CN114019110B - Workplace gas detector end cloud integration platform based on big data - Google Patents
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Abstract
The invention belongs to the technical field of gas detection, and discloses a big data-based cloud integrated platform at the gas detector end of a workplace, which comprises: a data collection module for collecting data about individual gas detectors deployed within a worksite; the data analysis module is used for carrying out temperature and humidity compensation calculation on detection data of the gas detector about the gas concentration and analyzing a diffusion path of leaked gas at a gas monitoring point; the data application module is used for providing various platform service functions based on the calculation result data of the data analysis module, and comprises a reminding message used for sending gas leakage to personnel according to the position coordinates of the personnel in the operation site.
Description
Technical Field
The invention belongs to the technical field of gas detection, and particularly relates to a cloud integrated platform at a gas detector end in a workplace based on big data.
Background
With the gradual expansion of industrial scale, especially the development of petroleum, chemical industry, coal, automobile and other industries, the variety and quantity of gas raw materials used in a working place, and the variety and quantity of gases generated in a production process are also increasing, wherein a part of gases may have flammable and explosive characteristics, and a part of gases may be toxic and harmful gases, which are easily harmful to human bodies, so it is necessary to research a method for automatically detecting gases in the working place, in the prior art, a proper gas detector is generally deployed in the working place, and the detection data of the gas detector is uploaded to a monitoring center, and when the detection data exceeds a threshold value, a reminder is given to relevant personnel, however, because the detection accuracy of the gas detector on the gas concentration is related to factors such as temperature and humidity, the problem in the prior art is that the detection accuracy of the gas detector on the gas concentration is not high, in addition, in the prior art, the diffusion path simulation of the leaked gas in the operation place is less, and personnel in the operation place are timely reminded to prevent the personnel from being harmed to health.
Disclosure of Invention
Aiming at the technical problems, the invention provides a big data-based cloud integrated platform at the gas detector end of a workplace, which mainly collects data about each gas detector deployed in the workplace, and uses a neural network to perform temperature and humidity compensation calculation on the detection data about the gas concentration of the gas detector, so as to obtain high-precision gas concentration detection data.
In order to achieve the above object, the present invention provides a big data based workplace gas detector end cloud integrated platform, which includes:
the data acquisition module is used for collecting data about each gas detector deployed in a workplace, wherein the data at least comprises detection data of the gas detector on gas concentration of a gas monitoring point, temperature data of the gas detector, temperature data of detected gas, humidity data of the detected gas and air humidity data of the gas monitoring point;
the data analysis module is used for carrying out temperature and humidity compensation calculation on the detection data of the gas detector about the gas concentration by using a neural network technology according to the data about each gas detector, so that high-precision gas concentration detection data are obtained, a three-dimensional coordinate system in a working place is established, and a diffusion path of leaked gas of a gas monitoring point is analyzed, so that the platform can send a reminding message to personnel in the working place in time;
the data analysis module is used for sending a reminding message of gas leakage to personnel in the operation site according to the position coordinates of the personnel in the operation site;
in the data analysis module, the establishment of a three-dimensional coordinate system in the operation site and the analysis of the diffusion path of the leaked gas at the gas monitoring point specifically include the following steps:
establishing a three-dimensional coordinate system by taking the ground in the operation site as an XOY plane and taking the upward direction vertical to the XOY plane as the positive direction of a Z axis, and respectively obtaining the position coordinates of each gas detector in the operation site under the three-dimensional coordinate system;
acquiring position coordinates of personnel in a workplace under the three-dimensional coordinate system through a mobile client in a data application module;
estimating a diffusion time of a leaked gas to reach position coordinates of a person in a work site under a three-dimensional coordinate system by using a diffusion path model of the leaked gas at a gas monitoring point, the diffusion path model being described as follows:
wherein, (x, y, z), x, y, z belongs to R, is the position coordinate of personnel in the operation site under a three-dimensional coordinate system, v, v & gt 0 is the leakage speed of the leaked gas on a gas monitoring point, alpha, pi/2 & gt alpha & gt 0 is the included angle between the leaked gas and the XOY plane of the three-dimensional coordinate system,
is the included angle between the projection of the leaked gas on the XOY plane and the X axis of the three-dimensional coordinate system, g is the gravity acceleration, h, h is more than 0, and is the coordinate of the gas detector on the Z axis of the three-dimensional coordinate systemThe value t, t > 0 is the time it takes for the leaking gas to reach the coordinates of the location where the personnel are located.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the cloud integrated platform at the gas detector end of the operation place based on the big data, data about each gas detector deployed in the operation place are collected through the data acquisition module, temperature and humidity compensation calculation is carried out on detection data about gas concentration of the gas detector through the data analysis module, a diffusion path of leaked gas at a gas monitoring point is analyzed, and finally various platform service functions are provided on the basis of calculation results of the data analysis module through the data application module;
2. the invention uses the neural network technology to carry out temperature and humidity compensation calculation on the detection data of the gas detector about the gas concentration, solves the problem of low detection precision of the gas detector on the gas concentration in the prior art, analyzes the diffusion path of the leaked gas of the gas monitoring point, and sends a reminding message of gas leakage to personnel according to the position coordinates of the personnel in the operation site, thereby avoiding health injury to the personnel.
Drawings
FIG. 1 is a composition structure diagram of a cloud integrated platform at a gas detector end of a big data-based workplace of the invention;
FIG. 2 is a block diagram of the data acquisition module of the present invention;
FIG. 3 is a block diagram of the data analysis module of the present invention;
FIG. 4 is a block diagram of the data application module of the present invention;
FIG. 5 is a flowchart illustrating the steps of temperature and humidity compensation calculation for the detected data related to the gas concentration according to the present invention;
FIG. 6 is a flow chart of the steps of constructing a training data set of a neural network model of the present invention;
fig. 7 is a flow chart of the steps of analyzing the diffusion path of the leaking gas according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
Referring to fig. 1, the invention provides a workplace gas detector end cloud integrated platform based on big data, which specifically comprises the following modules:
the data acquisition module is used for collecting data about each gas detector deployed in a workplace, and the data at least comprises detection data of the gas detector on gas concentration of a gas monitoring point, temperature data of the gas detector, temperature data of detected gas, humidity data of the detected gas and air humidity data of the gas monitoring point.
And the data analysis module is used for carrying out temperature and humidity compensation calculation on the detection data of the gas detector about the gas concentration by using a neural network technology according to the data about each gas detector, so that high-precision gas concentration detection data is obtained, and is also used for establishing a three-dimensional coordinate system in a workplace and analyzing a diffusion path of leaked gas at a gas monitoring point, so that the platform can send a reminding message to personnel in the workplace in time.
And the data analysis module is also used for sending a reminding message of gas leakage to personnel in the operation site according to the position coordinates of the personnel in the operation site.
Further, referring to fig. 2, the data acquisition module specifically includes a gas detector, a main controller, and a routing relay, where the gas detector is connected to the main controller through a bus to upload data about each gas detector to the main controller, and also receives a control command from the main controller, so as to perform field control on the gas detector, the main controller is connected to the routing relay, the main controller continuously uploads data about each gas detector to the routing relay, and also receives a control command from the routing relay, and the routing relay receives and forwards data about each gas detector and a control command about the gas detector through a network.
Further, referring to fig. 3, the cloud computing platform carried by the data analysis module includes a storage server and a computing server, the data analysis module receives data about each gas detector forwarded by the routing relay through a network, the storage server stores the data about each gas detector in real time, the computing server is configured to perform related computation based on the data about each gas detector, specifically includes performing temperature and humidity compensation computation on detected data about gas concentration of the gas detector, and analyzing a diffusion path of leaked gas at a gas monitoring point, the data analysis module returns a computation result upwards in the form of an interface, and meanwhile, the data analysis module can further send a control command for the gas detector downwards according to the computation result.
Specifically, after the data analysis module completes temperature and humidity compensation calculation on detection data related to gas concentration through a neural network, the method further comprises the step of judging the magnitude relation between high-precision gas concentration detection data and a gas concentration threshold, and when the magnitude relation is larger than or equal to the gas concentration threshold, the data analysis module sends the high-precision gas concentration detection data to a PC client and a mobile client of the data application module through the network, and simultaneously sends a field control command to the gas detector downwards to enable the gas detector to give an alarm.
Further, as shown in fig. 4, the data application module includes a PC client and a mobile client, the data application module performs data communication with the data analysis module through a network, the data application module receives calculation result data from the data analysis module and provides various platform service functions based on the calculation result data, and the mobile client is further configured to acquire position coordinates of a person in the workplace and send the position coordinates to the data analysis module, so that the position coordinates participate in calculation of the data analysis module.
Further, referring to fig. 5, the temperature and humidity compensation calculation of the detection data of the gas detector about the gas concentration by using the neural network model in the data analysis module specifically includes the following steps:
constructing a training data set of a neural network model, wherein the training data set comprises a plurality of sample data, and each sample data specifically comprises detection data of a gas detector for gas concentration, temperature data of the gas detector, temperature data of detected gas, humidity data of the detected gas, air humidity data of a gas monitoring point and high-precision detection data for gas concentration, wherein the detection data are used for completing temperature and humidity compensation calculation;
determining the number of neurons of an input layer, a hidden layer and an output layer according to the number of input data and the number of output data when the temperature and humidity compensation calculation is carried out on the neural network model, establishing the connection among the neurons of different layers, and completing the construction of the neural network model;
sequentially inputting sample data in the training data set into an input layer neuron of the constructed neural network model, forwarding the data through a hidden layer neuron until the output layer neuron generates an output result, calculating a training error of an output layer, and stopping training the model when the training error is smaller than an error threshold;
and the acquired data about the gas detector in the operation place is used as input data of the trained neural network model, and high-precision detection data of gas concentration, which is used for completing temperature and humidity compensation calculation, is obtained.
Specifically, by training a neural network model in advance using the sample data in the above-mentioned training data set, the neural network model can learn the internal relationship between the detection data of the gas detector for the gas concentration, the temperature data of the gas detector itself, the temperature data of the gas to be detected, the humidity data of the gas to be detected, and the air humidity data of the gas monitoring point, and the detection data of the gas concentration with high accuracy for which the calculation of temperature/humidity compensation is completed, and when the data on the gas detector in the work place is used as the input data of the neural network model after training, the detection data of the gas concentration with high accuracy for which the calculation of temperature/humidity compensation is completed can be obtained, so that the temperature data of the gas detector itself, the temperature data of the gas to be detected, and the humidity data of the gas to be detected are also eliminated, and air humidity data of the gas monitoring points, on the accuracy of the gas detector for the detection data of the gas concentration.
In addition, when the training error of the output layer is greater than the error threshold, the training error needs to be reversely propagated to the hidden layer neuron, the connection weight and the error of the neuron are adjusted according to the error of the hidden layer neuron, and the adjusted neural network model is continuously trained.
Further, referring to fig. 6, the above training data set for constructing the neural network model specifically includes the following steps:
establishing a basic data set according to the data range and concentration interval of the detection data of the gas detector on the gas concentration, the data range and temperature interval of the temperature data of the gas detector, the data range and temperature interval of the temperature data of the detected gas, the data range and humidity interval of the humidity data of the detected gas and the data range and humidity interval of the air humidity data of a gas monitoring point;
constructing a field data set using data collected by a data acquisition module regarding individual gas detectors deployed within a worksite;
extracting a certain amount of sample data from the basic data set and the field data set respectively to form a training data set phi for pre-training the neural network model,
wherein x isiI > 1 sample data in the basic dataset, pkK is more than 1, 1 is more than lambda is more than 0, and 1 is more than 1-lambda is more than 0.
Specifically, for the convenience of understanding the creation process of the basic data set, the data range of the detection data of the gas concentration is, for example, 100 to 950 × 10-6Concentration interval of 50X 10-6If the data range of the temperature data of the detected gas and the temperature data of the gas detector is 15 to 25 ℃, the temperature interval is 2 ℃, the data range of the humidity data of the detected gas and the air humidity data of the gas monitoring point is 40 to 60 percent RH, and the humidity interval is 5 percent RH, the basic data set comprises 18 × 6 × 6 × 5 × 16200 sample data in total, the sample data in the basic data set comprises the detection data of the gas concentration, the temperature data of the detected gas and the temperature data of the gas detector, and the humidity data of the detected gas and the air humidity data of the gas monitoring point, and is not limited to a small data range, and covers a large data range, so the sample data is more general.
In addition, the training data set for setting the neural network model is composed of a basic data set and a field data set, because the field data set is composed of data collected by a data acquisition module and about each gas detector deployed in a working site, sample data in the field data set is closer to the real situation in the working site, but detection data of gas concentration, temperature data of detected gas and temperature data of the gas detector, humidity data of the detected gas and air humidity data of a gas monitoring point, which are contained in the sample data, are usually limited in a certain data range, i.e. have no generality, if the neural network model is trained by using only the field data set, the neural network model may have an overfitting problem, so that the generalization performance of the neural network model is not high, and by adjusting the size of a lambda value, the specific quantity of the basic sample data and the field sample data which form the training data set can be changed, and the learning performance of the neural network model can be adjusted.
Further, referring to fig. 7, the data analysis module establishes a three-dimensional coordinate system in the operation site and analyzes a diffusion path of the leaking gas at the gas monitoring point, and the method specifically includes the following steps:
establishing a three-dimensional coordinate system by taking the ground in the operation place as an XOY plane and taking the upward direction vertical to the XOY plane as the positive direction of a Z axis, and respectively obtaining the position coordinates of each gas detector in the operation place under the three-dimensional coordinate system;
acquiring position coordinates of personnel in a workplace under the three-dimensional coordinate system through a mobile client in a data application module;
estimating a diffusion time of a leaked gas to reach position coordinates of a person in a work site under a three-dimensional coordinate system by using a diffusion path model of the leaked gas at a gas monitoring point, the diffusion path model being described as follows:
wherein, (x, y, z), x, y, z belongs to R, is the position coordinate of personnel in the operation site under a three-dimensional coordinate system, v, v is more than 0, is the leakage speed of the leaked gas on a gas monitoring point, alpha, pi/2 is more than alpha, is more than 0, is the included angle between the leaked gas and the XOY plane of the three-dimensional coordinate system,
the included angle between the projection of the leaked gas on the XOY plane and the X axis of the three-dimensional coordinate system is shown, g is the gravity acceleration, h and h are more than 0, the included angle is the coordinate value of the gas detector on the Z axis of the three-dimensional coordinate system, t and t are more than 0, and the time spent on the leaked gas reaching the position coordinate of the personnel is shown.
Specifically, when the three-dimensional coordinate system is established, the ground in the operation place is used as an XOY plane, the specific establishing mode of the X axis and the Y axis of the three-dimensional coordinate system is not limited in the present invention, in practical application, the X axis and the Y axis of the three-dimensional coordinate system can be established according to specific requirements, in addition, the obtained position coordinates of each gas detector in the operation place in the three-dimensional coordinate system, the values on the X axis and the Y axis jointly represent the plane position of the gas detector in the operation place, the value on the Z axis represents the height of the gas detector from the ground, the position coordinates of the person in the operation place in the three-dimensional coordinate system obtained by the mobile client in the data application module, the values on the X axis and the Y axis jointly represent the plane position of the person in the operation place, and the value on the Z axis represents the height of the person.
Specifically, the invention uses the release of a large amount of gas particles to represent the continuous discharge of the leaked gas, and simulates the diffusion path of the leaked gas by calculating the displacement of the gas particles in the operation site, so as to calculate the overall distribution of the gas particles in time and space, namely obtain the diffusion rule of the leaked gas, wherein, when calculating the displacement of the gas particles in the operation site, the invention does not consider the influence of the wind speed in the operation site on the diffusion of the leaked gas, and considers that the gas particles do uniform motion along the X axis and the Y axis of the three-dimensional coordinate system in the operation site after leaking from the gas monitoring point, and the gas particles do vertical upward throwing motion along the Z axis of the three-dimensional coordinate system in the operation site due to the action of gravity, thereby calculating the diffusion path model of the leaked gas of the gas monitoring point, in the invention, the position coordinates of the personnel in the operation site under the three-dimensional coordinate system are brought into the diffusion path model, so that the time spent by leaked gas reaching the position coordinates of the personnel can be estimated, the specific position of gas leakage and the specific time spent by gas reaching the position can be timely sent to the personnel, and the purposes of reminding the personnel to evacuate quickly and avoiding health injury to the personnel are achieved.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), Rambus (Rambus) direct RAM (RDRAM), direct bused dynamic RAM (DRDRAM), and bused dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. The utility model provides a workplace gas detector end cloud integration platform based on big data which characterized in that, the platform includes:
the data acquisition module is used for collecting data about each gas detector deployed in a workplace, wherein the data at least comprises detection data of the gas detector on gas concentration of a gas monitoring point, temperature data of the gas detector, temperature data of detected gas, humidity data of the detected gas and air humidity data of the gas monitoring point;
the data analysis module is used for carrying out temperature and humidity compensation calculation on the detection data of the gas detector about the gas concentration by using a neural network technology according to the data about each gas detector, so that high-precision gas concentration detection data are obtained, a three-dimensional coordinate system in a working place is established, and a diffusion path of leaked gas of a gas monitoring point is analyzed, so that the platform can send a reminding message to personnel in the working place in time;
the data analysis module is used for sending a reminding message of gas leakage to personnel in the operation site according to the position coordinates of the personnel in the operation site;
the data acquisition module specifically comprises gas detectors, a main controller and a routing relay, wherein the gas detectors are connected with the main controller through a bus to upload data about each gas detector to the main controller and receive control commands from the main controller so as to implement field control on the gas detectors, the main controller is connected with the routing relay, the main controller continuously uploads the data about each gas detector to the routing relay and receives the control commands from the routing relay, and the routing relay receives and forwards the data about each gas detector and the control commands for the gas detectors through a network;
in the data analysis module, a three-dimensional coordinate system in a working site is established, and a diffusion path of leaked gas of a gas monitoring point is analyzed, and the data analysis module specifically comprises the following steps:
establishing a three-dimensional coordinate system by taking the ground in the operation site as an XOY plane and taking the upward direction vertical to the XOY plane as the positive direction of a Z axis, and respectively obtaining the position coordinates of each gas detector in the operation site under the three-dimensional coordinate system;
acquiring position coordinates of personnel in a workplace under the three-dimensional coordinate system through a mobile client in a data application module;
estimating a diffusion time of a leaked gas to reach position coordinates of a person in a work site under a three-dimensional coordinate system by using a diffusion path model of the leaked gas at a gas monitoring point, the diffusion path model being described as follows:
wherein,
the position coordinates of the personnel in the operation site under the three-dimensional coordinate system,
the rate of leakage of leaking gas from the gas monitoring point,
the included angle between the leakage speed of the leaked gas emitted from the gas monitoring point and the XOY plane of the three-dimensional coordinate system,
the included angle between the projection of the leakage speed of the leakage gas emitted from the gas monitoring point on the XOY plane and the X axis of the three-dimensional coordinate system,
in order to be the acceleration of the gravity,
the coordinate value of the gas detector on the Z axis of the three-dimensional coordinate system,
the time it takes for the leaking gas to reach the coordinates of the location of the person.
2. The big data based workplace gas detector end cloud integrated platform according to claim 1, the system is characterized in that the data analysis module carries a cloud computing platform and comprises a storage server and a computing server, the data analysis module receives data which are forwarded by a routing relay and are related to each gas detector through a network, the storage server stores data related to each gas detector in real time, the calculation server is used for performing related calculation based on the data related to each gas detector, specifically comprises temperature and humidity compensation calculation of detection data of the gas detector related to gas concentration, and analyzing the diffusion path of the leaked gas of the gas monitoring point, returning the calculation result upwards by the data analysis module in the form of an interface, meanwhile, the data analysis module can also send a control command for the gas detector downwards according to the calculation result.
3. The big-data-based cloud integrated platform for the gas detector end of the workplace as claimed in claim 1, wherein the data application module comprises a PC client and a mobile client, the data application module is in data communication with the data analysis module through a network, the data application module receives calculation result data from the data analysis module and provides various platform service functions based on the calculation result data, and the mobile client is further used for obtaining position coordinates of personnel in the workplace and sending the position coordinates to the data analysis module so that the position coordinates can participate in calculation of the data analysis module.
4. The big-data-based workplace gas detector end cloud integrated platform according to claim 1, wherein a neural network model is used in the data analysis module to perform temperature and humidity compensation calculation on detection data of a gas detector about gas concentration, and the method specifically comprises the following steps:
constructing a training data set of a neural network model, wherein the training data set comprises a plurality of sample data, and each sample data specifically comprises detection data of a gas detector for gas concentration, temperature data of the gas detector, temperature data of detected gas, humidity data of the detected gas, air humidity data of a gas monitoring point and high-precision detection data for gas concentration, wherein the detection data are used for completing temperature and humidity compensation calculation;
determining the number of neurons of an input layer, a hidden layer and an output layer according to the number of input data and the number of output data when the temperature and humidity compensation calculation is carried out on the neural network model, establishing the connection among the neurons of different layers, and completing the construction of the neural network model;
sequentially inputting sample data in the training data set into an input layer neuron of the constructed neural network model, forwarding the data through a hidden layer neuron until the output layer neuron generates an output result, calculating a training error of an output layer, and stopping training the model when the training error is smaller than an error threshold;
and the acquired data about the gas detector in the operation place is used as input data of the trained neural network model, and high-precision detection data of gas concentration, which is used for completing temperature and humidity compensation calculation, is obtained.
5. The big-data-based workplace gas detector end cloud integrated platform according to claim 4, wherein the building of the training data set of the neural network model specifically comprises the following steps:
establishing a basic data set according to the data range and concentration interval of the detection data of the gas detector on the gas concentration, the data range and temperature interval of the temperature data of the gas detector, the data range and temperature interval of the temperature data of the detected gas, the data range and humidity interval of the humidity data of the detected gas and the data range and humidity interval of the air humidity data of a gas monitoring point;
constructing a field data set using data collected by a data acquisition module regarding individual gas detectors deployed within a worksite;
from the basic data set and the field data set, respectivelyExtracting a certain amount of sample data to form a training data set for pre-training the neural network model
Wherein
for example data in the underlying data set,
for example data in a field data set,
to scale the underlying sample data in the training dataset,
is the proportion of the field sample data in the training data set.
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