CN117495205A - Industrial Internet experiment system and method - Google Patents
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Abstract
The invention discloses an industrial Internet experiment system and a method, which belong to the field of digital information transmission, wherein an obtained quality monitoring value output in an actual experiment scene is compared with a set quality monitoring threshold value, if the quality monitoring value is greater than or equal to the set quality monitoring threshold value, the obtained quality monitoring value and the set quality monitoring threshold value are substituted into an adjustment value of set data of output experiment equipment in a production model for maintenance personnel to carry out parameter adjustment of the experiment equipment, the operation of the experiment equipment is carried out after the adjustment, if the quality monitoring value is smaller than the set quality monitoring threshold value, the parameter of the experiment equipment is not adjusted, the experiment equipment is normally operated, and the influence of environmental change is counteracted by rapidly adjusting the parameter of the experiment equipment along with the change of external environment parameters, so that the production quality of products is improved.
Description
Technical Field
The invention belongs to the technical field of digital information transmission, and particularly relates to an industrial Internet experiment system and method.
Background
The industrial internet experiment refers to a series of testing, verifying and optimizing activities performed in an actual production environment by building an industrial internet system and introducing new technology, equipment or management methods to improve production efficiency, reduce cost, optimize production flow and the like.
For example, a Chinese patent with an authorized bulletin number of CN115643123B discloses an Internet of things multi-network fusion experiment system and method, which particularly relates to the field of Internet of things, and is used for solving the problems that the current multi-network fusion monitoring system does not carry out targeted monitoring on the accuracy degree of an acquisition process and the implementation speed of a debugging scheme, so that the result of actual analysis is uncertain and the debugging effect is not timely; the system comprises a data acquisition module, an adjustment operation module, a communication gateway module and a data analysis module; according to the invention, the accuracy of the data acquisition process is judged, the feedback type circular acquisition is performed, the accuracy of data acquisition is ensured, corresponding adjustment means are formulated according to the acquired data, and the corresponding adjustment time is detected, so that the adjustment capability of the whole system is evaluated, and the follow-up personnel can conveniently overhaul.
Meanwhile, for example, in the Chinese patent with the authority of CN113489674B, a malicious traffic intelligent detection method and application for an internet of things system are provided, aiming at network traffic, brand new standardized processing is designed and applied, vectorization results are obtained, and then based on a network to be trained, which is designed and formed by sequentially passing through a multistage feature connection layer, a fusion layer and a classification layer, by taking each feature extraction network respectively corresponding to each preset vector type as input, training is performed to obtain a malicious traffic detection model, and the model can be applied to realize malicious traffic detection for target network traffic; the time sequence features, the short-time statistical features and the byte features of the flow are fused in the whole design scheme, so that the detection model is more powerful than other models, the experimental performance is better, and the robustness is stronger.
The problems proposed in the background art exist in the above patents: in the prior art, in the process of producing products by utilizing the industrial Internet of things, the production quality of the products is generally influenced by the external environment and the operation of production equipment, the operation of the production equipment cannot be changed along with the change of the external environment, so that the condition that the change of the external environment frequently occurs to influence the quality of the products is caused, the problems exist in the prior art, and in order to solve the problems, the application designs an industrial Internet experiment system and method.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an industrial Internet experiment system and method, the invention acquires parameter data information of all experimental equipment in an industrial Internet experiment scene, environmental data information in the scene obtained by sensing by a sensor and data of a production product in the experiment scene, the acquired data of the production product in the experiment scene is imported into a quality monitoring strategy to calculate a product quality monitoring value, the acquired historical parameter data information of all experimental equipment in the experiment scene, the historical environmental data information in the scene obtained by sensing by the sensor and the calculated historical product quality monitoring value are substituted into a production model construction strategy to construct a production model, the quality monitoring value obtained by outputting in the actual experiment scene is compared with a set quality monitoring threshold, if the quality monitoring value is larger than or equal to the set quality monitoring threshold, the obtained quality monitoring value and the set quality monitoring threshold are substituted into an adjusting value of the set data of the output experimental equipment in the production model for maintenance personnel to carry out parameter adjustment of the experimental equipment, if the quality monitoring value is smaller than the set quality monitoring threshold, the parameter of the equipment is not adjusted, the environment is changed along with the running of the equipment, and the environment is quickly influenced by the environment change of the experimental equipment is counteracted, so that the quality of the experimental parameter is changed.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an industrial Internet experimental method comprises the following specific steps:
s1, acquiring parameter data information of each experimental device in an industrial Internet experimental scene, environmental data information in the scene obtained by sensing a sensor and data of a production product in the experimental scene;
s2, importing the acquired data of the production products in the experimental scene into a quality monitoring strategy to calculate a product quality monitoring value;
s3, substituting the acquired historical parameter data information of each experimental device in the experimental scene, the historical environmental data information in the scene obtained by sensing by the sensor and the calculated historical product quality monitoring value into a production model construction strategy to construct a production model;
s4, comparing the quality monitoring value obtained by outputting in the actual experimental scene with a set quality monitoring threshold, if the quality monitoring value is greater than or equal to the set quality monitoring threshold, carrying out S5, and if the quality monitoring value is smaller than the set quality monitoring threshold, not adjusting parameters of experimental equipment, wherein the quality monitoring threshold comprises a pixel difference threshold and a distance difference threshold;
s5, substituting the obtained quality monitoring value and the set quality monitoring threshold value into a production model to output an adjusting value of the set data of the experimental equipment for maintenance personnel to adjust parameters of the experimental equipment, and performing operation of the experimental equipment after adjustment.
Specifically, the step S1 includes the following specific steps:
s11, acquiring an industrial Internet experimental scene, wherein parameter data information in the historical operation process of each experimental device in the experimental scene is acquired through an experimental device data acquisition module, and environment data information in the scene, which is obtained through sensing by an environment sensor, is acquired, wherein the parameter data information in the historical operation process of the experimental device comprises equipment parameter data such as operation voltage, current and the like of the experimental device in each historical environment, and the environment data information in the scene comprises environment parameter data such as temperature, humidity and the like in each historical environment;
s12, acquiring data of a produced product under the influence of a historical environment in a corresponding historical operation process, wherein the data of the produced product comprises pixel values of all pixel points of the product and relative distance specification data of all pixel points on the surface of the product, and the relative distance specification data is a distance between a corresponding position of each pixel point in a three-dimensional coordinate system and an origin of coordinates.
Specifically, the specific content of the quality monitoring policy in S2 is as follows:
s21, acquiring the acquired pixel value of each pixel of the production product and the relative distance specification data of each pixel on the surface of the product, and acquiring the pixel value of each pixel of the standard production product template and the relative distance specification data of each pixel on the surface of the product;
s22, importing the collected pixel values of all the pixels of the production product and the collected pixel values of all the pixels of the standard production product template into a pixel difference value calculation formula to calculate a pixel difference value, wherein the pixel difference value calculation formula is as follows:wherein n is the number of selected pixels on the product, < >>Pixel value of i-th pixel for producing product,>the pixel value of the ith pixel point corresponding to the standard production product template;
s23, acquiring the relative distance specification data of each pixel point on the surface of the product and importing the relative distance specification data of each pixel point on the surface of a standard production product template into a distance difference value calculation formula to calculate a distance difference value, wherein the distance difference value calculation formula is as follows:wherein->For the ith pixel point of the product surfaceThe relative distance specification data is used to determine the relative distance specification,the relative distance specification data of the ith pixel point on the surface of the standard production product template;
s24, the obtained pixel difference value and the distance difference value form a two-dimensional vector to be used as a quality monitoring value of the product.
Specifically, the specific content of the production model construction strategy in S3 is as follows:
s31, acquiring historical parameter data information of each experimental device in an experimental scene, in-scene historical environment data information obtained by sensing by a sensor and a calculated historical product quality monitoring value, constructing a deep learning neural network model which is input into the in-scene historical environment data information obtained by sensing by the sensor and the historical parameter data information of each experimental device in the experimental scene, and outputting the data as corresponding calculated historical product quality monitoring value data;
s32, dividing the acquired historical parameter data information of each experimental device in the experimental scene, the historical environmental data information in the scene sensed by the sensor and the calculated historical product quality monitoring value into a 70% parameter training set and a 30% parameter testing set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using 30% of parameter test sets, and outputting the optimal initial deep learning neural network model meeting the accuracy of the preset product quality monitoring value as the deep learning neural network model, wherein the output strategy formula of a specific neuron in the deep learning neural network model is as follows:wherein->For the output of n-layer s-term neurons, < ->For the n-1 layer neurons j and n layersConnection weight of s term neurons, +.>Representing the input of the n-1 layer neuron j, is->A bias representing the linear relationship of the n-1 layer neurons j to the n layer s term neurons, sim () represents a Sigmoid activation function, w is the number of n-1 layer neurons;
s33, selecting the deep learning neural network model which is output after training as a production model, acquiring an actual experimental scene, acquiring environment data information in the actual environment and parameter data information of experimental equipment, substituting the acquired environment data information in the actual environment and the parameter data information of the experimental equipment into the constructed production model, and outputting a quality monitoring value obtained in the actual experimental scene.
Specifically, the specific content of S4 includes the following specific steps:
s41, respectively comparing a pixel difference value and a distance difference value in a quality monitoring value obtained in an actual experimental scene with a set pixel difference threshold and a set distance difference threshold, and if the pixel difference value is greater than or equal to the pixel difference threshold and/or the distance difference value is greater than or equal to the distance difference threshold, performing S5;
s42, if the pixel difference value is not greater than or equal to the pixel difference threshold value and/or the distance difference value is greater than or equal to the distance difference threshold value, parameters of the experimental equipment are not adjusted, and the experimental equipment operates normally.
Specifically, the specific content of S5 includes the following specific steps:
s51, importing the set quality monitoring threshold value into a constructed production model, generating parameter data information of each experimental device in an experimental scene corresponding to the quality monitoring value, setting the parameter data as first device parameter data, and simultaneously generating parameter data information of each experimental device in the experimental scene corresponding to the set quality monitoring threshold value, and setting the parameter data information as second device parameter data;
s52, acquiring the obtained first equipment parameter data and second equipment parameter data, and adjusting the parameter data information of each experimental equipment to the second equipment parameter data by a maintainer;
s53, running the experimental equipment after adjustment.
The quality monitoring threshold value is calculated according to the extracted 1000 groups of data of the production defective products and the production defective products in the formula, and the pixel difference value and the distance difference value of the production defective products and the production defective products are substituted in the fitting software, so that the optimal quality monitoring threshold value for distinguishing the production defective products and the production defective products is obtained.
An industrial internet experimental system, which is realized based on the industrial internet experimental method, specifically comprises: the system comprises a data acquisition module, a quality detection value calculation module, a production model construction module, a data comparison module, a parameter adjustment module and a control module, wherein the data acquisition module is used for acquiring parameter data information of all experimental equipment in an industrial Internet experimental scene, environmental data information in the scene obtained by sensing a sensor and data of a production product in the experimental scene, and the quality detection value calculation module is used for guiding the acquired data of the production product in the experimental scene into a quality monitoring strategy to calculate a product quality monitoring value.
Specifically, the production model construction module is used for substituting the obtained historical parameter data information of each experimental device in the experimental scene, the historical environmental data information in the scene obtained by sensing by the sensor and the calculated historical product quality monitoring value into the production model construction strategy to construct a production model, the data comparison module is used for comparing the quality monitoring value obtained by outputting in the actual experimental scene with the set quality monitoring threshold value, and the parameter adjustment module is used for substituting the obtained quality monitoring value and the set quality monitoring threshold value into the production model to output the adjustment value of the set data of the experimental device for maintenance personnel to carry out parameter adjustment of the experimental device, and then carrying out operation of the experimental device.
Specifically, the control module is used for controlling the operation of the data acquisition module, the quality detection value calculation module, the production model construction module, the data comparison module and the parameter adjustment module.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes an industrial internet experimental method as described above by calling a computer program stored in the memory.
A computer readable storage medium storing instructions that when executed on a computer cause the computer to perform an industrial internet experimental method as described above.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, parameter data information of all experimental equipment in an industrial Internet experimental scene, environmental data information in the scene obtained by sensing by a sensor and data of production products in the experimental scene are obtained, the obtained data of the production products in the experimental scene are imported into a quality monitoring strategy to calculate a product quality monitoring value, the obtained historical parameter data information of all experimental equipment in the experimental scene, the historical environmental data information in the scene obtained by sensing by the sensor and the calculated historical product quality monitoring value are substituted into a production model construction strategy to construct a production model, the quality monitoring value obtained by outputting in the actual experimental scene is compared with a set quality monitoring threshold, if the quality monitoring value is greater than or equal to the set quality monitoring threshold, the obtained quality monitoring value and the set quality monitoring threshold are substituted into an adjusting value of the set data of the output experimental equipment in the production model to adjust parameters of the experimental equipment, the experimental equipment is operated after the adjustment, if the quality monitoring value is smaller than the set quality monitoring threshold, the parameters of the experimental equipment are not adjusted, the experimental equipment is normally operated, and the environmental parameters are quickly adjusted along with the change of external environmental parameters to offset the change, so that the quality of the produced quality is improved.
Drawings
FIG. 1 is a schematic flow chart of an industrial Internet experimental method of the invention.
Fig. 2 is a schematic diagram of a specific flow of step S2 of the industrial internet experimental method of the present invention.
Fig. 3 is a schematic diagram of a specific flow of step S3 of the industrial internet experimental method of the present invention.
FIG. 4 is a schematic diagram of an industrial Internet experimental system architecture according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1:
referring to fig. 1-3, an embodiment of the present invention is provided: an industrial Internet experimental method comprises the following specific steps:
s1, acquiring parameter data information of each experimental device in an industrial Internet experimental scene, environmental data information in the scene obtained by sensing a sensor and data of a production product in the experimental scene;
the following is a simple example code for acquiring device parameter data, environmental data and production product data in an industrial internet experimental scenario,
#include <stdio.h>
data structure of device parameter
typedef struct {
int deviceID;
float temperature;
float pressure;
float speed;
} DeviceData;
Data structure of the environment
typedef struct {
float temperature;
float humidity;
float luminosity;
} EnvironmentData;
Data structure of/(and/or production product
typedef struct {
int productID;
int quantity;
float weight;
} ProductData;
Device parameter data is/are acquired
DeviceData getDeviceData(int deviceID) {
DeviceData deviceData;
Where the corresponding device parameter data may be obtained from a device ID query database or other data source
deviceData.deviceID = deviceID;
deviceData.temperature = 25.5;
deviceData.pressure = 3.2;
deviceData.speed = 1200.0;
return deviceData;
}
Data of/acquisition environment
EnvironmentData getEnvironmentData() {
EnvironmentData environmentData;
Where environmental data sensed by the sensor can be obtained
environmentData.temperature = 26.0;
environmentData.humidity = 45.5;
environmentData.luminosity = 800.0;
return environmentData;
}
Data of production product
ProductData getProductData(int productID) {
ProductData productData;
Where the corresponding production product data may be obtained from a product ID query database or other data source
productData.productID = productID;
productData.quantity = 100;
productData.weight = 50.0;
return productData;
}
int main() {
Device parameter data is/are acquired
DeviceData deviceData = getDeviceData(1);
printf("Device ID: %d\n", deviceData.deviceID);
printf("Temperature: %.2f\n", deviceData.temperature);
printf("Pressure: %.2f\n", deviceData.pressure);
printf("Speed: %.2f\n", deviceData.speed);
Data of/acquisition environment
EnvironmentData environmentData = getEnvironmentData();
printf("Temperature: %.2f\n", environmentData.temperature);
printf("Humidity: %.2f\n", environmentData.humidity);
printf("Luminosity: %.2f\n", environmentData.luminosity);
Data of production product
ProductData productData = getProductData(1);
printf("Product ID: %d\n", productData.productID);
printf("Quantity: %d\n", productData.quantity);
printf("Weight: %.2f\n", productData.weight);
return 0;
}
This is just a simple example, and the actual code may involve more complex data acquisition logic and data processing, and the code may be modified and refined according to specific application scenarios and requirements;
s2, importing the acquired data of the production products in the experimental scene into a quality monitoring strategy to calculate a product quality monitoring value;
s3, substituting the acquired historical parameter data information of each experimental device in the experimental scene, the historical environmental data information in the scene obtained by sensing by the sensor and the calculated historical product quality monitoring value into a production model construction strategy to construct a production model;
s4, comparing the quality monitoring value obtained by outputting in the actual experimental scene with a set quality monitoring threshold, if the quality monitoring value is greater than or equal to the set quality monitoring threshold, carrying out S5, and if the quality monitoring value is smaller than the set quality monitoring threshold, not adjusting parameters of experimental equipment, wherein the quality monitoring threshold comprises a pixel difference threshold and a distance difference threshold;
s5, substituting the obtained quality monitoring value and the set quality monitoring threshold value into a production model to output an adjusting value of the set data of the experimental equipment for maintenance personnel to adjust parameters of the experimental equipment, and performing operation of the experimental equipment after adjustment;
in this embodiment, S1 includes the following specific steps:
s11, acquiring an industrial Internet experimental scene, wherein parameter data information in the historical operation process of each experimental device in the experimental scene is acquired through an experimental device data acquisition module, and environment data information in the scene, which is obtained through sensing by an environment sensor, is acquired, wherein the parameter data information in the historical operation process of the experimental device comprises equipment parameter data such as operation voltage, current and the like of the experimental device in each historical environment, and the environment data information in the scene comprises environment parameter data such as temperature, humidity and the like in each historical environment;
s12, acquiring data of a produced product under the influence of a historical environment in a corresponding historical operation process, wherein the data of the produced product comprises pixel values of all pixel points of the product and relative distance specification data of all pixel points on the surface of the product, and the relative distance specification data is a distance between a corresponding position of each pixel point in a three-dimensional coordinate system and an origin of coordinates;
in this embodiment, the specific content of the quality monitoring policy in S2 is as follows:
s21, acquiring the acquired pixel value of each pixel of the production product and the relative distance specification data of each pixel on the surface of the product, and acquiring the pixel value of each pixel of the standard production product template and the relative distance specification data of each pixel on the surface of the product;
s22, importing the collected pixel values of all the pixels of the production product and the collected pixel values of all the pixels of the standard production product template into a pixel difference value calculation formula to calculate a pixel difference value, wherein the pixel difference value calculation formula is as follows:wherein n is the number of selected pixels on the product, < >>Pixel value of i-th pixel for producing product,>the pixel value of the ith pixel point corresponding to the standard production product template;
s23, acquiring the relative distance specification data of each pixel point on the surface of the product and importing the relative distance specification data of each pixel point on the surface of a standard production product template into a distance difference value calculation formula to calculate a distance difference value, wherein the distance difference value calculation formula is as follows:wherein->For the relative distance specification data of the ith pixel point of the product surface,/for the product surface>The relative distance specification data of the ith pixel point on the surface of the standard production product template;
s24, forming a two-dimensional vector form by the acquired pixel difference value and the distance difference value to serve as a quality monitoring value of the product;
in this embodiment, the specific content of the production model construction strategy in S3 is:
s31, acquiring historical parameter data information of each experimental device in an experimental scene, in-scene historical environment data information obtained by sensing by a sensor and a calculated historical product quality monitoring value, constructing a deep learning neural network model which is input into the in-scene historical environment data information obtained by sensing by the sensor and the historical parameter data information of each experimental device in the experimental scene, and outputting the data as corresponding calculated historical product quality monitoring value data;
s32, dividing the acquired historical parameter data information of each experimental device in the experimental scene, the historical environmental data information in the scene sensed by the sensor and the calculated historical product quality monitoring value into a 70% parameter training set and a 30% parameter testing set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using 30% of parameter test sets, and outputting the optimal initial deep learning neural network model meeting the accuracy of the preset product quality monitoring value as the deep learning neural network model, wherein the output strategy formula of a specific neuron in the deep learning neural network model is as follows:wherein->For the output of n-layer s-term neurons, < ->For the connection weight of the n-1 layer neuron j and the n layer s item neuron,/->Representing the input of the n-1 layer neuron j, is->A bias representing the linear relationship of the n-1 layer neurons j to the n layer s term neurons, sim () represents a Sigmoid activation function, w is the number of n-1 layer neurons;
s33, selecting the deep learning neural network model which is output after training as a production model, acquiring an actual experimental scene, acquiring environmental data information in the actual environment and parameter data information of experimental equipment, substituting the acquired environmental data information in the actual environment and the parameter data information of the experimental equipment into the constructed production model, and outputting a quality monitoring value obtained in the actual experimental scene;
in this embodiment, the specific content of S4 includes the following specific steps:
s41, respectively comparing a pixel difference value and a distance difference value in a quality monitoring value obtained in an actual experimental scene with a set pixel difference threshold and a set distance difference threshold, and if the pixel difference value is greater than or equal to the pixel difference threshold and/or the distance difference value is greater than or equal to the distance difference threshold, performing S5;
s42, if the pixel difference value is not greater than or equal to the pixel difference threshold value and/or the distance difference value is greater than or equal to the distance difference threshold value, parameters of the experimental equipment are not adjusted, and the experimental equipment operates normally;
in this embodiment, the specific content of S5 includes the following specific steps:
s51, importing the set quality monitoring threshold value into a constructed production model, generating parameter data information of each experimental device in an experimental scene corresponding to the quality monitoring value, setting the parameter data as first device parameter data, and simultaneously generating parameter data information of each experimental device in the experimental scene corresponding to the set quality monitoring threshold value, and setting the parameter data information as second device parameter data;
s52, acquiring the obtained first equipment parameter data and second equipment parameter data, and adjusting the parameter data information of each experimental equipment to the second equipment parameter data by a maintainer;
s53, running experimental equipment after adjustment;
the quality monitoring threshold value is calculated according to the extracted 1000 groups of data of the production defective products and the production good products in the formula, and the pixel difference value and the distance difference value of the production defective products and the production good products are substituted in the fitting software to obtain the optimal quality monitoring threshold value for distinguishing the production defective products and the production good products.
The implementation of the embodiment can be realized: the method comprises the steps of obtaining parameter data information of all experimental equipment in an industrial Internet experimental scene, environmental data information in the scene obtained by sensing by a sensor and data of production products in the experimental scene, importing the obtained data of the production products in the experimental scene into a quality monitoring strategy to calculate a product quality monitoring value, substituting the obtained historical parameter data information of all experimental equipment in the experimental scene, the historical environmental data information in the scene obtained by sensing by the sensor and the calculated historical product quality monitoring value into a production model construction strategy to construct a production model, comparing the quality monitoring value obtained by outputting in the actual experimental scene with a set quality monitoring threshold, substituting the obtained quality monitoring value with the set quality monitoring threshold if the quality monitoring value is larger than or equal to the set quality monitoring threshold, substituting the set quality monitoring value of the set data of the output experimental equipment in the production model for maintenance personnel to carry out parameter adjustment of the experimental equipment, carrying out operation of the experimental equipment after adjustment, and if the quality monitoring value is smaller than the set quality monitoring threshold, not adjusting parameters of the experimental equipment, normally operating the experimental equipment, and rapidly carrying out adjustment on the equipment parameters along with change of external environment parameters to offset the influence of environmental change on the experimental equipment parameters, so that the production quality is improved.
Example 2:
as shown in fig. 4, an industrial internet experimental system is implemented based on the above industrial internet experimental method, which specifically includes: the system comprises a data acquisition module, a quality detection value calculation module, a production model construction module, a data comparison module, a parameter adjustment module and a control module, wherein the data acquisition module is used for acquiring parameter data information of all experimental equipment in an industrial Internet experimental scene, environmental data information in the scene obtained by sensing a sensor and data of production products in the experimental scene, and the quality detection value calculation module is used for leading the acquired data of the production products in the experimental scene into a quality monitoring strategy to calculate product quality monitoring values; the production model construction module is used for substituting the acquired historical parameter data information of each experimental device in the experimental scene, the historical environmental data information in the scene obtained by sensing by the sensor and the calculated historical product quality monitoring value into a production model construction strategy to construct a production model, the data comparison module is used for comparing the quality monitoring value obtained by outputting in the actual experimental scene with a set quality monitoring threshold value, and the parameter adjustment module is used for substituting the obtained quality monitoring value and the set quality monitoring threshold value into an adjustment value of the set data of the output experimental device in the production model to enable maintenance personnel to carry out parameter adjustment of the experimental device, and the experimental device is operated after adjustment; the control module is used for controlling the operation of the data acquisition module, the quality detection value calculation module, the production model construction module, the data comparison module and the parameter adjustment module.
Example 3:
the present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes an industrial internet experimental method as described above by calling a computer program stored in the memory.
The electronic device may vary greatly in configuration or performance, and can include one or more processors (Central Processing Units, CPU) and one or more memories, where the memories store at least one computer program that is loaded and executed by the processors to implement an industrial internet experimental method provided by the above method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4:
the present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
the computer program, when run on a computer device, causes the computer device to perform an industrial internet experimental method as described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one, and there may be additional partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit 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.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (9)
1. The industrial Internet experiment method is characterized by comprising the following specific steps of:
s1, acquiring parameter data information of each experimental device in an industrial Internet experimental scene, environmental data information in the scene obtained by sensing a sensor and data of a production product in the experimental scene;
s2, importing the acquired data of the production products in the experimental scene into a quality monitoring strategy to calculate a product quality monitoring value;
s3, substituting the acquired historical parameter data information of each experimental device in the experimental scene, the historical environmental data information in the scene obtained by sensing by the sensor and the calculated historical product quality monitoring value into a production model construction strategy to construct a production model;
s4, comparing the quality monitoring value obtained by outputting in the actual experimental scene with a set quality monitoring threshold, if the quality monitoring value is greater than or equal to the set quality monitoring threshold, carrying out S5, and if the quality monitoring value is smaller than the set quality monitoring threshold, not adjusting parameters of experimental equipment, wherein the quality monitoring threshold comprises a pixel difference threshold and a distance difference threshold;
s5, substituting the obtained quality monitoring value and the set quality monitoring threshold value into a production model to output an adjusting value of the set data of the experimental equipment for maintenance personnel to adjust parameters of the experimental equipment, and performing operation of the experimental equipment after adjustment;
the specific content of the quality monitoring strategy in the step S2 is as follows:
s21, acquiring the acquired pixel value of each pixel of the production product and the relative distance specification data of each pixel on the surface of the product, and acquiring the pixel value of each pixel of the standard production product template and the relative distance specification data of each pixel on the surface of the product;
s22, importing the collected pixel values of all the pixels of the production product and the collected pixel values of all the pixels of the standard production product template into a pixel difference value calculation formula to calculate a pixel difference value, wherein the pixel difference value calculation formula is as follows:wherein n is the number of selected pixels on the product, < >>Pixel value of i-th pixel for producing product,>the pixel value of the ith pixel point corresponding to the standard production product template;
s23, acquiring the relative distance specification data of each pixel point on the surface of the product and importing the relative distance specification data of each pixel point on the surface of a standard production product template into a distance difference value calculation formula to calculate a distance difference value, wherein the distance difference value calculation formula is as follows:wherein->Is the relative distance specification data of the ith pixel point of the product surface,the relative distance specification data of the ith pixel point on the surface of the standard production product template;
s24, forming a two-dimensional vector form by the acquired pixel difference value and the distance difference value to serve as a quality monitoring value of the product;
the specific content of the production model construction strategy in the S3 is as follows:
s31, acquiring historical parameter data information of each experimental device in an experimental scene, in-scene historical environment data information obtained by sensing by a sensor and a calculated historical product quality monitoring value, constructing a deep learning neural network model which is input into the in-scene historical environment data information obtained by sensing by the sensor and the historical parameter data information of each experimental device in the experimental scene, and outputting the data as corresponding calculated historical product quality monitoring value data;
s32, dividing the acquired historical parameter data information of each experimental device in the experimental scene, the historical environmental data information in the scene sensed by the sensor and the calculated historical product quality monitoring value into a 70% parameter training set and a 30% parameter testing set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using 30% of parameter test sets, and outputting the optimal initial deep learning neural network model meeting the accuracy of the preset product quality monitoring value as the deep learning neural network model, wherein the output strategy formula of a specific neuron in the deep learning neural network model is as follows:wherein->For the output of n-layer s-term neurons, < ->For the connection weight of the n-1 layer neuron j and the n layer s item neuron,/->Representing the input of the n-1 layer neuron j, is->A bias representing the linear relationship of the n-1 layer neurons j to the n layer s term neurons, sim () represents a Sigmoid activation function, w is the number of n-1 layer neurons;
s33, selecting the deep learning neural network model which is output after training as a production model, acquiring an actual experimental scene, acquiring environment data information in the actual environment and parameter data information of experimental equipment, substituting the acquired environment data information in the actual environment and the parameter data information of the experimental equipment into the constructed production model, and outputting a quality monitoring value obtained in the actual experimental scene.
2. The industrial internet experimental method according to claim 1, wherein S1 comprises the following specific steps:
s11, acquiring an industrial Internet experimental scene, acquiring parameter data information in the historical operation process of each experimental device in the experimental scene through an experimental device data acquisition module, and simultaneously acquiring environmental data information in the scene, which is obtained by sensing by an environmental sensor;
s12, acquiring data of a produced product under the influence of a history environment in a corresponding history operation process, wherein the data of the produced product comprises pixel values of all pixel points of the product and relative distance specification data of all pixel points on the surface of the product.
3. The industrial internet experimental method according to claim 2, wherein the specific content of S4 comprises the following specific steps:
s41, respectively comparing a pixel difference value and a distance difference value in a quality monitoring value obtained in an actual experimental scene with a set pixel difference threshold and a set distance difference threshold, and if the pixel difference value is greater than or equal to the pixel difference threshold and/or the distance difference value is greater than or equal to the distance difference threshold, performing S5;
s42, if the pixel difference value is not greater than or equal to the pixel difference threshold value and/or the distance difference value is greater than or equal to the distance difference threshold value, parameters of the experimental equipment are not adjusted, and the experimental equipment operates normally.
4. An industrial internet experimental method according to claim 3, wherein the specific content of S5 comprises the following specific steps:
s51, importing the set quality monitoring threshold value into a constructed production model, generating parameter data information of each experimental device in an experimental scene corresponding to the quality monitoring value, setting the parameter data as first device parameter data, and simultaneously generating parameter data information of each experimental device in the experimental scene corresponding to the set quality monitoring threshold value, and setting the parameter data information as second device parameter data;
s52, acquiring the obtained first equipment parameter data and second equipment parameter data, and adjusting the parameter data information of each experimental equipment to the second equipment parameter data by a maintainer;
s53, running the experimental equipment after adjustment.
5. Industrial internet experimental system, realized on the basis of an industrial internet experimental method according to any one of claims 1-4, characterized in that it comprises in particular: the system comprises a data acquisition module, a quality detection value calculation module, a production model construction module, a data comparison module, a parameter adjustment module and a control module, wherein the data acquisition module is used for acquiring parameter data information of all experimental equipment in an industrial Internet experimental scene, environmental data information in the scene obtained by sensing a sensor and data of a production product in the experimental scene, and the quality detection value calculation module is used for guiding the acquired data of the production product in the experimental scene into a quality monitoring strategy to calculate a product quality monitoring value.
6. The industrial internet experimental system as claimed in claim 5, wherein the production model construction module is configured to substitute the obtained historical parameter data information of each experimental device in the experimental scene, the historical environmental data information in the scene sensed by the sensor, and the calculated historical product quality monitoring value into the production model construction policy to construct the production model, the data comparison module is configured to compare the quality monitoring value output in the actual experimental scene with a set quality monitoring threshold, and the parameter adjustment module is configured to substitute the obtained quality monitoring value and the set quality monitoring threshold into an adjustment value of the set data of the experimental device in the production model to adjust parameters of the experimental device by a maintainer, and perform operation of the experimental device after adjustment.
7. The industrial internet experimental system of claim 6, wherein the control module is configured to control operations of the data acquisition module, the quality detection value calculation module, the production model construction module, the data comparison module, and the parameter adjustment module.
8. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor performs an industrial internet experimental method according to any one of claims 1-4 by invoking a computer program stored in the memory.
9. A computer-readable storage medium, characterized by: instructions stored thereon which, when executed on a computer, cause the computer to perform an industrial internet experimental method according to any of claims 1-4.
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