Material fireproof performance prediction method based on material crystallinity and related device

Abstract

The invention belongs to the technical field of power systems, and discloses a material fireproof performance prediction method based on material crystallinity and a related device. The material fireproof performance prediction method comprises the following steps: obtaining crystallinity data of a material of which the fireproof performance is to be predicted, and performing fireproof performance prediction by using a pre-constructed prediction model to obtain a fireproof performance prediction result of the material of which the fireproof performance is to be predicted; wherein the prediction model is a mathematical correlation model between the crystallinity and the fireproof performance, which is established by adopting a statistical analysis method according to known crystallinity data of a material to be subjected to fireproof performance prediction and a fireproof performance test result corresponding to the crystallinity data. According to the technical scheme disclosed by the invention, the fireproof performance can be quickly and accurately evaluated before the material is applied, the development efficiency of the fireproof material can be improved, the test period can be shortened, and a reliable basis can be provided for material selection under different environmental conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, relates to the field of performance prediction of protective materials, and in particular to a method for predicting the fire resistance of materials based on material crystallinity and related devices. Background Art

[0002] With the development of modern industry and technology, the fireproof performance of fireproof materials plays a vital role in many fields; for example, such as energy systems, urban pipeline tunnels, industrial facilities and high-voltage power transmission. The main function of fireproof materials is to keep them from burning when a fire occurs while ensuring the integrity of the overall structure and ensuring the safe and reliable operation of equipment and lines. With the diversification of application environments and increasingly complex working conditions, the fireproof performance of fireproof materials has been challenged. How to effectively evaluate and predict the fireproof performance of materials has become a technical problem that needs to be solved urgently.

[0003] At present, the fire resistance performance test methods of existing materials usually require long-term actual use tests or simulation tests in laboratory environments, which have defects such as long time consumption and high cost, and it is difficult to accurately predict the performance of materials under changing environmental conditions. In addition, most of the existing fire resistance performance evaluation methods focus on macroscopic parameters, ignoring the microstructure of the material, especially the profound influence of crystallinity on the fire resistance of the material. Explanatoryally, the fire resistance of the material is closely related to its microstructure, especially the crystallinity of the material, which has a direct impact on its performance. Further explanatory, crystallinity refers to the proportion of the crystalline part in the material, which has a direct impact on the molecular arrangement and electronic motion characteristics of the material; generally speaking, materials with high crystallinity usually have more regular molecular arrangements, and the thermal stability of the material is usually better; the atomic arrangement in the crystalline structure is more regular and compact, which enables the material to better resist thermal expansion and thermal shock at high temperatures; therefore, fire-resistant materials with high crystallinity usually have better high temperature resistance. Fireproof materials with high crystallinity can effectively isolate the spread of heat, maintain structural integrity at higher temperatures, are not easily softened or melted, and can inhibit the further spread of fire. On the contrary, materials with low crystallinity may cause degradation of thermodynamic, mechanical, and electrical properties due to irregular molecular arrangement and more internal defects.

[0004] In summary, there is an urgent need to develop a new material fire performance prediction scheme based on material crystallinity. Summary of the invention

[0005] The purpose of the present invention is to provide a method and a related device for predicting the fire resistance performance of materials based on the crystallinity of materials, so as to solve one or more of the above-mentioned technical problems. The technical solution disclosed in the present invention establishes an effective prediction model based on the quantitative analysis of the crystallinity, and the fire resistance performance of materials can be predicted based on this prediction model, which can quickly and accurately evaluate the fire resistance performance before the material is applied, improve the development efficiency of fireproof materials, reduce the test cycle, and provide a reliable basis for material selection under different environmental conditions.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for predicting the fire resistance of a material based on the crystallinity of the material, comprising the following steps: Obtaining crystallinity data of the material to be predicted for fire performance; Based on the acquired crystallinity data of the material to be predicted for fire resistance performance, a fire resistance performance prediction is performed using a pre-built prediction model to obtain a fire resistance performance prediction result of the material to be predicted for fire resistance performance; The prediction model is a mathematical correlation model between crystallinity and fire resistance established by statistical analysis method based on known crystallinity data of the material to be predicted for fire resistance and fire resistance test results corresponding to the crystallinity data.

[0007] A further improvement of the method of the present invention is that the step of obtaining the crystallinity data of the material to be predicted for fire resistance comprises: Obtaining crystallinity data of the material to be predicted for fire performance based on X-ray diffraction, differential scanning calorimetry or Fourier transform infrared spectroscopy; The crystallinity data of the material to be predicted for fire resistance includes the crystal phase content and the ordered data of molecular arrangement.

[0008] A further improvement of the method of the present invention is that the fire resistance performance prediction result of the material to be predicted includes one or more indicators of bending strength, elastic modulus, thermal conductivity and oxygen index fire resistance.

[0009] A further improvement of the method of the present invention is that the step of constructing the prediction model comprises: Based on material samples with different crystallinity for which fire protection performance is to be predicted, measuring and obtaining crystallinity data of each material sample; Based on the material samples to be predicted for fire resistance with different crystallinity and the selected fire resistance index, fire resistance tests are performed under different preset conditions to obtain the fire resistance test results of each material sample; Based on the crystallinity data of each material sample and the corresponding fire performance test results, multiple regression, random forest or neural network algorithm is selected for model training and verification to achieve the preset convergence conditions, obtain the trained mathematical association model and use it as a prediction model.

[0010] In a second aspect, the present invention provides a material fire performance prediction system based on material crystallinity, comprising: A data acquisition module, used to acquire crystallinity data of the material to be predicted for fire performance; A performance prediction module is used to predict the fire performance based on the acquired crystallinity data of the material to be predicted for fire performance, using a pre-built prediction model to obtain a fire performance prediction result of the material to be predicted for fire performance; The prediction model is a mathematical correlation model between crystallinity and fire resistance established by statistical analysis method based on known crystallinity data of the material to be predicted for fire resistance and fire resistance test results corresponding to the crystallinity data.

[0011] A further improvement of the system of the present invention is that the step of acquiring the crystallinity data of the material to be predicted for fire performance by the data acquisition module comprises: Obtaining crystallinity data of the material to be predicted for fire performance based on X-ray diffraction, differential scanning calorimetry or Fourier transform infrared spectroscopy; The crystallinity data of the material to be predicted for fire resistance includes the crystal phase content and the ordered data of molecular arrangement.

[0012] A further improvement of the system of the present invention is that the fire resistance performance prediction result of the material to be predicted includes one or more indicators of bending strength, elastic modulus, thermal conductivity and oxygen index fire resistance.

[0013] A further improvement of the system of the present invention is that the step of constructing the prediction model includes: Based on material samples with different crystallinity for which fire protection performance is to be predicted, measuring and obtaining crystallinity data of each material sample; Based on the material samples to be predicted for fire resistance with different crystallinity and the selected fire resistance index, fire resistance tests are performed under different preset conditions to obtain the fire resistance test results of each material sample; Based on the crystallinity data of each material sample and the corresponding fire performance test results, multiple regression, random forest or neural network algorithm is selected for model training and verification to achieve the preset convergence conditions, obtain the trained mathematical association model and use it as a prediction model.

[0014] According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for predicting the fire retardant properties of a material based on the crystallinity of the material as described in any one of the first aspect of the present invention is implemented.

[0015] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for predicting the fire resistance of a material based on the crystallinity of the material as described in any one of the first aspect of the present invention is implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a method for predicting the fire retardant properties of materials based on the crystallinity of the materials. By quantitatively analyzing the crystallinity of the materials and combining the actual performance of the fire retardant properties, an effective prediction model (i.e., a mathematical correlation model between the crystallinity of the materials and the fire retardant properties) is established, which can quickly and accurately evaluate the fire retardant properties of the materials before they are applied. The method of the present invention can improve the development efficiency of fire retardant materials, reduce the testing cycle, and provide a reliable basis for material selection under different environmental conditions. The application prospects of the technical solution of the present invention are very broad. By predicting different materials, their performance in electrical equipment can be evaluated in advance, and potential fire failure problems can be avoided. At the same time, the prediction method based on the crystallinity of the materials of the present invention can also provide a theoretical basis for the design of new high-fire retardant materials, which can promote the innovative development of materials science and electrical engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or 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 some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 is a schematic flow chart of a method for predicting fire resistance of a material based on material crystallinity in an embodiment of the present invention; Figure 2 It is a technical route schematic diagram of a method for predicting fire resistance of materials based on material crystallinity in an embodiment of the present invention; Figure 3 It is a schematic diagram of a material fire resistance performance prediction system based on material crystallinity in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments and technical solutions are only part of the embodiments of the present invention, not all of the embodiments.

[0020] All other embodiments obtained by those of ordinary skill in the art without creative work based on the technical solutions disclosed in the embodiments of the present invention belong to the scope of protection of the present invention. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] See also Figure 1 , a method for predicting the fire resistance of a material based on the crystallinity of the material provided by an embodiment of the present invention comprises the following steps: Step 1, obtaining crystallinity data of the material to be predicted for fire resistance; Step 2, based on the crystallinity data of the material to be predicted for fire resistance obtained in step 1, a prediction is performed using a pre-built prediction model to obtain a fire resistance prediction result of the material to be predicted for fire resistance; The prediction model is based on the known crystallinity data of the material to be predicted for fire resistance and the fire resistance test results corresponding to the crystallinity data, and a mathematical correlation model between the obtained crystallinity and fire resistance is established using a statistical analysis method.

[0022] In the technical solution of the embodiment of the present invention, the mathematical correlation model is a functional relationship between the crystallinity of the material and the fire resistance performance established through statistical analysis. This model can greatly reduce the workload of material testing and improve the accuracy of material performance estimation. In addition, the fire resistance is predicted from the characteristics of the material itself, and the crystallinity detection method is used. The crystallinity detection of the material consumes less materials and is usually non-destructive testing. Therefore, the technical solution of the embodiment of the present invention can also reduce material loss, improve material utilization, and achieve the purpose of reducing material testing costs. It has the advantages of high efficiency, economy, and accuracy.

[0023] The core of the technical solution provided by the embodiment of the present invention is to conduct a detailed quantitative analysis of the crystallinity of the material, and combine this analysis with the actual fireproof performance of the material. Through in-depth research and data mining, an efficient and accurate prediction model is established, that is, a mathematical correlation model between the crystallinity of the material and the fireproof performance. This model can quickly and accurately evaluate the fireproof performance of the material before it is officially put into use, greatly improving the efficiency of fireproof material development and significantly shortening the testing cycle. It should be further emphasized that the application value of the prediction method of the embodiment of the present invention is that it can not only provide a scientific and reliable basis for material selection under different environmental conditions, helping engineers and designers to quickly screen out the most suitable fireproof materials for specific application requirements from a large number of materials, but also effectively avoid the problem of fireproof failure caused by improper material selection, and ensure the stable operation of electrical equipment and power transmission.

[0024] In addition, the prediction method based on material crystallinity disclosed in the embodiment of the present invention also has far-reaching theoretical significance and practical value. It provides a solid theoretical basis for the design of new high-fire retardant materials, points out the research direction for material scientists and engineers, and helps to promote innovation and development in the fields of material science and fire protection. By deeply exploring the intrinsic connection between material crystallinity and fire-retardant material performance, we can better understand and utilize the characteristics of materials, opening up a broader space for future material research and development and application.

[0025] In the preferred technical solution of the embodiment of the present invention, the fire resistance test results of the material include one or more of bending strength, elastic modulus, thermal conductivity and oxygen index fire resistance.

[0026] See also Figure 2 In a preferred embodiment of the present invention, based on the crystallinity data of the material and the fire resistance test results, the steps of establishing a mathematical correlation model between crystallinity and fire resistance using a statistical analysis method specifically include: Step 1), obtaining the crystallinity data of the material; In a specific exemplary technical solution, techniques such as X-ray diffraction (XRD), differential scanning calorimetry (DSC) or Fourier transform infrared spectroscopy (FTIR) may be used to obtain the crystallinity data of the material.

[0027] Step 2), fire performance test; In a specific exemplary technical solution, the fire resistance performance of the material is tested under different environmental conditions (such as temperature, humidity, electric field strength, etc.) to obtain data such as the material's fire resistance bending strength, elastic modulus, thermal conductivity or oxygen index.

[0028] Step 3) Establishment of correlation model; In a specific exemplary technical solution, based on the crystallinity data of the material and its fire resistance test results, statistical analysis methods (such as regression analysis, machine learning, etc.) are used to establish a mathematical correlation model between crystallinity and fire resistance.

[0029] Step 4) prediction and verification; In a specific exemplary technical solution, the mathematical correlation model established in step 3) is used to predict the fire resistance of materials with different crystallinity, and the accuracy and applicability of the model are verified through experiments.

[0030] In the preferred technical solution of the embodiment of the present invention, by accurately characterizing the crystallinity characteristics of the material and comprehensively considering the fire resistance performance of the material under variable environmental conditions such as different temperatures, humidity, and electric field strengths, a quantitative mathematical correlation model between the crystallinity of the material and its fire resistance is established using data analysis and modeling techniques. This model can reveal the specific influence of changes in crystallinity on fire resistance, and can provide a scientific basis for the prediction and evaluation of the fire resistance of the material.

[0031] In a specific embodiment of the present invention, a method for predicting the fire resistance of a material based on the crystallinity of the material is provided, and the steps are as follows: Step 1: Material crystallinity characterization process, including: Use XRD, DSC or FTIR equipment to characterize the crystallinity of the target material, and obtain the crystal phase content and molecular arrangement order data by measuring the sample; For multiple groups of samples, the crystallinity of the samples is controlled by adjusting the preparation process (such as changing factors such as temperature, cooling rate or external stress) to form a group of materials with different crystallinity. The crystallinity of each sample is measured by characterization equipment and the corresponding data is recorded.

[0032] Step 2: Fire performance test process, including: The fire retardant performance test of the material will be carried out under different environmental conditions. In order to evaluate the fire retardant effect of the material in actual use, mechanical testing equipment and thermodynamic testing system are used. During the test, the ambient temperature, humidity and applied voltage are adjusted to measure the key fire retardant performance indicators of the material under different conditions, such as bending strength, elastic modulus, thermal conductivity, oxygen index fire retardant, etc. When designing the experiment, the test can be carried out in stages; for example, the bending strength, elastic modulus, thermal conductivity, oxygen index fire resistance, etc. of the material can be measured at room temperature first, and then the temperature and humidity can be gradually increased while performing performance tests to obtain complete environmental response data.

[0033] Step 3: Mathematical correlation model establishment and programming implementation process, including: After obtaining the material crystallinity and fire performance test data, data analysis software (including but not limited to MATLAB, Python, etc.) is used to train algorithms such as regression analysis or machine learning. In the specific exemplary technical solution, the model can select algorithms such as multivariate regression, random forest or neural network, including but not limited to the above algorithms.

[0034] When processing data, first use the crystallinity as the model input, and test the fire performance of materials with the same crystallinity. The test results are used as the output results of the corresponding independent variable model. The model is trained with real test data, and the above operation is repeated to test the fire performance of materials with different crystallinity, optimize the training model, and improve the model accuracy. In this process, different fire performance indicators are used as model dependent variables, and the model is trained and tested to correct its prediction level; in order to improve the accuracy of the model, a variety of variables (such as environmental conditions, etc.) can be introduced to optimize the prediction effect. After the model is trained, in actual use, the crystallinity of the material is used as the model input, and the corresponding fire performance indicator is used as the model output. The input and output are connected through multiple regression, random forest or neural network, etc., including but not limited to the above algorithms.

[0035] Step 4: Model validation and optimization process, including: To verify the accuracy of the model, we can compare the experimental and predicted results. Select a batch of samples that did not participate in the model training, measure their crystallinity, and use the model to predict their fire resistance. Then, conduct actual fire resistance tests on these samples and compare the errors between the predicted values ​​and the experimental values. If the predictions are consistent with the experimental results, it means that the model has a high prediction accuracy. If there is a large deviation, it is necessary to further optimize the model or supplement the experimental data to improve the accuracy and wide applicability of the prediction.

[0036] In a specific application embodiment of the present invention, in a certain electrical equipment manufacturing company, the R&D department is developing a new type of polymer fireproof material for use as a fireproof layer for high-voltage cables; in order to ensure the reliability of the material in practical applications, its fireproof performance needs to be accurately predicted. Since the crystallinity of the material has a significant impact on the fireproof performance, the fireproof performance prediction method based on the crystallinity of the material provided by the present invention includes the following steps: Step (1), obtaining crystallinity data of the material to be predicted for fire resistance, comprises: Step (1.1), select the test method: Considering the characteristics of polymer materials, X-ray diffraction (XRD) is selected as the main method to obtain crystallinity data. XRD can provide detailed information about the crystal phase content and molecular arrangement order of the material.

[0037] Step (1.2), implementing the test: preparing the polymer material to be tested; testing the sample using an X-ray diffractometer to obtain a diffraction pattern; analyzing the diffraction pattern to calculate the crystallinity data of the material, which may include indicators of crystal phase content and orderliness of molecular arrangement.

[0038] Step (2), building a prediction model, includes: Step (2.1), collect sample data: prepare a series of polymer material samples with different crystallinity; perform XRD test on each sample to obtain crystallinity data.

[0039] Step (2.2), conduct fire resistance test: select the fire resistance index; conduct fire resistance test on each sample under different preset conditions (such as temperature, humidity, etc.) and record the test results.

[0040] Step (2.3), model training and verification: organize the collected crystallinity data and fire test results to form a training data set; select the multivariate regression algorithm as the model training method; use the training data set to train the multivariate regression model, and adjust the model parameters so that the model can accurately reflect the relationship between crystallinity and fire resistance indicators; use cross-validation and other methods to verify the model to ensure the accuracy and stability of the model; when the model reaches the preset convergence condition, obtain the trained mathematical association model and use it as a prediction model.

[0041] Step (3), fire resistance performance prediction, includes: inputting the crystallinity data of the new material into the trained prediction model; the model outputs the fire resistance prediction result of the new material based on the input crystallinity data; based on the prediction result, evaluating whether the fire resistance performance of the new material meets the design requirements; if the prediction result does not meet the expectation, the material formula or processing technology can be adjusted, and then retesting and predicting until a satisfactory result is obtained.

[0042] In summary, by adopting the fire retardant performance prediction method based on material crystallinity of the present invention, the fire retardant performance of the newly developed polymer fire retardant material can be accurately predicted, thereby effectively shortening the material research and development cycle, reducing research and development costs, and improving the reliability and application performance of the material. The method of the present invention can provide strong technical support for the material research and development of the fire protection manufacturing industry.

[0043] In the above application embodiment, further exemplary explanations on constructing a prediction model are as follows: Collect sample data: Select a series of polymer material samples with different crystallinity, which should cover a wide range from low crystallinity to high crystallinity to ensure the generalization ability of the model; use a high-precision X-ray diffractometer to test each sample. During the test, ensure that the sample surface is flat and free of contamination to obtain an accurate diffraction pattern. By analyzing the diffraction pattern, calculate the crystallinity data of each sample, including the crystal phase content and the ordered index of molecular arrangement.

[0044] Conduct fire resistance test: determine fire resistance as the key indicator for evaluating fire resistance; set different test conditions, such as temperature (e.g., 25°C, 50°C, 75°C), humidity (e.g., 20%RH, 50%RH, 80%RH), and electric field strength (e.g., 10kV / mm, 20kV / mm, 30kV / mm) to comprehensively evaluate the fire resistance of samples under different environments. Under the set conditions, each sample is subjected to a fire resistance test, and the thermodynamic changes during the test and any abnormal phenomena are recorded. Furthermore, each sample is tested at least three times under each condition to ensure the reliability of the test results.

[0045] Model training and verification: The collected crystallinity data and fire test results are sorted to form a training data set containing multiple samples. Each sample includes the crystallinity data (crystalline phase content and molecular arrangement order) of the sample and the fire test results under different conditions. Based on the characteristics of the data and the analysis requirements, the multivariate regression algorithm is selected as the model training method. The multivariate regression algorithm can handle the relationship between multiple independent variables (such as crystallinity and test conditions) and the dependent variable (fire resistance), and is suitable for building a prediction model. The multivariate regression model is trained using the training data set. During the training process, the model parameters (such as regression coefficient, intercept, etc.) are adjusted to enable the model to accurately reflect the relationship between crystallinity and fire resistance. Optimization algorithms such as gradient descent are used to minimize the prediction error and improve the fitting effect of the model. The model is verified using methods such as cross-validation. The training data set is divided into several subsets, and one of the subsets is used as the validation set in turn, and the remaining subsets are used as the training set for model training. The accuracy and stability of the model are evaluated by comparing the prediction results of the model on the validation set with the actual test results. If the prediction error of the model is within an acceptable range, the model is considered to have converged. When the model reaches the preset convergence conditions (such as the prediction error is less than a certain threshold, the model parameter changes tend to be stable, etc.), a trained mathematical correlation model is obtained. The model can predict the fire resistance of polymers under different conditions based on the input crystallinity data.

[0046] For example, the model might look like this: Fire performance = f(crystalline phase content, molecular arrangement order, temperature, humidity, electric field strength); in, f is a function trained by a multivariate regression algorithm.

[0047] The following are device embodiments of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0048] See also Figure 3 In an embodiment of the present invention, a material fire performance prediction system based on material crystallinity is provided, comprising: A data acquisition module, used to acquire crystallinity data of the material to be predicted for fire performance; A performance prediction module is used to predict the fire performance based on the acquired crystallinity data of the material to be predicted for fire performance, using a pre-built prediction model to obtain a fire performance prediction result of the material to be predicted for fire performance; The prediction model is a mathematical correlation model between crystallinity and fire resistance established by statistical analysis method based on known crystallinity data of the material to be predicted for fire resistance and fire resistance test results corresponding to the crystallinity data.

[0049] The system provided by the embodiment of the present invention can realize the prediction of the fire resistance performance of different materials and provide quantitative fire resistance performance prediction results. The method is simple, efficient and easy to implement.

[0050] In one embodiment of the present invention, a computer device is provided, the computer device comprising a processor and a memory, the memory is used to store a computer program, the computer program comprises program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used to perform the operation of the material fire resistance prediction method based on material crystallinity.

[0051] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM (Random Access Memory) memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the material fire resistance prediction method based on material crystallinity in the above embodiment.

[0052] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program codes.

[0053] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0054] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting fire resistance of materials based on material crystallinity, characterized in that: The following steps are involved: Obtaining crystallinity data of the material to be predicted for fire performance; Based on the acquired crystallinity data of the material to be predicted for fire resistance performance, a fire resistance performance prediction is performed using a pre-built prediction model to obtain a fire resistance performance prediction result of the material to be predicted for fire resistance performance; The prediction model is a mathematical correlation model between crystallinity and fire resistance established by statistical analysis method based on known crystallinity data of the material to be predicted for fire resistance and fire resistance test results corresponding to the crystallinity data.

2. A method for predicting fire resistance of materials based on material crystallinity according to claim 1, characterized in that: The step of obtaining the crystallinity data of the material to be predicted for fire performance comprises: Obtaining crystallinity data of the material to be predicted for fire performance based on X-ray diffraction, differential scanning calorimetry or Fourier transform infrared spectroscopy; The crystallinity data of the material to be predicted for fire resistance includes the crystal phase content and the ordered data of molecular arrangement.

3. The method for predicting fire resistance of materials based on material crystallinity according to claim 1, characterized in that: The fire resistance prediction result of the material to be predicted includes one or more indicators of bending strength, elastic modulus, thermal conductivity and oxygen index fire resistance.

4. The method for predicting fire resistance of materials based on material crystallinity according to claim 1, characterized in that: The steps of constructing the prediction model include: Based on material samples with different crystallinity for which fire protection performance is to be predicted, measuring and obtaining crystallinity data of each material sample; Based on the material samples to be predicted for fire resistance with different crystallinity and the selected fire resistance index, fire resistance tests are performed under different preset conditions to obtain the fire resistance test results of each material sample; Based on the crystallinity data of each material sample and the corresponding fire performance test results, multiple regression, random forest or neural network algorithm is selected for model training and verification to achieve the preset convergence conditions, obtain the trained mathematical association model and use it as a prediction model.

5. A material fire performance prediction system based on material crystallinity, characterized in that: include: A data acquisition module, used to acquire crystallinity data of the material to be predicted for fire performance; A performance prediction module is used to predict the fire performance based on the acquired crystallinity data of the material to be predicted for fire performance, using a pre-built prediction model to obtain a fire performance prediction result of the material to be predicted for fire performance; The prediction model is a mathematical correlation model between crystallinity and fire resistance established by statistical analysis method based on known crystallinity data of the material to be predicted for fire resistance and fire resistance test results corresponding to the crystallinity data.

6. A material fire performance prediction system based on material crystallinity according to claim 5, characterized in that: The step of acquiring the crystallinity data of the material to be predicted for fire performance by the data acquisition module comprises: Obtaining crystallinity data of the material to be predicted for fire performance based on X-ray diffraction, differential scanning calorimetry or Fourier transform infrared spectroscopy; The crystallinity data of the material to be predicted for fire resistance includes the crystal phase content and the ordered data of molecular arrangement.

7. The material fire performance prediction system based on material crystallinity according to claim 5, characterized in that: The fire resistance prediction result of the material to be predicted includes one or more indicators of bending strength, elastic modulus, thermal conductivity and oxygen index fire resistance.

8. The material fire performance prediction system based on material crystallinity according to claim 5, characterized in that: The steps of constructing the prediction model include: Based on material samples with different crystallinity for which fire protection performance is to be predicted, measuring and obtaining crystallinity data of each material sample; Based on the material samples to be predicted for fire resistance with different crystallinity and the selected fire resistance index, fire resistance tests are performed under different preset conditions to obtain the fire resistance test results of each material sample; Based on the crystallinity data of each material sample and the corresponding fire performance test results, multiple regression, random forest or neural network algorithm is selected for model training and verification to achieve the preset convergence conditions, obtain the trained mathematical association model and use it as a prediction model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for predicting the fire resistance of a material based on the crystallinity of the material according to any one of claims 1 to 4 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the fire resistance performance of a material based on the crystallinity of the material according to any one of claims 1 to 4 is implemented.

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