Device heating simulation method and interactive system for user thermal experience research

Abstract

The present application discloses a device heating simulation method and interactive system for user thermal experience research. The method includes: collecting original operation data when the user performs a target task on the target device; reproducing the operation corresponding to the original operation data on the mirror device to obtain the corresponding three-dimensional temperature point cloud fusion map; obtaining the corresponding three-dimensional temperature point cloud fusion map under the same target task operated by different users; performing weighted averaging on multiple three-dimensional temperature point cloud fusion maps based on multiple determined map weights to obtain a typical temperature map corresponding to the target task, and storing the obtained typical temperature maps corresponding to different test tasks in a target database; based on the test task instructions received on the test device, obtaining the typical temperature map corresponding to the test task instructions in the target database, and executing the temperature distribution instructions corresponding to the typical temperature map on the test device. This method can achieve precise temperature control and provide reliable and accurate user thermal experience simulation.

Description

Technical Field

[0001] The present disclosure relates to the technical field of thermal experience research, and in particular to a device heating simulation method and an interactive system for user thermal experience research. Background Art

[0002] Currently, the number of touch-screen mobile phone users is growing rapidly worldwide. The heating of smart touch-screen mobile phones in various scenarios is an important factor affecting the user experience. In order to facilitate the research and testing of the user thermal experience of smartphones under different heating conditions, it is necessary to build a method and system for decoupling the control of the test machine temperature and other influencing factors. This provides a software and hardware testing basis for studying the impact of mobile phone heating on the user thermal experience in different scenarios.

[0003] The research schemes disclosed in the prior art mainly include: 1) simulating different heating conditions of mobile phones, tablets, chips, other devices or components by controlling heating resistors, etc., which has several shortcomings: First, heating resistors can effectively simulate devices with specific heating power, but it is difficult to effectively simulate the temperature distribution of mobile phones by controlling the heating power alone; 2) Temperature acquisition can effectively control the temperature in a closed loop, but due to the lack of control strategies and material layout, it will lead to inaccurate temperature control or too long temperature control time; 3) There is a lack of temperature maps under different usage conditions in typical scenarios of mobile phones, and the temperature simulation lacks basis and differences, and it is impossible to effectively test the impact boundaries of different heating scenarios and situations on the user's thermal experience; 4) There is a lack of scenario-based rapid temperature control methods, that is, decoupling and linking temperature control and scenario control.

[0004] Therefore, the traditional method of temperature control of the test machine is not accurate and efficient enough. For example, there is a lack of intelligent control strategies for temperature accuracy, temperature range, temperature rise and fall speed, scene superposition switching, etc. Due to the differences in temperature control between different users, the test results are inaccurate. Summary of the Invention

[0005] In view of this, the embodiments of the present disclosure provide a device heating simulation method and interactive system for user thermal experience research, which can solve the problems existing in the prior art such as the inability to effectively simulate the heating of target objects in different scenarios, the inability to explore the impact of the heating of target objects on the user's thermal experience in different scenarios, the inability to effectively and independently control the temperature and other factors, and the inability to ensure accurate temperature control in different scenarios.

[0006] In a first aspect, an embodiment of the present disclosure provides a device heating simulation method for user thermal experience research, comprising:

[0007] Collecting raw operation data when users perform target tasks on target devices;

[0008] Reproducing the operation corresponding to the original operation data on the mirror device, and obtaining a three-dimensional temperature point cloud fusion image corresponding to each operation;

[0009] Obtaining the three-dimensional temperature point cloud fusion image corresponding to the same target task performed by users with different operating habits;

[0010] Obtain the actual operation information of different users and determine the corresponding graph weights;

[0011] Performing weighted averaging on the plurality of three-dimensional temperature point cloud fusion images based on the plurality of image weights to obtain a typical temperature image corresponding to the target task, and storing the obtained different test tasks and their corresponding typical temperature images in a target database;

[0012] Based on the test task instruction received on the test device, a typical temperature map corresponding to the test task instruction is obtained in the target database and recorded as target temperature information;

[0013] A temperature distribution instruction corresponding to the target temperature information is executed on the test device.

[0014] Optionally, obtaining a three-dimensional temperature point cloud fusion image corresponding to each operation includes:

[0015] respectively collecting two-dimensional infrared image data of the mirror device at different preset sides;

[0016] respectively collecting three-dimensional point cloud data of the mirror device at different preset sides;

[0017] fusing the two-dimensional infrared image data from all sides to obtain a two-dimensional infrared fused image;

[0018] Fusing the three-dimensional point cloud data from all sides to obtain three-dimensional point cloud fusion information;

[0019] Splicing the two-dimensional infrared fusion image with the three-dimensional point cloud fusion information to obtain a three-dimensional temperature point cloud fusion image;

[0020] The mirror device and the target device are devices of the same model.

[0021] Optionally, after the mirror device completes a single operation each time, the mirror device is subjected to non-contact cooling;

[0022] When the temperature of the mirroring device drops to a preset temperature, the next operation is triggered.

[0023] Optionally, obtaining actual operation information of different users and determining corresponding graph weights includes:

[0024] Obtain the user's actual operation image;

[0025] Analyze the actual operation image based on preset standard operation actions to determine the operation completion level;

[0026] A corresponding graph weight is determined based on the completion level.

[0027] Optionally, executing a temperature distribution instruction corresponding to the target temperature information on the test device includes:

[0028] determining a coarse heating strategy and a fine heating strategy based on the target temperature information;

[0029] Performing rough heating on the test device according to the rough heating strategy to obtain a device in a rough heating state;

[0030] Performing fine heating on the rough heating state device based on the fine heating strategy to obtain a fine heating state device;

[0031] Collecting actual temperature distribution information of the precision heating state equipment;

[0032] Determine whether the temperature difference between the actual temperature distribution information obtained and the preset temperature distribution is within a preset range. If so, stop heating; if not, obtain temperature difference information, and analyze the temperature difference information based on the Floyd-Warshall algorithm to determine the shortest control path, and control the temperature of the precision heating state equipment to the preset temperature distribution based on the shortest control path.

[0033] Optionally, the determining whether the temperature difference between the acquired actual temperature distribution information and the preset temperature distribution is within a preset range includes:

[0034] Based on the actual temperature distribution information, obtaining a first distribution node set;

[0035] Based on the target temperature information, obtaining a second distribution node set;

[0036] Traversing the first distributed node set to determine a first local area of ​​each node;

[0037] Obtain a first temperature average value, a first temperature standard deviation, and a first temperature structure within the first local area;

[0038] traversing the second distributed node set to determine a second local area of ​​each node;

[0039] Obtain a second temperature average value, a second temperature standard deviation, and a second temperature structure within the second local area;

[0040] Obtaining a first coefficient based on all of the first temperature averages, the second temperature averages, and a first preset formula;

[0041] Obtaining a second coefficient based on all of the first temperature standard deviation, the second temperature standard deviation, and a second preset formula;

[0042] Obtaining a third coefficient based on all of the first temperature structure, the second temperature structure, and a third preset formula;

[0043] Obtaining a similarity degree based on the first coefficient, the second coefficient, the third coefficient, and a fourth preset formula;

[0044] Obtain a difference between the similarity degree and a preset threshold, and determine whether the difference is within a preset range.

[0045] In a second aspect, the present application discloses a device heating simulation method for user thermal experience research, comprising:

[0046] receiving a first interaction instruction from a user;

[0047] Based on the first interaction instruction, a typical temperature map and an ambient temperature corresponding to the first interaction instruction are obtained from a target database, and recorded as target temperature information;

[0048] Executing a temperature distribution instruction corresponding to the target temperature information on a test device;

[0049] In response to the user switching instruction, determining a second interaction instruction;

[0050] Controlling the test equipment to cool down to an initial temperature;

[0051] Acquire a typical temperature map corresponding to the second interaction instruction in a target database based on the second interaction instruction, and record the map as target temperature information;

[0052] A temperature distribution instruction corresponding to the target temperature information is executed on the test equipment.

[0053] In a third aspect, the present application discloses an experimental method for studying user thermal experience, comprising:

[0054] Collecting raw operation data when users perform target tasks on target devices;

[0055] Reproducing the operation corresponding to the original operation data on the mirror device, and obtaining a three-dimensional temperature point cloud fusion image corresponding to each operation;

[0056] Obtaining the three-dimensional temperature point cloud fusion image corresponding to the same target task performed by users with different operating habits;

[0057] Obtain the actual operation information of different users and determine the corresponding graph weights;

[0058] Based on the weights of the multiple atlases, a weighted average is performed on the multiple three-dimensional temperature point cloud fusion images to obtain a typical temperature atlas corresponding to the target task.

[0059] In a fourth aspect, the present application discloses an interactive system for user thermal experience research, comprising a testing machine and a temperature control workbench for placing the testing machine, wherein the temperature control workbench includes a cooling module and a non-contact temperature acquisition module;

[0060] The test machine includes a user thermal experience software interactive simulation module, a first side matrix temperature control module, a first side insulation module, a central module, a second side insulation module, a second side matrix temperature control module, and a shell simulation module, which are arranged in sequence: wherein the user thermal experience software interactive simulation module is used to receive interactive instructions of the user thermal experience; the first side matrix temperature control module is used to regulate the temperature distribution on the first side; the first side insulation module is used to block the heat of the first side matrix temperature control module; the central module includes a counterweight module and a power supply module, and the counterweight module is used to control the weight of the overall test machine to be consistent with the counterweight of the target simulation device; the second side insulation module is used to block the heat on the outside of the power supply module; the second side matrix temperature control module is used to regulate the temperature distribution on the second side; the shell simulation module is matched with the user thermal experience software interactive simulation module;

[0061] The power supply module is communicatively connected with the user thermal experience software interactive simulation module, the first-side matrix temperature control module, and the second-side matrix temperature control module for power supply;

[0062] The first-side matrix temperature control module includes a matrix heating resistor and several PWM temperature control circuits. The matrix heating resistor includes several independently arranged heating resistors. Insulating material is filled between two adjacent heating resistors, and the thermal conductivity of the insulating material is consistent with the thermal conductivity of the heating resistor; each heating resistor is connected to one of the PWM temperature control circuits; the second-side matrix temperature control module has the same structural setting as the first-side matrix temperature control module.

[0063] Optionally, the user thermal experience software interactive simulation module includes an interactive front-end submodule, a communication submodule and a background calculation submodule;

[0064] The interactive front-end submodule is used to collect interactive operations of the user's thermal experience;

[0065] The communication submodule is communicatively connected to the interactive front-end submodule, and is used to encode the user operation to obtain a coded signal;

[0066] The background computing submodule is signal-connected to the communication submodule, and is configured to operate on the received coded signal, obtain a temperature control strategy, and control the first-side matrix temperature control module and the second-side matrix temperature control module based on the temperature control strategy;

[0067] The temperature control strategy is a shortest control strategy for controlling the temperature distribution of the first-side matrix temperature control module and the second-side matrix temperature control module to a temperature distribution corresponding to target temperature information.

[0068] In a fifth aspect, the embodiments of the present disclosure further provide a computer device, which adopts the following technical solution:

[0069] The computer device comprises:

[0070] at least one processor; and,

[0071] a memory communicatively connected to the at least one processor; wherein,

[0072] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above-mentioned device heating simulation methods for user thermal experience research or test methods for user thermal experience research.

[0073] In a sixth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium storing computer instructions for enabling a computer to execute any of the above-described device heating simulation methods for user thermal experience research or experimental methods for user thermal experience research.

[0074] In a seventh aspect, an embodiment of the present disclosure further provides a computer program product, comprising a computer program / instruction, which implements the steps of any of the above methods when executed by a processor.

[0075] The device heating simulation method for user thermal experience research disclosed in this embodiment can obtain accurate user operations by collecting and analyzing user operation data; reproduce user operations through a mirror device, separate the user's actual operations from temperature collection, and do not affect each other, thereby ensuring that the three-dimensional temperature point cloud fusion map collected by the mirror device is accurate and reliable, that is, the collected temperature distribution information is not affected by the device temperature of the user's actual operation, and the target temperature information of different test tasks is accurately obtained; by storing and quickly retrieving the target temperature information in the database, the design plan can be quickly executed in the test phase, and the independent setting of the test device, the mirror device and the target device can realize independent control of temperature factors and non-temperature factors. According to the test task instructions received on the test device, the target temperature information corresponding to the test task instructions is obtained in the target database, and the temperature distribution instructions corresponding to the target temperature information are executed to display the heating, providing an accurate and reliable thermal experience.

[0076] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the following specifically cites preferred embodiments and describes them in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0078] Figure 1 A schematic flow chart of a device heating simulation method for user thermal experience research provided in an embodiment of the present disclosure.

[0079] Figure 2 Schematic diagram of the operation acquisition machine and the operation mirror machine provided in an embodiment of the present disclosure.

[0080] Figure 3 A schematic flow chart of a method for obtaining a three-dimensional temperature point cloud fusion image provided in an embodiment of the present disclosure.

[0081] Figure 4 A flowchart of a method for obtaining a three-dimensional temperature point cloud fusion image when the mirroring device is a mobile phone is provided in an embodiment of the present disclosure.

[0082] Figure 5 A schematic diagram of the mirroring device layout provided in an embodiment of the present disclosure.

[0083] Figure 6 A schematic diagram of the fusion of a three-dimensional temperature point cloud fusion map provided in an embodiment of the present disclosure.

[0084] Figure 7 A flowchart of a method for determining graph weights provided in an embodiment of the present disclosure.

[0085] Figure 8 A flowchart of a method for executing temperature distribution instructions corresponding to target temperature information on a test device provided in an embodiment of the present disclosure.

[0086] Figure 9 A method for determining whether the temperature difference between the actual temperature distribution information obtained and the preset temperature distribution is within a preset range is provided in an embodiment of the present disclosure.

[0087] Figure 10 A schematic flow chart of a device heating simulation method for user thermal experience research provided in an embodiment of the present disclosure.

[0088] Figure 11 A schematic diagram of the structure of a testing machine provided in an embodiment of the present disclosure.

[0089] Figure 12 A schematic diagram of the deployment of a communication base station for the communication submodule provided in an embodiment of the present disclosure.

[0090] Figure 13 A schematic diagram of the structure of a first-side matrix temperature control module provided in an embodiment of the present disclosure.

[0091] Figure 14 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure.

[0092] Description of reference numerals:

[0093] 10. Temperature control workbench; 20. Rapid cooling module; 30. Insulation fixture; 41. First infrared temperature measurement camera; 42. Second infrared temperature measurement camera; 43. Ambient temperature detection module; 51. First laser radar; 52. Second laser radar; 60. Mobile phone; 71. 2D infrared fusion image; 72. 3D point cloud fusion information; 80. 3D temperature point cloud fusion image; 90. Test machine; 91. User thermal experience software interactive simulation module; 911. Interactive front-end submodule; 912 , communication submodule; 913, background calculation submodule; 92, first side matrix temperature control module; 921, heating resistor; 922, PWM temperature control circuit; 93, first side insulation module; 94, center module; 941, counterweight module; 942, power module; 95, second side insulation module; 96, second side matrix temperature control module; 97, shell simulation module; 101, target workbench; 102, non-contact temperature acquisition module; 103, control background; 104, communication base station. DETAILED DESCRIPTION

[0094] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0095] It should be clear that the following embodiments of the present disclosure are described through specific concrete examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0096] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0097] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The illustrations only show components related to the present disclosure and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0098] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0099] Reference Figure 1 In the first aspect, the present application discloses a device heating simulation method for user thermal experience research, which specifically includes the following contents:

[0100] S100, collecting original operation data when a user performs a target task on a target device.

[0101] Among them, the target device is the operation acquisition machine.

[0102] Specifically, the user operates on the operation collection machine according to the test task, and the user operation (ie, original operation data) is collected in real time by the user operation collection program of the user operation collection machine silently in the background without affecting the user operation.

[0103] The target task (test task) is a list of different task scenarios established according to the research purpose, and the user completes the operation on the target device according to the list of different task scenarios.

[0104] Among them, user operations may include the user's click position, click time, sliding trajectory, key operations, etc.

[0105] S200: Reproduce the operation corresponding to the original operation data on the mirror device, and obtain a three-dimensional temperature point cloud fusion image corresponding to each operation.

[0106] The mirror device is an operation mirror device of the operation acquisition machine; for example, when the operation acquisition machine is a mobile phone, the mirror device is a mobile phone of the same model.

[0107] Specifically, the user's operations on the operation acquisition machine (i.e., the operations corresponding to the original operation data) are mirrored on the mirroring device, and the three-dimensional temperature point cloud fusion map of the operation mirroring machine is collected; the mirroring device can accurately simulate the user's operations on the actual device, and at the same time, the precise temperature distribution after each operation can be obtained in time to ensure the real-time and accuracy of the data.

[0108] Also refer to Figure 2 When users operate their mobile phones, their limbs are in direct contact with the phone, such as their palms, fingers, and face. If the temperature of the user when operating the phone is directly collected, such as with a built-in temperature sensor or infrared non-contact temperature measurement, the collected phone temperature information will include interference from the contact between the user's limbs and the phone. Therefore, by collecting user operations through an operation collector, mirroring the user operations to an operation mirror machine, and collecting the temperature distribution of the operation mirror machine, the influence of user limb contact on the phone's heating can be effectively avoided.

[0109] Furthermore, the user operations captured by the operation acquisition machine can be exported offline to an external storage device, and then imported into the operation mirror machine through the external storage. The operation mirror machine will reproduce all the user's operations on the user operation acquisition machine according to the collected user operation sequence. At the same time, the user operation mirror machine is observed through non-contact temperature measurement methods to obtain the temperature distribution of the user operation mirror machine. Because the operation mirror machine is free of user physical contact interference, the temperature changes caused by user operations reproduced by the operation mirror machine are completely the temperature changes caused by the operation mirror machine executing the user operations, that is, the typical temperature distribution of the user in the current environment and task scenario.

[0110] S300: Obtain a 3D temperature point cloud fusion image corresponding to users with different operating habits operating the same target task.

[0111] Among them, users with different operating habits include users of different age groups, users with different finger lengths, users with different levels of mobile phone usage, etc.

[0112] Specifically, by repeating S100 and S200 for different users, the corresponding three-dimensional temperature point cloud fusion map (i.e., temperature distribution information) for users with different operating habits operating the same target task can be obtained; obtaining temperature distribution maps under different operating habits provides diversified data support for subsequent analysis.

[0113] S400: Obtain actual operation information of different users and determine corresponding graph weights.

[0114] Based on the actual operation information of different users, the degree of completion of each user's actual operation is determined, that is, whether the completion is good or not. For users with higher completion rates, a higher graph weight can be set, and for users with lower completion rates, a lower graph weight can be set. Different users' behavioral habits have different effects on device heating, and assigning weights to them can more accurately reflect the actual situation.

[0115] S500, based on multiple atlas weights, weighted average is performed on multiple three-dimensional temperature point cloud fusion maps to obtain a typical temperature atlas corresponding to the target task, and the obtained different test tasks, ambient temperatures and their corresponding typical temperature atlases are stored in the target database.

[0116] Specifically, a weighted average algorithm can be used to average multiple three-dimensional temperature point cloud fusion maps according to their corresponding weights. The operating habits of multiple users can be comprehensively considered to obtain a more accurate typical temperature map, that is, a typical temperature map of the user under the corresponding ambient temperature, corresponding task scenario (i.e., test task name) and operating status (i.e., test task); the typical temperature map is stored in the target database together with the corresponding test task name to facilitate subsequent query and use, thereby improving work efficiency.

[0117] The ambient temperature may include: extreme cold, relatively low temperature, normal room temperature, relatively high temperature or extremely hot; and the operating state may be classified as high frequency operation or low frequency operation.

[0118] S600, based on the test task instruction received on the test device, obtain the typical temperature map and ambient temperature corresponding to the test task instruction in the target database, and record them as target temperature information.

[0119] Among them, the test equipment is a thermal experience interactive device.

[0120] Specifically, when the test equipment receives a specific test task instruction, it retrieves the typical temperature map corresponding to the task from the target database, and marks the retrieved typical temperature map and its corresponding ambient temperature as target temperature information for subsequent temperature distribution instruction execution; through database query, the required target temperature information can be quickly obtained, thereby improving test efficiency, ensuring that the test task accurately matches the corresponding target temperature information, and avoiding errors.

[0121] S700: Execute a temperature distribution instruction corresponding to the target temperature information on the test device.

[0122] Specifically, the temperature distribution instructions corresponding to the target temperature information are executed on the test device to display the heat. The ambient temperature can be executed first, and then the typical temperature map can be executed; further, the typical temperature map is applied to the test device to automatically generate corresponding temperature distribution instructions. The test device simulates the temperature distribution in the typical temperature map according to these instructions to complete the test task; by executing the temperature distribution instructions corresponding to the typical temperature map, the impact of user operations on device heating can be accurately simulated, helping the product design team to verify the thermal performance of the device, optimize the design, and improve the user experience.

[0123] The device heating simulation method for user thermal experience research disclosed in this embodiment can obtain accurate user operations by collecting and analyzing user operation data; reproduce user operations through a mirror device, separate the user's actual operations from temperature collection, and do not affect each other, thereby ensuring that the three-dimensional temperature point cloud fusion map collected by the mirror device is accurate and reliable, that is, the collected temperature distribution information is not affected by the device temperature of the user's actual operation, and the target temperature information of different test tasks is accurately obtained; by storing and quickly retrieving the target temperature information in the database, the design plan can be quickly executed in the test phase, and the independent setting of the test device, the mirror device and the target device can realize independent control of temperature factors and non-temperature factors. According to the test task instructions received on the test device, the target temperature information corresponding to the test task instructions is obtained in the target database, and the temperature distribution instructions corresponding to the target temperature information are executed to display the heating, providing an accurate and reliable thermal experience.

[0124] Reference Figure 3 ,The method for obtaining the three-dimensional temperature point cloud fusion image specifically includes the following contents:

[0125] A111 collects two-dimensional infrared image data of the mirror device at different preset sides.

[0126] In traditional methods, changes in external conditions (such as fluctuations in network signal strength and lighting) are difficult to fully control, resulting in temperature deviations in the test machine. In this step, by collecting two-dimensional infrared image data from different sides of the mirrored device, the temperature distribution of the device at different angles can be fully captured, avoiding the limitations of a single perspective.

[0127] A112 collects 3D point cloud data of the mirror device at different preset sides.

[0128] In traditional methods, temperature control is not accurate and efficient enough. In this step, by collecting three-dimensional point cloud data from different sides, the precise temperature distribution of the equipment in three-dimensional space can be obtained, thereby improving the accuracy of temperature control.

[0129] A113 fuses the two-dimensional infrared image data from all sides to obtain a two-dimensional infrared fused image.

[0130] By fusing the two-dimensional infrared image data from all sides, a two-dimensional infrared temperature distribution map of the entire device can be obtained. This method can comprehensively consider the temperature changes of the device on different sides and avoid temperature measurement errors caused by single-side data.

[0131] A114 fuses the 3D point cloud data from all sides to obtain 3D point cloud fusion information.

[0132] By fusing the three-dimensional point cloud data from all sides, the overall temperature distribution information of the device in three-dimensional space can be obtained. This method can accurately reflect the temperature changes of the device in three-dimensional space and improve the accuracy of temperature measurement.

[0133] A115, stitches the 2D infrared fusion image with the 3D point cloud fusion information to obtain a 3D temperature point cloud fusion image.

[0134] The mirror device and the target device are of the same model, ensuring that the user's actual operations on the target device can be 100% replicated on the mirror device.

[0135] Traditional methods lack intelligent control strategies and material layout, resulting in inaccurate temperature control or excessively long temperature control times. In this step, by splicing the two-dimensional infrared fusion image with the three-dimensional point cloud fusion information, a three-dimensional temperature point cloud fusion image that integrates temperature distribution and spatial position can be obtained. This method can more comprehensively and accurately obtain the temperature distribution of the equipment.

[0136] Reference Figure 4 When the mirroring device is a mobile phone, the method for obtaining the three-dimensional temperature point cloud fusion image specifically includes:

[0137] A121 collects two-dimensional infrared image data of the front side and two-dimensional infrared image data of the back side of the mirror device during operation using the first infrared temperature measurement camera and the second infrared temperature measurement camera.

[0138] By using the first and second infrared temperature measurement cameras to collect two-dimensional infrared image data of the front and back of the mobile phone during operation, the temperature distribution of the mobile phone at different angles can be fully captured, avoiding the limitations of a single perspective.

[0139] A122, based on the first laser radar and the second laser radar, respectively collects the front three-dimensional point cloud data and the back three-dimensional point cloud data of the mirror device during operation.

[0140] By using the first and second lidars to respectively collect three-dimensional point cloud data of the front and back sides of the mobile phone during operation, the precise temperature distribution of the mobile phone in three-dimensional space can be obtained.

[0141] A123, registering and fusing the front two-dimensional infrared image data and the back two-dimensional infrared image data to obtain a two-dimensional infrared fused image.

[0142] By aligning and fusing the two-dimensional infrared image data of the front and back, a two-dimensional infrared temperature distribution map of the entire mobile phone can be obtained. This method can comprehensively consider the temperature changes on the front and back of the mobile phone, avoiding temperature measurement errors caused by single-side data.

[0143] A124, registers and fuses the front 3D point cloud data with the back 3D point cloud data to obtain 3D point cloud fusion information.

[0144] By aligning and fusing the front and back three-dimensional point cloud data, the overall temperature distribution information of the mobile phone in three-dimensional space can be obtained. This method can accurately reflect the temperature changes of the mobile phone in three-dimensional space and improve the accuracy of temperature measurement.

[0145] A125, stitches the 2D infrared fusion image with the 3D point cloud fusion information to obtain a 3D temperature point cloud fusion image.

[0146] By stitching the two-dimensional infrared fusion image with the three-dimensional point cloud fusion information (i.e., registration fusion), a three-dimensional temperature point cloud fusion map that integrates temperature distribution and spatial position can be obtained. This method can more comprehensively and accurately reflect the temperature distribution of the mobile phone, thereby improving the efficiency and accuracy of temperature control.

[0147] Among them, the mirror device is adiabatically fixed; in traditional methods, changes in external conditions (such as ambient temperature, sunlight, etc.) will affect the temperature measurement of the test machine. By adiabatically fixing the mirror device (such as a mobile phone), the interference of external heat sources can be reduced, making the temperature measurement more accurate; since adiabatic fixation reduces the influence of external heat sources, the temperature stability of the mirror device during the test process is improved, which can effectively avoid temperature measurement errors caused by external temperature fluctuations.

[0148] In addition, adiabatic fixation can simplify the experimental setup and reduce the complexity of external condition control. This approach makes the experiment easier to operate and repeat, improving the repeatability and reliability of the experiment.

[0149] Furthermore, after each mirror device completes a single operation, the mirror device is cooled down in a non-contact manner (such as air cooling); when the temperature of the mirror device drops to a preset temperature, the next operation is triggered, which can effectively reduce the task switching waiting time.

[0150] By performing non-contact cooling after each mirroring device completes a single operation and triggering the next operation when the temperature of the mirroring device drops to a preset temperature, the task switching waiting time can be effectively reduced, the experimental efficiency can be improved, the accuracy and stability of the experimental results can be ensured, the controllability and flexibility of the experiment can be enhanced, the impact of differences in user usage habits can be reduced, and the experimental process can be simplified. These benefits further enhance the authenticity and effectiveness of the experimental results of mobile phone users' thermal experience research.

[0151] Furthermore, in order to ensure that users can quickly and efficiently experience multiple task scenarios and temperature levels, when the user intends to switch test tasks, the previous test machine can be placed on the temperature-controlled workbench and restored to its initial temperature through the rapid cooling module. The user can use other test machines on the temperature control console to experience the typical temperature of the same task scenario or other task scenarios.

[0152] Further references Figure 5 and Figure 6 When the mirroring device is a mobile phone, several mobile phones 60 are placed on the temperature control workbench 10. The user operates the mirroring machine to mirror the user's operation on the user operation acquisition machine, and the temperature acquisition module of the temperature control workbench collects the temperature distribution of the user operating the mirroring machine.

[0153] Specifically, all mobile phones are fixed by the heat-insulating fixing device 30, ensuring that the contact portion between the heat-insulating fixing device and the mobile phone is as small as possible and does not affect the temperature distribution and temperature collection of the mobile phone.

[0154] Each mobile phone is provided with a non-contact rapid cooling module 20 for rapidly cooling the mobile phone.

[0155] When the user places the mobile phone on an insulated fixture, the rapid cooling module will perform the cooling task according to the test task, lowering the temperature to near the preset temperature of the test task, reducing the waiting time for task switching.

[0156] In this embodiment, considering that the mobile phone is an ultra-thin device, the temperature of the side edges can be ignored, so only the front and back edges with larger areas are collected.

[0157] The temperature acquisition in this embodiment is all non-contact temperature measurement; specifically, when collecting temperature, the first infrared temperature measurement camera 41 collects two-dimensional infrared image data of the front side of the mirror device in operation, the first laser radar 51 collects three-dimensional point cloud data of the front side of the mirror device in operation, the second infrared temperature measurement camera 42 collects two-dimensional infrared image data of the back side of the mirror device in operation, and the second laser radar 52 collects three-dimensional point cloud data of the back side of the mirror device in operation; wherein, the two-dimensional infrared image can store temperature data on each pixel. At the same time, the ambient temperature detection module is set to perform real-time detection of the ambient temperature of the mobile phone. This module uses a high-precision temperature sensor to obtain ambient temperature data in real time, dynamically record changes in ambient temperature, and support real-time wireless communication transmission of temperature, providing external condition reference for temperature distribution modeling.

[0158] During the operation of the mobile phone device, the ambient temperature detection module 43 dynamically monitors the ambient temperature, and combines the task scenario (such as gaming, video playback, web browsing) and operation status (such as high-frequency operation, low-frequency operation) to ensure that the constructed temperature map can reflect the thermal distribution characteristics under actual usage conditions.

[0159] Specifically, the laser radars on both sides of the phone scan the point cloud of the phone, and through the key feature points on the edge of the phone, the three-dimensional point clouds on both sides of the phone are quickly spliced ​​to build a complete point cloud model of the phone. The coordinate system of the point cloud model is O m X m Y m Z m .

[0160] Specifically, the infrared temperature measurement camera and the laser radar need to be calibrated. The coordinate system of the infrared temperature measurement camera is O i X i Y i , the coordinate system of the laser radar is O l X l Y l Z l , through the transformation and unification of the spatial coordinate system, the coordinate system of the temperature acquisition module is obtained as t X t Y t Z t , which enables the projection of 2D infrared images into 3D space based on viewing angles. After calibration, the registration error between the 2D infrared image and 3D space can be within 0.1mm when the temperature acquisition module is 50cm away from the phone.

[0161] Specifically, when the temperature acquisition module collects the temperature distribution of the operating mirror machine, the point cloud emitted by the laser radar is matched with the mobile phone point cloud model to realize the temperature acquisition module coordinate system O t Xt Y t Z t With the point cloud model coordinate system O m X m Y m Z m The coordinate system is aligned to obtain the three-dimensional temperature point cloud coordinate system O tm X tm Y tm Z tm , the temperature of the two-dimensional infrared image can be mapped to the point cloud model, and the three-dimensional temperature point cloud P1(p1(t1,T 11 ,X1,Y1,Z1)、p2(t1,T 21 ,X2,Y2,Z2)、p3(t1,T 31 ,X3,Y3,Z3)、p4(t1,T 41 ,X4,Y4,Z4)......p n (t1,T n1 ,X n ,Y n ,Z n )).

[0162] Among them, p1, p2, p3...p n It is the point cloud model containing temperature information at time t1, T k1 It is point cloud p k The temperature at time t1, (X k , Y k , Z k ) is the point cloud p k The spatial coordinates of .

[0163] Specifically, under different typical ambient temperature levels and operating conditions, the user's time at different times t1, t2, t3...t m The three-dimensional temperature point cloud P1, P2, P3...P m , through the weighted average of the corresponding spatial point cloud, we get the point cloud p k Temperature T under typical temperature spectrum ko =(T k1 +T k2 +T k3 +......+T km ) / m, thus obtaining the typical temperature spectrum P of the user under typical ambient temperature and task o (p 1o (T 1o ,X1,Y1,Z1),p 2o (T 2o ,X2,Y2,Z2),p3o (T3 o ,X3,Y3,Z3)......p no (T no ,X n ,Y n ,Z n )).

[0164] Repeat the above steps to construct a typical temperature map for each typical ambient temperature, combined with different test tasks and operating states. Specifically, by taking the weighted average of the typical temperature maps of different users at each typical ambient temperature, combined with different test tasks and operating states, the typical temperature map of the task scenario at the current ambient temperature and operating state is obtained, that is, the typical temperature map P of different users (A, B, C, D...N) in the same task. Ao ,P Bo ,P Co ,P Do ......P No .

[0165] It should be noted that when it is necessary to collect the temperature of all sides of the device, a third infrared temperature measuring camera and a third laser radar can be added. The third infrared temperature measuring camera can be used to collect two-dimensional infrared image data of all sides of the mirroring device during operation, and the third laser radar can be used to collect three-dimensional point cloud data of all sides of the mirroring device during operation.

[0166] Reference Figure 7 , the method for determining the graph weight specifically includes the following:

[0167] S410: Acquire the user's actual operation image.

[0168] By obtaining the user's actual operation video, the specific operation steps and actions of the user when using the device (such as a mobile phone) can be intuitively recorded and observed. This method can avoid relying on the user's verbal description or memory, and improve the objectivity and authenticity of the experimental data.

[0169] S420: Analyze the actual operation image based on the preset standard operation action to determine the operation completion level.

[0170] Through preset standard operating actions, the actual operation images can be analyzed in detail to evaluate the completion of the user's operation. This method can objectively evaluate the user's operation level, avoid errors caused by subjective judgment, and improve the accuracy of experimental results.

[0171] S430: Determine the corresponding graph weight based on the completion level.

[0172] By matching the operation completion level with the graph weight, the impact of user operations can be quantified. This method can more accurately reflect the impact of different operations on device temperature changes, improving the reliability of temperature control and experimental results.

[0173] By acquiring actual user operation images, analyzing them based on preset standard operation actions, and determining the operation completion level and corresponding graph weight, the objectivity of experimental data can be improved, the accuracy and reliability of experimental results can be improved, the impact of operations can be quantified, the repeatability of experiments can be enhanced, the temperature control accuracy can be improved, and experimental analysis can be simplified. These benefits further enhance the authenticity and validity of the test results of mobile phone user thermal experience research.

[0174] Specifically, according to the performance of different users (A, B, C, D...N) in the task, different weights k are assigned to the typical temperature maps of different users. A 、k B 、k C 、k D ......k N Since different users have different effects on task execution, the weight of the typical temperature map of users who can complete the test task well is high, and the weight of the typical temperature map of users who cannot complete the test task well is low. The typical temperature map P of the test task t =P Ao ×k A +P Bo ×k B +P Co ×k C +......P No ×k N , where P Ao 、P Bo 、P Co 、P Do ......P No The corresponding three-dimensional temperature point cloud fusion image after operations by different users (A, B, C, D...N).

[0175] Specifically, typical temperature maps (3D temperature point clouds) will be stored in a unified format in the control backend database. Each set of data will be tagged with the corresponding ambient temperature, task scenario, and operation status. This data will cover the operation results of multiple users and multiple single-task scenarios, gradually enriching the mobile phone temperature map database to ensure that it covers a variety of real-world usage conditions.

[0176] To systematically manage this data, the database will utilize efficient data structures for storage. For example, typical temperature map data will be stored using compressed storage methods (such as Octree compression of point cloud data, which effectively reduces the storage requirements of large-scale point cloud data through recursive spatial partitioning and hierarchical data storage) to conserve storage space while ensuring query speed and data access efficiency. Each typical temperature map data entry will include metadata such as ambient temperature, mission scenario, and operational status to facilitate subsequent queries and analysis.

[0177] In addition, in order to better understand the relationship between ambient temperature, task operation scenarios and mobile phone temperature maps, this application establishes a multivariate regression model of ambient temperature-task scenario-operation-mobile phone temperature to predict and map temperature changes and presentations under different conditions. This regression model takes ambient temperature, task scenario, and operating status as independent variables, and the key features of the temperature map as dependent variables. By training this regression model, we can quickly and accurately predict the typical temperature maps that need to be retrieved and experienced by users based on the current ambient temperature conditions, task scenarios, and operating status in subsequent testing processes. The optimization and update of the model will be based on increasing data such as task scenarios and operations to ensure its adaptability and accuracy.

[0178] Furthermore, the actual operation images are analyzed based on preset standard operation actions to determine the specific plan for the operation completion level, including: breaking down the operations that the user may perform into a series of specific standard actions, such as opening an application, sliding the screen, clicking a button, entering text, etc.; setting specific specifications and requirements for each standard action, such as the starting and end positions of sliding the screen, the speed and strength of clicking a button, the correctness of entering text, etc.; setting reasonable time requirements for each standard action, for example, the time to open an application should be controlled within 2 seconds, and the time to enter text should be completed within 5 seconds, etc.; determining the execution order of each operation, for example, first open the application, then perform the sliding operation, and finally the clicking operation.

[0179] The analysis of actual operation images based on preset standard operation actions specifically includes: using image recognition and action recognition technology to identify and match each action in the actual operation image, comparing the user's operation action with the preset standard action, and judging whether it complies with the specification; conducting a normative evaluation of each recognized action, for example, judging whether the starting point and end point of sliding the screen meet the requirements, whether the speed and strength of clicking the button are up to standard, and the correctness of the input text; evaluating the execution time of each action, for example, recording the time to open the application to judge whether it is completed within the specified time; evaluating the overall sequence of operations, for example, judging whether the user opens the application first, then performs the sliding operation, and finally the clicking operation.

[0180] Determining the operation completion level specifically includes: dividing the operation completion level into different levels according to the results of action recognition and matching, normative evaluation, time evaluation and sequence evaluation, for example, excellent (meets all standards), good (basically meets the standards), general (partially meets the standards), and poor (does not meet the standards). The level division can quantify the user's operation completion level and improve the comparability and analyzability of the experimental results; according to different levels of completion, corresponding graph weights are assigned, for example, the weight of the excellent level is 1.0, the good level is 0.8, the general level is 0.6, and the poor level is 0.4. The weight distribution can reflect the impact of different operation completion levels on the temperature change of the equipment, thereby improving the accuracy of temperature control and the reliability of the experimental results.

[0181] By setting standards for detailed action decomposition, action standardization, action timing requirements, and action sequence, we ensure that every operation step is accurately recorded and evaluated. By utilizing action recognition and matching, standardization assessment, time assessment, and sequence assessment, we can accurately determine the user's operation completion. By grading and assigning weights, we can quantify the impact of operations and improve the accuracy and reliability of experimental results. These specific solutions further enhance the authenticity and validity of the results of mobile phone user thermal experience research experiments.

[0182] Reference Figure 8 The method of executing the temperature distribution instruction corresponding to the target temperature information on the test device specifically includes:

[0183] S710: Determine a coarse heating strategy and a fine heating strategy based on the target temperature information.

[0184] The coarse heating strategy includes first preset temperature information distributed in a matrix, and the first preset temperature is lower than the target temperature, ensuring that the device is heated up gradually to avoid thermal stress or damage that may be caused by a sudden increase in temperature.

[0185] Among them, the precise heating strategy includes the temperature difference to be heated, that is, the difference between the target temperature and the first preset temperature, and finely controls the step size of the temperature rise to ensure the accuracy and consistency of the temperature distribution.

[0186] In this step, the current typical temperature map can be automatically retrieved through the application of the established ambient temperature-task scenario-operation-mobile phone temperature multivariate prediction model, and the user can be informed of whether the temperature control is in place through a traffic light; when the user performs interactive operations in a relatively constant operating state under the current ambient temperature and task scenario, the temperature control strategy of the test machine remains unchanged.

[0187] S720 , performing rough heating on the test device according to the rough heating strategy to obtain a device in a rough heating state.

[0188] Use a coarse heating strategy to heat the device to the first preset temperature (i.e., roughly heat the device distribution to the preset temperature map under the current ambient temperature, test task scenario, and operating status), initially reaching a state close to the target temperature, preparing for subsequent refined heating and reducing the risks caused by sudden temperature changes.

[0189] Specifically, the preset temperature map under the current ambient temperature, test task scenario and operating state is Pt, the heating temperature of the test machine interaction front-end terminal module is Ti, and the target temperature information input to the test machine matrix temperature control module is Pt'=Pt-Ti.

[0190] S730 , performing fine heating on the device in the rough heating state based on the fine heating strategy to obtain the device in the fine heating state.

[0191] On the basis of coarse heating, a fine heating strategy is used to raise the device temperature to the target temperature. Through fine control, the device temperature is ensured to reach and stabilize within the target temperature range, thereby improving the accuracy of temperature control.

[0192] S740, collecting actual temperature distribution information of the equipment in the fine heating state.

[0193] Real-time monitoring of equipment temperature distribution provides accurate data for subsequent judgment and adjustment, ensuring the real-time and accuracy of temperature control.

[0194] S750, determine whether the temperature difference between the actual temperature distribution information obtained and the preset temperature distribution is within the preset range. If so, stop heating; if not, obtain temperature difference information, and analyze the temperature difference information based on the Floyd-Warshall algorithm to determine the shortest control path, and control the temperature of the precision heating state equipment to the preset temperature distribution based on the shortest control path.

[0195] In this step, the actual temperature distribution is compared with the preset temperature distribution (that is, the current temperature is continuously compared with the typical temperature map under the current task, ambient temperature and operating status) to determine whether the temperature difference is within the allowable range. If the temperature difference exceeds the range, the temperature difference information of each part is obtained; the Floyd-Warshall algorithm is used to analyze the temperature difference, determine the shortest control path, optimize the temperature control strategy, and adjust the temperature of the equipment based on the shortest control path to achieve the preset temperature distribution. Through fine control and optimization of the path, it is ensured that the equipment temperature accurately reaches the target distribution. The shortest path algorithm optimizes the control strategy, reduces unnecessary heating steps, improves the control efficiency, monitors and adjusts in real time, ensures the uniformity and stability of the temperature distribution, avoids local overheating or overcooling, accurately controls the temperature distribution, ensures the repeatability and accuracy of the experimental results, and enhances the credibility of the experimental data.

[0196] Reference Figure 9The method for determining whether the temperature difference between the actual temperature distribution information obtained and the preset temperature distribution is within a preset range specifically includes the following:

[0197] B100: Obtain a first distribution node set based on actual temperature distribution information.

[0198] Specifically, key nodes are extracted from the actual temperature distribution information to form a first distribution node set. By extracting the key nodes, the subsequent comparison and analysis process can be simplified and the calculation efficiency can be improved.

[0199] B200: Obtain a second distribution node set based on the target temperature information.

[0200] Specifically, key nodes are extracted from the target temperature information to form a second distributed node set, ensuring that the two compared node sets have the same structure and position, thereby improving the accuracy and consistency of the comparison.

[0201] B300, traverse the first distribution node set and determine the first local area of ​​each node;

[0202] A first temperature average value, a first temperature standard deviation, and a first temperature structure in a first local area are obtained.

[0203] B400, traverse the second distribution node set and determine the second local area of ​​each node;

[0204] A second temperature average value, a second temperature standard deviation, and a second temperature structure in the second local area are obtained.

[0205] For each node, its local area is determined, and the temperature average, temperature standard deviation and temperature structure in the area are calculated to ensure that the compared local areas have the same size and position, thereby improving the accuracy of the comparison.

[0206] Specifically, the calculation formula for the average temperature includes: Among them, T i is the temperature of the i-th point in the corresponding local area, and N is the number of points in the corresponding local area.

[0207] The calculation formula for temperature standard deviation includes:

[0208] The calculation formula of temperature structure includes:

[0209] B500, obtaining a first coefficient based on all first temperature averages, second temperature averages, and a first preset formula.

[0210] The first coefficient is calculated by a preset first formula to reflect the degree of difference in the average temperature values, quantify the difference in the average temperature values, and provide basic data for subsequent comprehensive evaluation.

[0211] Among them, the first preset formula is: Among them, μ p is the first temperature average, μ pt is the second temperature average value, and C1 is a non-zero constant used to prevent the denominator from being zero.

[0212] B600: Obtain a second coefficient based on all first temperature standard deviations, second temperature standard deviations, and a second preset formula.

[0213] The second preset formula is: Among them, σ p is the first temperature standard deviation, σ pt is the second temperature standard deviation, and C2 is a non-zero constant used to prevent the denominator from being zero.

[0214] B700, based on all the first temperature structures, the second temperature structures, and the third preset formula, obtain a third coefficient.

[0215] The third preset formula is: Among them, σ p is the first temperature structure, σ pt It is the second temperature structure, and C3 is a non-zero constant used to prevent the denominator from being zero.

[0216] B800, obtain a similarity degree based on the first coefficient, the second coefficient, the third coefficient, and the fourth preset formula.

[0217] Among them, the fourth preset formula is: SSIM(P,Pt)=l(P,Pt) α ×c(P,Pt) β ×s(P,Pt) γ , α, β, and γ are the relative importance of the metrics, all greater than 0.

[0218] B900, obtain the difference between the similarity degree and the preset threshold, and determine whether the difference is within the preset range YY+242300P.

[0219] The preset threshold is 1. When the difference is close to 0, it means that the similarity is close to 1. The difference is within the preset range, which means that the actual temperature distribution is consistent with the expected one.

[0220] Reference Figure 10 In a second aspect, the present application discloses a device heating simulation method for user thermal experience research, comprising:

[0221] S10: Receive a first interaction instruction from a user.

[0222] The interactive instruction is any one of the specified operation instructions.

[0223] S20 , based on the first interaction instruction, obtaining a typical temperature map and an ambient temperature corresponding to the first interaction instruction in a target database, and recording the result as target temperature information.

[0224] Acquiring typical temperature maps based on the target database reduces the time and complexity of manual setup, improves the efficiency and accuracy of data acquisition, and is suitable for test environments that require frequent switching between different temperature distributions.

[0225] S30, executing a temperature distribution instruction corresponding to the target temperature information on the test device.

[0226] By executing temperature distribution instructions on the test device, it can quickly respond to the user's switching instructions, adjust the device temperature in real time, and provide instant feedback. It is suitable for user thermal experience research that requires rapid adjustment of temperature distribution.

[0227] S40: Determine a second interaction instruction in response to the user switching instruction.

[0228] S50, controlling the test equipment to cool down to the initial temperature;

[0229] S60, obtaining a typical temperature map corresponding to the second interaction instruction from a target database based on the second interaction instruction, and recording the map as target temperature information;

[0230] S70: Execute a temperature distribution instruction corresponding to the target temperature information on the test device.

[0231] When the user switches the operation instruction, the solution disclosed in this embodiment can quickly control the temperature of the test equipment when executing the previous instruction, ensuring that the warmth of the test equipment does not affect the actual warmth presentation when executing the next instruction.

[0232] Among them, the temperature control time is less than the time it takes for the user to complete a single test task, so the user does not need to wait for the test machine temperature control when switching tasks.

[0233] Reference Figure 11 and Figure 12 In a third aspect, the present application discloses an interactive system for user thermal experience research, including a testing machine 90 and a temperature-controlled workbench (i.e., a target workbench 101) for placing the testing machine 90. The temperature-controlled workbench includes a cooling module (not shown) and a non-contact temperature acquisition module 102.

[0234] The testing machine 90 includes a user thermal experience software interactive simulation module 91, a first side matrix temperature control module 92, a first side insulation module 93, a central module 94, a second side insulation module 95, a second side matrix temperature control module 96, and a shell simulation module 97, which are arranged in sequence: among them, the user thermal experience software interactive simulation module 91 is used to receive interactive instructions of the user's thermal experience.

[0235] The first side matrix temperature control module 92 is used to regulate the temperature distribution on the first side; the first side insulation module 93 is used to prevent the heat of the first side matrix temperature control module 92 from diffusing away from the user thermal experience software interactive simulation module 91.

[0236] The central module 94 includes a counterweight module 941 and a power module 942. The counterweight module 941 is used to control the weight of the overall test machine 90 to be consistent with the counterweight of the target simulation device. Specifically, the weight and position of each group of components can be obtained by disassembling the same mobile phone, and the original components can be replaced with counterweight materials with the same area but thinner thickness. All counterweight materials are filled with thermal conductive materials and are in full contact with the mobile phone matrix temperature control module.

[0237] The second side insulation module 95 is used to block the heat of the power supply module 942 away from the side of the first side insulation module 93; the second side matrix temperature control module 96 is used to regulate the temperature distribution on the second side; the shell simulation module 97 is matched with the user thermal experience software interactive simulation module 91 and is used to encapsulate the first side matrix temperature control module 92, the first side insulation module 93, the central module 94, the second side insulation module 95, and the second side matrix temperature control module 96.

[0238] The first-side insulation module 93 and the second-side insulation module 95 are used to block the influence of the first-side matrix temperature control module 92 and the second-side matrix temperature control module 96 on the power module 942 .

[0239] The power supply module 942 is in communication with the user thermal experience software interactive simulation module 91 , the first-side matrix temperature control module 92 , and the second-side matrix temperature control module 96 for power supply.

[0240] Specifically, the user thermal experience software interactive simulation module 91 and the first side matrix temperature control module 92 and the second side matrix temperature control module 96 are completely independent modules with no interactive influence, that is, the user thermal experience software interactive simulation module 91 is a constant uniform low heat source, which does not affect the first side matrix temperature control module 92 and the second side matrix temperature control module 96. The temperature distribution of the first side matrix temperature control module 92 and the second side matrix temperature control module 96 does not affect the performance experience of the user thermal experience software interactive simulation module 91, and the user thermal experience software interactive simulation module 91 and the first side matrix temperature control module 92 and the second side matrix temperature control module 96 can be set in linkage according to the control background 103.

[0241] Furthermore, the user thermal experience software interactive simulation module 91 includes an interactive front-end submodule 911, a communication submodule 912 and a background computing submodule 913. The interactive front-end submodule 911 is used to collect user operations (i.e., interactive operations of user thermal experience); the communication submodule 912 is communicatively connected to the interactive front-end submodule 911, and is used to encode in combination with the current task scenario and user operations, and the encoded signal is transmitted to the background computing submodule 913 via wireless communication; the background computing submodule 913 is signal-connected to the communication submodule 912, and is used to perform calculations on the task scenario and user operations, and the calculation results are then fed back to the interactive front-end submodule 911 via the communication module, wherein the calculation results include a temperature control strategy, and the first-side matrix temperature control module 92 and the second-side matrix temperature control module 96 are controlled based on the temperature control strategy.

[0242] The temperature control strategy is the shortest control strategy for controlling the temperature distribution of the first-side matrix temperature control module 92 and the second-side matrix temperature control module 96 to a temperature distribution corresponding to the target temperature information.

[0243] The purpose of separating the interactive front-end submodule 911 and the background computing submodule 913 in physical space is to avoid the computing chip used for the background computing submodule 913 from having different temperatures due to different user operations, thereby affecting the user's thermal experience data.

[0244] Specifically, the power consumption of each screen area of ​​the interactive front-end terminal module 911 is fixed, that is, the user's differentiated interactive operations have no effect on the heat generation of each screen area. The heat generation of the interactive front-end terminal module 911 is uniform and much lower than that of the matrix temperature control module.

[0245] The communication submodule 912 uses a high-speed wireless network, such as 5.5G (5G-A mobile communication technology, enhanced version of 5G). The communication submodule 912 is a fixed frequency and fixed length communication, that is, the user's differentiated operations have no effect on the power consumption of the communication submodule 912.

[0246] Specifically, the communication submodule 912 communicates with the communication base station 104 at a fixed frequency. An independent phased array base station is deployed in the laboratory environment where the test machine 90 is located to ensure that when different users use the test machine 90 differently, the average communication delay time between the communication submodule 912 and the communication base station 104 is less than 5ms.

[0247] Furthermore, the communication base station 104 also receives the ambient temperature data of the mobile phone sent by the ambient temperature detection module 43 at a fixed frequency. The average communication delay time between the ambient temperature detection module 43 and the base station is less than YY+242300P

[0248] 5ms.

[0249] Reference Figure 13 The first-side matrix temperature control module 92 includes a matrix heating resistor 921 and a plurality of PWM temperature control circuits 922 (pulse width modulation circuits). The matrix heating resistor 921 includes a plurality of independently arranged heating resistors 921. Insulating material is filled between two adjacent heating resistors 921, and the thermal conductivity of the insulating material is consistent with the thermal conductivity of the heating resistor 921; each heating resistor 921 is connected to a PWM temperature control circuit 922.

[0250] Specifically, the PWM temperature control circuit 922 supplies a constant voltage, and controls the temperature of the matrix heating resistor 921 by controlling the frequency of on and off and the proportion of on and off. The heating power of each heating resistor 921 is equal to U 2 / R×conduction ratio / frequency, where U is the supply voltage of the power module 942 and R is the impedance of each heating resistor 921.

[0251] Specifically, the frequency of on and off of the PWM temperature control circuit 922, as well as the temperature control strategy of the on and off ratios, are transmitted by the control background 103 to the matrix temperature control module through the background calculation submodule 913 of the test machine 90, the base station, and the communication submodule 912.

[0252] In this embodiment, the second-side matrix temperature control module 96 has the same structure as the first-side matrix temperature control module 92 .

[0253] In specific operations, the target workbench 101 obtains the temperature distribution of the test machine 90 through the temperature acquisition module according to the test task, and feeds back the difference between the current temperature of the test machine 90 and the preset temperature of the test task to the control background 103. The control background 103 controls the matrix temperature control modules on both sides through the matrix temperature control method to precisely heat the temperature distribution of the test machine 90 to the preset temperature of the test task.

[0254] Specifically, the matrix temperature control method refers to: the control background 103 adjusts the heating of the matrix heating resistor 921 by controlling the parameters of the matrix temperature control module PWM temperature control circuit 922, and obtains the three-dimensional temperature point cloud of the test set through the temperature acquisition module. When the structural similarity index (SSIM) of the temperature distribution of the three-dimensional temperature point cloud and the typical temperature map is the largest, the temperature distribution of the matrix heating resistor 921 is controlled to be consistent with the typical temperature map.

[0255] The Floyd-Warshall algorithm is used to obtain the shortest path from the starting temperature distribution to the typical temperature map temperature distribution, achieving the shortest heating time and avoiding temperature overshoot that causes excessive temperature control time. The PWM temperature control circuit 922 parameters and the typical temperature map when the SSIM is maximum are fed into the deep neural network. By learning the relationship between the PWM temperature control circuit 922 parameters and the typical temperature map under different tasks of different users, a matrix temperature control method is formed.

[0256] Specifically, SSIM considers the temperature of each point cloud by moving point cloud by point cloud, and in each step, calculates the average temperature, standard deviation of temperature, and temperature structure in the local window.

[0257] By constructing three sets of comparison functions between the current temperature distribution P and the typical temperature spectrum temperature distribution Pt: average temperature comparison function l(P, Pt), c(P, Pt) and s(P, Pt).

[0258] The obtained SSIM(P,Pt)=l(P,Pt)α×c(P,Pt)β×s(P,Pt)γ, where α, β, and γ are the relative importance of the metrics, all greater than 0.

[0259] Specifically, the Floyd-Warshall algorithm means that for each current matrix heating resistor temperature distribution, it is the starting node, and the updated matrix heating resistor temperature distribution is the intermediate node k. If there is other intermediate node j that is better than the intermediate node k, then the starting node is updated through the intermediate node j, and the iteration is repeated to guide the shortest path between all nodes to be found. When the shortest path between each node is found, the shortest control strategy for the current temperature distribution of each matrix heating resistor to the typical temperature spectrum temperature distribution can be obtained.

[0260] According to the test task, the user can select the corresponding test machine 90 from the target workbench 101, and the user interacts with the user thermal experience software interactive simulation module 91 of the test machine 90. The interactive front-end submodule 911 collects user operations, and the communication submodule 912 encodes the user operations into wireless signals and transmits them to the background calculation submodule 913. The background calculation module calculates the user operations, and the calculation results are then fed back to the interactive front-end submodule 911 through the communication module.

[0261] Among them, the temperature control workbench will control the temperature of the test machine 90 degrees in advance to the typical temperature distribution of the test task according to the test task, and inform the user whether the temperature control is in place through traffic lights.

[0262] Before the user picks up the phone, the temperature control strategy of the test machine 90 will maintain the temperature distribution of the test machine 90 as a typical temperature map of the test task; after the user picks up the phone, the temperature control strategy of the test machine 90 remains unchanged.

[0263] When the user switches tasks, after placing the previous test machine 90 on the temperature control workbench, the control background 103 will set the temperature distribution of the test machine 90 to the temperature distribution of the typical temperature map of the next round of tasks according to the next round of test tasks.

[0264] Specifically, since the temperature control workbench can deploy multiple test machines 90 at the same time and set the test machine 90 to the temperature distribution of the typical temperature spectrum under the test task, the temperature control time is less than the time it takes for the user to complete a single test task. Therefore, the user does not need to wait for the test machine 90 to be temperature controlled when switching tasks.

[0265] Furthermore, the setting scheme of the test machine in this system can refer to the layout scheme of the mirror device in the device heating simulation method for user thermal experience research disclosed in the first aspect of this application, so it will not be repeated here.

[0266] This application aims to collect data from thermal experience tests of users with heating mobile phones, and establishes an interactive system for user thermal experience research. First, by operating a mirror machine and a non-contact temperature collection module, typical temperature maps of different test tasks are obtained. Compared with setting the engineering machine to a typical scene for testing, this can effectively avoid the heating differences of mobile phones caused by differences in engineering machine conditions (such as fluctuations in network signal strength, communication signal, power supply, lighting, etc.), and improve the effectiveness of collecting data from thermal experience tests of users with heating mobile phones.

[0267] The second is to use a matrix temperature control module to achieve high-precision distributed temperature control in different areas of the test machine. Compared with the use of chip power heating and resistor equivalent power heating, it can effectively avoid the problem of high-precision heating control in multiple areas and improve the effectiveness of thermal experience test data collection for mobile phone users.

[0268] The third is to propose a matrix temperature control method to quickly control the matrix temperature control module to fit the typical temperature spectrum. There is currently no other similar method. The temperature control method of the present invention comprehensively considers the materials, layout, power, weighting strategy and materials of the matrix temperature control module, and the user thermal experience software interactive simulation module materials. The application of this invention can improve the temperature control accuracy of the testing machine.

[0269] Fourth, by decoupling and linking the user thermal experience software interactive simulation module and the matrix temperature control module, independent control of temperature factors and non-temperature factors is achieved. Currently, there is no other effective method to achieve this function. The present invention sets the user thermal experience software interactive simulation module to local interaction, wireless transmission, and cloud-based background calculation to achieve unified power consumption of front-end interaction, and decouple the user thermal experience software interactive simulation module and the matrix temperature control module. By controlling the background, the test tasks are uniformly sent to the test machine and the temperature control workbench, thereby realizing temperature control and test scene setting based on the test tasks.

[0270] Fifth, by linking the temperature control workbench with the test machine, users can use different test machines to perform different tasks, reducing the temperature control waiting time for task switching. While the user is executing the test task, the temperature of other test machines can be quickly raised or lowered, and the task switching waiting time can be reduced to 0s.

[0271] In a fourth aspect, the present application provides an experimental method for user thermal experience research, comprising:

[0272] Collecting raw operation data when users perform target tasks on target devices;

[0273] Reproduce the operations corresponding to the original operation data on the mirror device and obtain the 3D temperature point cloud fusion image corresponding to each operation;

[0274] Obtain the corresponding 3D temperature point cloud fusion image when users with different operating habits perform the same target task;

[0275] Obtain the actual operation information of different users and determine the corresponding graph weights;

[0276] Based on multiple atlas weights, multiple 3D temperature point cloud fusion images are weighted averaged to obtain a typical temperature atlas corresponding to the target task.

[0277] The test method for user thermal experience research disclosed in this embodiment effectively avoids the limitations of typical usage scenarios by performing actual user operations on a target device and reproducing the actual operations on a mirror device set up independently from the target device. This method can capture real usage habits and changes in external conditions, so that the obtained test collection results are closer to actual usage; the operations corresponding to the original operation data are reproduced on the mirror device, and a three-dimensional temperature point cloud fusion map corresponding to each operation is obtained. This method can accurately control the temperature area and temperature changes, avoiding inaccurate test results caused by inaccurate temperature control.

[0278] In the same usage scenario, differences in usage habits of different users will lead to differences in the temperature of the test machine, which will become an interference factor in the user's thermal experience; this application can more comprehensively consider user differences and improve the validity and authenticity of the test results by obtaining the corresponding three-dimensional temperature point cloud fusion map when users with different operating habits operate the same target task.

[0279] In traditional methods, for the same mobile phone and the same typical usage scenario, when the test parameters are close to the limit, slight differences in the temperature of the test machine will cause large differences in the performance of the mobile phone. This application obtains the actual operation information of different users, determines the corresponding map weights, and performs weighted averaging on multiple three-dimensional temperature point cloud fusion maps based on multiple map weights to obtain a typical temperature map corresponding to the target task. This method can effectively handle the performance differences caused by temperature nonlinearity near the limit parameters, thereby improving the accuracy of the test results.

[0280] Traditional methods lack scenario-based rapid temperature control methods. This application decouples and links temperature control and scene control to quickly and accurately simulate temperature changes in different scenarios, avoiding the problem of inaccurate temperature control caused by scene switching.

[0281] The method disclosed in this application acquires an accurate three-dimensional temperature point cloud fusion map by collecting and reproducing the actual operation data of users, takes into account the differences in the operating habits of different users, and effectively solves the problems existing in the existing technology through weighted averaging and scenario-based rapid temperature control, thereby improving the authenticity and validity of the test results of mobile phone users' thermal experience research.

[0282] The computer device according to an embodiment of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache). The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, a flash memory, etc.

[0283] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the computer device to perform desired functions. In one embodiment of the present disclosure, the processor is used to execute the computer-readable instructions stored in the memory, causing the computer device to execute all or part of the steps of the device heating simulation method for user thermal experience research or the experimental method for user thermal experience research described in the aforementioned embodiments of the present disclosure.

[0284] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the scope of protection of this disclosure.

[0285] like Figure 14The present invention provides a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Figure 14 The computer device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0286] like Figure 14 As shown, the computer device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). Various programs and data required for the operation of the computer device are also stored in the RAM. The processor, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0287] Typically, the following devices can be connected to the I / O interface: input devices such as sensors or visual information acquisition devices; output devices such as display screens; storage devices such as tapes and hard disks; and communication devices. The communication device can allow the computer device to communicate with other devices (such as edge computing devices) wirelessly or by wire to exchange data. Figure 14 A computer device having various devices is shown, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0288] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of the device heating simulation method for user thermal experience research or the test method for user thermal experience research of the embodiment of the present disclosure are executed.

[0289] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0290] According to an embodiment of the present disclosure, a computer-readable storage medium stores non-transitory computer-readable instructions. When executed by a processor, the non-transitory computer-readable instructions execute all or part of the steps of the device heating simulation method for user thermal experience research or the test method for user thermal experience research described in the aforementioned embodiments of the present disclosure.

[0291] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

[0292] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0293] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this disclosure are merely illustrative and not restrictive, and should not be construed as necessarily possessed by each embodiment of the present disclosure. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, rather than as limitations. These details do not limit the present disclosure to necessarily being implemented using these specific details.

[0294] In the present disclosure, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "including," "comprising," "having," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0295] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0296] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0297] Various changes, substitutions, and modifications may be made to the technology described herein without departing from the teachings defined by the appended claims. Moreover, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of things, means, methods, and actions described above. Currently existing or later developed processes, machines, manufactures, compositions of things, means, methods, or actions that perform substantially the same function or achieve substantially the same results as the corresponding aspects described herein may be utilized. Accordingly, the appended claims include within their scope such processes, machines, manufactures, compositions of things, means, methods, or actions.

[0298] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0299] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A device heating simulation method for user thermal experience research, characterized in that: include: Collecting raw operation data when users perform target tasks on target devices; Reproducing the operation corresponding to the original operation data on the mirror device, and obtaining a three-dimensional temperature point cloud fusion image corresponding to each operation; Obtaining the three-dimensional temperature point cloud fusion image corresponding to the same target task performed by users with different operating habits; Obtain the actual operation information of different users and determine the corresponding graph weights; Performing weighted averaging on the plurality of three-dimensional temperature point cloud fusion images based on the plurality of image weights to obtain a typical temperature image corresponding to the target task, and storing the obtained different test tasks, ambient temperatures and their corresponding typical temperature images in a target database; Based on the test task instruction received on the test device, a typical temperature map and an ambient temperature corresponding to the test task instruction are obtained in the target database and recorded as target temperature information; A temperature distribution instruction corresponding to the target temperature information is executed on the test device.

2. The device heating simulation method for user thermal experience research according to claim 1, characterized in that: The obtaining of the three-dimensional temperature point cloud fusion image corresponding to each operation includes: respectively collecting two-dimensional infrared image data of the mirror device at different preset sides; respectively collecting three-dimensional point cloud data of the mirror device at different preset sides; fusing the two-dimensional infrared image data from all sides to obtain a two-dimensional infrared fused image; Fusing the three-dimensional point cloud data from all sides to obtain three-dimensional point cloud fusion information; Splicing the two-dimensional infrared fusion image with the three-dimensional point cloud fusion information to obtain a three-dimensional temperature point cloud fusion image; The mirror device and the target device are devices of the same model.

3. The device heating simulation method for user thermal experience research according to claim 2, characterized in that: After each time the mirror device completes a single operation, performing non-contact cooling on the mirror device; When the temperature of the mirroring device drops to a preset temperature, the next operation is triggered.

4. The device heating simulation method for user thermal experience research according to claim 1, characterized in that: The obtaining of actual operation information of different users and determining corresponding graph weights includes: Obtain the user's actual operation image; Analyze the actual operation image based on preset standard operation actions to determine the operation completion level; A corresponding graph weight is determined based on the completion level.

5. The device heating simulation method for user thermal experience research according to claim 1, characterized in that: Executing the temperature distribution instruction corresponding to the target temperature information on the test device includes: determining a coarse heating strategy and a fine heating strategy based on the target temperature information; Performing rough heating on the test device according to the rough heating strategy to obtain a device in a rough heating state; Performing fine heating on the rough heating state device based on the fine heating strategy to obtain a fine heating state device; Collecting actual temperature distribution information of the precision heating state equipment; Determine whether the temperature difference between the actual temperature distribution information obtained and the preset temperature distribution is within a preset range. If so, stop heating; if not, obtain temperature difference information, and analyze the temperature difference information based on the Floyd-Warshall algorithm to determine the shortest control path, and control the temperature of the precision heating state equipment to the preset temperature distribution based on the shortest control path.

6. The device heating simulation method for user thermal experience research according to claim 5, characterized in that: The determining whether the temperature difference between the acquired actual temperature distribution information and the preset temperature distribution information is within a preset range includes: Based on the actual temperature distribution information, obtaining a first distribution node set; Based on the target temperature information, obtaining a second distribution node set; Traversing the first distributed node set to determine a first local area of ​​each node; Obtain a first temperature average value, a first temperature standard deviation, and a first temperature structure within the first local area; Traversing the second distributed node set to determine a second local area of ​​each node; Obtain a second temperature average value, a second temperature standard deviation, and a second temperature structure within the second local area; Obtaining a first coefficient based on all of the first temperature averages, the second temperature averages, and a first preset formula; Obtaining a second coefficient based on all of the first temperature standard deviation, the second temperature standard deviation, and a second preset formula; Obtaining a third coefficient based on all of the first temperature structure, the second temperature structure, and a third preset formula; Obtaining a similarity degree based on the first coefficient, the second coefficient, the third coefficient, and a fourth preset formula; Obtain a difference between the similarity degree and a preset threshold, and determine whether the difference is within a preset range.

7. A test method for user thermal experience research, characterized in that: include: Collecting raw operation data when users perform target tasks on target devices; Reproducing the operation corresponding to the original operation data on the mirror device, and obtaining a three-dimensional temperature point cloud fusion image corresponding to each operation; Obtaining the three-dimensional temperature point cloud fusion image corresponding to the same target task performed by users with different operating habits; Obtain the actual operation information of different users and determine the corresponding graph weights; Based on the weights of the multiple atlases, a weighted average is performed on the multiple three-dimensional temperature point cloud fusion images to obtain a typical temperature atlas corresponding to the target task.

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