Temperature control method and system for a high and low temperature shock heat flux meter

Abstract

The present invention relates to the field of control, and particularly to a temperature control method and system for a high and low temperature shock heat flux meter. The method includes: collecting heat flux meter parameters; model training; using the trained prediction model to calculate the probability values of all possible observation state sequences from the current moment to the target moment when the hidden state is the target temperature, screening out the observation state sequences with probability values greater than the probability threshold as candidates for the optimal adjustment parameter set; calculating the adjustment difficulty of each observation state sequence in the optimal adjustment parameter set, and selecting the observation state sequence with the largest ratio of probability value to adjustment difficulty as the optimal parameter sequence; and adjusting the parameter settings of the heat flux meter from the current moment to the target moment according to the optimal parameter sequence. The present invention can dynamically find a set of optimal parameter adjustment sets through the prediction model based on the current heat flux meter temperature and the target heat flux meter temperature, so as to achieve precise temperature control.

Description

Technical Field

[0001] The present invention relates to the field of control. More specifically, the present invention relates to a temperature control method and system for a high and low temperature shock heat flux meter. Background Art

[0002] A high and low temperature shock heat flux meter is an experimental device used to simulate the thermal stress and thermal stability of materials under rapid temperature changes, and is applied to fields such as materials science, the electronics industry, and automobiles. It is of great significance for studying the thermal stability, thermal conductivity, and thermal stress performance of new materials, coatings, or composite materials, and for detecting and verifying the adaptability of electronic components, optical components, aerospace equipment, etc. to high and low temperature shocks. At the same time, it can also evaluate the effectiveness of the thermal management system in the face of rapid temperature changes.

[0003] The prior art, such as the patent application document with the publication number CN118550338A, discloses a temperature control method, a temperature control device, a temperature control equipment, and a storage medium. The temperature control method includes: obtaining temperature adjustment information of the temperature control equipment at each set temperature within a preset time period; performing statistical processing on the temperature adjustment information at each set temperature, calculating the temperature increase ratio and the temperature decrease ratio, and determining the target parameters at each set temperature based on these ratios; using the target parameters and the set temperature to construct a prediction model, adopting the fuzzy logic method, mapping the target parameters and the set temperature into fuzzy sets respectively, generating fuzzy rules and their supports, predicting the temperature through the fuzzy rules, and adjusting the current set temperature of the temperature control equipment to the target temperature range according to the result of the prediction model.

[0004] However, in practical applications, the temperature can change rapidly due to various factors (such as environmental changes, equipment status, etc.). The above patent application document lacks real-time temperature monitoring and feedback, resulting in the inability to adjust the temperature of the temperature control equipment in a timely and accurate manner. Summary of the Invention

[0005] To solve the technical problems of the limitations existing in the above temperature control method, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a temperature control method for a high and low temperature shock heat flux meter includes:

[0007] Collecting a historical parameter sequence of the heat flux meter at multiple historical moments; the historical parameter sequence includes the intake air temperature, intake air pressure, intake air flow rate, and the internal temperature of the heat flux meter; inputting each historical parameter sequence into a preset prediction model to obtain a trained prediction model;

[0008] Obtain the target temperature reached inside the heat flux meter within the target time, collect the internal temperature of the heat flux meter at the current moment, use the internal temperature of the heat flux meter at the current moment as the initial hidden state of the prediction model, and calculate the probability values of all observed state sequences in the prediction model when the hidden state is the target temperature from the current moment to the target moment; take the observed state sequences with probability values greater than the preset probability threshold as the optimal adjustment parameter set;

[0009] Calculate the adjustment difficulty of each observed state sequence in the optimal adjustment parameter set, and take the observed state sequence corresponding to the maximum value of the ratio of the probability value corresponding to the observed state sequence to the adjustment difficulty as the optimal parameter sequence;

[0010] Adjust the parameters of the heat flux meter from the current moment to the target moment according to the optimal parameter sequence.

[0011] By collecting historical parameter sequences and training a prediction model, the present invention can more accurately predict the change trend of the internal temperature of the heat flux meter. During the adjustment process, the internal temperature at the current moment is used as the initial hidden state, the probability values from the current moment to the target moment are calculated, and then the optimal adjustment parameter set is found. Further, by calculating the adjustment difficulty of each observed state sequence in the optimal adjustment parameter set and taking the ratio of the probability value to the adjustment difficulty as an evaluation index, the observed state sequence corresponding to the maximum ratio is selected as the optimal parameter sequence, which can minimize the adjustment difficulty while ensuring the temperature adjustment effect and improve the adjustment efficiency.

[0012] Preferably, the adjusting the parameters of the heat flux meter from the current moment to the target moment according to the optimal parameter sequence includes:

[0013] Analyze the optimal parameter sequence, and gradually adjust the intake air temperature, intake air pressure and intake air flow of the heat flux meter according to the parameter setting values obtained by the analysis.

[0014] By gradually adjusting parameters such as the intake air temperature, pressure and flow, the change of the internal temperature of the heat flux meter can be made smoother, avoiding excessive temperature fluctuations caused by parameter mutations, thereby ensuring the continuity and stability of temperature control during the entire adjustment process and enabling the heat flux meter to smoothly transition from the current temperature to the target temperature.

[0015] Preferably, the prediction model is a hidden Markov model; after the training of the hidden Markov model is completed, a hidden state transition probability matrix, an observation probability matrix and an initial probability distribution of the hidden state are obtained.

[0016] Preferably, the probability value of the observed state sequence satisfies the relational expression:

[0017] ; where is from the current moment to the target moment, the The probability value of an observation state sequence when the hidden state is the target temperature, is the th observation state sequence, at the target time the internal temperature of the heat flux meter, is the target temperature, , and are respectively the hidden state transition probability matrix, the observation probability matrix, and the initial probability distribution of the hidden state of the hidden Markov model, represents probability.

[0018] By calculating the probability value of the observation state sequence in the hidden state of the target temperature, it is possible to accurately predict or diagnose whether the heat flux meter may reach the target temperature in the future or currently.

[0019] Preferably, the calculation process of the adjustment difficulty includes:

[0020] Calculating the information entropy of the parameters included in each observation state sequence in the optimal adjustment parameter set, and taking the information entropy as the complexity of the corresponding parameter;

[0021] Calculating the average value of the complexities of all parameters in the observation state sequence to obtain the adjustment difficulty of the observation state sequence.

[0022] By calculating the information entropy of the parameters in each observation state sequence, the complexity of each parameter can be accurately quantified. The larger the information entropy, the higher the uncertainty of the parameter, that is, the more difficult its value is to predict or control, thus reflecting the higher complexity of the parameter; calculating the average value of the complexities of all parameters can obtain the overall adjustment difficulty of the observation state sequence. This index helps to comprehensively understand the complexity of the system or model and provides a basis for subsequent optimization or adjustment.

[0023] Preferably, the calculation process of the adjustment difficulty further includes:

[0024] Calculating the information entropy of the parameters included in each observation state sequence in the optimal adjustment parameter set; calculating the variance of the parameters included in each observation state sequence in the optimal adjustment parameter set;

[0025] Taking the product of the information entropy and the variance as the complexity of the parameter, and calculating the average value of the complexities of all parameters in the observation state sequence to obtain the adjustment difficulty of the observation state sequence.

[0026] Preferably, the calculation process of the adjustment difficulty further includes:

[0027] Calculating the synergy between pairwise parameters in the observation state sequence, and taking the average value of all synergies in the observation state sequence as the synergy factor;

[0028] After normalizing the ratio of the average complexity of all parameters in the observed state sequence to the cooperation factor, the adjustment difficulty is obtained.

[0029] By calculating the cooperation between pairwise parameters in the observed state sequence, the degree of interaction between parameters can be quantified. High cooperation indicates a strong mutual influence between parameters, while low cooperation indicates relative independence between parameters; normalizing the ratio of the average complexity of parameters to the cooperation factor, the obtained adjustment difficulty index comprehensively considers the complexity of parameters and the cooperation between parameters. This index can more comprehensively reflect the adjustment difficulty of the system and provide strong support for optimization decisions.

[0030] In a second aspect, a temperature control system for a high and low temperature shock heat flux meter includes: a processor and a memory, and the memory stores computer program instructions, which implement the above-mentioned temperature control method for the high and low temperature shock heat flux meter when the computer program instructions are executed by the processor.

[0031] The beneficial effects of the present invention are:

[0032] The temperature control method of the present invention can dynamically find a set of optimal heat flux meter parameter adjustment sets according to the current heat flux meter temperature and the target heat flux meter temperature through the hidden Markov model, so as to achieve precise temperature control. This method not only considers the probability of reaching the target temperature, but also considers the complexity and cooperation of the parameter sequence, ensuring the feasibility and effectiveness of parameter adjustment. Description of the Drawings

[0033] By referring to the following detailed description with reference to the drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0034] Figure 1 is a flowchart of the method from step S1 to step S5 in the temperature control method of a high and low temperature shock heat flux meter according to an embodiment of the present invention. Detailed Embodiments

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] An embodiment of the present invention discloses a temperature control method for a high and low temperature shock heat flux meter, referring to Figure 1, including steps S1 - S5, specifically as follows:

[0037] S1: Collect the historical parameter sequences of the heat flux meter at multiple historical moments; the historical parameter sequences include the intake air temperature, intake air pressure, intake air flow rate, and the internal temperature of the heat flux meter.

[0038] A heat flux meter is a device used to measure the heat flux density on the surface of an object, and its working principle is based on the basic physical laws of heat conduction and heat convection. In an impact heat flux meter, gas (usually air) is introduced into the instrument and impacts the sample to be measured through a specific channel, thereby generating heat flux. During this process, parameters such as the temperature, pressure, and flow rate of the gas will affect the internal temperature of the heat flux meter.

[0039] The intake air temperature directly affects the internal temperature distribution of the heat flux meter. When the intake air temperature rises, the internal temperature of the heat flux meter will also rise accordingly; the influence of the intake air pressure on the heat flux meter temperature is mainly reflected in the gas flow state. A higher intake air pressure usually means more intense gas flow, which will increase the heat exchange efficiency between the gas and the sample to be measured; the intake air flow rate determines the amount of gas passing through the heat flux meter per unit time. A larger intake air flow rate means more gas carrying more heat energy enters the interior of the heat flux meter.

[0040] In summary, the intake air temperature, intake air pressure, and intake air flow rate are the key factors affecting the internal temperature of the heat flux meter. Changes in these parameters will directly affect the heat exchange efficiency between the gas and the sample to be measured, thereby causing changes in the internal temperature of the heat flux meter. Therefore, by analyzing these intake parameters, the temperature state of the heat flux meter can be indirectly understood.

[0041] Specifically, use sensors to collect the intake air temperature, intake air pressure, intake air flow rate, and the internal temperature of the heat flux meter at each moment during the historical operation of the high - low temperature impact heat flux meter (such as collecting once per second), that is, one parameter sequence corresponds to one timestamp, and this parameter sequence contains the intake air temperature, intake air pressure, intake air flow rate, and internal temperature of the heat flux meter.

[0042] Furthermore, perform normalization processing on the above - mentioned historical parameter sequences.

[0043] S2: Input each historical parameter sequence into a preset prediction model to obtain a trained prediction model.

[0044] In the embodiment of the present invention, the Hidden Markov Model (HMM) is selected as the prediction model for the temperature control method of the high - low temperature impact heat flux meter, mainly for the following reasons:

[0045] 1. Separation of hidden states and observation states.

[0046] In the temperature control of a high-low temperature shock heat flux meter, the hidden state can be understood as the true temperature state inside the heat flux meter, which cannot be directly observed but can be inferred through other parameters. Parameters such as inlet temperature, inlet pressure, and inlet flow rate can be directly observed, and there is a specific relationship between them and the temperature state of the heat flux meter.

[0047] 2. Modeling ability of the sequence model.

[0048] The hidden Markov model is a statistical model for sequential data, which describes the transition probabilities between states and the emission probabilities from states to observations. In the temperature control of a high-low temperature shock heat flux meter, the temperature state and other parameters are sequential data that change over time. The hidden Markov model can capture the temporal dependence and correlation in these sequential data, thus more accurately predicting the future temperature state.

[0049] Specifically, take the inlet temperature, inlet pressure, and inlet flow rate as the observed states of the hidden Markov model, and take the internal temperature of the heat flux meter as the hidden state of the hidden Markov model. By training the hidden Markov model, obtain the transition probability matrix of the hidden state, the probability matrix of the observed state, and the initial probability distribution of the hidden state.

[0050] Among them, combine the inlet temperature, inlet pressure, and inlet flow rate at each moment as the observed state sequence.

[0051] It should be noted that the hidden Markov model is a prior art, and the specific training process will not be elaborated in detail.

[0052] S3: Obtain the target temperature reached inside the heat flux meter within the target time, collect the internal temperature of the heat flux meter at the current moment, use the internal temperature of the heat flux meter at the current moment as the initial hidden state of the prediction model, and calculate the probability values of all observed state sequences in the prediction model when the hidden state is the target temperature from the current moment to the target moment; take the observed state sequence with a probability value greater than the preset probability threshold as the optimal adjustment parameter set.

[0053] The temperature adjustment of the heat flux meter is a complex dynamic process, affected by various factors such as ambient temperature and heat conduction characteristics inside the heat flux meter. The relationships between these factors are often non-linear and uncertain, and it is difficult to directly describe them with a simple mathematical model. The hidden Markov chain can handle this uncertainty and complexity well by introducing hidden states to capture the dynamic changes inside the system.

[0054] By using a temperature sensor to obtain the current temperature of the heat flow meter as the initial hidden state of the hidden Markov chain, the current state can be combined with historical data. Using the state transition probability matrix and the observation probability matrix in the hidden Markov model, the possible paths to reach the target temperature at the target time can be predicted, which can more accurately reflect the state changes of the system at different time points and provide a basis for the selection of adjustment parameters.

[0055] Among them, the forward algorithm is an effective method in the hidden Markov model for calculating the probability of a given observation sequence. Through the forward algorithm, the probability values of each observation state sequence from the current time to the target time can be calculated, so as to evaluate the possibility of reaching the target temperature under different combinations of adjustment parameters.

[0056] Specifically, obtain the target temperature reached inside the heat flow meter within the target time (that is, the temperature value that is expected to be reached inside the heat flow meter during the test, usually set by the staff according to specific test requirements or standards).

[0057] Use a sensor to collect the internal temperature of the heat flow meter at the current time, and take the internal temperature of the heat flow meter at the current time as the initial hidden state of the hidden Markov model, which means starting from this temperature, simulating the dynamic change process of the internal temperature of the heat flow meter. During the model prediction process, find all possible observation state sequences or paths where the hidden state (i.e., the internal temperature of the heat flow meter) can reach the target temperature at the target time.

[0058] Among them, the observation state is the state that can be directly observed in the hidden Markov model. For the heat flow meter, the observation state may include the intake temperature, intake pressure, and intake flow measured by the temperature sensor. From the current time to the target time, there will be multiple possible observation state sequences, that is, different temperature change paths. Through the hidden Markov model, the probability value of each observation state sequence can be calculated, so as to evaluate the possibility of the heat flow meter reaching the target temperature from the current temperature under different conditions.

[0059] Furthermore, by applying the forward algorithm, calculate the probability values of all observation state sequences in the hidden Markov model from the current time to the target time when the target hidden state is the target temperature, that is, the satisfaction relationship is:

[0060]

[0061] In the formula, is the probability value of the th observation state sequence from the current time to the target time when the hidden state is the target temperature, is the th observation state sequence, is at the target time The internal temperature of the heat flux meter is the target temperature , and are respectively the hidden state transition probability matrix, the observation probability matrix and the initial probability distribution of the hidden state of the hidden Markov model represents probability

[0062] where represents that at the target time , the temperature inside the heat flux meter reaches the target temperature

[0063] According to the above calculation method, the probability values of all observed state sequences when the hidden state is the target temperature from the current time to the target time are calculated in the same way

[0064] The probability values calculated by the forward algorithm provide quantitative information about the relationship between the observed sequence and the hidden state. These probability values can be interpreted as the confidence that the system is in a specific hidden state (such as the target temperature) given the observed sequence. Further, setting a probability threshold can help screen out those observed state sequences that are most likely to lead to the target hidden state (target temperature). These sequences can be regarded as the optimal set of adjustment parameters because they are most likely to achieve the desired system state

[0065] Specifically, set the probability threshold to 0.6 (in practical applications, the choice of the threshold can be adjusted according to specific requirements. For example, if the system has high requirements for accuracy, a higher threshold can be set to ensure that only the most likely observed state sequences are selected; on the contrary, if the system requires more flexibility or robustness, a lower threshold can be set to include more candidate sequences), and use the observed state sequences with probability values greater than the probability threshold as the optimal set of adjustment parameters

[0066] Generally speaking, traditional temperature adjustment methods may require repeated trials and adjustments to find suitable parameters, which is time-consuming and inefficient. By using the hidden Markov chain and the forward algorithm, multiple possible combinations of adjustment parameters and their probability values can be calculated in a short time, quickly screening out the optimal solution, and improving the adjustment efficiency and accuracy

[0067] S4: Calculate the adjustment difficulty of each observed state sequence in the optimal set of adjustment parameters, and use the maximum value of the ratio of the probability value corresponding to the observed state sequence to the adjustment difficulty as the optimal parameter sequence

[0068] Among the observed state sequences of the optimal set of adjustment parameters, different parameters may have different impacts on the system and different complexities of adjustment. Therefore, by calculating the complexity of each parameter, the importance of each parameter can be better understood

[0069] First, select any observed state sequence. For the parameters in this observed state sequence, calculate their complexity by analyzing the information entropy. Then, the complexity of the parameters satisfies the following relationship:

[0070]

[0071] In the formula, is the complexity of the -th parameter in the -th observed state sequence, is the probability that the value corresponding to the -th parameter in the -th observed state sequence appears, is the total number of observed state sequences in the optimal adjustment parameter set, is the logarithmic function (exemplarily, with base 10).

[0072] Then, in the same way as calculating the complexity of the -th parameter in the -th observed state sequence above, the complexity of other parameters in this observed state sequence can be calculated. After obtaining the complexity of each parameter, average these complexities to obtain the adjustment difficulty of the observed state sequence.

[0073] In another embodiment, by introducing the variance of the parameter values, the complexity of the parameters is calculated, and the average value of the complexities of all parameters is used as the adjustment difficulty of this observed state sequence.

[0074] Among them, the complexity of the parameters after introducing the variance of the parameter values satisfies the following relationship:

[0075]

[0076] In the formula, is the complexity of the -th parameter in the -th observed state sequence, is the probability that the value corresponding to the -th parameter in the -th observed state sequence appears, is the total number of observed state sequences in the optimal adjustment parameter set, is the -th variance corresponding to the parameter.

[0077] It should be added that there are often mutual influence relationships between different parameters. Therefore, analyzing the synergy between parameters helps to formulate more effective parameter adjustment strategies.

[0078] First, calculate the synergy between pairwise parameters in the observed state sequence.

[0079] Exemplarily, select the two parameters of intake air temperature and intake air pressure, and calculate the synergy between the two, that is, the relational expression to be satisfied is:

[0080]

[0081] In the formula, is the intake air temperature and the intake air pressure between the synergy, is the intake air temperature and the intake air pressure between the covariance, is the intake air temperature of the standard deviation, is the intake air pressure of the standard deviation.

[0082] When the synergy between parameters is high, it means that the adjustment trends of these parameters are relatively consistent, and the overall stability of the system may be higher. On the contrary, if the synergy between parameters is low, the stability of the system may be poor, because the adjustment of parameters may lead to drastic changes in the system state.

[0083] According to the above calculation method of the synergy between the intake air temperature and the intake air pressure, the synergy of all in the observed state sequence can be obtained in the same way. Further, the average value of all synergies is calculated to obtain the synergy factor.

[0084] Furthermore, combining the complexity of a single parameter and the synergy between multiple parameters, the overall adjustment difficulty of the observed state sequence is calculated, that is, the relational expression to be satisfied is:

[0085]

[0086] In the formula, is the adjustment difficulty of the observed state sequence, is the average value of the complexities corresponding to all parameters in the observed state sequence, is the average value of the synergy between parameters in the observed state sequence, is the normalization process.

[0087] In the above calculation formula, the adjustment difficulty is closely related to the dispersion degree of parameter values and the interaction between parameters. Among them, the larger the variance, the more discrete the distribution of parameter values and the greater the difference between parameters; if the parameters are independent of each other, then each parameter can be considered separately when adjusted, which is relatively simple. However, if there is an interaction or dependence relationship between parameters, adjusting one parameter may affect the effects of other parameters. Therefore, the coordination and balance of multiple parameters need to be considered simultaneously.

[0088] Similarly, according to the above calculation method of the adjustment difficulty of the observation state sequence, the adjustment difficulties of other observation state sequences in the optimal adjustment parameter set can be calculated.

[0089] In summary, the probability values corresponding to the observation state sequences in the optimal adjustment parameter set reflect the likelihood of the corresponding sequences occurring in the prediction model. The higher the probability value, the more likely the sequence is to occur under the given model, which means that the model has a better fitting degree for this sequence. And the adjustment difficulty of the observation state sequence reflects the complexity required to adjust the parameters to a specific observation state sequence.

[0090] Taking into account both the probability and the adjustment difficulty comprehensively can enable the model to have better operability and practicality while maintaining good performance.

[0091] Specifically, the observation state sequence corresponding to the maximum value of the ratio of the probability value corresponding to the observation state sequence to the adjustment difficulty is used as the optimal parameter sequence. This optimal parameter sequence represents the optimal adjustment path of each parameter (inlet temperature, inlet pressure, inlet flow rate) of the heat flux meter from the current moment to the target moment.

[0092] By calculating the ratio of the probability value to the adjustment difficulty, these two factors can be balanced to a certain extent. Selecting the observation state sequence with the largest ratio means minimizing the adjustment difficulty while ensuring a relatively high probability, thereby achieving a balance between the model fitting effect and the feasibility of parameter adjustment.

[0093] S5: Adjust each parameter of the heat flux meter from the current moment to the target moment according to the optimal parameter sequence.

[0094] Analyze the optimal parameter sequence to obtain the specific set values of each parameter of the heat flux meter given in the optimal parameter sequence. These set values will guide the parameter adjustment of the heat flux meter in actual operation.

[0095] Starting from the current moment, gradually adjust the parameters of the heat flux meter. During and after the adjustment process, continuously monitor the operating state of the heat flux meter, especially the change in the internal temperature. If a large deviation is found between the actual parameters and the target parameters, or the performance of the heat flux meter does not meet the expectations, additional adjustments or diagnoses may be required.

[0096] An embodiment of the present invention also discloses a temperature control system for a high and low temperature shock heat flux meter, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the temperature control method of the high and low temperature shock heat flux meter according to the present invention is implemented.

[0097] The system further includes other components well-known to those skilled in the art such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be described in detail here.

[0098] In the present invention, the aforementioned memory may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory (RRAM), a dynamic random access memory (DRAM), a static random-access memory (SRAM), an enhanced dynamic random access memory (EDRAM), a high-bandwidth memory (HBM), a hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application program, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0099] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, for example, two, three, or more, etc., unless otherwise specifically defined.

[0100] Although this specification has shown and described multiple embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and concept of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in the practice of the present invention.

Claims

1. A temperature control method for a high and low temperature shock heat flux meter, characterized in that, Including: Collecting the historical parameter sequences of the heat flow meter at multiple historical moments; the historical parameter sequences include the intake air temperature, intake air pressure, intake air flow rate, and the internal temperature of the heat flow meter; inputting each historical parameter sequence into a preset prediction model to obtain a trained prediction model; Obtaining the target temperature reached inside the heat flow meter within the target time, collecting the internal temperature of the heat flow meter at the current moment, using the internal temperature of the heat flow meter at the current moment as the initial hidden state of the prediction model, and calculating the probability values of all observed state sequences in the prediction model when the hidden state is the target temperature from the current moment to the target moment; taking the observed state sequences with probability values greater than the preset probability threshold as the optimal adjustment parameter set; Calculating the adjustment difficulty of each observed state sequence in the optimal adjustment parameter set, and taking the observed state sequence corresponding to the maximum value of the ratio of the probability value corresponding to the observed state sequence to the adjustment difficulty as the optimal parameter sequence; Adjusting the parameters of the heat flow meter from the current moment to the target moment according to the optimal parameter sequence; The calculation process of the adjustment difficulty includes: Calculating the information entropy of the parameters included in each observed state sequence in the optimal adjustment parameter set, and taking the information entropy as the complexity of the corresponding parameter; Calculating the average value of the complexities of all parameters in the observed state sequence to obtain the adjustment difficulty of the observed state sequence.

2. The temperature control method of a high and low temperature shock heat flux meter according to claim 1, characterized in that, The adjusting the parameters of the heat flow meter from the current moment to the target moment according to the optimal parameter sequence includes: Parsing the optimal parameter sequence, and gradually adjusting the intake air temperature, intake air pressure, and intake air flow rate of the heat flow meter according to the parameter setting values obtained by parsing.

3. The temperature control method of a high and low temperature shock heat flux meter according to claim 2, characterized in that, The prediction model is a hidden Markov model; after the training of the hidden Markov model is completed, a hidden state transition probability matrix, an observation probability matrix, and an initial probability distribution of the hidden state are obtained.

4. The temperature control method of a high and low temperature shock heat flux meter according to claim 3, characterized in that, The probability value of the observed state sequence satisfies the relation: ; wherein, is the probability value of the -th observed state sequence from the current moment to the target moment when the hidden state is the target temperature, is the -th observed state sequence, is the internal temperature of the heat flowmeter at the target moment , is the target temperature, , and are respectively the hidden state transition probability matrix, the observation probability matrix and the initial probability distribution of the hidden state of the hidden Markov model, represents probability.

5. A temperature control method for a high and low temperature shock heat flux meter according to claim 1, characterized in that, The calculation process of the adjustment difficulty further includes: Calculating the information entropy of the parameters included in each observed state sequence in the optimal adjustment parameter set; calculating the variance of the parameters included in each observed state sequence in the optimal adjustment parameter set; Taking the product of the information entropy and the variance as the complexity of the parameter, calculating the average value of the complexities of all parameters in the observed state sequence to obtain the adjustment difficulty of the observed state sequence.

6. A temperature control method for a high and low temperature shock heat flux meter according to claim 5, characterized in that, The calculation process of the adjustment difficulty further includes: Calculating the synergy between pairwise parameters in the observed state sequence, and taking the average value of all synergies in the observed state sequence as the synergy factor; Normalizing the ratio of the average value of the complexities of all parameters in the observed state sequence to the synergy factor to obtain the adjustment difficulty.

7. A temperature control system for a high and low temperature shock heat flux meter, characterized in that, Including: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the temperature control method of the high and low temperature shock heat flow meter according to any one of claims 1-6 is implemented.

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