Quality inspection data analysis and acquisition system for horizontal double-suction centrifugal pump

Abstract

The invention provides a quality inspection data analysis and acquisition system for a horizontal double-suction centrifugal pump. The quality inspection data analysis and acquisition system comprises a parameter acquisition module, an analysis module, a comparison module, a storage module, an interaction module and an exception handling module, the parameter acquisition module is used for acquiring quality inspection data of the horizontal double-suction centrifugal pump, and the quality inspection data comprises flow, pressure, temperature and vibration data; the analysis module receives the quality inspection data collected by the parameter collection module, obtains a performance analysis coefficient through analysis and calculation according to the quality inspection data, generates a time coordinate according to the collected real-time quality inspection data, establishes a quality inspection data change curve of the horizontal double-suction centrifugal pump, and obtains a time coordinate point of the quality inspection data based on the change curve; and obtaining the trend change rate of the time coordinate point, and comparing the trend change rate of the time coordinate point with the trend change rate mean value under the corresponding data in the quality inspection data.

Description

Technical Field

[0001] The invention belongs to the field of data analysis, and in particular relates to a quality inspection data analysis and acquisition system for a horizontal double-suction centrifugal pump. Background Art

[0002] At present, as an important equipment of fluid machinery, horizontal double-suction centrifugal pumps are widely used in many fields such as water conservancy, chemical industry, and petroleum. The stability of its performance and the level of its efficiency are directly related to the safety and benefits of the entire production process. However, the traditional horizontal double-suction centrifugal pump quality inspection data analysis and collection system has many shortcomings, which greatly limit the performance optimization and fault prevention of the pump.

[0003] First, the accuracy and real-time performance of data collection in traditional systems need to be improved. Due to the complex internal structure of the pump and the changing working environment, traditional sensors often have difficulty accurately capturing the key parameters of the pump during operation, such as flow, pressure, vibration, etc. This not only leads to inaccurate data, but also makes it difficult for the system to detect abnormal conditions of the pump in a timely manner, thus delaying the best time to handle the fault.

[0004] Secondly, traditional systems have limitations in data processing and analysis. Existing data processing algorithms can only perform simple analysis on single-dimensional data, making it difficult to achieve a comprehensive and in-depth assessment of the pump's operating status. At the same time, due to the large amount of data and the variety of formats, traditional systems often lack effective data integration and mining capabilities, resulting in a large amount of valuable information being ignored or wasted.

[0005] Furthermore, the traditional system is deficient in terms of user experience and interactivity. The operation interface is complex and the functions are single, making it difficult for users to obtain the required information intuitively and conveniently. In addition, the system lacks intelligent alarm and early warning functions, and cannot provide reasonable maintenance suggestions or troubleshooting solutions in a timely manner according to the operating status of the pump.

[0006] In addition, traditional systems also have hidden dangers in terms of security and reliability. Due to the relatively closed system architecture and the lack of necessary security protection measures, they are vulnerable to external attacks or internal failures, resulting in data leakage or system paralysis. This will not only seriously affect the normal operation of the pump, but may also pose a threat to production safety.

[0007] The traditional horizontal double-suction centrifugal pump quality inspection data analysis and acquisition system has obvious deficiencies in data acquisition, processing, analysis, user experience, safety and reliability. Therefore, the development of a new type of horizontal double-suction centrifugal pump intelligent quality inspection data analysis and acquisition system to achieve comprehensive, accurate and real-time monitoring and analysis of the pump operation status has important practical significance and application value. Therefore, there is an urgent need for a quality inspection data analysis and acquisition system for horizontal double-suction centrifugal pumps. Summary of the invention

[0008] The present invention proposes a quality inspection data analysis and collection system for horizontal double-suction centrifugal pumps, which solves the problems of efficient analysis, abnormality detection and result feedback of quality inspection data of horizontal double-suction centrifugal pumps. Through the integrated data collection, analysis, comparison, storage, interaction and abnormality handling process, real-time monitoring and evaluation of pump performance is achieved, and the quality inspection efficiency and accuracy are improved.

[0009] The technical solution of the present invention is implemented as follows: a quality inspection data analysis and acquisition system for a horizontal double-suction centrifugal pump, comprising a parameter acquisition module, an analysis module, a comparison module, a storage module, an interaction module and an exception processing module for data interaction;

[0010] The parameter acquisition module is used to collect quality inspection data of the horizontal double-suction centrifugal pump, and the quality inspection data includes flow, pressure, temperature, and vibration data;

[0011] The analysis module receives the quality inspection data collected by the parameter acquisition module, obtains the performance analysis coefficient through analysis and calculation based on the quality inspection data, generates the time coordinate from the collected real-time quality inspection data, and establishes the quality inspection data change curve of the horizontal double-suction centrifugal pump, and obtains the time coordinate point of the quality inspection data based on the change curve; obtains the trend change rate at the time coordinate point, compares the trend change rate of the time coordinate point in the quality inspection data with the average trend change rate under the corresponding data, obtains the deviation from the average trend change rate to obtain the performance analysis coefficient, and imports the performance analysis coefficient into the comparison module;

[0012] The comparison module receives the performance analysis coefficient analyzed by the analysis module, and compares the comparison coefficient with the comparison threshold to obtain a comparison result;

[0013] The storage module receives the comparison result of the comparison module and stores the comparison result, and simultaneously feeds the comparison result back to the interaction module;

[0014] The interaction module receives the quality inspection data of the parameter acquisition module and the comparison result of the comparison module, and the interaction module interacts according to the quality inspection data and the comparison result;

[0015] The exception processing module performs an exception analysis on the quality inspection data of the parameter acquisition module according to the comparison result.

[0016] The traditional quality inspection process of horizontal double-suction centrifugal pumps often relies on manual inspection and empirical judgment, which is not only time-consuming and laborious, but also difficult to achieve comprehensive and real-time monitoring of pump performance. In addition, traditional methods usually lack in-depth analysis of quality inspection data and abnormality detection mechanisms, resulting in delayed problem discovery and affecting the stable operation of the equipment.

[0017] In contrast, this technical solution introduces a parameter acquisition module, an analysis module, a comparison module, a storage module, an interaction module, and an exception handling module to realize the automated collection and analysis of quality inspection data. First, the parameter acquisition module can collect key quality inspection data such as the flow, pressure, temperature, and vibration of the pump in real time and accurately, providing a reliable basis for subsequent analysis. Secondly, the analysis module calculates the performance analysis coefficient, establishes a quality inspection data change curve, and conducts in-depth analysis based on the trend change rate of the time coordinate point, so as to more accurately evaluate the performance status of the pump body. The comparison module compares the performance analysis coefficient according to the preset comparison threshold and quickly identifies abnormal data. The storage module is responsible for storing the comparison results and synchronously feeding them back to the interaction module so that users can understand the performance status of the pump body in real time. Finally, the exception handling module conducts in-depth analysis of the abnormal data, providing strong support for troubleshooting and prevention.

[0018] The biggest difference between this technical solution and existing technologies is its automated and intelligent quality inspection process. Through integrated module design, real-time collection, in-depth analysis and anomaly detection of quality inspection data are achieved, greatly improving the efficiency and accuracy of quality inspection. In addition, this solution also has good scalability and flexibility, and can adjust and optimize modules according to actual needs to meet quality inspection needs in different scenarios.

[0019] As a preferred implementation, a performance characteristic value is obtained through the performance analysis coefficient of the analysis module, a performance reference value is obtained based on the performance characteristic value, a comparison result of the performance analysis coefficient and a comparison threshold is obtained based on the performance reference value, the performance reference value is judged against a preset comparison threshold range, the minimum value of the analysis coefficient for which the performance reference value is greater than or equal to the minimum value of the comparison threshold range is marked as a first deviation coefficient, the maximum value of the analysis coefficient for which the performance reference value is less than or equal to the maximum value of the comparison threshold range is marked as a second deviation coefficient, the average value of the first deviation coefficient and the second deviation coefficient is obtained to obtain a comparison coefficient between the performance analysis coefficient and the comparison threshold; and the comparison coefficient is fed back to the comparison module.

[0020] As a preferred implementation, a performance characteristic value is calculated based on the performance analysis coefficient, and a performance judgment value is obtained according to the performance characteristic value. If the performance judgment value is greater than a preset minimum value of a comparison threshold range and less than a preset maximum value of a comparison threshold range, the time coordinate point is marked as a first change point; if the performance judgment value is less than a preset minimum value of a comparison threshold range or greater than a preset maximum value of a comparison threshold range, the time coordinate point is marked as a second change point; the number of first change points and the number of second change points in the quality inspection data are obtained, and are marked as C1 and C2 respectively; the ratio of C1 and C2 is obtained, and the ratio is compared with the preset ratio. If the ratio is less than the preset ratio, it is determined that the quality inspection data fluctuates slightly, and if the ratio is greater than the preset ratio, it is determined that the quality inspection data fluctuates significantly; the time coordinate point of the quality inspection data at which the quality inspection data is determined to fluctuate significantly is obtained, and it is marked as an abnormal time coordinate point, and whether the quality inspection data is abnormal is determined according to the abnormal time coordinate point.

[0021] As a preferred embodiment, the storage module receives the comparison results of the comparison module, revises the change curve of the quality inspection data according to the comparison results, stores the revised quality inspection data curve, and sets a storage time for the stored quality inspection data curve. If the quality inspection data curve reaches the storage time, the quality inspection data curve is deleted; if the storage time is not reached, the quality inspection data curve is not deleted.

[0022] As a preferred implementation, when revising, the abnormal data of the quality inspection data is obtained, the quality inspection data corresponding to the abnormal data is deleted to obtain a revised curve, the fitting curve coefficient is obtained based on the revised curve, the revised curve is fitted and analyzed to obtain a fitted quality inspection data curve, and the revised curve is adjusted based on the fitting curve coefficient to achieve revision of the quality inspection data curve.

[0023] As a preferred embodiment, the fitting analysis obtains the fitting quality inspection data curve by obtaining the original curve of the quality inspection data except the abnormal data as a reference curve, setting a time interval for the reference curve and dividing it into multiple time regions, and marking the number of time regions from top to bottom.

[0024] After adopting the above technical solution, the beneficial effects of the present invention are: this solution greatly shortens the quality inspection cycle and improves work efficiency through automated collection and analysis processes. At the same time, due to the use of deep analysis and abnormality detection mechanisms, potential problems can be discovered and handled in a timely manner, effectively avoiding downtime losses caused by failures. This solution can more accurately evaluate the performance status of the pump body by calculating the performance analysis coefficient, establishing a quality inspection data change curve, and analyzing the trend change rate based on time coordinate points. This refined analysis method not only improves the accuracy of the quality inspection results, but also provides strong support for subsequent troubleshooting and prevention.

[0025] This technical solution also has good user experience and interactivity. Through the design of the interactive module, users can understand the performance status of the pump body in real time, and query and export data as needed. This intuitive and convenient interactive method not only improves the user's work efficiency, but also enhances the user's trust and satisfaction with the quality inspection process. This technical solution realizes the efficiency, accuracy and intelligence of the quality inspection process of horizontal double-suction centrifugal pumps through integrated module design, automated collection and analysis processes, and deep analysis and anomaly detection mechanisms. Compared with the existing technology, this solution has shown significant advantages in quality inspection efficiency, accuracy and user experience, and has broad application prospects and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0027] Figure 1 It is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0028] 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.

[0029] Example:

[0030] like Figure 1 As shown in the figure, the quality inspection data analysis and acquisition system for horizontal double-suction centrifugal pumps is a comprehensive system that integrates multiple functional modules such as parameter acquisition, data analysis, comparison and judgment, data storage, result interaction and exception handling. Its core is to collect key quality inspection data (such as flow, pressure, temperature, vibration, etc.) of horizontal double-suction centrifugal pumps in real time during operation, and use advanced data analysis technology to deeply mine and analyze these data, so as to realize real-time monitoring and evaluation of pump performance.

[0031] The workflow of the system can be summarized into the following steps: first, the parameter acquisition module is responsible for collecting the quality inspection data of the pump body in real time; then, the analysis module receives this data and performs analysis and calculation based on a specific algorithm model to obtain the performance analysis coefficient; then, the comparison module compares the performance analysis coefficient with the preset comparison threshold to determine whether the pump body performance is within the normal range; then, the storage module stores the comparison results and synchronously feeds them back to the interaction module so that the user can view them in real time; finally, if the comparison results show that there is an abnormality in the pump body performance, the exception handling module will start the exception analysis process to further troubleshoot the problem.

[0032] Assume that you are in a horizontal double-suction centrifugal pump production workshop, where multiple horizontal double-suction centrifugal pumps are installed to provide stable fluid delivery services for the production line. In order to ensure the stable operation of the pump body, the above-mentioned quality inspection data analysis and acquisition system is used to monitor and evaluate it in real time.

[0033] 1. Parameter collection stage:

[0034] In the workshop, sensors are installed for each horizontal double-suction centrifugal pump to collect key quality inspection data such as flow, pressure, temperature and vibration in real time. These sensors transmit the collected data to the system's parameter acquisition module via wired or wireless means. After receiving the data, the parameter acquisition module will perform preliminary processing and verification to ensure the accuracy and integrity of the data.

[0035] 2. Data analysis phase:

[0036] After receiving the quality inspection data transmitted by the parameter acquisition module, the analysis module will immediately start the data analysis process. First, it will generate time coordinates based on the real-time quality inspection data and establish a quality inspection data change curve for the pump body. This curve can intuitively reflect the changing trend of the pump body performance over time. Next, the analysis module will obtain the time coordinate points of the quality inspection data based on the change curve and calculate the trend change rate at these points. Then, it will compare the trend change rate of each time coordinate point with the mean of the trend change rate under the corresponding data to obtain the deviation from the mean. This deviation value is the so-called performance analysis coefficient. The size of the performance analysis coefficient can reflect the stability and reliability of the pump body performance.

[0037] 3. Comparison and judgment stage:

[0038] After receiving the performance analysis coefficient transmitted by the analysis module, the comparison module will immediately start the comparison judgment process. It will compare the performance analysis coefficient with the preset comparison threshold. These comparison thresholds are determined based on the design parameters, operating conditions, historical data and other factors of the pump body. If the performance analysis coefficient is less than or equal to the comparison threshold, it means that the pump body performance is within the normal range; if the performance analysis coefficient is greater than the comparison threshold, it means that the pump body performance is abnormal.

[0039] 4. Data storage and result interaction stage:

[0040] After receiving the comparison results transmitted by the comparison module, the storage module will immediately store them. These stored data not only provide a basis for subsequent abnormal analysis, but also provide valuable data support for the performance evaluation and optimization of the pump body. At the same time, the storage module will also synchronously feedback the comparison results to the interactive module. The interactive module is a user-friendly interface that can intuitively display the comparison results to the user in the form of charts, reports, etc. The user can view the performance status of the pump body in real time through the interactive module, and perform data query, export and other operations as needed.

[0041] 5. Exception handling stage:

[0042] If the comparison results show that the pump performance is abnormal, the exception handling module will immediately start the abnormal analysis process. It will further investigate and analyze the pump performance based on the comparison results and the stored quality inspection data. The exception handling module will try to find out the cause of the performance abnormality, such as sensor failure, pump wear, fluid medium change, etc. Once the cause is found, the exception handling module will immediately generate an exception handling report and give corresponding handling suggestions. Users can perform maintenance, repair or replacement on the pump according to the suggestions in the exception handling report to ensure its stable operation.

[0043] In practical applications, the quality inspection data analysis and acquisition system not only improves the accuracy and efficiency of pump performance monitoring, but also provides strong data support for pump maintenance and management. By real-time monitoring and evaluation of the performance of the pump, users can promptly discover and deal with potential problems, thereby avoiding downtime losses and safety hazards caused by failures. At the same time, the system can also provide valuable data support for the performance optimization and improvement of the pump, helping users to continuously improve the operating efficiency and reliability of the pump.

[0044] The quality inspection data analysis and acquisition system for horizontal double-suction centrifugal pumps has significant advantages and broad application prospects. It can not only realize real-time monitoring and evaluation of pump performance, but also provide strong data support for pump maintenance and management. In the future development, there is reason to believe that the system will be widely used and promoted in more fields.

[0045] A performance characteristic value is obtained through the performance analysis coefficient of the analysis module, a performance reference value is obtained according to the performance characteristic value, a comparison result between the performance analysis coefficient and the comparison threshold is obtained according to the performance reference value, the performance reference value is judged with a preset comparison threshold range, the minimum value of the analysis coefficient when the performance reference value is greater than or equal to the minimum value of the comparison threshold range is marked as a first deviation coefficient, the maximum value of the analysis coefficient when the performance reference value is less than or equal to the maximum value of the comparison threshold range is marked as a second deviation coefficient, the average value of the first deviation coefficient and the second deviation coefficient is obtained to obtain a comparison coefficient between the performance analysis coefficient and the comparison threshold; and the comparison coefficient is fed back to the comparison module.

[0046] In practical applications, such as in the production and operation monitoring scenarios of horizontal double-suction centrifugal pumps, we use a more refined performance analysis method. First, through the analysis module, we not only calculate the performance analysis coefficients, but also further obtain the performance characteristic values ​​based on these coefficients. The performance characteristic value is a more detailed description of the pump performance, which may cover multiple dimensions such as flow stability, pressure fluctuation range, and temperature abnormality sensitivity. Then, we calculate the performance reference value based on the performance characteristic value, combined with historical data and pump design standards. This reference value represents the performance expectation of the pump under ideal conditions.

[0047] We compare the performance reference value with the preset comparison threshold range. The innovation of this step is that we not only pay attention to whether the performance analysis coefficient exceeds the threshold, but also obtain a more comprehensive and dynamic comparison coefficient by calculating the first deviation coefficient (the minimum value of the analysis coefficient when the performance reference value is close to but does not exceed the lower limit of the threshold) and the second deviation coefficient (the maximum value of the analysis coefficient when the performance reference value is close to but does not exceed the upper limit of the threshold). This comparison coefficient not only reflects the current state of the pump performance, but also indicates its possible future change trend. This comparison method is more accurate than traditional methods and can detect potential performance problems earlier.

[0048] The creative feature of this embodiment is that it introduces the concepts of performance characteristic value and performance reference value, and a method for comprehensively evaluating pump performance by calculating the first deviation coefficient and the second deviation coefficient. This method not only improves the accuracy of performance evaluation, but also provides more abundant information support for subsequent maintenance decisions.

[0049] A performance characteristic value is calculated based on the performance analysis coefficient, and a performance judgment value is obtained according to the performance characteristic value. If the performance judgment value is greater than the preset minimum value of the comparison threshold range and less than the preset maximum value of the comparison threshold range, the time coordinate point is marked as the first change point; if the performance judgment value is less than the preset minimum value of the comparison threshold range or greater than the preset maximum value of the comparison threshold range, the time coordinate point is marked as the second change point; the number of first change points and the number of second change points in the quality inspection data are obtained, and are marked as C1 and C2 respectively; the ratio of C1 and C2 is obtained, and the ratio is compared with the preset ratio. If the ratio is less than the preset ratio, it is determined that the quality inspection data fluctuates less, and if the ratio is greater than the preset ratio, it is determined that the quality inspection data fluctuates more; the time coordinate point of the quality inspection data at which the quality inspection data is determined to fluctuate more is obtained, and it is marked as an abnormal time coordinate point, and whether the quality inspection data is abnormal is determined according to the abnormal time coordinate point.

[0050] In another scenario, we use the performance judgment value to mark the change points in the quality inspection data and judge the data fluctuation accordingly. If the performance judgment value falls within the preset comparison threshold range, we mark it as the first change point, which usually means that the pump performance is in a stable state; if the performance judgment value exceeds the threshold range, it is marked as the second change point, which may indicate a potential performance problem. By counting the number of first and second change points (C1 and C2) and calculating their ratio, we can quantitatively evaluate the fluctuation of the quality inspection data. This method can detect small changes in the data earlier, so that timely measures can be taken to prevent potential failures. Compared with the prior art, this method improves the sensitivity and accuracy of data fluctuation analysis. Instead of focusing on the anomalies of a single data point, statistical analysis and ratio calculation are used to evaluate the fluctuation trend of the overall data. This method can detect small changes in the data earlier, so that timely measures can be taken to prevent potential failures. Compared with the prior art, this method improves the sensitivity and accuracy of data fluctuation analysis.

[0051] The storage module receives the comparison result of the comparison module, revises the change curve of the quality inspection data according to the comparison result, stores the revised quality inspection data curve, and sets a storage time for the stored quality inspection data curve. If the quality inspection data curve reaches the storage time, the quality inspection data curve is deleted. If the storage time is not reached, the quality inspection data curve is not deleted.

[0052] When revising the quality inspection data curve, we adopted a more refined fitting analysis method. First, by identifying abnormal data and deleting the corresponding quality inspection data, we obtained a revised curve. Then, based on this revised curve, we calculated the fitting curve coefficients and used these coefficients to perform fitting analysis on the curve to obtain a smoother and more accurate fitting quality inspection data curve. Finally, the revised curve was adjusted according to the fitting curve coefficients to achieve the final revision of the curve.

[0053] The innovation of this implementation is that it not only focuses on deleting abnormal data, but also restores the integrity and accuracy of the data through fitting analysis. This method not only improves the effect of data revision, but also provides a more reliable basis for subsequent data analysis and performance evaluation. Compared with the prior art, this method improves the accuracy and efficiency of data revision.

[0054] When making revisions, the abnormal data of the quality inspection data is obtained, the quality inspection data corresponding to the abnormal data is deleted to obtain a revision curve, the fitting curve coefficient is obtained based on the revision curve, the revision curve is fit analyzed to obtain a fitted quality inspection data curve, and the revision curve is adjusted based on the fitting curve coefficient to realize the revision of the quality inspection data curve.

[0055] When revising the quality inspection data curve, we adopted a more refined fitting analysis method. First, by identifying abnormal data and deleting the corresponding quality inspection data, we obtained a revised curve. Then, based on this revised curve, we calculated the fitting curve coefficients and used these coefficients to perform fitting analysis on the curve to obtain a smoother and more accurate fitting quality inspection data curve. Finally, the revised curve was adjusted according to the fitting curve coefficients to achieve the final revision of the curve.

[0056] The innovation of this implementation is that it not only focuses on deleting abnormal data, but also restores the integrity and accuracy of the data through fitting analysis. This method not only improves the effect of data revision, but also provides a more reliable basis for subsequent data analysis and performance evaluation. Compared with the prior art, this method improves the accuracy and efficiency of data revision.

[0057] The fitting analysis obtains the fitting quality inspection data curve by obtaining the original curve of the quality inspection data except the abnormal data as the reference curve, setting the time interval for the reference curve and segmenting it to obtain multiple time regions, and marking the number of time regions from top to bottom. In the fitting analysis process, we adopted a more refined time region segmentation method. First, the original curve of the quality inspection data except the abnormal data is selected as the reference curve. Then, the time interval is set and the reference curve is segmented to obtain multiple time regions. These time regions are marked with different numbers from top to bottom to facilitate subsequent analysis and processing.

[0058] The innovation of this step is that it enables us to more accurately capture the changing characteristics of data in different time periods by segmenting the time region. This method not only improves the accuracy of fitting analysis, but also provides more detailed information support for subsequent performance evaluation and maintenance decisions. Compared with existing technologies, this method improves the sophistication and accuracy of data analysis and performance evaluation.

[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A quality inspection data analysis and acquisition system for a horizontal double-suction centrifugal pump, characterized in that: Including data interaction between parameter acquisition module, analysis module, comparison module, storage module, interaction module and exception handling module; The parameter acquisition module is used to collect quality inspection data of the horizontal double-suction centrifugal pump, and the quality inspection data includes flow, pressure, temperature, and vibration data; The analysis module receives the quality inspection data collected by the parameter acquisition module, obtains the performance analysis coefficient through analysis and calculation based on the quality inspection data, generates the time coordinate from the collected real-time quality inspection data, and establishes the quality inspection data change curve of the horizontal double-suction centrifugal pump, and obtains the time coordinate point of the quality inspection data based on the change curve; obtains the trend change rate at the time coordinate point, compares the trend change rate of the time coordinate point in the quality inspection data with the average trend change rate under the corresponding data, obtains the deviation from the average trend change rate to obtain the performance analysis coefficient, and imports the performance analysis coefficient into the comparison module; The comparison module receives the performance analysis coefficient analyzed by the analysis module, and compares the comparison coefficient with the comparison threshold to obtain a comparison result; The storage module receives the comparison result of the comparison module and stores the comparison result, and simultaneously feeds the comparison result back to the interaction module; The interaction module receives the quality inspection data of the parameter acquisition module and the comparison result of the comparison module, and the interaction module interacts according to the quality inspection data and the comparison result; The exception processing module performs an exception analysis on the quality inspection data of the parameter acquisition module according to the comparison result.

2. A quality inspection data analysis and acquisition system for a horizontal double-suction centrifugal pump as claimed in claim 1, characterized in that: A performance characteristic value is obtained through the performance analysis coefficient of the analysis module, a performance reference value is obtained according to the performance characteristic value, a comparison result between the performance analysis coefficient and the comparison threshold is obtained according to the performance reference value, the performance reference value is judged with a preset comparison threshold range, the minimum value of the analysis coefficient when the performance reference value is greater than or equal to the minimum value of the comparison threshold range is marked as a first deviation coefficient, the maximum value of the analysis coefficient when the performance reference value is less than or equal to the maximum value of the comparison threshold range is marked as a second deviation coefficient, the average value of the first deviation coefficient and the second deviation coefficient is obtained to obtain a comparison coefficient between the performance analysis coefficient and the comparison threshold; and the comparison coefficient is fed back to the comparison module.

3. A quality inspection data analysis and acquisition system for a horizontal double-suction centrifugal pump as claimed in claim 1, characterized in that: A performance characteristic value is calculated based on the performance analysis coefficient, and a performance judgment value is obtained according to the performance characteristic value. If the performance judgment value is greater than the preset minimum value of the comparison threshold range and less than the preset maximum value of the comparison threshold range, the time coordinate point is marked as the first change point; if the performance judgment value is less than the preset minimum value of the comparison threshold range or greater than the preset maximum value of the comparison threshold range, the time coordinate point is marked as the second change point; the number of the first change points and the number of the second change points in the quality inspection data are obtained, and are marked as C1 and C2 respectively; Obtain the ratio of C1 and C2, and compare the ratio with the preset ratio. If the ratio is smaller than the preset ratio, it is determined that the fluctuation of the quality inspection data is small. If the ratio is larger than the preset ratio, it is determined that the fluctuation of the quality inspection data is large. Obtain the time coordinate point of the quality inspection data at which the fluctuation of the quality inspection data is large, mark it as an abnormal time coordinate point, and determine whether the quality inspection data is abnormal based on the abnormal time coordinate point.

4. A quality inspection data analysis and acquisition system for a horizontal double-suction centrifugal pump as claimed in claim 1, characterized in that: The storage module receives the comparison result of the comparison module, revises the change curve of the quality inspection data according to the comparison result, stores the revised quality inspection data curve, and sets a storage time for the stored quality inspection data curve. If the quality inspection data curve reaches the storage time, the quality inspection data curve is deleted. If the storage time is not reached, the quality inspection data curve is not deleted.

5. A quality inspection data analysis and acquisition system for a horizontal double-suction centrifugal pump as claimed in claim 1, characterized in that: When making revisions, the abnormal data of the quality inspection data is obtained, the quality inspection data corresponding to the abnormal data is deleted to obtain a revision curve, the fitting curve coefficient is obtained based on the revision curve, the revision curve is fit analyzed to obtain a fitted quality inspection data curve, and the revision curve is adjusted based on the fitting curve coefficient to realize the revision of the quality inspection data curve.

6. A quality inspection data analysis and acquisition system for a horizontal double-suction centrifugal pump as claimed in claim 5, characterized in that: The fitting analysis obtains the fitting quality inspection data curve by obtaining the original curve of the quality inspection data except the abnormal data as a reference curve, setting a time interval for the reference curve and dividing it into multiple time regions, and marking the number of time regions from top to bottom.

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