An R & D production data monitoring system and method based on the Internet of Things

Abstract

The present invention relates to the technical field of research and development production, and specifically relates to a research and development production data monitoring system and method based on the Internet of Things, including: analyzing the research and development production data of the acquisition nodes to obtain missing characterization values, analyzing the missing data types of abnormal acquisition nodes based on the analysis signals to obtain missing continuous values, analyzing the correlation between the missing data types and adjacent data types based on the low signal of the continuity of the missing data to obtain the correlation coefficient deviation value, determining the average value of the missing adjacent ratio based on the filling signal, and performing a filling operation on the missing data types in the type data group. By distinguishing between continuous type missing and interval type missing, and by analyzing the correlation between the missing data types and adjacent data types, the present invention can determine whether the missing data can be reasonably filled based on adjacent data, avoiding unnecessary re-acquisition and improving the efficiency of data processing.

Description

Technical Field

[0001] The present invention relates to the technical field of R & D and production, and particularly relates to a R & D and production data monitoring system and method based on the Internet of Things. Background Art

[0002] In the modern R & D and production process, whether it is in the fields of industrial manufacturing, biopharmaceutical R & D, or electronic information product development, the real-time and accurate monitoring of production and R & D data is crucial. Traditional data monitoring methods often rely on manual regular inspections and records. This method is not only inefficient, prone to human errors, but also difficult to capture sudden abnormal situations in the production and R & D process in a timely manner. With the booming development of the Internet of Things technology, its capabilities of real-time perception, interconnection, and intelligent processing have brought new opportunities for R & D and production data monitoring.

[0003] However, the existing technologies lack pertinence in dealing with missing data, and cannot judge whether it is necessary to re-collect data based on continuous missing and intermittent missing. When dealing with intermittent missing types, it cannot reasonably use adjacent data to fill in the missing data, but blindly re-collects. At the same time, there is a lack of a comprehensive monitoring and early warning mechanism for R & D and production area data, making it difficult to discover potential risks in the production process in a timely manner. Managers cannot make decisions in a timely manner, which is not conducive to reducing production risks and improving production efficiency.

[0004] Therefore, we propose a R & D and production data monitoring system and method based on the Internet of Things. Summary of the Invention

[0005] The purpose of the present invention is to provide a R & D and production data monitoring system and method based on the Internet of Things to solve at least one of the above-mentioned existing technical problems.

[0006] In the first aspect, the present invention provides a R & D and production data monitoring method based on the Internet of Things, which specifically includes:

[0007] Step 1: During the acquisition period, analyze the R & D and production data of the acquisition nodes to obtain a missing characterization value. If the missing characterization value is less than the missing characterization threshold, generate an analysis signal;

[0008] Step 2: Based on the analysis signal, analyze the missing data types of the abnormal acquisition nodes to obtain a missing continuity value. If the missing continuity value is greater than the missing continuity threshold, generate a high missing data continuity signal;

[0009] Step 3: Based on the low missing data continuity signal, analyze the correlation between the missing data type and the adjacent data types to obtain a correlation coefficient deviation value. If the correlation coefficient deviation value is less than or equal to the correlation coefficient deviation threshold, generate a filling signal;

[0010] Step 4: Based on the filling signal, determine the mean of the missing adjacent ratio, and perform a filling operation on the missing data types in the type data group.

[0011] In a second aspect, the present invention provides a research and development production data monitoring system based on the Internet of Things, specifically including:

[0012] Missing data judgment module: During the acquisition period, analyze the research and development production data of the acquisition node to obtain a missing characterization value. If the missing characterization value is less than the missing characterization threshold, generate an analysis signal.

[0013] Missing continuity judgment module: Based on the analysis signal, analyze the missing data types of the abnormal acquisition node to obtain a missing continuity value. If the missing continuity value is greater than the missing continuity threshold, generate a high missing data continuity signal.

[0014] Filling analysis module: Based on the low missing data continuity signal, analyze the correlation between the missing data type and the adjacent data type to obtain a correlation coefficient deviation value. If the correlation coefficient deviation value is less than or equal to the correlation coefficient deviation threshold, generate a filling signal.

[0015] Data filling module: Based on the filling signal, determine the mean of the missing adjacent ratio, and perform a filling operation on the missing data types in the type data group.

[0016] Advantages of the present invention:

[0017] 1. By analyzing the research and development production data of the acquisition node during the acquisition period, the present invention obtains a missing characterization value. If the missing characterization value is less than the missing characterization threshold, an analysis signal is generated. By analyzing the types of research and development production data at the acquisition node, the present invention can timely detect the situation of missing data types, judge the degree of data missing according to the missing characterization value, re-acquire the data with a high degree of missing, generate an analysis signal for the data with a low degree of missing, and continue the subsequent analysis, improving the efficiency and pertinence of data processing, contributing to improving the reliability of research and development production data acquisition, and providing strong support for research and development production decisions based on these data.

[0018] 2. Based on the analysis signal, the present invention analyzes the missing data types of abnormal acquisition nodes to obtain missing continuous values. If the missing continuous value is greater than the missing continuous threshold, a high signal of missing data continuity is generated. Based on the low signal of missing data continuity, the correlation between the missing data type and the adjacent data type is analyzed to obtain the correlation coefficient deviation value. If the correlation coefficient deviation value is less than or equal to the correlation coefficient deviation threshold, a filling signal is generated. Based on the filling signal, the average value of the missing adjacent ratio is determined, and the filling operation is performed on the missing data type in the type data group. By distinguishing between continuous type missing and interval type missing, the present invention can clearly identify the specific pattern of data missing, help quickly locate the characteristics of data missing, and provide a basis for subsequent targeted processing. By calculating the missing continuous value, it helps to decide whether it is necessary to re-collect the R & D production data, ensuring the reliability and integrity of the data. For interval type missing, by analyzing the correlation between the missing data type and the adjacent data type, it can be judged whether the missing data can be reasonably filled based on the adjacent data, avoiding unnecessary re-collection and improving the efficiency of data processing. Taking the average value of the missing adjacent ratio as the filling coefficient, the missing data is filled based on this, providing a scientific and reasonable method for data filling, making the filled data have a certain degree of rationality and credibility, and improving the usability of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 is a flowchart of a method for monitoring R & D production data based on the Internet of Things in an embodiment of the present invention;

[0021] Figure 2 is a block diagram of a system for monitoring R & D production data based on the Internet of Things in an embodiment of the present invention;

[0022] Figure 3 is a schematic structural diagram of a device for monitoring R & D production data based on the Internet of Things in an embodiment of the present invention;

[0023] Reference numerals in the drawings: 3. Computer device; 301. Processor; 302. Memory; 303. Computer program. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] Embodiment 1

[0026] Figure 1 The figure is a flowchart of a method for monitoring R & D and production data based on the Internet of Things provided in Embodiment 1 of the present invention. A method for monitoring R & D and production data based on the Internet of Things can be executed by a system for monitoring R & D and production data based on the Internet of Things. A system for monitoring R & D and production data based on the Internet of Things can be implemented by software and / or hardware, and can be configured in a device for monitoring R & D and production data based on the Internet of Things. Optionally, a device for monitoring R & D and production data based on the Internet of Things can be an electronic device, which can be a notebook, a desktop computer, a smart tablet, etc., and the embodiments of the present invention do not limit this.

[0027] As Figure 1 shown, a method for monitoring R & D and production data based on the Internet of Things provided in the embodiments of the present invention specifically includes:

[0028] Step 1: During the acquisition period, analyze the R & D and production data of the acquisition node to obtain a missing characterization value. If the missing characterization value is less than the missing characterization threshold, generate an analysis signal.

[0029] Preset an acquisition period, and divide the acquisition period into several acquisition nodes with equal time intervals.

[0030] It should be noted that the acquisition period includes but is not limited to 10 days, 20 days, 30 days.

[0031] Based on any one acquisition node;

[0032] Use an Internet of Things sensor to obtain the R & D and production data of the acquisition node. Among them, the Internet of Things sensor includes but is not limited to a vibration sensor and a pressure sensor.

[0033] Count the types of R & D and production data at the acquisition node, and compare and analyze the types of R & D and production data at the acquisition node with the standard types of R & D and production data. Specifically:

[0034] If the types of R & D and production data at the acquisition node are less than the standard types of R & D and production data, mark this acquisition node as an abnormal type acquisition node.

[0035] If the number of types of R & D and production data at the acquisition node is greater than or equal to the standard number of types of R & D and production data, then mark the acquisition node as a normal number acquisition node;

[0036] It should be noted that the standard types of R & D and production data are set by those skilled in the art according to past historical experience;

[0037] Based on any acquisition node with an abnormal number;

[0038] Obtain the difference between the number of types of R & D and production data of the acquisition node with an abnormal number and the standard number of types of R & D and production data, and take the absolute value of the difference to obtain the abnormal number deviation. Then, perform a ratio process on the abnormal number deviation and the standard number of types of R & D and production data to obtain the abnormal number deviation ratio;

[0039] Extract the missing types of R & D and production data in the acquisition node with an abnormal number and mark them as missing data types. Sum up the quantities corresponding to all missing data types to obtain the sum of missing type quantities. Then, perform a ratio process on the sum of missing type quantities and the quantity corresponding to the standard types of R & D and production data to obtain the missing type degree ratio;

[0040] Sum up the abnormal number deviation ratio and the missing type degree ratio to calculate and obtain the missing characterization value;

[0041] Compare the missing characterization value with the missing characterization threshold:

[0042] If the missing characterization value is greater than or equal to the missing characterization threshold, it indicates that the degree of missing R & D and production data collected during the acquisition cycle is high. Re - collect the R & D and production data to generate an acquisition signal;

[0043] If the missing characterization value is less than the missing characterization threshold, it indicates that the degree of missing R & D and production data collected during the acquisition cycle is low, and an analysis signal is generated;

[0044] The technical solution of this embodiment is as follows: During the acquisition cycle, analyze the R & D and production data of the acquisition node to obtain the missing characterization value. If the missing characterization value is less than the missing characterization threshold, an analysis signal is generated. By analyzing the number of types of R & D and production data at the acquisition node, the present invention can timely detect the situation of missing data types, judge the degree of data missing according to the missing characterization value, re - collect the data with a high degree of missing, and generate an analysis signal for the data with a low degree of missing to continue the subsequent analysis, improving the efficiency and pertinence of data processing, contributing to improving the reliability of R & D and production data acquisition, and providing strong support for R & D and production decisions based on these data.

[0045] Embodiment Two

[0046] Such as Figure 1As shown in the figure, a method for monitoring R & D and production data based on the Internet of Things provided by an embodiment of the present invention specifically includes:

[0047] Step 2: Based on the analysis signal, analyze the missing data types of abnormal acquisition nodes to obtain missing continuous values. If the missing continuous value is greater than the missing continuous threshold, a high signal of missing data continuity is generated;

[0048] Based on the abnormal type number of acquisition nodes, if the number of continuously occurring missing data types ≥ 2, it indicates a continuous type of missing, and a continuous type of missing signal is generated. If the number of continuously occurring missing data types < 2, it indicates an intermittent type of missing, and an intermittent type of missing signal is generated;

[0049] Exemplarily, assume that R & D and production data during the assembly process of a certain electronic product are collected. The collection period is 10 days, which is divided into 10 acquisition nodes, with each day being an acquisition node. The data types collected include 5 types: temperature, humidity, part vibration frequency, part pressure, and production line speed. The standard number of R & D and production data types is these 5 types;

[0050] After comparison, it is found that the two data types of humidity and part vibration frequency are missing; because the number of continuously occurring missing data types is 2, meeting the condition of "the number of continuously occurring missing data types ≥ 2", so this belongs to a continuous type of missing, and the system will generate a continuous type of missing signal;

[0051] At the 7th acquisition node, only the data type of part pressure is found to be missing. Since the number of continuously occurring missing data types is 1, meeting the condition of "the number of continuously occurring missing data types < 2", this therefore belongs to an intermittent type of missing, and the system will generate an intermittent type of missing signal;

[0052] For another example, at the 8th and 9th acquisition nodes, it is found that the two data types of production line speed and temperature are missing respectively. Each node has only one data type missing, and the number of continuously occurring missing data types for each is less than 2. Therefore, both of these two nodes belong to the intermittent type of missing, and the system will generate intermittent type of missing signals for the 8th and 9th acquisition nodes respectively;

[0053] Count the number of occurrences of the continuous type of missing signal and mark it as the number of continuous type of missing times LQ;

[0054] Through the formula: Calculate the missing continuous value QL, where m represents the number of acquisition nodes;

[0055] Compare the missing continuous value with the missing continuous threshold:

[0056] If the number of consecutive missing values is greater than the missing consecutive threshold, it indicates that the R & D production data needs to be recollected to generate a high signal for the continuity of missing data;

[0057] If the number of consecutive missing values is less than or equal to the missing consecutive threshold, a low signal for the continuity of missing data is generated;

[0058] Step 3: Based on the low signal for the continuity of missing data, analyze the correlation between the missing data type and the adjacent data type to obtain the correlation coefficient deviation value. If the correlation coefficient deviation value is less than or equal to the correlation coefficient deviation threshold, a filling signal is generated;

[0059] Extract the adjacent data type when the missing signal of the interval type is generated. Combine the previous data type or the next data type in the adjacent data type with the missing data type to obtain a type data group, and analyze the correlation between the missing data type and the adjacent data type in the type data group. Specifically:

[0060] Based on any type data group, obtain the data values of the missing data type and the adjacent data type in the same historical period. Mark the data value corresponding to the missing data type as the missing data value, and mark the data value corresponding to the adjacent data type as the adjacent data value;

[0061] Divide the historical period into several historical time points with equal time intervals;

[0062] Through the formula: Calculate the correlation coefficient r between the missing data type and the adjacent data type;

[0063] where n is the number of samples, and are the observed values of the i-th historical time point of the variable missing data value and the adjacent data value respectively, and are the sample means of the variable missing data value and the adjacent data value respectively;

[0064] Take the absolute value of the correlation coefficient r to obtain the absolute value of the correlation coefficient, and subtract the absolute value of the correlation coefficient from 1 to obtain the correlation coefficient deviation value;

[0065] Compare the correlation coefficient deviation value with the correlation coefficient deviation threshold:

[0066] If the correlation coefficient deviation value is less than or equal to the correlation coefficient deviation threshold, it means that the correlation between the missing data value and the adjacent data value in the historical period is high, that is, the correlation between the missing data type and the adjacent data type in the type data group in the historical period is high, and a filling signal is generated;

[0067] If the deviation value of the correlation coefficient is greater than the correlation coefficient deviation threshold, it indicates that the correlation between the missing data value and the adjacent data value in the historical period is low, that is, the correlation between the missing data type and the adjacent data type in the type data group in the historical period is low, and a non-filling signal is generated to re-collect the missing data type;

[0068] Step Four: Based on the filling signal, determine the average missing adjacent ratio, and perform a filling operation on the missing data type in the type data group;

[0069] Specifically, the ratio of the missing data value to the adjacent data value at the historical time point is processed to obtain the missing adjacent ratio, and the average value of the missing adjacent ratios at all historical time points is obtained by summing and averaging;

[0070] The average missing adjacent ratio is used as the filling coefficient, and the filling coefficient is multiplied by the adjacent data value to obtain the missing data value to be filled. Based on the missing data value to be filled, the data of the current missing data type is filled;

[0071] The technical solution of this embodiment is as follows: Based on the analysis signal, analyze the missing data type of the abnormal acquisition node to obtain the missing continuous value. If the missing continuous value is greater than the missing continuous threshold, a high missing data continuity signal is generated. Based on the low missing data continuity signal, analyze the correlation between the missing data type and the adjacent data type to obtain the correlation coefficient deviation value. If the correlation coefficient deviation value is less than or equal to the correlation coefficient deviation threshold, a filling signal is generated. Based on the filling signal, determine the average missing adjacent ratio, and perform a filling operation on the missing data type in the type data group. By distinguishing between continuous type missing and interval type missing, the present invention can clearly identify the specific pattern of data missing, help quickly locate the characteristics of data missing, and provide a basis for subsequent targeted processing. By calculating the missing continuous value, it helps to determine whether it is necessary to re-collect the R & D production data, ensuring the reliability and integrity of the data. For interval type missing, by analyzing the correlation between the missing data type and the adjacent data type, it can be judged whether the missing data can be reasonably filled based on the adjacent data, avoiding unnecessary re-collection and improving the efficiency of data processing. Using the average missing adjacent ratio as the filling coefficient and filling the missing data based on this provides a scientific and reasonable method for data filling, making the filled data have a certain degree of rationality and credibility, and improving the usability of the data.

[0072] Embodiment Three

[0073] As Figure 2 shown, a research and development production data monitoring system based on the Internet of Things provided by an embodiment of the present invention specifically includes:

[0074] Missing data judgment module: During the acquisition period, analyze the R & D and production data of the acquisition nodes to obtain a missing characterization value. If the missing characterization value is less than the missing characterization threshold, generate an analysis signal;

[0075] Missing continuity judgment module: Based on the analysis signal, analyze the missing data types of abnormal acquisition nodes to obtain a missing continuity value. If the missing continuity value is greater than the missing continuity threshold, generate a high missing data continuity signal;

[0076] Filling analysis module: Based on the low missing data continuity signal, analyze the correlation between the missing data type and the adjacent data types to obtain a correlation coefficient deviation value. If the correlation coefficient deviation value is less than or equal to the correlation coefficient deviation threshold, generate a filling signal;

[0077] Data filling module: Based on the filling signal, determine the average missing adjacent ratio and perform a filling operation on the missing data types in the type data group.

[0078] Embodiment 4

[0079] Refer to Figure 3 , the embodiment of the present invention also provides a computer device 3, including: a memory 302, a processor 301, and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, it implements a method for monitoring R & D and production data based on the Internet of Things as described in any one of the above methods.

[0080] The computer device 3 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art can understand that Figure 3 merely examples of the computer device 3, which do not constitute a limitation on the computer device 3, may include more or fewer components than shown in the figure, or combine some components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0081] The so-called processor 301 may be a central processing unit (CPU), and this processor 301 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0082] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as the hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 3. Further, the memory 302 may also include both the internal storage unit and the external storage device of the computer device 3. The memory 302 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program, etc. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0083] Embodiment Five

[0084] The embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements a method for monitoring R & D and production data based on the Internet of Things as described in any one of the above methods.

[0085] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the method of the above embodiment in this application, a computer program can be used to instruct the relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0086] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0087] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0088] In the embodiments disclosed in this application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0089] Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0090] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0092] The above has described in detail an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for monitoring R & D and production data based on the Internet of Things, characterized in that, Including: Step 1: During the acquisition period, analyze the R & D production data of the acquisition nodes to obtain missing characterization values. If the missing characterization value is less than the missing characterization threshold, generate an analysis signal; The method for obtaining the missing characterization value is as follows: Obtain the R & D production data of the acquisition nodes, and analyze to obtain the abnormal species deviation ratio and the missing type degree ratio; Sum the abnormal species deviation ratio and the missing type degree ratio, and calculate to obtain the missing characterization value; The abnormal species deviation ratio is the ratio of the abnormal species deviation to the number of standard R & D production data species; The missing type degree ratio is the ratio of the sum of the missing type quantities to the number of standard R & D production data types; The sum of the missing type quantities is the sum of the quantities corresponding to all missing data types; The missing data type is the R & D production data type missing in the acquisition node with abnormal species; Step 2: Based on the analysis signal, analyze the missing data types of the abnormal acquisition nodes to obtain missing continuous values. If the missing continuous value is less than or equal to the missing continuous threshold, generate a low missing data continuity signal; Step 3: Based on the low missing data continuity signal, analyze the correlation between the missing data type and the adjacent data types to obtain a correlation coefficient deviation value. If the correlation coefficient deviation value is less than or equal to the correlation coefficient deviation threshold, generate a filling signal; Step 4: Based on the filling signal, determine the average missing adjacent ratio, and perform a filling operation on the missing data types in the type data group; The specific operation of filling the missing data types in the type data group is as follows: Obtain the average missing adjacent ratio, use the average missing adjacent ratio as the filling coefficient, multiply the filling coefficient by the adjacent data value to obtain the missing data value to be filled, and based on the missing data value to be filled, perform a filling operation on the data of the current missing data type; The method for obtaining the average missing adjacent ratio is as follows: Perform a ratio process on the missing data value and the adjacent data value at the historical time point to obtain the missing adjacent ratio, sum and average all the missing adjacent ratios at the historical time points to obtain the average missing adjacent ratio; The adjacent data value is the data value corresponding to the adjacent data type.

2. The method for monitoring R & D and production data based on the Internet of Things according to claim 1, wherein The method for obtaining the abnormal species deviation ratio is as follows: Preset an acquisition period, and divide the acquisition period into several acquisition nodes with equal time intervals; Based on any one acquisition node; Obtain the R & D production data of the acquisition node, count the number of R & D production data species at the acquisition node, and compare the number of R & D production data species at the acquisition node with the number of standard R & D production data species: If the number of R & D production data species at the acquisition node is less than the number of standard R & D production data species, mark this acquisition node as an acquisition node with abnormal species; Based on any one acquisition node with abnormal species; Take the difference between the number of R & D production data species of the acquisition node with abnormal species and the number of standard R & D production data species, and take the absolute value of the difference to obtain the abnormal species deviation. Perform a ratio process on the abnormal species deviation and the number of standard R & D production data species to obtain the abnormal species deviation ratio.

3. The method for monitoring R & D and production data based on the Internet of Things according to claim 2, wherein, The method for obtaining the abnormal species deviation ratio is as follows: Extract the missing R & D production data types in the abnormal species collection node, mark them as missing data types, sum up the quantities corresponding to all the missing data types to obtain the sum of missing type quantities, and perform a ratio process on the sum of missing type quantities and the quantity corresponding to the standard R & D production data types to obtain the missing type degree ratio.

4. A method for monitoring R & D and production data based on the Internet of Things according to claim 3, characterized in that, The acquisition method of the missing continuous value is as follows: Based on the abnormal species collection node, if the quantity of continuously appearing missing data types ≥ 2, it indicates a continuous type of missing, generate a continuous type of missing signal; if the quantity of continuously appearing missing data types < 2, it indicates an intermittent type of missing, generate an intermittent type of missing signal. Count the number of times the continuous type of missing signal appears and mark it as the continuous type of missing times LQ. Through the formula: The missing consecutive value QL is calculated, where m represents the number of acquisition nodes.

5. A method for monitoring R & D and production data based on the Internet of Things according to claim 4, characterized in that, The acquisition method of the correlation coefficient deviation value is as follows: Extract the adjacent data types when the intermittent type of missing signal is generated, combine the previous data type or the next data type in the adjacent data types with the missing data type to obtain a type data group. Based on the type data group, analyze the missing data value corresponding to the missing data type and the adjacent data value corresponding to the adjacent data type to obtain the correlation coefficient r. Take the absolute value of the correlation coefficient r to obtain the absolute value of the correlation coefficient, and perform a subtraction process on 1 and the absolute value of the correlation coefficient to obtain the correlation coefficient deviation value.

6. The method for monitoring R & D and production data based on the Internet of Things according to claim 5, characterized in that, The acquisition method of the correlation coefficient r is as follows: Based on any type data group, obtain the data values of the missing data type and the adjacent data type in the same historical period in the type data group. Mark the data value corresponding to the missing data type as the missing data value, and mark the data value corresponding to the adjacent data type as the adjacent data value. Divide the historical period into several historical time points with equal time intervals. Calculate the correlation coefficient r between the missing data type and the adjacent data type through the Pearson correlation coefficient formula.

7. An R & D and production data monitoring system based on the Internet of Things, which implements the R & D and production data monitoring method based on the Internet of Things according to any one of claims 1-6, characterized in that, It includes: Missing data judgment module: During the acquisition period, analyze the R & D production data of the acquisition node to obtain the missing characterization value. If the missing characterization value is less than the missing characterization threshold, generate an analysis signal. The acquisition method of the missing characterization value is as follows: Obtain the R & D production data of the acquisition node, and analyze to obtain the abnormal species deviation ratio and the missing type degree ratio. Sum up the abnormal species deviation ratio and the missing type degree ratio, and calculate to obtain the missing characterization value. The abnormal species deviation ratio is the ratio of the abnormal species deviation to the number of standard R & D production data types. The missing type degree ratio is the ratio of the sum of missing type quantities to the number of standard R & D production data types. The sum of missing type quantities is the sum of the quantities corresponding to all missing data types. The missing data type is the R & D production data type missing in the abnormal species collection node. Missing continuity judgment module: Based on the analysis signal, analyze the missing data types of the abnormal acquisition node to obtain the missing continuous value. If the missing continuous value is less than or equal to the missing continuous threshold, generate a low missing data continuity signal. Filling analysis module: Based on the low-signal of the continuity of missing data, analyze the correlation between the missing data type and the adjacent data type to obtain the deviation value of the correlation coefficient. If the deviation value of the correlation coefficient is less than or equal to the correlation coefficient deviation threshold, generate a filling signal; Data filling module: Based on the filling signal, determine the average value of the missing adjacent ratio, and perform a filling operation on the missing data type in the type data group; The specific operation of filling the missing data type in the type data group is as follows: Obtain the average value of the missing adjacent ratio, use the average value of the missing adjacent ratio as the filling coefficient, multiply the filling coefficient by the adjacent data value to obtain the missing data value to be filled, and based on the missing data value to be filled, perform a filling operation on the data of the current missing data type; The obtaining method of the average value of the missing adjacent ratio is as follows: Perform a ratio operation on the missing data value and the adjacent data value at the historical time point to obtain the missing adjacent ratio, sum up the missing adjacent ratios at all historical time points and take the average value to obtain the average value of the missing adjacent ratio; The adjacent data value is the data value corresponding to the adjacent data type.

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