Test power supply temperature monitoring and cooling control method

Abstract

The invention relates to a test power supply temperature monitoring and cooling control method, which belongs to the technical field of test power supply temperature control, and comprises the following steps of: dynamically tracking temperature data of test power supplies through a temperature tracking model of the test power supplies to obtain a test temperature evaluation index of each test power supply; the temperature state data of the test power supply are identified based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply, and finally the cooling characteristic data of the cooling system in unit time are obtained. And generating a related cooling plan according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system in unit time, and cooling according to the related cooling plan. According to the invention, the real-time temperature condition of the test power supply is intelligently tracked by constructing the temperature tracking model of the test power supply, so that the temperature condition of the test power supply is automatically identified, and the test power supply is adaptively cooled through a genetic algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of test temperature control, and in particular to a test power supply temperature monitoring and temperature reduction control method. Background Art

[0002] The test power supply must meet the requirements of the equivalent grid power supply capacity, that is, it must be able to provide energy for a sufficient duration as the primary requirement. The primary problem that the energy storage distribution transformer short-circuit impulse power supply solves is that the stored energy can meet the requirements for completing an impulse test. The power supply is automatically controlled by power electronics, and the impulse current waveform under the standard required capacity can be obtained through automatic constant voltage compensation technology, initial voltage regulation technology, and initial phase angle regulation technology. The main purpose of the short-circuit impulse test is to verify the dynamic stability of the distribution transformer. The dynamic stability is closely related to the short-circuit current. Therefore, the energy storage impulse power supply equipment must first be able to provide energy for a sufficient duration, and at the same time meet the transient current peak allowable deviation of no more than 5% of the calculated value, and the steady-state current effective value allowable deviation of no more than 10% of the calculated value. The energy storage test power supply stores the power supply energy through the energy storage device. During the test, a short-circuit impulse test can be completed only by the stored energy. By outputting the rated test voltage through the power electronic conversion device, the short-circuit impulse test of the distribution transformer can be completed. Many users who need to complete the short-circuit impulse test of the distribution transformer can realize the short-circuit impulse test of the distribution transformer under the existing power supply capacity conditions. However, during the test, as the test power supply is used for a longer time, it will generate heat due to its internal resistance. The heat will be detrimental to the discharge of the test power supply, resulting in the test work not being carried out as planned. Summary of the invention

[0003] The invention overcomes the shortcomings of the prior art and provides a test power supply temperature monitoring and temperature reduction control method.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides a test power supply temperature monitoring and cooling control method, comprising:

[0006] Acquire real-time temperature data of the test power supply in each area, and construct a real-time temperature model diagram of the test power supply based on the real-time temperature data of the test power supply in each area;

[0007] Constructing a temperature tracking model for a test power supply, dynamically tracking the temperature data of the test power supply through the temperature tracking model for the test power supply, and obtaining a test temperature evaluation index for each test power supply;

[0008] Identify the temperature status data of the test power supply through tracking based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply;

[0009] Acquire cooling characteristic data of the cooling system within a unit time, generate a relevant cooling plan according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and perform cooling according to the relevant cooling plan.

[0010] Furthermore, in the present method, real-time temperature data of the test power supply in each area is obtained, and a real-time temperature model diagram of the test power supply is constructed based on the real-time temperature data of the test power supply in each area, specifically including:

[0011] Obtaining the size information of the test power supply, and constructing a three-dimensional model through a three-dimensional modeling software according to the size information of the test power supply, obtaining a three-dimensional modeling diagram of the test power supply, and obtaining the temperature information of each position of the test power supply through an infrared sensor;

[0012] Setting the rendering color corresponding to each temperature information, and rendering the three-dimensional modeling diagram of the test power supply according to the rendering color corresponding to each temperature information and the temperature information of each position of the test power supply, and obtaining the temperature rendering information of the test power supply at each moment;

[0013] By dynamically simulating the three-dimensional modeling diagram of the test power supply and the temperature rendering information of the test power supply at each moment, a three-dimensional dynamic modeling diagram of the test power supply is generated, and the three-dimensional dynamic modeling diagram of the test power supply is output as a real-time temperature model diagram of the test power supply.

[0014] Furthermore, in this method, a temperature tracking model of the test power supply is constructed, and the temperature data of the test power supply is dynamically tracked by the temperature tracking model of the test power supply, specifically:

[0015] A temperature tracking model of a test power supply is constructed based on a deep neural network, a graph neural network is introduced, the temperature tracking model of the test power supply is input into the graph neural network, and the temperature rendering information in the temperature tracking model of the test power supply is used as a graph node;

[0016] Introducing a cyclic spatial attention mechanism, inputting the graph node into the cyclic spatial attention mechanism, obtaining a temperature feature graph, and performing a SoftMax operation on each temperature feature data in the temperature feature graph;

[0017] Through the SoftMax operation, a normalized feature map with spatial attention is obtained, and an inner product operation is performed on the normalized feature map with spatial attention and the temperature rendering information, so as to focus the attention on the temperature rendering information and generate a temperature attention feature map;

[0018] The temperature attention feature map is input into the hidden layer, and works in coordination with the hidden state in the hidden layer to update the hidden layer state and dynamically track the temperature data of the test power supply.

[0019] Furthermore, in the present method, by tracking, the temperature status data of the test power supply is identified based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply, specifically:

[0020] Acquire temperature information of a test power supply, and determine whether the temperature information of the test power supply is greater than a test temperature evaluation index of the test power supply;

[0021] When the temperature information of the test power supply is greater than the test temperature evaluation index of the test power supply, the test power supply corresponding to the temperature information greater than the test temperature evaluation index of the test power supply is regarded as a test power supply in an abnormal temperature state;

[0022] When the temperature information of the test power supply is not greater than the test temperature evaluation index of the test power supply, the test power supply corresponding to the temperature information not greater than the test temperature evaluation index of the test power supply is taken as the test power supply in the normal temperature state.

[0023] Furthermore, in this method, the cooling characteristic data of the cooling system within a unit time is obtained, specifically:

[0024] The cooling change characteristic data of the cooling system within a unit time under various working parameters are obtained through big data, and a cooling characteristic data prediction model is constructed based on a recursive neural network;

[0025] Inputting the temperature drop variation characteristic data of the temperature drop system within a unit time under each working parameter into the temperature drop characteristic data prediction model for training, and obtaining the trained temperature drop characteristic data prediction model;

[0026] The cooling change characteristic data of the cooling system within unit time under each working parameter within the preset time is obtained, and the cooling change characteristic data of the cooling system within unit time under each working parameter within the preset time is input into the cooling characteristic data prediction model for prediction, so as to obtain the cooling characteristic data of the cooling system within unit time within the current preset time period.

[0027] Furthermore, in this method, a relevant cooling plan is generated according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and cooling is performed according to the relevant cooling plan, specifically:

[0028] Acquire temperature status data of the test power supply within a preset time, and acquire temperature rise characteristic data according to the temperature status data of the test power supply within the preset time, and determine whether the temperature rise characteristic data is greater than the temperature reduction characteristic data of the cooling system within the unit time;

[0029] Introducing a genetic algorithm, setting a genetic algebra based on the genetic algorithm, and reinitializing the working parameters of the cooling system when the heating characteristic data is greater than the cooling characteristic data of the cooling system within the unit time;

[0030] Acquire cooling characteristic data of the cooling system within a unit time under the working parameters of the current cooling system, and if the heating characteristic data is greater than the cooling characteristic data of the cooling system within the unit time, perform inheritance based on the genetic algebra to adjust the working parameters of the current cooling system;

[0031] If the temperature rise characteristic data is not greater than the temperature reduction characteristic data of the cooling system within the unit time, the working parameters of the current cooling system are output, and a relevant cooling plan is generated, and cooling is performed according to the relevant cooling plan.

[0032] A second aspect of the present invention provides a test power supply temperature monitoring and cooling control system, the system comprising a memory and a processor, the memory comprising a test power supply temperature monitoring and cooling control method program, and when the test power supply temperature monitoring and cooling control method program is executed by the processor, the steps of any one of the test power supply temperature monitoring and cooling control methods are implemented.

[0033] A third aspect of the present invention provides an electronic device, comprising:

[0034] A temperature model diagram construction module is used to obtain real-time temperature data of the test power supply in each area, and to construct a real-time temperature model diagram of the test power supply based on the real-time temperature data of the test power supply in each area;

[0035] A temperature tracking module for a test power supply is used to construct a temperature tracking model for the test power supply, dynamically track the temperature data of the test power supply through the temperature tracking model for the test power supply, and obtain a test temperature evaluation index for each test power supply;

[0036] A temperature status data identification module, which identifies the temperature status data of the test power supply by tracking based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply;

[0037] A cooling plan generation module obtains cooling characteristic data of a cooling system within a unit time, generates a relevant cooling plan according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and performs cooling according to the relevant cooling plan.

[0038] The fourth aspect of the present invention provides a computer-readable storage medium, which includes a test power supply temperature monitoring and cooling control method program. When the test power supply temperature monitoring and cooling control method program is executed by a processor, the steps of any one of the test power supply temperature monitoring and cooling control methods are implemented.

[0039] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0040] The present invention obtains the real-time temperature data of the test power supply in each area, constructs the real-time temperature model diagram of the test power supply based on the real-time temperature data of the test power supply in each area, and then constructs the temperature tracking model of the test power supply, dynamically tracks the temperature data of the test power supply through the temperature tracking model of the test power supply, and obtains the test temperature evaluation index of each test power supply, thereby identifying the temperature state data of the test power supply based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply through tracking, and finally obtains the cooling characteristic data of the cooling system within a unit time, generates a related cooling plan according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and performs cooling according to the related cooling plan. The present invention intelligently tracks the real-time temperature condition of the test power supply by constructing the temperature tracking model of the test power supply, thereby automatically identifying the temperature condition of the test power supply, and then adaptively cools the test power supply through a genetic algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0042] Figure 1 The overall flow chart of the test power supply temperature monitoring and cooling control method is shown;

[0043] Figure 2 A partial flow chart of the test power supply temperature monitoring and temperature reduction control method is shown;

[0044] Figure 3 The system block diagram of the test power supply temperature monitoring and cooling control system is shown;

[0045] Figure 4 A device schematic diagram of an electronic device is shown. DETAILED DESCRIPTION

[0046] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0048] like Figure 1 As shown, the first aspect of the present invention provides a test power supply temperature monitoring and cooling control method, comprising:

[0049] S102: Acquire real-time temperature data of the test power supply in each area, and construct a real-time temperature model diagram of the test power supply based on the real-time temperature data of the test power supply in each area;

[0050] S104: constructing a temperature tracking model for the test power supply, dynamically tracking the temperature data of the test power supply through the temperature tracking model for the test power supply, and obtaining a test temperature evaluation index for each test power supply;

[0051] S106: Identifying the temperature status data of the test power supply through tracking based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply;

[0052] S108: Acquire cooling characteristic data of the cooling system within a unit time, generate a relevant cooling plan according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and perform cooling according to the relevant cooling plan.

[0053] It should be noted that the present invention constructs a temperature tracking model of the test power supply to intelligently track the real-time temperature of the test power supply, thereby automatically identifying the temperature of the test power supply, and then adaptively cooling the test power supply through a genetic algorithm.

[0054] Furthermore, in the present method, real-time temperature data of the test power supply in each area is obtained, and a real-time temperature model diagram of the test power supply is constructed based on the real-time temperature data of the test power supply in each area, specifically including:

[0055] Obtain the size information of the test power supply, and construct a three-dimensional model through a three-dimensional modeling software according to the size information of the test power supply, obtain a three-dimensional modeling diagram of the test power supply, and obtain the temperature information of each position of the test power supply through an infrared sensor;

[0056] Set the rendering color corresponding to each temperature information, and render the three-dimensional modeling diagram of the test power supply according to the rendering color corresponding to each temperature information and the temperature information of each position of the test power supply, and obtain the temperature rendering information of the test power supply at each moment;

[0057] By dynamically simulating the three-dimensional modeling diagram of the test power supply and the temperature rendering information of the test power supply at each moment, a three-dimensional dynamic modeling diagram of the test power supply is generated, and the three-dimensional dynamic modeling diagram of the test power supply is output as a real-time temperature model diagram of the test power supply.

[0058] It should be noted that the three-dimensional modeling software includes SolidWorks, Maya software, UG software, etc. The real-time temperature dynamic model diagram of the test power supply can be constructed through this method.

[0059] like Figure 2 As shown, further, in this method, a temperature tracking model of the test power supply is constructed, and the temperature data of the test power supply is dynamically tracked by the temperature tracking model of the test power supply, specifically:

[0060] S202: construct a temperature tracking model of the test power supply based on a deep neural network, introduce a graph neural network, input the temperature tracking model of the test power supply into the graph neural network, and use the temperature rendering information in the temperature tracking model of the test power supply as a graph node;

[0061] S204: Introduce a cyclic spatial attention mechanism, input the graph node into the cyclic spatial attention mechanism, obtain a temperature feature map, and perform a SoftMax operation on each temperature feature data in the temperature feature map;

[0062] S206: Obtain a normalized feature map with spatial attention through a SoftMax operation, and perform an inner product operation on the normalized feature map with spatial attention and the temperature rendering information, focus the attention on the temperature rendering information, and generate a temperature attention feature map;

[0063] S208: Input the temperature attention feature map into the hidden layer, and work together with the hidden state in the hidden layer to update the hidden layer state and dynamically track the temperature data of the test power supply.

[0064] It should be noted that the present invention integrates the recurrent attention mechanism to focus on the temperature rendering information rather than the three-dimensional model diagram of the test power supply, thereby suppressing the interference of multi-scale data on the training of the model, thereby improving the tracking accuracy of the test power supply.

[0065] Furthermore, in the present method, by tracking, the temperature status data of the test power supply is identified based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply, specifically:

[0066] Acquire temperature information of the test power supply, and determine whether the temperature information of the test power supply is greater than a test temperature evaluation index of the test power supply;

[0067] When the temperature information of the test power supply is greater than the test temperature evaluation index of the test power supply, the test power supply corresponding to the temperature information greater than the test temperature evaluation index of the test power supply is regarded as the test power supply in an abnormal temperature state;

[0068] When the temperature information of the test power supply is not greater than the test temperature evaluation index of the test power supply, the test power supply corresponding to the temperature information not greater than the test temperature evaluation index of the test power supply is taken as the test power supply in the normal temperature state.

[0069] It should be noted that the test temperature evaluation index is a temperature index threshold set by the user, such as 50 degrees Celsius.

[0070] Furthermore, in this method, the cooling characteristic data of the cooling system within a unit time is obtained, specifically:

[0071] The cooling change characteristic data of the cooling system within a unit time under various working parameters are obtained through big data, and a cooling characteristic data prediction model is constructed based on a recursive neural network;

[0072] Inputting the temperature change characteristic data of the temperature reduction system within a unit time under each working parameter into the temperature reduction characteristic data prediction model for training, and obtaining the trained temperature reduction characteristic data prediction model;

[0073] The cooling change characteristic data of the cooling system within unit time under various working parameters within the preset time are obtained, and the cooling change characteristic data of the cooling system within unit time under various working parameters within the preset time are input into the cooling characteristic data prediction model for prediction, so as to obtain the cooling characteristic data of the cooling system within unit time within the current preset time period.

[0074] It should be noted that the working parameters include data such as the air supply volume per unit time, the cold air volume per unit time, and the cold water flow rate per unit time. The cooling change characteristic data includes the cooling amount per unit time. Since the cooling equipment will degrade in performance after a certain number of years of use, this method can obtain the cooling characteristic data of the cooling system per unit time within the current preset time period under each working parameter.

[0075] Furthermore, in this method, a relevant cooling plan is generated according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and cooling is performed according to the relevant cooling plan, specifically:

[0076] Acquire the temperature state data of the test power supply within a preset time, and acquire the temperature rise characteristic data according to the temperature state data of the test power supply within the preset time, and judge whether the temperature rise characteristic data is greater than the temperature drop characteristic data of the cooling system within a unit time;

[0077] A genetic algorithm is introduced, and a genetic algebra is set based on the genetic algorithm. When the heating characteristic data is greater than the cooling characteristic data of the cooling system within a unit time, the working parameters of the cooling system are reinitialized;

[0078] Acquire the cooling characteristic data of the cooling system within a unit time under the working parameters of the current cooling system. If the heating characteristic data is greater than the cooling characteristic data of the cooling system within a unit time, perform inheritance based on genetic algebra to adjust the working parameters of the current cooling system.

[0079] If the heating characteristic data is not greater than the cooling characteristic data of the cooling system within a unit time, the working parameters of the current cooling system are output, and a relevant cooling plan is generated, and the cooling is performed according to the relevant cooling plan.

[0080] It should be noted that this method can adaptively cool the test power supply so that the discharge performance of the test power supply meets the predetermined requirements. The temperature rise characteristic data is the temperature rise data per unit time, such as a temperature rise of 0.1 degrees Celsius within one minute.

[0081] like Figure 3 As shown, the second aspect of the present invention provides a test power supply temperature monitoring and cooling control system 3, the system includes a memory 31 and a processor 32, the memory 31 includes a test power supply temperature monitoring and cooling control method program, and when the test power supply temperature monitoring and cooling control method program is executed by the processor 32, any step of the test power supply temperature monitoring and cooling control method is implemented.

[0082] like Figure 4 As shown, the third aspect of the present invention provides an electronic device, including:

[0083] A temperature model diagram construction module 10 is used to obtain real-time temperature data of the test power supply in each area, and to construct a real-time temperature model diagram of the test power supply based on the real-time temperature data of the test power supply in each area;

[0084] The test power supply temperature tracking module 20 constructs a test power supply temperature tracking model, dynamically tracks the temperature data of the test power supply through the test power supply temperature tracking model, and obtains the test temperature evaluation index of each test power supply;

[0085] The temperature state data identification module 30 identifies the temperature state data of the test power supply by tracking based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply;

[0086] The cooling plan generation module 40 obtains cooling characteristic data of the cooling system within a unit time, generates a relevant cooling plan according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and performs cooling according to the relevant cooling plan.

[0087] Furthermore, in the present device, real-time temperature data of the test power supply in each area is obtained, and a real-time temperature model diagram of the test power supply is constructed based on the real-time temperature data of the test power supply in each area, specifically including:

[0088] Obtain the size information of the test power supply, and construct a three-dimensional model through a three-dimensional modeling software according to the size information of the test power supply, obtain a three-dimensional modeling diagram of the test power supply, and obtain the temperature information of each position of the test power supply through an infrared sensor;

[0089] Set the rendering color corresponding to each temperature information, and render the three-dimensional modeling diagram of the test power supply according to the rendering color corresponding to each temperature information and the temperature information of each position of the test power supply, and obtain the temperature rendering information of the test power supply at each moment;

[0090] By dynamically simulating the three-dimensional modeling diagram of the test power supply and the temperature rendering information of the test power supply at each moment, a three-dimensional dynamic modeling diagram of the test power supply is generated, and the three-dimensional dynamic modeling diagram of the test power supply is output as a real-time temperature model diagram of the test power supply.

[0091] Furthermore, in this device, a temperature tracking model of the test power supply is constructed, and the temperature data of the test power supply is dynamically tracked by the temperature tracking model of the test power supply, specifically:

[0092] A temperature tracking model of the test power supply is constructed based on a deep neural network. The graph neural network is introduced, the temperature tracking model of the test power supply is input into the graph neural network, and the temperature rendering information in the temperature tracking model of the test power supply is used as a graph node.

[0093] Introduce the cyclic spatial attention mechanism, input the graph nodes into the cyclic spatial attention mechanism, obtain the temperature feature map, and perform SoftMax operation on each temperature feature data in the temperature feature map;

[0094] Through the SoftMax operation, a normalized feature map with spatial attention is obtained, and an inner product operation is performed on the normalized feature map with spatial attention and the temperature rendering information to focus attention on the temperature rendering information and generate a temperature attention feature map;

[0095] The temperature attention feature map is input into the hidden layer and works together with the hidden state in the hidden layer to update the hidden layer state and dynamically track the temperature data of the test power supply.

[0096] Furthermore, in the present device, by tracking, the temperature status data of the test power supply is identified based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply, specifically:

[0097] Acquire temperature information of the test power supply, and determine whether the temperature information of the test power supply is greater than a test temperature evaluation index of the test power supply;

[0098] When the temperature information of the test power supply is greater than the test temperature evaluation index of the test power supply, the test power supply corresponding to the temperature information greater than the test temperature evaluation index of the test power supply is regarded as the test power supply in an abnormal temperature state;

[0099] When the temperature information of the test power supply is not greater than the test temperature evaluation index of the test power supply, the test power supply corresponding to the temperature information not greater than the test temperature evaluation index of the test power supply is taken as the test power supply in the normal temperature state.

[0100] Furthermore, in this device, the cooling characteristic data of the cooling system within a unit time is obtained, specifically:

[0101] The cooling change characteristic data of the cooling system within a unit time under various working parameters are obtained through big data, and a cooling characteristic data prediction model is constructed based on a recursive neural network;

[0102] Inputting the temperature change characteristic data of the temperature reduction system within a unit time under each working parameter into the temperature reduction characteristic data prediction model for training, and obtaining the trained temperature reduction characteristic data prediction model;

[0103] The cooling change characteristic data of the cooling system within unit time under various working parameters within the preset time are obtained, and the cooling change characteristic data of the cooling system within unit time under various working parameters within the preset time are input into the cooling characteristic data prediction model for prediction, so as to obtain the cooling characteristic data of the cooling system within unit time within the current preset time period.

[0104] Furthermore, in this device, a relevant cooling plan is generated according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and cooling is performed according to the relevant cooling plan, specifically:

[0105] Acquire the temperature state data of the test power supply within a preset time, and acquire the temperature rise characteristic data according to the temperature state data of the test power supply within the preset time, and judge whether the temperature rise characteristic data is greater than the temperature drop characteristic data of the cooling system within a unit time;

[0106] A genetic algorithm is introduced, and a genetic algebra is set based on the genetic algorithm. When the heating characteristic data is greater than the cooling characteristic data of the cooling system within a unit time, the working parameters of the cooling system are reinitialized;

[0107] Acquire the cooling characteristic data of the cooling system within a unit time under the working parameters of the current cooling system. If the heating characteristic data is greater than the cooling characteristic data of the cooling system within a unit time, perform inheritance based on genetic algebra to adjust the working parameters of the current cooling system.

[0108] If the heating characteristic data is not greater than the cooling characteristic data of the cooling system within a unit time, the working parameters of the current cooling system are output, and a relevant cooling plan is generated, and the cooling is performed according to the relevant cooling plan.

[0109] The fourth aspect of the present invention provides a computer-readable storage medium, which includes a test power supply temperature monitoring and cooling control method program. When the test power supply temperature monitoring and cooling control method program is executed by a processor, any step of the test power supply temperature monitoring and cooling control method is implemented.

[0110] In summary, the present invention obtains the real-time temperature data of the test power supply in each area, constructs a real-time temperature model diagram of the test power supply based on the real-time temperature data of the test power supply in each area, and then constructs a temperature tracking model of the test power supply, dynamically tracks the temperature data of the test power supply through the temperature tracking model of the test power supply, and obtains the test temperature evaluation index of each test power supply, thereby identifying the temperature state data of the test power supply based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply through tracking, and finally obtains the cooling characteristic data of the cooling system within a unit time, generates a related cooling plan according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and performs cooling according to the related cooling plan. The present invention intelligently tracks the real-time temperature condition of the test power supply by constructing a temperature tracking model of the test power supply, thereby automatically identifying the temperature condition of the test power supply, and then adaptively cools the test power supply through a genetic algorithm.

[0111] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0112] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0113] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0114] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0115] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0116] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A test power supply temperature monitoring and cooling control method, characterized in that: include: Acquire real-time temperature data of the test power supply in each area, and construct a real-time temperature model diagram of the test power supply based on the real-time temperature data of the test power supply in each area; Constructing a temperature tracking model for a test power supply, dynamically tracking the temperature data of the test power supply through the temperature tracking model for the test power supply, and obtaining a test temperature evaluation index for each test power supply; Identify the temperature status data of the test power supply through tracking based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply; Acquire cooling characteristic data of the cooling system within a unit time, generate a relevant cooling plan according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and perform cooling according to the relevant cooling plan.

2. A test power supply temperature monitoring and cooling control method according to claim 1, characterized in that: Acquiring real-time temperature data of the test power supply in each area, and constructing a real-time temperature model diagram of the test power supply based on the real-time temperature data of the test power supply in each area, specifically including: Obtaining the size information of the test power supply, and constructing a three-dimensional model through a three-dimensional modeling software according to the size information of the test power supply, obtaining a three-dimensional modeling diagram of the test power supply, and obtaining the temperature information of each position of the test power supply through an infrared sensor; Setting the rendering color corresponding to each temperature information, and rendering the three-dimensional modeling diagram of the test power supply according to the rendering color corresponding to each temperature information and the temperature information of each position of the test power supply, and obtaining the temperature rendering information of the test power supply at each moment; By dynamically simulating the three-dimensional modeling diagram of the test power supply and the temperature rendering information of the test power supply at each moment, a three-dimensional dynamic modeling diagram of the test power supply is generated, and the three-dimensional dynamic modeling diagram of the test power supply is output as a real-time temperature model diagram of the test power supply.

3. A test power supply temperature monitoring and cooling control method according to claim 1, characterized in that: A temperature tracking model of the test power supply is constructed, and the temperature data of the test power supply is dynamically tracked by the temperature tracking model of the test power supply, specifically: A temperature tracking model of a test power supply is constructed based on a deep neural network, a graph neural network is introduced, the temperature tracking model of the test power supply is input into the graph neural network, and the temperature rendering information in the temperature tracking model of the test power supply is used as a graph node; Introducing a cyclic spatial attention mechanism, inputting the graph node into the cyclic spatial attention mechanism, obtaining a temperature feature graph, and performing a SoftMax operation on each temperature feature data in the temperature feature graph; Through the SoftMax operation, a normalized feature map with spatial attention is obtained, and an inner product operation is performed on the normalized feature map with spatial attention and the temperature rendering information, so as to focus the attention on the temperature rendering information and generate a temperature attention feature map; The temperature attention feature map is input into the hidden layer, and works in coordination with the hidden state in the hidden layer to update the hidden layer state and dynamically track the temperature data of the test power supply.

4. A test power supply temperature monitoring and cooling control method according to claim 1, characterized in that: By tracking, the temperature status data of the test power supply is identified based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply, specifically: Acquire temperature information of a test power supply, and determine whether the temperature information of the test power supply is greater than a test temperature evaluation index of the test power supply; When the temperature information of the test power supply is greater than the test temperature evaluation index of the test power supply, the test power supply corresponding to the temperature information greater than the test temperature evaluation index of the test power supply is regarded as a test power supply in an abnormal temperature state; When the temperature information of the test power supply is not greater than the test temperature evaluation index of the test power supply, the test power supply corresponding to the temperature information not greater than the test temperature evaluation index of the test power supply is taken as the test power supply in the normal temperature state.

5. A test power supply temperature monitoring and cooling control method according to claim 1, characterized in that: Get the cooling characteristic data of the cooling system within a unit time, specifically: The cooling change characteristic data of the cooling system within a unit time under various working parameters are obtained through big data, and a cooling characteristic data prediction model is constructed based on a recursive neural network; Inputting the temperature drop variation characteristic data of the temperature drop system within a unit time under each working parameter into the temperature drop characteristic data prediction model for training, and obtaining the trained temperature drop characteristic data prediction model; The cooling change characteristic data of the cooling system within unit time under each working parameter within the preset time is obtained, and the cooling change characteristic data of the cooling system within unit time under each working parameter within the preset time is input into the cooling characteristic data prediction model for prediction, so as to obtain the cooling characteristic data of the cooling system within unit time within the current preset time period.

6. A test power supply temperature monitoring and cooling control method according to claim 1, characterized in that: Generate a relevant cooling plan according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and perform cooling according to the relevant cooling plan, specifically: Acquire temperature status data of the test power supply within a preset time, and acquire temperature rise characteristic data according to the temperature status data of the test power supply within the preset time, and determine whether the temperature rise characteristic data is greater than the temperature reduction characteristic data of the cooling system within the unit time; Introducing a genetic algorithm, setting a genetic algebra based on the genetic algorithm, and reinitializing the working parameters of the cooling system when the heating characteristic data is greater than the cooling characteristic data of the cooling system within the unit time; Acquire cooling characteristic data of the cooling system within a unit time under the working parameters of the current cooling system, and if the heating characteristic data is greater than the cooling characteristic data of the cooling system within the unit time, perform inheritance based on the genetic algebra to adjust the working parameters of the current cooling system; If the temperature rise characteristic data is not greater than the temperature reduction characteristic data of the cooling system within the unit time, the working parameters of the current cooling system are output, and a relevant cooling plan is generated, and cooling is performed according to the relevant cooling plan.

7. A test power supply temperature monitoring and cooling control system, characterized in that: The system includes a memory and a processor, wherein the memory includes a test power supply temperature monitoring and cooling control method program. When the test power supply temperature monitoring and cooling control method program is executed by the processor, the steps of the test power supply temperature monitoring and cooling control method as described in any one of claims 1-6 are implemented.

8. An electronic device, characterized in that: include: A temperature model diagram construction module is used to obtain real-time temperature data of the test power supply in each area, and to construct a real-time temperature model diagram of the test power supply based on the real-time temperature data of the test power supply in each area; A temperature tracking module for a test power supply is used to construct a temperature tracking model for the test power supply, dynamically track the temperature data of the test power supply through the temperature tracking model for the test power supply, and obtain a test temperature evaluation index for each test power supply; A temperature status data identification module, which identifies the temperature status data of the test power supply by tracking based on the test temperature evaluation index of the test power supply and the temperature tracking model of the test power supply; A cooling plan generation module obtains cooling characteristic data of a cooling system within a unit time, generates a relevant cooling plan according to the temperature state data of the test power supply and the cooling characteristic data of the cooling system within a unit time, and performs cooling according to the relevant cooling plan.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a test power supply temperature monitoring and cooling control method program. When the test power supply temperature monitoring and cooling control method program is executed by a processor, the steps of the test power supply temperature monitoring and cooling control method as described in any one of claims 1-6 are implemented.

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