Oil product storage quality evaluation system based on data analysis

Abstract

The invention belongs to the field of oil product storage, relates to a data analysis technology, is used for solving the problem that in the prior art, when the oil product storage quality is abnormal, feedback cannot be conducted in time, and particularly relates to an oil product storage quality evaluation system based on data analysis. The quality evaluation platform is in communication connection with an automatic monitoring module, an automatic evaluation module, a regular monitoring module and a database; the automatic monitoring module is used for automatically monitoring and analyzing the quality of the stored oil products: dividing an oil product storage area into a plurality of monitoring areas, marking oil product storage tanks in the monitoring areas as monitoring objects, and setting a plurality of automatic monitoring time points with equal time intervals in a regular monitoring period; according to the invention, the quality of the stored oil product can be automatically monitored and analyzed, the storage quality parameters of the oil product in the oil product storage tank are automatically obtained at the automatic monitoring time point and are analyzed and calculated to obtain the self-monitoring coefficient, and the storage state of the oil product is fed back and evaluated according to the self-monitoring coefficient.

Description

Technical Field

[0001] The invention belongs to the field of oil storage and relates to data analysis technology, in particular to an oil storage quality assessment system based on data analysis. Background Art

[0002] Oil products refer to petroleum products, including but not limited to gasoline, diesel, lubricants, etc. These products are widely used in transportation, industry, agriculture and other fields, and are indispensable energy and raw materials in modern society. The quality of oil products directly affects its performance and safety. Therefore, there are strict standards and regulations for the production, storage, transportation and use of oil products.

[0003] The oil storage quality assessment system in the prior art can only perform random sampling inspections on the oil storage quality on a regular basis, and cannot provide timely feedback when the oil storage quality is abnormal. At the same time, the random sampling method has low sampling specificity, resulting in the sampling inspection results being unable to reflect the actual storage status of the overall oil.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide an oil storage quality assessment system based on data analysis, which is used to solve the problem that the prior art cannot provide timely feedback when the oil storage quality is abnormal;

[0006] The technical problem to be solved by the present invention is: how to provide an oil storage quality assessment system based on data analysis that can provide timely feedback when the oil storage quality is abnormal.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An oil storage quality assessment system based on data analysis includes a quality assessment platform, wherein the quality assessment platform is communicatively connected with an automatic monitoring module, an automatic assessment module, a regular monitoring module and a database;

[0009] The automatic monitoring module is used to automatically monitor and analyze the quality of stored oil products: divide the oil storage area into several monitoring areas, mark the oil storage tanks located in the monitoring area as monitoring objects, generate a monitoring cycle, set several regular monitoring time points with equal time intervals in the monitoring cycle, and form a regular monitoring period by adjacent regular monitoring time points. Set several automatic monitoring time points with equal time intervals in the regular monitoring period, obtain the temperature data WD, liquid level data YW and concentration data ND of the monitoring object in the monitoring area at the automatic monitoring time point, and perform numerical calculation to obtain the self-monitoring coefficient ZJ of the monitoring object at the automatic monitoring time point; mark the monitoring object as a self-monitoring normal object or a self-monitoring abnormal object according to the self-monitoring coefficient ZJ;

[0010] The automatic evaluation module is used to evaluate and analyze the oil quality monitoring data at the automatic monitoring time point;

[0011] The periodic monitoring module is used to verify and analyze the quality of stored oil products.

[0012] Furthermore, the process of acquiring the temperature data WD of the monitored object includes: acquiring the temperature value of the oil product in the monitored object, acquiring the standard temperature range of the oil product in the monitored object through a database, marking the average value of the maximum value and the minimum value of the standard temperature range as the standard temperature value, and marking the absolute value of the difference between the oil product temperature value and the standard temperature value as the temperature data WD of the monitored object; the process of acquiring the liquid level data YW includes: acquiring the liquid level value of the oil product in the monitored object, and marking the difference between the liquid level value of the monitored object at the last automatic monitoring time point and the current liquid level value as the liquid level data YW of the monitored object; the process of acquiring the concentration data ND includes: acquiring the concentration value of the oil product in the monitored object, acquiring the standard concentration range of the oil product in the monitored object through a database, marking the average value of the maximum value and the minimum value of the standard concentration range as the standard concentration value, and marking the absolute value of the difference between the oil product concentration value and the standard concentration value as the concentration data ND.

[0013] Furthermore, the specific process of marking the monitored object as a self-monitoring normal object or a self-monitoring abnormal object includes: obtaining the self-monitoring threshold ZJmax of the monitored object through the database, and comparing the self-monitoring coefficient ZJ of the monitored object at the automatic monitoring time point with the self-monitoring threshold ZJmax: if the self-monitoring coefficient ZJ is less than the self-monitoring threshold ZJmax, it is determined that the oil storage quality of the monitored object at the automatic monitoring time point meets the requirements, and the corresponding monitored object is marked as a self-monitoring normal object; if the self-monitoring coefficient ZJ is greater than or equal to the self-monitoring threshold ZJmax, it is determined that the oil storage quality of the monitored object at the automatic monitoring time point does not meet the requirements, and the corresponding monitored object is marked as a self-monitoring abnormal object.

[0014] Furthermore, the specific process of the automatic evaluation module evaluating and analyzing the oil quality monitoring data at the automatic monitoring time point includes: marking the ratio of the number of self-monitoring abnormal objects marked at the automatic monitoring time point to the total number of monitoring objects as the self-monitoring abnormality coefficient at the automatic monitoring time point, and judging whether the overall oil storage status at the automatic monitoring time point meets the requirements through the self-monitoring abnormality coefficient.

[0015] Furthermore, the specific process of determining whether the overall storage status of the oil product at the automatic monitoring time point meets the requirements includes: comparing the self-monitoring abnormality coefficient with the preset self-monitoring abnormality threshold: if the self-monitoring abnormality coefficient is less than the self-monitoring abnormality threshold, it is determined that the overall storage status of the oil product meets the requirements, and an instant verification signal is generated and the instant verification signal and all self-monitoring abnormal objects are sent to the mobile phone terminal of the manager through the quality assessment platform; if the self-monitoring abnormality coefficient is greater than or equal to the self-monitoring abnormality threshold, it is determined that the overall storage status of the oil product does not meet the requirements, and an interval adjustment signal is generated and sent to the periodic monitoring module through the quality assessment platform. At the same time, the time difference between the next periodic monitoring time point and the current automatic monitoring time point is marked as the adjustment value, and all subsequent periodic monitoring time points within the monitoring period are adjusted forward, and the adjustment duration is the adjustment value.

[0016] Furthermore, the regular monitoring module verifies and analyzes the quality of the stored oil products at the regular monitoring time point or when receiving the interval adjustment signal: the verification and analysis methods include performing distillation range detection, freezing point detection, acidity detection and corrosiveness detection on the oil product samples.

[0017] Furthermore, the sample acquisition process for verification analysis at regular monitoring time points includes: marking the monitoring objects that are marked as self-monitoring normal objects at all automatic monitoring time points in the regular monitoring period as sampling objects, summing and averaging the self-monitoring coefficients ZJ of the sampling objects at all automatic monitoring time points to obtain the sampling coefficients of the sampling objects, arranging the sampling objects in descending order of the sampling coefficient values ​​to obtain a sampling sequence, and intercepting the first L1 sampling objects from the sampling sequence for verification sample collection; and collecting verification samples from all self-monitoring abnormal objects when the interval adjustment signal is received.

[0018] The present invention has the following beneficial effects:

[0019] 1. The automatic monitoring module can automatically monitor and analyze the quality of stored oil products. The automatic monitoring time point can be set during the regular monitoring period. At the automatic monitoring time point, the storage quality parameters of the oil products in the oil storage tank can be automatically obtained and analyzed and calculated to obtain the self-monitoring coefficient. The storage status of the oil products can be fed back and evaluated based on the self-monitoring coefficient.

[0020] 2. The automatic evaluation module can evaluate and analyze the oil quality monitoring data at the automatic monitoring time point, and provide feedback on the overall storage status of the oil at the automatic monitoring time point according to the marking status of the self-monitoring abnormal object, so as to generate different processing signals according to the feedback results, and adjust the regular monitoring time point when necessary to ensure the timeliness of the verification analysis;

[0021] 3. The quality of stored oil products can be verified and analyzed through the regular monitoring module. On the basis of automatic monitoring, high-precision detection methods such as distillation detection and condensation point detection are added to improve the monitoring accuracy while ensuring the efficiency of storage quality monitoring. At the same time, different trigger conditions can be set to ensure timely feedback when oil storage is abnormal;

[0022] 4. The quality assessment module can be used to conduct periodic assessment and analysis of oil storage quality, and a comprehensive analysis can be conducted on multiple storage quality parameters to obtain an assessment coefficient. The assessment coefficient can be used to provide feedback on the overall oil storage status. At the same time, combined with distribution analysis, the monitoring period of storage quality abnormalities can be optimized for decision analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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 work.

[0024] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0025] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0026] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0027] Embodiment 1: Figure 1 As shown, an oil storage quality assessment system based on data analysis includes a quality assessment platform, and the quality assessment platform is communicatively connected with an automatic monitoring module, an automatic assessment module, a regular monitoring module and a database.

[0028] The automatic monitoring module is used to automatically monitor and analyze the quality of stored oil products: the oil storage area is divided into several monitoring areas, the oil storage tanks located in the monitoring area are marked as monitoring objects, a monitoring cycle is generated, several regular monitoring time points with equal time intervals are set within the monitoring cycle, and a regular monitoring period is composed of adjacent regular monitoring time points. Several automatic monitoring time points with equal time intervals are set within the regular monitoring period, and the temperature data WD, liquid level data YW and concentration data ND of the monitoring objects in the monitoring area are obtained at the automatic monitoring time points.

[0029] The process of acquiring the temperature data WD of the monitored object includes: acquiring the temperature value of the oil product in the monitored object, acquiring the standard temperature range of the oil product in the monitored object through a database, marking the average value of the maximum value and the minimum value of the standard temperature range as the standard temperature value, and marking the absolute value of the difference between the oil product temperature value and the standard temperature value as the temperature data WD of the monitored object.

[0030] The process of acquiring the liquid level data YW includes: acquiring the liquid level value of the oil in the monitored object, and marking the difference between the liquid level value of the monitored object at the last automatic monitoring time point and the current liquid level value as the liquid level data YW of the monitored object.

[0031] The process of acquiring the concentration data ND includes: acquiring the oil concentration value in the monitored object, acquiring the standard concentration range of the oil in the monitored object through the database, marking the average of the maximum and minimum values ​​of the standard concentration range as the standard concentration value, and marking the absolute value of the difference between the oil concentration value and the standard concentration value as the concentration data ND.

[0032] By formula The self-monitoring coefficient ZJ of the monitored object at the automatic monitoring time point is obtained, where k1, k2 and k3 are all proportional coefficients, and k1>k2>k3>1; the self-monitoring threshold ZJmax of the monitored object is obtained through the database, and the self-monitoring coefficient ZJ of the monitored object at the automatic monitoring time point is compared with the self-monitoring threshold ZJmax: if the self-monitoring coefficient ZJ is less than the self-monitoring threshold ZJmax, it is determined that the oil storage quality of the monitored object at the automatic monitoring time point meets the requirements, and the corresponding monitored object is marked as a self-monitoring normal object; if the self-monitoring coefficient ZJ is greater than or equal to the self-monitoring threshold ZJmax, it is determined that the oil storage quality of the monitored object at the automatic monitoring time point does not meet the requirements, and the corresponding monitored object is marked as a self-monitoring abnormal object; the automatic monitoring time point is set within the regular monitoring period, and the storage quality parameters of the oil in the oil storage tank are automatically obtained at the automatic monitoring time point, and the self-monitoring coefficient is obtained by analysis and calculation, and the oil storage status is fed back and evaluated according to the self-monitoring coefficient.

[0033] The automatic evaluation module is used to evaluate and analyze the oil quality monitoring data at the automatic monitoring time point: the ratio of the number of self-monitoring abnormal objects marked at the automatic monitoring time point to the total number of monitored objects is marked as the self-monitoring abnormality coefficient at the automatic monitoring time point, and the self-monitoring abnormality coefficient is compared with the preset self-monitoring abnormality threshold: if the self-monitoring abnormality coefficient is less than the self-monitoring abnormality threshold, an instant verification signal is generated and the instant verification signal and all self-monitoring abnormal objects are sent to the mobile phone terminal of the manager through the quality evaluation platform; if the self-monitoring abnormality coefficient is greater than or equal to the self-monitoring abnormality threshold, an interval adjustment signal is generated and the interval adjustment signal is sent to the regular monitoring module through the quality evaluation platform, and the time difference between the next regular monitoring time point and the current automatic monitoring time point is marked as the adjustment value, and all subsequent regular monitoring time points within the monitoring cycle are adjusted forward, and the adjustment duration is the adjustment value; according to the marking status of the self-monitoring abnormal object, the overall storage status of the oil at the automatic monitoring time point is fed back, so as to generate different processing signals according to the feedback results, and adjust the regular monitoring time points when necessary to ensure the timeliness of the verification analysis.

[0034] The periodic monitoring module is used to verify and analyze the quality of stored oil products at the periodic monitoring time point or when the interval adjustment signal is received: the verification and analysis methods include distillation range detection, condensation point detection, acidity detection and corrosion detection of oil samples; the sample acquisition process for verification and analysis at the periodic monitoring time point includes: marking the monitoring objects marked as normal self-monitoring objects at all automatic monitoring time points in the periodic monitoring period as sampling objects, summing and averaging the self-monitoring coefficients ZJ of the sampling objects at all automatic monitoring time points to obtain the sampling coefficients of the sampling objects, arranging the sampling objects in descending order according to the sampling coefficient values ​​to obtain a sampling sequence, and intercepting the first L1 sampling objects from the sampling sequence for verification sample collection; L1 is a numerical constant, and the specific value of L1 is set by the management personnel; verification samples are collected from all self-monitoring abnormal objects when the interval adjustment signal is received; on the basis of automatic monitoring, high-precision detection methods such as distillation detection and condensation point detection are added to improve the monitoring accuracy while ensuring the efficiency of storage quality monitoring, and different trigger conditions are set to ensure the timeliness of feedback when the oil storage is abnormal.

[0035] Embodiment 2: Figure 2 As shown, the quality assessment platform is also communicatively connected with a quality assessment module and a distribution analysis module.

[0036] The quality assessment module is used to evaluate and analyze the quality of oil products: at the end of the monitoring cycle, the self-monitoring abnormal data ZY, interval adjustment data JT and self-monitoring average data ZP of the monitoring cycle are obtained. The process of obtaining the self-monitoring abnormal data ZY includes: marking the number of times the monitored object is marked as a self-monitoring abnormal object during the monitoring cycle as the abnormal value of the monitored object, marking the ratio of the sum of the abnormal values ​​of all monitored objects to the number of monitored objects as the self-monitoring abnormal data ZY, the interval adjustment data JT is the number of times the regular monitoring module receives the interval adjustment signal during the monitoring cycle, and the process of obtaining the self-monitoring average data ZP includes: summing and averaging the self-monitoring coefficients ZJ of all monitored objects at all automatic monitoring time points to obtain to the self-monitoring average data ZP; the evaluation coefficient PG of the monitoring period is obtained through the formula PG=m1*ZY+m2*JT+m3*ZP, wherein m1, m2 and m3 are all proportional coefficients, and m1>m2>m3>1; the evaluation threshold PGmax is obtained through the database, and the evaluation coefficient PG is compared with the evaluation threshold PGmax: if the evaluation coefficient PG is less than the evaluation threshold PGmax, it is determined that the overall oil storage quality within the monitoring period meets the requirements; if the evaluation coefficient PG is greater than or equal to the evaluation threshold PGmax, it is determined that the overall oil storage quality within the monitoring period does not meet the requirements, and a distribution analysis signal is generated and sent to the distribution analysis module through the quality assessment platform.

[0037] The distribution analysis module is used to analyze the distribution status of self-monitoring abnormal objects within the monitoring period: the abnormal values ​​of all monitored objects in the monitoring area are summed to obtain the concentrated value of the monitoring area, the variance of the concentrated values ​​of all monitoring areas is calculated to obtain the distribution coefficient, the distribution threshold is obtained through the database, and the distribution coefficient is compared with the distribution threshold: if the distribution coefficient is less than the distribution threshold, an optimization signal is generated and sent to the mobile terminal of the manager through the quality assessment platform. After receiving the optimization signal, the manager can optimize the storage tank and the acquisition terminal for several monitoring objects with the largest abnormal value; if the distribution coefficient is greater than or equal to the distribution threshold, a regional optimization signal is generated and sent to the mobile terminal of the manager through the quality assessment platform. After receiving the regional optimization signal, the manager can optimize the environmental control and regional management for several monitoring areas with the largest concentrated value; a comprehensive analysis is performed on multiple storage quality parameters to obtain the evaluation coefficient, and the overall oil storage status is fed back through the evaluation coefficient. At the same time, the monitoring period of storage quality abnormality is optimized and analyzed in combination with the distribution analysis.

[0038] A data analysis-based oil storage quality assessment system obtains temperature data WD, liquid level data YW and concentration data ND of a monitored object in a monitoring area at an automatic monitoring time point during operation, performs numerical calculation to obtain a self-monitoring coefficient ZJ, and marks the monitored object as a self-monitoring normal object or a self-monitoring abnormal object through the self-monitoring coefficient ZJ; marks the ratio of the number of self-monitoring abnormal objects marked at the automatic monitoring time point to the total number of monitored objects as the self-monitoring abnormal coefficient at the automatic monitoring time point, determines whether the overall storage quality of the oil product at the automatic monitoring time point meets the requirements through the self-monitoring abnormal coefficient, and adjusts all subsequent regular monitoring time points within the monitoring period forward if the requirements are not met, and performs verification and analysis on the quality of the stored oil product at the regular monitoring time point or when an interval adjustment signal is received.

[0039] 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 claims, they should all fall within the protection scope of the present invention.

[0040] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula A technician in this field collects multiple groups of sample data and sets a corresponding self-monitoring coefficient for each group of sample data; substitutes the set self-monitoring coefficient and the collected sample data into the formula, any three formulas constitute a three-variable linear equation system, and the calculated coefficients are screened and averaged to obtain the values ​​of k1, k2 and k3, which are 4.32, 2.85 and 2.61 respectively;

[0041] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding self-monitoring coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the self-monitoring coefficient is proportional to the value of the temperature data.

[0042] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0043] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An oil storage quality assessment system based on data analysis, characterized in that: It includes a quality assessment platform, which is communicatively connected with an automatic monitoring module, an automatic assessment module, a regular monitoring module and a database; The automatic monitoring module is used to automatically monitor and analyze the quality of stored oil products: divide the oil storage area into several monitoring areas, mark the oil storage tanks located in the monitoring area as monitoring objects, generate a monitoring cycle, set several regular monitoring time points with equal time intervals in the monitoring cycle, and form a regular monitoring period by adjacent regular monitoring time points. Set several automatic monitoring time points with equal time intervals in the regular monitoring period, obtain the temperature data WD, liquid level data YW and concentration data ND of the monitoring object in the monitoring area at the automatic monitoring time point, and perform numerical calculation to obtain the self-monitoring coefficient ZJ of the monitoring object at the automatic monitoring time point; mark the monitoring object as a self-monitoring normal object or a self-monitoring abnormal object according to the self-monitoring coefficient ZJ; The automatic evaluation module is used to evaluate and analyze the oil quality monitoring data at the automatic monitoring time point; The periodic monitoring module is used to verify and analyze the quality of stored oil products.

2. The oil storage quality assessment system based on data analysis according to claim 1 is characterized in that: The process of acquiring the temperature data WD of the monitored object includes: acquiring the temperature value of the oil product in the monitored object, acquiring the standard temperature range of the oil product in the monitored object through a database, marking the average value of the maximum value and the minimum value of the standard temperature range as the standard temperature value, and marking the absolute value of the difference between the oil product temperature value and the standard temperature value as the temperature data WD of the monitored object; the process of acquiring the liquid level data YW includes: acquiring the liquid level value of the oil product in the monitored object, and marking the difference between the liquid level value of the monitored object at the last automatic monitoring time point and the current liquid level value as the liquid level data YW of the monitored object; the process of acquiring the concentration data ND includes: acquiring the concentration value of the oil product in the monitored object, acquiring the standard concentration range of the oil product in the monitored object through a database, marking the average value of the maximum value and the minimum value of the standard concentration range as the standard concentration value, and marking the absolute value of the difference between the oil product concentration value and the standard concentration value as the concentration data ND.

3. The oil storage quality assessment system based on data analysis according to claim 2 is characterized in that: The specific process of marking the monitored object as a self-monitoring normal object or a self-monitoring abnormal object includes: obtaining the self-monitoring threshold ZJmax of the monitored object through the database, and comparing the self-monitoring coefficient ZJ of the monitored object at the automatic monitoring time point with the self-monitoring threshold ZJmax: if the self-monitoring coefficient ZJ is less than the self-monitoring threshold ZJmax, it is determined that the oil storage quality of the monitored object at the automatic monitoring time point meets the requirements, and the corresponding monitored object is marked as a self-monitoring normal object; if the self-monitoring coefficient ZJ is greater than or equal to the self-monitoring threshold ZJmax, it is determined that the oil storage quality of the monitored object at the automatic monitoring time point does not meet the requirements, and the corresponding monitored object is marked as a self-monitoring abnormal object.

4. The oil storage quality assessment system based on data analysis according to claim 3 is characterized in that: The specific process of the automatic evaluation module evaluating and analyzing the oil quality monitoring data at the automatic monitoring time point includes: marking the ratio of the number of self-monitoring abnormal objects marked at the automatic monitoring time point to the total number of monitoring objects as the self-monitoring abnormality coefficient at the automatic monitoring time point, and judging whether the overall oil storage status at the automatic monitoring time point meets the requirements through the self-monitoring abnormality coefficient.

5. The oil storage quality assessment system based on data analysis according to claim 4 is characterized in that: The specific process of determining whether the overall storage status of the oil product at the automatic monitoring time point meets the requirements includes: comparing the self-monitoring abnormality coefficient with the preset self-monitoring abnormality threshold: if the self-monitoring abnormality coefficient is less than the self-monitoring abnormality threshold, it is determined that the overall storage status of the oil product meets the requirements, and an instant verification signal is generated and the instant verification signal and all self-monitoring abnormal objects are sent to the mobile phone terminal of the manager through the quality assessment platform; if the self-monitoring abnormality coefficient is greater than or equal to the self-monitoring abnormality threshold, it is determined that the overall storage status of the oil product does not meet the requirements, and an interval adjustment signal is generated and sent to the periodic monitoring module through the quality assessment platform. At the same time, the time difference between the next periodic monitoring time point and the current automatic monitoring time point is marked as the adjustment value, and all subsequent periodic monitoring time points within the monitoring period are adjusted forward, and the adjustment duration is the adjustment value.

6. The oil storage quality assessment system based on data analysis according to claim 5 is characterized in that: The regular monitoring module verifies and analyzes the quality of the stored oil products at the regular monitoring time point or when receiving the interval adjustment signal: the verification and analysis methods include distillation range detection, freezing point detection, acidity detection and corrosiveness detection of the oil product samples.

7. The oil storage quality assessment system based on data analysis according to claim 6, characterized in that: The sample acquisition process for verification analysis at regular monitoring time points includes: marking the monitoring objects that are marked as self-monitoring normal objects at all automatic monitoring time points in the regular monitoring period as sampling objects, summing and averaging the self-monitoring coefficients ZJ of the sampling objects at all automatic monitoring time points to obtain the sampling coefficients of the sampling objects, arranging the sampling objects in descending order of the sampling coefficient values ​​to obtain a sampling sequence, and intercepting the first L1 sampling objects from the sampling sequence for verification sample collection; and collecting verification samples from all self-monitoring abnormal objects when the interval adjustment signal is received.

Images