An oil filtering device and its usage method

Abstract

The present invention relates to the technical field of oil filtration, and particularly relates to an oil filtration device and a usage method. The oil filtration device includes a filter body, a data acquisition component, and a data processor. The data acquisition component acquires the oil state data at the inlet and outlet of the filter body within a target time period. The data processor acquires the suspected abnormal state data in the oil state data and the corresponding suspected moments, and based on the suspected abnormal state data at each suspected moment, obtains the target suspected pollution degree at each suspected moment; based on the numerical change trend and time continuity of the target suspected pollution degrees at all suspected moments, obtains the pseudo-abnormal degree of the target time period, and further obtains the true pollution degree at the end moment of the target time period. According to the true pollution degree, obtains the next backwashing moment, improves the accuracy of the backwashing moment, and ultimately improves the accuracy of the backwashing start control and the efficiency of the oil filtration device.

Description

Technical Field

[0001] The present invention relates to the technical field of oil filtration, and particularly relates to an oil filtration device and a usage method thereof. Background Art

[0002] An oil filtration device is used to filter impurities from oil. To ensure the impurity filtration efficiency, the oil filtration device is usually equipped with a flushing and sewage discharging device, or the oil filtration device is integrally provided with the flushing and sewage discharging device to form an automatic backwashing oil filtration device, which is used to perform regular or irregular backwashing on the oil filtration device to wash away the filtered impurities.

[0003] As Figure 1 shown, it is a common structure of an oil filtration device, including: a liquid inlet 1, a liquid outlet 2, a filter screen 3, a slag discharge port 4, and a flushing guide valve 5. The existing automatic backwashing strategy is: set a fixed pressure difference threshold, and when the actual pressure difference data of the oil liquid at the liquid inlet 1 and the liquid outlet 2 reaches the set pressure difference threshold, the backwashing is automatically started. However, the method of controlling the start of backwashing by using a fixed pressure difference threshold does not consider the actual state change of the oil during the filtration process of the oil filtration device. For example, the change in the oil temperature will change the viscosity of the oil medium, and the viscosity will affect the flow rate of the oil liquid through the filter screen 3, resulting in a large error between the pressure difference data of the oil liquid and the true pressure difference data.

[0004] Therefore, in the existing backwashing method of the oil filtration device, the backwashing start time is inaccurately obtained, resulting in a low accuracy of the backwashing start control, thereby affecting the operation efficiency of the oil filtration device. Summary of the Invention

[0005] In view of this, in order to solve the technical problem that the backwashing start time in the existing backwashing start control method of the oil filtration device is inaccurately obtained, the present invention provides an oil filtration device and a usage method thereof.

[0006] The specific technical solutions adopted are as follows:

[0007] An embodiment of the present invention provides an oil filtration device, including a filter body, the filter body includes a liquid inlet and a liquid outlet, and the oil filtration device further includes:

[0008] a data acquisition component, configured to obtain the oil state data of the liquid inlet and the liquid outlet within a target time period;

[0009] A data processor is configured to obtain suspected abnormal status data in the petroleum status data and corresponding suspected moments; based on the suspected abnormal status data at each suspected moment, obtain the target suspected pollution degree at each suspected moment; according to the numerical change trend and time continuity of the target suspected pollution degrees at all suspected moments, obtain the pseudo-abnormal degree of the target time period; according to the pseudo-abnormal degree, obtain the true pollution degree of the last suspected moment in the target time period; and according to the true pollution degree, obtain the next backwashing moment.

[0010] In some possible implementation manners, the petroleum status data includes pressure, temperature, and flow rate.

[0011] Obtaining the suspected abnormal status data in the petroleum status data and corresponding suspected moments includes:

[0012] Obtain the petroleum status data of the liquid inlet at each sampling moment within the target time period, map it into a three-dimensional coordinate system, and then perform clustering to obtain discrete points outside the cluster as the first initial discrete points; the sampling moment corresponding to the first initial discrete points is the first initial suspected moment.

[0013] Obtain the petroleum status data of the liquid outlet at each sampling moment within the target time period, map it into a three-dimensional coordinate system, and then perform clustering to obtain discrete points outside the cluster as the second initial discrete points; the sampling moment corresponding to the second initial discrete points is the second initial suspected moment.

[0014] Obtain the coincident initial suspected moments among the first initial suspected moment and the second initial suspected moment, the coincident initial suspected moments are the suspected moments, and the petroleum status data at the coincident initial suspected moments is the suspected abnormal status data.

[0015] In some possible implementation manners, based on the suspected abnormal status data at each suspected moment, obtaining the target suspected pollution degree at each suspected moment includes:

[0016] Based on the abnormal conditions of the pressure and flow rate at the liquid inlet and the liquid outlet in the suspected abnormal status data at each suspected moment, obtain the initial suspected pollution degree at each suspected moment.

[0017] Based on the correlation between the temperature change at the liquid inlet and the liquid outlet and the change of the initial suspected pollution degree in the initial suspected abnormal status data at each suspected moment, adjust the initial suspected pollution degree to obtain the target suspected pollution degree.

[0018] In some possible implementation manners, based on the abnormal conditions of the pressure and flow rate of the liquid inlet and the liquid outlet in the suspected abnormal state data at each suspected moment, obtaining the initial suspected contamination degree at each suspected moment includes:

[0019] Obtaining the discrete distance between the pressure of the suspected abnormal state data at the first suspected moment of the liquid inlet and the pressure characteristic positions of each of the clusters, and obtaining a first pressure discrete distance according to each of the discrete distances of the pressure; obtaining the discrete distance between the flow rate of the suspected abnormal state data at the first suspected moment of the liquid inlet and the flow rate characteristic positions of each of the clusters, and obtaining a first flow rate discrete distance according to each of the discrete distances of the flow rate; the first suspected moment is any one of the suspected moments;

[0020] Obtaining the discrete distance between the pressure of the suspected abnormal state data at the first suspected moment of the liquid outlet and the pressure characteristic positions of each of the clusters, and obtaining a second pressure discrete distance according to each of the discrete distances of the pressure; obtaining the discrete distance between the flow rate of the suspected abnormal state data at the first suspected moment of the liquid outlet and the flow rate characteristic positions of each of the clusters, and obtaining a second flow rate discrete distance according to each of the discrete distances of the flow rate;

[0021] Obtaining the pressure discrete distance difference at the first suspected moment according to the first pressure discrete distance and the second pressure discrete distance, and obtaining the flow rate discrete distance difference at the first suspected moment according to the first flow rate discrete distance and the second flow rate discrete distance;

[0022] Obtaining the initial suspected contamination degree at the first suspected moment according to the pressure discrete distance difference and the flow rate discrete distance difference.

[0023] In some possible implementation manners, the process of obtaining the correlation relationship includes:

[0024] Obtaining the temperature differences at the first suspected moment and the previous consecutive preset number of suspected moments to form a temperature difference sequence; the temperature difference is the difference between the temperatures in the suspected abnormal state data of the liquid inlet and the liquid outlet at the corresponding suspected moment;

[0025] Obtaining the correlation degree between the temperature difference sequence and the suspected contamination degree sequence at the first suspected moment; the suspected contamination degree sequence is composed of the suspected contamination degrees at the first suspected moment and the previous consecutive preset number of suspected moments.

[0026] In some possible implementation manners, the process of obtaining the target suspected contamination degree includes:

[0027] Obtaining an adjustment coefficient according to the correlation degree at the first suspected moment, and the adjustment coefficient is negatively correlated with the correlation degree;

[0028] Adjust the initial suspected pollution degree according to the adjustment coefficient to obtain the target suspected pollution degree at the first suspected moment.

[0029] In some possible implementation manners, the specific numerical change trend is: the proportion of the number of suspected moments in the target suspected pollution degree sequence that meet the preset conditions; the preset condition is: the target suspected pollution degree at the second suspected moment is greater than the target suspected pollution degree at the third suspected moment; the second suspected moment is any suspected moment, and the third suspected moment is the previous suspected moment of the second suspected moment; the target suspected pollution degree sequence is composed of the target suspected pollution degrees of all suspected moments.

[0030] The process of obtaining the time continuity includes: obtaining the variance of the time interval sequence, where the time interval sequence is composed of the time intervals between all adjacent sampling moments in the target suspected pollution degree sequence; obtaining the time continuity according to the variance of the time interval sequence, and the time continuity is negatively correlated with the variance of the time interval sequence.

[0031] In some possible implementation manners, based on the pseudo-abnormal degree, obtaining the true pollution degree at the last suspected moment in the target time period includes:

[0032] Obtain a refinement coefficient according to the pseudo-abnormal degree, and the refinement coefficient is negatively correlated with the pseudo-abnormal degree;

[0033] Obtain the true pollution degree according to the refinement coefficient and the target suspected pollution degree at the last suspected moment.

[0034] In some possible implementation manners, according to the true pollution degree, obtaining the next backwashing moment includes:

[0035] Obtain the pollution degree difference between the preset true pollution degree threshold and the true pollution degree;

[0036] Obtain the next backwashing moment according to the pollution degree difference and the unit time change rate of the pollution degree; the unit time change rate of the pollution degree is calculated from the true pollution degree and the characteristic time length of the target time period; the characteristic time length is the time interval between the start moment and the last suspected moment of the target time period.

[0037] A method for using an oil filtration device includes the following steps:

[0038] Obtain the next backwashing moment in the above-mentioned oil filtration device;

[0039] When the next backwashing moment arrives, control the start of the flushing device configured with the oil filtering device to perform backwashing on the oil filtering device.

[0040] The present invention has the following technical effects including but not limited to: The oil state data at the liquid inlet and the liquid outlet can reflect the actual operating state of the oil filtering device. Based on the oil state data at the liquid inlet and the liquid outlet, and in combination with the actual operating state of the oil filtering device reflected in the oil state data during the target time period, the next backwashing moment closely related to the actual operating state is obtained, improving the accuracy of obtaining the backwashing moment, ultimately enhancing the precision of backwashing start control, and improving the operating efficiency of the oil filtering device. Description of the Drawings

[0041] Figure 1 is a schematic mechanical structure diagram of an existing oil filtering device;

[0042] Figure 2 is a schematic electrical composition diagram of an oil filtering device provided by the present invention;

[0043] Figure 3 is a specific schematic electrical composition diagram of an oil filtering device provided by the present invention;

[0044] Figure 4 is a data processing flow chart of an oil filtering device provided by the present invention;

[0045] Figure 5 is a schematic three-dimensional coordinate system diagram;

[0046] Figure 6 is a schematic diagram of the acquisition process of suspected abnormal state data and corresponding suspected moments;

[0047] Figure 7 is a schematic diagram of the acquisition process of the target suspected pollution degree;

[0048] Figure 8 is a schematic diagram of the acquisition process of the initial suspected pollution degree;

[0049] Figure 9 is a schematic diagram of the acquisition process of the acquisition process of the correlation;

[0050] Figure 10 is a schematic diagram of the acquisition process of the target suspected pollution degree;

[0051] Figure 11 is a schematic diagram of the acquisition process of the true pollution degree;

[0052] Figure 12 is a schematic diagram of the acquisition process of the next backwashing moment;

[0053] Figure 13 It is an overall flow chart of the method of using a petroleum filtering device;

[0054] In the figure, 1 is the liquid inlet, 2 is the liquid outlet, 3 is the filter screen, 4 is the slag discharge port, and 5 is the flushing guide valve. DETAILED DESCRIPTION

[0055] An embodiment of a petroleum filtering device:

[0056] This embodiment provides a petroleum filtering device, including a filter body, the filter body such as Figure 1 As shown, the filter body includes a liquid inlet 1 and a liquid outlet 2. Of course, the filter body also includes other components, such as: a filter screen 3, a slag discharge port 4 and a flushing guide valve 5. Usually, the petroleum filtering device is equipped with a flushing device. As a common component, the flushing device usually includes a flushing controller, a flushing motor and a flushing pump. The flushing controller is connected to the flushing motor and the flushing guide valve 5 by signal, and is used to send relevant control instructions to the flushing motor and the flushing guide valve 5. The flushing motor is connected to the flushing pump in a transmission manner, and the flushing pipeline of the flushing pump is connected to the flushing guide valve 5. The filtering process of the petroleum filtering device and the control of the flushing process are both existing technologies and will not be repeated here.

[0057] When the petroleum filtration device is in operation, the internal filtration efficiency will gradually deteriorate due to the accumulation of dirt and residue on the filter 3, resulting in a high pressure at the liquid inlet 1 and a low pressure at the liquid outlet 2. At this time, backwashing is required to remove the dirt on the filter 3. Normally, the accumulation of dirt has the characteristic of slowly increasing, that is, the data obtained is relatively continuous within a period of time after backwashing. However, when filtering petroleum, it is impossible to guarantee that the quality of the input oil is consistent during the monitoring stage, and there may be fluctuations, resulting in discontinuous data. Therefore, discontinuous data points may be due to high pollution.

[0058] like Figure 2 As shown, the petroleum filtering device also includes a data acquisition component and a data processor, and the data acquisition component and the data processor are signal connected.

[0059] The data acquisition component is used to obtain the petroleum status data of the liquid inlet 1 and the liquid outlet 2 within the target time period. Among them, the target time period is a preset time period, and the length of the time period is set according to actual needs. In this embodiment, in order to ensure the reliability of flushing, the starting moment of the target time period is the starting moment when the normal petroleum filtration starts after the end of the last backwashing. Since the main purpose of the data processor is to judge the backwashing conditions of the petroleum filtration device, remove pseudo-abnormal data, and accurately measure the timing of the next backwashing of the petroleum filtration device. Therefore, the end moment of the target time period is earlier than the starting moment of the next backwashing. So, the time length of the target time period is determined according to the time range of the time intervals between a large number of adjacent two backwashings in the past, such as 25 minutes. Then, within 25 minutes after the end of the last backwashing, it is impossible to start the next backwashing. Even in extreme cases, after the end of the last backwashing, at least more than 25 minutes of interval is required to possibly start the next backwashing.

[0060] The data acquisition component needs to ensure that it does not affect the normal flow of petroleum inside the petroleum filtration device, and as much as possible, make the monitoring positions of the data acquisition component at the liquid inlet 1 and the liquid outlet 2 close to the core flow area of the petroleum, so as to accurately monitor the petroleum status data. Then, first, the data acquisition component can adopt miniaturized detection devices, and the data acquisition component can use wireless communication to transmit data to the data processor. For example, the data acquisition component is equipped with a low-power wireless communication module such as a Bluetooth module, and the data processor is also equipped with the same type of wireless communication module, so that signal transmission lines can be dispensed with, further avoiding affecting the normal operation of the petroleum filtration device and reducing power consumption at the same time. Correspondingly, a dedicated power supply needs to be set in the data acquisition component, and a micro power supply such as a button battery can be used to reduce the volume and further avoid affecting the normal operation of the petroleum filtration device.

[0061] As a specific implementation, the petroleum status data obtained by the data acquisition component is pressure, temperature, and flow rate. Among them, the pressure is the oil pressure of the petroleum, the temperature is the temperature of the petroleum, and the flow rate is the flow rate of the petroleum. Then, the data acquisition component includes a first data acquisition unit and a second data acquisition unit, as Figure 3 shown, the first data acquisition unit includes a first pressure sensor, a first temperature sensor, and a first flow sensor. The first data acquisition unit is arranged at the liquid inlet 1 and is used to detect the pressure, temperature, and flow rate at the liquid inlet 1. The second data acquisition unit includes a second pressure sensor, a second temperature sensor, and a second flow sensor. The second data acquisition unit is arranged at the liquid outlet 2 and is used to detect the pressure, temperature, and flow rate at the liquid outlet 2. It should be understood that each of the above sensors can be a small sensor, and the specific model is set according to actual needs.

[0062] It should be noted that the acquisition frequencies of the above-mentioned various sensors are kept consistent. For a certain sampling moment, the acquired data includes the pressure, temperature, and flow rate at the liquid inlet 1 and the pressure, temperature, and flow rate at the liquid outlet 2 at this sampling moment.

[0063] The data processor is used to process data according to the petroleum state data to obtain the next backwashing moment. The hardware devices of the data processor can be configured as data processing chips such as a CPU or a single-chip microcomputer.

[0064] The data processing strategy executed by the data processor is as Figure 4 shown and includes the following steps:

[0065] Step S1: Obtain the suspected abnormal state data and the corresponding suspected moments in the petroleum state data.

[0066] The petroleum state data can reflect the actual state of the petroleum during the filtration process. According to the actual state, the suspected abnormal state data can be obtained from it, and thus the suspected moments corresponding to the suspected abnormal state data can be obtained.

[0067] In this embodiment, a three-dimensional coordinate system is constructed, as Figure 5 shown. The X-axis of the three-dimensional coordinate system is temperature, the Y-axis is pressure, and the Z-axis is flow rate.

[0068] The acquisition process of the suspected abnormal state data and the corresponding suspected moments is as Figure 6 shown and includes:

[0069] Step S1-1: Obtain the petroleum state data of the liquid inlet at each sampling moment within the target time period, map it into the three-dimensional coordinate system, and perform clustering to obtain the discrete points outside the cluster as the first initial discrete points; the sampling moments corresponding to the first initial discrete points are the first initial suspected moments;

[0070] Step S1-2: Obtain the petroleum state data of the liquid outlet at each sampling moment within the target time period, map it into the three-dimensional coordinate system, and perform clustering to obtain the discrete points outside the cluster as the second initial discrete points; the sampling moments corresponding to the second initial discrete points are the second initial suspected moments;

[0071] Step S1-3: Obtain the overlapping initial suspected moments among the first initial suspected moments and the second initial suspected moments. The overlapping initial suspected moments are the suspected moments, and the petroleum state data at the overlapping initial suspected moments is the suspected abnormal state data.

[0072] For the petroleum state data of the liquid inlet 1:

[0073] Since the target time period includes multiple sampling moments, pressure, temperature, and flow rate data at the liquid inlet 1 can be obtained at each sampling moment. Each sampling moment corresponds to a three-dimensional coordinate point, and the three-dimensional coordinate point is composed of the pressure, temperature, and flow rate data at the corresponding sampling moment. Therefore, obtaining the three-dimensional coordinate points of the liquid inlet 1 at each sampling moment within the target time period is expressed as , where is the pressure at the x-th sampling moment, is the temperature at the x-th sampling moment, is the flow rate at the x-th sampling moment. Then it is mapped into a three-dimensional coordinate system, so that there are multiple three-dimensional coordinate points in the three-dimensional coordinate system.

[0074] Perform a clustering operation on the three-dimensional coordinate points in the three-dimensional coordinate system. In this embodiment, the clustering algorithm is an existing algorithm, which is set according to actual needs. For example, the K-means clustering algorithm, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm, etc. are used. If the K-means clustering algorithm is used, the number of clusters, that is, the value of K, needs to be set in advance. For example, K = 3. The DBSCAN clustering algorithm does not require setting the number of clusters. This embodiment takes the DBSCAN clustering algorithm as an example.

[0075] The neighborhood size during DBSCAN clustering is set according to actual needs. As a specific implementation, a suitable neighborhood needs to be selected during DBSCAN clustering to ensure the performance of clustering. An overly large neighborhood distance is likely to misestimate discrete points into the normal range, and an overly small neighborhood is likely to regard normal points as discrete points. Since the data continuously increases over time, the selected neighborhood size is the mean value of adjacent data in space. Therefore, the selected neighborhood calculation method is:

[0076] ;

[0077] In the formula, represents the neighborhood selection size in DBSCAN, represents the average change in temperature within the target time period; represents the average change in flow rate within the target time period; represents the average change in pressure within the target time period. The overall calculation formula represents the distance of the average change in three-dimensional space.

[0078] Among them, for any one of the average changes in temperature, flow rate, and pressure, the calculation method is as follows:

[0079] ;

[0080] In the formula, represents the average change amount of the y-th type of petroleum state data, and n represents the number of sampling times within the target time period. represents the y-th type of petroleum state data at the x-th sampling time. represents the y-th type of petroleum state data at the (x + 1)-th sampling time; when y = 1, 2, and 3, the y-th type of petroleum state data represents temperature, pressure, and flow rate respectively.

[0081] The number of clusters generated after clustering is determined by the actual clustering result. During the petroleum filtration process, the differences between the data in the normal state are small, and it is easy to classify the data in the normal state into one category during clustering, while the differences between the data in the suspected abnormal state are large and are likely to become isolated discrete points. As Figure 5 shown, the coordinate points within the dashed ellipse area represent discrete points. Therefore, by clustering the petroleum state data at the liquid inlet 1, clusters and discrete points outside the clusters can be obtained. The obtained discrete points are used as the first initial discrete points, and the sampling times corresponding to the first initial discrete points are the first initial suspected times.

[0082] Using the above process, the same clustering process is performed on the petroleum state data at the liquid outlet 2 to obtain discrete points outside the clusters, which are used as the second initial discrete points, and the sampling times corresponding to the second initial discrete points are the second initial suspected times.

[0083] Therefore, based on the liquid inlet 1, multiple first initial discrete points and their corresponding first initial suspected times will be obtained. Based on the liquid outlet 2, multiple second initial discrete points and their corresponding second initial suspected times will be obtained.

[0084] By comprehensively analyzing the liquid inlet 1 and the liquid outlet 2, the coincident initial suspected times among the first initial suspected times and the second initial suspected times are obtained, that is, the same initial suspected times among the first initial suspected times and the second initial suspected times. The coincident initial suspected times are used as the required suspected times, and the petroleum state data at the coincident initial suspected times are used as the required suspected abnormal state data.

[0085] The suspected times and the corresponding suspected abnormal state data obtained in this step represent points that suddenly fluctuate compared to the whole, which do not conform to the continuous and gradually increasing characteristics of the pollution situation of the filter screen 3 and are suspected to be points with pollution characteristics.

[0086] Step S2: Based on the suspected abnormal state data at each suspected time, obtain the target suspected pollution degree at each suspected time.

[0087] The suspected abnormal state data at each suspected moment reflects the suspected contamination degree of the filter screen 3 at the corresponding suspected moment. Therefore, based on the suspected abnormal state data at each suspected moment, the target suspected contamination degree at each suspected moment is obtained. As a specific implementation, as Figure 7 shown, a specific implementation process is given:

[0088] Step S2-1: Based on the abnormal conditions of the pressure and flow rate at the liquid inlet and the liquid outlet in the suspected abnormal state data at each suspected moment, the initial suspected contamination degree at each suspected moment is obtained.

[0089] The severity of the contamination of the filter screen 3 is usually reflected in the abnormal conditions of the pressure and the flow rate at the liquid inlet 1 and the liquid outlet 2. For example, the greater the pressure difference and the flow rate difference at the same sampling moment between the liquid inlet 1 and the liquid outlet 2, the higher the severity of the contamination of the filter screen 3. Therefore, based on the abnormal conditions of the pressure and the flow rate at the liquid inlet 1 and the liquid outlet 2 in the suspected abnormal state data at each suspected moment, the initial suspected contamination degree at each suspected moment is obtained.

[0090] Specifically, as Figure 8 shown, the process of obtaining the initial suspected contamination degree includes:

[0091] Step S2-1-1: Obtain the discrete distances between the pressure of the suspected abnormal state data at the first suspected moment of the liquid inlet and the pressure characteristic positions of each cluster, and obtain the first pressure discrete distance according to the discrete distances of the pressure; obtain the discrete distances between the flow rate of the suspected abnormal state data at the first suspected moment of the liquid inlet and the flow rate characteristic positions of each cluster, and obtain the first flow rate discrete distance according to the discrete distances of the flow rate; the first suspected moment is any suspected moment;

[0092] Step S2-1-2: Obtain the discrete distances between the pressure of the suspected abnormal state data at the first suspected moment of the liquid outlet and the pressure characteristic positions of each cluster, and obtain the second pressure discrete distance according to the discrete distances of the pressure; obtain the discrete distances between the flow rate of the suspected abnormal state data at the first suspected moment of the liquid outlet and the flow rate characteristic positions of each cluster, and obtain the second flow rate discrete distance according to the discrete distances of the flow rate;

[0093] Step S2-1-3: Obtain the pressure discrete distance difference at the first suspected moment according to the first pressure discrete distance and the second pressure discrete distance, and obtain the flow rate discrete distance difference at the first suspected moment according to the first flow rate discrete distance and the second flow rate discrete distance;

[0094] Step S2-1-4: Obtain the initial suspected contamination degree at the first suspected moment according to the pressure discrete distance difference and the flow rate discrete distance difference.

[0095] For the sake of convenience of explanation, the first suspected moment is set as any one of the suspected moments. For the liquid inlet 1:

[0096] The cluster corresponding to the liquid inlet 1 can have only one or multiple. Obtain the discrete distance between the pressure of the suspected abnormal state data at the first suspected moment of the liquid inlet 1 and the pressure characteristic positions of each cluster corresponding to the liquid inlet 1. Among them, the pressure characteristic position of the cluster can be the pressure center position of the corresponding cluster. The calculation method of the pressure center position of the cluster is: obtain the average value of the pressures of all the suspected abnormal state data in the cluster, and this pressure average value is the pressure center position of the cluster. Calculate the absolute value of the difference between the pressure of the suspected abnormal state data at the first suspected moment of the liquid inlet 1 and the pressure average values of each cluster corresponding to the liquid inlet 1, and use it as the pressure discrete distance corresponding to each cluster. Thus, obtain the pressure discrete distances corresponding to each cluster, and then calculate the average value of the pressure discrete distances of all the clusters as the first pressure discrete distance at the first suspected moment of the liquid inlet 1.

[0097] Similarly, obtain the discrete distance between the flow rate of the suspected abnormal state data at the first suspected moment of the liquid inlet 1 and the flow rate characteristic positions of each cluster corresponding to the liquid inlet 1. Among them, the flow rate characteristic position of the cluster can be the flow rate center position of the cluster. The calculation method of the flow rate center position of the cluster is: obtain the average value of the flow rates of all the suspected abnormal state data in the cluster, and this flow rate average value is the flow rate center position of the cluster. Calculate the absolute value of the difference between the flow rate of the suspected abnormal state data at the first suspected moment of the liquid inlet 1 and the flow rate average values of each cluster corresponding to the liquid inlet 1, and use it as the flow rate discrete distance corresponding to each cluster. Thus, obtain the flow rate discrete distances corresponding to each cluster, and then calculate the average value of the flow rate discrete distances of all the clusters as the first flow rate discrete distance at the first suspected moment of the liquid inlet 1.

[0098] Similarly, adopt the above calculation process of the first pressure discrete distance and the first flow rate discrete distance. For the liquid outlet 2: obtain the second pressure discrete distance and the second flow rate discrete distance at the first suspected moment of the liquid outlet 2.

[0099] Then, calculate the absolute value of the difference between the first pressure discrete distance and the second pressure discrete distance as the pressure discrete distance difference at the first suspected moment, and calculate the absolute value of the difference between the first flow discrete distance and the second flow discrete distance as the flow discrete distance difference at the first suspected moment. The greater the pressure discrete distance difference, the greater the pressure difference between the liquid inlet 1 and the liquid outlet 2 at the first suspected moment, and the higher the possible pollution degree. The greater the flow discrete distance difference, the greater the flow difference between the liquid inlet 1 and the liquid outlet 2 at the first suspected moment, and the higher the possible pollution degree. Therefore, based on the pressure discrete distance difference and the flow discrete distance difference, obtain the initial suspected pollution degree at the first suspected moment. The initial suspected pollution degree is positively correlated with both the pressure discrete distance difference and the flow discrete distance difference. In this embodiment, the product of the pressure discrete distance difference and the flow discrete distance difference at the first suspected moment after normalization is used as the initial suspected pollution degree at the first suspected moment.

[0100] The normalization in this embodiment can adopt relevant normalization methods in the prior art, such as: , where Z is the normalized value, z is the input quantity, is the exponential function with the natural constant e as the base.

[0101] Thus, obtain the initial suspected pollution degree at each suspected moment.

[0102] Step S2-2: Based on the correlation between the temperature change of the liquid inlet and the liquid outlet and the change of the initial suspected pollution degree in the initial suspected abnormal state data at each suspected moment, adjust the initial suspected pollution degree to obtain the target suspected pollution degree.

[0103] Temperature is a key factor affecting the changes in pressure and flow. There is a certain correlation between temperature and the viscosity of the oil. When the temperature changes, the viscosity of the oil also changes. Generally speaking, when the temperature rises, the viscosity of the oil will decrease, resulting in an increasing trend in pressure and flow, which will further lead to a relatively high pollution degree compared to the actual situation. Therefore, based on the correlation between the temperature change of the liquid inlet 1 and the liquid outlet 2 and the change of the initial suspected pollution degree in the initial suspected abnormal state data at each suspected moment, adjust the initial suspected pollution degree to obtain the target suspected pollution degree.

[0104] As Figure 9 shown, the process of obtaining the correlation includes:

[0105] Step S2-2-1: Obtain the temperature differences of a continuous preset number of suspected moments before and including the first suspected moment to form a temperature difference sequence; the temperature difference is the difference in temperature in the suspected abnormal state data of the liquid inlet and the liquid outlet at the corresponding suspected moment;

[0106] Step S2-2-2: Obtain the correlation degree between the temperature difference sequence and the suspected contamination degree sequence at the first suspected moment; the suspected contamination degree sequence is composed of the suspected contamination degrees at the first suspected moment and the previous consecutive preset number of suspected moments.

[0107] Among them, the consecutive preset number is set according to actual needs, such as 5. Moreover, the preset number of suspected moments is a plurality of consecutive suspected moments before the first suspected moment, and the last suspected moment among the preset number of suspected moments is the previous suspected moment adjacent to the first suspected moment. The temperature difference is the difference in temperature in the suspected abnormal state data of the liquid inlet 1 and the liquid outlet 2 corresponding to the suspected moment. For example: the temperature difference at the first suspected moment is the difference in temperature between the suspected abnormal state data of the liquid inlet 1 at the first suspected moment and the suspected abnormal state data of the liquid outlet 2 at the first suspected moment. The temperature difference in this embodiment is specifically the absolute value of the temperature difference. Set the fourth suspected moment as any one of the previous consecutive preset number of suspected moments before the first suspected moment, then the temperature difference at the fourth suspected moment is the difference in temperature between the suspected abnormal state data of the liquid inlet 1 at the fourth suspected moment and the suspected abnormal state data of the liquid outlet 2 at the fourth suspected moment. Thus, the temperature differences of the previous consecutive preset number of suspected moments before the first suspected moment are obtained.

[0108] The temperature differences of the first suspected moment and the previous consecutive preset number of suspected moments form a temperature difference sequence corresponding to the first suspected moment in chronological order.

[0109] Since each suspected moment corresponds to a suspected contamination degree, then, obtain the suspected contamination degrees at the first suspected moment and the previous consecutive preset number of suspected moments, and according to the suspected contamination degrees at the first suspected moment and the previous consecutive preset number of suspected moments, form a suspected contamination degree sequence corresponding to the first suspected moment in chronological order.

[0110] The correlation degree between the temperature difference sequence and the suspected contamination degree sequence at the first suspected moment can be the Pearson correlation coefficient or the cosine similarity, and the implementer selects according to the actual situation. As an optimization method, the temperature difference sequence and the suspected contamination degree sequence can be curve-fitted first, and then the correlation degree of the two fitted curves is obtained.

[0111] Then, according to the correlation relationship at the first suspected moment, adjust the initial suspected contamination degree at the first suspected moment to obtain the target suspected contamination degree at the first suspected moment. In this embodiment, as Figure 10 shown, the process of obtaining the target suspected contamination degree includes:

[0112] Step S2-2-3: Obtain an adjustment coefficient according to the correlation degree at the first suspected moment, where the adjustment coefficient is negatively correlated with the correlation degree;

[0113] Step S2-2-4: Adjust the initial suspected pollution degree according to the adjustment coefficient to obtain the target suspected pollution degree at the first suspected moment.

[0114] When the temperature increases, it will cause an increasing trend in pressure and flow, further resulting in a pollution degree that is higher than the actual value. Therefore, if the change in temperature is more consistent with the change in the suspected pollution degree, that is, the correlation degree between the temperature difference sequence and the suspected pollution degree sequence at the first suspected moment is higher, it is considered that the possibility of data abnormality caused by temperature is higher, and then the true pollution degree is correspondingly lowered. Therefore, an adjustment coefficient is obtained according to the correlation degree at the first suspected moment, and the adjustment coefficient is negatively correlated with the correlation degree. It should be understood that the adjustment coefficient is a value less than or equal to 1, and the adjustment coefficient needs to be within a certain range, otherwise the target suspected pollution degree obtained after adjustment may be too small, thus affecting the accuracy. Therefore, as a specific implementation manner, a specific quantization method of the adjustment coefficient is given as follows:

[0115] ;

[0116] where, represents the adjustment coefficient at the i-th suspected moment, represents the correlation degree at the i-th suspected moment, represents the normalization function. The i-th suspected moment represents any suspected moment.

[0117] Then multiply the adjustment coefficient at the first suspected moment by the initial suspected pollution degree at the first suspected moment, and the product obtained is the target suspected pollution degree at the first suspected moment.

[0118] Thus, the target suspected pollution degrees at each suspected moment are obtained.

[0119] Step S3: Obtain the pseudo-abnormality degree of the target time period according to the numerical change trend and time continuity of the target suspected pollution degrees at all suspected moments.

[0120] Construct a target suspected pollution degree sequence by arranging the target suspected pollution degrees at all suspected moments in time sequence. The actual abnormal situation in the target time period is related to both the numerical change trend and time continuity of the target suspected pollution degree sequence. Therefore, the pseudo-abnormality degree of the target time period can be obtained according to the numerical change trend and time continuity of the target suspected pollution degree sequence.

[0121] As a specific implementation, the numerical change trend represents an upward trend in the value of the target suspected pollution degree. The numerical change trend is specifically: the proportion of the number of suspected moments that meet the preset conditions in the target suspected pollution degree sequence. The preset condition is that the target suspected pollution degree at the second suspected moment is greater than the target suspected pollution degree at the third suspected moment; the second suspected moment is any suspected moment, and the third suspected moment is the previous suspected moment of the second suspected moment. That the target suspected pollution degree at the second suspected moment is greater than the target suspected pollution degree at the third suspected moment means greater than the target suspected pollution degree at the previous adjacent suspected moment. Then, the numerical change trend is essentially the proportion of the number of suspected moments whose target suspected pollution degree is greater than that of the previous adjacent suspected moment, that is, the ratio of the obtained number to the total number of suspected moments. As other implementation manners, the following calculations can also be performed: obtain the difference between the target suspected pollution degree at the previous moment and the target suspected pollution degree at the next suspected moment among two adjacent suspected moments in the target suspected pollution degree sequence, and then obtain the number of differences greater than 0 from all the differences between two adjacent suspected moments. A difference greater than 0 indicates that the target suspected pollution degree at the previous moment is greater than the target suspected pollution degree at the next suspected moment.

[0122] The process of obtaining time continuity is as follows: obtain the sampling moments corresponding to each target suspected pollution degree in the target suspected pollution degree sequence to form a sampling moment sequence; obtain the time intervals between every two adjacent sampling moments in the sampling moment sequence, and all the time intervals between two adjacent sampling moments form a time interval sequence; then, obtain the time interval variance of the time interval sequence. The smaller the time interval variance, the higher the time continuity of the target suspected pollution degree sequence. Therefore, finally, the time interval variance is negatively correlated and normalized, and the obtained result is the time continuity.

[0123] The negative correlation normalization in this embodiment can adopt the existing relevant normalization methods, such as: , where Z is the value after negative correlation normalization, z is the input quantity, is the exponential function with the natural constant e as the base.

[0124] For the normal pollution situation of the filter screen 3, the dirt accumulation is slow and rising. Then, the more it belongs to the normal pollution situation, the higher the time continuity and the higher the numerical change trend. Therefore, according to the numerical change trend and time continuity of the target suspected pollution degree at all suspected moments, obtain the pseudo-abnormal degree of the target time period. As a specific implementation, the product of the numerical change trend and time continuity is used as the pseudo-abnormal degree of the target time period.

[0125] Step S4: Obtain the true pollution degree of the last suspected moment in the target time period according to the pseudo-abnormal degree.

[0126] Based on the pseudo - anomaly degree of the obtained target time period, the true pollution degree at the last suspected moment in the target time period can be obtained. The true pollution degree is ultimately used to obtain the next backwash moment.

[0127] As a specific implementation manner, as Figure 11 shown, a specific calculation process of the true pollution degree is given as follows:

[0128] Step S4 - 1: Obtain a refinement coefficient according to the pseudo - anomaly degree, where the refinement coefficient is negatively correlated with the pseudo - anomaly degree;

[0129] Step S4 - 2: Obtain the true pollution degree according to the refinement coefficient and the target suspected pollution degree at the last suspected moment.

[0130] Specifically, the last suspected moment in the target time period is obtained from the target time period, and the corresponding target suspected pollution degree at the last suspected moment is obtained. The refinement coefficient, as an adjustment coefficient for the target suspected pollution degree at the last suspected moment, is obtained from the pseudo - anomaly degree. The refinement coefficient is negatively correlated with the pseudo - anomaly degree. As a specific implementation manner, the difference between the value 1 and the pseudo - anomaly degree is used as the refinement coefficient. Finally, according to the refinement coefficient and the target suspected pollution degree at the last suspected moment, the true pollution degree at the last suspected moment is obtained. As a specific implementation manner, the product of the refinement coefficient and the target suspected pollution degree at the last suspected moment is used as the true pollution degree at the last suspected moment. Since the later the time, the more serious the pollution of the filter screen 3 and the more complex the pollution situation, the last suspected moment in the target time period is very close to, or even equal to, the cut - off moment of the target time period. Therefore, the true pollution degree at the last suspected moment represents the true pollution degree of the filter screen 3 at the end of the target time period.

[0131] Step S5: Obtain the next backwash moment according to the true pollution degree.

[0132] According to the true pollution degree at the last suspected moment, obtain the next backwash moment. As a specific implementation manner, as Figure 12 shown, a specific obtaining process of the next backwash moment is given as follows:

[0133] Step S5 - 1: Obtain the pollution degree difference between the preset true pollution degree threshold and the true pollution degree;

[0134] Step S5-2: Obtain the next backwashing moment according to the difference in pollution degree and the change rate of pollution degree per unit time; the change rate of pollution degree per unit time is calculated from the actual pollution degree and the characteristic time length of the target time period; the characteristic time length is the time interval between the starting moment of the target time period and the last suspected moment.

[0135] Preset an actual pollution degree threshold, which is obtained by experience. As a specific example, it is set to 0.64. This preset actual pollution degree threshold represents the critical pollution degree value at which the next backwashing can be performed.

[0136] The difference in pollution degree between the preset actual pollution degree threshold and the actual pollution degree represents the gap between the actual pollution degree and the preset actual pollution degree threshold. In this embodiment, the difference between the preset actual pollution degree threshold and the actual pollution degree is used as the difference in pollution degree. The change rate of pollution degree per unit time represents the change of pollution degree per unit time. In this embodiment, the change rate of pollution degree per unit time is calculated from the actual pollution degree and the characteristic time length of the target time period. As a specific implementation manner, the ratio of the actual pollution degree to the characteristic time length of the target time period is used as the change rate of pollution degree per unit time. The characteristic time length is the time interval between the starting moment of the target time period and the last suspected moment. It should be noted that if the actual scenario is not considered, in principle, there is a possibility that the starting moment of the target time period and the last suspected moment are the same moment, that is, the obtained time interval is 0. However, in the actual process of oil filtration, the later the time, the more pollutants gradually accumulate on the filter screen 3, the more serious the pollution of the filter screen 3, and the more complex the pollution situation. That is, the later the time, the more suspected moments there may be, so there will inevitably be multiple suspected moments in the target time period, and there is no such situation where the time interval is 0. Moreover, even if there is a situation where the time interval is 0, the process of obtaining the next backwashing moment in this embodiment is not implemented in this case.

[0137] Finally, based on the difference in the degree of contamination and the rate of change of the degree of contamination per unit time, the time interval between the last suspected moment and the next backwash moment in the target time period is obtained. Specifically, the product of the difference in the degree of contamination and the rate of change of the degree of contamination per unit time is used as the time interval between the last suspected moment and the next backwash moment. This time interval represents how long it takes to reach the preset true contamination threshold after the last suspected moment, that is, how long it takes to initiate backwashing. Thus, the next backwash moment is obtained based on this time interval. It should be understood that if the time interval between the last suspected moment and the next backwash moment is less than or equal to the time interval between the last suspected moment in the target time period and the end moment of the target time period, that is, the obtained next backwash moment is earlier than or equal to the end moment of the target time period, then the end moment of the target time period is taken as the next backwash moment.

[0138] In the following, after obtaining the next backwash moment, the data processor uses means such as timer timing. When the next backwash moment arrives, relevant control instructions are output to perform backwash control.

[0139] An embodiment of the usage method of an oil filtration device:

[0140] This embodiment provides a usage method of an oil filtration device. The object of this usage method is the oil filtration device in the above-mentioned oil filtration device embodiment, and the specific data required is the next backwash moment obtained in the above-mentioned oil filtration device embodiment. As Figure 13 shown, the process of this usage method is as follows:

[0141] Step S100: Obtain the next backwash moment in the oil filtration device;

[0142] Step S200: When the next backwash moment arrives, control the flushing device configured with the oil filtration device to start to perform backwashing on the oil filtration device.

[0143] The oil filtration device is equipped with a flushing device, and the data processor is signal-connected to the flushing device. When the next backwash moment arrives, the data processor sends a start instruction to the flushing device. Specifically: the data processor outputs a start instruction to the flushing controller in the flushing device, and the flushing controller sends control instructions to the flushing motor and the flushing guide valve 5 to control the start of the flushing motor and the conduction of the flushing guide valve 5 to perform backwashing on the oil filtration device. It should be understood that when the next backwash moment arrives or is about to arrive, the data processor also sends a stop instruction to the oil filtration device to stop oil filtration to ensure reliable backwashing.

[0144] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A petroleum filtering device, comprising a filter body, wherein the filter body comprises a liquid inlet and a liquid outlet, wherein: The petroleum filtering device also includes: A data acquisition component, used for acquiring the oil status data of the liquid inlet and the liquid outlet within a target time period; A data processor is used to obtain suspected abnormal state data and corresponding suspected moments in the petroleum state data; based on the suspected abnormal state data at each suspected moment, obtain the target suspected contamination degree at each suspected moment; according to the numerical change trend and time continuity of the target suspected contamination degree at all suspected moments, obtain the pseudo abnormal degree of the target time period; according to the pseudo abnormal degree, obtain the real contamination degree at the last suspected moment in the target time period; according to the real contamination degree, obtain the next backwashing moment; The oil status data includes pressure, temperature and flow rate; Obtaining suspected abnormal state data in the oil state data and the corresponding suspected time, including: The oil state data of the liquid inlet at each sampling time within the target time period is obtained, and the data is mapped to a three-dimensional coordinate system and clustered to obtain discrete points outside the cluster as first initial discrete points; the sampling time corresponding to the first initial discrete point is the first initial suspected time; The oil state data of the liquid outlet at each sampling time within the target time period is obtained, and the data is mapped to a three-dimensional coordinate system and clustered to obtain discrete points outside the cluster as second initial discrete points; the sampling time corresponding to the second initial discrete point is the second initial suspected time; The overlapping initial suspected moment between the first initial suspected moment and the second initial suspected moment is obtained, the overlapping initial suspected moment is the suspected moment, and the oil state data at the overlapping initial suspected moment is the suspected abnormal state data.

2. The petroleum filtering device according to claim 1, characterized in that: Based on the suspected abnormal state data at each suspected moment, the target suspected pollution degree at each suspected moment is obtained, including: Based on the abnormal conditions of the pressure and flow rate of the liquid inlet and the liquid outlet in the suspected abnormal state data at each suspected moment, obtaining the initial suspected contamination degree at each suspected moment; Based on the correlation between the temperature change of the liquid inlet and the liquid outlet and the change of the initial suspected contamination degree in the initial suspected abnormal state data at each suspected moment, the initial suspected contamination degree is adjusted to obtain the target suspected contamination degree.

3. The petroleum filtering device according to claim 2, characterized in that: Based on the abnormal conditions of the pressure and flow rate of the liquid inlet and the liquid outlet in the suspected abnormal state data at each suspected moment, the initial suspected contamination degree at each suspected moment is obtained, including: Obtaining discrete distances between the pressure of the suspected abnormal state data of the liquid inlet at the first suspected moment and the pressure characteristic positions of each of the clusters, and obtaining a first pressure discrete distance according to each discrete distance of the pressure; obtaining discrete distances between the flow of the suspected abnormal state data of the liquid inlet at the first suspected moment and the flow characteristic positions of each of the clusters, and obtaining a first flow discrete distance according to each discrete distance of the flow; the first suspected moment is any suspected moment; Obtaining the discrete distances between the pressure of the suspected abnormal state data of the liquid outlet at the first suspected moment and the pressure characteristic positions of each of the clusters, and obtaining the second pressure discrete distance according to each discrete distance of the pressure; obtaining the discrete distances between the flow rate of the suspected abnormal state data of the liquid outlet at the first suspected moment and the flow characteristic positions of each of the clusters, and obtaining the second flow discrete distance according to each discrete distance of the flow rate; Obtain a pressure discrete distance difference at a first suspected moment according to the first pressure discrete distance and the second pressure discrete distance, and obtain a flow discrete distance difference at a first suspected moment according to the first flow discrete distance and the second flow discrete distance; An initial suspected contamination degree at a first suspected moment is obtained according to the pressure discrete distance difference and the flow discrete distance difference.

4. The petroleum filtering device according to claim 3, characterized in that: The process of obtaining the correlation relationship includes: Obtaining the temperature difference between the first suspected moment and a preset number of suspected moments before the first suspected moment to form a temperature difference sequence; the temperature difference is the temperature difference in the suspected abnormal state data of the liquid inlet and the liquid outlet corresponding to the suspected moment; The correlation between the temperature difference sequence and the suspected pollution degree sequence at the first suspected moment is obtained; the suspected pollution degree sequence is composed of the suspected pollution degrees at the first suspected moment and the preset number of consecutive suspected moments before the first suspected moment.

5. The petroleum filtering device according to claim 4, characterized in that: The process of obtaining the suspected contamination degree of the target includes: Acquire an adjustment coefficient according to the correlation degree at the first suspected moment, wherein the adjustment coefficient is negatively correlated with the correlation degree; The initial suspected pollution level is adjusted according to the adjustment coefficient to obtain a target suspected pollution level at the first suspected moment.

6. The petroleum filtering device according to claim 1, characterized in that: The numerical variation trend is specifically: the proportion of the number of suspected moments that meet the preset conditions in the target suspected pollution degree sequence; the preset condition is: the target suspected pollution degree at the second suspected moment is greater than the target suspected pollution degree at the third suspected moment; the second suspected moment is any suspected moment, and the third suspected moment is the suspected moment before the second suspected moment; the target suspected pollution degree sequence is composed of the target suspected pollution degrees at all suspected moments; The process of acquiring the time continuity includes: acquiring the variance of the time interval sequence, wherein the time interval sequence is composed of the time intervals between all two adjacent sampling moments in the target suspected contamination degree sequence; and obtaining the time continuity according to the variance of the time interval sequence, wherein the time continuity is negatively correlated with the variance of the time interval sequence.

7. The petroleum filtering device according to claim 1, characterized in that: Based on the pseudo-abnormality level, obtaining the real pollution level at the last suspected moment in the target time period includes: According to the pseudo-abnormality degree, obtaining a perfection coefficient, wherein the perfection coefficient is negatively correlated with the pseudo-abnormality degree; The actual pollution degree is obtained according to the perfection coefficient and the target suspected pollution degree at the last suspected moment.

8. The petroleum filtering device according to claim 1, characterized in that: According to the actual pollution degree, the next backwashing time is obtained, including: Obtaining a pollution degree difference between a preset real pollution degree threshold and the real pollution degree; The next backwashing time is obtained according to the pollution degree difference and the pollution degree change rate per unit time; the pollution degree change rate per unit time is calculated by the actual pollution degree and the characteristic time length of the target time period; the characteristic time length is the time interval between the start time of the target time period and the last suspected time.

9. A method for using a petroleum filtering device, characterized in that: The steps include: Obtaining the next backwashing time in the petroleum filtering device according to any one of claims 1 to 8; When the next backwashing time comes, the flushing device matched with the oil filter device is controlled to start so as to backwash the oil filter device.

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