CN111307480A - Embedded heat pipe-based heat transfer management system, method and storage medium - Google Patents
Embedded heat pipe-based heat transfer management system, method and storage medium Download PDFInfo
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- CN111307480A CN111307480A CN202010106001.XA CN202010106001A CN111307480A CN 111307480 A CN111307480 A CN 111307480A CN 202010106001 A CN202010106001 A CN 202010106001A CN 111307480 A CN111307480 A CN 111307480A
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Abstract
The invention belongs to the technical field of heat pipe heat transfer management, and discloses a heat transfer management system based on an embedded heat pipe, a method and a storage medium, wherein a temperature detection module detects heat pipe temperature data through a temperature sensor, and a heat loss detection module detects heat pipe heat loss data through heat pipe monitoring equipment; according to the detected numerical value, the central control module controls the heat transfer efficiency calculation module, and the calculation program calculates the heat transfer efficiency data of the heat pipe; the control fault diagnosis module diagnoses the fault of the heat pipe connection battery through the diagnosis circuit; and predicting the service life of the heat pipe by using a prediction program through a heat pipe service life prediction module according to the heat transfer efficiency and the battery fault data result. According to the invention, the fault diagnosis module can diagnose the short circuit and leakage condition of the battery at the initial stage of short circuit occurrence and before high temperature occurs, and the maximum temperature rise problem caused by short circuit can be accurately predicted.
Description
Technical Field
The invention belongs to the technical field of heat pipe heat transfer management, and particularly relates to a heat transfer management system and method based on an embedded heat pipe and a storage medium.
Background
The heat pipe (heat pipe) technology is widely applied to the industries of aerospace, military industry and the like in the past, and since the heat pipe (heat pipe) technology is introduced into the radiator manufacturing industry, the design idea of the traditional radiator is changed for people, the single heat radiation mode that a better heat radiation effect is obtained by only depending on a high-air-volume motor is eliminated, the heat pipe technology is adopted to ensure that the heat radiator can obtain a satisfactory effect even if the heat radiator adopts a low-rotating-speed low-air-volume motor, the noise problem which troubles air cooling heat radiation is well solved, and the heat radiation industry is opened up. However, the existing embedded heat pipe-based heat transfer and management system cannot accurately diagnose the battery fault connected by the heat pipe; meanwhile, the heat conductivity of the heat pipe cannot be accurately tested.
In summary, the problems of the prior art are as follows: the existing embedded heat pipe-based heat transfer management system cannot accurately diagnose the battery fault connected by the heat pipe; meanwhile, the heat conductivity of the heat pipe cannot be accurately tested.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a heat transfer and management system and method based on an embedded heat pipe and a storage medium.
The invention is realized in this way, a heat transfer management system based on embedded heat pipe, the heat transfer management system based on embedded heat pipe includes:
the temperature detection module is connected with the central control module and used for detecting the temperature data of the heat pipe through the temperature sensor; the process of processing the acquired signals by the temperature detection module is as follows: detecting the temperature change through a sensitive element, and converting a signal by using a conversion element; the converted signal is transmitted to a processing circuit; the processing circuit carries out denoising amplification on the acquired signals to obtain signals without noise; the signal without noise is transmitted to a central control module; the process of denoising the collected signals through the processing circuit comprises the following steps: carrying out wavelet decomposition on the acquired temperature signals to obtain each scale coefficient of the temperature data; carrying out threshold processing on the temperature data according to each scale coefficient of the temperature data; after the processing is finished, wavelet reconstruction is adopted to obtain a signal without noise;
the heat loss detection module is connected with the central control module and used for detecting heat loss data of the heat pipe through the heat pipe monitoring equipment;
the central control module is connected with the temperature detection module, the heat loss detection module, the heat transfer efficiency calculation module, the fault diagnosis module, the heat pipe testing module, the heat pipe service life prediction module and the display module and is used for controlling each module to normally work through the main control computer; the central control module extracts corresponding data characteristics according to the heat pipe temperature data, the heat pipe loss data, the heat pipe heat transfer efficiency data and the fault diagnosis result; establishing a corresponding training set according to the extracted data characteristics; establishing a corresponding test set according to the heat pipe temperature data, the heat pipe loss data, the heat pipe heat transfer efficiency data and the fault diagnosis result; calculating the difference between the object to be tested and the training set, and selecting the attributes of N training objects within the range of the difference as the neighbors of the test data; classifying the test data according to the attributes of the N training objects;
the heat transfer efficiency calculation module is connected with the central control module and used for calculating heat transfer efficiency data of the heat pipe through a calculation program;
the fault diagnosis module is connected with the central control module and is used for diagnosing the fault of the heat pipe connection battery through the diagnosis circuit;
the heat pipe testing module is connected with the central control module and used for testing the heat conduction performance of the heat pipe through testing equipment;
the heat pipe service life prediction module is connected with the central control module and used for predicting the service life of the heat pipe through a prediction program; storing the data in a database according to the detected heat pipe temperature data, heat pipe loss data, heat pipe heat transfer efficiency data and fault diagnosis results, accumulating the data and analyzing the data; after the data accumulation analysis is completed, determining a model and characteristics of the heat pipe with faults; predicting the service life of the heat pipe according to the model and the characteristics of the heat pipe with faults;
and the display module is connected with the central control module and used for displaying the detected heat pipe temperature, heat loss data, heat transfer efficiency, diagnosis results, test results and service life prediction results through a display.
Another object of the present invention is to provide an embedded heat pipe-based heat management method using the embedded heat pipe heat transfer management system, including:
testing the heat conduction performance of a heat pipe by utilizing test equipment in a heat pipe test module;
step two, in the process of testing the heat conduction performance of the heat pipe, the temperature detection module detects the temperature data of the heat pipe through a temperature sensor, and the heat loss detection module detects the heat loss data of the heat pipe through heat pipe monitoring equipment;
step three, according to the detected numerical value, the central control module controls the heat transfer efficiency calculation module, and the calculation program calculates the heat transfer efficiency data of the heat pipe; the control fault diagnosis module diagnoses the fault of the heat pipe connection battery through the diagnosis circuit;
fourthly, predicting the service life of the heat pipe by a prediction program through a heat pipe service life prediction module according to the heat transfer efficiency and the battery fault data result;
and fifthly, the central control module controls the display module to display the detected heat pipe temperature, heat loss data, heat transfer efficiency, diagnosis result, test result and service life prediction result by using the display.
Further, in the step one, the heat pipe testing module testing method is as follows:
preparing a heat pipe testing device, wherein the heat pipe testing device comprises a constant-temperature water tank, a heat pipe positioning jig and a tester, hot water with constant temperature is filled in the constant-temperature water tank, the heat pipe positioning jig is movably arranged above the constant-temperature water tank and used for clamping and fixing first ends of the heat pipes, and the tester is connected to the heat pipe positioning jig and used for measuring the temperature of the first ends of the heat pipes;
when the heat pipe positioning jig is in a first movable position, the first ends of the heat pipes are clamped and fixed on the heat pipe positioning jig, and the second ends of the heat pipes are suspended above the water surface of hot water; then the heat pipe positioning jig is positioned at a second movable position, and the second end of the heat pipe is immersed in hot water for heating and timing; after the heat pipes are soaked in hot water and heated for a period of time, the temperature of the first end of each heat pipe at the moment is read by the tester, and if the temperature of the first end of one heat pipe is higher than a specific value, the heat conduction performance of the heat pipe can be judged to meet the requirement.
Further, in the second step, the process of processing the acquired signal by the temperature detection module is as follows:
detecting the temperature change through a sensitive element, and converting a signal by using a conversion element; the converted signal is transmitted to a processing circuit;
the processing circuit carries out denoising amplification on the acquired signals to obtain signals without noise; the signal without noise is passed to a central control module.
Further, the process of denoising the collected signal by the processing circuit comprises:
carrying out wavelet decomposition on the acquired temperature signals to obtain each scale coefficient of the temperature data;
carrying out threshold processing on the temperature data according to each scale coefficient of the temperature data; and after the processing is finished, wavelet reconstruction is adopted to obtain a signal without noise.
Further, in the third step, the process of classifying the data by the central control module is as follows:
extracting corresponding data characteristics according to the heat pipe temperature data, the heat pipe loss data, the heat pipe heat transfer efficiency data and the fault diagnosis result; establishing a corresponding training set according to the extracted data characteristics; establishing a corresponding test set according to the heat pipe temperature data, the heat pipe loss data, the heat pipe heat transfer efficiency data and the fault diagnosis result;
calculating the difference between the object to be tested and the training set, and selecting the attributes of N training objects within the range of the difference as the neighbors of the test data;
and classifying the test data according to the attributes of the N training objects.
Further, in the third step, the process of fusing the data detected by the plurality of temperature sensors by the central control module is as follows:
the central control module receives data information transmitted by a plurality of temperature sensors and extracts corresponding technical characteristics by using corresponding algorithms;
establishing a feature vector with a representative function according to the extracted technical features;
carrying out pattern recognition processing on the characteristic vector, and carrying out description calibration on a detection target; establishing corresponding relevance according to the content of the description calibration; and simultaneously synthesizing the data by using a fusion algorithm.
Further, in the third step, the fault diagnosis module diagnosis method is as follows:
step a, configuring diagnosis parameters of an upper computer, starting the upper computer, and initializing a sampling frequency f, a current threshold value Is and an electric quantity threshold value Cs;
b, monitoring a current signal I by the upper computer through the current sensor in real time, if I Is less than Is, continuously monitoring the current signal by the upper computer through the current sensor in real time, repeating the b, if I Is more than or equal to Is, triggering a battery external short circuit fault diagnosis and maximum temperature rise prediction mechanism, and entering the c;
and C, the upper computer collects and stores the current signal Ii at the time ti according to the sampling frequency f, calculates the electric quantity C released by the external short circuit, and calculates the relation as follows:
wherein N is the number of samples after the occurrence of the external short circuit;
step d, diagnosing whether the external short circuit causes battery leakage, if C is larger than or equal to Cs, the battery is diagnosed as not having leakage, displaying the result on the upper computer interface, and going to step e, if C is smaller than Cs, diagnosing that leakage occurs, displaying the result on the upper computer interface, and going to step f;
step e, processing by a first neural network, wherein the upper computer inputs the electric quantity C calculated in the step C into a pre-established and trained BP neural network 1 to obtain the output of the BP neural network 1, and the output is the predicted value delta Tmax of the maximum temperature rise of the external short circuit fault of the battery;
and f, processing by a second neural network, wherein the upper computer inputs the electric quantity C calculated in the step C into a pre-established and trained BP second neural network to obtain the output of the BP second neural network, and the output is the predicted value delta Tmax of the maximum temperature rise of the external short circuit fault of the battery.
Further, the process of establishing and training the BP first neural network and the BP second neural network specifically comprises the following steps:
(1) determining training samples of the BP first neural network and the BP second neural network;
(2) establishing the BP first neural network and the BP second neural network;
(3) respectively training the BP first neural network and the BP second neural network;
(4) and determining the optimal BP first neural network and BP second neural network.
Further, in the fourth step, the prediction process of the heat pipe life prediction module on the life of the heat pipe is as follows:
storing the data in a database according to the detected heat pipe temperature data, heat pipe loss data, heat pipe heat transfer efficiency data and fault diagnosis results, accumulating the data and analyzing the data;
after the data accumulation analysis is completed, determining a model and characteristics of the heat pipe with faults;
and predicting the service life of the heat pipe according to the fault model and the fault characteristics of the heat pipe.
The invention has the advantages and positive effects that: according to the invention, the fault diagnosis module can diagnose the short circuit and leakage condition of the battery at the initial stage of short circuit occurrence and before high temperature occurs, and accurately predict the problem of maximum temperature rise caused by short circuit, so that a good basis can be provided for the protection and further intervention of external short circuit faults of the power battery; meanwhile, the first ends of the heat pipes are clamped and fixed on the heat pipe positioning jig through the heat pipe testing module, and the second ends of the heat pipes are suspended above hot water; then the heat pipe positioning jig is pivoted to a second movable position, so that the second end of the heat pipe is soaked in hot water for heating and timing is started; after the second ends of the heat pipes are heated for a period of time, the temperature of the first end of each heat pipe at the moment is read by the tester; if the temperature of the first end of a certain heat pipe is higher than a specific value, the heat conduction performance of the heat pipe can be judged to meet the requirement, and therefore the accuracy of the heat conduction performance test of the heat pipe is greatly improved.
Drawings
Fig. 1 is a schematic diagram of an embedded heat pipe-based heat transfer management system according to an embodiment of the present invention.
In the figure: 1. a temperature detection module; 2. a heat loss detection module; 3. a central control module; 4. a heat transfer efficiency calculation module; 5. a fault diagnosis module; 6. a heat pipe testing module; 7. a heat pipe life prediction module; 8. and a display module.
Fig. 2 is a flowchart of an embedded heat pipe based management method according to an embodiment of the present invention.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, an embedded heat pipe-based heat transfer management system according to an embodiment of the present invention includes: the system comprises a temperature detection module 1, a heat loss detection module 2, a central control module 3, a heat transfer efficiency calculation module 4, a fault diagnosis module 5, a heat pipe test module 6, a heat pipe service life prediction module 7 and a display module 8.
And the temperature detection module 1 is connected with the central control module 3 and used for detecting the temperature data of the heat pipe through a temperature sensor.
And the heat loss detection module 2 is connected with the central control module 3 and is used for detecting heat loss data of the heat pipe through the heat pipe monitoring equipment.
And the central control module 3 is connected with the temperature detection module 1, the heat loss detection module 2, the heat transfer efficiency calculation module 4, the fault diagnosis module 5, the heat pipe test module 6, the heat pipe service life prediction module 7 and the display module 8 and is used for controlling the normal work of each module through a main control computer.
And the heat transfer efficiency calculation module 4 is connected with the central control module 3 and is used for calculating heat transfer efficiency data of the heat pipe through a calculation program.
And the fault diagnosis module 5 is connected with the central control module 3 and is used for diagnosing the faults of the heat pipe connection battery through a diagnosis circuit.
And the heat pipe testing module 6 is connected with the central control module 3 and is used for testing the heat conduction performance of the heat pipe through testing equipment.
And the heat pipe service life prediction module 7 is connected with the central control module 3 and used for predicting the service life of the heat pipe through a prediction program.
And the display module 8 is connected with the central control module 3 and used for displaying the detected heat pipe temperature, heat loss data, heat transfer efficiency, diagnosis results, test results and service life prediction results through a display.
As shown in fig. 2, an embedded heat pipe based management method according to an embodiment of the present invention includes:
s101: the heat conduction performance of the heat pipe is tested by utilizing the test equipment in the heat pipe test module.
S102: in the process of testing the heat conduction performance of the heat pipe, the temperature detection module detects the temperature data of the heat pipe through the temperature sensor, and the heat loss detection module detects the heat loss data of the heat pipe through the heat pipe monitoring equipment.
S103: and according to the detected numerical value, the central control module controls the heat transfer efficiency calculation module, and the calculation program calculates the heat transfer efficiency data of the heat pipe. And the control fault diagnosis module diagnoses the faults of the heat pipe connection battery through the diagnosis circuit.
S104: and predicting the service life of the heat pipe by using a prediction program through a heat pipe service life prediction module according to the heat transfer efficiency and the battery fault data result.
S105: the central control module controls the display module to display the detected heat pipe temperature, heat loss data, heat transfer efficiency, diagnosis result, test result and service life prediction result by using the display.
The invention is further described with reference to specific examples.
Example 1
The process of processing the acquired signals by the temperature detection module 1 which is connected with the central control module 3 and used for detecting the temperature data of the heat pipe through the temperature sensor provided by the invention is as follows:
the temperature conversion is detected by the sensitive element, and the signal is converted by the conversion element. The converted signal is passed to a processing circuit.
The processing circuit carries out denoising and amplification on the acquired signals to obtain signals without noise. The signal without noise is passed to a central control module.
The process of denoising the acquired signal by the processing circuit comprises the following steps:
and carrying out wavelet decomposition on the acquired temperature signals to obtain each scale coefficient of the temperature data.
And carrying out threshold processing on the temperature data according to each scale coefficient of the temperature data. And after the processing is finished, wavelet reconstruction is adopted to obtain a signal without noise.
The invention provides a process for classifying data by a central control module 3 which is connected with a temperature detection module 1, a heat loss detection module 2, a heat transfer efficiency calculation module 4, a fault diagnosis module 5, a heat pipe test module 6, a heat pipe service life prediction module 7 and a display module 8 and is used for controlling each module to normally work through a main control computer, wherein the process comprises the following steps:
and extracting corresponding data characteristics according to the heat pipe temperature data, the heat pipe loss data, the heat pipe heat transfer efficiency data and the fault diagnosis result. And establishing a corresponding training set according to the extracted data characteristics. And establishing a corresponding test set according to the heat pipe temperature data, the heat pipe loss data, the heat pipe heat transfer efficiency data and the fault diagnosis result.
And calculating the difference between the object to be tested and the training set, and selecting the attributes of the N training objects within the range of the difference as the neighbors of the test data.
And classifying the test data according to the attributes of the N training objects.
Example 2
The central control module 3 is connected with the temperature detection module 1, the heat loss detection module 2, the heat transfer efficiency calculation module 4, the fault diagnosis module 5, the heat pipe test module 6, the heat pipe service life prediction module 7 and the display module 8, is used for controlling each module to normally work through a main control computer, and aims to prevent errors from occurring in the process of measuring the temperature of the heat pipe by a single temperature sensor. A plurality of temperature sensors are required to measure the temperature in the heat pipe. The plurality of temperature sensors transmit the detected data to the central control module 3, and the process of fusing the data detected by the plurality of temperature sensors is as follows:
the central control module receives data information transmitted by the plurality of temperature sensors and extracts corresponding technical characteristics by using corresponding algorithms.
And establishing a feature vector with a representative function according to the extracted technical features.
And carrying out pattern recognition processing on the characteristic vector, and carrying out description and calibration on the detection target. And establishing corresponding relevance according to the contents of the description calibration. And simultaneously synthesizing the data by using a fusion algorithm.
Example 3
The fault diagnosis method in the fault diagnosis module 5 provided by the invention is as follows:
step a, configuring diagnosis parameters of an upper computer, starting the upper computer, and initializing a sampling frequency f, a current threshold value Is and an electric quantity threshold value Cs.
And b, monitoring the current signal I by the upper computer through the current sensor in real time, if I Is less than Is, continuously monitoring the current signal by the upper computer through the current sensor in real time, repeating the b, if I Is more than or equal to Is, triggering a battery external short circuit fault diagnosis and maximum temperature rise prediction mechanism, and entering the c.
And C, the upper computer collects and stores the current signal Ii at the time ti according to the sampling frequency f, calculates the electric quantity C released by the external short circuit, and calculates the relation as follows:
where N is the number of samples after the external short occurs.
And d, diagnosing whether the external short circuit causes the leakage of the battery, if C is larger than or equal to Cs, the battery is diagnosed as not having the leakage, displaying the result on the upper computer interface, and going to the e step, if C is smaller than Cs, the battery is diagnosed as having the leakage, displaying the result on the upper computer interface, and going to the f step.
And e, processing by using a first neural network, and inputting the electric quantity C calculated in the step C into a pre-established and trained BP neural network 1 by using the upper computer to obtain the output of the BP neural network 1, wherein the output is the predicted value delta Tmax of the maximum temperature rise of the external short circuit fault of the battery.
And f, processing by a second neural network, wherein the upper computer inputs the electric quantity C calculated in the step C into a pre-established and trained BP second neural network to obtain the output of the BP second neural network, and the output is the predicted value delta Tmax of the maximum temperature rise of the external short circuit fault of the battery.
Example 4
The establishing and training process of the BP first neural network and the BP second neural network provided by the invention specifically comprises the following steps:
(1) and determining training samples of the BP first neural network and the BP second neural network.
(2) And establishing the BP first neural network and the BP second neural network.
(3) And respectively training the BP first neural network and the BP second neural network.
(4) And determining the optimal BP first neural network and BP second neural network.
The testing method of the heat pipe testing module 6 provided by the invention comprises the following steps:
preparing a heat pipe testing device, wherein the heat pipe testing device comprises a constant-temperature water tank, a heat pipe positioning jig and a tester, hot water with constant temperature is filled in the constant-temperature water tank, the heat pipe positioning jig is movably arranged above the constant-temperature water tank and used for clamping and fixing the first ends of the heat pipes, and the tester is connected to the heat pipe positioning jig and used for measuring the temperature of the first ends of the heat pipes.
When the heat pipe positioning jig is in the first movable position, the first ends of the heat pipes are clamped and fixed on the heat pipe positioning jig, and the second ends of the heat pipes are suspended above the water surface of hot water. Then the heat pipe positioning jig is positioned at a second movable position, and the second end of the heat pipe is immersed in hot water for heating and timing. After the heat pipes are soaked in hot water and heated for a period of time, the temperature of the first end of each heat pipe at the moment is read by the tester, and if the temperature of the first end of one heat pipe is higher than a specific value, the heat conduction performance of the heat pipe can be judged to meet the requirement.
The prediction process of the heat pipe service life by the heat pipe service life prediction module 7 which is connected with the central control module 3 and used for predicting the service life of the heat pipe through a prediction program provided by the invention is as follows:
and storing the data in a database according to the detected heat pipe temperature data, heat pipe loss data, heat pipe heat transfer efficiency data and fault diagnosis results, accumulating the data and analyzing the data.
And after the data accumulation analysis is completed, determining a model and characteristics of the heat pipe with faults.
And predicting the service life of the heat pipe according to the fault model and the fault characteristics of the heat pipe.
Example 5
When the heat pipe testing device works, firstly, the heat conducting performance of the heat pipe is tested by utilizing the testing equipment in the heat pipe testing module 6. In the process of testing the heat conduction performance of the heat pipe, the temperature detection module 1 detects the temperature data of the heat pipe through the temperature sensor, and the heat loss detection module 2 detects the heat loss data of the heat pipe through the heat pipe monitoring equipment. According to the detected numerical value, the central control module 3 controls the heat transfer efficiency calculation module, and the calculation program calculates the heat transfer efficiency data of the heat pipe. And the control fault diagnosis module 5 diagnoses the fault of the heat pipe connection battery through a diagnosis circuit. And according to the heat transfer efficiency and the battery fault data result, predicting the service life of the heat pipe by using a prediction program through a heat pipe service life prediction module 7. The central control module 3 controls the display module to display the detected heat pipe temperature, heat loss data, heat transfer efficiency, diagnosis result, test result and service life prediction result by using the display.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (10)
1. An embedded heat pipe based heat transfer management system, comprising:
the temperature detection module is connected with the central control module and used for detecting the temperature data of the heat pipe through the temperature sensor; the process of processing the acquired signals by the temperature detection module is as follows: detecting the temperature change through a sensitive element, and converting a signal by using a conversion element; the converted signal is transmitted to a processing circuit; the processing circuit carries out denoising amplification on the acquired signals to obtain signals without noise; the signal without noise is transmitted to a central control module; the process of denoising the collected signals through the processing circuit comprises the following steps: carrying out wavelet decomposition on the acquired temperature signals to obtain each scale coefficient of the temperature data; carrying out threshold processing on the temperature data according to each scale coefficient of the temperature data; after the processing is finished, wavelet reconstruction is adopted to obtain a signal without noise;
the heat loss detection module is connected with the central control module and used for detecting heat loss data of the heat pipe through the heat pipe monitoring equipment;
the central control module is connected with the temperature detection module, the heat loss detection module, the heat transfer efficiency calculation module, the fault diagnosis module, the heat pipe testing module, the heat pipe service life prediction module and the display module and is used for controlling each module to normally work through the main control computer; the central control module extracts corresponding data characteristics according to the heat pipe temperature data, the heat pipe loss data, the heat pipe heat transfer efficiency data and the fault diagnosis result; establishing a corresponding training set according to the extracted data characteristics; establishing a corresponding test set according to the heat pipe temperature data, the heat pipe loss data, the heat pipe heat transfer efficiency data and the fault diagnosis result; calculating the difference between the object to be tested and the training set, and selecting the attributes of N training objects within the range of the difference as the neighbors of the test data; classifying the test data according to the attributes of the N training objects;
the heat transfer efficiency calculation module is connected with the central control module and used for calculating heat transfer efficiency data of the heat pipe through a calculation program;
the fault diagnosis module is connected with the central control module and is used for diagnosing the fault of the heat pipe connection battery through the diagnosis circuit;
the heat pipe testing module is connected with the central control module and used for testing the heat conduction performance of the heat pipe through testing equipment;
the heat pipe service life prediction module is connected with the central control module and used for predicting the service life of the heat pipe through a prediction program; storing the data in a database according to the detected heat pipe temperature data, heat pipe loss data, heat pipe heat transfer efficiency data and fault diagnosis results, accumulating the data and analyzing the data; after the data accumulation analysis is completed, determining a model and characteristics of the heat pipe with faults; predicting the service life of the heat pipe according to the model and the characteristics of the heat pipe with faults;
and the display module is connected with the central control module and used for displaying the detected heat pipe temperature, heat loss data, heat transfer efficiency, diagnosis results, test results and service life prediction results through a display.
2. An embedded heat pipe management method based on the embedded heat pipe heat transfer management system according to claim 1, wherein the embedded heat pipe management method comprises:
testing the heat conduction performance of a heat pipe by utilizing test equipment in a heat pipe test module;
step two, in the process of testing the heat conduction performance of the heat pipe, the temperature detection module detects the temperature data of the heat pipe through a temperature sensor, and the heat loss detection module detects the heat loss data of the heat pipe through heat pipe monitoring equipment;
step three, according to the detected numerical value, the central control module controls the heat transfer efficiency calculation module, and the calculation program calculates the heat transfer efficiency data of the heat pipe; the control fault diagnosis module diagnoses the fault of the heat pipe connection battery through the diagnosis circuit;
fourthly, predicting the service life of the heat pipe by a prediction program through a heat pipe service life prediction module according to the heat transfer efficiency and the battery fault data result;
and fifthly, the central control module controls the display module to display the detected heat pipe temperature, heat loss data, heat transfer efficiency, diagnosis result, test result and service life prediction result by using the display.
3. The embedded heat pipe based heat management method according to claim 2, wherein in the first step, the heat pipe testing module testing method comprises the following steps:
preparing a heat pipe testing device, wherein the heat pipe testing device comprises a constant-temperature water tank, a heat pipe positioning jig and a tester, hot water with constant temperature is filled in the constant-temperature water tank, the heat pipe positioning jig is movably arranged above the constant-temperature water tank and used for clamping and fixing first ends of the heat pipes, and the tester is connected to the heat pipe positioning jig and used for measuring the temperature of the first ends of the heat pipes;
when the heat pipe positioning jig is in a first movable position, the first ends of the heat pipes are clamped and fixed on the heat pipe positioning jig, and the second ends of the heat pipes are suspended above the water surface of hot water; then the heat pipe positioning jig is positioned at a second movable position, and the second end of the heat pipe is immersed in hot water for heating and timing; after the heat pipes are soaked in hot water and heated for a period of time, the temperature of the first end of each heat pipe at the moment is read by the tester, and if the temperature of the first end of one heat pipe is higher than a specific value, the heat conduction performance of the heat pipe can be judged to meet the requirement.
4. The embedded heat pipe based management method according to claim 2, wherein in the second step, the processing of the collected signals by the temperature detection module comprises:
detecting the temperature change through a sensitive element, and converting a signal by using a conversion element; the converted signal is transmitted to a processing circuit;
the processing circuit carries out denoising amplification on the acquired signals to obtain signals without noise; the signal without noise is transmitted to a central control module;
the process of denoising the acquired signal by the processing circuit comprises the following steps:
carrying out wavelet decomposition on the acquired temperature signals to obtain each scale coefficient of the temperature data;
carrying out threshold processing on the temperature data according to each scale coefficient of the temperature data; and after the processing is finished, wavelet reconstruction is adopted to obtain a signal without noise.
5. The embedded heat pipe management method according to claim 2, wherein in the third step, the central control module classifies the data as follows:
extracting corresponding data characteristics according to the heat pipe temperature data, the heat pipe loss data, the heat pipe heat transfer efficiency data and the fault diagnosis result; establishing a corresponding training set according to the extracted data characteristics; establishing a corresponding test set according to the heat pipe temperature data, the heat pipe loss data, the heat pipe heat transfer efficiency data and the fault diagnosis result;
calculating the difference between the object to be tested and the training set, and selecting the attributes of N training objects within the range of the difference as the neighbors of the test data;
and classifying the test data according to the attributes of the N training objects.
6. The embedded heat pipe management method according to claim 2, wherein in the third step, the process of fusing the data detected by the plurality of temperature sensors by the central control module is as follows:
the central control module receives data information transmitted by a plurality of temperature sensors and extracts corresponding technical characteristics by using corresponding algorithms;
establishing a feature vector with a representative function according to the extracted technical features;
carrying out pattern recognition processing on the characteristic vector, and carrying out description calibration on a detection target; establishing corresponding relevance according to the content of the description calibration; and simultaneously synthesizing the data by using a fusion algorithm.
7. The embedded heat pipe based heat management method according to claim 2, wherein in the third step, the fault diagnosis module diagnosis method comprises the following steps:
step a, configuring diagnosis parameters of an upper computer, starting the upper computer, and initializing a sampling frequency f, a current threshold value Is and an electric quantity threshold value Cs;
b, monitoring a current signal I by the upper computer through the current sensor in real time, if I Is less than Is, continuously monitoring the current signal by the upper computer through the current sensor in real time, repeating the b, if I Is more than or equal to Is, triggering a battery external short circuit fault diagnosis and maximum temperature rise prediction mechanism, and entering the c;
and C, the upper computer collects and stores the current signal Ii at the time ti according to the sampling frequency f, calculates the electric quantity C released by the external short circuit, and calculates the relation as follows:
wherein N is the number of samples after the occurrence of the external short circuit;
step d, diagnosing whether the external short circuit causes battery leakage, if C is larger than or equal to Cs, the battery is diagnosed as not having leakage, displaying the result on the upper computer interface, and going to step e, if C is smaller than Cs, diagnosing that leakage occurs, displaying the result on the upper computer interface, and going to step f;
step e, processing by a first neural network, wherein the upper computer inputs the electric quantity C calculated in the step C into a pre-established and trained BP neural network 1 to obtain the output of the BP neural network 1, and the output is the predicted value delta Tmax of the maximum temperature rise of the external short circuit fault of the battery;
step f, processing by a second neural network, wherein the upper computer inputs the electric quantity C calculated in the step C into a pre-established and trained BP second neural network to obtain the output of the BP second neural network, and the output is the predicted value delta Tmax of the maximum temperature rise of the external short circuit fault of the battery;
the establishment and training process of the BP first neural network and the BP second neural network specifically comprises the following steps:
(1) determining training samples of the BP first neural network and the BP second neural network;
(2) establishing the BP first neural network and the BP second neural network;
(3) respectively training the BP first neural network and the BP second neural network;
(4) and determining the optimal BP first neural network and BP second neural network.
8. The embedded heat pipe based management method according to claim 2, wherein in the fourth step, the prediction process of the heat pipe life prediction module on the life of the heat pipe is as follows:
storing the data in a database according to the detected heat pipe temperature data, heat pipe loss data, heat pipe heat transfer efficiency data and fault diagnosis results, accumulating the data and analyzing the data;
after the data accumulation analysis is completed, determining a model and characteristics of the heat pipe with faults;
and predicting the service life of the heat pipe according to the fault model and the fault characteristics of the heat pipe.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for implementing the embedded heat pipe based management method according to any one of claims 2-8 when executed on an electronic device.
10. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the embedded heat pipe-based thermal management method of any one of claims 2-8.
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