table of the humanoid robot, where the cooling data table records several cooling states of the humanoid robot, and each cooling state corresponds to a range of cooling efficiency coefficients; Calculating an efficiency coefficient based on the calibrated temperature rise data and the actual temperature rise data to obtain a target efficiency coefficient; Performing matching analysis on the range of cooling efficiency coefficients in the cooling data table according to the target efficiency coefficient to obtain the cooling capacity analysis result of the humanoid robot.

6. The method according to claim 5, wherein The method further includes: Performing matching mapping on the range of cooling efficiency coefficients in the cooling data table according to the target efficiency coefficient to obtain a target cooling state; Obtaining several adjacent cooling states, where the adjacent cooling states are the historical cooling states of the humanoid robot in an adjacent time window, and the adjacent time window is a time window adjacent to the time window of the target cooling state; Performing cooling capacity analysis on the target cooling state according to all the adjacent cooling states to obtain the cooling capacity analysis result of the humanoid robot.

7. The method according to claim 3, wherein The expression form of the theoretical temperature rise model is: Among them, ΔT is the first temperature rise data; I is the working 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 heating component; c is the specific heat capacity of the heating component; Δt is the time window.

8. An analysis system for the cooling capacity of a humanoid robot, characterized in that, Applied to a humanoid robot, the humanoid robot includes a heating component and a cooling structure, and the system includes: A first processing unit, configured to obtain the ambient temperature, the first temperature and operating current of the heating component, and the second temperature of the cooling structure; A second processing unit, configured to perform temperature rise hybrid 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 the actual temperature rise data of the heating component, where the time window of the actual temperature rise data is the same as the time window of the calibrated temperature rise data; A fourth processing unit, configured to perform cooling analysis on the actual temperature rise data according to the calibrated temperature rise data to obtain the cooling capacity analysis result of the humanoid robot.

9. An electronic device, characterized in that, Including: At least one processor; At least one memory, configured to store 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 1-7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that, The program executable by the processor, when executed by the processor, is used to implement the method according to any one of claims 1-7.

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