A smart electronic collision ammonia molecule dissociation control system

Abstract

This invention discloses an intelligent electron collision ammonia molecule dissociation control system, belonging to the field of dissociation control technology. It includes an observation platform, a transmission module, an amplification module, a dissociation module, a data acquisition module, a spectrometer, an analysis and control module, an operation analysis module, and a block storage module. The observation platform receives operational information from each submodule and provides real-time feedback to researchers. The transmission module emits a femtosecond laser. This invention enables automated modeling and parameter seeking, improves control accuracy, reduces limitations in use, and simplifies operation for researchers. It also reduces data processing energy consumption, effectively improves data processing efficiency, and ensures the security of research data.

Description

Technical Field

[0001] This invention relates to the field of dissociation control technology, and in particular to an intelligent electronic collision ammonia molecule dissociation control system. Background Technology

[0002] Dissociation, also known as molecular dissociation, in chemistry refers to the process by which molecules separate or thermally decompose into two or more parts. It has two meanings: one refers to the separation of a diatomic gas into its constituent atoms upon heating; the other refers to the separation of a compound into positively and negatively charged ions in water. Both of these chemical reactions are labeled as dissociation. In the second case, dissociation is synonymous with ionization. It can occur in gaseous, liquid, solid, or solution states. With a deeper understanding of light field waveforms, it has become possible to control the amplitude, spectrum, and phase of light fields. Among these, research on the precise control of ultrafast femtosecond laser fields in the time-frequency domain for the directional asymmetric dissociation of molecules is of great significance for the precise control of molecular dynamics and the coherent regulation of chemical reactions.

[0003] Existing intelligent electron collision ammonia molecule dissociation control systems cannot achieve automated modeling and parameter finding, have low control accuracy, and are highly limited in application. In addition, existing intelligent electron collision ammonia molecule dissociation control systems have high data processing energy consumption, low processing efficiency, and poor data security. Therefore, we propose an intelligent electron collision ammonia molecule dissociation control system. Summary of the Invention

[0004] The purpose of this invention is to address the deficiencies in the existing technology by proposing an intelligent electronic collision ammonia molecule dissociation control system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] An intelligent electronic collision ammonia molecule dissociation control system includes an observation platform, a transmission module, an amplification module, a dissociation module, a data acquisition module, a spectrometer, an analysis and control module, an operation analysis module, and a block storage module;

[0007] The observation platform is used to receive the operating information of each submodule and provide real-time feedback to researchers for viewing;

[0008] The emission module is used to emit femtosecond lasers;

[0009] The amplification module is used to amplify the femtosecond laser emitted by the emission module;

[0010] The dissociation module is used to receive femtosecond laser light and dissociate ammonia molecules;

[0011] The data acquisition module is used to collect information during the dissociation process in real time.

[0012] The spectrometer is used to collect and display the waveforms during the dissociation of ammonia molecules;

[0013] The analysis and control module is used to analyze ammonia dissociation information and perform detection and regulation.

[0014] The operation analysis module is used to receive operational information from researchers for risk analysis.

[0015] The block storage module is used to record this dissociation process and store it on the blockchain.

[0016] As a further aspect of the present invention, the specific steps for the dissociation of ammonia molecules in the dissociation module are as follows:

[0017] Step 1: The amplified femtosecond laser is split into two beams, a fundamental frequency beam and a frequency-doubled beam, by a dichroic mirror in the dissociation module. Then, the horizontally polarized fundamental frequency beam and the vertically polarized frequency-doubled beam are finally combined by another dichroic mirror to form a two-color laser field.

[0018] Step 2: Another set of emitting modules emits continuous laser light, which is coupled to the optical path through the dichroic mirror in the dissociation module, and then coupled out from the second dichroic mirror. After that, the interference fringes formed by the two paths of light are obtained by CCD imaging, and the phase of the interference fringes is extracted to perform phase compensation for the frequency-doubled light.

[0019] Step 3: The dual-color laser pulse is introduced into the spectrometer and focused by the concave mirror into an ultrasonic molecular beam composed of 50% nitrogen and 50% argon. The initial dissociation position of electrons is obtained according to the ADK ionization theory, and the initial longitudinal momentum and transverse momentum are Gaussian distributed for optical field coupling dissociation.

[0020] As a further aspect of the present invention, the specific steps for detection and regulation by the analysis and control module are as follows:

[0021] Step (1): The analysis and control module extracts past dissociation records from the block storage module and extracts data from each dissociation record to construct a sample dataset. It calculates the standard deviation of the sample dataset and filters out abnormal data in the sample dataset based on the calculated standard deviation.

[0022] Step (2): Standardize and normalize the remaining data, divide the processed data into training set and test set, set a set of convolutional neural network parameters, determine the number of neurons in each neural network layer, and use the Gaussian function as the activation function of each neuron.

[0023] Step (3): Input the training set into the input layer of the neural network and determine the center vector to obtain the linear combination of the output. Then, use the least squares recursive method to obtain the energy function after multiple rounds of learning. When the energy function value is less than the target error, the training process ends and the control model is output. Then, import the test set into the control model for testing to obtain the loss value. If the loss value does not meet the preset expected value, the parameters of the control model are updated.

[0024] Step (4): The control model receives various data from the data acquisition module, preprocesses the data, and then obtains the ammonia molecule dissociation curve through input, convolution, pooling, full connection and output, and adjusts it according to the generated curve.

[0025] As a further aspect of the present invention, the specific formula for calculating the standard deviation in step (1) is as follows:

[0026]

[0027]

[0028] Among them, v n Let s be the data deviation of the sample dataset, and s be the standard deviation. If any data x i deviation v n Satisfy | v n If |>3σ, then the data is considered abnormal and is removed.

[0029] The specific formula for the Gaussian function mentioned in step (2) is as follows:

[0030]

[0031] In the formula, R y (z) is the output of the y-th unit in the hidden layer, σ y Here, z represents the normalized parameter of the y-th hidden node function, z represents the input vector of the input layer, and c... y Let R be the cluster center vector of the hidden layer function of the y-th unit, where R is the cluster center vector of the hidden layer function. y When (z) is larger, z and c y The closer the distance.

[0032] As a further aspect of the present invention, the specific steps for updating the control model parameters are as follows:

[0033] Step 1: Initialize the network connection weights within the specified range of the control model, then submit training samples from the set of input and output pairs during training, compare the expected network output with the actual network output, and then calculate the local error of all neurons.

[0034] Step 2: When the local error exceeds the preset threshold of the staff, the weights of the control model are trained and updated according to the learning rule equation, and all possible data results are listed according to the preset learning rate and step size.

[0035] Step 3: For each set of data, select any subset as the test set and the remaining subsets as the training set. After training the test model, perform detection on the test set and calculate the root mean square error of the detection results.

[0036] Step 4: Replace the test set with another subset, and then take the remaining subset as the training set. Calculate the root mean square error again until all data have been predicted once. Select the combination parameters corresponding to the minimum root mean square error as the optimal parameters in the data interval and replace the original parameters of the control model.

[0037] As a further aspect of the present invention, the specific steps of the risk analysis in the operation analysis module are as follows:

[0038] Step 1: The operation analysis module deploys relevant data acquisition plugins on the observation platforms of different systems or obtains the operation data recorded in the observation platform through the syslog server, filters out the operation data that meets the preset conditions, and then processes the filtered operation data into detection data in a unified format.

[0039] The second step is to match the recorded operational behaviors with abnormal behavior characteristics in the processed detection data, generate corresponding alarm information based on the matching results, calculate the risk score of each alarm information and output the calculation results, then feed the alarm information back to the relevant maintenance personnel and interrupt the relevant operation process.

[0040] As a further aspect of the present invention, the specific steps for the block storage module to be stored on the blockchain are as follows:

[0041] Step 1: Preprocess the data from this dismantling process into blocks that meet the conditions. When the block is added to the network, each node in the blockchain network generates a local public-private key pair as its own identifier in the network. When a node is waiting for its local role to become a candidate node, it broadcasts a leader application to other nodes in the network and sends it.

[0042] Step II: Once the candidate node becomes the leader node, the other nodes become follower nodes. The leader node then broadcasts block record information. After receiving the information, the follower nodes broadcast the received information to other follower nodes and record the number of repetitions. They then use the information with the most repetitions to generate the block header and send a verification request to the leader node. After successful verification, the leader node sends an add command and enters a dormant period. After receiving the confirmation information, the follower nodes add the newly generated blocks to the blockchain and return their candidate status.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] 1. This intelligent electronic collision ammonia molecule dissociation control system uses a dissociation module to compensate for and construct a dual-color laser field to dissociate ammonia molecules. Simultaneously, the analysis and control module extracts past dissociation data from the block storage module, filters out abnormal data, preprocesses the remaining data, and divides the processed data into training and testing sets. A convolutional neural network is then constructed, and the training set is input into the neural network's input layer for training. When the energy function value is less than the target error, the training process ends and the control model is output. The testing set is then imported into the control model for testing to obtain the loss value. If the loss value does not meet the preset expectation, the parameters of the control model are updated. The control model receives various data acquired by the data acquisition module, preprocesses each data point, and then obtains the ammonia molecule dissociation curve through input, convolution, pooling, full connection, and output. Adjustments are made based on the generated curve. This system enables automated modeling and parameter finding, improves control accuracy, reduces limitations, and simplifies operation for researchers.

[0045] 2. This invention preprocesses the data from the dissociation process into blocks that meet certain conditions. When a block is added to the network, each node in the blockchain generates a local public-private key pair as its identifier. When a node is waiting to become a candidate node, it broadcasts a leader application to other nodes in the network. Once the candidate node becomes the leader node, the other nodes become follower nodes. The leader node then broadcasts the block record information. After receiving the information, the follower nodes broadcast the received information to other follower nodes and record the number of repetitions. The node then uses the information with the most repetitions to generate the block header and sends a verification application to the leader node. After successful verification, the leader node sends an add command and enters a dormant period. After receiving confirmation, the follower nodes add the newly generated blocks to the blockchain and return their candidate identities. This reduces data processing energy consumption, effectively improves data processing efficiency, and ensures the security of research data. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0047] Figure 1 This is a system block diagram of an intelligent electron collision ammonia molecule dissociation control system proposed in this invention. Detailed Implementation

[0048] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0049] Example 1

[0050] Reference Figure 1 A smart electronic collision ammonia molecule dissociation control system includes an observation platform, a transmission module, an amplification module, a dissociation module, a data acquisition module, a spectrometer, an analysis and control module, an operation analysis module, and a block storage module.

[0051] The observation platform is used to receive the operating information of each submodule and provide real-time feedback to researchers; the emission module is used to emit femtosecond lasers; and the amplification module is used to amplify the femtosecond lasers emitted by the emission module.

[0052] The dissociation module is used to receive femtosecond lasers and dissociate ammonia molecules.

[0053] Specifically, the amplified femtosecond laser is split into two beams, a fundamental beam and a frequency-doubled beam, by a dichroic mirror in the dissociation module. The horizontally polarized fundamental beam and the vertically polarized frequency-doubled beam are then combined by another dichroic mirror to form a two-color laser field. Another set of emission modules emits continuous laser light, which is coupled to the optical path by a dichroic mirror in the dissociation module and then coupled out from a second dichroic mirror. The interference fringes formed by the two beams are then captured by a CCD image, and the phase of the interference fringes is extracted to compensate for the phase of the frequency-doubled beam. The two-color laser field pulse is then introduced into a spectrometer and focused by a concave mirror into an ultrasonic molecular beam composed of 50% nitrogen and 50% argon. The initial dissociation position of the electrons is obtained according to the ADK ionization theory, and the initial longitudinal and transverse momentum are Gaussian distributed for optical field coupling and dissociation.

[0054] The data acquisition module is used to collect information during the dissociation process in real time; the spectrometer is used to collect and display the waveforms of ammonia molecules during dissociation; and the analysis and control module is used to analyze the ammonia analysis and dissociation information and to perform detection and regulation.

[0055] Specifically, the analysis and control module extracts past dissociation records from the block storage module and extracts data from each dissociation record to construct a sample dataset. It calculates the standard deviation of this sample dataset and filters out outliers based on the calculated standard deviation. The remaining data is then standardized and normalized, and divided into training and testing sets. A set of convolutional neural network parameters is set, and the number of neurons in each neural network layer is determined. A Gaussian function is used as the activation function for each neuron. The training set is then input into the neural network input layer, and a center vector is determined to obtain a linear combination of outputs. The least squares recursive method is then used to obtain the energy function after multiple rounds of learning. When the energy function value is less than the target error, the training process ends, and the control model is output. The test set is then imported into the control model for testing to obtain the loss value. If the loss value does not meet the preset expectation, the parameters of the control model are updated. The control model receives various data acquired by the data acquisition module and preprocesses them. Then, through input, convolution, pooling, fully connected layers, and output, it obtains the ammonia molecule dissociation curve and makes adjustments based on the generated curve.

[0056] In this embodiment, the network connection weights are initialized within the specified range of the control model. Then, training samples are submitted from the set of input and output pairs during training, and the expected network output is compared with the actual network output. The local error of all neurons is then calculated. When the local error exceeds the preset threshold, the weights of the control model are trained and updated according to the learning rule equation. All possible data results are listed according to the preset learning rate and step size. For each set of data, any subset is selected as the test set, and the remaining subsets are used as the training set. After training the test model, the test set is tested, and the root mean square error of the test results is calculated. The test set is replaced with another subset, and the remaining subset is used as the training set. The root mean square error is calculated again until all data is predicted once. The combined parameters corresponding to the minimum root mean square error are selected as the optimal parameters within the data range and replace the original parameters of the control model.

[0057] It should be further explained that the specific formula for calculating the standard deviation is as follows:

[0058]

[0059]

[0060] Among them, v n Let s be the data deviation of the sample dataset, and s be the standard deviation. If any data x i deviation v n Satisfy | v n If |>3σ, then the data is considered abnormal and is removed.

[0061] The specific formula for the Gaussian function is as follows:

[0062]

[0063] In the formula, R y (z) is the output of the y-th unit in the hidden layer, σ y Here, z represents the normalized parameter of the y-th hidden node function, z represents the input vector of the input layer, and c... y Let R be the cluster center vector of the hidden layer function of the y-th unit, where R is the cluster center vector of the hidden layer function. y When (z) is larger, z and c y The closer the distance.

[0064] Example 2

[0065] Reference Figure 1 A smart electronic collision ammonia molecule dissociation control system includes an observation platform, a transmission module, an amplification module, a dissociation module, a data acquisition module, a spectrometer, an analysis and control module, an operation analysis module, and a block storage module.

[0066] The operation analysis module is used to receive operational information from researchers for risk analysis.

[0067] Specifically, the operation analysis module deploys relevant data acquisition plugins on different system observation platforms or obtains operation data recorded on the observation platform through a syslog server, filters out operation data that meets preset conditions, processes the filtered operation data into detection data in a unified format, matches the operation behaviors recorded in the processed detection data with abnormal behavior characteristics, generates corresponding alarm information based on the matching results, calculates the risk score of each alarm information and outputs the calculation results, then feeds the alarm information back to the relevant maintenance personnel and interrupts the relevant operation process.

[0068] The block storage module is used to record this dismantling process and store it on the blockchain.

[0069] Specifically, the block storage module preprocesses the data from this dismantling process into blocks that meet the conditions. When a block is added to the network, each node in the blockchain network generates a local public-private key pair as its own identifier in the network. When a node is waiting for its local role to become a candidate node, it broadcasts a leader application to other nodes in the network and sends it. When the candidate node becomes the leader node, the other nodes become follower nodes. Then, the leader node broadcasts the block record information. After receiving the information, the follower nodes broadcast the received information to other follower nodes and record the number of repetitions. They use the information with the most repetitions to generate the block header and send a verification application to the leader node. After the verification is successful, the leader node sends an add command and enters a dormant period. After receiving the confirmation information, the follower nodes add the newly generated blocks to the blockchain and return the candidate identity.

Claims

1. A smart electronic collision ammonia molecule dissociation control system, characterized in that, It includes an observation platform, a transmission module, an amplification module, a dissociation module, a data acquisition module, a spectrometer, an analysis and control module, an operation and analysis module, and a block storage module; The observation platform is used to receive the operating information of each submodule and provide real-time feedback to researchers for viewing; The emission module is used to emit femtosecond lasers; The amplification module is used to amplify the femtosecond laser emitted by the emission module; The dissociation module is used to receive femtosecond laser light and dissociate ammonia molecules; The data acquisition module is used to collect information during the dissociation process in real time. The spectrometer is used to collect and display the waveforms during the dissociation of ammonia molecules; The analysis and control module is used to analyze ammonia dissociation information and perform detection and regulation. The operation analysis module is used to receive operational information from researchers for risk analysis. The block storage module is used to record this dissociation process and store it on the blockchain. The specific steps for the dissociation of ammonia molecules in the dissociation module are as follows: Step 1: The amplified femtosecond laser is split into two beams, a fundamental frequency beam and a frequency-doubled beam, by a dichroic mirror in the dissociation module. Then, the horizontally polarized fundamental frequency beam and the vertically polarized frequency-doubled beam are finally combined by another dichroic mirror to form a two-color laser field. Step 2: Another set of emitting modules emits continuous laser light, which is coupled to the optical path through the dichroic mirror in the dissociation module, and then coupled out from the second dichroic mirror. After that, the interference fringes formed by the two beams of CCD imaging are obtained, and the phase of the interference fringes is extracted to perform phase compensation for the frequency-doubled light. Step 3: The dual-color laser pulse is introduced into the spectrometer and focused by the concave mirror into an ultrasonic molecular beam composed of 50% nitrogen and 50% argon. The initial dissociation position of the electron is obtained according to the ADK ionization theory, and the initial longitudinal momentum and transverse momentum are Gaussian distributed for optical field coupling dissociation. The specific steps for detection and regulation by the analysis and control module are as follows: Step (1): The analysis and control module extracts past dissociation records from the block storage module and extracts data from each dissociation record to construct a sample dataset. It calculates the standard deviation of the sample dataset and filters out abnormal data in the sample dataset based on the calculated standard deviation. Step (2): Standardize and normalize the remaining data, divide the processed data into training set and test set, set a set of convolutional neural network parameters, determine the number of neurons in each neural network layer, and use the Gaussian function as the activation function of each neuron. Step (3): Input the training set into the input layer of the neural network and determine the center vector to obtain the linear combination of the output. Then, use the least squares recursive method to obtain the energy function after multiple rounds of learning. When the energy function value is less than the target error, the training process ends and the control model is output. Then, import the test set into the control model for testing to obtain the loss value. If the loss value does not meet the preset expected value, the parameters of the control model are updated. Step (4): The control model receives various data from the data acquisition module, preprocesses the data, and then obtains the ammonia molecule dissociation curve through input, convolution, pooling, full connection and output, and adjusts it according to the generated curve.

2. The intelligent electron collision ammonia molecule dissociation control system according to claim 1, characterized in that, The specific formula for calculating the standard deviation in step (1) is as follows: （1） （2） in, For the data bias of the sample dataset, Let be the standard deviation, if any data deviation satisfy If the data is abnormal, it will be removed. The specific formula for the Gaussian function mentioned in step (2) is as follows: （3） In the formula, It is the output of the y-th unit in the hidden layer. It is the normalized parameter of the function of the y-th hidden node. Represents the input vector of the input layer. Let be the cluster center vector of the hidden layer function of the y-th unit, where, when When it is larger, and The closer the distance.

3. The intelligent electron collision ammonia molecule dissociation control system according to claim 1, characterized in that, The specific steps for updating the parameters of the regulation model are as follows: Step 1: Initialize the network connection weights within the specified range of the control model, then submit training samples from the set of input and output pairs during training, compare the expected network output with the actual network output, and then calculate the local error of all neurons. Step 2: When the local error exceeds the preset threshold of the staff, the weights of the control model are trained and updated according to the learning rule equation, and all possible data results are listed according to the preset learning rate and step size. Step 3: For each set of data, select any subset as the test set and the remaining subsets as the training set. After training the test model, perform detection on the test set and calculate the root mean square error of the detection results. Step 4: Replace the test set with another subset, and then take the remaining subset as the training set. Calculate the root mean square error again until all data have been predicted once. Select the combination parameters corresponding to the minimum root mean square error as the optimal parameters in the data interval and replace the original parameters of the control model.

4. The intelligent electron collision ammonia molecule dissociation control system according to claim 1, characterized in that, The specific steps of the risk analysis in the operation analysis module are as follows: Step 1: The operation analysis module deploys relevant data acquisition plugins on the observation platforms of different systems or obtains the operation data recorded in the observation platform through the syslog server, filters out the operation data that meets the preset conditions, and then processes the filtered operation data into detection data in a unified format. The second step is to match the recorded operational behaviors with abnormal behavior characteristics in the processed detection data, generate corresponding alarm information based on the matching results, calculate the risk score of each alarm information and output the calculation results, then feed the alarm information back to the relevant maintenance personnel and interrupt the relevant operation process.

5. The intelligent electron collision ammonia molecule dissociation control system according to claim 1, characterized in that, The specific steps for up-to-chain storage of the block storage module are as follows: Step 1: Preprocess the data from this dismantling process into blocks that meet the conditions. When the block is added to the network, each node in the blockchain network generates a local public-private key pair as its own identifier in the network. When a node is waiting for its local role to become a candidate node, it broadcasts a leader application to other nodes in the network and sends it. Step II: Once the candidate node becomes the leader node, the other nodes become follower nodes. The leader node then broadcasts block record information. After receiving the information, the follower nodes broadcast the received information to other follower nodes and record the number of repetitions. They then use the information with the most repetitions to generate the block header and send a verification request to the leader node. After successful verification, the leader node sends an add command and enters a dormant period. After receiving the confirmation information, the follower nodes add the newly generated blocks to the blockchain and return their candidate status.

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