Temperature prediction method and device, computer equipment, storage medium and program product

Abstract

The invention relates to a temperature prediction method and device, computer equipment, a storage medium and a program product. The method comprises the steps that in response to a temperature prediction instruction of a combustion object, object parameters of the combustion object and medium parameters of a cooling medium are obtained, and the convective heat transfer coefficient and the object temperature of the combustion object at different prediction moments are determined according to the object parameters, the medium parameters and a heat dissipation cooling model; the heat dissipation cooling model represents a function relationship among the object parameters, the medium parameters, the convective heat transfer coefficient and the object temperature, and is used for predicting the object temperature at the next prediction moment according to the object temperature at the previous prediction moment. According to the method, continuous prediction of the object temperature at the next prediction moment is obtained based on the object temperature prediction at the previous prediction moment, the heat dissipation cooling model can accurately represent the function relation among the object parameters, the medium parameters, the convective heat transfer coefficient and the object temperature, and the accuracy of the predicted object temperature can be correspondingly improved.

Description

Technical Field

[0001] The present application relates to the field of temperature measurement technology, and in particular to a temperature prediction method, device, computer equipment, storage medium and program product. Background Art

[0002] With the continuous development of new energy technologies, new energy equipment that uses batteries as energy supply has also been increasingly used.

[0003] Taking new energy vehicles as an example, it is inevitable that the vehicle will catch fire due to battery thermal runaway or other reasons during use, and it is necessary to take cooling and fire extinguishing measures to rescue the vehicle. In related technologies, it is usually necessary to estimate the temperature of the vehicle during the cooling process based on the actual situation on site to guide the arrangement of fire extinguishing measures.

[0004] However, the related art has a problem of poor prediction accuracy when estimating the temperature of a vehicle. Summary of the invention

[0005] Based on this, it is necessary to provide a temperature prediction method, device, computer equipment, storage medium and program product to address the above technical problems.

[0006] In a first aspect, an embodiment of the present application provides a temperature prediction method, the method comprising:

[0007] In response to a temperature prediction instruction of a burning object, obtaining object parameters of the burning object and medium parameters of a cooling medium;

[0008] According to the object parameters, medium parameters and heat dissipation and cooling model, the convective heat transfer coefficient and object temperature of the burning object at different prediction times are determined; the heat dissipation and cooling model characterizes the functional relationship between the object parameters, medium parameters, convective heat transfer coefficient and object temperature, and is used to predict the object temperature at the next prediction time based on the object temperature at the previous prediction time.

[0009] In the embodiment of the present application, the object temperature of the burning object at different prediction times is predicted based on the object parameters of the burning object, the medium parameters of the cooling medium, and the heat dissipation and cooling model. Not only is the continuous prediction of the object temperature at the next prediction time achieved based on the object temperature prediction at the previous prediction time, but the heat dissipation and cooling model can accurately characterize the functional relationship between the object parameters, the medium parameters, the convective heat transfer coefficient and the object temperature, which can correspondingly improve the accuracy of the predicted object temperature.

[0010] In one embodiment, the temperature prediction instruction carries the name of the burning object material, the characteristic parameters of the burning object, the name of the cooling medium material and the characteristic parameters of the cooling medium; obtaining the object parameters of the burning object and the medium parameters of the cooling medium includes:

[0011] Determine the general parameters of the burning object according to the name of the burning object material, and determine the general parameters of the cooling medium according to the name of the cooling medium material;

[0012] The characteristic parameters of the combustion object and the common parameters of the combustion object are determined as object parameters, and the characteristic parameters of the cooling medium and the common parameters of the cooling medium are determined as medium parameters.

[0013] In an embodiment of the present application, the temperature prediction instruction carries the material name and characteristic parameters of the burning object and the cooling medium, and the general parameters can be determined based on the material name, thereby obtaining object parameters including the characteristic parameters of the burning material and the general parameters of the burning material, and medium parameters including the characteristic parameters of the cooling medium and the general parameters of the cooling medium, thereby simplifying the information that the user needs to input and improving the convenience of temperature prediction.

[0014] In one embodiment, determining the general parameters of the burning object according to the name of the burning object material includes:

[0015] Determine the material thermal parameters of the burning object in the first database according to the name of the burning object as the general parameters of the burning object;

[0016] Determine the general parameters of the cooling medium according to the name of the cooling medium substance, including:

[0017] The material thermal parameters of the cooling medium are determined in the second database according to the material name of the cooling medium as the general parameters of the cooling medium.

[0018] In the embodiment of the present application, a database of thermal parameters of various substances is preconfigured so that corresponding thermal parameters can be directly read from the database according to the substance name as common parameters, thereby improving the convenience and efficiency of parameter acquisition.

[0019] In one embodiment, the heat dissipation and cooling model includes a heat transfer coefficient formula and an object temperature formula; according to the object parameters, the medium parameters and the heat dissipation and cooling model, the convective heat transfer coefficient and the object temperature of the combustion object at different prediction times are determined, including:

[0020] The object parameters and medium parameters are input into the heat dissipation and cooling model, and the convective heat transfer coefficient of the combustion object at different prediction times is determined by the heat transfer coefficient formula;

[0021] Based on the convection heat transfer coefficient at different prediction times, the object temperature of the burning object at different prediction times is determined by the object temperature formula.

[0022] In the embodiment of the present application, the formula is solidified in the program, and the user only needs to input relevant parameter information to obtain the convective heat transfer coefficient and the object temperature of the burning object at different prediction times, thereby realizing the temperature prediction of the burning object in the cooling scenario, thereby reducing the application difficulty of the temperature prediction method.

[0023] In one embodiment, the convective heat transfer coefficient of the combustion object at different prediction times is determined by a heat transfer coefficient formula, including:

[0024] Determine the relative heat transfer intensity at different prediction times based on the object parameters and the medium parameters; the relative heat transfer intensity is used to characterize the relative intensity of convective heat transfer and conductive heat transfer between the burning object and the cooling medium;

[0025] For each prediction moment, the relative heat transfer intensity, the thermal conductivity in the medium parameters, and the object length in the object parameters are substituted into the heat transfer coefficient formula to obtain the convective heat transfer coefficient at the prediction moment.

[0026] In the embodiment of the present application, in the above method, the convective heat transfer coefficient at different prediction times is determined based on the heat transfer coefficient formula solidified in the program, which simplifies the process of determining the convective heat transfer coefficient, improves the efficiency of determining the convective heat transfer coefficient, and simultaneously improves the temperature prediction efficiency.

[0027] In one embodiment, the cooling medium is water, and the medium parameters include the gas-liquid phase transition temperature of water; the relative heat transfer intensity at different prediction times is determined according to the object parameters and the medium parameters, including:

[0028] When the object temperature at the last prediction moment is greater than or equal to the gas-liquid phase transition temperature of water, the relative heat transfer intensity at the next prediction moment adjacent to the last prediction moment is determined according to the object parameters, medium parameters and the two-phase heat transfer intensity formula; when the next prediction moment is the first prediction moment, the object temperature at the last prediction moment is the initial temperature of the burning object;

[0029] When the object temperature at the last prediction moment is lower than the gas-liquid phase transition temperature of water, the relative heat transfer intensity at the next prediction moment adjacent to the last prediction moment is determined according to the object parameters, the medium parameters and the single-phase heat transfer intensity formula.

[0030] In an embodiment of the present application, different heat transfer intensity formulas are selected based on the object temperature of the burning object at the previous prediction moment to determine the relative heat transfer intensity at the next prediction moment, so as to adapt to the actual heat dissipation conditions in the cooling scenario when the cooling medium is water, thereby improving the matching degree between the temperature prediction process and the actual heat dissipation conditions, thereby improving the accuracy of the obtained relative heat transfer intensity, and simultaneously improving the accuracy of the temperature prediction.

[0031] In one embodiment, determining the relative heat transfer intensity at a next prediction moment adjacent to a previous prediction moment according to the object parameters and the medium parameters and the two-phase heat transfer intensity formula includes:

[0032] Determine the flow characteristic parameters of the cooling medium, the droplet deformation scale and the heat transfer influence according to the medium parameters;

[0033] Determine the measurement value of superheat and latent heat between the cooling medium and the burning object at the last prediction moment according to the medium parameters and the object parameters;

[0034] The flow characteristic parameters, droplet deformation scale, heat transfer influence, and the measured values ​​of superheat and latent heat at the previous prediction moment are substituted into the two-phase heat transfer intensity formula to obtain the relative heat transfer intensity at the next prediction moment.

[0035] In the embodiment of the present application, the relative heat transfer intensity at the next prediction moment is determined based on the two-phase heat transfer intensity formula, which is adapted to the actual heat dissipation situation dominated by water evaporation heat dissipation in the cooling scenario, thereby improving the accuracy of the relative heat transfer intensity and temperature prediction under the actual heat dissipation situation.

[0036] In one embodiment, determining the relative heat transfer intensity at a next prediction moment adjacent to a previous prediction moment according to the object parameters and the medium parameters and the single-phase heat transfer intensity formula includes:

[0037] Determine the flow characteristic parameters of the cooling medium, the droplet deformation scale and the heat transfer influence according to the medium parameters;

[0038] Determine the temperature difference between the cooling medium and the burning object at the last prediction moment according to the medium parameters and the object parameters;

[0039] The flow characteristic parameters, droplet deformation scale, heat transfer influence and temperature difference value at the previous prediction moment are substituted into the single-phase heat transfer intensity formula to obtain the relative heat transfer intensity at the next prediction moment.

[0040] In the embodiment of the present application, the relative heat transfer intensity at the next prediction moment is determined based on the single-phase heat transfer intensity formula, which is adapted to the actual heat dissipation situation dominated by water convection heat dissipation in the cooling scenario, thereby improving the accuracy of the relative heat transfer intensity and temperature prediction under the actual heat dissipation situation.

[0041] In one embodiment, the object parameters include the initial temperature, density, specific heat capacity, thermal conductivity and thickness of the burning object; the medium parameters include the initial temperature of the cooling medium; the heat dissipation and cooling model includes the object temperature formula; determining the object temperature of the burning object at different prediction times includes:

[0042] For each prediction moment, the combustion duration, convection heat transfer coefficient, initial temperature of the burning object, density, specific heat capacity, thermal conductivity, object thickness and initial temperature of the cooling medium at the corresponding prediction moment are substituted into the object temperature formula to obtain the object temperature at the prediction moment.

[0043] In the embodiment of the present application, the object temperature formula is solidified in the program, and the user only needs to input relevant parameter information to obtain the object temperature of the burning object at different prediction times, thereby improving the convenience of applying the temperature prediction method and simultaneously improving the prediction efficiency.

[0044] In one embodiment, the method further comprises:

[0045] Generate a temperature change curve of the burning object based on the temperature of the object at different prediction moments.

[0046] In the embodiment of the present application, a temperature change curve of a burning object is generated, and the obtained object temperature is displayed graphically, so that users can intuitively understand the temperature change characteristics of the burning object in the cooling scenario, thereby improving the display effect of the obtained object temperature.

[0047] In one embodiment, the method further comprises:

[0048] receiving an adjustment operation for at least one of an object parameter and a medium parameter, and generating a new temperature prediction instruction;

[0049] In response to the new temperature prediction instruction, the new object temperature of the combustion object at different prediction times is determined according to the heat dissipation and temperature reduction model.

[0050] In an embodiment of the present application, a new temperature prediction instruction is generated based on the parameter adjustment operation, and a new round of temperature prediction is performed in response to the new temperature prediction instruction, which can adapt to the adjustment of fire extinguishing measures so that users can choose appropriate fire extinguishing measures, thereby improving the practicality of the temperature prediction method.

[0051] In a second aspect, an embodiment of the present application further provides a temperature prediction device, the device comprising:

[0052] A parameter acquisition module, used for acquiring object parameters of the combustion object and medium parameters of the cooling medium in response to a parameter input operation;

[0053] The temperature prediction module is used to determine the convective heat transfer coefficient and object temperature of the burning object at different prediction times based on the object parameters, medium parameters and heat dissipation and cooling model; the heat dissipation and cooling model characterizes the functional relationship between the object parameters, medium parameters, convective heat transfer coefficient and object temperature, and is used to predict the object temperature at the next prediction time based on the object temperature at the previous prediction time.

[0054] In a third aspect, an embodiment of the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the temperature prediction method provided in any one of the embodiments of the first aspect are implemented.

[0055] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the temperature prediction method provided in any one of the embodiments in the first aspect above are implemented.

[0056] In a fifth aspect, an embodiment of the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps in the temperature prediction method provided in any one of the embodiments in the first aspect above.

[0057] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, 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 application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] FIG1 is a diagram showing the internal structure of a computer device in one embodiment;

[0059] FIG2 is a schematic flow chart of a temperature prediction method in one embodiment;

[0060] FIG3 is a schematic diagram of a process for determining object parameters and medium parameters in one embodiment;

[0061] FIG4 is a schematic diagram of a process for determining the temperature of an object in one embodiment;

[0062] FIG5 is a schematic diagram of a process for determining a convective heat transfer coefficient in one embodiment;

[0063] FIG6 is a schematic diagram of a process for determining relative heat transfer intensity in one embodiment;

[0064] FIG7 is a schematic diagram of a process for determining relative heat transfer intensity in another embodiment;

[0065] FIG8 is a schematic diagram of a process for determining relative heat transfer intensity in another embodiment;

[0066] FIG. 9 is a graph showing the temperature of a burning object over time in one embodiment;

[0067] FIG10 is a schematic flow chart of a temperature prediction method in another embodiment;

[0068] FIG11 is a schematic flow chart of a temperature prediction method in another embodiment;

[0069] FIG. 12 is a structural block diagram of a battery lithium deposition detection device in one embodiment. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the term "including" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0072] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0073] In the description of the embodiments of the present application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In the description of the embodiments of the present application, the term "plurality" refers to more than two (including two), unless otherwise clearly and specifically defined.

[0074] With the continuous development of new energy technologies, new energy equipment that uses batteries as energy supply has also been increasingly used.

[0075] Taking new energy vehicles as an example, spontaneous combustion of the vehicle due to battery thermal runaway or other reasons is inevitable during use. It is necessary to cool down the vehicle and extinguish the fire for rescue.

[0076] In the related technology, professionals are usually required to estimate the temperature of the vehicle during the cooling process according to the actual situation on site, timely assess the risks, and guide the arrangement of fire-fighting measures, so as to reduce resource waste and personal injury, etc. In order to reduce the subjective influence of personnel, infrared temperature measuring equipment is introduced in the related technology to detect the temperature of burning vehicles. However, the temperature measurement principle of the infrared temperature measuring equipment is to use an infrared camera to convert the infrared radiation signal on the surface of the object into an electrical signal, and then obtain the surface temperature of the object. The infrared radiation signal deep in the object is blocked by the surface material and cannot be transmitted. The related technology directly uses the surface temperature measured by the infrared temperature measuring equipment as the vehicle temperature, or further predicts the internal temperature of the vehicle based on the surface temperature as the vehicle temperature.

[0077] However, in cooling scenarios where fire extinguishing agents such as water are used to extinguish and rescue burning vehicles, the large amount of water vapor produced by water at high temperatures will greatly affect the conduction of infrared radiation signals, making it impossible for infrared thermal imaging to effectively measure the surface temperature of objects.

[0078] Therefore, the related art has a problem of poor prediction accuracy when estimating the temperature of the vehicle.

[0079] Based on this, the applicant designed a temperature prediction method, which predicts the object temperature of the burning object at different prediction times through a heat dissipation and cooling model that can accurately characterize the functional relationship between object parameters, medium parameters, convective heat transfer coefficient and object temperature, thereby achieving the technical effect of improving the accuracy of the predicted object temperature.

[0080] In one embodiment, a temperature prediction method is provided, and the method is applied to the computing device in FIG. 1 as an example for explanation. The computer device includes a processor 102, a memory 103, an input device 104, a communication interface 105, and a display unit 106 connected through a system bus 101. Among them, the processor 102 of the computer device is used to provide computing and control capabilities. The memory 103 of the computer device includes a non-volatile storage medium 1031 and an internal memory 1032. The non-volatile storage medium 1031 stores an operating system and a computer program. The internal memory 1032 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium 1031. The communication interface 105 of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor 102, a temperature prediction method is implemented.

[0081] Those skilled in the art will understand that the structure shown in FIG. 1 is merely a block diagram of a partial structure related to the embodiment of the present application, and does not constitute a limitation on the computer device to which the embodiment of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0082] In one embodiment, the present application provides a temperature prediction method, as shown in FIG2 , the embodiment includes the following steps:

[0083] S210. In response to a temperature prediction instruction of a burning object, obtain object parameters of the burning object and medium parameters of a cooling medium.

[0084] The temperature prediction instruction is triggered based on the user input operation. The burning object is the burning object in the cooling scene, such as a vehicle, a battery or an iron door. The cooling medium is the medium used to cool the burning object in the cooling scene, such as water or dry ice. The cooling scene is an application scenario in which a cooling medium is used to cool the burning object.

[0085] Optionally, the computer device triggers the generation of a temperature prediction instruction for the burning object under the input operation of the user, and the computer device responds to the temperature prediction instruction to obtain object parameters of the burning object and medium parameters of the cooling medium based on the user input information.

[0086] For example, the computer device may display an information input interface, and the user may input object parameters of the burning object and medium parameters of the cooling medium in the information input interface, and click OK to trigger the generation of a temperature prediction instruction for the burning object. The computer device directly obtains the object parameters of the burning object and medium parameters of the cooling medium input by the user in response to the temperature prediction instruction.

[0087] S220. Determine the convective heat transfer coefficient and object temperature of the burning object at different prediction times based on the object parameters, medium parameters and the heat dissipation and cooling model; the heat dissipation and cooling model characterizes the functional relationship between the object parameters, medium parameters, convective heat transfer coefficient and object temperature, and is used to predict the object temperature at the next prediction time based on the object temperature at the previous prediction time.

[0088] The heat dissipation and cooling model is a mathematical model pre-stored in a computer device and is used to predict the object temperature of a burning object in a cooling scenario.

[0089] It should be noted that the burning object in the cooling scenario will dissipate heat and cool down under the action of the cooling medium, and the object temperature of the burning object will change over time. The heat dissipation and cooling model can obtain the object temperature of the burning object at different prediction times based on the heat transfer relationship between the burning object and the cooling medium.

[0090] Optionally, after obtaining the object parameters of the burning object and the medium parameters of the cooling medium, the computer device may input the object parameters of the burning object and the medium parameters of the cooling medium into the heat dissipation and cooling model to run the heat dissipation and cooling model, and obtain the convective heat transfer coefficient of the burning object at different prediction moments based on the input object parameters and medium parameters, and predict the object temperature at the next prediction moment based on the object temperature at the previous prediction moment according to the convective heat transfer coefficient at different moments. In the case where the next prediction moment is the first prediction moment, the object temperature at the previous prediction moment is the initial temperature of the burning object in the object parameters.

[0091] In the embodiment of the present application, by responding to the temperature prediction instruction of the burning object, the object parameters of the burning object and the medium parameters of the cooling medium are obtained, so as to determine the convective heat transfer coefficient and the object temperature of the burning object at different prediction moments according to the object parameters, the medium parameters and the heat dissipation and cooling model; the heat dissipation and cooling model characterizes the functional relationship between the object parameters, the medium parameters, the convective heat transfer coefficient and the object temperature, and is used to predict the object temperature at the next prediction moment according to the object temperature at the previous prediction moment. In the above method, the object temperature of the burning object at different prediction moments is predicted based on the object parameters of the burning object, the medium parameters of the cooling medium and the heat dissipation and cooling model, which not only realizes the continuous prediction of the object temperature at the next prediction moment based on the object temperature prediction at the previous prediction moment, but also the heat dissipation and cooling model can accurately characterize the functional relationship between the object parameters, the medium parameters, the convective heat transfer coefficient and the object temperature, which can correspondingly improve the accuracy of the predicted object temperature.

[0092] The user input information received by the computer device includes the name of the burning object material, the characteristic parameters of the burning object, the name of the cooling medium material and the characteristic parameters of the cooling medium, and the generated temperature prediction instruction correspondingly carries the name of the burning object material, the characteristic parameters of the burning object, the name of the cooling medium material and the characteristic parameters of the cooling medium. Based on this, in one embodiment, as shown in FIG3 , the object parameters of the burning object and the medium parameters of the cooling medium are obtained in the above S210, including:

[0093] S310. Determine the general parameters of the combustion object according to the name of the material of the combustion object, and determine the general parameters of the cooling medium according to the name of the material of the cooling medium.

[0094] Among them, the name of the burning object material is the name of the main material constituting the burning object, such as iron, wood or plastic. The general parameters of the burning object are related to the main material constituting the burning object, and are usually parameters that do not change under normal conditions of the main material, such as thermal conductivity, specific heat capacity or density and other material thermal parameters. The name of the cooling medium material is the name of the main material constituting the cooling medium, such as water or carbon dioxide. The general parameters of the cooling medium are related to the main material constituting the cooling medium, and are usually parameters that do not change under normal conditions of the main material, such as surface tension, kinematic viscosity, specific heat capacity or density.

[0095] Optionally, the computer device can read the burning object material name and the cooling medium material name carried in the temperature prediction instruction to determine the general parameters of the burning object according to the burning object material name, and determine the general parameters of the cooling medium according to the cooling medium material name.

[0096] Exemplarily, the computer device can be connected to the Internet to obtain the general parameters of the burning object according to the name of the burning object material, and obtain the general parameters of the cooling medium according to the name of the cooling medium material.

[0097] S320: Determine the characteristic parameters of the combustion object and the common parameters of the combustion object as object parameters, and determine the characteristic parameters of the cooling medium and the common parameters of the cooling medium as medium parameters.

[0098] The characteristic parameters of the burning object are the parameters unique to the burning object in the current cooling scene, such as size or initial temperature. The characteristic parameters of the cooling medium are the parameters unique to the cooling medium in the current cooling scene, such as flow rate, particle size or initial temperature.

[0099] Optionally, the computer device can directly read the characteristic parameters of the combustion object carried in the temperature prediction instruction, and use the characteristic parameters of the combustion object and the general parameters of the combustion object as the object parameters of the combustion object. Similarly, the computer device can also directly read the characteristic parameters of the cooling medium carried in the temperature prediction instruction, and use the characteristic parameters of the cooling medium and the general parameters of the cooling medium as the medium parameters of the cooling medium.

[0100] In the embodiment of the present application, the temperature prediction instruction carries the name of the burning object, the characteristic parameters of the burning object, the name of the cooling medium, and the characteristic parameters of the cooling medium, so as to determine the general parameters of the burning object according to the name of the burning object, and determine the general parameters of the cooling medium according to the name of the cooling medium, and determine the characteristic parameters of the burning object and the general parameters of the burning object as object parameters, and determine the characteristic parameters of the cooling medium and the general parameters of the cooling medium as medium parameters. In the above method, the temperature prediction instruction carries the name of the burning object and the characteristic parameters of the cooling medium, and the general parameters can be determined based on the name of the material, so as to obtain the object parameters including the characteristic parameters of the burning material and the general parameters of the burning material, and the medium parameters including the characteristic parameters of the cooling medium and the general parameters of the cooling medium, which simplifies the information that the user needs to input and improves the convenience of temperature prediction.

[0101] To improve the efficiency of obtaining the universal parameters, in one embodiment, determining the universal parameters of the burning object according to the name of the burning object material in S310 includes:

[0102] The material thermal parameters of the burning object are determined in the first database according to the material name of the burning object as the universal parameters of the burning object.

[0103] The first database is a database preset in the computer device, and the first database includes material thermal parameters corresponding to a plurality of materials. The material thermal parameters of the burning object are parameters related to the heat conduction of the burning material. For example, the material thermal parameters of the burning object may include thermal conductivity, specific heat capacity or density, etc.

[0104] Optionally, the computer device may determine the material thermal parameters corresponding to the material name of the burning object in the first database by table lookup according to the material name of the burning object, as the general parameters of the burning object.

[0105] Accordingly, in the above S310, the general parameters of the cooling medium are determined according to the name of the cooling medium substance, including:

[0106] The material thermal parameters of the cooling medium are determined in the second database according to the material name of the cooling medium as the general parameters of the cooling medium.

[0107] The second database is a database preset in the computer device, and the second database includes thermal parameters of multiple substances. The thermal parameters of the cooling medium are parameters related to the heat conduction of the cooling medium. For example, the thermal parameters of the cooling medium may include surface tension, kinematic viscosity, specific heat capacity or density, etc.

[0108] Optionally, the computer device may determine the material thermal parameters corresponding to the cooling medium material name in the second database by looking up the cooling medium material name as the cooling medium general parameters.

[0109] It should be noted that the first database and the second database can be two independent databases, the first database includes the material thermal parameters of the main substances constituting the combustion object, and includes the material thermal parameters of multiple main substances, and the second database includes the material thermal parameters of the main substances constituting the cooling medium, and also includes the material thermal parameters of multiple main substances. The computer device can determine the material thermal parameters of the combustion object in the first database and the material thermal parameters of the cooling medium in the second database respectively. The first database and the second database can be the same parameter database, and the parameter database includes multiple material thermal parameters, including not only the material thermal parameters of different main substances constituting the combustion object, but also the material thermal parameters of different main substances constituting the cooling medium. Exemplarily, the above database can be presented in the form of a data table, such as the following Table 1 which exemplarily shows the material thermal parameters of multiple common substances.

[0110]

[0111] In the embodiment of the present application, the thermal parameters of the burning object are determined in the first database according to the name of the burning object as the general parameters of the burning object, and the thermal parameters of the cooling medium are determined in the second database according to the name of the cooling medium as the general parameters of the cooling medium. In the above method, by pre-configuring a database of thermal parameters of various substances, the corresponding thermal parameters can be directly read from the database according to the name of the substance as the general parameters, which improves the convenience and efficiency of parameter acquisition.

[0112] The heat dissipation and cooling model includes a heat transfer coefficient formula and an object temperature formula. The heat transfer coefficient formula is used to determine the convective heat transfer coefficient at different prediction moments. The object temperature formula is used to determine the object temperature of the burning object at the corresponding prediction moment based on the convective heat transfer coefficient at different prediction moments. Therefore, in one embodiment, as shown in FIG4 , the above S220, based on the object parameters, the medium parameters and the heat dissipation and cooling model, determines the convective heat transfer coefficient and the object temperature of the burning object at different prediction moments, including:

[0113] S410, inputting the object parameters and the medium parameters into the heat dissipation and cooling model, and determining the convective heat transfer coefficient of the combustion object at different prediction times through the heat transfer coefficient formula.

[0114] The heat transfer coefficient formula is an expression that characterizes the functional relationship between some parameters of the object parameters and the medium parameters and the convective heat transfer coefficient. The convective heat transfer coefficient is used to characterize the heat dissipation efficiency of the burning object in the current cooling scenario, that is, how fast the burning object cools down.

[0115] Optionally, after obtaining the object parameters of the burning object and the medium parameters of the cooling medium, the computer device may input the object parameters and the medium parameters into the heat dissipation and cooling model, and substitute some parameters into the heat transfer coefficient formula to obtain the convective heat transfer coefficient at the prediction time corresponding to a prediction cycle. Exemplarily, the current time is t0, the preset cycle is t, and the convective heat transfer coefficient h at the prediction time t1=t0+t can be obtained through the heat transfer coefficient formula.

[0116] S420. Based on the convective heat transfer coefficient at different prediction times, determine the object temperature of the burning object at different prediction times by using the object temperature formula.

[0117] The object temperature formula is an expression that characterizes the functional relationship between object parameters, some parameters of medium parameters, convection heat transfer coefficient, and object temperature.

[0118] Optionally, after obtaining the convective heat transfer coefficient at different prediction moments, for each prediction moment, the computer device may substitute the convective heat transfer coefficient at the prediction moment and some parameters of the object parameters and the medium parameters into the object temperature formula to obtain the object temperature of the burning object at the corresponding prediction moment. Exemplarily, continuing the above example, the object temperature T1 corresponding to the prediction moment t1 may be obtained based on the convective heat transfer coefficient h at the prediction moment t1 through the object temperature formula.

[0119] In the embodiment of the present application, the heat dissipation and cooling model includes a heat transfer coefficient formula and an object temperature formula. The object parameters and medium parameters are input into the heat dissipation and cooling model. The convective heat transfer coefficient of the burning object at different prediction times is determined by the heat transfer coefficient formula. Based on the convective heat transfer coefficient at different prediction times, the object temperature of the burning object at different prediction times is determined by the object temperature formula. In the above method, the formula is solidified in the program, and the user only needs to input the relevant parameter information to obtain the convective heat transfer coefficient and object temperature of the burning object at different prediction times, thereby realizing the temperature prediction of the burning object in the cooling scenario, thereby reducing the application difficulty of the temperature prediction method.

[0120] The heat transfer coefficient formula is an expression that characterizes the functional relationship between the relative heat transfer intensity, the thermal conductivity of the cooling medium, the object length of the burning object, and the convective heat transfer coefficient. In one embodiment, as shown in FIG5 , the convective heat transfer coefficient of the burning object at different prediction times is determined by the heat transfer coefficient formula in S410, including:

[0121] S510. Determine the relative heat transfer intensity at different prediction times according to the object parameters and the medium parameters; the relative heat transfer intensity is used to characterize the relative intensity of convective heat transfer and conductive heat transfer between the combustion object and the cooling medium.

[0122] Optionally, the computer device may obtain relevant parameters among the object parameters and the medium parameters according to the calculation formula of the relative heat transfer intensity, and substitute the obtained relevant parameters into the calculation formula of the relative heat transfer intensity to obtain the relative heat transfer intensity at different prediction times.

[0123] S520. For each prediction moment, substitute the relative heat transfer intensity, the thermal conductivity in the medium parameters, and the object length in the object parameters into the heat transfer coefficient formula to obtain the convective heat transfer coefficient at the prediction moment.

[0124] The object length is the length of the longest side of the burning object.

[0125] Optionally, after obtaining the relative heat transfer intensity at different prediction moments, the computer device can obtain the thermal conductivity in the medium parameters and the object length in the object parameters, and for each prediction moment, substitute the relative heat transfer intensity at the prediction moment, the thermal conductivity in the medium parameters and the object length into the heat transfer coefficient formula to obtain the convective heat transfer coefficient at the corresponding prediction moment.

[0126] Exemplarily, the Nusselt number is used to characterize the relative heat transfer intensity, and the heat transfer coefficient formula is as follows:

[0127]

[0128] Among them, h represents the convective heat transfer coefficient, Nu represents the Nusselt number, λ represents the thermal conductivity of the cooling medium, and d represents the object length of the burning object.

[0129] In an embodiment of the present application, the relative heat transfer intensity at different prediction moments is determined according to the object parameters and the medium parameters. For each prediction moment, the relative heat transfer intensity, the thermal conductivity in the medium parameters and the object length in the object parameters are substituted into the heat transfer coefficient formula to obtain the convective heat transfer coefficient at the prediction moment. The relative heat transfer intensity is used to characterize the relative intensity of convective heat transfer and conductive heat transfer between the burning object and the cooling medium. In the above method, the convective heat transfer coefficient at different prediction moments is determined based on the heat transfer coefficient formula solidified in the program, which simplifies the determination process of the convective heat transfer coefficient, improves the efficiency of determining the convective heat transfer coefficient, and simultaneously improves the temperature prediction efficiency.

[0130] When the cooling medium is water, the medium parameters include the gas-liquid phase transition temperature of water. Based on this, in one embodiment, as shown in FIG6 , the relative heat transfer intensity at different prediction times is determined according to the object parameters and the medium parameters in S510, including:

[0131] S610. When the object temperature at the previous prediction moment is greater than or equal to the gas-liquid phase transition temperature of water, determine the relative heat transfer intensity at the next prediction moment adjacent to the previous prediction moment based on the object parameters, the medium parameters and the two-phase heat transfer intensity formula; when the next prediction moment is the first prediction moment, the object temperature at the previous prediction moment is the initial temperature of the burning object.

[0132] Among them, the cooling medium is water, and the two-phase heat transfer intensity formula represents the expression of the relative heat transfer intensity when water evaporation is the dominant heat dissipation in the cooling scenario. The gas-liquid phase transition temperature represents the critical temperature at which the cooling medium undergoes a gas-liquid phase transition. For example, when the cooling medium is water, the gas-liquid phase transition temperature is the boiling point of water.

[0133] It should be noted that, when the next prediction time is not the first prediction time, the object temperature at the previous prediction time is the object temperature predicted by the heat dissipation and cooling model. The initial temperature of the burning object can be predetermined.

[0134] Optionally, the computer device may select an expression of relative heat transfer intensity based on the object temperature at the last prediction moment, so as to determine the relative heat transfer intensity at the next prediction moment by using the selected expression of relative heat transfer intensity. When the cooling medium is water, the computer device may compare the object temperature obtained at the last prediction moment with the boiling point temperature of water, and when the object temperature is greater than or equal to the boiling point temperature of water (water evaporation is dominant in the cooling scenario), select the two-phase heat transfer intensity formula to determine the relative heat transfer intensity at the next prediction moment adjacent to the last prediction moment according to the object parameters and the medium parameters and the two-phase heat transfer intensity formula.

[0135] S620. When the object temperature at the last prediction moment is lower than the gas-liquid phase transition temperature of water, determine the relative heat transfer intensity at the next prediction moment adjacent to the last prediction moment according to the object parameters, the medium parameters and the single-phase heat transfer intensity formula.

[0136] Among them, the cooling medium is water, and the single-phase heat transfer intensity formula represents the expression of relative heat transfer intensity when the cooling scenario is dominated by water convection heat dissipation.

[0137] Optionally, when the cooling medium is water, the computer device may compare the object temperature obtained at the last prediction moment with the boiling point temperature of water. When the object temperature is lower than the boiling point temperature of water (water convection heat dissipation is dominant in the cooling scenario), the single-phase heat transfer intensity formula is selected to determine the relative heat transfer intensity at the next prediction moment adjacent to the last prediction moment based on the object parameters and the medium parameters and the single-phase heat transfer intensity formula.

[0138] In the embodiment of the present application, the cooling medium is water, and the medium parameters include the gas-liquid phase transition temperature of water. When the object temperature at the last prediction moment is greater than or equal to the gas-liquid phase transition temperature of water, the relative heat transfer intensity at the next prediction moment adjacent to the last prediction moment is determined according to the object parameters, the medium parameters and the two-phase heat transfer intensity formula. When the object temperature at the last prediction moment is less than the gas-liquid phase transition temperature of water, the relative heat transfer intensity at the next prediction moment adjacent to the last prediction moment is determined according to the object parameters, the medium parameters and the single-phase heat transfer intensity formula. When the next prediction moment is the first prediction moment, the object temperature at the last prediction moment is the initial temperature of the burning object. In the above method, different heat transfer intensity formulas are selected based on the object temperature of the burning object at the last prediction moment to determine the relative heat transfer intensity at the next prediction moment, so as to adapt to the actual heat dissipation situation in the cooling scene when the cooling medium is water, thereby improving the matching degree between the temperature prediction process and the actual heat dissipation situation, thereby improving the accuracy of the obtained relative heat transfer intensity, and simultaneously improving the accuracy of the temperature prediction.

[0139] Regarding the case where the relative heat transfer intensity is determined based on the two-phase heat transfer intensity formula, in one embodiment, as shown in FIG. 7 , the above S610 determines the relative heat transfer intensity at the next prediction moment adjacent to the previous prediction moment based on the object parameters and the medium parameters and the two-phase heat transfer intensity formula, including:

[0140] S710, determining the flow characteristic parameters, droplet deformation scale and heat transfer influence of the cooling medium according to the medium parameters.

[0141] Among them, the flow characteristic parameter is used to characterize the flow condition of the cooling medium. Exemplarily, the flow characteristic parameter can be characterized by the Reynolds number. The droplet deformation scale is used to characterize the scale of droplet deformation and breakup in the cooling medium. Exemplarily, the droplet deformation scale can be characterized by the Weber number. The heat transfer influence quantity is used to characterize the measure of the mutual influence of energy and momentum migration in the cooling medium. Exemplarily, the heat transfer influence quantity can be characterized by the Prandtl number.

[0142] Optionally, the computer device may call relevant parameters in the medium parameters of the cooling medium to calculate the flow characteristic parameters, droplet deformation scale and heat transfer influence of the cooling medium. For example, the cooling medium is water, and the flow characteristic parameter is the Reynolds number (Re), the droplet deformation scale is the Weber number (We), and the heat transfer influence is the Prandtl number (Pr). The calculation formulas of the parameters are as follows:

[0143]

[0144]

[0145]

[0146] in, represents the density of water, Indicates the water flow rate, represents the diameter of the water droplet, represents the kinematic viscosity of water, is the surface tension of water, is the specific heat capacity of water, Represents the thermal conductivity of water.

[0147] S720. Determine the measurement values ​​of superheat and latent heat between the cooling medium and the burning object at the last prediction moment according to the medium parameters and the object parameters.

[0148] The medium parameters include the specific heat capacity, saturation temperature and vaporization heat of the cooling medium, and the object parameters include the initial temperature of the burning object.

[0149] Optionally, when the last prediction moment is the first prediction moment, the computer device can calculate the measurement value of superheat and latent heat between the cooling medium and the combustion object at the last moment based on the specific heat capacity, saturation temperature and vaporization heat of the cooling medium and the initial temperature of the combustion object. Exemplarily, the cooling medium is water, and the calculation formula of the measurement value (Ja) of superheat and latent heat between the cooling medium and the combustion object is as follows:

[0150]

[0151] Where T represents the temperature of the burning object at the last prediction moment, represents the saturation temperature of water, Represents the heat of vaporization of water.

[0152] Optionally, when the last prediction moment is not the first prediction moment, the computer device can calculate the measurement value of superheat and latent heat between the cooling medium and the burning object based on the specific heat capacity, saturation temperature and heat of vaporization of the cooling medium and the object temperature of the burning object predicted in the last time.

[0153] S730, substituting the flow characteristic parameters, droplet deformation scale, heat transfer influence quantity, and the measurement values ​​of superheat and latent heat at the previous prediction moment into the two-phase heat transfer intensity formula to obtain the relative heat transfer intensity at the next prediction moment.

[0154] Among them, the two-phase heat transfer intensity formula is an expression that characterizes the functional relationship between flow characteristic parameters, droplet deformation scale, heat transfer influence quantity, superheat and latent heat measurement values, and relative heat transfer intensity.

[0155] Optionally, the computer device can substitute the obtained flow characteristic parameters, droplet deformation scale, heat transfer influence quantity and the measurement values ​​of superheat and latent heat at the previous prediction moment into the two-phase heat transfer intensity formula to obtain the relative heat transfer intensity at the next prediction moment.

[0156] Exemplarily, the Nusselt number is used to characterize the relative heat transfer intensity, and the two-phase heat transfer intensity formula is as follows:

[0157]

[0158] Wherein, ae is a parameter that needs to be calibrated to determine the two-phase heat transfer intensity formula. For example, the two-phase heat transfer intensity formula is as follows:

[0159]

[0160] In the embodiment of the present application, the flow characteristic parameters, droplet deformation scale and heat transfer influence of the cooling medium are determined according to the medium parameters, and the measurement values ​​of superheat and latent heat between the cooling medium and the burning object at the last prediction moment are determined according to the medium parameters and the object parameters, so as to substitute the flow characteristic parameters, droplet deformation scale, heat transfer influence and the measurement values ​​of superheat and latent heat at the last prediction moment into the two-phase heat transfer intensity formula to obtain the relative heat transfer intensity at the next prediction moment. In the above method, the relative heat transfer intensity at the next prediction moment is determined based on the two-phase heat transfer intensity formula, which is suitable for the actual heat dissipation situation dominated by water evaporation heat dissipation in the cooling scene, and improves the accuracy of the relative heat transfer intensity and temperature prediction under the actual heat dissipation situation.

[0161] Regarding the case where the relative heat transfer intensity is determined based on the single-phase heat transfer intensity formula, in one embodiment, as shown in FIG8 , the above S620 determines the relative heat transfer intensity at the next prediction moment adjacent to the previous prediction moment according to the object parameters and the medium parameters and the single-phase heat transfer intensity formula, including:

[0162] S810. Determine the flow characteristic parameters, droplet deformation scale, and heat transfer influence of the cooling medium according to the medium parameters.

[0163] Optionally, the computer device may call relevant parameters in the medium parameters of the cooling medium to calculate the flow characteristic parameters, droplet deformation scale, and heat transfer influence of the cooling medium. Exemplarily, the flow characteristic parameter is the Reynolds number (Re), the droplet deformation scale is the Weber number (We), and the heat transfer influence is the Prandtl number (Pr). For the specific calculation process, see S710.

[0164] S820. Determine the temperature difference between the cooling medium and the burning object at the last prediction moment according to the medium parameters and the object parameters.

[0165] The medium parameters include the initial temperature and saturation temperature of the cooling medium, and the object parameters include the initial temperature of the burning object.

[0166] Optionally, when the last prediction moment is the first prediction moment, the computer device may calculate the temperature difference value between the cooling medium and the burning object at the last moment based on the initial temperature and saturation temperature of the cooling medium and the initial temperature of the burning object. For example, the temperature difference value ( ) is calculated as follows:

[0167]

[0168] in, represents the initial temperature of water.

[0169] Optionally, when the last prediction moment is not the first prediction moment, the computer device can calculate the temperature difference between the cooling medium and the burning object based on the initial temperature and saturation temperature of the cooling medium and the object temperature of the burning object predicted last time.

[0170] S830, substituting the flow characteristic parameters, droplet deformation scale, heat transfer influence amount and temperature difference value at the previous prediction time into the single-phase heat transfer intensity formula to obtain the relative heat transfer intensity at the next prediction time.

[0171] Among them, the single-phase heat transfer intensity formula is an expression that characterizes the functional relationship between flow characteristic parameters, droplet deformation scale, heat transfer influence quantity, temperature difference value and relative heat transfer intensity.

[0172] Optionally, the computer device can substitute the obtained flow characteristic parameters, droplet deformation scale, heat transfer influence and temperature difference value at the last prediction moment into the two-phase heat transfer intensity formula to obtain the relative heat transfer intensity at the next prediction moment.

[0173] For example, the Nusselt number is used to characterize the relative heat transfer intensity, and the single-phase heat transfer intensity formula is as follows:

[0174]

[0175] Wherein, af is a parameter required for calibration to determine the single-phase heat transfer intensity formula. For example, the single-phase heat transfer intensity formula is as follows:

[0176]

[0177] In the embodiment of the present application, the flow characteristic parameters, droplet deformation scale and heat transfer influence of the cooling medium are determined according to the medium parameters, and the temperature difference value between the cooling medium and the burning object at the last prediction moment is determined according to the medium parameters and the object parameters, so as to substitute the flow characteristic parameters, droplet deformation scale, heat transfer influence and the temperature difference value at the last prediction moment into the single-phase heat transfer intensity formula to obtain the relative heat transfer intensity at the next prediction moment. In the above method, the relative heat transfer intensity at the next prediction moment is determined based on the single-phase heat transfer intensity formula, which is suitable for the actual heat dissipation situation dominated by water convection heat dissipation in the cooling scene, and improves the accuracy of the relative heat transfer intensity and temperature prediction under the actual heat dissipation situation.

[0178] When the object parameters include the initial temperature, density, specific heat capacity, thermal conductivity and object thickness of the burning object, the medium parameters include the initial temperature of the cooling medium, and the heat dissipation and cooling model includes the object temperature formula, in one embodiment, determining the object temperature of the burning object at different prediction times in S220 includes:

[0179] For each prediction moment, the combustion duration, convection heat transfer coefficient, initial temperature of the burning object, density, specific heat capacity, thermal conductivity, object thickness and initial temperature of the cooling medium at the corresponding prediction moment are substituted into the object temperature formula to obtain the object temperature at the prediction moment.

[0180] Among them, the object temperature formula is derived from the expression of the macroscopic heat conduction relationship between the burning object and the cooling medium. The derivation process is as follows:

[0181]

[0182]

[0183]

[0184]

[0185] It represents the total heat transfer of the burning object. represents the density of the burning object, C represents the specific heat capacity of the burning object, V represents the volume of the burning object, represents the initial temperature of the burning object, Indicates the temperature of the burning object when the heat transfer ends infinitely (usually the initial temperature of the cooling medium), It indicates the heat loss after the combustion object starts to transfer heat. is the thermal conductivity of the burning object, is the thickness of the burning object.

[0186] Transcendental equation The solution is related to the thickness and material of the burning object and can be obtained based on an empirical formula. For example, the empirical formula is as follows:

[0187]

[0188] Wherein, a and b represent empirical parameters, for example, a=0.402, b=1.699.

[0189] Because the heat change △Q divided by the density of the burning object Specific heat capacity ,volume The temperature change △T can be obtained. △Q is , △T is . Expressing the temperature of the burning object at the time corresponding to the burning time t, the following formula can be obtained:

[0190]

[0191] The object temperature formula can be derived accordingly:

[0192]

[0193] Optionally, for each prediction moment, the computer device can substitute the combustion duration, convection heat transfer coefficient, initial temperature of the burning object, density, specific heat capacity, thermal conductivity, object thickness and initial temperature of the cooling medium at the corresponding prediction moment into the object temperature formula to calculate the object temperature at the corresponding prediction moment.

[0194] In the embodiment of the present application, the object parameters include the initial temperature, density, specific heat capacity, thermal conductivity and object thickness of the burning object, and the medium parameters include the initial temperature of the cooling medium. For each prediction moment, the combustion duration, convection heat transfer coefficient, initial temperature, density, specific heat capacity, thermal conductivity, object thickness and initial temperature of the cooling medium corresponding to the prediction moment are substituted into the object temperature formula to obtain the object temperature at the prediction moment. In the above method, the object temperature formula is solidified in the program, and the user only needs to input the relevant parameter information to obtain the object temperature of the burning object at different prediction moments, which improves the convenience of the application of the temperature prediction method and simultaneously improves the prediction efficiency.

[0195] To graphically display the obtained object temperature, in one embodiment, the method further includes:

[0196] Generate a temperature change curve of the burning object based on the temperature of the object at different prediction moments.

[0197] The temperature change curve is used to characterize the change of the temperature of the burning object over time in the cooling scene. For example, the temperature change curve of the burning object can be a curve graph or a bar graph.

[0198] Optionally, the computer device can use the prediction time as the horizontal axis and the object temperature as the vertical axis, and generate a curve graph or a bar graph of the object temperature changing over time based on the object temperature at different prediction times, so as to reflect the changing trend of the object temperature over time, such as before time t1, the cooling rate is low, after time t1, the cooling rate increases, or after time t2, the object temperature drops below the critical temperature of thermal runaway, etc.

[0199] In practical applications, computer equipment can also divide the object temperature at different predicted moments into multiple temperature intervals (such as high, medium, and low temperature zones), and generate a pie chart with the sector area representing the duration to reflect the duration of the temperature zone in which the object temperature is located. For example, the combustion temperature is in the high temperature zone for T1 duration, in the medium temperature zone for T2 duration, and in the low temperature zone for T3 duration.

[0200] For example, the computer device may call Excel to generate a temperature change curve of the burning object. For example, the computer device imports the temperature change curve of the burning object generated by the object temperature at different prediction moments into Excel, forming a table with one column for different prediction moments and another column for the object temperature at the corresponding prediction moment, and based on the table, generates a curve graph of the object temperature of the burning object changing over time as shown in FIG9 .

[0201] In the embodiment of the present application, a temperature change curve of a burning object is generated according to the temperature of the object at different predicted moments. In the above method, a temperature change curve of a burning object is generated, and the obtained object temperature is graphically displayed, so that the user can intuitively understand the temperature change characteristics of the burning object in the cooling scene, and improve the display effect of the obtained object temperature.

[0202] In actual applications, the user may also adjust parameters to generate new temperature prediction instructions. Therefore, in one embodiment, as shown in FIG10 , the method further includes:

[0203] S1010: Receive an adjustment operation for at least one of an object parameter and a medium parameter, and generate a new temperature prediction instruction.

[0204] After obtaining the object temperature of the burning object at different prediction times in the cooling scenario, the user can adjust at least one of the object parameter and the medium parameter based on the actual cooling demand to start a new round of temperature prediction. Adjusting at least one of the object parameter and the medium parameter corresponds to adjusting the fire extinguishing measures.

[0205] Optionally, the user performs an input operation on the computer device to adjust at least one of the object parameters and medium parameters previously input, which may be a parameter in the object parameters or a parameter in the medium parameters. The computer device correspondingly receives the adjustment operation for at least one of the object parameters and medium parameters, triggering the generation of a new temperature prediction instruction. Exemplarily, the computer device may directly modify the parameter adjusted by the user in the previously generated temperature prediction instruction, thereby generating a new temperature prediction instruction.

[0206] S1020. In response to a new temperature prediction instruction, determine a new object temperature of the burning object at different prediction times according to a heat dissipation and cooling model.

[0207] Optionally, after generating the new temperature prediction instruction, the computer device responds to the new temperature prediction instruction and determines the new object temperature of the burning object at different prediction times according to the heat dissipation and cooling model to perform a new round of temperature prediction. The specific process can be referred to the embodiments corresponding to the aforementioned Figures 1 to 10, which will not be repeated here.

[0208] In the embodiment of the present application, a new temperature prediction instruction is generated by receiving an adjustment operation for at least one of the object parameter and the medium parameter, and in response to the new temperature prediction instruction, the new object temperature of the burning object at different prediction moments is determined according to the heat dissipation and cooling model. In the above method, a new temperature prediction instruction is generated based on the parameter adjustment operation, and a new round of temperature prediction is performed in response to the new temperature prediction instruction, which can be adapted to the adjustment of fire extinguishing measures so that the user can select appropriate fire extinguishing measures, thereby improving the practicality of the temperature prediction method.

[0209] To facilitate understanding by those skilled in the art, the temperature prediction method provided by the present application is described in detail below. As shown in FIG11 , the method may include:

[0210] S1101, in response to a temperature prediction instruction of a burning object, determining a material thermal parameter of the burning object in a first database according to a material name of the burning object carried in the temperature prediction instruction as a general parameter of the burning object;

[0211] S1102, determining the material thermal parameters of the cooling medium in the second database according to the cooling medium material name carried in the temperature prediction instruction as the cooling medium general parameters;

[0212] S1103, determining the combustion object characteristic parameters and the combustion object common parameters carried in the temperature prediction instruction as object parameters of the combustion object;

[0213] S1104, determining the cooling medium characteristic parameters and cooling medium general parameters carried in the temperature prediction instruction as medium parameters of the cooling medium;

[0214] S1105, determining the flow characteristic parameters, droplet deformation scale and heat transfer influence amount of the cooling medium according to the medium parameters;

[0215] S1106, determining the measurement values ​​of superheat and latent heat between the cooling medium and the burning object at the last prediction moment according to the medium parameters and the object parameters;

[0216] S1107, determining the temperature difference value between the cooling medium and the burning object at the last prediction moment according to the medium parameters and the object parameters;

[0217] S1108. When the object temperature at the last prediction moment is greater than or equal to the gas-liquid phase transition temperature of water, substitute the flow characteristic parameters, droplet deformation scale, heat transfer influence quantity, and the measurement values ​​of superheat and latent heat at the last prediction moment into the two-phase heat transfer intensity formula in the heat dissipation and cooling model to obtain the relative heat transfer intensity at the next prediction moment; when the next prediction moment is the first prediction moment, the object temperature at the last prediction moment is the initial temperature of the burning object;

[0218] S1109, when the object temperature at the last prediction moment is lower than the gas-liquid phase transition temperature of water, substitute the flow characteristic parameters, droplet deformation scale, heat transfer influence amount and temperature difference value at the last prediction moment into the single-phase heat transfer intensity formula in the heat dissipation and cooling model to obtain the relative heat transfer intensity at the next prediction moment;

[0219] S1110, for each prediction moment, substitute the combustion duration, convection heat transfer coefficient, initial temperature of the burning object in the object parameters, density, specific heat capacity, thermal conductivity, object thickness, and initial temperature of the cooling medium in the medium parameters corresponding to the prediction moment into the object temperature formula in the heat dissipation and cooling model to obtain the object temperature at the corresponding prediction moment;

[0220] S1111, generating a temperature change curve of a burning object according to the temperature of the object at different prediction moments;

[0221] S1112, receiving an adjustment operation for at least one of the object parameter and the medium parameter, and generating a new temperature prediction instruction;

[0222] S1113. In response to the new temperature prediction instruction, determine the new object temperature of the burning object at different prediction times according to the heat dissipation and cooling model.

[0223] It should be noted that for the description in the above S1101-S1113, reference may be made to the relevant description in the above embodiment, and the effects are similar, which will not be repeated in this embodiment.

[0224] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0225] In one embodiment, as shown in FIG. 12 , a temperature prediction device is provided, including: a parameter acquisition module 1201 and a temperature prediction module 1202; wherein:

[0226] The parameter acquisition module 1201 is used to obtain the object parameters of the burning object and the medium parameters of the cooling medium in response to the temperature prediction instruction of the burning object;

[0227] The temperature prediction module 1202 is used to determine the convective heat transfer coefficient and object temperature of the burning object at different prediction times based on the object parameters, medium parameters and the heat dissipation and cooling model; the heat dissipation and cooling model characterizes the functional relationship between the object parameters, medium parameters, convective heat transfer coefficient and object temperature, and is used to predict the object temperature at the next prediction time based on the object temperature at the previous prediction time.

[0228] Each module in the above-mentioned temperature prediction device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0229] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of any one of the above-mentioned temperature prediction methods when executing the computer program.

[0230] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned temperature prediction methods are implemented.

[0231] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of any of the above-mentioned temperature prediction methods when executed by a processor.

[0232] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0233] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0234] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A temperature prediction method, characterized in that: The method comprises: In response to a temperature prediction instruction of a burning object, obtaining object parameters of the burning object and medium parameters of a cooling medium; According to the object parameters, the medium parameters and the heat dissipation and cooling model, the convective heat transfer coefficient and the object temperature of the burning object at different prediction times are determined; the heat dissipation and cooling model characterizes the functional relationship between the object parameters, the medium parameters, the convective heat transfer coefficient and the object temperature, and is used to predict the object temperature at the next prediction time based on the object temperature at the previous prediction time.

2. The method according to claim 1, characterized in that: The temperature prediction instruction carries the name of the burning object material, the characteristic parameters of the burning object, the name of the cooling medium material and the characteristic parameters of the cooling medium; the obtaining of the object parameters of the burning object and the medium parameters of the cooling medium includes: Determine the general parameters of the combustion object according to the name of the combustion object substance, and determine the general parameters of the cooling medium according to the name of the cooling medium substance; The characteristic parameters of the combustion object and the universal parameters of the combustion object are determined as the object parameters, and the characteristic parameters of the cooling medium and the universal parameters of the cooling medium are determined as the medium parameters.

3. The method according to claim 2, characterized in that The step of determining the general parameters of the burning object according to the name of the burning object substance includes: Determine the material thermal parameters of the burning object in the first database according to the material name of the burning object as the general parameters of the burning object; The determining of the cooling medium general parameters according to the cooling medium substance name includes: The material thermal parameters of the cooling medium are determined in the second database according to the material name of the cooling medium as the general parameters of the cooling medium.

4. The method according to any one of claims 1 to 3, characterized in that The heat dissipation and cooling model includes a heat transfer coefficient formula and an object temperature formula; the convective heat transfer coefficient and the object temperature of the combustion object at different prediction times are determined according to the object parameters, the medium parameters and the heat dissipation and cooling model, including: Inputting the object parameters and the medium parameters into the heat dissipation and cooling model, and determining the convective heat transfer coefficient of the combustion object at different prediction times by using the heat transfer coefficient formula; Based on the convection heat transfer coefficients at the different prediction moments, the object temperature of the combustion object at the different prediction moments is determined by the object temperature formula.

5. The method according to claim 4, characterized in that Determining the convective heat transfer coefficient of the combustion object at different prediction times by using the heat transfer coefficient formula includes: Determine the relative heat transfer intensity at different prediction times according to the object parameters and the medium parameters; the relative heat transfer intensity is used to characterize the relative intensity of convective heat transfer and conductive heat transfer between the combustion object and the cooling medium; For each prediction moment, the relative heat transfer intensity, the thermal conductivity in the medium parameters, and the object length in the object parameters are substituted into the heat transfer coefficient formula to obtain the convective heat transfer coefficient at the prediction moment.

6. The method according to claim 5, characterized in that The cooling medium is water, and the medium parameters include the gas-liquid phase transition temperature of water; and determining the relative heat transfer intensity at different prediction times according to the object parameters and the medium parameters includes: In the case where the object temperature at the last prediction moment is greater than or equal to the gas-liquid phase transition temperature of the water, the relative heat transfer intensity at the next prediction moment adjacent to the last prediction moment is determined according to the object parameters, the medium parameters and the two-phase heat transfer intensity formula; in the case where the next prediction moment is the first prediction moment, the object temperature at the last prediction moment is the initial temperature of the burning object; When the object temperature at the last prediction moment is lower than the gas-liquid phase transition temperature of the water, the relative heat transfer intensity at the next prediction moment adjacent to the last prediction moment is determined according to the object parameters, the medium parameters and the single-phase heat transfer intensity formula.

7. The method according to claim 6, characterized in that The determining, based on the object parameters, the medium parameters and the two-phase heat transfer intensity formula, the relative heat transfer intensity at the next prediction moment adjacent to the previous prediction moment comprises: Determining the flow characteristic parameters, droplet deformation scale and heat transfer influence amount of the cooling medium according to the medium parameters; Determine the measurement value of superheat and latent heat between the cooling medium and the burning object at the last prediction moment according to the medium parameters and the object parameters; The flow characteristic parameters, the droplet deformation scale, the heat transfer influence amount, and the measurement values ​​of superheat and latent heat at the previous prediction moment are substituted into the two-phase heat transfer intensity formula to obtain the relative heat transfer intensity at the next prediction moment.

8. The method according to claim 6, characterized in that The determining, according to the object parameters, the medium parameters and the single-phase heat transfer intensity formula, the relative heat transfer intensity at the next prediction moment adjacent to the previous prediction moment comprises: Determining the flow characteristic parameters, droplet deformation scale and heat transfer influence amount of the cooling medium at the last prediction moment according to the medium parameters; Determine the temperature difference between the cooling medium and the burning object at the last prediction moment according to the medium parameters and the object parameters; The flow characteristic parameters, the droplet deformation scale, the heat transfer influence amount and the temperature difference value at the previous prediction moment are substituted into the single-phase heat transfer intensity formula to obtain the relative heat transfer intensity at the next prediction moment.

9. The method according to any one of claims 1 to 3, characterized in that: The object parameters include the initial temperature, density, specific heat capacity, thermal conductivity and thickness of the burning object, and the medium parameters include the initial temperature of the cooling medium; The heat dissipation and cooling model includes an object temperature formula; Determining the object temperature of the burning object at different prediction times includes: For each prediction moment, the combustion duration corresponding to the prediction moment, the convective heat transfer coefficient, the initial temperature of the burning object, the density, specific heat capacity, thermal conductivity, object thickness and the initial temperature of the cooling medium are substituted into the object temperature formula to obtain the object temperature at the prediction moment.

10. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: A temperature variation curve of the burning object is generated according to the temperature of the object at the different prediction moments.

11. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: receiving an adjustment operation for at least one of the object parameter and the medium parameter, and generating a new temperature prediction instruction; In response to the new temperature prediction instruction, a new object temperature of the combustion object at different prediction times is determined according to the heat dissipation and temperature reduction model.

12. A temperature prediction device, characterized in that: The device comprises: A parameter acquisition module, used for acquiring object parameters of the combustion object and medium parameters of the cooling medium in response to a parameter input operation; A temperature prediction module is used to determine the convective heat transfer coefficient and the object temperature of the burning object at different prediction times based on the object parameters, the medium parameters and the heat dissipation and cooling model; the heat dissipation and cooling model characterizes the functional relationship between the object parameters, the medium parameters, the convective heat transfer coefficient and the object temperature, and is used to predict the object temperature at the next prediction time based on the object temperature at the previous prediction time.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 11 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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