Data conversion method and system based on big data and electronic equipment

Abstract

The invention relates to the technical field of internet data conversion security services, and particularly discloses a data conversion method and system based on big data and electronic equipment. Therefore, the optimal data conversion process of the to-be-converted data is matched, the characteristics of different data can be better adapted, the conversion accuracy and efficiency are improved, the data conversion stability of the to-be-converted data is obtained by evaluating the conversion process parameters of the to-be-converted data, potential problems in the data conversion process can be found in time, the conversion process is optimized, and the conversion efficiency is improved. According to the method, the reliability and consistency of data conversion are ensured, and the conversion accuracy of the to-be-converted data is more comprehensively judged based on the conversion result of the to-be-converted data and the data conversion stability, so that data conversion is fed back, the judgment accuracy and credibility are improved, and powerful support is provided for quality control of data conversion.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet data conversion security service, and in particular to a method, system and electronic equipment based on big data data conversion. Background Art

[0002] With the rapid development of big data technology, data conversion, as a key link in the data processing process, has become increasingly important. Many existing data conversion methods are too focused on the analysis of the conversion process, but ignore the quality of the data conversion results, thereby introducing abnormal data conversion results and making the data conversion results unable to effectively serve the system's business decision-making needs.

[0003] For example, the invention patent with announcement number CN107491515B announces an intelligent power distribution and utilization data conversion method based on a big data platform, including: integrating the original data of various power distribution and utilization business systems through a data interface, and storing them in a data cache area; migrating the original data of each business system from the data cache area to the original library to provide a data basis for subsequent data cleaning and analysis; processing the original data with the help of the big data platform, and storing the processed data in an intermediate library according to a new table structure as the original data for subsequent data analysis; migrating the analyzed data from the intermediate library to the result library to create a global index.

[0004] For example, the invention patent with announcement number CN111061788B announces a multi-source heterogeneous data conversion and integration system based on cloud architecture and its implementation method. The system includes a development module, a management and monitoring module, a scheduling service module, a resource database, a file transfer component, a message transfer component and a cross-domain routing service module; based on the SG-ETL data conversion and integration tool, it supports various data sources, including various relational databases, data files, big data platform components, etc., realizes the development of data conversion and integration functions from the data source to the target end, completes the ETL process configuration development work in a graphical way, and realizes the scheduling and monitoring of massive data conversion jobs.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technologies have at least the following technical problems: the above-mentioned applications all focus one-sidedly on data conversion operations. If only the data conversion process is analyzed, errors and inconsistencies in the data conversion results may be introduced, thereby affecting subsequent data analysis and causing misleading conclusions in the conversion results of the system data. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method, system and electronic device based on big data data conversion, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a data conversion method based on big data, including: S1. Based on the conversion preparation of the data to be converted, the environmental parameters of the data to be converted are obtained, the environmental stability coefficient of the data to be converted is analyzed, and the data conversion process of the data to be converted is matched; S2. Based on the conversion process of the data to be converted, the conversion process parameters of the data to be converted are obtained, thereby evaluating the data conversion stability of the data to be converted; S3. Based on the conversion result of the data to be converted and the data conversion stability, the conversion accuracy of the data to be converted is determined, thereby providing feedback on the data conversion.

[0008] As a further method, the data conversion process of matching the data to be converted is specifically a data conversion process corresponding to the environmental stability coefficient of the data to be converted and each environmental stability coefficient interval stored in the data integration library, thereby matching the data conversion process of the data to be converted.

[0009] As a further method, the data conversion is fed back, and the specific feedback process is: the conversion accuracy of the data to be converted is compared with the conversion accuracy threshold. If the conversion accuracy of the data to be converted is less than the conversion accuracy threshold, feedback is given to the data conversion.

[0010] The second aspect of the present invention provides a data conversion system based on big data, including: a data conversion preparation analysis module, which is used to obtain environmental parameters of the data to be converted based on the conversion preparation of the data to be converted, analyze the environmental stability coefficient of the data to be converted, and match the data conversion process of the data to be converted; a data conversion process evaluation module, which is used to obtain conversion process parameters of the data to be converted based on the conversion process of the data to be converted, thereby evaluating the data conversion stability of the data to be converted; a data conversion result feedback module, which is used to determine the conversion accuracy of the data to be converted based on the conversion result of the data to be converted and the data conversion stability, thereby providing feedback on the data conversion.

[0011] A third aspect of the present invention provides an electronic device that applies a method for data conversion based on big data, wherein the electronic device includes a memory for storing computer program instructions and a processor for executing program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute the method for data conversion based on big data.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0013] (1) The present invention provides a data conversion method, system and electronic device based on big data. By analyzing the environmental parameters of the data to be converted, the different environmental parameters may affect the characteristics and conversion effect of the data, thereby matching the best data conversion process for the data to be converted, which can better adapt to the characteristics of different data and improve the accuracy and efficiency of the conversion. By evaluating the conversion process parameters of the data to be converted, the data conversion stability of the data to be converted is obtained, which helps to timely discover potential problems in the data conversion process, optimize the conversion process, ensure the reliability and consistency of data conversion, and more comprehensively determine the conversion accuracy of the data to be converted based on the conversion results of the data to be converted and the data conversion stability, thereby providing feedback on the data conversion, improving the accuracy and credibility of the judgment, and providing quality control for data conversion. It provides strong support; specifically, the environmental stability coefficient of the fuel combustion process data can be obtained first, thereby matching the best data conversion process of the fuel combustion process data, and obtaining the data conversion stability of the fuel combustion process data by evaluating the conversion process parameters of the fuel combustion process data, optimizing the data conversion process, and finally based on the conversion result of the fuel combustion process data, that is, the fuel combustion process data visualization chart, and the data conversion stability, the conversion accuracy of the fuel combustion process data can be more comprehensively determined, and by minimizing the complexity of data analysis and the amount of data required, it can quickly respond to evaluate the accuracy of the data conversion process, reduce the amount of data transmission and optimize the data analysis process, while ensuring the accuracy of the fuel combustion process data visualization chart, providing precise data support for parameter adjustment of the fuel combustion process.

[0014] (2) The present invention obtains the data conversion stability of the data to be converted by comprehensively analyzing the processing rate of the driving device to which the data to be converted belongs, the discrete value of power fluctuation, and the discrete value of the captured data packet load. By analyzing the hardware performance parameters to guide the data conversion process, seamless connection between software and hardware can be achieved, which helps to give full play to the performance advantages of the hardware, improve the processing rate of the hardware, and thus improve the efficiency and stability of data conversion.

[0015] (3) The present invention obtains the stability coefficient of the environment to which the data to be converted belongs by comprehensively analyzing the average network bandwidth of the network environment to which the data to be converted belongs, the driving load factor of the driving device environment to which the data to be converted belongs, and the total number of data attribute types of the data environment to which the data to be converted belongs. Comprehensive analysis from multiple dimensions can optimize resource allocation and improve data conversion adaptability. Comprehensive evaluation helps to reduce the one-sided analysis caused by analyzing the fluctuations of a single factor, thereby improving the overall stability and success rate of data conversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0017] Figure 1 It is a schematic diagram of the method steps of the present invention;

[0018] Figure 2 It is a schematic diagram of system module connection of the present invention;

[0019] Figure 3 It is a schematic diagram of the pressure curve of the internal combustion engine involved in the present invention.

[0020] Figure markings: 1. Reference length of the horizontal axis segment; 2. Length of the horizontal axis segment of missing data; 3. Data detection point; 4. The horizontal coordinate position point of each data point. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 As shown, the first aspect of the present invention provides a data conversion method based on big data, including: S1. Based on the conversion preparation of the data to be converted, obtain the environmental parameters of the data to be converted, analyze the environmental stability coefficient of the data to be converted, and match the data conversion process of the data to be converted.

[0023] In an example embodiment, the application field of the conversion preparation of the above-mentioned data to be converted is the conversion preparation of pressure data of a fuel combustion process based on the field of industrial manufacturing technology. The average network bandwidth of the network environment to which the pressure data belongs, the driving load factor of the driving device environment to which the pressure data belongs, and the total number of data attribute types of the pressure data can be obtained, the stability coefficient of the environment to which the pressure data belongs can be analyzed, and the data conversion process of the pressure data can be matched, thereby converting the pressure data into a pressure data report. The data conversion process supports the collection and conversion of high-precision pressure data, thereby promoting the unified management and application of pressure data and promoting the informatization development in the field of industrial manufacturing technology.

[0024] In a specific embodiment, the present invention obtains the stability coefficient of the environment to which the data to be converted belongs by comprehensively analyzing the average network bandwidth of the network environment to which the data to be converted belongs, the drive load factor of the drive device environment to which the data to be converted belongs, and the total number of data attribute types of the data environment to which the data to be converted belongs. Comprehensive analysis from multiple dimensions can optimize resource allocation and improve data conversion adaptability. Comprehensive evaluation helps to reduce the one-sided analysis caused by analyzing the fluctuations of a single factor, thereby improving the overall stability and success rate of data conversion.

[0025] Specifically, the data conversion process of the data to be converted is matched as follows: the data conversion process corresponding to the environmental stability coefficient of the data to be converted and the environmental stability coefficient intervals stored in the data integration library is matched, thereby matching the data conversion process of the data to be converted; specifically, each environmental stability coefficient interval corresponds to the data conversion process, and the environmental stability coefficient interval to which the environmental stability coefficient of the data to be converted belongs is queried, then the data conversion process corresponding to the environmental stability coefficient interval is the data conversion process of the data to be converted; the data conversion process corresponding to the above-mentioned environmental stability coefficient intervals is formulated by a data system operation and maintenance specialist according to a design parameter based on a big data data conversion system. In an example embodiment, the data conversion process of the data to be converted is specifically as follows: the data to be converted in a relational database (such as an Oracle database) is migrated to a distributed system platform for data analysis, and a visualization curve is generated through a matrix laboratory. The process supports the loading of various types of structured data, supports flexible and general data format descriptions, including fields contained in the data to be converted, delimiters of each field, field types, etc., and supports the transmission of tuple sequences with delimiters.

[0026] The above data conversion process can be specifically applied to but not limited to the field of industrial manufacturing technology. Through this data conversion process, equipment operation data, such as operating efficiency, load conditions, etc., are converted into real-time status monitoring visualization indicators to monitor equipment status in real time, predict equipment failures, and achieve preventive maintenance.

[0027] Furthermore, the analysis of the stability coefficient of the environment to which the data to be converted belongs is specifically carried out as follows: extracting the real-time network bandwidth of the network environment to which the data to be converted belongs within the conversion environment analysis period from the environmental parameters to which the data to be converted belongs, and performing mean processing to obtain the average network bandwidth of the network environment to which the data to be converted belongs; the above-mentioned environmental parameters to which the data to be converted belongs, in a specific embodiment, may represent the environmental parameters of the fuel combustion process data in the fuel full process system, including but not limited to the real-time network bandwidth of the network environment to which the fuel combustion process data belongs within the conversion environment analysis period; the above-mentioned conversion environment analysis period represents the time period for analyzing the conversion environment of the data to be converted, and its specific duration is determined by the data system operation and maintenance specialist according to actual conditions; the real-time network bandwidth of the network environment to which the above-mentioned conversion data belongs within the conversion environment analysis period may be measured by a network testing tool (such as a network speed tester). In an example embodiment, if the network environment to which the fuel combustion process data in the field of industrial manufacturing technology belongs is a Wi-Fi network, the real-time network bandwidth of the Wi-Fi network within the conversion environment analysis period may be measured by a network speed tester, and the average network bandwidth may be obtained by performing mean processing.

[0028] The average CPU usage rate and average memory occupancy rate of the driving device environment to which the data to be converted belongs during the conversion environment analysis period are extracted from the environment parameters to which the data to be converted belongs, and the load factor corresponding to the average CPU usage rate and the load factor corresponding to the average memory occupancy rate are extracted from the data integration library, and the product result of the average CPU usage rate and the load factor corresponding to the average CPU usage rate, and the product result of the average memory occupancy rate and the load factor corresponding to the average memory occupancy rate are accumulated to obtain the driving load factor of the driving device environment to which the data to be converted belongs; the average CPU usage rate and the average memory occupancy rate of the driving device environment to which the data to be converted belongs during the conversion environment analysis period refer to reading the real-time CPU usage rate and the real-time memory occupancy rate of the driving device environment to which the data to be converted belongs from the task manager of the driving device to which the data to be converted belongs during the conversion environment analysis period, and averaging them to obtain the CPU usage rate and the real-time memory occupancy rate of the driving device environment to which the data to be converted belongs during the conversion environment analysis period. PU average usage rate and average memory occupancy rate. In an example embodiment, the real-time CPU usage rate and real-time memory occupancy rate of the computer device during the conversion environment analysis cycle can be read from the task manager of the computer device to which the fuel combustion process data in the field of industrial manufacturing technology belongs; the load factor corresponding to the above-mentioned average CPU usage rate and the load factor corresponding to the average memory occupancy rate are weighted coefficients, which are formulated by the data system operation and maintenance specialist according to the performance of the drive device and the usage of the drive device, and are respectively used to indicate the contribution of the average CPU usage rate to the overall load of the drive device and the contribution of the average memory occupancy rate to the overall load of the drive device. The load factor corresponding to the average CPU usage rate and the load factor corresponding to the average memory occupancy rate are stored in the data integration library; it should be explained that the drive device includes but is not limited to a computer, and any electronic device that can run a method based on big data data conversion can be marked as a drive device to which the data to be converted belongs.

[0029] In an example embodiment, the average CPU usage and average memory occupancy of fuel combustion process data in the field of industrial manufacturing technology during the conversion environment analysis cycle are 70% and 80% respectively, and the load factors corresponding to the average CPU usage and the load factors corresponding to the average memory occupancy are 0.8 and 0.7 respectively. Then the driving load factor = 70%*0.8+80%*0.7=112%.

[0030] Obtain and count the total number of data attribute types of the data environment to which the data to be converted belongs. The specific acquisition process is: locate the relational database to which the data to be converted belongs, obtain the data attribute types of the data environment to which the data to be converted belongs by querying the metadata API of the relational database, and count the total number of data attribute types. For example, in the fuel full process system, the fuel combustion process data is August 7, 2023, cylinder, and 1.5 tons, which are respectively derived from the time attribute, location attribute, and total fuel amount attribute, and the total number of data attribute types is comprehensively expressed as 3.

[0031] The stability coefficient of the environment to which the data to be converted belongs is obtained by comprehensive analysis. In this exemplary embodiment, the stability coefficient of the environment to which the data to be converted belongs is obtained by comprehensive analysis of the average network bandwidth of the network environment to which the data to be converted belongs, the drive load factor of the drive device environment to which the data to be converted belongs, and the total number of data attribute types of the data environment to which the data to be converted belongs. It is used to analyze the value of the stability of the environment to which the data to be converted belongs. The specific expression is:

[0032]

[0033] Among them, ENI is the environmental stability coefficient of the data to be converted. When the attribute types of the data to be converted are complex and diverse, the network bandwidth demand increases accordingly. If the network bandwidth is insufficient, the data transmission speed will slow down, resulting in the time for the driver device to process data being extended, which directly leads to an increase in the average CPU usage rate and the average memory occupancy rate of the driver device. Because the driver device needs more time to process the accumulated data to be converted, this will further increase the load of the driver device, which may have an adverse effect on the stability and overall performance of the driver device. Therefore, a higher network bandwidth indicates that the data to be converted can be transmitted and converted faster, reducing the network waiting time and potential delays. A lower driver load factor indicates that the system resources of the driver device (such as CPU and memory) are used more easily, which is conducive to its stable data processing. The diversity of data attribute types may increase the complexity of data processing and the potential risk of errors. Therefore, the three parameters are comprehensively analyzed into an environmental stability coefficient, which can quantitatively evaluate the stability state of the environment in which the current data to be converted is located, so that when selecting a data conversion process and adjusting a resource allocation based on a large data conversion system, it can be based on more scientific and accurate information to improve the stability and efficiency of data conversion.

[0034] MP is the average network bandwidth of the network environment to which the data to be converted belongs. It refers to the average amount of data that can be transmitted per unit time in the network environment to which the data to be converted belongs. It reflects the overall level of transmission capacity of the network environment to which the data to be converted belongs and is one of the important indicators for measuring network performance.

[0035] SLF is the driver load factor of the driver device environment to which the data to be converted belongs. It reflects the usage of key resources such as the CPU and memory of the driver device and is an important indicator for measuring the operating performance of the driver device. If the driver load factor is large, the operating pressure of the driver device is relatively large.

[0036] DLY is the total number of data attribute types of the data environment to which the data to be converted belongs, reflecting the complexity and diversity of the data environment. For the data to be converted, the diversity of data attribute types may mean that more data types and conversion logic need to be processed during the data conversion process, increasing the complexity and difficulty of data conversion.

[0037] e is a natural constant, ls1 is the impact factor corresponding to the average network bandwidth unit value preset in the data integration library, which represents the degree of influence of the average network bandwidth unit value on the environmental stability coefficient of the data to be converted. The fitting curve corresponding to the environmental stability coefficient is obtained by fitting the relationship between the total number of historical concurrent activities, the average strength of historical network signals and the environmental stability coefficient, and the fitting curve corresponding to the environmental stability coefficient is brought into the fitting curve according to the real-time total number of concurrent activities and the average strength of network signals, thereby obtaining the impact factor corresponding to the average network bandwidth unit value in this example. The value range of this value in this example is 0 to 1.

[0038] ls2 is the impact factor corresponding to the unit value of the driving load factor preset in the data integration library, which represents the degree of influence of the unit value of the driving load factor on the environmental stability coefficient of the data to be converted. The fitting curve corresponding to the environmental stability coefficient is obtained by fitting the relationship between the historical average CPU usage rate, the historical average memory occupancy rate and the environmental stability coefficient, and the fitting curve corresponding to the environmental stability coefficient is brought into the fitting curve according to the real-time average CPU usage rate and the average memory occupancy rate, thereby obtaining the impact factor corresponding to the unit value of the driving load factor in this example. The value range of this value in this example is 0 to 1.

[0039] ls3 is the impact factor corresponding to the total number of data attribute type unit values ​​preset in the data integration library, which represents the degree of influence of the total number of data attribute type unit values ​​on the environmental stability coefficient of the data to be converted. The fitting curve corresponding to the environmental stability coefficient is obtained by fitting the relationship between the historical data demand, the historical data storage capacity and the environmental stability coefficient, and the fitting curve corresponding to the environmental stability coefficient is brought into the fitting curve according to the real-time data demand and the data storage capacity, thereby obtaining the impact factor corresponding to the total number of data attribute type unit values ​​in this example. The value range of this value in this example is 0 to 1.

[0040] In this exemplary embodiment, the change table of the environmental stability coefficient of the above-mentioned data to be converted and its corresponding parameters is shown in Table 1:

[0041] Table 1 Changes in the environmental stability coefficient of the data to be converted and its corresponding parameters

[0042]

[0043]

[0044] In this example embodiment, the value of the impact factor corresponding to the unit value of the average network bandwidth is set to 0.15, the value of the impact factor corresponding to the unit value of the drive load factor is set to 0.35, and the value of the impact factor corresponding to the unit value of the total number of data attribute types is set to 0.2. It can be seen from Table 1 that there are only slight differences in the total number of data attribute types. When the average network bandwidth is larger and the drive load factor is smaller, the environmental stability coefficient shows a larger trend. With higher transmission capacity and lower system burden, the data environment is more stable and reliable, which is conducive to improving the efficiency of data transmission.

[0045] S2. Based on the conversion process of the data to be converted, obtain the conversion process parameters of the data to be converted, thereby evaluating the data conversion stability of the data to be converted.

[0046] In an example embodiment, the conversion process of the above-mentioned data to be converted is based on the conversion process of the fuel combustion process data, and the processing rate, power fluctuation discrete value and captured data packet load discrete value of the driving device to which the fuel combustion process data belongs are obtained, thereby evaluating the data conversion stability of the fuel combustion process data, and accurately analyzing the data conversion process of the fuel combustion process data, which can ensure that a visualized simulation image of the fuel combustion process data is obtained. The technician sets parameters of the fuel combustion process based on the visualized simulation image of the fuel combustion process data, such as combustion temperature, to adapt to the combustion requirements of different materials and realize the automation and intelligence of the fuel combustion process.

[0047] In a specific embodiment, the present invention obtains the data conversion stability of the data to be converted by comprehensively analyzing the processing rate of the driving device to which the data to be converted belongs, the discrete value of power fluctuations, and the discrete value of the captured data packet load, and guides the data conversion process by analyzing hardware performance parameters. This can achieve seamless docking between software and hardware, help to give full play to the performance advantages of the hardware, improve the processing rate of the hardware, and thus improve the efficiency and stability of data conversion.

[0048] Specifically, the conversion process parameters of the data to be converted are obtained, specifically referring to the conversion process parameters of the driving device to which the data to be converted belongs, and are intended to analyze the multi-dimensional factors in the data conversion process. If the data conversion process does not take into account the specific parameters and limitations of the driving device, the data to be converted may be lost, damaged or erroneous during the conversion process, which may affect subsequent data analysis, processing or decision-making processes.

[0049] Furthermore, the data conversion stability of the data to be converted is evaluated, and the specific evaluation process is: extracting the number of bytes processed by the driving device to which the data to be converted belongs within the data conversion cycle from the conversion process parameters of the driving device to which the data to be converted belongs, and performing ratio processing with the duration corresponding to the data conversion cycle to obtain the processing rate of the driving device to which the data to be converted belongs; the above-mentioned data conversion cycle refers to the interval between the start time point of the data conversion process and the end time point of the data conversion process, and its specific duration is extracted from an operation log generated based on a big data data conversion system; the number of bytes processed by the driving device to which the data to be converted belongs within the data conversion cycle can be extracted from the log data recorded by a third-party performance monitoring tool (such as an operation and maintenance monitor, which is used to monitor various network parameters, server health indicators and the integrity of the application, and it provides powerful data collection, reporting and user interface to help users understand the operating status of the device system in real time), such as extracting the number of bytes processed by computer equipment to which fuel combustion process data in the field of industrial manufacturing technology belongs within the data conversion cycle.

[0050] The power of each detection sample of the central processor of the drive device to which the data to be converted belongs within the data conversion cycle is extracted from the conversion process parameters of the drive device to which the data to be converted belongs, and the standard deviation processing is performed, and the obtained result is marked as the power fluctuation discrete value of the drive device to which the data to be converted belongs; the power of each detection sample of the central processor of the drive device to which the data to be converted belongs within the data conversion cycle refers to the data system operation and maintenance specialist randomly arranging several time points as each detection sample time point within the data conversion cycle, and reading the power of the central processor at each detection sample time point from the performance monitoring tool provided by the drive device operating system, that is, the power of each detection sample of the central processor of the drive device to which the data to be converted belongs within the data conversion cycle, such as the power of each detection sample of the central processor of the computer device to which the fuel combustion process data belongs in the field of industrial manufacturing technology within the data conversion cycle. Assuming that AGP is the result of the mean value processing of each detection sample power, the specific standard deviation processing process of the power fluctuation discrete value of the computer device to which the fuel combustion process data belongs is: GP a is the ath detection sample power, a is the number of each detection sample power, a={1,2,3,...,s}, and s is the total number of detection sample powers.

[0051] The load of each captured data packet of the driving device to which the data to be converted belongs within the data conversion cycle is extracted from the conversion process parameters of the driving device to which the data to be converted belongs, and a standard deviation analysis is performed to obtain a discrete value of the load of the captured data packet of the driving device to which the data to be converted belongs. The load of each captured data packet of the driving device to which the data to be converted belongs within the data conversion cycle can be quickly located from the data packet capture file recorded by the network packet analysis software by using the filter function, thereby reading the load of each captured data packet from the data packet capture file. The specific standard deviation processing process of the discrete value of the load of the captured data packet of the driving device to which the data to be converted belongs is the same as the specific standard deviation processing process of the discrete value of the power fluctuation of the driving device to which the data to be converted belongs, such as extracting the load of each captured data packet of the computer device to which the fuel combustion process data belongs within the data conversion cycle, and performing a standard deviation analysis to obtain the discrete value of the load of the captured data packet of the computer device to which the fuel combustion process data belongs.

[0052] Thus, the data conversion stability of the data to be converted is comprehensively evaluated. In this embodiment, the data conversion stability of the data to be converted is obtained by comprehensively analyzing the processing rate of the driving device to which the data to be converted belongs, the power fluctuation discrete value, and the captured data packet load discrete value. It is a numerical value used to evaluate the stability of the data conversion process of the data to be converted. The specific expression is:

[0053]

[0054] Among them, FRC is the data conversion stability of the data to be converted. A large discrete value of the captured data packet load directly indicates that a data conversion system based on big data is in an unstable state. This instability causes the power fluctuation of the driving device to intensify when processing data conversion, making it difficult to maintain a stable and efficient processing rate. At the same time, it makes it difficult for the driving device to predict and adapt to load changes, thereby affecting the overall stability and efficiency of data processing. Therefore, a higher processing rate can process the data to be converted faster and more stably. Smaller power fluctuations may reduce the errors that occur when the driving device processes the data to be converted. A small discrete value of the captured data packet load indicates that a data conversion system based on big data is in a stable and controllable operating state, ensuring the continuity and consistency of the processing of the data to be converted. A comprehensive analysis of the above three parameters can more comprehensively reflect the actual operating state of the data to be converted during the data conversion process, help identify potential unstable factors and deal with unstable situations during the data conversion process, thereby ensuring the continuity and accuracy of the data conversion process.

[0055] CV is the processing rate of the drive device to which the data to be converted belongs. A higher processing rate indicates that the drive device can complete the data conversion task more quickly, reduce waiting delays and improve the efficiency of data conversion.

[0056] PV is the power fluctuation discrete value of the drive device to which the data to be converted belongs. It is a parameter that reflects the power stability of the drive device during operation. The smaller the power fluctuation discrete value, the smaller the power fluctuation and the more stable the operation of the drive device, which helps to reduce data processing errors and risks caused by unstable power.

[0057] HV is the discrete value of the captured data packet load of the driving device to which the data to be converted belongs. It is used to describe the fluctuation of the data packet load captured by the driving device from a big data data conversion system, revealing the inherent stability level of a big data data conversion system. The smaller the discrete value of the captured data packet load, the more stable the system can maintain a stable processing state, ensure the continuity of the data to be converted, and reduce the possibility of missing data to be converted, thereby ensuring the accuracy and reliability of the data conversion process.

[0058] e is a natural constant, bo1 is an influence factor corresponding to the processing rate unit value preset in the data integration library, which represents the influence degree of the processing rate unit value on the data conversion stability of the data to be converted. The fitting curve corresponding to the data conversion stability is obtained by fitting the relationship between the historical drive device read and write speed, the historical processing data volume and the data conversion stability, and the fitting curve corresponding to the data conversion stability is brought into the fitting curve according to the real-time drive device read and write speed and the processing data volume, thereby obtaining the influence factor corresponding to the processing rate unit value in this example. The value range of this value in this example is 0 to 1.

[0059] The influence factor corresponding to the power fluctuation discrete value unit value preset in the bo2 data integration library represents the influence degree of the power fluctuation discrete value unit value on the data conversion stability of the data to be converted. The fitting curve corresponding to the data conversion stability is obtained by fitting the relationship between the historical average input voltage, the historical average input current and the data conversion stability, and the fitting curve corresponding to the data conversion stability is introduced according to the real-time average input voltage and the average input current, thereby obtaining the influence factor corresponding to the power fluctuation discrete value unit value in this example. The value range of this value in this example is 0 to 1.

[0060] bo3 is the impact factor corresponding to the discrete value unit value of the captured data packet payload preset in the data integration library, which represents the influence degree of the discrete value unit value of the captured data packet payload on the data conversion stability of the data to be converted. The fitting curve corresponding to the data conversion stability is obtained by fitting the relationship between the historical network bandwidth, the historical network congestion and the data conversion stability, and the fitting curve corresponding to the data conversion stability is introduced according to the real-time network bandwidth and network congestion, thereby obtaining the impact factor corresponding to the discrete value unit value of the captured data packet payload in this example. The value range of this value in this example is 0 to 1.

[0061] S3. Based on the conversion result of the data to be converted and the data conversion stability, the conversion accuracy of the data to be converted is determined, thereby providing feedback on the data conversion.

[0062] Specifically, the feedback on the conversion of the data is as follows: the conversion accuracy of the data to be converted is compared with the conversion accuracy threshold. If the conversion accuracy of the data to be converted is less than the conversion accuracy threshold, feedback is given on the conversion of the data; the above conversion accuracy threshold represents the minimum value of the reasonable range of conversion accuracy of the data to be converted, which is formulated by the data system operation and maintenance specialist based on historical experience; if the conversion accuracy of the data to be converted is less than the conversion accuracy threshold, it indicates that under the current conversion conditions, the data conversion process adopted fails to effectively meet the accuracy requirements of data processing, thereby revealing that the conversion process is insufficient for the data to be converted, and therefore feedback is required. The specific feedback The feedback process is as follows: a feedback report is generated based on a big data data conversion system, which includes parameters involved in the data conversion process based on a big data data conversion method. The data system operation and maintenance specialist can optimize the system or the environment to which the system belongs based on the feedback report, thereby ensuring the improvement of data conversion quality to meet increasingly complex and changeable business needs; if the conversion accuracy of the data to be converted is greater than or equal to the conversion accuracy threshold, there is no need to provide feedback, and the result of the data conversion can be directly used for business decision-making; if the conversion accuracy of the fuel combustion process data is less than the conversion accuracy threshold, it indicates that the data conversion process of the fuel combustion process data fails to effectively meet the accuracy requirements of data processing.

[0063] Specifically, the conversion accuracy of the data to be converted, in this embodiment, is obtained by comprehensive analysis of the data conversion stability of the data to be converted, the abnormality rate and the missing rate of the sample curve to which the data to be converted belongs, and is used to determine the conversion accuracy of the data to be converted. The specific expression is:

[0064]

[0065] Among them, FI is the conversion accuracy of the data to be converted, and the stability in the data conversion process is the guarantee of data conversion accuracy. If the data conversion stability is low, that is, the data conversion process shows low robustness, it will inevitably cause significant missing phenomena in the data curve after data conversion, which is reflected in a higher missing rate. The aggravation of the missing phenomenon will cause the generated curve trajectory to deviate significantly from the expected reference curve contour, and finally show a trend of rising abnormality rate. Therefore, ensuring that the data conversion stability is at a high level can ensure that the conversion result can keep the error small. The small abnormality rate and missing rate can directly indicate that the data conversion accuracy is high. By comprehensively analyzing these three indicators, we can have a more comprehensive and in-depth understanding of the quality and reliability of data conversion, and thus provide more accurate data support and guarantee for the decision-making process of data conversion.

[0066] FRC is the data conversion stability of the data to be converted. It is obtained by comprehensive analysis of the processing rate of the drive device to which the data to be converted belongs, the discrete value of power fluctuation and the discrete value of the captured data packet load. It is a numerical value used to evaluate the stability of the data conversion process of the data to be converted.

[0067] OD is the abnormal rate of the sample curve to which the data to be converted belongs, which refers to the ratio of the total number of abnormal data detection points in the sample curve to which the data to be converted belongs to the total number of data detection points. The deviation value of the abnormal data detection point may be caused by distortion in the data conversion process.

[0068] DLR is the missing rate of the sample curve to which the data to be converted belongs, which refers to the proportion of missing data points in the sample curve to which the data to be converted belongs. The missing data points may be caused by technical limitations or error processing in the conversion process.

[0069] TM is the weight factor corresponding to the conversion stability preset in the data integration library, which represents the numerical value of the proportion of conversion stability to the conversion accuracy of the data to be converted. The fitting curve corresponding to the conversion accuracy is obtained by fitting the relationship between the historical conversion error rate, the historical conversion requirement accuracy and the conversion accuracy, and the fitting curve corresponding to the conversion accuracy is brought into the fitting curve according to the real-time conversion error rate and the conversion requirement accuracy, thereby obtaining the weight factor corresponding to the conversion stability in this example. The value range of this value in this example is 0 to 1.

[0070] ra1 is the impact factor corresponding to the abnormality rate unit value preset in the data integration library, which represents the degree of influence of the abnormality rate unit value on the conversion accuracy of the data to be converted. The fitting curve corresponding to the conversion accuracy is obtained by fitting the relationship between the historical abnormality frequency, the historical abnormality degree and the conversion accuracy, and the fitting curve corresponding to the conversion accuracy is brought into the fitting curve according to the real-time abnormality frequency and the abnormality degree, thereby obtaining the impact factor corresponding to the abnormality rate unit value in this example. The value range of this value in this example is 0 to 1.

[0071] ra2 is the impact factor corresponding to the missing rate unit value preset in the data integration library, which represents the degree of influence of the missing rate unit value on the conversion accuracy of the data to be converted. The fitting curve corresponding to the conversion accuracy is obtained by fitting the relationship between the total number of historical missing values, historical business sensitivity and conversion accuracy, and the fitting curve corresponding to the conversion accuracy is brought into the fitting curve according to the real-time total number of missing values ​​and business sensitivity, thereby obtaining the impact factor corresponding to the missing rate unit value in this example. The value range of this value in this example is 0 to 1.

[0072] Furthermore, the conversion accuracy of the data to be converted is determined by the following specific determination process: extracting the sample curve to which the data to be converted belongs from the conversion result of the data to be converted, marking each data detection point on the sample curve to which the data to be converted belongs, and extracting the reference curve to which the data to be converted belongs from the data integration library, performing difference processing on the ordinate corresponding to each data detection point on the sample curve to which the data to be converted belongs and the ordinate corresponding to the data detection point on the reference curve to which the data to be converted belongs, obtaining each deviation value of the sample curve to which the data to be converted belongs, comparing the difference value with the deviation threshold, marking the data detection point corresponding to each deviation value greater than the deviation threshold as an abnormal data detection point, counting the total number of abnormal data detection points, performing ratio processing with the total number of data detection points, and obtaining the abnormal rate of the sample curve to which the data to be converted belongs; the above-mentioned sample curve to which the data to be converted belongs refers to a conversion result generated in the data conversion process of the system mentioned in the present invention, which is drawn by the matrix laboratory according to the data points received in the conversion process, such as Figure 3 As shown, it is specifically an internal combustion engine pressure curve, which represents the changing relationship between the internal combustion engine cylinder pressure and the crankshaft angle. Its horizontal axis is the crankshaft angle in degrees, and the vertical axis is the cylinder pressure in kilopascals. In the intake stroke, as the piston moves downward, the volume in the cylinder increases, and the pressure usually decreases. However, due to the opening of the intake valve and the action of the intake system, the pressure in the cylinder will gradually rise until the end of the intake stroke; the reference curve to which the above-mentioned data to be converted belongs refers to the curve drawn by the data system operation and maintenance specialist based on the original data of the data to be converted, such as the internal combustion engine pressure reference curve drawn using the original data of the internal combustion engine pressure and the crankshaft angle; the above-mentioned data detection points are randomly arranged on the sample curve by the data system operation and maintenance specialist, for example Figure 3 The data detection point 3 in the figure is a point randomly arranged on the internal combustion engine pressure curve by the data system operation and maintenance specialist; the above-mentioned deviation threshold represents the minimum value of the reasonable range of the deviation value, which is set by the data system operation and maintenance specialist. In this example embodiment, it is assumed that a certain deviation value is 0.9 due to the loss of precision in the data conversion process. In view of the strict requirements of cylinder pressure control on precision, any slight deviation may have a significant impact on the performance and safety of the internal combustion engine. Therefore, the value of the deviation threshold is set to 0.3. Therefore, the deviation value is greater than the deviation threshold, and the data detection point corresponding to the deviation value is marked as an abnormal data detection point.

[0073] The horizontal coordinate position point of each data point of the sample curve to which the data to be converted belongs is obtained, and adjacent horizontal coordinate position points are subjected to difference processing to obtain the length of each horizontal coordinate segment of the sample curve to which the data to be converted belongs, and the mode of each horizontal coordinate segment length is located and marked as the horizontal coordinate segment reference length, and each horizontal coordinate segment length greater than the horizontal coordinate segment reference length is compared with the horizontal coordinate segment reference length, and each horizontal coordinate segment length greater than the horizontal coordinate segment reference length is marked as the horizontal coordinate segment length of each missing data, and the total length of the missing data horizontal coordinate segment of the sample curve to which the data to be converted belongs is accumulated and the ratio is processed with the horizontal coordinate segment reference length, and the obtained value is subjected to difference processing with the value of the total number of missing data horizontal coordinate segments, and the result of the difference processing is marked as the total number of missing values ​​of the sample curve to which the data to be converted belongs, and data analysis is performed with the total number of data points to obtain the missing rate of the sample curve to which the data to be converted belongs; the mode of the above-mentioned horizontal coordinate segment lengths can be obtained by analysis through statistical analysis software (such as social science statistical software package); the horizontal coordinate position point of each data point of the sample curve to which the data to be converted belongs, in Figure 3 In the internal combustion engine pressure curve shown, the horizontal coordinate position 4 of each data point is only for illustration, aiming to intuitively display the data distribution and facilitate observation and analysis. In the actual operation of the internal combustion engine, the crankshaft angle is continuous and constantly changing, and its change reflects the continuity of the working process in the cylinder. When monitoring the cylinder pressure, in order to obtain accurate and continuous data to analyze the working state of the cylinder, it is usually measured and recorded after the crankshaft rotates through the same fixed angle. This fixed crankshaft angle increment constitutes the horizontal coordinate segment reference length 1, which represents the horizontal coordinate position interval between adjacent data points. If Figure 3 The length of the horizontal axis segment exceeds the reference length of the horizontal axis segment, indicating that there are missing data points in the horizontal axis segment, so it is marked as the missing data horizontal axis segment length, such as Figure 3The length of the horizontal axis segment of 2 is missing data. This missing data may be caused by errors in the data acquisition program or packet loss in the data conversion process. It should be explained that the length of the horizontal axis segment of the missing data is an integer multiple of the reference length of the horizontal axis segment. Because data acquisition is designed according to fixed intervals, in this example embodiment, it is collected according to fixed crankshaft angle increments. Therefore, when a problem occurs and data is missing, what is missing is usually one or more complete acquisition cycles, so it is an integer multiple.

[0074] It should be explained that the missing rate of the sample curve to which the above-mentioned data to be converted belongs refers to the total number of missing values ​​divided by the cumulative result of the total number of missing values ​​and the total number of data points. In an example embodiment, assuming that the reference length of the horizontal axis segment is 5 mm, the lengths of the horizontal axis segments of each missing data are 15 mm, 10 mm, and 15 mm respectively, then the total length of the horizontal axis segment of the missing data is 40 mm, and the result of ratio processing with the reference length of the horizontal axis segment is 8. There are 3 missing data horizontal axis segment lengths, then 8 is subtracted from the number of missing data horizontal axis segment lengths (3), and the result 5 is marked as the total number of missing values ​​of the sample curve to which the data to be converted belongs. Assuming that the total number of data points is 95, the missing rate is 5 / (5+95)=0.05.

[0075] At the same time, the data conversion stability of the data to be converted is comprehensively determined, thereby comprehensively determining the conversion accuracy of the data to be converted.

[0076] In a specific embodiment, the present invention provides a data conversion method, system and electronic device based on big data. By analyzing the environmental parameters of the data to be converted, the different environmental parameters may affect the characteristics of the data and the conversion effect, thereby matching the best data conversion process for the data to be converted, which can better adapt to the characteristics of different data and improve the accuracy and efficiency of the conversion. By evaluating the conversion process parameters of the data to be converted, the data conversion stability of the data to be converted is obtained, which helps to timely discover potential problems in the data conversion process, optimize the conversion process, ensure the reliability and consistency of data conversion, and more comprehensively determine the conversion accuracy of the data to be converted based on the conversion results of the data to be converted and the data conversion stability, thereby providing feedback on the data conversion, improving the accuracy and credibility of the judgment, and providing quality control for data conversion. It provides strong support; specifically, the dependent variable in the environmental stability coefficient formula to which the fuel combustion process data belongs can be obtained first, thereby matching the best data conversion process for the fuel combustion process data, and obtaining the data conversion stability of the fuel combustion process data by evaluating the conversion process parameters of the fuel combustion process data, thereby optimizing the data conversion process, and finally based on the conversion result of the fuel combustion process data, that is, the fuel combustion process data visualization chart, and the data conversion stability, the conversion accuracy of the fuel combustion process data can be more comprehensively determined, and by minimizing the complexity of data analysis and the amount of data required, it can quickly respond to the accuracy of the data conversion process, reduce the amount of data transmission and optimize the data analysis process, while ensuring the accuracy of the fuel combustion process data visualization chart, and providing precise data support for parameter adjustment of the fuel combustion process.

[0077] Reference Figure 2 As shown, the second aspect of the present invention provides a data conversion system based on big data, including: a data conversion preparation analysis module, a data conversion process evaluation module and a data conversion result feedback module.

[0078] The second aspect of the present invention provides a data conversion system based on big data, which also includes a data integration library, wherein the data integration library is used to store the load factor corresponding to the average CPU usage rate and the load factor corresponding to the average memory occupancy rate, the impact factor corresponding to the unit value of the average network bandwidth, the impact factor corresponding to the unit value of the drive load factor, the impact factor corresponding to the unit value of the total number of data attribute types, the data conversion process corresponding to each environmental stability coefficient interval, the impact factor corresponding to the unit value of the processing rate, the impact factor corresponding to the unit value of the power fluctuation discrete value, the impact factor corresponding to the unit value of the captured data packet load, the reference curve to which the data to be converted belongs, the weight factor corresponding to the conversion stability, the impact factor corresponding to the unit value of the abnormality rate, and the impact factor corresponding to the unit value of the missing rate.

[0079] The data conversion preparation analysis module is connected to the data conversion process evaluation module and the data integration library respectively, the data conversion process evaluation module is connected to the data conversion result feedback module and the data integration library respectively, and the data conversion result feedback module is connected to the data integration library.

[0080] The data conversion preparation analysis module is used to obtain environmental parameters of the data to be converted based on the conversion preparation of the data to be converted, analyze the stability coefficient of the environment to which the data to be converted belongs, and match the data conversion process of the data to be converted.

[0081] The data conversion process evaluation module is used to obtain conversion process parameters of the data to be converted based on the conversion process of the data to be converted, thereby evaluating the data conversion stability of the data to be converted.

[0082] The data conversion result feedback module is used to determine the conversion accuracy of the data to be converted based on the conversion result of the data to be converted and the data conversion stability, thereby providing feedback on the data conversion.

[0083] A third aspect of the present invention provides an electronic device that applies a method for data conversion based on big data, wherein the electronic device includes a memory for storing computer program instructions and a processor for executing program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute the method for data conversion based on big data.

[0084] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for data conversion based on big data, characterized in that: include: S1. Based on the conversion preparation of the data to be converted, obtain the environmental parameters of the data to be converted, analyze the environmental stability coefficient of the data to be converted, and match the data conversion process of the data to be converted; S2. Based on the conversion process of the data to be converted, obtaining the conversion process parameters of the data to be converted, thereby evaluating the data conversion stability of the data to be converted; S3. Based on the conversion result of the data to be converted and the data conversion stability, the conversion accuracy of the data to be converted is determined, thereby providing feedback on the data conversion.

2. The method for data conversion based on big data according to claim 1, characterized in that: The analysis of the environmental stability coefficient of the data to be converted is specifically carried out as follows: Extracting the real-time network bandwidth of the network environment to which the data to be converted belongs within the conversion environment analysis period from the environment parameters to which the data to be converted belongs, and performing mean processing to obtain the average network bandwidth of the network environment to which the data to be converted belongs; Extracting the average CPU usage rate and the average memory occupancy rate of the driving device environment to which the data to be converted belongs during the conversion environment analysis period from the environment parameters to which the data to be converted belongs, and extracting the load factor corresponding to the average CPU usage rate and the load factor corresponding to the average memory occupancy rate from the data integration library, and accumulating the product result of the average CPU usage rate and the load factor corresponding to the average CPU usage rate, and the product result of the average memory occupancy rate and the load factor corresponding to the average memory occupancy rate, to obtain the driving load factor of the driving device environment to which the data to be converted belongs; The total number of data attribute types of the data environment to which the data to be converted belongs is obtained and counted, and the stability coefficient of the environment to which the data to be converted belongs is obtained through comprehensive analysis.

3. The method for data conversion based on big data according to claim 1, characterized in that: The data conversion process of matching the data to be converted is specifically as follows: The data conversion process corresponding to the environmental stability coefficient of the data to be converted is matched with the data conversion process corresponding to each environmental stability coefficient interval stored in the data integration library, thereby matching the data conversion process of the data to be converted.

4. The method for data conversion based on big data according to claim 1, characterized in that: The acquisition of the conversion process parameters of the data to be converted specifically refers to the conversion process parameters of the driving device to which the data to be converted belongs.

5. The method for data conversion based on big data according to claim 1, characterized in that: The specific evaluation process of evaluating the data conversion stability of the data to be converted is as follows: Extract the number of bytes processed by the driving device to which the data to be converted belongs within the data conversion cycle from the conversion process parameters of the driving device to which the data to be converted belongs, and perform ratio processing with the duration corresponding to the data conversion cycle to obtain the processing rate of the driving device to which the data to be converted belongs; Extracting the power of each detection sample of the central processor of the driving device to which the data to be converted belongs within the data conversion cycle from the conversion process parameters of the driving device to which the data to be converted belongs, performing standard deviation processing, and marking the obtained result as the power fluctuation discrete value of the driving device to which the data to be converted belongs; The load of each captured data packet of the driving device to which the data to be converted belongs within the data conversion cycle is extracted from the conversion process parameters of the driving device to which the data to be converted belongs, and the standard deviation analysis is performed to obtain the discrete value of the load of the captured data packet of the driving device to which the data to be converted belongs, thereby comprehensively evaluating the data conversion stability of the data to be converted.

6. The method for data conversion based on big data according to claim 1, characterized in that: The specific determination process of determining the conversion accuracy of the data to be converted is as follows: Extracting a sample curve to which the data to be converted belongs from the conversion result of the data to be converted, marking each data detection point on the sample curve to which the data to be converted belongs, and extracting a reference curve to which the data to be converted belongs from the data integration library, performing difference processing on the ordinate corresponding to each data detection point on the sample curve to which the data to be converted belongs and the ordinate corresponding to the data detection point on the reference curve to which the data to be converted belongs, obtaining each deviation value of the sample curve to which the data to be converted belongs, comparing the difference value with the deviation threshold, marking the data detection points corresponding to each deviation value greater than the deviation threshold as abnormal data detection points, counting the total number of abnormal data detection points, performing ratio processing with the total number of data detection points, and obtaining the abnormal rate of the sample curve to which the data to be converted belongs; Obtain the horizontal coordinate position point of each data point of the sample curve to which the data to be converted belongs, perform difference processing on adjacent horizontal coordinate position points to obtain the length of each horizontal coordinate segment of the sample curve to which the data to be converted belongs, locate the mode of the length of each horizontal coordinate segment, and mark it as the reference length of the horizontal coordinate segment, compare the length of each horizontal coordinate segment with the reference length of the horizontal coordinate segment, mark the length of each horizontal coordinate segment that is greater than the reference length of the horizontal coordinate segment as the length of each missing data horizontal coordinate segment, add up to obtain the total length of the missing data horizontal coordinate segment of the sample curve to which the data to be converted belongs, perform ratio processing with the reference length of the horizontal coordinate segment, perform difference processing on the obtained value and the value of the total number of missing data horizontal coordinate segments, mark the result of the difference processing as the total number of missing values ​​of the sample curve to which the data to be converted belongs, and perform data analysis with the total number of data points to obtain the missing rate of the sample curve to which the data to be converted belongs; At the same time, the data conversion stability of the data to be converted is comprehensively determined, thereby comprehensively determining the conversion accuracy of the data to be converted.

7. The method for data conversion based on big data according to claim 6, characterized in that: The conversion accuracy of the data to be converted is specifically expressed as: Among them, FI is the conversion accuracy of the data to be converted, FRC is the data conversion stability of the data to be converted, OD is the abnormality rate of the sample curve to which the data to be converted belongs, DLR is the missing rate of the sample curve to which the data to be converted belongs, TM is the weight factor corresponding to the conversion stability preset in the data integration library, ra1 is the impact factor corresponding to the unit value of the abnormality rate preset in the data integration library, and ra2 is the impact factor corresponding to the unit value of the missing rate preset in the data integration library.

8. The method for data conversion based on big data according to claim 1, characterized in that: The feedback of the data conversion is performed, and the specific feedback process is as follows: The conversion accuracy of the data to be converted is compared with the conversion accuracy threshold. If the conversion accuracy of the data to be converted is less than the conversion accuracy threshold, feedback is given on the conversion of the data.

9. A data conversion system based on big data, characterized by: include: The data conversion preparation analysis module is used to obtain the environmental parameters of the data to be converted based on the conversion preparation of the data to be converted, analyze the environmental stability coefficient of the data to be converted, and match the data conversion process of the data to be converted; A data conversion process evaluation module, used to obtain conversion process parameters of the data to be converted based on the conversion process of the data to be converted, thereby evaluating the data conversion stability of the data to be converted; The data conversion result feedback module is used to determine the conversion accuracy of the data to be converted based on the conversion result of the data to be converted and the data conversion stability, thereby providing feedback on the data conversion.

10. An electronic device using the big data conversion method according to any one of claims 1 to 8, the electronic device comprising a memory for storing computer program instructions and a processor for executing program instructions, wherein: When the computer program instructions are executed by the processor, the electronic device is triggered to execute a big data data conversion method as described in any one of claims 1 to 8.

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