IoT Data Acquisition and Analysis Methods in Smart Manufacturing

Abstract

This invention discloses a method for IoT data acquisition and analysis in intelligent manufacturing, belonging to the field of intelligent manufacturing technology. The method includes the following steps: S1: Real-time acquisition of IoT data during the intelligent manufacturing process; S2: Processing the IoT data to determine more accurate IoT data for evaluation; S3: Encrypting and transmitting the processed, more accurate IoT data to ensure secure transmission. This invention solves the problem that existing methods cannot collect and analyze IoT data in the intelligent manufacturing process in real time, resulting in an inability to fully grasp the IoT data and thus poor control over the intelligent manufacturing process. This invention ensures the security of IoT data transmission, enables real-time acquisition and timely analysis of IoT data during the intelligent manufacturing process, allowing for a comprehensive understanding of the IoT data and improving the control over the intelligent manufacturing process.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to an Internet of Things data collection and analysis method in intelligent manufacturing. Background Art

[0002] Intelligent manufacturing, which originated from the research on artificial intelligence, is a human-machine integrated intelligent system composed of intelligent machines and human experts. It can perform intelligent activities in the manufacturing process, such as analysis, reasoning, judgment, conception and decision-making. Through the cooperation between people and intelligent machines, it can expand, extend and partially replace the mental labor of human experts in the manufacturing process. It updates the concept of manufacturing automation and expands it to flexibility, intelligence and high integration.

[0003] The Chinese patent with publication number CN115776524B discloses a multi-level scheduling and transmission system for massive data of the Internet of Things for intelligent manufacturing, including a data acquisition module, a data processing module and a data transmission module. The data acquisition module is used to obtain a task set, and the data processing module is used to: construct multiple priorities and obtain time slices of each priority, obtain the initial priority of each task according to the priority coefficient of each task, process each task once at each priority, calculate the remaining priority coefficient of each task, obtain the priority of each task according to the remaining priority coefficient of each task and the interval range of each priority, process each task again at each priority, and reallocate the priority of each task once for each task processed until each task is processed; the data transmission module is used to transmit the relevant data obtained after each task is processed, so as to realize the efficient transmission of functional manufacturing Internet of Things data; however, the patent has the following defects:

[0004] The existing IoT data in the intelligent manufacturing process cannot be collected and analyzed in real time, which makes it impossible to fully grasp the IoT data in the intelligent manufacturing process, and thus the control effect of the intelligent manufacturing process is poor. Summary of the invention

[0005] The purpose of the present invention is to provide a method for collecting and analyzing Internet of Things data in intelligent manufacturing, which can ensure the security of Internet of Things data transmission, and can collect and analyze Internet of Things data in the intelligent manufacturing process in real time, so as to fully grasp the Internet of Things data in the intelligent manufacturing process, thereby improving the management and control effect of the intelligent manufacturing process, and solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The IoT data collection and analysis method in intelligent manufacturing includes the following steps:

[0008] S1: Real-time collection of equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process, and determination of IoT data in the intelligent manufacturing process;

[0009] S2: Process the IoT data collected in real time during the smart manufacturing process to determine IoT data for more accurate evaluation;

[0010] S3: Encrypt and transmit the processed IoT data with more accurate evaluation, and determine the IoT data for secure transmission;

[0011] S4: Evaluate and analyze the securely transmitted IoT data based on IoT data standards to determine IoT data evaluation and analysis results;

[0012] S5: Conduct mining and analysis on the evaluation and analysis results of IoT data, determine the IoT data management and control plan, and manage IoT data based on the IoT data management and control plan.

[0013] Preferably, in S1, the IoT data in the intelligent manufacturing process is collected in real time, and the equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process are collected in real time, and the following operations are performed:

[0014] Based on sensors and combined with radio frequency identification technology, barcode scanners and monitoring equipment, real-time acquisition of equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process;

[0015] Based on the real-time acquired equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data, determine the IoT data in the intelligent manufacturing process;

[0016] Among them, the sensors include temperature sensors, humidity sensors, pressure sensors, current sensors, voltage sensors and power sensors;

[0017] Based on temperature sensors, humidity sensors, pressure sensors, current sensors, voltage sensors and power sensors, the ambient temperature, ambient humidity, equipment pressure, equipment current, equipment voltage and equipment power information in the intelligent manufacturing process are obtained respectively;

[0018] Based on radio frequency identification technology, non-contact two-way data communication is carried out through wireless radio frequency, and electronic tags or radio frequency cards are read and written using wireless radio frequency to determine device identification information;

[0019] Based on the barcode scanner reading device barcode information, the light emitted by the light source is irradiated onto the barcode symbol through the optical system and reflected onto the optical instrument of the barcode scanner. Through photoelectric conversion, it is interpreted by the decoder as a digital signal directly accepted by the computer to obtain the information contained in the device barcode;

[0020] Based on monitoring equipment, real-time monitoring of equipment and intelligent manufacturing process is carried out in the intelligent manufacturing process.

[0021] Preferably, real-time collection of IoT data in the intelligent manufacturing process includes real-time collection of equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process, and also includes:

[0022] Real-time detection of sensor data collection response time, sensitivity and linearity;

[0023] The first operation stability evaluation coefficient is obtained by using the data acquisition response time of the sensor; wherein the first operation stability evaluation coefficient is obtained by the following formula:

[0024]

[0025] Wherein, t represents the first operation stability evaluation coefficient; n represents the number of data collection times of the sensor; T i represents the data collection response time corresponding to the i-th data collection; T e Indicates the maximum acquisition response time allowed when the sensor operates normally; T max Indicates the maximum data collection response time that occurs in n data collections of the sensor;

[0026] The second operation stability evaluation coefficient is obtained by using the sensitivity of the sensor; wherein the second operation stability evaluation coefficient is obtained by the following formula:

[0027]

[0028] Wherein, p represents the second operation stability evaluation coefficient; p min It represents the minimum sensitivity of the sensor in n data collections; p e Indicates the minimum sensitivity allowed for the sensor to operate normally; p i represents the sensor sensitivity corresponding to the i-th data collection;

[0029] Obtaining a sensor operation evaluation parameter using the first operation stability evaluation coefficient and the second operation stability evaluation coefficient in combination with linearity;

[0030] When the sensor operation evaluation parameter is lower than a preset parameter threshold, the sensor is judged to be operating abnormally and an abnormal alarm is issued.

[0031] Preferably, the first operation stability evaluation coefficient and the second operation stability evaluation coefficient are used in combination with linearity to obtain the sensor operation evaluation parameter, including:

[0032] Extracting a preset linearity monitoring time period;

[0033] Extracting the maximum deviation value between the actual output of the sensor corresponding to each linearity monitoring time period and the output on the fitting straight line;

[0034] The linearity evaluation coefficient is obtained by using the maximum deviation value between the actual output of the sensor corresponding to each linearity monitoring time period and the output on the fitting straight line; wherein the linearity evaluation coefficient is obtained by the following formula:

[0035]

[0036] Where I represents the linearity evaluation coefficient; m represents the total number of linearity monitoring time periods; X i Indicates the maximum deviation value corresponding to the i-th linearity monitoring time period; X m Indicates the output value corresponding to the full scale of the sensor; X c Indicates the maximum deviation value allowed under normal sensor operation; X max Indicates the maximum deviation value actually occurring in the sensor;

[0037] The sensor operation evaluation parameter is obtained by using the first operation stability evaluation coefficient and the second operation stability evaluation coefficient in combination with the linearity evaluation coefficient, wherein the sensor operation evaluation parameter is obtained by the following formula:

[0038] W=[1+(pt)·I]·W0

[0039] Among them, W represents the sensor operation evaluation parameter; W0 represents the preset operation evaluation parameter reference value; I represents the linearity evaluation coefficient; p represents the second operation stability evaluation coefficient; t represents the first operation stability evaluation coefficient.

[0040] Preferably, in S2, the IoT data collected in real time during the intelligent manufacturing process is processed, and the following operations are performed:

[0041] Obtain IoT data in smart manufacturing processes;

[0042] Based on the sequential retrieval method, the IoT data in the intelligent manufacturing process is retrieved;

[0043] Filter out invalid and missing data that are not valuable for IoT data collection and analysis contained in IoT data in the intelligent manufacturing process;

[0044] Identify IoT data that is valuable for IoT data collection and analysis;

[0045] Obtain the retrieved IoT data that is valuable for IoT data collection and analysis;

[0046] Based on data standardization methods, the retrieved IoT data that is valuable for IoT data collection and analysis is converted;

[0047] Eliminate the dimension effect of IoT data and make IoT data have a unified measurement standard;

[0048] Determine IoT data with unified standardization;

[0049] Obtain the converted IoT data with unified standardization;

[0050] Integrate IoT data with unified standardization, so as to combine and integrate IoT data with unified standardization;

[0051] Identify IoT data for more accurate assessment.

[0052] Preferably, in S3, the processed IoT data with more accurate evaluation is encrypted for transmission, and the following operations are performed:

[0053] Obtain IoT data for more accurate evaluation;

[0054] Based on the needs of IoT data collection and analysis, the processing terminal encrypts and transmits more accurate IoT data to the analysis terminal for evaluation;

[0055] The processing terminal encrypts the processed IoT data with more accurate evaluation, and transmits the encrypted IoT data to the analysis terminal;

[0056] After the analysis terminal receives the encrypted IoT data sent by the processing terminal, the analysis terminal decrypts the encrypted IoT data to determine the securely transmitted IoT data.

[0057] Preferably, in S3, when the processing terminal encrypts and transmits the IoT data for more accurate evaluation to the analysis terminal, the following operations are performed:

[0058] The processing terminal sends a data transmission request to the analysis terminal;

[0059] After receiving the data transmission request sent by the processing terminal, the analysis terminal checks its own transmission channel, and sends data transmission feedback to the processing terminal according to the transmission channel inspection result;

[0060] After the processing terminal receives the data transmission feedback sent by the analysis terminal, it establishes a transmission connection between the processing terminal and the analysis terminal based on the data transmission feedback, and based on the established transmission connection, the processing terminal sends a decryption key to the analysis terminal for the analysis terminal to decrypt the encrypted IoT data.

[0061] Preferably, in S4, the securely transmitted IoT data is evaluated and analyzed based on IoT data standards, and the following operations are performed:

[0062] Get securely transmitted IoT data;

[0063] Based on the IoT data collection and analysis needs in smart manufacturing, and based on securely transmitted IoT data, the pre-set IoT data standards in the smart manufacturing process are indexed;

[0064] Evaluate and analyze securely transmitted IoT data based on IoT data standards to determine IoT data evaluation and analysis results;

[0065] Among them, if the securely transmitted IoT data is within the IoT data standard range, the IoT data evaluation and analysis result is that the IoT data is normal;

[0066] Among them, if the securely transmitted IoT data is not within the scope of the IoT data standard, the IoT data evaluation and analysis result will be IoT data anomaly.

[0067] Preferably, in S5, mining and analysis are performed on the IoT data evaluation and analysis results, and the following operations are performed:

[0068] Obtain IoT data evaluation and analysis results;

[0069] Based on the evaluation and analysis results of IoT data, carry out in-depth mining and relevant analysis of IoT data to determine the IoT data management and control plan;

[0070] Among them, IoT data is controlled based on IoT data control solutions;

[0071] When the IoT data is normal, the IoT data is stored and visualized;

[0072] When the IoT data is abnormal, timely warnings will be issued for the IoT data, and maintenance personnel will be remotely guided to maintain the intelligent manufacturing process.

[0073] Preferably, in S5, the IoT data is stored and visualized by performing the following operations:

[0074] Get IoT data;

[0075] Store and back up IoT data, and perform statistical analysis on IoT data;

[0076] The IoT data can be visualized in the form of charts and images.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] 1. The present invention determines the Internet of Things data in the intelligent manufacturing process by collecting equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in real time in the intelligent manufacturing process, determines the Internet of Things data with more accurate evaluation by processing the Internet of Things data in the intelligent manufacturing process collected in real time, determines the Internet of Things data with secure transmission by encrypting and transmitting the processed Internet of Things data with more accurate evaluation, and ensures the security of Internet of Things data transmission.

[0079] 2. The present invention evaluates and analyzes the securely transmitted IoT data based on the IoT data standard, determines the IoT data evaluation and analysis results, determines the IoT data management and control plan by mining and analyzing the IoT data evaluation and analysis results, and manages the IoT data based on the IoT data management and control plan. The IoT data in the intelligent manufacturing process can be collected and analyzed in real time and in a timely manner, so that the IoT data in the intelligent manufacturing process can be fully mastered, thereby improving the management and control effect of the intelligent manufacturing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 The present invention is a flow chart of the method for collecting and analyzing Internet of Things data in intelligent manufacturing. DETAILED DESCRIPTION

[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] In order to solve the existing problem that the IoT data in the intelligent manufacturing process cannot be collected and analyzed in real time, which makes it impossible to fully grasp the IoT data in the intelligent manufacturing process, and thus the control effect of the intelligent manufacturing process is poor, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0083] The IoT data collection and analysis method in intelligent manufacturing includes the following steps:

[0084] S1: Real-time collection of equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process, and determination of IoT data in the intelligent manufacturing process;

[0085] In this embodiment, as the preferred technical solution of the present invention, the IoT data in the intelligent manufacturing process is collected in real time, and the device status information, device operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process are collected in real time, and the following operations are performed:

[0086] Based on sensors and combined with radio frequency identification technology, barcode scanners and monitoring equipment, real-time acquisition of equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process;

[0087] Based on the real-time acquired equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data, determine the IoT data in the intelligent manufacturing process;

[0088] Among them, the sensors include temperature sensors, humidity sensors, pressure sensors, current sensors, voltage sensors and power sensors;

[0089] Based on temperature sensors, humidity sensors, pressure sensors, current sensors, voltage sensors and power sensors, the ambient temperature, ambient humidity, equipment pressure, equipment current, equipment voltage and equipment power information in the intelligent manufacturing process are obtained respectively;

[0090] Based on radio frequency identification technology, non-contact two-way data communication is carried out through wireless radio frequency, and electronic tags or radio frequency cards are read and written using wireless radio frequency to determine device identification information;

[0091] Based on the barcode scanner reading device barcode information, the light emitted by the light source is irradiated onto the barcode symbol through the optical system and reflected onto the optical instrument of the barcode scanner. Through photoelectric conversion, it is interpreted by the decoder as a digital signal directly accepted by the computer to obtain the information contained in the device barcode;

[0092] Based on monitoring equipment, real-time monitoring of equipment and intelligent manufacturing process is carried out in the intelligent manufacturing process.

[0093] Specifically, real-time collection of IoT data in the intelligent manufacturing process includes real-time collection of equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process, and also includes:

[0094] Real-time detection of sensor data collection response time, sensitivity and linearity;

[0095] The first operation stability evaluation coefficient is obtained by using the data acquisition response time of the sensor; wherein the first operation stability evaluation coefficient is obtained by the following formula:

[0096]

[0097] Wherein, t represents the first operation stability evaluation coefficient; n represents the number of data collection times of the sensor; T i represents the data collection response time corresponding to the i-th data collection; T e Indicates the maximum acquisition response time allowed when the sensor operates normally; T max Indicates the maximum data collection response time that occurs in n data collections of the sensor;

[0098] The second operation stability evaluation coefficient is obtained by using the sensitivity of the sensor; wherein the second operation stability evaluation coefficient is obtained by the following formula:

[0099]

[0100] Wherein, p represents the second operation stability evaluation coefficient; p min It represents the minimum sensitivity of the sensor in n data collections; p e Indicates the minimum sensitivity allowed for the sensor to operate normally; p i represents the sensor sensitivity corresponding to the i-th data collection;

[0101] Obtaining a sensor operation evaluation parameter using the first operation stability evaluation coefficient and the second operation stability evaluation coefficient in combination with linearity;

[0102] When the sensor operation evaluation parameter is lower than a preset parameter threshold, the sensor is judged to be operating abnormally and an abnormal alarm is issued.

[0103] The technical effects of the above technical solution are: real-time collection of IoT data in the intelligent manufacturing process, including equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data, ensuring real-time monitoring of the entire manufacturing process. Real-time detection of the sensor's data collection response time, sensitivity and linearity provides a comprehensive evaluation basis for the sensor's operating stability.

[0104] The first operation stability evaluation coefficient is calculated through the collected data. This coefficient takes into account the response time of the sensor in multiple data collections and its relationship with the maximum collection response time allowed during normal operation, and can accurately reflect the operation stability of the sensor. The second operation stability evaluation coefficient is calculated based on the sensitivity of the sensor. By comparing the minimum sensitivity with the minimum sensitivity allowed for normal operation, the stability of the sensor is further evaluated. Combining the first operation stability evaluation coefficient, the second operation stability evaluation coefficient and the linearity, the sensor operation evaluation parameters are obtained, which provides a comprehensive quantitative indicator for the comprehensive performance of the sensor. When the sensor operation evaluation parameters are lower than the preset parameter threshold, the system can quickly determine the abnormal operation of the sensor and issue an abnormal alarm, which helps to discover and solve problems in a timely manner and reduce failures and downtime in the production process.

[0105] Through real-time monitoring and stability evaluation of the sensor's operating status, enterprises can adjust production plans based on data analysis results, optimize resource allocation, reduce energy and material consumption, reduce waste, and improve resource utilization efficiency. At the same time, this technical solution also helps to improve the automation level of the production line, reduce human intervention, and improve production efficiency and product quality.

[0106] In summary, this technical solution realizes accurate evaluation and monitoring of sensor operation stability by real-time collection and analysis of IoT data in the intelligent manufacturing process, which helps to optimize resource allocation, improve production efficiency, reduce production costs, and enhance the stability and reliability of the production process.

[0107] Specifically, the first operation stability evaluation coefficient and the second operation stability evaluation coefficient are combined with linearity to obtain the sensor operation evaluation parameter, including:

[0108] Extracting a preset linearity monitoring time period;

[0109] Extracting the maximum deviation value between the actual output of the sensor corresponding to each linearity monitoring time period and the output on the fitting straight line;

[0110] The linearity evaluation coefficient is obtained by using the maximum deviation value between the actual output of the sensor corresponding to each linearity monitoring time period and the output on the fitting straight line; wherein the linearity evaluation coefficient is obtained by the following formula:

[0111]

[0112] Where I represents the linearity evaluation coefficient; m represents the total number of linearity monitoring time periods; X i Indicates the maximum deviation value corresponding to the i-th linearity monitoring time period; X m Indicates the output value corresponding to the full scale of the sensor; Xc Indicates the maximum deviation value allowed under normal operation of the sensor; X max Indicates the maximum deviation value actually occurring in the sensor;

[0113] The sensor operation evaluation parameter is obtained by using the first operation stability evaluation coefficient and the second operation stability evaluation coefficient in combination with the linearity evaluation coefficient, wherein the sensor operation evaluation parameter is obtained by the following formula:

[0114] W=[1+(pt)·I]·W0

[0115] Among them, W represents the sensor operation evaluation parameter; W0 represents the preset operation evaluation parameter reference value; I represents the linearity evaluation coefficient; p represents the second operation stability evaluation coefficient; t represents the first operation stability evaluation coefficient.

[0116] The technical effect of the above technical solution is: the technical solution comprehensively and accurately evaluates the operating status of the sensor by comprehensively considering multiple factors such as the data acquisition response time, sensitivity and linearity of the sensor. This comprehensive evaluation method can more accurately reflect the actual performance of the sensor and provide reliable data support for subsequent decision-making. By extracting the preset linearity monitoring time period and calculating the maximum deviation value between the actual output of the sensor and the output on the fitting line in each time period, the linearity evaluation coefficient is obtained. This method can accurately quantify the linearity performance of the sensor and help to promptly discover and solve potential linearity problems.

[0117] Based on the calculation results of the sensor operation evaluation parameters, when the parameter is lower than the preset parameter threshold, the system can quickly determine that the sensor is operating abnormally and issue an abnormal alarm. This real-time monitoring and alarm mechanism helps to detect and handle sensor failures in a timely manner, reducing downtime and losses in the production process. By real-time monitoring and evaluating the operating status of sensors, companies can adjust production plans based on data analysis results, optimize resource allocation, reduce unnecessary waste, and improve production efficiency. At the same time, this technical solution also helps to improve the automation level of production lines, reduce the need for human intervention, and further improve production efficiency and product quality. Enhance the stability and reliability of the production process: By comprehensively and comprehensively evaluating the operating status of sensors, this technical solution can detect and solve potential faults and problems in a timely manner, thereby enhancing the stability and reliability of the production process. This is of great significance for improving product quality, reducing production costs, and meeting customer needs.

[0118] In summary, this technical solution achieves a comprehensive and accurate evaluation of the sensor's operating status by comprehensively considering multiple performance indicators of the sensor. It has significant technical effects such as real-time monitoring, precise quantitative evaluation, optimized resource allocation, improved production efficiency, and enhanced stability and reliability of the production process.

[0119] S2: Process the IoT data collected in real time during the smart manufacturing process to determine IoT data for more accurate evaluation;

[0120] In this embodiment, as a preferred technical solution of the present invention, the IoT data collected in real time during the intelligent manufacturing process is processed, and the following operations are performed:

[0121] Obtain IoT data in smart manufacturing processes;

[0122] Based on the sequential retrieval method, the IoT data in the intelligent manufacturing process is retrieved;

[0123] Filter out invalid and missing data that are not valuable for IoT data collection and analysis contained in IoT data in the intelligent manufacturing process;

[0124] Identify IoT data that is valuable for IoT data collection and analysis;

[0125] Obtain the retrieved IoT data that is valuable for IoT data collection and analysis;

[0126] Based on data standardization methods, the retrieved IoT data that is valuable for IoT data collection and analysis is converted;

[0127] Eliminate the dimension effect of IoT data and make IoT data have a unified measurement standard;

[0128] Determine IoT data with unified standardization;

[0129] Obtain the converted IoT data with unified standardization;

[0130] Integrate IoT data with unified standardization, so as to combine and integrate IoT data with unified standardization;

[0131] Identify IoT data for more accurate assessment.

[0132] S3: Encrypt and transmit the processed IoT data with more accurate evaluation, and determine the IoT data for secure transmission;

[0133] In this embodiment, as a preferred technical solution of the present invention, the processed IoT data with more accurate evaluation is encrypted and transmitted, and the following operations are performed:

[0134] Obtain IoT data for more accurate evaluation;

[0135] Based on the needs of IoT data collection and analysis, the processing terminal encrypts and transmits more accurate IoT data to the analysis terminal for evaluation;

[0136] The processing terminal encrypts the processed IoT data with more accurate evaluation, and transmits the encrypted IoT data to the analysis terminal;

[0137] After the analysis terminal receives the encrypted IoT data sent by the processing terminal, the analysis terminal decrypts the encrypted IoT data to determine the securely transmitted IoT data.

[0138] In this embodiment, as a preferred technical solution of the present invention, when the processing terminal encrypts and transmits more accurate IoT data to the analysis terminal, the following operations are performed:

[0139] The processing terminal sends a data transmission request to the analysis terminal;

[0140] After receiving the data transmission request sent by the processing terminal, the analysis terminal checks its own transmission channel, and sends data transmission feedback to the processing terminal according to the transmission channel inspection result;

[0141] After the processing terminal receives the data transmission feedback sent by the analysis terminal, it establishes a transmission connection between the processing terminal and the analysis terminal based on the data transmission feedback, and based on the established transmission connection, the processing terminal sends a decryption key to the analysis terminal for the analysis terminal to decrypt the encrypted IoT data.

[0142] S4: Evaluate and analyze the securely transmitted IoT data based on IoT data standards to determine IoT data evaluation and analysis results;

[0143] In this embodiment, as a preferred technical solution of the present invention, the securely transmitted IoT data is evaluated and analyzed based on the IoT data standard, and the following operations are performed:

[0144] Get securely transmitted IoT data;

[0145] Based on the IoT data collection and analysis needs in smart manufacturing, and based on securely transmitted IoT data, the pre-set IoT data standards in the smart manufacturing process are indexed;

[0146] Evaluate and analyze securely transmitted IoT data based on IoT data standards to determine IoT data evaluation and analysis results;

[0147] Among them, if the securely transmitted IoT data is within the IoT data standard range, the IoT data evaluation and analysis result is that the IoT data is normal;

[0148] Among them, if the securely transmitted IoT data is not within the scope of the IoT data standard, the IoT data evaluation and analysis result will be IoT data anomaly.

[0149] It should be noted that the IoT data transmitted securely is evaluated and analyzed based on the IoT data standard, where the results of the IoT data evaluation and analysis are shown in Table 1:

[0150] Table 1: Results of IoT data evaluation and analysis;

[0151]

[0152] Therefore, by evaluating and analyzing the securely transmitted IoT data based on the IoT data standard and determining the IoT data evaluation and analysis results, the IoT data in the intelligent manufacturing process can be analyzed in a timely manner, so that the IoT data in the intelligent manufacturing process can be fully mastered.

[0153] S5: Conduct mining and analysis on the evaluation and analysis results of IoT data, determine the IoT data management and control plan, and manage IoT data based on the IoT data management and control plan.

[0154] In this embodiment, as a preferred technical solution of the present invention, the IoT data evaluation and analysis results are mined and analyzed, and the following operations are performed:

[0155] Obtain IoT data evaluation and analysis results;

[0156] Based on the evaluation and analysis results of IoT data, carry out in-depth mining and relevant analysis of IoT data to determine the IoT data management and control plan;

[0157] Among them, IoT data is controlled based on IoT data control solutions;

[0158] When the IoT data is normal, the IoT data is stored and visualized;

[0159] When the IoT data is abnormal, timely warnings will be issued for the IoT data, and maintenance personnel will be remotely guided to maintain the intelligent manufacturing process.

[0160] It should be noted that the IoT data is controlled based on the IoT data control solution, where the control of IoT data is shown in Table 2:

[0161] Table 2: Control of IoT data;

[0162]

[0163] Therefore, by mining and analyzing the results of IoT data evaluation and analysis, an IoT data management and control plan is determined, and IoT data is managed based on the IoT data management and control plan, so that the IoT data in the intelligent manufacturing process can be fully mastered, thereby improving the management and control effect of the intelligent manufacturing process.

[0164] In this embodiment, as a preferred technical solution of the present invention, the IoT data is stored and visualized, and the following operations are performed:

[0165] Get IoT data;

[0166] Store and back up IoT data, and perform statistical analysis on IoT data;

[0167] The IoT data can be visualized in the form of charts and images.

[0168] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0169] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The method for collecting and analyzing Internet of Things data in intelligent manufacturing is characterized by: The steps include: S1: Real-time collection of equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process, and determination of IoT data in the intelligent manufacturing process; S2: Process the IoT data collected in real time during the smart manufacturing process to determine IoT data for more accurate evaluation; S3: Encrypt and transmit the processed IoT data with more accurate evaluation, and determine the IoT data for secure transmission; S4: Evaluate and analyze the securely transmitted IoT data based on IoT data standards to determine IoT data evaluation and analysis results; S5: Conduct mining and analysis on the IoT data evaluation and analysis results, determine the IoT data management and control plan, and manage the IoT data based on the IoT data management and control plan; In S1, real-time collection of IoT data in the intelligent manufacturing process, real-time collection of equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process, and the following operations are performed: Based on sensors and combined with radio frequency identification technology, barcode scanners and monitoring equipment, real-time acquisition of equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process; Based on the real-time acquired equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data, determine the IoT data in the intelligent manufacturing process; Among them, the sensors include temperature sensors, humidity sensors, pressure sensors, current sensors, voltage sensors and power sensors; Based on temperature sensors, humidity sensors, pressure sensors, current sensors, voltage sensors and power sensors, the ambient temperature, ambient humidity, equipment pressure, equipment current, equipment voltage and equipment power information in the intelligent manufacturing process are obtained respectively; Based on radio frequency identification technology, non-contact two-way data communication is carried out through wireless radio frequency, and electronic tags or radio frequency cards are read and written using wireless radio frequency to determine device identification information; Based on the barcode scanner reading device barcode information, the light emitted by the light source is irradiated onto the barcode symbol through the optical system and reflected onto the optical instrument of the barcode scanner. Through photoelectric conversion, it is interpreted by the decoder as a digital signal directly accepted by the computer to obtain the information contained in the device barcode; Real-time monitoring of equipment and intelligent manufacturing process in the intelligent manufacturing process based on monitoring equipment; Real-time collection of IoT data in the intelligent manufacturing process Real-time collection of equipment status information, equipment operation data, manufacturing environment data and intelligent manufacturing data in the intelligent manufacturing process, including: Real-time detection of sensor data collection response time, sensitivity and linearity; The first operation stability evaluation coefficient is obtained by using the data acquisition response time of the sensor; wherein the first operation stability evaluation coefficient is obtained by the following formula: Wherein, t represents the first operation stability evaluation coefficient; n represents the number of data collection times of the sensor; T i represents the data collection response time corresponding to the i-th data collection; T e Indicates the maximum acquisition response time allowed when the sensor operates normally; T max Indicates the maximum data collection response time that occurs in n data collections of the sensor; The second operation stability evaluation coefficient is obtained by using the sensitivity of the sensor; wherein the second operation stability evaluation coefficient is obtained by the following formula: Wherein, p represents the second operation stability evaluation coefficient; p min It represents the minimum sensitivity of the sensor in n data collections; p e Indicates the minimum sensitivity allowed for the sensor to operate normally; p i represents the sensor sensitivity corresponding to the i-th data collection; Obtaining a sensor operation evaluation parameter using the first operation stability evaluation coefficient and the second operation stability evaluation coefficient in combination with linearity; When the sensor operation evaluation parameter is lower than a preset parameter threshold, the sensor is judged to be operating abnormally and an abnormal alarm is issued; The sensor operation evaluation parameter is obtained by using the first operation stability evaluation coefficient and the second operation stability evaluation coefficient in combination with linearity, including: Extracting a preset linearity monitoring time period; Extracting the maximum deviation value between the actual output of the sensor corresponding to each linearity monitoring time period and the output on the fitting straight line; The linearity evaluation coefficient is obtained by using the maximum deviation value between the actual output of the sensor corresponding to each linearity monitoring time period and the output on the fitting straight line; wherein the linearity evaluation coefficient is obtained by the following formula: Where I represents the linearity evaluation coefficient; m represents the total number of linearity monitoring time periods; X i Indicates the maximum deviation value corresponding to the i-th linearity monitoring time period; X m Indicates the output value corresponding to the full scale of the sensor; X c Indicates the maximum deviation value allowed under normal operation of the sensor; X max Indicates the maximum deviation value actually occurring in the sensor; The sensor operation evaluation parameter is obtained by using the first operation stability evaluation coefficient and the second operation stability evaluation coefficient in combination with the linearity evaluation coefficient, wherein the sensor operation evaluation parameter is obtained by the following formula: Among them, W represents the sensor operation evaluation parameter; W0 represents the preset operation evaluation parameter reference value; I represents the linearity evaluation coefficient; p represents the second operation stability evaluation coefficient; t represents the first operation stability evaluation coefficient.

2. The method for collecting and analyzing IoT data in intelligent manufacturing according to claim 1 is characterized in that: In S2, the IoT data collected in real time during the intelligent manufacturing process is processed, and the following operations are performed: Obtain IoT data in smart manufacturing processes; Based on the sequential retrieval method, the IoT data in the intelligent manufacturing process is retrieved; Filter out invalid and missing data that are not valuable for IoT data collection and analysis contained in IoT data in the intelligent manufacturing process; Identify IoT data that is valuable for IoT data collection and analysis; Obtain the retrieved IoT data that is valuable for IoT data collection and analysis; Based on data standardization methods, the retrieved IoT data that is valuable for IoT data collection and analysis is converted; Eliminate the dimension effect of IoT data and make IoT data have a unified measurement standard; Determine IoT data with unified standardization; Obtain the converted IoT data with unified standardization; Integrate IoT data with unified standardization, so as to combine and integrate IoT data with unified standardization; Identify IoT data for more accurate assessment.

3. The method for collecting and analyzing Internet of Things data in intelligent manufacturing according to claim 2 is characterized in that: In S3, the processed IoT data with more accurate evaluation is encrypted and transmitted, and the following operations are performed: Obtain IoT data for more accurate evaluation; Based on the needs of IoT data collection and analysis, the processing terminal encrypts and transmits more accurate IoT data to the analysis terminal for evaluation; The processing terminal encrypts the processed IoT data with more accurate evaluation, and transmits the encrypted IoT data to the analysis terminal; After the analysis terminal receives the encrypted IoT data sent by the processing terminal, the analysis terminal decrypts the encrypted IoT data to determine the securely transmitted IoT data.

4. The method for collecting and analyzing Internet of Things data in intelligent manufacturing according to claim 3 is characterized in that: In S3, when the processing terminal encrypts and transmits more accurate IoT data to the analysis terminal, the following operations are performed: The processing terminal sends a data transmission request to the analysis terminal; After receiving the data transmission request sent by the processing terminal, the analysis terminal checks its own transmission channel, and sends data transmission feedback to the processing terminal according to the transmission channel inspection result; After the processing terminal receives the data transmission feedback sent by the analysis terminal, it establishes a transmission connection between the processing terminal and the analysis terminal based on the data transmission feedback, and based on the established transmission connection, the processing terminal sends a decryption key to the analysis terminal for the analysis terminal to decrypt the encrypted IoT data.

5. The method for collecting and analyzing Internet of Things data in intelligent manufacturing according to claim 4 is characterized in that: In S4, the securely transmitted IoT data is evaluated and analyzed based on the IoT data standard, and the following operations are performed: Get securely transmitted IoT data; Based on the IoT data collection and analysis needs in smart manufacturing, and based on securely transmitted IoT data, the pre-set IoT data standards in the smart manufacturing process are indexed; Evaluate and analyze securely transmitted IoT data based on IoT data standards to determine IoT data evaluation and analysis results; Among them, if the securely transmitted IoT data is within the IoT data standard range, the IoT data evaluation and analysis result is that the IoT data is normal; Among them, if the securely transmitted IoT data is not within the scope of the IoT data standard, the IoT data evaluation and analysis result will be IoT data anomaly.

6. The method for collecting and analyzing Internet of Things data in intelligent manufacturing according to claim 5 is characterized in that: In S5, the IoT data evaluation and analysis results are mined and analyzed, and the following operations are performed: Obtain IoT data evaluation and analysis results; Based on the evaluation and analysis results of IoT data, carry out in-depth mining and relevant analysis of IoT data to determine the IoT data management and control plan; Among them, IoT data is controlled based on IoT data control solutions; When the IoT data is normal, the IoT data is stored and visualized; When the IoT data is abnormal, timely warnings will be issued for the IoT data, and maintenance personnel will be remotely guided to maintain the intelligent manufacturing process.

7. The method for collecting and analyzing Internet of Things data in intelligent manufacturing according to claim 6 is characterized in that: In S5, the IoT data is stored and visualized, and the following operations are performed: Get IoT data; Store and back up IoT data, and perform statistical analysis on IoT data; The IoT data can be visualized in the form of charts and images.

Images