Preparation method of explosion-proof protective composite material

Abstract

The invention discloses a preparation method of an explosion-proof protective composite material, which comprises the following steps of: acquiring a real-time thermocompression temperature collected by a temperature sensor, and carrying out time sequence segmentation and local time sequence association on the real-time thermocompression temperature by adopting a data processing and analysis algorithm based on deep learning, so as to obtain a real-time thermocompression temperature; therefore, the recommended value of the pressing pressure at the next time point is automatically obtained according to the aggregation representation characteristics of the real-time hot pressing temperatures in the local time sequence. Therefore, by monitoring and analyzing the temperature change in the hot pressing process in real time, the pressing pressure can be dynamically adjusted according to the fine time sequence change, so that the forming quality of the composite material is ensured, the consistency of the product quality is improved, and the automation degree in the hot pressing process of the material is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent preparation, and more specifically, to a method for preparing an explosion-proof protective composite material. Background Art

[0002] With the increasing safety requirements in modern society, explosion-proof composite materials are playing an increasingly important role in military, civil construction, transportation and other fields. Such materials need to have good mechanical properties, impact resistance and temperature regulation capabilities to ensure stable safety protection performance in extreme environments.

[0003] Chinese patent CN111231461A discloses an explosion-proof protective composite material, a preparation method thereof and explosion-proof protective clothing, which uses a composite material layer woven with a Steiner minimum tree topological structure as an outer layer to enhance the stability and puncture and ballistic resistance of the material, and a flexible material layer filled with a phase change cold storage composition as an inner layer, which can relieve the stuffiness of the wearer when working and improve the wearing comfort, thereby comprehensively improving the explosion-proof protective performance of the composite material and the wearer's comfort experience.

[0004] In the preparation process of explosion-proof protective composite materials, the adhesive-coated materials need to be hot-pressed to ensure a strong connection between the composite structure layer and the flexible material layer, and to ensure the structural integrity and performance stability of the composite material. In particular, during the hot pressing process, precise control of pressure is the key to ensuring the quality of material molding. However, traditional control methods usually rely on experience and fixed operating parameters and lack a real-time feedback mechanism. This means that once the pressure parameters are set, it is difficult to dynamically adjust them according to changes in the actual process. In other words, with slight changes in production conditions, these fixed rules are difficult to guarantee the best control effect, and are prone to over- or under-compensation, thus affecting the consistency and reliability of product quality.

[0005] Therefore, an optimized method for preparing explosion-proof protective composite materials is desired. Summary of the invention

[0006] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a method for preparing an explosion-proof protective composite material, which obtains the real-time hot pressing temperature collected by a temperature sensor, and adopts a data processing and analysis algorithm based on deep learning to perform time series segmentation and local time series association on the real-time hot pressing temperature, so as to automatically obtain the recommended value of the pressing pressure at the next time point according to the aggregation representation characteristics of each real-time hot pressing temperature in the local time series. In this way, by real-time monitoring and analyzing the temperature changes during the hot pressing process, the pressing pressure can be dynamically adjusted according to these subtle time series changes to ensure the molding quality of the composite material and improve the consistency of product quality, thereby improving the degree of automation in the material hot pressing process.

[0007] According to one aspect of the present application, a method for preparing an explosion-proof protective composite material is provided, which comprises: using at least two high-performance fibers for interlaced weaving to form a composite structure layer; making a flexible substrate containing a plurality of hidden holes, injecting a phase change cold storage material into the plurality of hidden holes to obtain a flexible material layer containing a phase change cold storage material; placing the composite structure layer and the flexible material layer on both sides of a center layer coated with an adhesive to obtain an adhesive material, and performing a hot pressing process on the adhesive material at a temperature of 55 to 65° C. by a hot pressing device to obtain an explosion-proof protective composite material;

[0008] The adhesive material is subjected to a heat pressing process at a temperature of 55 to 65° C. by a heat pressing device, and is characterized by comprising:

[0009] Acquire the time series of the real-time heat pressing temperature collected by the temperature sensor;

[0010] Sequence segmentation is performed on the time series of the real-time hot pressing temperature to obtain a set of local time series of the real-time hot pressing temperature;

[0011] Inputting each local time series of the real-time hot pressing temperature in the set of local time series of the real-time hot pressing temperature into a pressing temperature time series feature extractor to obtain a set of hot pressing temperature local time series associated feature vectors;

[0012] Inputting the set of the local time series associated feature vectors of the hot pressing temperature into the time series node energy significance feature forward propagation optimizer to obtain the hot pressing temperature time series attenuation aggregation representation vector as the hot pressing temperature time series attenuation aggregation representation feature;

[0013] Based on the thermal pressing temperature time series decay aggregation representation characteristics, an optimization instruction is obtained, and the optimization instruction is used to represent a recommended decoding value of the pressing pressure at the next time point.

[0014] Compared with the prior art, the preparation method of explosion-proof protective composite material provided by the present application obtains the real-time hot pressing temperature collected by the temperature sensor, and adopts the data processing and analysis algorithm based on deep learning to perform time series segmentation and local time series association on the real-time hot pressing temperature, so as to automatically obtain the recommended value of the pressing pressure at the next time point according to the aggregation representation characteristics of each real-time hot pressing temperature in the local time series. In this way, by real-time monitoring and analyzing the temperature changes in the hot pressing process, the pressing pressure can be dynamically adjusted according to these subtle time series changes to ensure the molding quality of the composite material and improve the consistency of product quality, thereby improving the degree of automation in the material hot pressing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 This is a flow chart of a method for preparing an explosion-proof protective composite material according to an embodiment of the present application;

[0017] Figure 2 A data flow diagram of a method for preparing an explosion-proof protective composite material according to an embodiment of the present application;

[0018] Figure 3 This is a flow chart of sub-step S3 of the method for preparing the explosion-proof protective composite material according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0020] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0021] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0022] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0023] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0024] Traditional control methods usually rely on experience and fixed operating parameters and lack a real-time feedback mechanism. This means that once the pressure parameters are set, it is difficult to dynamically adjust them according to changes in the actual process. In other words, with slight changes in production conditions, these fixed rules are difficult to guarantee the best control effect, and are prone to over- or under-compensation, thereby affecting the consistency and reliability of product quality. Therefore, an optimized method for preparing explosion-proof protective composite materials is desired.

[0025] In the technical solution of the present application, a method for preparing an explosion-proof protective composite material is proposed. Figure 1 The flowchart is a method for preparing an explosion-proof protective composite material according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of the method for preparing the explosion-proof protective composite material according to the embodiment of the present application. Figure 1 and Figure 2 As shown, according to the preparation method of the explosion-proof protective composite material of the embodiment of the present application, the steps include: S1, using at least two high-performance fibers for interlaced weaving to form a composite structure layer; S2, making a flexible substrate containing a plurality of hidden holes, injecting a phase change cold storage material into the plurality of hidden holes to obtain a flexible material layer containing a phase change cold storage material; S3, placing the composite structure layer and the flexible material layer on both sides of a center layer coated with an adhesive to obtain an adhesive material, and performing a hot pressing treatment on the adhesive material at a temperature of 55 to 65°C by a hot pressing device to obtain an explosion-proof protective composite material.

[0026] In particular, the S1 uses at least two high-performance fibers to be interlaced and woven to form a composite structure layer. It should be understood that a composite structure layer with a complex structure and high toughness can be created by interlacing. This structure can effectively resist impact loads and fracture propagation, thereby improving the toughness and impact resistance of the composite material. Different types of fibers have different mechanical properties, and interlaced weaving can utilize their synergy to enhance the explosion resistance of the composite material.

[0027] In particular, the S2 is to prepare a flexible substrate containing a plurality of hidden holes, and inject a phase change cold storage material into the plurality of hidden holes to obtain a flexible material layer containing the phase change cold storage material. The phase change cold storage material has the ability to absorb or release a large amount of heat within a specific temperature range. Injecting it into the hidden holes of the flexible substrate can effectively store cold.

[0028] In particular, in S3, the composite structure layer and the flexible material layer are placed on both sides of the center layer coated with an adhesive to obtain an adhesive material, and the adhesive material is subjected to a hot pressing process at a temperature of 55 to 65° C. by a hot pressing device to obtain an explosion-proof protective composite material. In a specific example of the present application, Figure 3 As shown, the S3 includes: S31, obtaining the time series of the real-time hot pressing temperature collected by the temperature sensor; S32, performing sequence segmentation on the time series of the real-time hot pressing temperature to obtain a set of local time series of the real-time hot pressing temperature; S33, inputting each local time series of the real-time hot pressing temperature in the set of local time series of the real-time hot pressing temperature into the pressing temperature timing feature extractor to obtain a set of local time series associated feature vectors of the hot pressing temperature; S34, inputting the set of local time series associated feature vectors of the hot pressing temperature into the timing node energy significance feature forward propagation optimizer to obtain a hot pressing temperature timing attenuation aggregation representation vector as a hot pressing temperature timing attenuation aggregation representation feature; S35, obtaining an optimization instruction based on the hot pressing temperature timing attenuation aggregation representation feature, and the optimization instruction is used to represent the recommended decoding value of the pressing pressure at the next time point.

[0029] Specifically, the S31 and S32 obtain the time series of the real-time hot pressing temperature collected by the temperature sensor; and perform sequence segmentation on the time series of the real-time hot pressing temperature to obtain a set of local time series of the real-time hot pressing temperature. Considering that the real-time hot pressing temperature in different local time periods in the time series of the real-time hot pressing temperature shows different change trends and fluctuations. Based on this, in order to better capture and carefully analyze the change characteristic information of the local hot pressing temperature in the real-time hot pressing temperature, in the technical solution of the present application, the time series of the real-time hot pressing temperature is sequence segmented based on a predetermined time scale to divide the entire time series according to the time window size, and obtain a set of local time series of the real-time hot pressing temperature.

[0030] Specifically, in S33, each local time series of the real-time hot pressing temperature in the set of local time series of the real-time hot pressing temperature is input into the pressing temperature time series feature extractor to obtain a set of local time series associated feature vectors of the hot pressing temperature. Considering that different correlation relationships exist between different local time spans in the local time series of each real-time hot pressing temperature in the set of local time series of the real-time hot pressing temperature, the Bi-LSTM model can simultaneously process the past and future information in the time series data, is good at modeling long-term and short-term time series dependencies, and can better extract the complex time series features contained in the temperature series. Therefore, in the technical solution of the present application, each local time series of the real-time hot pressing temperature in the set of local time series of the real-time hot pressing temperature is input into the pressing temperature time series feature extractor based on the Bi-LSTM model to obtain a set of local time series associated feature vectors of the hot pressing temperature. In particular, the essence of the pressing temperature time series feature extractor based on the Bi-LSTM model is the Bi-LSTM model.

[0031] Specifically, in S34, the set of the local temporal correlation feature vectors of the hot pressing temperature is input into the forward propagation optimizer of the energy significance feature of the timing node to obtain the hot pressing temperature temporal attenuation aggregation representation vector as the hot pressing temperature temporal attenuation aggregation representation feature. Considering the different significance and importance shown in each local temporal correlation feature of the hot pressing temperature in the set of the local temporal correlation feature vectors of the hot pressing temperature, and in order to be able to give corresponding attention to each local temporal correlation feature vector of the hot pressing temperature, so as to more comprehensively characterize and aggregate the hot pressing temperature features in the entire time domain, thereby highlighting the features of the key time points, in the technical solution of the present application, the set of the local temporal correlation feature vectors of the hot pressing temperature is input into the forward propagation optimizer of the energy significance feature of the timing node to obtain the hot pressing temperature temporal attenuation aggregation representation vector. It is worth mentioning that the forward propagation optimizer of the energy significance feature of the timing node quantifies the significance feature information of each node and enhances and aggregates the significance of each local temporal correlation feature of the hot pressing temperature based on the law that the energy decays over time.

[0032] In an embodiment of the present application, the set of the hot pressing temperature local time series associated feature vectors is input into the time node energy significance feature forward propagation optimizer to obtain the hot pressing temperature time series attenuation aggregation representation vector as the hot pressing temperature time series attenuation aggregation representation feature, including: first, based on the mean and standard deviation of each hot pressing temperature local time series associated feature vector in the set of the hot pressing temperature local time series associated feature vectors, the energy significance descriptor of each hot pressing temperature local time series associated feature vector is calculated to obtain a sequence of hot pressing temperature energy significance descriptors, so as to quantify the importance of the hot pressing temperature at different time points; then, the timestamps of each hot pressing temperature local time series associated feature vector in the sequence of the hot pressing temperature local time series associated feature vectors are extracted to obtain a sequence of hot pressing temperature timestamps; that is, the timestamp is extracted from each hot pressing temperature local time series associated feature vector to retain the dynamic law of the hot pressing temperature change over time. Subsequently, the local time series correlation feature vector of the hot pressing temperature corresponding to the current time point in the set of the local time series correlation feature vector of the hot pressing temperature is taken as the current node feature vector, and the local time series correlation feature vectors of the hot pressing temperature corresponding to other time points are taken as the historical node feature vectors to obtain a sequence of the current local time series correlation feature vector of the hot pressing temperature and the historical local time series correlation feature vector of the hot pressing temperature; the node feature vector of the current time point in the sequence is regarded as the current local time series correlation feature vector of the hot pressing temperature, and the others are regarded as the historical local time series correlation feature vectors of the hot pressing temperature to distinguish the current and historical node feature vectors, so as to better capture the key influence of the current temperature change on the final pressure. Further, the energy time decay factor of each historical local time series correlation feature vector of the hot pressing temperature in the sequence of the historical local time series correlation feature vector of the hot pressing temperature is calculated relative to the current local time series correlation feature vector of the hot pressing temperature to obtain a sequence of the energy time decay factor of the hot pressing temperature; here, the energy time decay factor of each historical local time series correlation feature vector of the hot pressing temperature is calculated to reduce the relative importance of the historical feature vector and highlight the influence of the current feature. Then, taking the sequence of the hot pressing temperature energy significant descriptors as positive adjustment factors and the sequence of the hot pressing temperature energy time attenuation factors as negative adjustment factors, the sequence of the historical hot pressing temperature local time series correlation feature vectors is subjected to time series propagation aggregation to obtain the historical hot pressing temperature time series propagation aggregation representation feature vector; finally, the historical hot pressing temperature time series propagation aggregation representation feature vector and the current hot pressing temperature local time series correlation feature vector are fused to obtain the hot pressing temperature time series attenuation aggregation representation vector.

[0033] The process of calculating the energy significant descriptors of the local time series associated feature vectors of the hot pressing temperature based on the mean and standard deviation of each local time series associated feature vector of the hot pressing temperature in the set of the local time series associated feature vectors of the hot pressing temperature to obtain a sequence of energy significant descriptors of the hot pressing temperature includes: calculating the mean and standard deviation of the local time series associated feature vectors of the hot pressing temperature to obtain the mean of the local time series associated feature of the hot pressing temperature and the standard deviation of the local time series associated feature of the hot pressing temperature; calculating the energy significant descriptors of the local time series associated feature vectors of the hot pressing temperature and the hot pressing temperature; The positional difference of the mean value of the local timing association feature of temperature is obtained to obtain the local timing association difference vector of heat pressing temperature; the fourth power of each feature value in the local timing association difference vector of heat pressing temperature is calculated to obtain the local timing association modulation difference vector of heat pressing temperature; the expected value of the local timing association modulation difference vector of heat pressing temperature is calculated to obtain the local timing association expected value of heat pressing temperature; the local timing association expected value of heat pressing temperature is divided by the fourth power of the standard deviation of the local timing association feature of heat pressing temperature to obtain the energy significant descriptor of heat pressing temperature.

[0034] More specifically, the process of calculating the energy time decay factor of each historical hot pressing temperature local time series associated feature vector in the sequence of the historical hot pressing temperature local time series associated feature vector relative to the current hot pressing temperature local time series associated feature vector to obtain the sequence of hot pressing temperature energy time decay factors includes: respectively subtracting the hot pressing temperature timestamp corresponding to the current hot pressing temperature local time series associated feature vector from the hot pressing temperature timestamp corresponding to each historical hot pressing temperature local time series associated feature vector and rounding down to obtain the sequence of historical hot pressing temperature time span values; calculating the After the sequence of historical hot pressing temperature time span values ​​is divided by the square of the time attenuation inverse scaling parameter by position to obtain a sequence of historical hot pressing temperature time span square modulation values, the sequence of historical hot pressing temperature time span square modulation values ​​is multiplied by the attenuation rate positive scaling parameter by position to obtain a sequence of hot pressing temperature time series span energy attenuation coefficients; each hot pressing temperature time series span energy attenuation coefficient in the sequence of hot pressing temperature time series span energy attenuation coefficients is used as an exponential power to calculate the natural exponential function value with the natural constant e as the base to obtain the sequence of hot pressing temperature energy time attenuation factors. Furthermore, using the sequence of the heat pressing temperature energy significant descriptors as positive adjustment factors and the sequence of the heat pressing temperature energy time decay factors as negative adjustment factors, the sequence of the historical heat pressing temperature local time series associated feature vectors is subjected to time series propagation aggregation to obtain a historical heat pressing temperature time series propagation aggregation representation feature vector, including: dividing the sequence of the heat pressing temperature energy significant descriptors by the sequence of the heat pressing temperature energy time decay factors by position to obtain a sequence of heat pressing temperature adjustment factors, and calculating the positionally weighted sum of the sequence of the heat pressing temperature adjustment factors and the sequence of the historical heat pressing temperature local time series associated feature vectors to obtain the historical heat pressing temperature time series propagation aggregation representation feature vector.

[0035] In summary, in the above embodiment, the set of the local timing-related feature vectors of the hot pressing temperature is input into the forward propagation optimizer of the energy significance feature of the timing node to obtain the hot pressing temperature timing attenuation aggregation representation vector as the hot pressing temperature timing attenuation aggregation representation feature, including: inputting the set of the local timing-related feature vectors of the hot pressing temperature into the forward propagation optimizer of the energy significance feature of the timing node, and processing with the following forward propagation formula to obtain the hot pressing temperature timing attenuation aggregation representation vector; wherein the forward propagation formula is:

[0036] X={x 1 , x 2 , …x k-1 , x k}

[0037]

[0038]

[0039]

[0040]

[0041] Wherein, X is the set of local time series correlation feature vectors of the thermal pressing temperature, x k-1 and x k are the (k-1)th and kth local timing-related feature vectors of the thermal pressing temperature in the set of local timing-related feature vectors of the thermal pressing temperature, respectively; k represents the number of feature vectors in the set of local timing-related feature vectors of the thermal pressing temperature, and x ij is the eigenvalue of each position in the i-th local time series associated feature vector of the thermal pressing temperature in the set of the local time series associated feature vector of the thermal pressing temperature, E(·) is the calculated expected value, μ i and σ i 2 are the mean and variance of the local time series associated feature vector of the i-th thermal pressing temperature, L is the number of eigenvalues ​​in each local time series associated feature vector of the thermal pressing temperature, is the energy-significant descriptor of the kth local temporal correlation feature vector of thermal pressing temperature, is the energy-significant descriptor of the local temporal correlation feature vector of the i-th thermal bonding temperature, t k and t i The distribution represents the timestamp of the kth and ith local time series correlation feature vectors of the thermal pressing temperature, is a rounding operation, δ is the time decay inverse scaling parameter, β is the decay rate positive scaling parameter, θ and γ are weight hyperparameters, exp(·) represents the exponential function value with the natural constant e as the base, and f c is a vector representing the thermal compression temperature time series decay polymerization.

[0042] Specifically, the S35 obtains an optimization instruction based on the heat pressing temperature time series decay aggregation representation feature, and the optimization instruction is used to represent the recommended decoded value of the pressing pressure at the next time point. In a specific example of the present application, the heat pressing temperature time series decay aggregation representation vector is input into a decoder-based pressing pressure optimizer to obtain an optimization instruction, and the optimization instruction is used to represent the recommended decoded value of the pressing pressure at the next time point. That is, the heat pressing temperature time series decay aggregation representation obtained by energy significant propagation aggregation using the set of local time series associated feature vectors of the heat pressing temperature is classified and processed to automatically obtain the recommended value of the pressing pressure at the next time point. In this way, by real-time monitoring and analyzing the temperature changes during the heat pressing process, the pressing pressure can be dynamically adjusted according to these subtle time series changes to ensure the molding quality of the composite material and improve the consistency of product quality, thereby improving the degree of automation in the material heat pressing process.

[0043] Here, the set of local timing-related feature vectors of the hot pressing temperature respectively express the short-range and long-range bidirectional timing correlation characteristics of the real-time hot pressing temperature in the local time domain. Therefore, when performing global time domain feature forward propagation optimization based on the energy significance of the timing nodes, the full-time domain timing offset caused by the timing node energy significance difference caused by the timing feature differences in each local time domain is taken into account, so that the selective local time domain feature propagation state space representation in the distributed field has a regression understanding imbalance relative to the global time domain timing semantic feature representation. Therefore, it is expected to further improve the full-time domain aggregation sequence regression understanding consistency of the hot pressing temperature timing attenuation aggregation representation vector, thereby improving the accuracy of the optimization instructions obtained by the input decoder-based pressing pressure optimizer.

[0044] Therefore, the present application considers optimizing the thermal pressing temperature timing decay aggregation representation vector when the thermal pressing temperature timing decay aggregation representation vector is input into the pressing pressure optimizer based on the decoder to obtain the optimization instruction, and the optimization process includes:

[0045] Determine the number of zero eigenvalues ​​in the thermal pressing temperature time series decay aggregation representation vector, and calculate the reciprocal of the logarithm of the number of zero eigenvalues ​​with base two and the exponential value of the reciprocal of the number of zero eigenvalues ​​with base natural constants to obtain a first thermal pressing temperature time series decay aggregation representation value and a second thermal pressing temperature time series decay aggregation representation value:

[0046] α=[log 2 (n)] -1

[0047] β=exp[(n) -1 ]

[0048] Wherein, n is the number of zero eigenvalues ​​in the thermal pressing temperature time series decay aggregation representation vector, α is the first thermal pressing temperature time series decay aggregation representation value, β is the second thermal pressing temperature time series decay aggregation representation value, and exp(·) is an exponential function with a natural constant as the base;

[0049] Specifically, it can be obtained by subtracting the zero norm of the thermal pressing temperature time series decay polymerization representation vector from the length of the thermal pressing temperature time series decay polymerization representation vector, that is:

[0050] n=L-}} V}} 0

[0051] Wherein, V represents the thermal pressing temperature time series decay aggregation representation vector, L represents the length of the thermal pressing temperature time series decay aggregation representation vector, ||V|| 0 represents the zero norm of the thermal pressing temperature time series decay aggregation representation vector, and n is the number of zero eigenvalues ​​in the thermal pressing temperature time series decay aggregation representation vector;

[0052] Then, the power function of each eigenvalue of the thermal pressing temperature time-series decay polymerization representation vector with the inverse of the number of zero eigenvalues ​​as the exponent is calculated to obtain the thermal pressing temperature time-series decay polymerization structure state vector, that is:

[0053]

[0054] in, Each eigenvalue of the thermal pressing temperature time series decay polymerization representation vector is a power function with the inverse of the number of zero eigenvalues ​​as the exponent, V 1 The state vector of the polymer structure representing the time series decay of the thermal pressing temperature;

[0055] The heat pressing temperature time series decay polymerization structure state vector is multiplied by the first heat pressing temperature time series decay polymerization representation value to obtain the heat pressing temperature time series decay polymerization information representation vector, that is:

[0056] V 2 =α⊙ V 1

[0057] Among them, V 1 represents the state vector of the thermal pressing temperature time series decay polymerization structure, α is the first thermal pressing temperature time series decay polymerization representation value, ⊙ is the position point multiplication, V 2 A vector representing the thermal pressing temperature time series decay aggregation information;

[0058] The autocorrelation matrix of the heat pressing temperature time series decay polymerization structure state vector is respectively multiplied by the second heat pressing temperature time series decay polymerization representation value and the weight hyperparameter to obtain the heat pressing temperature time series decay polymerization regression understanding matrix, that is:

[0059]

[0060] Wherein, V is the thermal pressing temperature time series attenuation aggregate representation vector, w is the weight hyperparameter, n is the number of zero eigenvalues ​​in the thermal pressing temperature time series attenuation aggregate representation vector, β is the second thermal pressing temperature time series attenuation aggregate representation value, represents the power function of calculating each eigenvalue of the thermal pressing temperature time series decay polymerization representation vector with the inverse of the number of zero eigenvalues ​​as the exponent, T represents the transpose of the vector, ⊙ represents the point multiplication by position, is a matrix multiplication, M is the thermal pressing temperature time series decay polymerization regression understanding matrix;

[0061] The heat pressing temperature time series attenuation aggregation information representation vector is matrix-multiplied with the heat pressing temperature time series attenuation aggregation regression understanding matrix to obtain the optimized heat pressing temperature time series attenuation aggregation representation vector, that is:

[0062]

[0063] Here, V 2 The thermal bonding temperature time series decay aggregation information representation vector, is a matrix multiplication, M is the thermal pressing temperature timing decay aggregation regression understanding matrix, and V′ is the optimized thermal pressing temperature timing decay aggregation representation vector. Finally, the optimized thermal pressing temperature timing decay aggregation representation vector is input into the pressing pressure optimizer based on the decoder to obtain the optimization instruction.

[0064] Based on this, by modeling the zero dimension of the vector field of the heat pressing temperature time series decay aggregation representation vector as a global field redundant dependency, the effective long-range modeling of the heat pressing temperature time series decay aggregation representation vector under the field linear complexity is realized, so that in the feature regression process of the heat pressing temperature time series decay aggregation representation vector, the structured correlation self-distillation between information representation and regression-like understanding is performed in the selective feature enhancement state space based on feature distribution, so as to maintain the overall information complexity of the feature distribution of the heat pressing temperature time series decay aggregation representation vector, and promote the remote dynamic regression understanding balance of its feature convergence, so as to improve the regression understanding consistency between the feature regression and feature extraction processes, and improve the accuracy of the optimization instructions obtained by the heat pressing temperature time series decay aggregation representation vector input based on the decoder. In this way, by real-time monitoring and analysis of the temperature changes in the heat pressing process, the pressing pressure can be dynamically adjusted according to these subtle time series changes to ensure the molding quality of the composite material and improve the consistency of product quality, thereby improving the degree of automation in the material heat pressing process.

[0065] In summary, the preparation method of the explosion-proof protective composite material according to the embodiment of the present application is explained, which obtains the real-time hot pressing temperature collected by the temperature sensor, and adopts the data processing and analysis algorithm based on deep learning to perform time series segmentation and local time series association on the real-time hot pressing temperature, so as to automatically obtain the recommended value of the pressing pressure at the next time point according to the aggregation representation characteristics of each real-time hot pressing temperature in the local time series. In this way, by real-time monitoring and analyzing the temperature changes during the hot pressing process, the pressing pressure can be dynamically adjusted according to these subtle time series changes to ensure the molding quality of the composite material and improve the consistency of product quality, thereby improving the degree of automation in the material hot pressing process.

[0066] In another example, the preparation of the explosion-proof protective composite material can be achieved by the following steps:

[0067] 1. Material preparation: When the temperature is below 15°C, components A and B need to be heated and kept warm, and the viscosity should be controlled to be less than 2000cps;

[0068] 2. Equipment debugging: During construction, the high-temperature, high-pressure impact mixing equipment specified by our company must be used.

[0069] 3. Before spraying, check the hydraulic pressure of materials A and B (2500-3000psi). If the hydraulic pressure difference is more than 500psi, the pressure must be released and the material must be discharged before restarting the machine for debugging.

[0070] 4. The metal substrate should be treated to Sa2.5 level and painted with matching special primer. Before construction, ensure that the spraying surface is clean, dry, dust-free, and shield the areas that do not need spraying; other substrates should be treated according to actual conditions.

[0071] 5. During construction, the spray distance should be controlled at 50-100cm, and the last shot should cover about 40%-60% of the previous shot. The travel speed should be based on effectively controlling the sagging.

[0072] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for preparing an explosion-proof protective composite material, comprising: using at least two high-performance fibers to interweave to form a composite structure layer; making a flexible substrate containing a plurality of hidden holes, injecting a phase change cold storage material into the plurality of hidden holes to obtain a flexible material layer containing a phase change cold storage material; placing the composite structure layer and the flexible material layer on both sides of a central layer coated with an adhesive to obtain an adhesive material, and performing a hot pressing process on the adhesive material at a temperature of 55 to 65° C. by a hot pressing device to obtain an explosion-proof protective composite material; in, The adhesive material is subjected to a heat pressing process at a temperature of 55 to 65° C. by a heat pressing device, characterized in that it includes: Acquire the time series of real-time heat pressing temperature collected by the temperature sensor; Sequence segmentation is performed on the time series of the real-time hot pressing temperature to obtain a set of local time series of the real-time hot pressing temperature; Inputting each local time series of the real-time hot pressing temperature in the set of local time series of the real-time hot pressing temperature into a pressing temperature time series feature extractor to obtain a set of hot pressing temperature local time series associated feature vectors; Inputting the set of the local time series associated feature vectors of the hot pressing temperature into the time series node energy significance feature forward propagation optimizer to obtain the hot pressing temperature time series attenuation aggregation representation vector as the hot pressing temperature time series attenuation aggregation representation feature; Based on the thermal pressing temperature time series decay aggregation representation characteristics, an optimization instruction is obtained, and the optimization instruction is used to represent a recommended decoding value of the pressing pressure at the next time point.

2. The method for preparing the explosion-proof protective composite material according to claim 1, characterized in that: The time series of the real-time hot pressing temperature is segmented to obtain a set of local time series of the real-time hot pressing temperature, including: based on a predetermined time scale, the time series of the real-time hot pressing temperature is segmented to obtain a set of local time series of the real-time hot pressing temperature.

3. The method for preparing the explosion-proof protective composite material according to claim 2, characterized in that: Input each local time series of the real-time hot pressing temperature in the set of local time series of the real-time hot pressing temperature into the pressing temperature time series feature extractor to obtain a set of local time series associated feature vectors of the hot pressing temperature, including: input each local time series of the real-time hot pressing temperature in the set of local time series of the real-time hot pressing temperature into the pressing temperature time series feature extractor based on the Bi-LSTM model to obtain a set of local time series associated feature vectors of the hot pressing temperature.

4. The method for preparing the explosion-proof protective composite material according to claim 3, characterized in that: Inputting the set of the local time series associated feature vectors of the thermal pressing temperature into the time series node energy significance feature forward propagation optimizer to obtain the thermal pressing temperature time series attenuation aggregation representation vector, including: Based on the mean and standard deviation of each local time series correlation feature vector of the thermal pressing temperature in the set of local time series correlation feature vectors of the thermal pressing temperature, calculating the energy significant descriptor of each local time series correlation feature vector of the thermal pressing temperature to obtain a sequence of energy significant descriptors of the thermal pressing temperature; Extracting the timestamp of each heat pressing temperature local time series associated feature vector in the sequence of heat pressing temperature local time series associated feature vectors to obtain a sequence of heat pressing temperature timestamps; The local time series correlation feature vector of the hot pressing temperature corresponding to the current time point in the set of the local time series correlation feature vectors of the hot pressing temperature is used as the current node feature vector and the local time series correlation feature vectors of the hot pressing temperature corresponding to other time points are used as the historical node feature vectors to obtain a sequence of the current local time series correlation feature vector of the hot pressing temperature and the historical local time series correlation feature vectors of the hot pressing temperature; Calculating the energy time decay factor of each historical hot pressing temperature local time series associated characteristic vector in the sequence of the historical hot pressing temperature local time series associated characteristic vectors relative to the current hot pressing temperature local time series associated characteristic vector to obtain a sequence of hot pressing temperature energy time decay factors; Using the sequence of the heat pressing temperature energy significant descriptors as positive adjustment factors and the sequence of the heat pressing temperature energy time decay factors as negative adjustment factors, the sequence of the historical heat pressing temperature local time series associated feature vectors is subjected to time series propagation aggregation to obtain a historical heat pressing temperature time series propagation aggregation representation feature vector; The historical hot pressing temperature time series propagation aggregation representation feature vector and the current hot pressing temperature local time series association feature vector are fused to obtain the hot pressing temperature time series attenuation aggregation representation vector.

5. The method for preparing the explosion-proof protective composite material according to claim 4, characterized in that: Based on the mean and standard deviation of each local time series correlation feature vector of the thermal pressing temperature in the set of the local time series correlation feature vectors of the thermal pressing temperature, calculating the energy significant descriptors of each local time series correlation feature vector of the thermal pressing temperature to obtain a sequence of energy significant descriptors of the thermal pressing temperature, including: Calculating the mean and standard deviation of the local time series correlation feature vector of the thermal pressing temperature respectively to obtain the mean of the local time series correlation feature of the thermal pressing temperature and the standard deviation of the local time series correlation feature of the thermal pressing temperature; Calculating the position difference between the local time series correlation feature vector of the thermal pressing temperature and the mean value of the local time series correlation feature of the thermal pressing temperature to obtain the local time series correlation difference vector of the thermal pressing temperature; Calculating the fourth power of each eigenvalue in the local timing correlation difference vector of the thermal pressing temperature to obtain the local timing correlation modulation difference vector of the thermal pressing temperature; Calculating the expected value of the local timing correlation modulation difference vector of the thermal pressing temperature to obtain the local timing correlation expected value of the thermal pressing temperature; The expected value of the local temporal correlation of the thermal pressing temperature is divided by the fourth power of the standard deviation of the local temporal correlation feature of the thermal pressing temperature to obtain the thermal pressing temperature energy significant descriptor.

6. The method for preparing the explosion-proof protective composite material according to claim 5, characterized in that: Calculating the energy time decay factor of each historical hot pressing temperature local time series associated feature vector in the sequence of the historical hot pressing temperature local time series associated feature vectors relative to the current hot pressing temperature local time series associated feature vector to obtain a sequence of hot pressing temperature energy time decay factors, including: Subtracting the hot pressing temperature timestamp corresponding to the current hot pressing temperature local time series associated feature vector from the hot pressing temperature timestamp corresponding to each historical hot pressing temperature local time series associated feature vector and rounding down to obtain a sequence of historical hot pressing temperature time span values; After calculating the square of the sequence of historical hot pressing temperature time span values ​​divided by the time attenuation inverse scaling parameter by position to obtain a sequence of historical hot pressing temperature time span square modulation values, the sequence of historical hot pressing temperature time span square modulation values ​​is multiplied by the attenuation rate positive scaling parameter by position to obtain a sequence of hot pressing temperature time series span energy attenuation coefficients; Taking each heat pressing temperature time series span energy attenuation coefficient in the sequence of the heat pressing temperature time series span energy attenuation coefficient as an exponential power, a natural exponential function value with a natural constant e as the base is calculated to obtain the sequence of the heat pressing temperature energy time attenuation factor.

7. The method for preparing the explosion-proof protective composite material according to claim 6, characterized in that: Taking the sequence of the heat pressing temperature energy significant descriptors as positive adjustment factors and the sequence of the heat pressing temperature energy time decay factors as negative adjustment factors, the sequence of the historical heat pressing temperature local time series associated feature vectors is subjected to time series propagation aggregation to obtain the historical heat pressing temperature time series propagation aggregation representation feature vector, including: dividing the sequence of the heat pressing temperature energy significant descriptors by the sequence of the heat pressing temperature energy time decay factors by position to obtain the sequence of heat pressing temperature adjustment factors, and calculating the positionally weighted sum of the sequence of the heat pressing temperature adjustment factors and the sequence of the historical heat pressing temperature local time series associated feature vectors to obtain the historical heat pressing temperature time series propagation aggregation representation feature vector.

8. The method for preparing the explosion-proof protective composite material according to claim 7, characterized in that: Based on the thermal pressing temperature timing decay aggregation representation feature, an optimization instruction is obtained, including: inputting the thermal pressing temperature timing decay aggregation representation vector into a pressing pressure optimizer based on a decoder to obtain the optimization instruction.

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