A high vacuum oil injection system

Abstract

The present invention relates to the field of vacuum oil injection technology, and discloses a high vacuum oil injection system, including a data acquisition module, a data processing module, a data calculation module, a data analysis module and a feedback module. The system realizes real-time monitoring and quality evaluation of the oil injection process by integrating four data sets and performing multi-level comparative analysis. Finally, an accurate quality report is generated through the feedback module to help operators quickly respond to potential problems. The overall system has brought significant improvements in improving the stability of the oil injection process, reducing human errors, and improving efficiency, and significantly extended the service life of the pressure transmitter. The system not only uses real-time data under the current timestamp, but also analyzes by comparing similar data under the previous timestamp. Through this comparative analysis of time series data, the system can identify the trend and amplitude of parameter changes, help determine whether abnormalities occur during the oil injection process, and discover potential quality problems in advance.

Description

Technical Field

[0001] The invention relates to the technical field of vacuum oil injection, in particular to a high vacuum oil injection system. Background Art

[0002] The high vacuum oil injection system is a technology used to inject specific liquids (usually insulating oil, silicone oil, etc.) into the interior of a device under a vacuum environment. It is commonly used in the production and manufacturing of industrial components such as precision equipment, sensors, and pressure transmitters. The system injects oil under high vacuum conditions, avoiding the risk of air or other impurities mixing into the oil in traditional oil injection methods, thereby improving the sealing, stability, and durability of the equipment.

[0003] Pressure transmitters are an indispensable part of industrial automation systems, and their stable performance directly affects the operation of the entire system. However, in the manufacturing process of remote sealing components of pressure transmitters, the oil injection method of the filling fluid has an important impact on its performance. Traditional oil injection methods often cannot provide a sufficient vacuum environment, resulting in air mixing in the filling fluid, which in turn affects the performance and service life of the pressure transmitter. Summary of the invention

[0004] The purpose of the present invention is to provide a high vacuum oil filling system to solve the problem that the traditional oil filling method proposed in the above background technology often cannot provide a sufficient vacuum environment, resulting in air mixing in the filling liquid, thereby affecting the performance and service life of the pressure transmitter.

[0005] To achieve the above object, the present invention provides the following technical solutions: a high vacuum oil injection system, comprising a data acquisition module, a data processing module, a data calculation module, a data analysis module and a feedback module;

[0006] The data acquisition module is used to acquire multi-source data of the oil injection process and preset data;

[0007] The data processing module is used to preprocess the multi-source data acquired by the data acquisition module, and organize the preprocessed multi-source data into a first data set, a second data set, and a third data set;

[0008] The data calculation module is used to integrate and calculate the first data set, the second data set and the third data set generated by the data processing module into a quality coefficient GLC;

[0009] The data analysis module includes a first analysis unit and a second analysis unit, wherein the first analysis unit is used to compare the quality coefficient GLC calculated by the data calculation module with a preset first threshold value Y, thereby generating a first comparison result, and judging whether the current oil injection meets the quality requirements according to the first comparison result;

[0010] If the first comparison result shows that the quality requirement is not met, the second analysis unit integrates and calculates the quality coefficient GLC and the preset first threshold value Y to generate a magnitude coefficient P, and compares the magnitude coefficient P with the preset second threshold value R to generate a second comparison result, and classifies the abnormal level of the current quality according to the second comparison result;

[0011] The feedback module sends corresponding result information to the terminal according to the second comparison result.

[0012] Preferably, the data acquisition module includes a first acquisition unit, a second acquisition unit, a third acquisition unit and a fourth acquisition unit;

[0013] The first acquisition unit is used to acquire pressure output data, the second acquisition unit is used to acquire temperature data, and the third acquisition unit is used to acquire liquid viscosity data;

[0014] Pressure output data includes input pressure, output pressure and response time;

[0015] Temperature data includes ambient temperature, transmitter temperature, and temperature change rate;

[0016] Liquid viscosity data includes viscosity value, viscosity change rate and impurity content;

[0017] Integrate pressure output data, temperature data, and liquid viscosity data into multi-source data.

[0018] Preferably, the data processing module includes a preprocessing unit and an integration unit, the preprocessing unit is used to preprocess, split and dimensionlessly transform the multi-source data collected by the data acquisition module, and the integration unit is used to organize the multi-source data after processing by the preprocessing unit into a first data set, a second data set and a third data set.

[0019] Preferably, the preprocessing unit splits the pressure output data into preset pressure output data and real-time pressure output data, splits the temperature data into preset temperature data and real-time temperature data, and splits the liquid viscosity data into preset liquid viscosity data and real-time liquid viscosity data.

[0020] Preferably, the first data set includes a preset input pressure Amax and real-time input pressures A1, A2, A3, ...An, a preset output pressure Bmax and real-time output pressures B1, B2, B3, ...Bn, a preset response time Cmax and real-time response times C1, C2, C3, ...Cn;

[0021] The second data set includes a preset ambient temperature Dmax and real-time ambient temperatures D1, D2, D3, ...Dn, a preset transmitter temperature Emax and real-time transmitter temperatures E1, E2, E3, ...En, a preset temperature change rate Fmax and real-time temperature change rates F1, F2, F3, ...Fn;

[0022] The third data set includes a preset viscosity value Gmax and real-time viscosity values ​​G1, G2, G3, ... Gn, a preset viscosity change rate Hmax and real-time viscosity change rates H1, H2, H3, ... Hn, a preset impurity content Imax and real-time impurity content I1, I2, I3, ... In.

[0023] Preferably, the data calculation module calculates and obtains the quality coefficient GLC by the following formula:

[0024] ;

[0025] In the formula: a1, a2 and a3 are weight values, and the values ​​of a1, a2 and a3 are adjusted and set by the user, S1 is the first reference coefficient, S2 is the second reference coefficient, and S3 is the third reference coefficient.

[0026] Preferably, the calculation module calculates and obtains the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3 respectively through the following formulas;

[0027] ;

[0028] ;

[0029] ;

[0030] Wherein: An-1, Bn-1, Cn-1, Dn-1, En-1, Fn-1, Gn-1, Hn-1, In-1 are the real-time input pressure, real-time output pressure, real-time response time, real-time ambient temperature, real-time transmitter temperature, real-time temperature change rate, real-time viscosity value, real-time viscosity change rate and real-time impurity content at the n-1 timestamp respectively;

[0031] An, Bn, Cn, Dn, En, Fn, Gn, Hn, and In are the real-time input pressure, real-time output pressure, real-time response time, real-time ambient temperature, real-time transmitter temperature, real-time temperature change rate, real-time viscosity value, real-time viscosity change rate, and real-time impurity content at timestamp n, respectively;

[0032] X is the correction factor.

[0033] Preferably, the correction coefficient X is calculated by the following formula:

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] Wherein: Amax, Bmax, Cmax, Dmax, Emax, Fmax, Gmax, Hmax, Imax are real-time input pressure, preset output pressure, preset response time, preset ambient temperature, preset transmitter temperature, preset temperature change rate, preset viscosity value, preset viscosity change rate and preset impurity content, respectively;

[0039] An, Bn, Cn, Dn, En, Fn, Gn, Hn, and In are the real-time input pressure, real-time output pressure, real-time response time, real-time ambient temperature, real-time transmitter temperature, real-time temperature change rate, real-time viscosity value, real-time viscosity change rate, and real-time impurity content at timestamp n, respectively.

[0040] Preferably, the first comparison result is as follows:

[0041] when When , it means that the current oil injection has quality abnormality.

[0042] Preferably, the second comparison result is as follows;

[0043] when When , it means that the current oil injection system is in the first-level quality abnormality;

[0044] when When , it means that the current oil injection system is in the second-level quality abnormality;

[0045] when When , it means that the current oil injection system is in the third level of quality abnormality;

[0046] The magnitude coefficient P is calculated using the following formula:

[0047] ;

[0048] Where GLC is the quality coefficient and Y is the first threshold.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The system integrates four data sets and conducts multi-level comparative analysis to achieve real-time monitoring and quality assessment of the oil injection process. Finally, accurate quality reports are generated through the feedback module to help operators quickly respond to potential problems. The overall system has brought significant improvements in improving the stability of the oil injection process, reducing human errors, and improving efficiency, and has significantly extended the service life of the pressure transmitter.

[0051] 2. This system not only uses the real-time data at the current timestamp, but also analyzes it by comparing the same data at the previous timestamp. Through this comparative analysis of time series data, the system can identify the trend and amplitude of parameter changes, help determine whether there are abnormalities in the oil injection process, and discover potential quality problems in advance. In order to ensure the accuracy of the calculation, the system also designs a correction coefficient X to correct the deviation in the data. The calculation of this correction coefficient is based on the comparison of preset parameters and real-time data, so that the system can be dynamically adjusted according to the changes in real-time parameters during the oil injection process to ensure the reliability of the final quality assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a system flow chart of the present invention.

[0053] In the figure: 1. Data acquisition module; 11. First acquisition unit; 12. Second acquisition unit; 13. Third acquisition unit; 2. Data processing module; 21. Preprocessing unit; 22. Integration unit; 3. Data calculation module; 4. Data analysis module; 41. First analysis unit; 42. Second analysis unit; 5. Feedback module. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1: Please refer to Figure 1 , a high vacuum oil injection system, a data acquisition module 1, a data processing module 2, a data calculation module 3, a data analysis module 4 and a feedback module 5;

[0056] The data acquisition module 1 is used to acquire multi-source data of the oil injection process and preset data;

[0057] The data processing module 2 is used to preprocess the multi-source data acquired by the data acquisition module 1, and organize the preprocessed multi-source data into a first data set, a second data set, and a third data set;

[0058] The data calculation module 3 is used to integrate the first data set, the second data set and the third data set generated by the data processing module 2 and calculate the quality coefficient GLC;

[0059] The data analysis module 4 includes a first analysis unit 41 and a second analysis unit 42. The first analysis unit 41 is used to compare the quality coefficient GLC calculated by the data calculation module 3 with a preset first threshold value Y, thereby generating a first comparison result, and judging whether the current oil injection meets the quality requirements according to the first comparison result;

[0060] If the first comparison result shows that the quality requirement is not met, the second analysis unit 42 integrates and calculates the quality coefficient GLC and the preset first threshold value Y to generate a magnitude coefficient P, and compares the magnitude coefficient P with the preset second threshold value R to generate a second comparison result, and classifies the abnormal level of the current quality according to the second comparison result;

[0061] The feedback module 5 sends corresponding result information to the terminal according to the second comparison result.

[0062] In this embodiment: Data acquisition module 1 is responsible for acquiring necessary data during the oil filling process and from preset data. It can monitor various parameters of the oil filling process in real time, such as vacuum, pressure, temperature and filling liquid status, so as to ensure accurate control of the entire oil filling process. By collecting these data, the system can perform subsequent data processing and analysis to ensure the transparency and accuracy of the oil filling process. The main task of this module is to provide an accurate and real-time data basis for subsequent processing, so as to achieve the purpose of improving data accuracy and reliability;

[0063] The data processing module 2 pre-processes the multi-source data acquired by the data acquisition module 1, cleans and filters the noise or erroneous data, and organizes the multi-source data into the first data set, the second data set, and the third data set. Through this pre-processing, the system can more efficiently analyze the key data in the oil injection process, while ensuring the accuracy of subsequent calculations and analysis. This module completes the task of converting disorganized raw data into usable information, achieving the purpose of improving data quality and reducing data processing errors;

[0064] The data calculation module 3 integrates the first data set, the second data set and the third data set generated by the data processing module 2 and performs comprehensive calculations to obtain the quality coefficient GLC of the oil injection system. This coefficient is a direct reflection of the quality of the oil injection process and represents the performance of the system in terms of vacuum, liquid viscosity, temperature stability, etc. This module completes the data calculation task, provides a quantitative basis for subsequent quality evaluation, and achieves the purpose of accurately measuring the quality of the oil injection process;

[0065] The data analysis module 4 compares the calculated quality coefficient GLC with the preset first threshold value Y, generates the first comparison result, and determines whether the oil filling process meets the quality requirements. If it does not meet the standard, it will continue to integrate and calculate the quality coefficient GLC and the first threshold value Y to generate the magnitude coefficient P, and further compare it with the preset second threshold value R to generate the second comparison result and classify the current quality abnormality level. The task of this module is to ensure the oil filling quality through double comparison, and at the same time provide accurate judgment basis for abnormal situations, so as to achieve the purpose of improving detection accuracy and abnormal management capabilities;

[0066] Feedback module 5 generates a corresponding quality report based on the second comparison result generated by data analysis module 4, and feeds back the abnormal quality level to the system operator. This module ensures the real-time monitoring system's immediate feedback on the oil injection quality, making it easier for operators to take corrective measures in a timely manner. Its main task is to provide users with detailed quality information and improvement suggestions, thereby achieving the purpose of improving the system's responsiveness and automation control level;

[0067] The optimized high vacuum oil filling system significantly improves the accuracy of data collection and processing, and has a higher degree of automation. The system integrates four data sets and conducts multi-level comparative analysis to achieve real-time monitoring and quality assessment of the oil filling process. Finally, accurate quality reports are generated through the feedback module to help operators quickly respond to potential problems. The overall system has brought significant improvements in improving the stability of the oil filling process, reducing human errors, and improving efficiency, and has significantly extended the service life of the pressure transmitter.

[0068] Example 2: Please refer to Figure 1 , the data acquisition module 1 includes a first acquisition unit 11, a second acquisition unit 12 and a third acquisition unit 13;

[0069] The first acquisition unit 11 is used to acquire pressure output data, the second acquisition unit 12 is used to acquire temperature data, and the third acquisition unit 13 is used to acquire liquid viscosity data;

[0070] Pressure output data includes input pressure, output pressure and response time;

[0071] Temperature data includes ambient temperature, transmitter temperature, and temperature change rate;

[0072] Liquid viscosity data includes viscosity value, viscosity change rate and impurity content;

[0073] Integrate pressure output data, temperature data, and liquid viscosity data into multi-source data.

[0074] In this embodiment: through the first acquisition unit 11, the system can accurately capture the subtle changes in the pressure output during the oil injection process, thereby effectively monitoring and controlling the pressure environment of the oil injection. This improvement ensures that the filling liquid is always in the optimal pressure state during the injection process, avoiding performance instability caused by pressure fluctuations.

[0075] Through the second acquisition unit 12, the system can monitor temperature changes in real time to ensure that the oil injection process always remains within the ideal temperature range, avoiding the negative impact of temperature fluctuations on liquid properties, thereby improving the stability and service life of the equipment.

[0076] The third acquisition unit 13 can provide real-time feedback on the liquid state during the oil filling process, helping the system to dynamically adjust according to actual conditions to ensure the consistency and quality of the filling liquid. This improvement greatly enhances the fine control capability of the oil filling process and avoids system failure or error caused by unstable viscosity.

[0077] By subdividing the data acquisition module into independent acquisition units such as pressure, temperature, and viscosity, the optimized high vacuum oil injection system achieves more accurate multi-parameter monitoring and control. This structure not only improves the grasp and response speed of key parameters, but also reduces failures caused by inaccurate data collection, thereby further improving the performance and service life of the pressure transmitter. There are significant improvements in accuracy, stability, and automation levels, which brings better system performance than traditional oil injection methods.

[0078] Example 3: Please refer to Figure 1 The data processing module 2 includes a preprocessing unit 21 and an integration unit 22. The preprocessing unit 21 is used to preprocess, split and dimensionlessly process the multi-source data collected by the data collection module 1. The integration unit 22 is used to organize the multi-source data processed by the preprocessing unit 21 into a first data set, a second data set and a third data set.

[0079] The preprocessing unit 21 splits the pressure output data into preset pressure output data and real-time pressure output data, splits the temperature data into preset temperature data and real-time temperature data, splits the liquid viscosity data into preset liquid viscosity data and real-time liquid viscosity data, and splits the vacuum data into preset vacuum data and real-time vacuum data.

[0080] The first data set includes a preset input pressure Amax and real-time input pressures A1, A2, A3, ...An, a preset output pressure Bmax and real-time output pressures B1, B2, B3, ...Bn, a preset response time Cmax and real-time response times C1, C2, C3, ...Cn;

[0081] The second data set includes a preset ambient temperature Dmax and real-time ambient temperatures D1, D2, D3, ...Dn, a preset transmitter temperature Emax and real-time transmitter temperatures E1, E2, E3, ...En, a preset temperature change rate Fmax and real-time temperature change rates F1, F2, F3, ...Fn;

[0082] The third data set includes preset viscosity value Gmax and real-time viscosity values ​​G1, G2, G3, ...Gn, preset viscosity change rate Hmax and real-time viscosity change rate H1, H2, H3, ...Hn, preset impurity content Imax and real-time impurity content I1, I2, I3, ...In.

[0083] In this embodiment: the preprocessing unit 21 can split and process the multi-source data from the data acquisition module 1, and in a dimensionless manner, uniformly process the data of different dimensions, split and process the preset data and the real-time data, so that the system can more accurately monitor and analyze the changes in the oil injection process, ensuring that the system can detect deviations and make adjustments in time. The dimensionless processing converts different types of data into comparable standard forms, which is convenient for subsequent integration and calculation, and reduces calculation errors.

[0084] The integration unit 22 is responsible for arranging the pre-processed multi-source data into a first data set, a second data set and a third data set. Each data set contains different preset values ​​and real-time data, so that the system can better manage multi-dimensional oil injection data. By classifying and arranging different parameter data into different data sets, the system can more clearly handle changes in various aspects, thereby enhancing the monitoring and optimization of complex processes. The classified data can be directly input into the subsequent calculation module, which greatly reduces the processing time and improves the response speed of the overall system.

[0085] By splitting, preprocessing and integrating data, the system has achieved significant improvements in accuracy, data comparability and processing efficiency. Compared with traditional technologies, the optimized system can detect problems earlier and more accurately, and quickly take necessary adjustment measures, effectively improving the quality control level of the entire oil injection process, thereby extending the service life of the pressure transmitter and improving the overall efficiency and reliability of the industrial automation system.

[0086] Embodiment 4: The data calculation module 3 calculates and obtains the quality coefficient GLC through the following formula;

[0087] ;

[0088] In the formula: a1, a2 and a3 are weight values, and the values ​​of a1, a2 and a3 are adjusted and set by the user, S1 is the first reference coefficient, S2 is the second reference coefficient, and S3 is the third reference coefficient.

[0089] The calculation module calculates and obtains the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3 respectively through the following formulas;

[0090] ;

[0091] ;

[0092] ;

[0093] Wherein: An-1, Bn-1, Cn-1, Dn-1, En-1, Fn-1, Gn-1, Hn-1, In-1 are the real-time input pressure, real-time output pressure, real-time response time, real-time ambient temperature, real-time transmitter temperature, real-time temperature change rate, real-time viscosity value, real-time viscosity change rate and real-time impurity content at the n-1 timestamp respectively;

[0094] An, Bn, Cn, Dn, En, Fn, Gn, Hn, and In are the real-time input pressure, real-time output pressure, real-time response time, real-time ambient temperature, real-time transmitter temperature, real-time temperature change rate, real-time viscosity value, real-time viscosity change rate, and real-time impurity content at timestamp n, respectively;

[0095] X is the correction factor.

[0096] The correction coefficient X is calculated by the following formula:

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] Wherein: Amax, Bmax, Cmax, Dmax, Emax, Fmax, Gmax, Hmax, Imax are real-time input pressure, preset output pressure, preset response time, preset ambient temperature, preset transmitter temperature, preset temperature change rate, preset viscosity value, preset viscosity change rate and preset impurity content, respectively;

[0102] An, Bn, Cn, Dn, En, Fn, Gn, Hn, and In are the real-time input pressure, real-time output pressure, real-time response time, real-time ambient temperature, real-time transmitter temperature, real-time temperature change rate, real-time viscosity value, real-time viscosity change rate, and real-time impurity content at timestamp n, respectively.

[0103] In this embodiment: the system provides user-adjustable weight values ​​for a1, a2, and a3. This improvement allows users to dynamically adjust weights according to specific application requirements, ensuring that the contribution of different reference coefficients to the final quality coefficient GLC can be optimized according to actual conditions. In this way, in different application scenarios, the system can be flexibly adjusted according to different production environments or material characteristics, improving the adaptability and performance of the system.

[0104] The calculation formula of each reference coefficient includes a series of key oil injection parameters, such as input pressure, output pressure, response time, ambient temperature, transmitter temperature, temperature change rate, liquid viscosity and impurity content, etc. This multi-parameter coupling calculation method can more comprehensively reflect the real-time status and environmental conditions of the oil injection system, thereby improving the monitoring accuracy during the oil injection process.

[0105] This system not only uses the real-time data at the current timestamp, but also analyzes it by comparing the same data at the previous timestamp. Through this comparative analysis of time series data, the system can identify the trend and amplitude of parameter changes, help determine whether there are abnormalities in the oil injection process, and discover potential quality problems in advance. In order to ensure the accuracy of the calculation, the system also designs a correction coefficient X to correct the deviation in the data. The calculation of this correction coefficient is based on the comparison of preset parameters and real-time data, so that the system can be dynamically adjusted according to the changes in real-time parameters during the oil injection process to ensure the reliability of the final quality assessment.

[0106] Example 5: The first comparison result is as follows;

[0107] when When , it means that the current oil injection has quality abnormality.

[0108] The results of the second comparison are as follows;

[0109] when When , it means that the current oil injection system is in the first-level quality abnormality;

[0110] when When , it means that the current oil injection system is in the second-level quality abnormality;

[0111] when When , it means that the current oil injection system is in the third level of quality abnormality;

[0112] The magnitude coefficient P is calculated using the following formula:

[0113] ;

[0114] Where GLC is the quality coefficient and Y is the first threshold.

[0115] In this embodiment, by comparing the quality coefficient GLC with the first threshold value Y, the system can accurately determine the quality status of the current oil product. When Y, it means that no abnormality is detected during the oil filling process and the oil quality meets the preset standard; when GLC When Y is reached, it means that the oil quality is abnormal. Compared with the traditional oil injection quality monitoring method, this method can obtain and calculate the oil injection quality data in real time, and automatically identify the status of the oil, reduce manual misjudgment and delayed response, and ensure the high stability of oil injection quality.

[0116] The improvement of the second comparison result is reflected in the hierarchical management of oil quality anomalies. By classifying the anomalies into three levels, the system can take different countermeasures according to the severity of the anomaly. When the system identifies it as a first-level quality abnormality, it means the problem is relatively minor; when When , it indicates secondary quality abnormality and the problem is more serious; This multi-level early warning mechanism allows the system to provide timely feedback when a minor anomaly occurs, preventing a minor problem from turning into a major failure, thereby improving the safety and reliability of the system.

[0117] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0118] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A high vacuum oil injection system, characterized in that: A data acquisition module (1), a data processing module (2), a data calculation module (3), a data analysis module (4) and a feedback module (5); The data acquisition module (1) is used to acquire multi-source data of the oil injection process and preset data; The data processing module (2) is used to pre-process the multi-source data acquired by the data acquisition module (1), and organize the pre-processed multi-source data into a first data set, a second data set, and a third data set; The data calculation module (3) is used to integrate the first data set, the second data set and the third data set generated by the data processing module (2) and calculate the quality coefficient GLC; The data analysis module (4) comprises a first analysis unit (41) and a second analysis unit (42); the first analysis unit (41) is used to compare the quality coefficient GLC calculated by the data calculation module (3) with a preset first threshold value Y, thereby generating a first comparison result, and judging whether the current oil injection meets the quality requirements based on the first comparison result; If the first comparison result shows that the quality requirement is not met, the second analysis unit (42) integrates and calculates the quality coefficient GLC and the preset first threshold value Y to generate a magnitude coefficient P, and compares the magnitude coefficient P with the preset second threshold value R to generate a second comparison result, and classifies the abnormal level of the current quality according to the second comparison result; The feedback module (5) sends corresponding result information to the terminal according to the second comparison result.

2. A high vacuum oil injection system according to claim 1, characterized in that: The data acquisition module (1) comprises a first acquisition unit (11), a second acquisition unit (12) and a third acquisition unit (13); The first acquisition unit (11) is used to acquire pressure output data, the second acquisition unit (12) is used to acquire temperature data, and the third acquisition unit (13) is used to acquire liquid viscosity data; Pressure output data includes input pressure, output pressure and response time; Temperature data includes ambient temperature, transmitter temperature, and temperature change rate; Liquid viscosity data includes viscosity value, viscosity change rate and impurity content; Integrate pressure output data, temperature data, and liquid viscosity data into multi-source data.

3. A high vacuum oil injection system according to claim 2, characterized in that: The data processing module (2) comprises a preprocessing unit (21) and an integration unit (22), wherein the preprocessing unit (21) is used to preprocess, split and dimensionlessly process the multi-source data collected by the data collection module (1), and the integration unit (22) is used to organize the multi-source data processed by the preprocessing unit (21) into a first data set, a second data set and a third data set.

4. A high vacuum oil injection system according to claim 3, characterized in that: The preprocessing unit (21) splits the pressure output data into preset pressure output data and real-time pressure output data, splits the temperature data into preset temperature data and real-time temperature data, and splits the liquid viscosity data into preset liquid viscosity data and real-time liquid viscosity data.

5. A high vacuum oil injection system according to claim 4, characterized in that: The first data set includes a preset input pressure Amax and real-time input pressures A1, A2, A3, ...An, a preset output pressure Bmax and real-time output pressures B1, B2, B3, ...Bn, a preset response time Cmax and real-time response times C1, C2, C3, ...Cn; The second data set includes a preset ambient temperature Dmax and real-time ambient temperatures D1, D2, D3, ...Dn, a preset transmitter temperature Emax and real-time transmitter temperatures E1, E2, E3, ...En, a preset temperature change rate Fmax and real-time temperature change rates F1, F2, F3, ...Fn; The third data set includes a preset viscosity value Gmax and real-time viscosity values ​​G1, G2, G3, ... Gn, a preset viscosity change rate Hmax and real-time viscosity change rates H1, H2, H3, ... Hn, a preset impurity content Imax and real-time impurity content I1, I2, I3, ... In.

6. A high vacuum oil injection system according to claim 5, characterized in that: The data calculation module (3) calculates and obtains the quality coefficient GLC by the following formula; ； In the formula: a1, a2 and a3 are weight values, and the values ​​of a1, a2 and a3 are adjusted and set by the user, S1 is the first reference coefficient, S2 is the second reference coefficient, and S3 is the third reference coefficient.

7. A high vacuum oil injection system according to claim 6, characterized in that: The data calculation module (3) calculates and obtains the first reference coefficient S1, the second reference coefficient S2 and the third reference coefficient S3 respectively through the following formulas; ； ； ； Wherein: An-1, Bn-1, Cn-1, Dn-1, En-1, Fn-1, Gn-1, Hn-1, In-1 are the real-time input pressure, real-time output pressure, real-time response time, real-time ambient temperature, real-time transmitter temperature, real-time temperature change rate, real-time viscosity value, real-time viscosity change rate and real-time impurity content at the n-1 timestamp respectively; An, Bn, Cn, Dn, En, Fn, Gn, Hn, and In are the real-time input pressure, real-time output pressure, real-time response time, real-time ambient temperature, real-time transmitter temperature, real-time temperature change rate, real-time viscosity value, real-time viscosity change rate, and real-time impurity content at timestamp n, respectively; X is the correction factor.

8. A high vacuum oil injection system according to claim 7, characterized in that: The correction coefficient X is calculated by the following formula: ； ； ； ； Amax, Bmax, Cmax, Dmax, Emax, Fmax, Gmax, Hmax, Imax, respectively, are real-time input pressure, preset output pressure, preset response time, preset ambient temperature, preset transmitter temperature, preset temperature change rate, preset viscosity value, preset viscosity change rate, and preset impurity content; An, Bn, Cn, Dn, En, Fn, Gn, Hn, and In are the real-time input pressure, real-time output pressure, real-time response time, real-time ambient temperature, real-time transmitter temperature, real-time temperature change rate, real-time viscosity value, real-time viscosity change rate, and real-time impurity content at timestamp n, respectively.

9. A high vacuum oil injection system according to claim 1, characterized in that: The first comparison result is as follows: when When , it means that the current oil injection has quality abnormality.

10. A high vacuum oil injection system according to claim 1, characterized in that: The second comparison result is as follows: when When , it means that the current oil injection system is in the first-level quality abnormality; when When , it means that the current oil injection system is in the second-level quality abnormality; when When , it means that the current oil injection system is in the third level of quality abnormality; The magnitude coefficient P is calculated using the following formula: ； Where GLC is the quality coefficient and Y is the first threshold.

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