Intelligent data storage and integration method and system based on Internet of Things, and storage medium

Abstract

The invention discloses an intelligent data storage and integration method and system based on the Internet of Things and a storage medium. The method relates to the technical field of data storage, and comprises the following steps: collecting and processing intelligent data, storing and integrating data; analyzing the intelligent data storage and integration data; and comprehensively analyzing, optimizing and adjusting the accuracy of intelligent data graph integration. According to the method, the intelligent data storage integrated data is collected and processed, the intelligent data storage integrated data is analyzed, the intelligent data storage integrated data quality evaluation index and the intelligent data storage integrated graph generation reliability evaluation index are obtained, comprehensive analysis is carried out, threshold comparison is carried out, and the reliability evaluation index of the intelligent data storage integrated data is obtained. According to the method for optimizing and adjusting the accuracy of intelligent data graphic integration, the accuracy of the intelligent data storage and integration method is improved, and the problem that the accuracy of the intelligent data storage and integration method is insufficient in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of data storage technology, and in particular to an intelligent data storage integration method, system and storage medium based on the Internet of Things. Background Art

[0002] As the IoT industry chain is divided into multiple links, including chips, sensors, controllers, communication modules, communication networks, IoT platforms, IoT terminals, intelligent platforms, etc. These links together form the foundation of IoT technology and support its application in various industries, including the mobile phone panel industry. IoT technology collects information about objects or processes that need to be monitored, connected, and interacted in real time through various information sensors, radio frequency identification technology, global positioning systems, etc., and realizes ubiquitous connection between objects and objects, objects and people through the network, and realizes intelligent perception, identification and management of objects and processes. The emergence of the IoT has broadened the product application model and prompted enterprises to transform from traditional business models to smart ports.

[0003] The existing intelligent data storage and integration system of the Internet of Things can collect device status data, such as battery power, signal strength, temperature, etc. through big data collection and storage technology. Data transmission uses the MQTT interface protocol, and Kafka is used for data reception and push; through the Internet of Things big data storage and management technology, large-scale distributed file systems such as HDFS are used to support big data storage.

[0004] For example, the invention patent application with publication number CN105447132A discloses a four-layer geographic data storage system for IoT applications, including a four-layer architecture of geographic nodes, logical nodes, application nodes, and storage nodes, wherein: geographic nodes are used to accurately describe the geometric shape of locations in applications; application nodes are used to describe objects containing geographic location information in IoT applications; logical nodes are the core structure of the organization space in the present invention, which organizes geographically meaningful points in a hierarchical structure and associates geographic nodes, application nodes, and storage nodes respectively. It provides a flexible and efficient method for data query and processing operations; storage nodes are used to efficiently process different types of data, requiring the mixed use of SQL, NoSQL, and distributed file systems.

[0005] For example, the invention patent application with publication number: CN109033387A discloses an Internet of Things search system, method and storage medium that integrates multi-source data, including: a multi-source data access module, used to access, clean, classify and store multi-source data; wherein the multi-source data includes Internet of Things device data, industry-level data and Internet data; a data storage and analysis module, used to implement differentiated storage of multi-source data, perform fusion analysis and index establishment on the data in each database, and provide data search and search result sorting and filtering services; an application service module, used to receive a search request initiated by a user through a user terminal, and obtain corresponding search results from the data storage and analysis module according to the search request to return them to the user terminal.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] This method is applied in data analysis and graphics generation scenarios, but there is a problem of insufficient accuracy assessment of intelligent data storage and integration. The data generated by IoT devices contains noise, which affects the accuracy of intelligent data analysis and the reliability of graphics generation. At the same time, the intelligent data generated by IoT devices needs to be synchronized to the central storage system in real time. Since it is impossible to efficiently process large amounts of real-time data streams, graphics generation is delayed, and there is a problem of insufficient accuracy of the intelligent data storage and integration method. Summary of the invention

[0008] The embodiments of the present application solve the problem of insufficient accuracy of the intelligent data storage integration method in the prior art by providing an intelligent data storage integration method, system and storage medium based on the Internet of Things, thereby improving the accuracy of the intelligent data storage integration method.

[0009] The embodiment of the present application provides an intelligent data storage integration method based on the Internet of Things, comprising the following steps: collecting and processing intelligent data storage integration data; analyzing the intelligent data storage integration data to obtain an intelligent data storage integration data quality assessment index and an intelligent data storage integration graph generation reliability assessment index; comprehensively analyzing to obtain an intelligent data graph integration accuracy assessment index; comparing and analyzing the intelligent data storage integration data quality assessment index with a first threshold of the intelligent data storage integration data quality assessment index, and optimizing the intelligent data storage integration data quality method; comparing and analyzing the intelligent data storage integration graph generation reliability assessment index with a second threshold of the intelligent data storage integration graph generation reliability assessment index, and optimizing and adjusting the intelligent data storage integration graph generation reliability method; comparing and analyzing the intelligent data graph integration accuracy assessment index with a comprehensive threshold of the intelligent data graph integration accuracy assessment index, and optimizing and adjusting the intelligent data graph integration accuracy method.

[0010] Furthermore, the specific steps of collecting and processing intelligent data storage and integration data are: collecting intelligent data storage and integration raw data through Internet of Things devices; cleaning and denoising the intelligent data storage and integration raw data to obtain intelligent data storage and integration data, and the intelligent data storage and integration data includes intelligent data storage and integration data quality data and intelligent data storage and integration graphics generation reliability data.

[0011] Furthermore, the specific steps for obtaining the intelligent data storage integration data quality assessment index are: obtaining the intelligent data noise of the preset intelligent data storage integration data quality time detection point through the data analysis server; obtaining the intelligent data density of the preset intelligent data storage integration data quality time detection point through the data mining library; obtaining the graph generation speed of the preset intelligent data storage integration data quality time detection point through the APM program performance management tool; obtaining the intelligent data update frequency of the preset intelligent data storage integration data quality time detection point through the data stream processing platform; obtaining the intelligent data delay of the preset intelligent data storage integration data quality time detection point through the NTP server; the intelligent data storage integration data quality data includes intelligent data noise, intelligent data density, graph generation speed, intelligent data update frequency and intelligent data delay; and obtaining the intelligent data storage integration data quality assessment index according to the intelligent data storage integration data quality data analysis.

[0012] Furthermore, the specific steps for obtaining the reliability evaluation index of intelligent data storage integrated graphics generation are: obtaining the intelligent data noise of the preset intelligent data storage integrated graphics generation reliability detection point through data analysis software; obtaining the intelligent data density of the preset intelligent data storage integrated graphics generation reliability detection point through data visualization tools; obtaining the graphic generation speed of the preset intelligent data storage integrated graphics generation reliability detection point through performance monitoring tools; obtaining the number of graphic data points of the preset intelligent data storage integrated graphics generation reliability detection segment through a graphical user interface; obtaining the number of graphic inclusion dimensions of the preset intelligent data storage integrated graphics generation reliability detection segment through a data visualization tool; obtaining the intelligent data integration rate of the preset intelligent data storage integrated graphics generation reliability detection point through an ETL tool; the intelligent data storage integrated graphics generation reliability data includes intelligent data noise, intelligent data density, graphic generation speed, number of graphic data points, number of graphic inclusion dimensions and intelligent data integration rate; and obtaining the reliability evaluation index of intelligent data storage integrated graphics generation according to the intelligent data storage integrated graphics generation reliability data analysis.

[0013] Furthermore, the specific steps of the comprehensive analysis to obtain the intelligent data graphics integration accuracy evaluation index are: obtaining the graphic pixel density of the preset intelligent data graphics integration accuracy detection point through image editing software; and obtaining the intelligent data graphics integration accuracy evaluation index through comprehensive analysis of the intelligent data storage integration graphic generation reliability evaluation index, graphic pixel density and intelligent data storage integration data quality evaluation index.

[0014] Furthermore, the specific steps of the method for optimizing the data quality of intelligent data storage and integration are: if the intelligent data storage and integration data quality assessment index is lower than or equal to the first threshold of the intelligent data storage and integration data quality assessment index, there is no need to optimize the intelligent data storage and integration data quality; if the intelligent data storage and integration data quality assessment index is greater than the first threshold of the intelligent data storage and integration data quality assessment index, then the difference between the intelligent data storage and integration data quality assessment index and the first threshold of the intelligent data storage and integration data quality assessment index is used to match the corresponding adjustment plan in the intelligent data storage and integration database.

[0015] Furthermore, the specific steps of optimizing and adjusting the reliability method of intelligent data storage integrated graphics generation are as follows: if the intelligent data storage integrated graphics generation reliability assessment index is greater than or equal to the second threshold value of the intelligent data storage integrated graphics generation reliability assessment index, there is no need to optimize and adjust the intelligent data storage integrated graphics generation reliability method; if the intelligent data storage integrated graphics generation reliability assessment index is lower than the second threshold value of the intelligent data storage integrated graphics generation reliability assessment index, then the difference between the intelligent data storage integrated graphics generation reliability assessment index and the second threshold value of the intelligent data storage integrated graphics generation reliability assessment index is used to match the corresponding adjustment plan in the intelligent data storage integration database.

[0016] Furthermore, the specific steps of the method for optimizing and adjusting the accuracy of intelligent data graphics integration are: extracting the comprehensive threshold of the intelligent data graphics integration accuracy assessment index in the intelligent data storage and integration database, and comparing the intelligent data graphics integration accuracy assessment index with the comprehensive threshold of the intelligent data graphics integration accuracy assessment index; if the intelligent data graphics integration accuracy assessment index is greater than or equal to the comprehensive threshold of the intelligent data graphics integration accuracy assessment index, there is no need to optimize and adjust the intelligent data graphics integration accuracy; if the intelligent data graphics integration accuracy assessment index is lower than the comprehensive threshold of the intelligent data graphics integration accuracy assessment index, then the difference between the intelligent data graphics integration accuracy assessment index and the comprehensive threshold of the intelligent data graphics integration accuracy assessment index is used to match the corresponding adjustment plan in the intelligent data storage and integration database.

[0017] The embodiment of the present application provides an intelligent data storage integration system based on the Internet of Things, including an intelligent data storage integration data acquisition module, an intelligent data storage integration data analysis module, a comprehensive analysis module and an optimization and adjustment module for the reliability method of intelligent data storage integration graphics generation: the intelligent data storage integration data acquisition module is used to collect and process intelligent data storage integration data; the intelligent data storage integration data analysis module is used to analyze the intelligent data storage integration data to obtain the intelligent data storage integration data quality evaluation index and the intelligent data storage integration graphics generation reliability evaluation index; the comprehensive analysis module is used to comprehensively analyze and obtain the intelligent data graphics integration accuracy evaluation index; the optimization and adjustment module for the reliability method of intelligent data storage integration graphics generation is used to compare and analyze the intelligent data storage integration data quality evaluation index with the first threshold of the intelligent data storage integration data quality evaluation index, and optimize the intelligent data storage integration data quality method; compare and analyze the intelligent data storage integration graphics generation reliability evaluation index with the second threshold of the intelligent data storage integration graphics generation reliability evaluation index, and optimize and adjust the intelligent data storage integration graphics generation reliability method; compare and analyze the intelligent data graphics integration accuracy evaluation index with the comprehensive threshold of the intelligent data graphics integration accuracy evaluation index, and optimize and adjust the intelligent data graphics integration accuracy method.

[0018] An embodiment of the present application provides a computer-readable storage medium for storing a program, which, when executed by a processor, implements an intelligent data storage and integration method based on the Internet of Things.

[0019] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. By collecting and processing intelligent data storage and integration data, analyzing the intelligent data storage and integration data, obtaining the intelligent data storage and integration data quality assessment index and the intelligent data storage and integration graphics generation reliability assessment index, comprehensively analyzing and comparing thresholds, optimizing and adjusting the intelligent data graphics integration accuracy method, and improving the accuracy of the intelligent data storage and integration method.

[0021] 2. By analyzing the intelligent data storage and integration data, we can obtain the intelligent data storage and integration data quality assessment index and the intelligent data storage and integration graphic generation reliability assessment index, which will help identify problems in data management, such as data inconsistency, missing or errors, thereby promoting the optimization of data management processes, and making data analysis and interpretation more intuitive and easy to understand.

[0022] 3. Through comprehensive analysis and threshold comparison, optimize and adjust the intelligent data graphics integration accuracy method to ensure that the data displayed in the graphics is more accurate, reduce misleading information, and improve the credibility of the data. Through accurate data graphics, resources can be allocated more effectively and resource utilization efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of an intelligent data storage and integration method based on the Internet of Things provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of an intelligent data graphics integration accuracy evaluation index function provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of the structure of an intelligent data storage and integration system based on the Internet of Things provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application solve the problem of insufficient accuracy of the intelligent data storage integration method in the prior art by providing an intelligent data storage integration method, system and storage medium based on the Internet of Things. By collecting and processing intelligent data storage integration data, analyzing the intelligent data storage integration data, obtaining an intelligent data storage integration data quality assessment index and an intelligent data storage integration graph generation reliability assessment index, comprehensively analyzing and performing threshold comparison, optimizing and adjusting the intelligent data graph integration accuracy method, and improving the accuracy of the intelligent data storage integration method.

[0027] The technical solution in the embodiment of the present application is to solve the above-mentioned problem that the intelligent data storage and integration method is not accurate enough. The overall idea is as follows:

[0028] By collecting and processing intelligent data storage and integration data, analyzing the intelligent data storage and integration data, obtaining the intelligent data storage and integration data quality assessment index and the intelligent data storage and integration graphics generation reliability assessment index, comprehensively analyzing and performing threshold comparisons, optimizing and adjusting the intelligent data graphics integration accuracy method, and improving the accuracy of the intelligent data storage and integration method.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0030] like Figure 1As shown, it is a flow chart of the intelligent data storage integration method based on the Internet of Things provided in an embodiment of the present application. The method is applied to an intelligent data storage integration system based on the Internet of Things, and the method includes the following steps: collecting and processing intelligent data storage integration data; analyzing the intelligent data storage integration data to obtain an intelligent data storage integration data quality assessment index and an intelligent data storage integration graphic generation reliability assessment index; comprehensively analyzing to obtain an intelligent data graphic integration accuracy assessment index; comparing and analyzing the intelligent data storage integration data quality assessment index with the first threshold of the intelligent data storage integration data quality assessment index, and optimizing the intelligent data storage integration data quality method; comparing and analyzing the intelligent data storage integration graphic generation reliability assessment index with the second threshold of the intelligent data storage integration graphic generation reliability assessment index, and optimizing and adjusting the intelligent data storage integration graphic generation reliability method; comparing and analyzing the intelligent data graphic integration accuracy assessment index with the comprehensive threshold of the intelligent data graphic integration accuracy assessment index, and optimizing and adjusting the intelligent data graphic integration accuracy method.

[0031] Furthermore, the specific steps of collecting and processing intelligent data storage and integration data are: collecting intelligent data storage and integration raw data through Internet of Things devices; cleaning and denoising the intelligent data storage and integration raw data to obtain intelligent data storage and integration data, and the intelligent data storage and integration data includes intelligent data storage and integration data quality data and intelligent data storage and integration graphics generation reliability data.

[0032] Furthermore, the specific steps for obtaining the intelligent data storage integration data quality evaluation index are: obtaining the intelligent data noise of the preset intelligent data storage integration data quality time detection point through the data analysis server; obtaining the intelligent data density of the preset intelligent data storage integration data quality time detection point through the data mining library; obtaining the graph generation speed of the preset intelligent data storage integration data quality time detection point through the APM program performance management tool; obtaining the intelligent data update frequency of the preset intelligent data storage integration data quality time detection point through the data stream processing platform; obtaining the intelligent data delay of the preset intelligent data storage integration data quality time detection point through the NTP server; the intelligent data storage integration data quality data includes intelligent data noise, intelligent data density, graph generation speed, intelligent data update frequency and intelligent data delay; and obtaining the intelligent data storage integration data quality evaluation index according to the intelligent data storage integration data quality data analysis.

[0033] In this embodiment, the specific method of analyzing and obtaining the intelligent data storage integration data quality evaluation index is as follows:

[0034]

[0035] μ1+μ2+μ3=1;

[0036] The preset intelligent data storage integration data quality time detection points are numbered in sequence, S0 represents the number of the intelligent data storage integration data quality time detection point, S0=1,2,...,S, S represents the total number of intelligent data storage integration data quality time detection points.

[0037] Represents the intelligent data storage integration data quality assessment index at the S0th intelligent data storage integration data quality time detection point.

[0038] It represents the intelligent data noise impact coefficient at the S0th intelligent data storage integration data quality time detection point.

[0039] Represents the intelligent data density impact coefficient at the S0th intelligent data storage integration data quality time detection point.

[0040] It represents the graphics generation speed at the S0th intelligent data storage integration data quality time detection point.

[0041] ξ1 represents the standard value of the graphics generation speed, which is a preset standard value of the graphics generation speed obtained from the intelligent data storage and integration database, and can be the average value of the graphics generation speed at the preset intelligent data storage and integration data quality time detection point from the historical database.

[0042] Represents the intelligent data delay at the S0th intelligent data storage integration data quality time detection point.

[0043] Y1 represents the intelligent data delay threshold, which is a preset intelligent data delay threshold obtained from the intelligent data storage integration database, and can be the average value of the intelligent data delay at the preset intelligent data storage integration data quality time detection point from the historical database.

[0044] Represents the intelligent data update frequency at the S0th intelligent data storage integration data quality time detection point.

[0045] G1 represents the intelligent data update frequency threshold, which is the preset intelligent data update frequency threshold obtained from the intelligent data storage integration database, and can be the average intelligent data update frequency under the preset intelligent data storage integration data quality time detection point from the historical database.

[0046] HS0 Represents the intelligent data noise at the S0th intelligent data storage integration data quality time detection point.

[0047] H1 represents the intelligent data noise standard value, which is the preset intelligent data noise standard value obtained from the intelligent data storage integration database, and can be the intelligent data noise average value at the preset intelligent data storage integration data quality time detection point from the historical database.

[0048] N S0 Represents the intelligent data density at the S0th intelligent data storage integration data quality time detection point.

[0049] N1 represents the intelligent data density standard value, which is the preset intelligent data density standard value obtained from the intelligent data storage integration database, and can be the average value of the intelligent data density at the preset intelligent data storage integration data quality time detection point from the historical database.

[0050] μ1 is a preset graphic generation speed influencing weight factor obtained from the intelligent data storage integration database.

[0051] μ2 is a preset intelligent data delay impact weight factor obtained from the intelligent data storage integration database.

[0052] μ3 is a preset intelligent data update frequency influencing weight factor obtained from the intelligent data storage integration database.

[0053] It is the preset intelligent data noise impact factor obtained from the intelligent data storage integration database.

[0054] It is a preset intelligent data density impact factor obtained from the intelligent data storage integration database.

[0055] The preset graphic generation speed influence weight factor, the preset intelligent data delay influence weight factor and the preset intelligent data update frequency influence weight factor respectively represent the numerical value of the influence of graphic generation speed, intelligent data delay and intelligent data update frequency on the intelligent data storage integration data quality assessment index, representing the proportion of graphic generation speed, intelligent data delay and intelligent data update frequency in the influence on the intelligent data storage integration data quality assessment index.

[0056] The preset weight factors affecting the graphics generation speed, the preset weight factors affecting the intelligent data delay and the preset weight factors affecting the intelligent data update frequency are obtained through mapping relationships. For example, a mapping set of graphics generation speed, intelligent data delay and intelligent data update frequency and their corresponding weights is established through the relationship between the graphics generation speed, intelligent data delay and intelligent data update frequency in the historical data and the data transmission speed. The preset graphics generation speed weight factor, the preset intelligent data delay weight factor and the preset intelligent data update frequency weight factor corresponding to the mapping set are obtained by inputting the real-time graphics generation speed, intelligent data delay and intelligent data update frequency.

[0057] The preset intelligent data noise impact factor and the preset intelligent data density impact factor respectively represent the numerical values ​​of the influence of intelligent data noise and intelligent data density on the intelligent data storage integration data quality assessment index, and represent the proportion of intelligent data noise and intelligent data density in the influence on the intelligent data storage integration data quality assessment index.

[0058] The preset intelligent data noise influencing factor and the preset intelligent data density influencing factor are obtained through mapping relationships. For example, a mapping set of intelligent data noise and intelligent data density and their corresponding weights is established respectively through the relationship between the intelligent data noise and intelligent data density in the historical data and the graphics generation speed. The corresponding preset intelligent data noise influencing factor and preset intelligent data density influencing factor in the mapping set are obtained by inputting real-time intelligent data noise and intelligent data density.

[0059] Intelligent data noise refers to errors or outliers in the data, and intelligent data density refers to the amount of data per unit volume or unit area. The higher the intelligent data noise, the lower the actual density of the data, because the noise may obscure the real data points, and the intelligent data density is reduced; the higher the intelligent data noise, the more preprocessing and cleaning are required to improve data quality, and the speed of graphic generation is slowed down; the higher the intelligent data noise, the more frequent data updates are required to correct errors, and the frequency of intelligent data updates is slowed down; the greater the intelligent data noise, the delay in data processing, because extra time is required to identify and correct the noise, and the greater the intelligent data delay; the higher the intelligent data density, the more detailed information can be provided, but it also requires more complex processing, which increases the burden of graphic generation, thereby affecting the speed of graphic generation, and the slower the graphic generation speed.

[0060] The higher the square of the difference between the intelligent data noise and the standard value of the intelligent data noise, the lower the data quality will be. There is a positive correlation between the square of the difference between the intelligent data noise and the standard value of the intelligent data noise and the data quality assessment index of the intelligent data storage integration. The larger the intelligent data storage integration data quality assessment index is; the larger the square of the difference between the intelligent data density and the standard value of the intelligent data density, the overfitting or difficulty in analysis will be caused. There is a positive correlation between the square of the difference between the intelligent data density and the standard value of the intelligent data density and the data quality assessment index of the intelligent data storage integration. The larger the intelligent data storage integration data quality assessment index is; the larger the absolute value of the difference between the graphics generation speed and the standard value of the graphics generation speed, the more likely it is that the data cleaning and verification steps will be neglected, and the graphics generation will be difficult. The absolute value of the difference between the speed and the standard value of the graphics generation speed and the square of the standard value of the intelligent data density are positively correlated with the intelligent data storage and integration data quality assessment index, and the larger the intelligent data storage and integration data quality assessment index is; the faster the intelligent data update frequency, the faster it can reflect the latest information status, and the faster the intelligent data update frequency is negatively correlated with the intelligent data storage and integration data quality assessment index, and the smaller the intelligent data storage and integration data quality assessment index is; the greater the intelligent data delay, the slower the data is processed and integrated, increasing the risk of outdated data, and the intelligent data delay is positively correlated with the intelligent data storage and integration data quality assessment index, and the larger the intelligent data storage and integration data quality assessment index is.

[0061] Furthermore, the specific steps for obtaining the reliability evaluation index of intelligent data storage integrated graphics generation are: obtaining the intelligent data noise of the preset intelligent data storage integrated graphics generation reliability detection point through data analysis software; obtaining the intelligent data density of the preset intelligent data storage integrated graphics generation reliability detection point through data visualization tools; obtaining the graphics generation speed of the preset intelligent data storage integrated graphics generation reliability detection point through performance monitoring tools; obtaining the number of graphics data points of the preset intelligent data storage integrated graphics generation reliability detection segment through a graphical user interface; obtaining the number of graphics inclusion dimensions of the preset intelligent data storage integrated graphics generation reliability detection segment through a data visualization tool; obtaining the intelligent data integration rate of the preset intelligent data storage integrated graphics generation reliability detection point through an ETL tool; the intelligent data storage integrated graphics generation reliability data includes intelligent data noise, intelligent data density, graphics generation speed, number of graphics data points, number of graphics inclusion dimensions and intelligent data integration rate; and obtaining the reliability evaluation index of intelligent data storage integrated graphics generation according to the intelligent data storage integrated graphics generation reliability data analysis.

[0062] In this embodiment, the specific method of analyzing and obtaining the reliability evaluation index of intelligent data storage integrated graphics generation is as follows:

[0063]

[0064] ρ1+ρ2+ρ3=1; φ1+φ2=1;

[0065] The preset intelligent data storage integrated graphics generation reliability detection points are numbered in sequence, K0 represents the number of the intelligent data storage integrated graphics generation reliability detection point under the T0th intelligent data storage integrated graphics generation reliability detection segment, K0 = 1, 2, ..., K, K represents the total number of intelligent data storage integrated graphics generation reliability detection points.

[0066] The preset intelligent data storage integrated graphics generation reliability time is divided into intelligent data storage integrated graphics generation reliability detection segments of the same time length, T0 represents the number of the intelligent data storage integrated graphics generation reliability detection segment, T0 = 1, 2, ..., T, T represents the total number of intelligent data storage integrated graphics generation reliability detection segments.

[0067] Represents the intelligent data storage integrated graphics generation reliability evaluation index of the K0th intelligent data storage integrated graphics generation reliability detection point.

[0068] It represents the intelligent data noise correction coefficient at the K0th intelligent data storage integrated graphics generation reliability detection point.

[0069] It represents the intelligent data density correction coefficient at the K0th intelligent data storage integration graph generation reliability detection point.

[0070] It represents the graphics generation speed at the K0th intelligent data storage integrated graphics generation reliability detection point.

[0071] A1 represents the standard value of the graphics generation speed, which is a preset standard value of the graphics generation speed obtained from the intelligent data storage integration database, and can be the average value of the graphics generation speed under the preset intelligent data storage integration graphics generation reliability detection point from the historical database.

[0072] It represents the intelligent data integration rate at the K0th intelligent data storage integration graph generation reliability detection point.

[0073] C1 represents the intelligent data integration rate threshold, which is a preset intelligent data integration rate threshold obtained from the intelligent data storage integration database, and can be the average value of the intelligent data integration rate at the reliability detection point generated by the preset intelligent data storage integration graph from the historical database.

[0074] It represents the graphics data presentation completeness coefficient under the T0th intelligent data storage integrated graphics generation reliability detection segment.

[0075] Indicates the number of data points in the graph under the T0th intelligent data storage integrated graph generation reliability detection segment.

[0076] P1 represents the standard value of the number of data points in the graph, which is the standard value of the number of data points in the preset graph obtained from the intelligent data storage integration database, and can be the average value of the number of data points in the graph under the reliability detection point generated by the preset intelligent data storage integration graph in the historical database.

[0077] Indicates the number of dimensions contained in the graph under the T0th intelligent data storage integration graph generation reliability detection segment.

[0078] Q1 represents the standard value of the number of dimensions contained in the graphic, which is the preset standard value of the number of dimensions contained in the graphic obtained from the intelligent data storage integration database, and can be the average value of the number of dimensions contained in the graphic under the reliability detection point generated by the preset intelligent data storage integration graphic from the historical database.

[0079] ρ1 is a preset graphic generation speed influencing weight factor obtained from the intelligent data storage integration database.

[0080] ρ2 is a preset intelligent data integration rate influencing weight factor obtained from the intelligent data storage integration database.

[0081] ρ3 is a weight factor influencing the completeness coefficient of the preset graphic data obtained from the intelligent data storage integration database.

[0082] φ1 is a preset intelligent data noise correction factor obtained from the intelligent data storage integration database.

[0083] φ2 is a preset intelligent data density correction factor obtained from the intelligent data storage integration database.

[0084] The preset graphics generation speed influencing weight factor, the preset intelligent data integration rate influencing weight factor and the preset graphics data presentation completeness coefficient influencing weight factor respectively indicate the numerical value of the influence of graphics generation speed, intelligent data integration rate and graphics data presentation completeness coefficient on the intelligent data storage integration graphics generation reliability assessment index, indicating the proportion of graphics generation speed, intelligent data integration rate and graphics data presentation completeness coefficient in the influence of intelligent data storage integration graphics generation reliability assessment index.

[0085] The weight factors affecting the preset graphics generation speed, the weight factors affecting the preset intelligent data integration rate and the weight factors affecting the preset graphics data presentation completeness coefficient are obtained through mapping relationships. For example, a mapping set of graphics generation speed, intelligent data integration rate and graphics data presentation completeness coefficient and their corresponding weights is established respectively through the relationship between the graphics generation speed, intelligent data integration rate and graphics data presentation completeness coefficient in historical data and the memory capacity. The corresponding preset graphics generation speed weight factors, preset intelligent data integration rate weight factors and preset graphics data presentation completeness coefficient weight factors in the mapping set are obtained by inputting the real-time graphics generation speed, intelligent data integration rate and graphics data presentation completeness coefficient.

[0086] The preset intelligent data noise correction factor and the preset intelligent data density correction factor respectively represent the numerical values ​​of the influence of intelligent data noise and intelligent data density on the intelligent data storage integration data quality assessment index, and represent the proportion of intelligent data noise and intelligent data density in the influence on the intelligent data storage integration data quality assessment index.

[0087] The preset intelligent data noise correction factor and the preset intelligent data density correction factor are obtained through mapping relationships. For example, a mapping set of intelligent data noise and intelligent data density and their corresponding weights is established respectively through the relationship between the intelligent data noise and intelligent data density in the historical data and the data collection frequency. The corresponding preset intelligent data noise correction factor and preset intelligent data density correction factor in the mapping set are obtained by inputting real-time intelligent data noise and intelligent data density.

[0088] Intelligent data noise usually refers to errors or outliers in the data, and intelligent data density refers to the amount of data per unit volume or unit area. The increase in intelligent data noise leads to a decrease in intelligent data density because the noise will mask the true data pattern; the higher the intelligent data noise, the more preprocessing and cleaning are required to improve data quality, and the slower the graphics generation speed; the higher the intelligent data density, the more data points there are in the same display area, and the more graphic data points there are; the more graphic data points there are, the longer it takes to process and render the graphics, and the slower the graphics generation speed; the increase in the number of dimensions contained in the graphics increases the complexity of the data and the difficulty of interpreting the graphics, the increase in the number of graphic data points, and the slower the graphics generation speed; the faster the intelligent data integration rate, the faster the data can be processed and prepared, and the faster the graphics generation speed.

[0089] The higher the square of the difference between the intelligent data noise and the standard value of the intelligent data noise, the more errors will be introduced or the true representation of the data will be distorted. The square of the difference between the intelligent data noise and the standard value of the intelligent data noise is negatively correlated with the reliability evaluation index of the intelligent data storage integrated graphic generation, and the smaller the reliability evaluation index of the intelligent data storage integrated graphic generation is; the larger the square of the difference between the intelligent data density and the intelligent data density, the more difficult the graphics will be to interpret. The square of the difference between the intelligent data density and the intelligent data density is negatively correlated with the reliability evaluation index of the intelligent data storage integrated graphic generation, and the smaller the reliability evaluation index of the intelligent data storage integrated graphic generation is; the larger the absolute value of the difference between the graphic generation speed and the standard value of the graphic generation speed, the more insufficient the data processing will be. The absolute value of the difference between the graphic generation speed and the standard value of the graphic generation speed is negatively correlated with the reliability evaluation index of the intelligent data storage integrated graphic generation, and the reliability evaluation index of the intelligent data storage integrated graphic generation is The smaller the index is; the larger the square of the difference between the number of graphic data points and the standard value of the number of graphic data points, the higher the complexity of the graphic and the more difficult it is to analyze. There is a negative correlation between the square of the difference between the number of graphic data points and the standard value of the number of graphic data points and the reliability assessment index of intelligent data storage integration graphic generation, and the smaller the reliability assessment index of intelligent data storage integration graphic generation is; the larger the square of the difference between the number of graphic dimensions and the standard value of the number of graphic dimensions, the more difficult the graphic is to understand and the reliability is reduced. There is a negative correlation between the square of the difference between the number of graphic dimensions and the standard value of the number of graphic dimensions and the reliability assessment index of intelligent data storage integration graphic generation, and the smaller the reliability assessment index of intelligent data storage integration graphic generation is; the faster the intelligent data integration rate is, the faster the data can be processed and updated. There is a positive correlation between the intelligent data integration rate and the reliability assessment index of intelligent data storage integration graphic generation, and the larger the reliability assessment index of intelligent data storage integration graphic generation is.

[0090] Furthermore, the specific steps of comprehensive analysis to obtain the intelligent data graphics integration accuracy evaluation index are: obtaining the graphic pixel density of the preset intelligent data graphics integration accuracy detection point through image editing software; and obtaining the intelligent data graphics integration accuracy evaluation index through comprehensive analysis of the intelligent data storage integration graphic generation reliability evaluation index, graphic pixel density and intelligent data storage integration data quality evaluation index.

[0091] In this embodiment, the specific method of obtaining the intelligent data graph integration accuracy evaluation index through analysis is as follows:

[0092]

[0093] σ1+σ2+σ3=1;

[0094] The preset intelligent data graphics integration accuracy time detection points are numbered in sequence, L0 represents the number of the intelligent data graphics integration accuracy time detection point, L0=1,2,...,L, L represents the total number of the intelligent data graphics integration accuracy time detection points.

[0095] ζ represents the intelligent data graphics integration accuracy evaluation index.

[0096] Represents the intelligent data storage integrated graphics generation reliability evaluation index of the K0th intelligent data storage integrated graphics generation reliability detection point.

[0097] Represents the graphic pixel density at the L0th intelligent data graphic integration accuracy time detection point.

[0098] U1 represents the graphic pixel density threshold, which is a preset graphic pixel density threshold obtained from the intelligent data storage integration database, and can be the average graphic pixel density under the preset intelligent data graphic integration accuracy detection point from the historical database.

[0099] Represents the intelligent data storage integration data quality assessment index at the S0th intelligent data storage integration data quality time detection point.

[0100] σ1 is a weight factor influencing the reliability assessment index generated by the preset intelligent data storage integration graph obtained from the intelligent data storage integration database.

[0101] σ2 is a preset graphic pixel density influence weight factor obtained from the intelligent data storage integration database.

[0102] σ3 is the influencing weight factor of the preset intelligent data storage integration data quality assessment index obtained from the intelligent data storage integration database.

[0103] The preset intelligent data storage integration graphics generation reliability assessment index influencing weight factor, the preset graphics pixel density influencing weight factor and the preset intelligent data storage integration data quality assessment index influencing weight factor respectively represent the numerical values ​​of the influence of the intelligent data storage integration graphics generation reliability assessment index, graphics pixel density and intelligent data storage integration data quality assessment index on the intelligent data graphics integration accuracy assessment index, and represent the proportion of the intelligent data storage integration graphics generation reliability assessment index, graphics pixel density and intelligent data storage integration data quality assessment index in the influence on the intelligent data graphics integration accuracy assessment index.

[0104] The preset intelligent data storage integrated graphics generation reliability assessment index influencing weight factors, the preset graphics pixel density influencing weight factors and the preset intelligent data storage integrated data quality assessment index influencing weight factors are obtained through mapping relationships. For example, through the relationship between the intelligent data storage integrated graphics generation reliability assessment index, graphics pixel density and intelligent data storage integrated data quality assessment index and the graphics resolution in the historical data, a mapping set of intelligent data storage integrated graphics generation reliability assessment index, graphics pixel density and intelligent data storage integrated data quality assessment index and their corresponding weights is established respectively. By inputting the real-time intelligent data storage integrated graphics generation reliability assessment index, graphics pixel density and intelligent data storage integrated data quality assessment index, the corresponding preset intelligent data storage integrated graphics generation reliability assessment index influencing weight factors, preset graphics pixel density influencing weight factors and preset intelligent data storage integrated data quality assessment index influencing weight factors in the mapping set are obtained.

[0105] Table 1 is an example table of the intelligent data graphics integration accuracy evaluation index. The example parameters in Table 1 only take the parameters under one intelligent data graphics integration accuracy detection point for illustration. The weight factor σ1 is set to 0.4, the weight factor σ2 is set to 0.3, the weight factor σ3 is set to 0.3, and the Q1 graphic pixel density threshold is 2. Table 1 is shown below.

[0106] Table 1 Example table of intelligent data graphics integration accuracy evaluation index

[0107]

[0108] The higher the graphic pixel density, the richer the details displayed by the graphic, which helps to improve the reliability assessment index of the graphic, and the larger the reliability assessment index of intelligent data storage integration graphic generation; the data quality is poor, contains errors or missing values, and the generated graphics are inaccurate, the larger the intelligent data storage integration data quality assessment index is, and the lower the intelligent data storage integration graphic generation reliability assessment index is; the graphic pixel density affects the clarity of data presentation, and the higher the graphic pixel density, the smaller the intelligent data storage integration data quality assessment index is.

[0109] It can be seen from Table 1 that the higher the reliability evaluation index of intelligent data storage integration graphics generation, the lower the error rate in the graphics generation process, the more stable and accurate the presentation of intelligent data, the reliability evaluation index of intelligent data storage integration graphics generation is positively correlated with the intelligent data graphics integration accuracy evaluation index, and the larger the intelligent data graphics integration accuracy evaluation index is; the higher the graphics pixel density, the enhanced graphics clarity and detail display, which helps to understand and analyze the data more accurately, the graphics pixel density is positively correlated with the intelligent data graphics integration accuracy evaluation index, and the larger the intelligent data graphics integration accuracy evaluation index is; the larger the intelligent data storage integration data quality evaluation index is, the worse the data quality is, the lower the graphics integration accuracy evaluation index based on data generation is, the intelligent data storage integration data quality evaluation index is negatively correlated, and the intelligent data storage integration data quality evaluation index is smaller.

[0110] like Figure 2 As shown, it is a schematic diagram of the intelligent data graphics integration accuracy evaluation index function provided in an embodiment of the present application; x is the positive semi-axis of the horizontal coordinate, y is the positive semi-axis of the vertical coordinate, the weight factor σ1 is set to 0.4, the weight factor σ2 is set to 0.3, the weight factor σ3 is set to 0.3, and the pixel density of U1 graphic is 2.

[0111] Curve a indicates that if the graphic pixel density is set to a fixed value of 1, the intelligent data storage integration data quality assessment index is set to a fixed value of 1, the intelligent data storage integration graphic generation reliability assessment index is x, and the intelligent data graphic integration accuracy assessment index is y, the intelligent data graphic integration accuracy assessment index increases with the increase of the intelligent data storage integration graphic generation reliability assessment index.

[0112] Furthermore, the specific steps of the method for optimizing the data quality of intelligent data storage and integration are as follows: if the intelligent data storage and integration data quality assessment index is lower than or equal to the first threshold of the intelligent data storage and integration data quality assessment index, there is no need to optimize the intelligent data storage and integration data quality; if the intelligent data storage and integration data quality assessment index is greater than the first threshold of the intelligent data storage and integration data quality assessment index, then the corresponding adjustment plan in the intelligent data storage and integration database is matched through the difference between the intelligent data storage and integration data quality assessment index and the first threshold of the intelligent data storage and integration data quality assessment index.

[0113] In this embodiment, it is assumed that the intelligent data storage integration data quality assessment index is 2, the first threshold of the intelligent data storage integration data quality assessment index obtained from the intelligent data storage integration database is 1, and the corresponding difference is 1. Then, the adjustment scheme corresponding to the difference between the intelligent data storage integration data quality assessment index and the first threshold of the intelligent data storage integration data quality assessment index matched from the intelligent data storage integration database is 1. The adjustment scheme is: with the increase in the number of IoT devices, the amount of collected data has increased sharply, which has brought pressure to storage and transmission. The burden of storage and processing is reduced by adopting data compression technology. For example, a lightweight compression algorithm such as LZ4 or Snappy is used to compress sensor data in real time, reduce data storage requirements, and speed up data transmission in the network. The original data stream processing process is complicated, resulting in data update delays, which affects the real-time performance of monitoring graphics. By optimizing the data stream processing process, the update delay is reduced. For example, by using Apache Message queue technologies such as Kafka optimize data flows to ensure that data can flow quickly and reduce delays in the processing chain. There is a delay from the data source to the generation of graphics, which causes the monitoring graphics to fail to reflect the machine status in real time. By adopting real-time data processing technology, the delay from the data source to the generation of graphics can be reduced. For example, real-time data processing frameworks such as Apache Flink are used to perform real-time analysis of sensor data, and instant data push is achieved through technologies such as WebSocket to ensure real-time updates of monitoring graphics, so as to reduce the data quality assessment index of intelligent data storage integration.

[0114] Furthermore, the specific steps for optimizing and adjusting the reliability method of intelligent data storage integrated graphics generation are as follows: if the intelligent data storage integrated graphics generation reliability evaluation index is greater than or equal to the second threshold value of the intelligent data storage integrated graphics generation reliability evaluation index, there is no need to optimize and adjust the intelligent data storage integrated graphics generation reliability method; if the intelligent data storage integrated graphics generation reliability evaluation index is lower than the second threshold value of the intelligent data storage integrated graphics generation reliability evaluation index, then the difference between the intelligent data storage integrated graphics generation reliability evaluation index and the second threshold value of the intelligent data storage integrated graphics generation reliability evaluation index is used to match the corresponding adjustment plan in the intelligent data storage integration database.

[0115] In this embodiment, assuming that the reliability evaluation index of intelligent data storage integrated graphics generation is 3, the second threshold value of the reliability evaluation index of intelligent data storage integrated graphics generation obtained from the intelligent data storage integrated database is 4, and the corresponding difference is -1, then the adjustment scheme corresponding to the difference between the reliability evaluation index of intelligent data storage integrated graphics generation and the second threshold value of the reliability evaluation index of intelligent data storage integrated graphics generation obtained from the intelligent data storage integrated database is -1 is as follows: use an adaptive filter to process the data to reduce noise. For example, the system uses an adaptive filtering algorithm, such as a Kalman filter, to process the real-time intelligent data storage. The stored integrated data is filtered to smooth short-term fluctuations and reveal the true intelligent data storage integration; the sampling frequency of the original intelligent data storage integration data is too high, resulting in too much data, which is not conducive to fast graphics generation and analysis. According to the analysis requirements, the data is downsampled to increase the data density for specific analysis. For example, the transaction data originally updated every second is downsampled to an average value every 5 minutes. This not only reduces the amount of data, but also maintains the representativeness of the data, making it easier to generate smoother graphics; when processing a large amount of real-time data streams, the data volume surges, resulting in slow data integration and analysis processes. Through parallel processing technology, the data processing speed is improved. For example, using Apache Spark clusters to process data, a large amount of transaction data is divided into small batches, and data integration and analysis tasks are performed in parallel on multiple nodes to ensure the rapid generation of real-time graphics, so as to improve the reliability evaluation index of intelligent data storage integration graphics generation.

[0116] Furthermore, the specific steps of optimizing and adjusting the intelligent data graphics integration accuracy method are as follows: extracting the comprehensive threshold of the intelligent data graphics integration accuracy assessment index in the intelligent data storage and integration database, and comparing the intelligent data graphics integration accuracy assessment index with the comprehensive threshold of the intelligent data graphics integration accuracy assessment index; if the intelligent data graphics integration accuracy assessment index is greater than or equal to the comprehensive threshold of the intelligent data graphics integration accuracy assessment index, there is no need to optimize and adjust the intelligent data graphics integration accuracy; if the intelligent data graphics integration accuracy assessment index is lower than the comprehensive threshold of the intelligent data graphics integration accuracy assessment index, then matching the corresponding adjustment plan in the intelligent data storage and integration database through the difference between the intelligent data graphics integration accuracy assessment index and the comprehensive threshold of the intelligent data graphics integration accuracy assessment index.

[0117] In this embodiment, assuming that the intelligent data graph integration accuracy evaluation index is 4, the intelligent data graph integration accuracy evaluation index comprehensive threshold obtained from the intelligent data storage integration database is 6, and the corresponding difference is -2, then the intelligent data graph integration accuracy evaluation index obtained from the intelligent data storage integration database and the intelligent data graph integration accuracy evaluation index comprehensive threshold value is -2. The corresponding adjustment scheme is: Since the data comes from different sources, there are deviations, which leads to inaccurate data in the graph. By standardizing the data, the deviations between different data sources are eliminated. For example, the user behavior data is processed using the Z-score standardization method to ensure that each data point is standardized based on the mean and standard deviation of the data set to which it belongs, thereby eliminating the source data. Deviation; If the graphics generation process blocks the response of the user interface and affects the user experience, the asynchronous processing method can be used to allow the graphics generation to be performed in the background. For example, the asynchronous programming characteristics of Node.js or Python can be used to execute the graphics generation task in the background. The user interface can continue to respond to user operations without being stuck due to graphics generation; If the generated graphics contain too many dimensions, resulting in an imbalance in the ratio of the number of data points to the number of dimensions, making the graphics difficult to understand, the number of dimensions contained in the graphics can be adjusted to ensure that the ratio of the number of data points to the number of dimensions is appropriate. For example, the data analysis team optimizes the graphic report, removes some unimportant dimensions, and retains only key dimensions such as the update frequency of intelligent data graphics integration, so that the graphics are clearer and easier to understand, so as to improve the intelligent data graphics integration accuracy evaluation index.

[0118] like Figure 3As shown, it is a structural schematic diagram of an intelligent data storage integration system based on the Internet of Things provided in an embodiment of the present application. The intelligent data storage integration system based on the Internet of Things provided in an embodiment of the present application includes: an intelligent data storage integration data acquisition module, an intelligent data storage integration data analysis module, a comprehensive analysis module and an optimization and adjustment intelligent data storage integration graph generation reliability method module: an intelligent data storage integration data acquisition module: used to collect and process intelligent data storage integration data; an intelligent data storage integration data analysis module: used to analyze the intelligent data storage integration data to obtain an intelligent data storage integration data quality evaluation index and an intelligent data storage integration graph generation reliability evaluation index; a comprehensive analysis module: Used for comprehensive analysis to obtain the intelligent data graphics integration accuracy evaluation index; optimize and adjust the intelligent data storage integrated graphics generation reliability method module: used to compare and analyze the intelligent data storage integration data quality evaluation index with the first threshold of the intelligent data storage integration data quality evaluation index, and optimize the intelligent data storage integration data quality method; compare and analyze the intelligent data storage integrated graphics generation reliability evaluation index with the second threshold of the intelligent data storage integrated graphics generation reliability evaluation index, and optimize and adjust the intelligent data storage integrated graphics generation reliability method; compare and analyze the intelligent data graphics integration accuracy evaluation index with the comprehensive threshold of the intelligent data graphics integration accuracy evaluation index, and optimize and adjust the intelligent data graphics integration accuracy method.

[0119] Among them, the embodiment of the present application also provides a computer-readable storage medium for storing a program, which, when executed by a processor, implements an intelligent data storage and integration method based on the Internet of Things.

[0120] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0124] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0125] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent data storage and integration method based on the Internet of Things, characterized in that: The following steps are involved: Collect and process intelligent data, store and integrate data; Analyze the intelligent data storage integration data to obtain the intelligent data storage integration data quality evaluation index and the intelligent data storage integration graphic generation reliability evaluation index; Comprehensive analysis yields an intelligent data graphics integration accuracy assessment index; Compare and analyze the intelligent data storage integration data quality assessment index with the first threshold of the intelligent data storage integration data quality assessment index to optimize the intelligent data storage integration data quality method; Compare and analyze the reliability evaluation index of intelligent data storage integrated graphics generation with the second threshold of the reliability evaluation index of intelligent data storage integrated graphics generation, and optimize and adjust the reliability method of intelligent data storage integrated graphics generation; Compare and analyze the intelligent data graphics integration accuracy evaluation index with the comprehensive threshold of the intelligent data graphics integration accuracy evaluation index, and optimize and adjust the intelligent data graphics integration accuracy method.

2. The intelligent data storage integration method based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of collecting and processing intelligent data storage and integration data are as follows: Collect intelligent data through IoT devices to store and integrate raw data; The original data of intelligent data storage and integration is cleaned and denoised to obtain intelligent data storage and integration data, wherein the intelligent data storage and integration data includes intelligent data storage and integration data quality data and intelligent data storage and integration graph generation reliability data.

3. The intelligent data storage integration method based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of obtaining the intelligent data storage integration data quality assessment index are as follows: Get the preset intelligent data storage and integrate the intelligent data noise of the data quality time detection point through the data analysis server; Get the intelligent data density of preset intelligent data storage and integrated data quality time detection points through the data mining library; Get the preset intelligent data storage and integration data quality time detection point graphics generation speed through the APM program performance management tool; Get the intelligent data update frequency of preset intelligent data storage and integrated data quality time detection points through the data stream processing platform; Get the preset intelligent data storage and integrate the intelligent data delay of the data quality time detection point through the NTP server; The intelligent data storage integrated data quality data includes intelligent data noise, intelligent data density, graph generation speed, intelligent data update frequency and intelligent data delay; According to the intelligent data storage integration data quality data analysis, the intelligent data storage integration data quality evaluation index is obtained.

4. The intelligent data storage integration method based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of obtaining the intelligent data storage integrated graphics generation reliability evaluation index are: Get the preset intelligent data storage and integration graphics to generate intelligent data noise of reliability detection points through data analysis software; Get the intelligent data density of reliability test points generated by preset intelligent data storage integration graphics through data visualization tools; Get the graphics generation speed of the preset intelligent data storage integrated graphics generation reliability detection point through the performance monitoring tool; Obtain the number of graphic data points of the reliability detection segment of the preset intelligent data storage integrated graphic generation through the graphical user interface; The number of dimensions of the graphics including the preset intelligent data storage integration graphics generation reliability detection segment is obtained through data visualization tools; Get the intelligent data integration rate of the reliability inspection point generated by the preset intelligent data storage integration graph through the ETL tool; The intelligent data storage integration graph generation reliability data includes intelligent data noise, intelligent data density, graph generation speed, number of graph data points, number of graph dimensions and intelligent data integration rate; According to the reliability data analysis of intelligent data storage integrated graphics generation, the reliability evaluation index of intelligent data storage integrated graphics generation is obtained.

5. The intelligent data storage integration method based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of obtaining the intelligent data graphics integration accuracy evaluation index through comprehensive analysis are as follows: Obtain the graphic pixel density of the preset intelligent data graphic integration accuracy detection point through the image editing software; The intelligent data graphics integration accuracy evaluation index is obtained by comprehensive analysis of the intelligent data storage integration graphics generation reliability evaluation index, graphics pixel density and intelligent data storage integration data quality evaluation index.

6. The intelligent data storage integration method based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of the method for optimizing intelligent data storage and integrating data quality are as follows: If the intelligent data storage integration data quality assessment index is lower than or equal to the first threshold value of the intelligent data storage integration data quality assessment index, it is not necessary to optimize the intelligent data storage integration data quality; If the intelligent data storage integration data quality assessment index is greater than the first threshold of the intelligent data storage integration data quality assessment index, the corresponding adjustment plan in the intelligent data storage integration database is matched through the difference between the intelligent data storage integration data quality assessment index and the first threshold of the intelligent data storage integration data quality assessment index.

7. The intelligent data storage integration method based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of the method for optimizing and adjusting the reliability of intelligent data storage integrated graphics generation are as follows: If the reliability evaluation index of intelligent data storage integrated graphics generation is greater than or equal to the second threshold value of the reliability evaluation index of intelligent data storage integrated graphics generation, there is no need to optimize and adjust the reliability method of intelligent data storage integrated graphics generation; If the intelligent data storage integrated graphics generation reliability assessment index is lower than the second threshold value of the intelligent data storage integrated graphics generation reliability assessment index, the corresponding adjustment plan in the intelligent data storage integration database is matched through the difference between the intelligent data storage integrated graphics generation reliability assessment index and the second threshold value of the intelligent data storage integrated graphics generation reliability assessment index.

8. The intelligent data storage integration method based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of the method for optimizing and adjusting the accuracy of intelligent data graphics integration are as follows: Extracting the comprehensive threshold of the intelligent data graphics integration accuracy evaluation index in the intelligent data storage integration database, and comparing the intelligent data graphics integration accuracy evaluation index with the comprehensive threshold of the intelligent data graphics integration accuracy evaluation index; If the intelligent data graphics integration accuracy evaluation index is greater than or equal to the intelligent data graphics integration accuracy evaluation index comprehensive threshold, there is no need to optimize and adjust the intelligent data graphics integration accuracy; If the intelligent data graphics integration accuracy assessment index is lower than the comprehensive threshold of the intelligent data graphics integration accuracy assessment index, the corresponding adjustment plan in the intelligent data storage integration database is matched through the difference between the intelligent data graphics integration accuracy assessment index and the comprehensive threshold of the intelligent data graphics integration accuracy assessment index.

9. The intelligent data storage integration system based on the Internet of Things is characterized by: It includes intelligent data storage integrated data acquisition module, intelligent data storage integrated data analysis module, comprehensive analysis module and optimization and adjustment intelligent data storage integrated graphics generation reliability method module: Intelligent data storage and integration data acquisition module: used to collect and process intelligent data storage and integration data; Intelligent data storage integration data analysis module: used to analyze the intelligent data storage integration data to obtain the intelligent data storage integration data quality evaluation index and the intelligent data storage integration graph generation reliability evaluation index; Comprehensive analysis module: used for comprehensive analysis to obtain the intelligent data graphics integration accuracy evaluation index; Optimizing and adjusting the intelligent data storage integration graph generation reliability method module: used to compare and analyze the intelligent data storage integration data quality assessment index with the first threshold of the intelligent data storage integration data quality assessment index, and optimize the intelligent data storage integration data quality method; Compare and analyze the reliability evaluation index of intelligent data storage integrated graphics generation with the second threshold of the reliability evaluation index of intelligent data storage integrated graphics generation, and optimize and adjust the reliability method of intelligent data storage integrated graphics generation; Compare and analyze the intelligent data graphics integration accuracy evaluation index with the comprehensive threshold of the intelligent data graphics integration accuracy evaluation index, and optimize and adjust the intelligent data graphics integration accuracy method.

10. A computer-readable storage medium for storing a program, wherein when the program is executed by a processor, the intelligent data storage and integration method based on the Internet of Things as described in any one of claims 1 to 8 is implemented.

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