CN117996911A - Automatic overheat protection method and system for intelligent charging gun - Google Patents
Automatic overheat protection method and system for intelligent charging gun Download PDFInfo
- Publication number
- CN117996911A CN117996911A CN202410144699.2A CN202410144699A CN117996911A CN 117996911 A CN117996911 A CN 117996911A CN 202410144699 A CN202410144699 A CN 202410144699A CN 117996911 A CN117996911 A CN 117996911A
- Authority
- CN
- China
- Prior art keywords
- temperature
- charging gun
- model
- intelligent charging
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000012544 monitoring process Methods 0.000 claims abstract description 192
- 238000012549 training Methods 0.000 claims abstract description 63
- 238000011161 development Methods 0.000 claims abstract description 35
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000012360 testing method Methods 0.000 claims abstract description 26
- 230000006870 function Effects 0.000 claims description 59
- 238000010606 normalization Methods 0.000 claims description 37
- 238000011176 pooling Methods 0.000 claims description 27
- 230000009022 nonlinear effect Effects 0.000 claims description 12
- 210000002569 neuron Anatomy 0.000 claims description 9
- 230000004913 activation Effects 0.000 claims description 7
- 238000010276 construction Methods 0.000 claims description 7
- 238000005457 optimization Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 3
- 238000011056 performance test Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 description 9
- 230000000694 effects Effects 0.000 description 5
- 238000007726 management method Methods 0.000 description 5
- 238000013473 artificial intelligence Methods 0.000 description 4
- 238000004590 computer program Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000007418 data mining Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 238000011423 initialization method Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to the field of remote monitoring, and discloses a method and a system for automatic overheat protection of an intelligent charging gun, wherein the method comprises the following steps: dividing the normalized data into training data and test data; constructing a temperature monitoring model framework of the intelligent charging gun, initializing weight and bias parameters of the temperature monitoring model framework to obtain an initial temperature monitoring model, and performing forward propagation processing on the training data by using the initial temperature monitoring model to obtain model prediction temperature; and calculating a loss gradient between the predicted temperature of the model and the preset real tag temperature, optimizing an initial temperature monitoring model to obtain an optimized temperature monitoring model, monitoring the temperature value of the intelligent charging gun by using the optimized temperature monitoring model, analyzing the temperature development curve of the intelligent charging gun, constructing an overheat protection instruction of the intelligent charging gun, and executing overheat protection of the intelligent charging gun based on the overheat protection instruction. The invention can improve the timeliness of automatic overheat protection of the intelligent charging gun.
Description
Technical Field
The invention relates to the field of remote monitoring, in particular to an automatic overheat protection method and system for an intelligent charging gun.
Background
The intelligent charging gun is charging equipment integrated with advanced sensing technology, a microprocessor, a communication interface and control logic, can carry out safe and efficient energy transmission with a high-voltage battery system of an Electric Vehicle (EV), is designed and implemented by an automatic overheat protection system of the intelligent charging gun, and aims to improve the safety of the charging equipment, reduce faults caused by overheat and potential fire risks and ensure the safety of users and property. With the development of intelligent charging technology, these protective measures will become more intelligent and efficient, providing a more reliable user experience.
The automatic overheat protection of the current intelligent charging gun monitors the temperature of the charging gun by using an electronic sensor, and measures are automatically taken to prevent equipment overheat when abnormal temperature rise is detected.
Disclosure of Invention
The invention provides a method and a system for automatic overheat protection of an intelligent charging gun, and mainly aims to improve the timeliness of the automatic overheat protection of the intelligent charging gun.
In order to achieve the above object, the present invention provides a method for automatic overheat protection of an intelligent charging gun, comprising:
Acquiring charging data of an intelligent charging gun, carrying out normalization processing on the charging data to obtain normalization data, and dividing the normalization data into training data and test data;
Constructing a temperature monitoring model framework of the intelligent charging gun, initializing weight and bias parameters of the temperature monitoring model framework to obtain an initial temperature monitoring model, and performing forward propagation processing on the training data by using the initial temperature monitoring model to obtain model prediction temperature;
Constructing a loss function of the initial temperature monitoring model, calculating a loss gradient between the model predicted temperature and a preset real label temperature by using the loss function, and adjusting the weight and the bias parameter based on the loss gradient to obtain an adjustment weight and an adjustment bias parameter;
Optimizing the initial temperature monitoring model based on the adjustment weight and the adjustment bias parameter to obtain an optimized temperature monitoring model, and analyzing the monitoring model performance of the optimized temperature monitoring model by utilizing the test data;
When the monitoring model meets the preset requirement, monitoring the temperature value of the intelligent charging gun by using the optimized temperature monitoring model, constructing a temperature curve of the intelligent charging gun based on the temperature value, identifying a temperature influence factor of the intelligent charging gun, analyzing a temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor, constructing an overheat protection instruction of the intelligent charging gun based on the temperature development curve, and executing overheat protection of the intelligent charging gun based on the overheat protection instruction.
Optionally, the normalizing the charging data to obtain normalized data includes:
Identifying a data attribute of the charging data;
Identifying a data extremum of the charging data based on the data attribute;
Calculating a data normalization value of the charging data based on the data extremum;
and carrying out normalization processing on the charging data based on the data normalization value to obtain normalized data.
Optionally, the constructing a temperature monitoring model architecture of the intelligent charging gun includes:
Identifying charging gun characteristics of the intelligent charging gun;
Analyzing the temperature monitoring requirement of the intelligent charging gun based on the charging gun characteristics;
determining a temperature monitoring component of the intelligent charging gun based on the temperature monitoring requirement;
and constructing a temperature monitoring model framework of the intelligent charging gun based on the temperature monitoring component.
Optionally, the performing forward propagation processing on the training data by using the initial temperature monitoring model to obtain a model predicted temperature includes:
Performing feature extraction on the training data by utilizing a convolution layer in the initial temperature monitoring model to obtain a training data feature map;
downsampling the training data feature map by using a pooling layer in the initial temperature monitoring model to obtain a downsampled feature map;
introducing nonlinear properties into the downsampled feature map by using an activation function in the initial temperature monitoring model to obtain a nonlinear property feature map;
and connecting the nonlinear property characteristic graphs by using a full-connection layer in the initial temperature monitoring model to obtain a model prediction temperature.
Optionally, the step of downsampling the training data feature map by using a pooling layer in the initial temperature monitoring model to obtain a downsampled feature map includes:
Based on the training data feature map, a maximum pooling window of the pooling layer is calculated using the following formula:
Wherein MaxPool (S) represents a maximum pooling window of the pooling layer, S represents a training data feature map, and S ac represents an element of training data feature map coordinates (a, c);
And based on the maximum pooling window, utilizing a pooling layer in the initial temperature monitoring model to perform downsampling on the training data feature map to obtain a downsampled feature map.
Optionally, the calculating the loss gradient between the model predicted temperature and the preset real tag temperature using the loss function includes:
Calculating a loss value of the model predicted temperature and a preset real label temperature by using the loss function;
calculating a neuron gradient of the model predicted temperature corresponding to the initial temperature monitoring model based on the loss value;
calculating a bias gradient and a weight gradient of the initial temperature monitoring model through the neuron gradient;
a loss gradient of the initial temperature monitoring model is determined based on the bias gradient and the weight gradient.
Optionally, the calculating the loss value of the model predicted temperature and the preset real tag temperature by using the loss function includes:
calculating a loss value of the model predicted temperature and a preset real tag temperature by using the loss function, wherein the loss function R:
Wherein τ represents a loss value of the model predicted temperature and a preset real tag temperature, M represents the number of model predicted temperatures, F v represents the v-th model predicted temperature, The true tag temperature, which represents the predicted temperature of the v-th model.
Optionally, the adjusting the weight and the bias parameter based on the loss gradient, to obtain an adjusted weight and an adjusted bias parameter, includes:
based on the loss gradient, the weight and the bias parameter are adjusted by using the following formula to obtain the adjusted weight and the adjusted bias parameter:
Wherein sigma represents the adjustment weight, epsilon represents the adjustment bias parameter, alpha represents the learning rate, Representing the weight gradient corresponding to the loss gradient,/>Representing the loss gradient versus bias gradient, R represents the loss function,/>Representing the partial derivative.
Optionally, the analyzing the temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor includes:
Identifying a factor influence relation between the temperature influence factor and a temperature value corresponding to the temperature curve;
marking the state of the influence factor of the temperature influence factor;
Analyzing the temperature development state of the intelligent charging gun based on the influence factor state and the factor influence relation;
And constructing a temperature development curve of the intelligent charging gun based on the temperature development state.
In order to solve the above problems, the present invention further provides a system for automatic overheat protection of an intelligent charging gun, the system comprising:
the data dividing module is used for acquiring charging data of the intelligent charging gun, carrying out normalization processing on the charging data to obtain normalization data, and dividing the normalization data into training data and test data;
The initial model construction module is used for constructing a temperature monitoring model framework of the intelligent charging gun, initializing weight and bias parameters of the temperature monitoring model framework to obtain an initial temperature monitoring model, and carrying out forward propagation processing on the training data by using the initial temperature monitoring model to obtain a model prediction temperature;
The model optimization module is used for constructing a loss function of the initial temperature monitoring model, calculating a loss gradient between the model predicted temperature and a preset real label temperature by using the loss function, and adjusting the weight and the bias parameter based on the loss gradient to obtain an adjustment weight and an adjustment bias parameter;
the model performance test module is used for optimizing the initial temperature monitoring model based on the adjustment weight and the adjustment bias parameter to obtain an optimized temperature monitoring model, and analyzing the monitoring model performance of the optimized temperature monitoring model by utilizing the test data;
And the overheat protection instruction construction module is used for monitoring the temperature value of the intelligent charging gun by using the optimized temperature monitoring model when the monitoring model meets the preset requirement, constructing a temperature curve of the intelligent charging gun based on the temperature value, identifying a temperature influence factor of the intelligent charging gun, analyzing a temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor, constructing an overheat protection instruction of the intelligent charging gun based on the temperature development curve, and executing overheat protection of the intelligent charging gun based on the overheat protection instruction.
According to the embodiment of the invention, the normalization processing is carried out on the charging data to obtain the normalization data, so that effective comparison and mathematical operation can be ensured between different data, and especially in the fields of machine learning and data mining, the convergence speed of an algorithm can be increased, and a model is more stable; optionally, the embodiment of the invention can train and predict the later model by dividing the label data into training data and test data, thereby improving the effect of model prediction; optionally, in the embodiment of the invention, the weight and the bias parameter of the temperature monitoring model framework are initialized to obtain the initial temperature monitoring model, so that the training speed and the final performance of the model can be improved; further, the embodiment of the invention utilizes the initial temperature monitoring model to perform forward propagation processing on the training data to obtain a model predicted temperature, so that whether a temperature predicted value is accurate or not can be judged to evaluate the model performance, thereby providing a data basis for later model training. Therefore, the method and the system for automatic overheat protection of the intelligent charging gun can improve the timeliness of automatic overheat protection of the intelligent charging gun.
Drawings
FIG. 1 is a flow chart illustrating a method for automatic overheat protection of an intelligent charging gun according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a system for automatic overheat protection of an intelligent charging gun according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an electronic device of a system for automatic overheat protection of an intelligent charging gun according to an embodiment of the present invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an automatic overheat protection method for an intelligent charging gun. The execution main body of the method for automatically overheat protecting the intelligent charging gun comprises, but is not limited to, at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the method for automatic overheat protection of the intelligent charging gun can be performed by software or hardware installed in a terminal device or a server device, wherein the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (ContentDelivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for automatic overheat protection of an intelligent charging gun according to an embodiment of the invention is shown. In this embodiment, the method for automatic overheat protection of the intelligent charging gun includes:
S1, acquiring charging data of an intelligent charging gun, carrying out normalization processing on the charging data to obtain normalization data, and dividing the normalization data into training data and test data.
In the embodiment of the invention, the charging data refers to a data set generated in the charging process of the intelligent charging gun, such as data of current, voltage, temperature and the like.
Furthermore, the embodiment of the invention obtains the normalized data by carrying out normalization processing on the charging data, so that effective comparison and mathematical operation can be ensured between different data, and particularly in the fields of machine learning and data mining, the convergence speed of an algorithm can be accelerated, and the model is more stable. The normalized data refers to a data set obtained by normalizing the charging data.
As an embodiment of the present invention, the normalizing the charging data to obtain normalized data includes: identifying a data attribute of the charging data; identifying a data extremum of the charging data based on the data attribute; calculating a data normalization value of the charging data based on the data extremum; and carrying out normalization processing on the charging data based on the data normalization value to obtain normalized data.
The data attribute refers to a characteristic attribute of the charging data, such as a charging rate, a charging power, a voltage and current, a charging efficiency and the like, and the data normalization refers to a value of the charging data obtained through a normalization function.
Optionally, the embodiment of the invention can train and predict the later model by dividing the label data into the training data and the test data, thereby improving the model prediction effect. The training data is data for training a model, and the test data is data for testing performance of the model. The training data and the test data are divided by percentages, the training data occupies 80% of the normalized data, and the test data occupies 20% of the normalized data.
S2, constructing a temperature monitoring model framework of the intelligent charging gun, initializing weight and bias parameters of the temperature monitoring model framework to obtain an initial temperature monitoring model, and performing forward propagation processing on the training data by using the initial temperature monitoring model to obtain model prediction temperature.
According to the embodiment of the invention, the data foundation can be improved for the construction of the later-stage temperature monitoring model by constructing the temperature monitoring model framework of the intelligent charging gun. The temperature monitoring model architecture refers to an architecture for constructing the intelligent charging gun temperature monitoring model, such as a convolution layer, a pooling layer, an activation function, a full connection layer and the like.
As one embodiment of the present invention, the constructing a temperature monitoring model architecture of the intelligent charging gun includes: identifying charging gun characteristics of the intelligent charging gun; analyzing the temperature monitoring requirement of the intelligent charging gun based on the charging gun characteristics; determining a temperature monitoring component of the intelligent charging gun based on the temperature monitoring requirement; and constructing a temperature monitoring model framework of the intelligent charging gun based on the temperature monitoring component.
The charging gun features refer to feature attributes of the intelligent charging gun, such as structural features of charging, rated power and the like, the temperature monitoring requirement refers to requirements of temperature monitoring, such as real-time monitoring, accurate control, early warning function and the like, of the intelligent charging gun, and the temperature monitoring component refers to component architectures, such as a convolution layer, a pooling layer, an activation function, a full-connection layer and the like, required for the temperature monitoring of the intelligent charging gun.
Optionally, in the embodiment of the invention, the weight and the bias parameter of the temperature monitoring model framework are initialized to obtain the initial temperature monitoring model, so that the training speed and the final performance of the model can be improved. The initial temperature monitoring model is a model obtained by initializing the weight and the bias parameters of the temperature monitoring model framework. In detail, the initializing of the weight and bias parameters of the temperature monitoring model architecture may be performed by an initialization method provided by a deep learning framework (e.g., tensorFlow, pyTorch, etc.).
Further, the embodiment of the invention carries out forward propagation processing on the training data by utilizing the initial temperature monitoring model to obtain the model predicted temperature, and can judge whether the temperature predicted value is accurate to evaluate the model performance, thereby providing a data basis for later model training. The model predicted temperature is a value obtained by the initial temperature monitoring model through temperature monitoring of the training data.
Further, as an embodiment of the present invention, the performing forward propagation processing on the training data by using the initial temperature monitoring model to obtain a model predicted temperature includes: performing feature extraction on the training data by utilizing a convolution layer in the initial temperature monitoring model to obtain a training data feature map; downsampling the training data feature map by using a pooling layer in the initial temperature monitoring model to obtain a downsampled feature map; introducing nonlinear properties into the downsampled feature map by using an activation function in the initial temperature monitoring model to obtain a nonlinear property feature map; and connecting the nonlinear property characteristic graphs by using a full-connection layer in the initial temperature monitoring model to obtain a model prediction temperature.
The method comprises the steps of extracting features in input data, capturing features with different scales by setting different convolution kernel sizes and numbers, wherein the pooling layer is used for reducing the dimension of a feature map, reducing the calculation amount and retaining important feature information, the activation function is used for introducing nonlinear properties, the common activation function comprises ReLU, sigmoid, tanh and other functions, the fully connected layer is used for connecting the outputs of the previous layers and obtaining a final prediction result through matrix multiplication and offset addition, the training data feature map is a feature map extracted through the convolution layer, the downsampling feature map is a feature map obtained by extracting useful information of the feature map, and the nonlinear property feature map is a result obtained by introducing nonlinear properties to the downsampled feature map.
Optionally, as an optional embodiment of the present invention, the downsampling the training data feature map with the pooling layer in the initial temperature monitoring model to obtain a downsampled feature map includes: based on the training data feature map, a maximum pooling window of the pooling layer is calculated using the following formula:
Wherein MaxPool (S) represents a maximum pooling window of the pooling layer, S represents a training data feature map, and S ac represents an element of training data feature map coordinates (a, c);
And based on the maximum pooling window, utilizing a pooling layer in the initial temperature monitoring model to perform downsampling on the training data feature map to obtain a downsampled feature map.
S3, constructing a loss function of the initial temperature monitoring model, calculating a loss gradient between the model predicted temperature and a preset real label temperature by using the loss function, and adjusting the weight and the bias parameter based on the loss gradient to obtain an adjustment weight and an adjustment bias parameter.
The embodiment of the invention can measure the difference between the predicted value and the actual temperature value of the model by constructing the loss function of the initial temperature monitoring model. Wherein the loss function refers to a function that calculates the difference between the model predicted value and the actual temperature value, such as Mean Square Error (MSE) and Mean Absolute Error (MAE).
Optionally, the embodiment of the invention calculates the loss gradient between the model predicted temperature and the preset real label temperature by using the loss function, so that the prediction effect of the initial temperature monitoring model can be identified, the model optimization is performed, and the performance of the model is improved. Wherein the loss gradient refers to the derivative of the loss function to model parameters (weights and biases) that indicates how the parameters should be adjusted to reduce losses.
As an embodiment of the present invention, the calculating the loss gradient between the model predicted temperature and the preset real tag temperature using the loss function includes: calculating a loss value of the model predicted temperature and a preset real label temperature by using the loss function; calculating a neuron gradient of the model predicted temperature corresponding to the initial temperature monitoring model based on the loss value; calculating a bias gradient and a weight gradient of the initial temperature monitoring model through the neuron gradient; a loss gradient of the initial temperature monitoring model is determined based on the bias gradient and the weight gradient.
The method comprises the steps of calculating a model predictive temperature, calculating a neuron gradient, calculating a bias gradient and a weight gradient, wherein the loss value is a difference value between the model predictive temperature and a preset real label temperature, the neuron gradient is a loss gradient of each layer of neurons corresponding to the initial temperature monitoring model through a chain rule, and the bias gradient and the weight gradient are loss gradients of biases and weights corresponding to the initial temperature monitoring model.
Optionally, as an optional embodiment of the present invention, the calculating, using the loss function, a loss value of the model predicted temperature and a preset real tag temperature includes: calculating a loss value of the model predicted temperature and a preset real tag temperature by using the loss function, wherein the loss function R:
Wherein τ represents a loss value of the model predicted temperature and a preset real tag temperature, M represents the number of model predicted temperatures, F v represents the v-th model predicted temperature, The true tag temperature, which represents the predicted temperature of the v-th model.
According to the embodiment of the invention, the weight and the bias parameter are adjusted based on the loss gradient, and the performance of the model can be improved by adjusting the weight and the bias parameter. Wherein, the adjustment weight and the adjustment bias parameter refer to the adjusted model weight and parameter.
Optionally, as an embodiment of the present invention, said adjusting the weight and the bias parameter based on the loss gradient, to obtain an adjusted weight and an adjusted bias parameter includes: based on the loss gradient, the weight and the bias parameter are adjusted by using the following formula to obtain the adjusted weight and the adjusted bias parameter:
Wherein sigma represents the adjustment weight, epsilon represents the adjustment bias parameter, alpha represents the learning rate, Representing the weight gradient corresponding to the loss gradient,/>Representing the loss gradient versus bias gradient, R represents the loss function,/>Representing the partial derivative.
And S4, optimizing the initial temperature monitoring model based on the adjustment weight and the adjustment bias parameter to obtain an optimized temperature monitoring model, and analyzing the monitoring model performance of the optimized temperature monitoring model by utilizing the test data.
In the embodiment of the invention, the optimized temperature monitoring model refers to a model obtained by adjusting the weight and the bias parameter corresponding to the initial temperature monitoring model. The prediction accuracy of the model can be improved.
According to the embodiment of the invention, the performance of the model can be further improved by analyzing the performance of the monitoring model of the optimized temperature monitoring model by using the test data, so that the foundation is improved for the later model temperature prediction. The performance of the monitoring model refers to the accuracy of the optimized temperature monitoring model on temperature monitoring. The analyzing the monitoring model performance of the optimized temperature monitoring model may be analyzed by calculating the recall of the optimized temperature monitoring model.
S5, when the monitoring model meets the preset requirement, monitoring the temperature value of the intelligent charging gun by using the optimized temperature monitoring model, constructing a temperature curve of the intelligent charging gun based on the temperature value, identifying a temperature influence factor of the intelligent charging gun, analyzing a temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor, constructing an overheat protection instruction of the intelligent charging gun based on the temperature development curve, and executing overheat protection of the intelligent charging gun based on the overheat protection instruction.
In the embodiment of the invention, the temperature value refers to a temperature value output by the charging data of the intelligent charging gun monitored by the optimized temperature monitoring model, the temperature curve refers to a relation curve between the temperature value and time of the intelligent charging gun, the temperature curve can be constructed through a curve function, and the temperature influence factor refers to factors influencing the temperature of the intelligent charging gun, such as charging power, charging time and the like.
Further, according to the embodiment of the invention, based on the temperature curve and the temperature influence factor, the temperature development curve of the intelligent charging gun is analyzed to predict the future trend of the temperature of the intelligent charging gun, so that possible temperature anomalies can be timely identified, and the timeliness of monitoring the temperature anomalies of the intelligent charging gun is improved. The temperature development curve refers to a trend of continuous temperature change of the intelligent charging gun.
Further, as an embodiment of the present invention, the analyzing the temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor includes: identifying a factor influence relation between the temperature influence factor and a temperature value corresponding to the temperature curve; marking the state of the influence factor of the temperature influence factor; analyzing the temperature development state of the intelligent charging gun based on the influence factor state and the factor influence relation; and constructing a temperature development curve of the intelligent charging gun based on the temperature development state.
The factor influence relation refers to the association relation between the temperature influence factor and the temperature value corresponding to the temperature curve. For example, the higher the charging power is, the faster the temperature of the intelligent charging gun continuously rises, the state of the influencing factor refers to the current value of the temperature influencing factor, for example, the charging power, the charging time and the like, and the state of temperature development refers to the value of continuous change of the temperature of the intelligent charging gun.
Optionally, in an embodiment of the present invention, the overheat protection instruction refers to an instruction for eliminating overheat of the smart charging gun, for example, an instruction for power failure, an instruction for power reduction, and the like. And the overheat protection instruction for constructing the intelligent charging gun can select a preset overheat protection instruction by identifying and analyzing charging data corresponding to the temperature abnormal point of the temperature development curve. The abnormal temperature point is a coordinate point exceeding a preset temperature threshold value in the temperature development curve.
According to the embodiment of the invention, the overheat protection of the intelligent charging gun is executed based on the overheat protection instruction, so that the timeliness of overheat protection of the intelligent charging gun can be improved.
According to the embodiment of the invention, the normalization processing is carried out on the charging data to obtain the normalization data, so that effective comparison and mathematical operation can be ensured between different data, and especially in the fields of machine learning and data mining, the convergence speed of an algorithm can be increased, and a model is more stable; optionally, the embodiment of the invention can train and predict the later model by dividing the label data into training data and test data, thereby improving the effect of model prediction; optionally, in the embodiment of the invention, the weight and the bias parameter of the temperature monitoring model framework are initialized to obtain the initial temperature monitoring model, so that the training speed and the final performance of the model can be improved; further, the embodiment of the invention utilizes the initial temperature monitoring model to perform forward propagation processing on the training data to obtain a model predicted temperature, so that whether a temperature predicted value is accurate or not can be judged to evaluate the model performance, thereby providing a data basis for later model training. Therefore, the method for automatically overheat protecting the intelligent charging gun can improve the timeliness of the automatic overheat protection of the intelligent charging gun.
Fig. 2 is a functional block diagram of a system for automatic overheat protection of an intelligent charging gun according to an embodiment of the present invention.
The system 200 for automatic overheat protection of an intelligent charging gun of the present invention can be installed in an electronic device. Depending on the functions implemented, the system 200 for automatic overheat protection of the intelligent charging gun may include a data partitioning module 201, an initial model building module 202, a model optimization module 203, a model performance testing module 204, and an overheat protection instruction building module 205. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the data dividing module 201 is configured to obtain charging data of an intelligent charging gun, normalize the charging data to obtain normalized data, and divide the normalized data into training data and test data;
The initial model construction module 202 is configured to construct a temperature monitoring model architecture of the intelligent charging gun, initialize weights and bias parameters of the temperature monitoring model architecture to obtain an initial temperature monitoring model, and perform forward propagation processing on the training data by using the initial temperature monitoring model to obtain a model prediction temperature;
the model optimization module 203 is configured to construct a loss function of the initial temperature monitoring model, calculate a loss gradient between the model predicted temperature and a preset real tag temperature using the loss function, and adjust the weight and the bias parameter based on the loss gradient, to obtain an adjusted weight and an adjusted bias parameter;
the model performance test module 204 is configured to optimize the initial temperature monitoring model based on the adjustment weight and the adjustment bias parameter to obtain an optimized temperature monitoring model, and analyze a monitoring model performance of the optimized temperature monitoring model using the test data;
The overheat protection instruction construction module 205 is configured to monitor a temperature value of the intelligent charging gun using the optimized temperature monitoring model when the monitoring model meets a preset requirement, construct a temperature curve of the intelligent charging gun based on the temperature value, identify a temperature influence factor of the intelligent charging gun, analyze a temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor, construct an overheat protection instruction of the intelligent charging gun based on the temperature development curve, and execute overheat protection of the intelligent charging gun based on the overheat protection instruction.
In detail, each module in the system 200 for automatic overheat protection of an intelligent charging gun in the embodiment of the present invention adopts the same technical means as the method for automatic overheat protection of an intelligent charging gun in the drawings when in use, and can produce the same technical effects, which are not described here again.
The embodiment of the invention provides electronic equipment for realizing the method for automatically overheat protecting the intelligent charging gun.
Referring to fig. 3, the electronic device may include a processor 30, a memory 31, a communication bus 32, and a communication interface 33, and may further include a computer program stored in the memory 31 and executable on the processor 30, such as a method program for automatic overheat protection of an intelligent charging gun.
The processor may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory (e.g., executing a program for automatic overheat protection of an intelligent charging gun, etc.), and calling data stored in the memory.
The memory includes at least one type of readable storage medium including flash memory, removable hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), etc. that are provided on the electronic device. Further, the memory may also include both internal storage units and external storage devices of the electronic device. The memory can be used for storing application software installed in the electronic equipment and various data, such as codes of programs based on automatic overheat protection of the intelligent charging gun, and the like, and can be used for temporarily storing data which are already output or are to be output.
The communication bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory and at least one processor or the like.
The communication interface is used for communication between the electronic equipment and other equipment, and comprises a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and preferably, the power source may be logically connected to the at least one processor through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The program stored by the memory in the electronic device for automatic overheat protection of the intelligent charging gun is a combination of a plurality of instructions, and when running in the processor, the program can realize:
Acquiring charging data of an intelligent charging gun, carrying out normalization processing on the charging data to obtain normalization data, and dividing the normalization data into training data and test data;
Constructing a temperature monitoring model framework of the intelligent charging gun, initializing weight and bias parameters of the temperature monitoring model framework to obtain an initial temperature monitoring model, and performing forward propagation processing on the training data by using the initial temperature monitoring model to obtain model prediction temperature;
Constructing a loss function of the initial temperature monitoring model, calculating a loss gradient between the model predicted temperature and a preset real label temperature by using the loss function, and adjusting the weight and the bias parameter based on the loss gradient to obtain an adjustment weight and an adjustment bias parameter;
Optimizing the initial temperature monitoring model based on the adjustment weight and the adjustment bias parameter to obtain an optimized temperature monitoring model, and analyzing the monitoring model performance of the optimized temperature monitoring model by utilizing the test data;
When the monitoring model meets the preset requirement, monitoring the temperature value of the intelligent charging gun by using the optimized temperature monitoring model, constructing a temperature curve of the intelligent charging gun based on the temperature value, identifying a temperature influence factor of the intelligent charging gun, analyzing a temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor, constructing an overheat protection instruction of the intelligent charging gun based on the temperature development curve, and executing overheat protection of the intelligent charging gun based on the overheat protection instruction.
Specifically, the specific implementation method of the above instruction by the processor may refer to descriptions of related steps in the corresponding embodiment of the drawings, which are not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
Acquiring charging data of an intelligent charging gun, carrying out normalization processing on the charging data to obtain normalization data, and dividing the normalization data into training data and test data;
Constructing a temperature monitoring model framework of the intelligent charging gun, initializing weight and bias parameters of the temperature monitoring model framework to obtain an initial temperature monitoring model, and performing forward propagation processing on the training data by using the initial temperature monitoring model to obtain model prediction temperature;
Constructing a loss function of the initial temperature monitoring model, calculating a loss gradient between the model predicted temperature and a preset real label temperature by using the loss function, and adjusting the weight and the bias parameter based on the loss gradient to obtain an adjustment weight and an adjustment bias parameter;
Optimizing the initial temperature monitoring model based on the adjustment weight and the adjustment bias parameter to obtain an optimized temperature monitoring model, and analyzing the monitoring model performance of the optimized temperature monitoring model by utilizing the test data;
When the monitoring model meets the preset requirement, monitoring the temperature value of the intelligent charging gun by using the optimized temperature monitoring model, constructing a temperature curve of the intelligent charging gun based on the temperature value, identifying a temperature influence factor of the intelligent charging gun, analyzing a temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor, constructing an overheat protection instruction of the intelligent charging gun based on the temperature development curve, and executing overheat protection of the intelligent charging gun based on the overheat protection instruction.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method for automatic overheat protection of an intelligent charging gun, the method comprising:
Acquiring charging data of an intelligent charging gun, carrying out normalization processing on the charging data to obtain normalization data, and dividing the normalization data into training data and test data;
Constructing a temperature monitoring model framework of the intelligent charging gun, initializing weight and bias parameters of the temperature monitoring model framework to obtain an initial temperature monitoring model, and performing forward propagation processing on the training data by using the initial temperature monitoring model to obtain model prediction temperature;
Constructing a loss function of the initial temperature monitoring model, calculating a loss gradient between the model predicted temperature and a preset real label temperature by using the loss function, and adjusting the weight and the bias parameter based on the loss gradient to obtain an adjustment weight and an adjustment bias parameter;
Optimizing the initial temperature monitoring model based on the adjustment weight and the adjustment bias parameter to obtain an optimized temperature monitoring model, and analyzing the monitoring model performance of the optimized temperature monitoring model by utilizing the test data;
When the monitoring model meets the preset requirement, monitoring the temperature value of the intelligent charging gun by using the optimized temperature monitoring model, constructing a temperature curve of the intelligent charging gun based on the temperature value, identifying a temperature influence factor of the intelligent charging gun, analyzing a temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor, constructing an overheat protection instruction of the intelligent charging gun based on the temperature development curve, and executing overheat protection of the intelligent charging gun based on the overheat protection instruction.
2. The method for automatic overheat protection of an intelligent charging gun according to claim 1, wherein the normalizing the charging data to obtain normalized data comprises:
Identifying a data attribute of the charging data;
Identifying a data extremum of the charging data based on the data attribute;
Calculating a data normalization value of the charging data based on the data extremum;
and carrying out normalization processing on the charging data based on the data normalization value to obtain normalized data.
3. The method for automatic overheat protection of an intelligent charging gun according to claim 1, wherein the constructing a temperature monitoring model architecture of the intelligent charging gun comprises:
Identifying charging gun characteristics of the intelligent charging gun;
Analyzing the temperature monitoring requirement of the intelligent charging gun based on the charging gun characteristics;
determining a temperature monitoring component of the intelligent charging gun based on the temperature monitoring requirement;
and constructing a temperature monitoring model framework of the intelligent charging gun based on the temperature monitoring component.
4. The method for automatic overheat protection of an intelligent charging gun according to claim 1, wherein the forward propagation processing is performed on the training data by using the initial temperature monitoring model to obtain a model predicted temperature, comprising:
Performing feature extraction on the training data by utilizing a convolution layer in the initial temperature monitoring model to obtain a training data feature map;
downsampling the training data feature map by using a pooling layer in the initial temperature monitoring model to obtain a downsampled feature map;
introducing nonlinear properties into the downsampled feature map by using an activation function in the initial temperature monitoring model to obtain a nonlinear property feature map;
and connecting the nonlinear property characteristic graphs by using a full-connection layer in the initial temperature monitoring model to obtain a model prediction temperature.
5. The method for automatic overheat protection of an intelligent charging gun according to claim 4, wherein the step of downsampling the training data feature map by a pooling layer in the initial temperature monitoring model to obtain a downsampled feature map comprises:
Based on the training data feature map, a maximum pooling window of the pooling layer is calculated using the following formula:
Wherein MaxPool (S) represents a maximum pooling window of the pooling layer, S represents a training data feature map, and S ac represents an element of training data feature map coordinates (a, c);
And based on the maximum pooling window, utilizing a pooling layer in the initial temperature monitoring model to perform downsampling on the training data feature map to obtain a downsampled feature map.
6. The method of intelligent charge gun automatic overheat protection of claim 1, wherein the calculating a loss gradient between the model predicted temperature and a preset true tag temperature using the loss function comprises:
Calculating a loss value of the model predicted temperature and a preset real label temperature by using the loss function;
calculating a neuron gradient of the model predicted temperature corresponding to the initial temperature monitoring model based on the loss value;
calculating a bias gradient and a weight gradient of the initial temperature monitoring model through the neuron gradient;
a loss gradient of the initial temperature monitoring model is determined based on the bias gradient and the weight gradient.
7. The method of intelligent gun automatic overheat protection as claimed in claim 6, wherein the calculating the loss value of the model predicted temperature and the preset real tag temperature using the loss function includes:
calculating a loss value of the model predicted temperature and a preset real tag temperature by using the loss function, wherein the loss function R:
Wherein τ represents a loss value of the model predicted temperature and a preset real tag temperature, M represents the number of model predicted temperatures, F v represents the v-th model predicted temperature, The true tag temperature, which represents the predicted temperature of the v-th model.
8. The method of intelligent gun automatic overheat protection of claim 1, wherein adjusting the weights and the bias parameters based on the loss gradient results in adjusting weights and adjusting bias parameters, comprising:
based on the loss gradient, the weight and the bias parameter are adjusted by using the following formula to obtain the adjusted weight and the adjusted bias parameter:
Wherein sigma represents the adjustment weight, epsilon represents the adjustment bias parameter, alpha represents the learning rate, Representing the weight gradient corresponding to the loss gradient,/>Representing the loss gradient versus bias gradient, R represents the loss function,/>Representing the partial derivative.
9. The method of automatic overheat protection for an intelligent charging gun of claim 1, wherein the analyzing the temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor comprises:
Identifying a factor influence relation between the temperature influence factor and a temperature value corresponding to the temperature curve;
marking the state of the influence factor of the temperature influence factor;
Analyzing the temperature development state of the intelligent charging gun based on the influence factor state and the factor influence relation;
And constructing a temperature development curve of the intelligent charging gun based on the temperature development state.
10. A system for intelligent charging gun automatic overheat protection, characterized by a method for performing the intelligent charging gun automatic overheat protection as claimed in any one of claims 1 to 9, the system comprising:
the data dividing module is used for acquiring charging data of the intelligent charging gun, carrying out normalization processing on the charging data to obtain normalization data, and dividing the normalization data into training data and test data;
The initial model construction module is used for constructing a temperature monitoring model framework of the intelligent charging gun, initializing weight and bias parameters of the temperature monitoring model framework to obtain an initial temperature monitoring model, and carrying out forward propagation processing on the training data by using the initial temperature monitoring model to obtain a model prediction temperature;
The model optimization module is used for constructing a loss function of the initial temperature monitoring model, calculating a loss gradient between the model predicted temperature and a preset real label temperature by using the loss function, and adjusting the weight and the bias parameter based on the loss gradient to obtain an adjustment weight and an adjustment bias parameter;
the model performance test module is used for optimizing the initial temperature monitoring model based on the adjustment weight and the adjustment bias parameter to obtain an optimized temperature monitoring model, and analyzing the monitoring model performance of the optimized temperature monitoring model by utilizing the test data;
And the overheat protection instruction construction module is used for monitoring the temperature value of the intelligent charging gun by using the optimized temperature monitoring model when the monitoring model meets the preset requirement, constructing a temperature curve of the intelligent charging gun based on the temperature value, identifying a temperature influence factor of the intelligent charging gun, analyzing a temperature development curve of the intelligent charging gun based on the temperature curve and the temperature influence factor, constructing an overheat protection instruction of the intelligent charging gun based on the temperature development curve, and executing overheat protection of the intelligent charging gun based on the overheat protection instruction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410144699.2A CN117996911A (en) | 2024-01-31 | 2024-01-31 | Automatic overheat protection method and system for intelligent charging gun |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410144699.2A CN117996911A (en) | 2024-01-31 | 2024-01-31 | Automatic overheat protection method and system for intelligent charging gun |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117996911A true CN117996911A (en) | 2024-05-07 |
Family
ID=90900371
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410144699.2A Pending CN117996911A (en) | 2024-01-31 | 2024-01-31 | Automatic overheat protection method and system for intelligent charging gun |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117996911A (en) |
-
2024
- 2024-01-31 CN CN202410144699.2A patent/CN117996911A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112148577B (en) | Data anomaly detection method and device, electronic equipment and storage medium | |
| CN112306835B (en) | User data monitoring and analyzing method, device, equipment and medium | |
| CN111694844B (en) | Enterprise operation data analysis method and device based on configuration algorithm and electronic equipment | |
| CN116630766B (en) | Multi-source information data processing system, method and equipment | |
| CN111339072B (en) | User behavior-based change value analysis method and device, electronic equipment and medium | |
| CN112434308B (en) | Application vulnerability detection method and device, electronic equipment and computer storage medium | |
| CN116015922B (en) | Network security situation analysis method, device and equipment of electric power Internet of things | |
| CN116755985A (en) | Platform intelligent monitoring management system based on big data and data analysis | |
| CN113295399B (en) | Converter valve element state evaluation method, device, electronic equipment and storage medium | |
| CN117557199B (en) | Intelligent warehousing method, system and storage medium based on mathematical model | |
| CN111652282B (en) | Big data-based user preference analysis method and device and electronic equipment | |
| CN117474180A (en) | Regional power supply optimization system, method and medium based on power distribution cabinet adjustment | |
| CN116089250B (en) | Man-machine interaction optimization management system and management method | |
| CN117996911A (en) | Automatic overheat protection method and system for intelligent charging gun | |
| CN113469237B (en) | User intention recognition method, device, electronic equipment and storage medium | |
| CN116227911A (en) | Risk monitoring and early warning method, device, equipment and medium for fault power failure event | |
| CN112434947A (en) | Intelligent evaluation method and equipment for power distribution network and storage medium | |
| CN116155770B (en) | Internet of things security situation assessment method, device and equipment under multi-source data fusion | |
| CN117250522B (en) | Data modeling method and system applied to retired battery management | |
| CN117314205B (en) | Method and device for reporting detection results of compartment interstitial evaluation items | |
| CN118155424B (en) | Road safety management method and system for realizing expressway based on data interaction | |
| CN112700006B (en) | Network architecture searching method, device, electronic equipment and medium | |
| CN113706292B (en) | Credit card testing method, device, equipment and medium based on virtual data | |
| CN117235480B (en) | Screening method and system based on big data under data processing | |
| CN118134115A (en) | Safety management method and system applied to electronic auxiliary material processing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |