An Abnormal Early Warning Method for the Operating Data of a Collection Tank

Abstract

The present invention provides a method for abnormal warning of the operation data of a collection tank, including: determining the liquid level change trend according to the liquid level change amounts of the collection tank to be detected in the first time period, the second time period, and the target time period; if the liquid level change trend is tending to be stable, comparing the operation data in the first time period, the second time period, the target time period, and the historical time period to determine the operation safety state in the third time period; if the liquid level change trend is that there is a liquid level change, determining the operation safety state in the third time period according to the motion states of a number of operation data in the first time period, the second time period, and the target time period in the operation data trajectory diagram; if the operation safety state in the third time period is an abnormal state, sending an emergency braking instruction to the control module of the collection tank to be detected to reduce the monitoring computing power of the operation data of the collection tank to be detected and achieve the purpose of abnormal warning.

Description

Background Art

[0002] The collection tank is used to hold liquid media. Since different media have different conditions during storage or transportation, the operating data for the safety monitoring of the collection tank during the handling of different media will also be different, and the set monitoring thresholds for the operating data will also be different. Therefore, when the collection tank stores or transports media, it is necessary to monitor the operating safety of the collection tank in real time. When the operating data of the collection tank is in an abnormal state, an abnormal alarm is sent to the control center in a timely manner.

[0003] The current safety operation monitoring of the collection tank is achieved by monitoring all the operating data of the collection tank (such as pressure values, temperature values, etc.) in real time. However, this monitoring method will result in an excessive amount of processed operating data and cannot adaptively adjust the monitoring thresholds for the operating data according to different media. Therefore, the safety operation monitoring results of the collection tank will have errors. Moreover, since this safety operation monitoring method of the collection tank is a real-time monitoring, an abnormal alarm will only be issued when the operating data is detected to be abnormal at the current moment. Since the collection tank has already experienced an abnormal failure when the abnormal alarm is issued, even if the staff promptly closes the collection tank or takes other emergency measures, it may still cause economic losses to the collection tank. Therefore, the current safety operation monitoring method of the collection tank cannot provide early warning of abnormalities, has a large amount of data processing, will occupy a large amount of computing power of the monitoring system, and there are still certain errors in the monitoring results. Summary of the Invention

[0004] In view of the above technical problems, the technical solution adopted by the present invention is as follows:

[0005] According to one aspect of the present application, there is provided a method for early warning of abnormal operating data of a collection tank, which is applied to a system for early warning of abnormal operating data of a collection tank. The system for early warning of abnormal operating data of a collection tank is connected to a collection tank to be detected;

[0006] Among them, the method for early warning of abnormal operating data of a collection tank includes the following steps:

[0007] Step S100: Determine the corresponding liquid level change trend of the collection tank to be detected according to the liquid level change amount of the collection tank to be detected in the first time period, the liquid level change amount of the collection tank to be detected in the second time period, and the liquid level change amount of the collection tank to be detected in the target time period;

[0008] Among them, the lengths of the first time period, the second time period, and the target time period are the same, and the end moment of the first time period is the start moment of the second time period, the end moment of the second time period is the start moment of the target time period, and the end moment of the target time period is the current moment;

[0009] Step S200: If the liquid level change trend corresponding to the collection tank to be detected tends to be stable, then execute Step S300; otherwise, execute Step S400;

[0010] Step S300: By comparing a number of operation data of the collection tank to be detected in the first time period, a number of operation data in the second time period, a number of operation data in the target time period, and a number of operation data in the historical time period, to determine the operation safety state of the collection tank to be detected in the third time period, and execute Step S500;

[0011] Wherein, the end time of the historical time period is the start time of the first time period; the length of the third time period is the same as the length of the target time period, and the start time of the third time period is the end time of the target time period;

[0012] Step S400: According to a number of operation data of the collection tank to be detected in the first time period, the second time period, and the target time period, and the motion state in the operation data trajectory diagram corresponding to the collection tank to be detected, to determine the operation safety state of the collection tank to be detected in the third time period, and execute Step S500;

[0013] Wherein, the operation data trajectory diagram is obtained by training a number of operation data of the collection tank to be detected in the historical time period;

[0014] Step S500: If the operation safety state of the collection tank to be detected in the third time period is an abnormal state, then send an emergency braking instruction to the control module of the collection tank to be detected.

[0015] According to one aspect of the present application, there is provided a non-transitory computer-readable storage medium, in which at least one instruction or at least one program segment is stored, and the at least one instruction or the at least one program segment is loaded and executed by a processor to implement the above-mentioned method for warning of abnormal operation data of the collection tank.

[0016] According to one aspect of the present application, there is provided an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0017] The present invention has at least the following beneficial effects:

[0018] The method for warning of abnormal operation data of the collection tank of the present invention first determines the liquid level change trend corresponding to the collection tank to be detected according to the liquid level change amount of the collection tank to be detected in the first time period, the liquid level change amount of the collection tank to be detected in the second time period, and the liquid level change amount of the collection tank to be detected in the target time period. If the liquid level change trend corresponding to the collection tank to be detected is tending to be stable, then by comparing a number of operation data of the collection tank to be detected in the first time period, a number of operation data in the second time period, a number of operation data in the target time period, and a number of operation data in the historical time period, to determine the operation safety state of the collection tank to be detected in the third time period. On the contrary, if the liquid level change trend is that there is a liquid level change, then according to a number of operation data of the collection tank to be detected in the first time period, the second time period, and the target time period, and the motion state in the operation data trajectory diagram corresponding to the collection tank to be detected, to determine the operation safety state of the collection tank to be detected in the third time period. Finally, if the operation safety state of the collection tank to be detected in the third time period is an abnormal state, an emergency braking instruction is sent to the control module of the collection tank to be detected. By judging the liquid level change amount of the collection tank to be detected, to determine the liquid level change trend of the collection tank to be detected and the judgment method of the operation data of the collection tank to be detected, and then by different judgment methods to process the operation data in the first time period, the second time period, and the target time period, to reduce the monitoring computing power of the operation data of the collection tank to be detected, and by judging the operation data in the first time period, the second time period, and the target time period, to predict the operation safety state of the collection tank to be detected in the third time period, so as to achieve the purpose of abnormal warning. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 It is a flowchart of the method for warning of abnormal operation data of the collection tank provided by the embodiment of the present invention. Detailed Embodiments

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0022] This application proposes a method for abnormal warning of the operation data of a collection tank. This method is applied to an abnormal warning system for the operation data of a collection tank. The abnormal warning system for the operation data of a collection tank is connected to a collection tank to be detected. The abnormal warning system for the operation data of a collection tank is used to perform abnormal detection on the collection tank to be detected. A number of detection mechanisms (such as temperature sensors, pressure sensors, ultrasonic sensors, etc.) are arranged in the collection tank to be detected. Each detection mechanism corresponds to a detection type (such as for temperature detection, for pressure detection, for detecting the wall thickness of the collection tank, etc.). The detection mechanism is used to perform data detection on the collection tank to be detected (such as the temperature sensor detects the temperature of the medium in the collection tank to be detected, and the ultrasonic sensor detects the wall thickness of the collection tank to be detected, etc.).

[0023] Among them, as Figure 1 shown, the method for abnormal warning of the operation data of the collection tank proposed in this application includes the following steps:

[0024] Step S100: Determine the liquid level change trend corresponding to the collection tank to be detected according to the liquid level change amount of the collection tank to be detected in the first time period, the liquid level change amount of the collection tank to be detected in the second time period, and the liquid level change amount of the collection tank to be detected in the target time period;

[0025] The lengths of the first time period, the second time period, and the target time period are the same, and the end time of the first time period is the start time of the second time period, the end time of the second time period is the start time of the target time period, and the end time of the target time period is the current time.

[0026] By determining the liquid level change trend of the medium carried by the collection tank to be detected in the time period from the start time of the first time period to the current time, the specific abnormal detection method for the collection tank to be detected is determined (that is, step S300 and step S400).

[0027] Further, step S100 includes steps S110 - S170:

[0028] Step S110: Obtain the liquid level value corresponding to each detection moment of the collection tank to be detected in the first time period to obtain the first liquid level value list A1 = (A 11 , A 21 ,..., A i1 ,..., A n1 ); where i = 1, 2,..., n; n is the number of detection moments in the first time period; A i1 is the liquid level value corresponding to the i-th detection moment of the collection tank to be detected in the first time period; the time period length between every two adjacent detection moments is the same;

[0029] Step S120. Determine the liquid level change amount B1 of the collection tank to be detected within the first time period according to the first liquid level value list A1 = (∑ n i=1 A i1 ) / n;

[0030] Step S130. Obtain the liquid level values corresponding to each detection moment of the collection tank to be detected within the second time period to obtain the second liquid level value list A2 = (A 12 , A 22 ,..., A i2 ,..., A n2 ); where A i2 is the liquid level value corresponding to the i-th detection moment of the collection tank to be detected within the second time period; the time interval length between every two adjacent detection moments within the second time period is the same as the time interval length between every two adjacent detection moments within the first time period;

[0031] Step S140. Determine the liquid level change amount B2 of the collection tank to be detected within the second time period according to the second liquid level value list A2 = (∑ n i=1 A i2 ) / n;

[0032] Step S150. Obtain the liquid level values corresponding to each detection moment of the collection tank to be detected within the target time period to obtain the target liquid level value list A3 = (A 13 , A 23 ,..., A i3 ,..., A n3 ); where A i3 is the liquid level value corresponding to the i-th detection moment of the collection tank to be detected within the target time period; the time interval length between every two adjacent detection moments within the target time period is the same as the time interval length between every two adjacent detection moments within the first time period;

[0033] Step S160. Determine the liquid level change amount B3 of the collection tank to be detected within the target time period according to the target liquid level value list A3 = (∑ n i=1 A i3 ) / n;

[0034] Step S170. If |B1 - B2| ≤ B0 and |B2 - B3| ≤ B0, then determine that the liquid level change trend of the collection tank to be detected tends to be stable; otherwise, determine that the liquid level change trend of the collection tank to be detected has a liquid level change; where B0 is a preset liquid level change difference threshold.

[0035] If the liquid level change trend corresponding to the collection tank to be detected is stable from the first time period to the target time period, it indicates that the liquid level of the medium in the collection tank to be detected has not changed significantly from the first time period to the target time period, which can show that the collection tank to be detected is in the medium storage state from the first time period to the target time period; on the contrary, if the liquid level change trend corresponding to the collection tank to be detected has a liquid level change from the first time period to the target time period, it indicates that the liquid level of the medium in the collection tank to be detected has changed significantly from the first time period to the target time period, which can show that the collection tank to be detected is in the medium transfer state from the first time period to the target time period.

[0036] Step S200: If the liquid level change trend corresponding to the collection tank to be detected is stable, then execute step S300; otherwise, execute step S400.

[0037] If the liquid level change trend corresponding to the collection tank to be detected is stable, it indicates that the state of the medium carried in the collection tank to be detected is relatively stable, and the possibility of generating an abnormal fault is small. Therefore, the abnormal detection method of data vector comparison described in step S300 is adopted. This method only needs to perform vector comparison on several operation data in each time period with the historical operation data. Compared with the method of real-time comparison of operation data in the prior art, the data processing volume is reduced.

[0038] If the liquid level change trend corresponding to the collection tank to be detected has a liquid level change, it indicates that the state of the medium carried in the collection tank to be detected is relatively fluctuating, and the possibility of generating an abnormal fault is large. Therefore, the method of abnormal detection by viewing the trend of the trajectory points in the operation data trajectory diagram described in step S400 is adopted to improve the accuracy of abnormal detection and early warning of the collection tank to be detected. Moreover, the abscissa and ordinate of the trajectory points in the operation data trajectory diagram are determined by the method of principal component analysis for the operation data of abnormal detection. Compared with the method of detecting all operation data in the prior art, the data processing volume is further reduced.

[0039] Step S300: By comparing several operation data of the collection tank to be detected in the first time period, several operation data in the second time period, several operation data in the target time period, and several operation data in the historical time period, to determine the operation safety state of the collection tank to be detected in the third time period, and execute step S500.

[0040] The end time of the historical time period is the start time of the first time period; the length of the third time period is the same as the length of the target time period, and the start time of the third time period is the end time of the target time period, that is, the third time period is a future time period.

[0041] Further, step S300 includes steps S310 - S360:

[0042] Step S310: Obtain the mean value of each running data of the collection tank to be detected within the first time period, so as to obtain the first running data vector C1 = (C 11 , C 21 ,..., C j1 ,..., C k1 ); where j = 1, 2,..., k; k is the number of running data of the collection tank to be detected; C j1 is the mean value of the j-th running data of the collection tank to be detected within the first time period;

[0043] Step S320: Obtain the mean value of each running data of the collection tank to be detected within the second time period, so as to obtain the second running data vector C2 = (C 12 , C 22 ,..., C j2 ,..., C k2 ); where C j2 is the mean value of the j-th running data of the collection tank to be detected within the second time period;

[0044] Step S330: Obtain the mean value of each running data of the collection tank to be detected within the target time period, so as to obtain the target running data vector C3 = (C 13 , C 23 ,..., C j3 ,..., C k3 ); where C j3 is the mean value of the j-th running data of the collection tank to be detected within the target time period;

[0045] Step S340: Obtain each historical running data vector corresponding to the collection tank to be detected, so as to obtain the historical running data vector list D = (D1, D2,..., D h ,..., D m ); where h = 1, 2,..., m; m is the number of historical sub-time periods within the historical time period; D h is the historical running data vector corresponding to the h-th historical sub-time period of the collection tank to be detected within the historical time period;

[0046] D h = (D 1h , D 2h ,..., D jh ,..., D kh ); D jh is the mean value of the j-th running data of the collection tank to be detected within the h-th historical sub-time period;

[0047] The length of each historical sub - time period is equal to the length of the target time period. The start time of the h - th historical sub - time period is the end time of the (h - 1)-th historical sub - time period, and the end time of the h - th historical sub - time period is the start time of the (h + 1)-th historical sub - time period. The end time of the m - th historical sub - time period is the start time of the first time period.

[0048] Step S350: Determine a number of target historical operation data vectors from a number of historical operation data vectors according to the medium carried by the collection tank to be detected at the current moment.

[0049] Since different media carried in the collection tank to be detected will result in different detection thresholds for the operation data of the collection tank to be detected (for example, the detection temperature threshold when the medium is water is different from the detection temperature threshold when the medium is oil), therefore, in order to improve the accuracy of anomaly detection, it is necessary to screen out the target historical operation data vectors corresponding to the medium carried by the collection tank to be detected at the current moment from the historical operation data vectors, and then perform vector comparison. Only such targeted operation data comparison can improve the anomaly detection accuracy of the collection tank to be detected.

[0050] Further, step S350 includes steps S351 - S355:

[0051] Step S351: Obtain the medium identifier E0 corresponding to the medium carried in the collection tank to be detected at the current moment.

[0052] Step S352: Obtain the medium identifier corresponding to each historical operation data vector in the historical operation data vector list D to obtain the medium identifier list E=(E1, E2,..., E h ,..., E m ); where E h is the medium identifier corresponding to the medium carried at the end time of the h - th historical sub - time period of the collection tank to be detected.

[0053] Step S353: Traverse each medium identifier in the medium identifier list E in turn. If E h =E0, then determine D h as the intermediate historical operation data vector to obtain the intermediate historical operation data vector list F=(F1, F2,..., F a ,..., F b ); where a = 1, 2,..., b; b is the number of determined intermediate historical operation data vectors; F a is the a - th determined intermediate historical operation data vector.

[0054] Step S354: If the historical sub - time period corresponding to F c-2 , F c-1The corresponding historical sub - time period, F c The corresponding historical sub - time period, F c+1 If the corresponding historical sub - time periods are consecutive adjacent historical sub - time periods within the historical time period, then F c is determined as the target historical operation data vector; where c = 3,..., b - 1;

[0055] Determine the intermediate historical operation data vector with consecutive historical sub - time periods as the target historical operation data vector, so that the time periods corresponding to the determined target historical operation data vector and the target operation data vector are both consecutive adjacent time periods, in order to further improve the accuracy of the matching degree of vector comparison.

[0056] Step S355: If the number of target historical operation data vectors in the intermediate historical operation data vector list F is less than the preset target vector number threshold, then execute step S400.

[0057] If the number of target historical operation data vectors in the intermediate historical operation data vector list F is less than the target vector number threshold, it means that the number of determined target historical operation data vectors is small. If these target historical operation data vectors are used for vector comparison, due to the small number of comparison groups, the obtained comparison results will be too rough. Therefore, precise detection is performed through the detection method described in step S400.

[0058] Step S360: Compare according to the first operation data vector C1, the second operation data vector C2, the target operation data vector C3, and the target historical operation data vector to determine the operation safety state of the to - be - detected collection tank in the third time period.

[0059] Furthermore, step S360 includes steps S361 - S364:

[0060] Step S361: Obtain each target historical operation data vector to obtain the target historical operation data vector list G=(G1, G2,..., G d ,..., G e ); where d = 1, 2,..., e; e is the number of determined target historical operation data vectors; G d is the d - th determined target historical operation data vector;

[0061] Step S362: Traverse each target historical operation data vector in the target historical operation data vector list G. If, in the intermediate historical operation data vector list F, the matching degree between the second intermediate historical operation data vector before G d and the first operation data vector C1 is greater than the preset matching degree threshold, and before G dThe matching degree between the first intermediate historical operation data vector before and the second operation data vector C2 is greater than a preset matching degree threshold, and G d The matching degree with the target operation data vector C3 is greater than a preset matching degree threshold, then in the list F of intermediate historical operation data vectors, the first intermediate historical operation data vector after G d will be determined as the key historical operation data vector;

[0062] Step S363: Obtain the operation safety status identifier corresponding to each key historical operation data vector;

[0063] The operation safety status identifier represents the operation safety status of the to-be-detected collection tank during the historical sub-time period corresponding to the key historical operation data vector corresponding to this operation safety status identifier.

[0064] For example, when the operation safety status identifier is 1, it means that the operation safety status of the to-be-detected collection tank during the historical sub-time period corresponding to the key historical operation data vector corresponding to this operation safety status identifier is a safe status; when the operation safety status identifier is 0, it means that the operation safety status of the to-be-detected collection tank during the historical sub-time period corresponding to the key historical operation data vector corresponding to this operation safety status identifier is an abnormal status.

[0065] Step S364: If, among all the operation safety status identifiers, the ratio of the number of operation safety status identifiers representing the operation safety status as abnormal status to the total number of operation safety status identifiers is greater than a preset abnormal ratio threshold, then determine that the operation safety status of the to-be-detected collection tank during the third time period is an abnormal status.

[0066] Step S400: According to a number of operation data of the to-be-detected collection tank during the first time period, the second time period, and the target time period, determine the motion state in the operation data trajectory diagram corresponding to the to-be-detected collection tank, so as to determine the operation safety status of the to-be-detected collection tank during the third time period, and execute step S500;

[0067] Among them, the operation data trajectory diagram is obtained by training a number of operation data of the to-be-detected collection tank during the historical time period. Specifically, the operation data trajectory diagram is obtained through step S401 - step S402:

[0068] Step S401: According to each operation data of the to-be-detected collection tank, determine the first target operation data and the second target operation data corresponding to the to-be-detected collection tank;

[0069] Furthermore, step S401 includes step S4011 - step S4016:

[0070] Step S4011: Obtain the operation data identifier corresponding to each operation data of the collection tank to be detected, so as to obtain the operation data identifier list H = (H1, H2,..., H j ,..., H k ); where H j is the operation data identifier corresponding to the j-th operation data of the collection tank to be detected;

[0071] Step S4012: Perform principal component analysis on H1, H2,..., H j ,..., H k to obtain several groups of operation data;

[0072] There is a correlation between several operation data corresponding to the same group of operation data.

[0073] The principal component analysis algorithm is used to reduce the detection dimension of the operation data. By performing principal component analysis on all operation data, several groups of operation data are obtained to represent the influence degree of different groups of operation data on the abnormal detection result of the collection tank to be detected. The principal component analysis algorithm (Principal Component Analysis, PCA) is an existing data processing algorithm, so it will not be elaborated here.

[0074] Step S4013: Obtain the influence value of the detection item corresponding to several operation data in each group of operation data on the safe operation result of the collection tank to be detected, so as to obtain the operation data influence value corresponding to each group of operation data, and determine the operation data influence value list I = (I1, I2,..., I p ,..., I q ); where p = 1, 2,..., q; q is the number of groups of operation data; I p is the operation data influence value corresponding to the p-th group of operation data; I p is the sum of the influence values of the detection items corresponding to several operation data in the p-th group of operation data on the safe operation result of the collection tank to be detected;

[0075] The influence value of the detection item corresponding to the operation data (i.e., the detection category, such as a group of operation data for detecting the pressure value and a group of operation data for detecting the temperature value, etc.) on the safe operation result of the collection tank to be detected is a value preset by the staff and can be obtained by the staff through statistics of historical detection results.

[0076] Step S4014: Determine the group of operation data corresponding to MAX(I) as the first group of operation data, and several operation data in the first group of operation data are the first initial operation data; where MAX() is a preset maximum value determination function;

[0077] The first set of operation data is represented as the operation data included therein having the greatest impact on the safe operation result of the collection tank to be detected among all operation data.

[0078] Step S4015: In the operation data impact value list I, determine the operation data groups corresponding to the operation data impact values that are only less than MAX(I) as the second operation data groups, and several operation data in the second operation data groups are the second initial operation data;

[0079] Step S4016: Perform data integration processing on several first initial operation data and several second initial operation data respectively to obtain first target operation data and second target operation data.

[0080] The method of performing data integration processing on several first initial operation data and several second initial operation data respectively can be average value processing, logarithmic processing, or other data integration methods.

[0081] Step S402: Input the first target operation data and the second target operation data into a preset data detection model to obtain an operation data trajectory graph output by the data detection model;

[0082] The data detection model is trained based on several operation data of the collection tank to be detected in a historical time period.

[0083] Among them, the abscissa of the trajectory point of the operation data trajectory graph represents the first target operation data corresponding to the collection tank to be detected; the ordinate of the trajectory point of the operation data trajectory graph represents the second target operation data corresponding to the collection tank to be detected.

[0084] By performing principal component analysis on all operation data, the first target operation data with the greatest impact on the abnormal detection result and the second target operation data with the abnormal detection result impact degree second only to the first target operation data are obtained, and only these two groups of operation data are monitored for abnormalities, so as to reduce the monitoring and processing amount of operation data while ensuring the accuracy of abnormal detection.

[0085] Furthermore, step S402 includes steps S4021 - S4027:

[0086] Step S4021: Obtain the parameter values of each operation data corresponding to the first operation data group in each historical sub - time period and the parameter values of each operation data corresponding to the second operation data group in each historical sub - time period;

[0087] Step S4022: Determine the sum of the parameter values of several operation data corresponding to the first operation data group in the same historical sub - time period as the first historical operation data parameter value;

[0088] Step S4023: Determine the sum of the parameter values of several pieces of operation data corresponding to the second operation data group within the same historical sub - time period as the second historical operation data parameter value;

[0089] Step S4024: Obtain the operation safety status identifier of the to - be - detected collection tank within each historical sub - time period;

[0090] Step S4025: Input the first historical operation data parameter value, the second historical operation data parameter value, and the operation safety status identifier corresponding to each historical sub - time period into a preset data detection model for training to obtain an operation data trajectory graph; the abscissa of each trajectory point in the operation data trajectory graph is the first historical operation data parameter value, and the ordinate of each trajectory point in the operation data trajectory graph is the second historical operation data parameter value;

[0091] The data detection model can adopt an existing mathematical model, and the training method is also an existing data model training method.

[0092] Step S4026: Determine the trajectory points in the initial data trajectory graph where the operation safety status identifier indicates that the operation safety status of the to - be - detected collection tank is in a normal state as operation safety trajectory points;

[0093] Step S4027: Determine the area enclosed by several operation safety trajectory points as the operation safety area.

[0094] Further, step S400 includes steps S410 - S470:

[0095] Step S410: Determine the sum of the parameter values of several pieces of operation data corresponding to the first operation data group within the first time period as the first target operation data parameter value, and determine the sum of the parameter values of several pieces of operation data corresponding to the second operation data group as the second target operation data parameter value;

[0096] Step S420: Determine the sum of the parameter values of several pieces of operation data corresponding to the first operation data group within the second time period as the third target operation data parameter value, and determine the sum of the parameter values of several pieces of operation data corresponding to the second operation data group as the fourth target operation data parameter value;

[0097] Step S430: Determine the sum of the parameter values of several pieces of operation data corresponding to the first operation data group within the target time period as the fifth target operation data parameter value, and determine the sum of the parameter values of several pieces of operation data corresponding to the second operation data group as the sixth target operation data parameter value;

[0098] Step S440: Input the first target operation data parameter value and the second target operation data parameter value into the data detection model to obtain the first trajectory point of the to - be - detected collection tank in the operation data trajectory graph within the first time period;

[0099] Step S450: Input the third target operating data parameter value and the fourth target operating data parameter value into the data detection model to obtain the second trajectory point of the to-be-detected collection tank in the operating data trajectory graph during the second time period;

[0100] Step S460: Input the fifth target operating data parameter value and the sixth target operating data parameter value into the data detection model to obtain the third trajectory point of the to-be-detected collection tank in the operating data trajectory graph during the target time period;

[0101] Step S470: If the first trajectory point, the second trajectory point, and the third trajectory point are all within the operating safety area, and the length between the third trajectory point and the nearest boundary point of the operating safety area is greater than the preset safety distance threshold, then determine that the operating safety state of the to-be-detected collection tank during the third time period is the normal state; otherwise, determine that the operating safety state of the to-be-detected collection tank during the third time period is the abnormal state.

[0102] If the first trajectory point, the second trajectory point, and the third trajectory point are all within the operating safety area, and the length between the third trajectory point and the nearest boundary point of the operating safety area is greater than the preset safety distance threshold, it means that the to-be-detected collection tank is in a safe state during the target time period and has no tendency to extend outside the operating safety area. Then, it can be determined that the operating safety state of the to-be-detected collection tank during the third time period in the future is also the normal state.

[0103] On the contrary, if the length between the third trajectory point and the nearest boundary point of the operating safety area is less than or equal to the preset safety distance threshold, it means that although the to-be-detected collection tank is in a safe state during the target time period, it has a tendency to extend outside the operating safety area. Then, for the operating safety of the to-be-detected collection tank, determine the operating safety state of the to-be-detected collection tank during the third time period as the abnormal state to notify the staff to detect the to-be-detected collection tank.

[0104] Step S500: If the operating safety state of the to-be-detected collection tank during the third time period is the abnormal state, send an emergency braking instruction to the control module of the to-be-detected collection tank.

[0105] If the operating safety state of the to-be-detected collection tank during the third time period is the abnormal state, it means that the to-be-detected collection tank may have an abnormal failure during the third time period in the future. Then, send an emergency braking instruction to the control module of the to-be-detected collection tank, or send an alarm signal to the control system of the staff to remind the staff to check the to-be-detected collection tank to achieve the purpose of abnormal warning.

[0106] The abnormal warning method for the operating data of the collection tank of the present invention first determines the liquid level change trend corresponding to the collection tank to be detected according to the liquid level change amount of the collection tank to be detected in the first time period, the liquid level change amount of the collection tank to be detected in the second time period, and the liquid level change amount of the collection tank to be detected in the target time period. If the liquid level change trend corresponding to the collection tank to be detected is tending to be stable, then by comparing a number of operating data of the collection tank to be detected in the first time period, a number of operating data in the second time period, a number of operating data in the target time period, and a number of operating data in the historical time period, to determine the operating safety status of the collection tank to be detected in the third time period. On the contrary, if the liquid level change trend is that there is a liquid level change, then according to a number of operating data of the collection tank to be detected in the first time period, the second time period, and the target time period, and the motion state in the operating data trajectory diagram corresponding to the collection tank to be detected, to determine the operating safety status of the collection tank to be detected in the third time period. Finally, if the operating safety status of the collection tank to be detected in the third time period is an abnormal state, an emergency braking instruction is sent to the control module of the collection tank to be detected. By judging the liquid level change amount of the collection tank to be detected, to determine the liquid level change trend of the collection tank to be detected, and the judgment method of the operating data of the collection tank to be detected, and then by different judgment methods to process the operating data in the first time period, the second time period, and the target time period, to reduce the monitoring computing power of the operating data of the collection tank to be detected, and by judging the operating data in the first time period, the second time period, and the target time period, to predict the operating safety status of the collection tank to be detected in the third time period, so as to achieve the purpose of abnormal warning.

[0107] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product runs on an electronic device, the program code is used to cause the electronic device to execute the steps in the method according to various exemplary embodiments of the present invention described above in this specification.

[0108] In addition, although the steps of the methods in the present disclosure are described in a specific order in the drawings, however, this does not require or imply that these steps must be executed in this specific order, or that all the steps shown must be executed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0109] Those skilled in the art can easily understand from the description of the above embodiments that the exemplary embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0110] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is further provided.

[0111] Those skilled in the art can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuits", "modules", or "systems" here.

[0112] The electronic device according to this embodiment of the present invention. The electronic device is only an example and should not impose any limitations on the functions and usage scopes of the embodiments of the present invention.

[0113] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the above at least one processor, the above at least one storage, and a bus connecting different system components (including the storage and the processor).

[0114] Among them, the storage stores program codes, and the program codes can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0115] The storage may include a readable medium in the form of a volatile storage, such as a random access storage (RAM) and / or a cache storage, and may further include a read-only storage (ROM).

[0116] The storage may further include a program / utility tool having a set (at least one) of program modules. Such program modules include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples.

[0117] The bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of the various bus architectures.

[0118] The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device, and / or communicate with any device that enables the electronic device to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface. Moreover, the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter.

[0119] In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium on which a program product capable of implementing the above methods in this specification is stored. In some possible implementation manners, various aspects of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0120] In addition, the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, rather than for limiting purposes. It is easy to understand that the processes shown in the above drawings do not indicate or limit the time sequence of these processes. Additionally, it is also easy to understand that these processes can be executed synchronously or asynchronously, for example, in multiple modules.

[0121] It should be noted that although several modules or units of devices for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0122] As described above, the above are only specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for abnormal warning of the operation data of a collection tank, characterized in that, Applied to the abnormal warning system for the operation data of the collection tank, the abnormal warning system for the operation data of the collection tank is connected to the collection tank to be detected. A number of detection mechanisms are arranged in the collection tank to be detected, and each detection mechanism corresponds to a detection type. The detection mechanism is used to detect the data of the collection tank to be detected; Among them, the abnormal warning method for the operation data of the collection tank includes the following steps: Step S100: Determine the liquid level change trend corresponding to the collection tank to be detected according to the liquid level change amount of the collection tank to be detected in the first time period, the liquid level change amount of the collection tank to be detected in the second time period, and the liquid level change amount of the collection tank to be detected in the target time period; The lengths of the first time period, the second time period, and the target time period are the same, and the end time of the first time period is the start time of the second time period, the end time of the second time period is the start time of the target time period, and the end time of the target time period is the current time; Step S200: If the liquid level change trend corresponding to the collection tank to be detected is tending to be stable, then execute step S300; otherwise, execute step S400; Step S300: Determine the operation safety state of the collection tank to be detected in the third time period by comparing a number of operation data of the collection tank to be detected in the first time period, a number of operation data in the second time period, a number of operation data in the target time period, and a number of operation data in the historical time period, and execute step S500; The end time of the historical time period is the start time of the first time period; the length of the third time period is the same as the length of the target time period, and the start time of the third time period is the end time of the target time period; Step S400: Determine the operation safety state of the collection tank to be detected in the third time period according to the motion state of the collection tank to be detected in the corresponding operation data trajectory diagram of a number of operation data in the first time period, the second time period, and the target time period, and execute step S500; the operation data trajectory diagram is obtained by training a number of operation data of the collection tank to be detected in the historical time period; Step S500: If the operation safety state of the collection tank to be detected in the third time period is an abnormal state, then send an emergency braking instruction to the control module of the collection tank to be detected; Among them, the operation data trajectory diagram is obtained through step S401 - step S402: Step S401: Determine the first target operation data and the second target operation data corresponding to the collection tank to be detected according to each operation data of the collection tank to be detected; each operation data is obtained by the corresponding detection mechanism; Step S402: Input the first target operation data and the second target operation data into a preset data detection model to obtain the operation data trajectory diagram output by the data detection model; the data detection model is obtained by training a number of operation data of the collection tank to be detected in the historical time period; The abscissa of the trajectory point of the operating data trajectory diagram represents the first target operating data corresponding to the to-be-detected collection tank; the ordinate of the trajectory point of the operating data trajectory diagram represents the second target operating data corresponding to the to-be-detected collection tank.

2. The method according to claim 1, characterized in that, The step S100 includes: Step S110: Obtain the liquid level values corresponding to each detection moment of the to-be-detected collection tank within the first time period, so as to obtain the first liquid level value list A1 = (A 11 , A 21 ,..., A i1 ,..., A n1 ); where i = 1, 2,..., n; n is the number of detection moments within the first time period; A i1 is the liquid level value corresponding to the i-th detection moment of the to-be-detected collection tank within the first time period; the time interval length between every two adjacent detection moments is the same; Step S120. Determine the liquid level change amount B1 of the collection tank to be detected within the first time period according to the first liquid level value list A1, where B1 = (∑ n i=1 A i1 ) / n; Step S130: Obtain the liquid level values corresponding to each detection moment of the to-be-detected collection tank within the second time period, so as to obtain a second liquid level value list A2 = (A 12 , A 22 ,..., A i2 ,..., A n2 ); where A i2 is the liquid level value corresponding to the i-th detection moment of the to-be-detected collection tank within the second time period; the time length between every two adjacent detection moments within the second time period is the same as the time length between every two adjacent detection moments within the first time period; Step S140. Determine the liquid level change amount B2 of the to-be-detected collection tank within the second time period according to the second liquid level value list A2, where B2 = (∑ n i=1 A i2 ) / n; Step S150: Obtain the liquid level values corresponding to each detection moment of the to-be-detected collection tank within the target time period, so as to obtain a target liquid level value list A3 = (A 13 , A 23 ,..., A i3 ,..., A n3 ); where A i3 is the liquid level value corresponding to the i-th detection moment of the to-be-detected collection tank within the target time period; the time length between every two adjacent detection moments within the target time period is the same as the time length between every two adjacent detection moments within the first time period; Step S160. Determine the liquid level change amount B3 of the to-be-detected collection tank within the target time period according to the target liquid level value list A3, where B3 = (∑ n i=1 A i3 ) / n; Step S170: If |B1 - B2| ≤ B0 and |B2 - B3| ≤ B0, then determine that the liquid level change trend corresponding to the to-be-detected collection tank tends to be stable; otherwise, determine that the liquid level change trend corresponding to the to-be-detected collection tank has a liquid level change; where B0 is a preset liquid level change difference threshold.

3. The method according to claim 2, wherein The step S300 includes: Step S310: Obtain the mean value of each operating data of the to-be-detected collection tank within the first time period, so as to obtain the first operating data vector C1 = (C 11 , C 21 ,..., C j1 ,..., C k1 ); where j = 1, 2,..., k; k is the number of operating data of the to-be-detected collection tank; C j1 is the mean value of the j-th operating data of the to-be-detected collection tank within the first time period; Step S320: Obtain the mean value of each operating data of the to-be-detected collection tank within the second time period to obtain a second operating data vector C2 = (C 12 , C 22 ,..., C j2 ,..., C k2 ); where C j2 is the mean value of the j-th operating data of the to-be-detected collection tank within the second time period; Step S330: Obtain the mean value of each operating data of the to-be-detected collection tank within the target time period to obtain a target operating data vector C3 = (C 13 , C 23 ,..., C j3 ,..., C k3 ); where C j3 is the mean value of the j-th operating data of the to-be-detected collection tank within the target time period; Step S340: Obtain each historical operation data vector corresponding to the to-be-detected collection tank to obtain a historical operation data vector list D = (D1, D2,..., D h ,..., D m ); where h = 1, 2,..., m; m is the number of historical sub-time periods within the historical time period; D h is the historical operation data vector corresponding to the h-th historical sub-time period of the to-be-detected collection tank within the historical time period; the length of each historical sub-time period is equal to the length of the target time period, and the start time of the h-th historical sub-time period is the end time of the (h - 1)-th historical sub-time period, and the end time of the h-th historical sub-time period is the start time of the (h + 1)-th historical sub-time period; the end time of the m-th historical sub-time period is the start time of the first time period; D h =(D 1h , D 2h ,..., D jh ,..., D kh ); D jh is the mean value of the j-th running data of the collection tank to be detected in the h-th historical sub-time period; Step S350: Determine a number of target historical operating data vectors from a number of the historical operating data vectors according to the medium carried by the to-be-detected collection tank at the current moment. Step S360: Compare the first operating data vector C1, the second operating data vector C2, the target operating data vector C3, and the target historical operating data vectors to determine the operating safety state of the to-be-detected collection tank in the third time period.

4. The method according to claim 3, wherein The step S350 includes: Step S351: Obtain the medium identifier E0 corresponding to the medium carried in the to-be-detected collection tank at the current moment. Step S352: Obtain the medium identifier corresponding to each historical operation data vector in the historical operation data vector list D, so as to obtain a medium identifier list E = (E1, E2,..., E h ,..., E m ); where E h is the medium identifier corresponding to the medium carried by the to-be-detected collection tank at the end moment of the h-th historical sub-time period; Step S353: Traverse each medium identifier in the medium identifier list E in sequence. If E h = E0, then set D h as the intermediate historical operation data vector to obtain the intermediate historical operation data vector list F = (F1, F2,..., F a ,..., F b ); where a = 1, 2,..., b; b is the number of determined intermediate historical operation data vectors; F a is the a-th determined intermediate historical operation data vector. Step S354, if F c-2 corresponding historical sub - time period, F c-1 corresponding historical sub - time period, F c corresponding historical sub - time period, F c+1 corresponding historical sub - time periods within the historical time period are consecutive and adjacent historical sub - time periods, then F c is determined as the target historical operation data vector; where c = 3,..., b - 1.

5. The method according to claim 4, wherein The step S354 includes: Step S355: If the number of target historical operating data vectors in the intermediate historical operating data vector list F is less than a preset target vector number threshold, then execute step S400.

6. The method according to claim 5, wherein The step S360 includes: Step S361: Obtain each of the target historical operation data vectors to obtain a list G = (G1, G2,..., G d ,..., G e ); where d = 1, 2,..., e; e is the number of determined target historical operation data vectors; G d is the d-th determined target historical operation data vector; Step S362, traverse each target historical operation data vector in the target historical operation data vector list G. If, in the intermediate historical operation data vector list F, the matching degree between the second intermediate historical operation data vector before G d and the first operation data vector C1 is greater than a preset matching degree threshold, and the matching degree between the first intermediate historical operation data vector before G d and the second operation data vector C2 is greater than a preset matching degree threshold, and the matching degree between G d and the target operation data vector C3 is greater than a preset matching degree threshold, then determine the first intermediate historical operation data vector after G d in the intermediate historical operation data vector list F as the key historical operation data vector; Step S363: Obtain the operating safety state identifier corresponding to each of the key historical operating data vectors; the operating safety state identifier represents the operating safety state of the to-be-detected collection tank in the historical sub-time period corresponding to the key historical operating data vector corresponding to the operating safety state identifier. Step S364: If the ratio of the number of operating safety state identifiers representing the abnormal state in all the operating safety state identifiers to the total number of the operating safety state identifiers is greater than a preset abnormal ratio threshold, then determine that the operating safety state of the to-be-detected collection tank in the third time period is an abnormal state.

7. The method according to claim 6, characterized in that The step S401 includes: Step S4011: Obtain the operation data identifier corresponding to each operation data of the to-be-detected collection tank, so as to obtain an operation data identifier list H = (H1, H2,..., H j ,..., H k ); where H j is the operation data identifier corresponding to the j-th operation data of the to-be-detected collection tank; Step S4012, perform principal component analysis on H1, H2,..., H j ,..., H k to obtain a number of operating data groups; there is a correlation between the corresponding operating data in the same operating data group; Step S4013: Obtain the influence values of the corresponding detection items of several pieces of operation data in each of the operation data groups on the safe operation result of the collection tank to be detected, so as to obtain the operation data influence value corresponding to each operation data group, and determine the operation data influence value list I = (I1, I2,..., I p ,..., I q ); where p = 1, 2,..., q; q is the number of operation data groups; I p is the operation data influence value corresponding to the p-th operation data group; I p is the sum of the influence values of the detection items corresponding to several pieces of operation data in the p-th operation data group on the safe operation result of the collection tank to be detected; Step S4014: Determine the operating data group corresponding to MAX(I) as the first operating data group, and a number of operating data in the first operating data group are the first initial operating data; where MAX() is a preset maximum value determination function. Step S4015: Determine the operating data group corresponding to the operating data influence value that is only less than MAX(I) in the operating data influence value list I as the second operating data group, and a number of operating data in the second operating data group are the second initial operating data. Step S4016: Perform data integration processing on a number of the first initial operating data and a number of the second initial operating data respectively to obtain the first target operating data and the second target operating data.

8. The method according to claim 7, wherein The step S402 includes: Step S4021: Obtain the parameter values of each piece of operation data corresponding to the first operation data group in each historical sub-time period and the parameter values of each piece of operation data corresponding to the second operation data group in each historical sub-time period; Step S4022: Determine the first historical operation data parameter value as the sum of the parameter values of several pieces of operation data corresponding to the first operation data group in the same historical sub-time period; Step S4023: Determine the second historical operation data parameter value as the sum of the parameter values of several pieces of operation data corresponding to the second operation data group in the same historical sub-time period; Step S4024: Obtain the operation safety status identifier of the to-be-detected collection tank in each historical sub-time period; Step S4025: Input the first historical operation data parameter value, the second historical operation data parameter value, and the operation safety status identifier corresponding to each historical sub-time period into a preset data detection model for training to obtain an operation data trajectory graph; the abscissa of each trajectory point in the operation data trajectory graph is the first historical operation data parameter value, and the ordinate of each trajectory point in the operation data trajectory graph is the second historical operation data parameter value; Step S4026: Determine the trajectory points in the initial data trajectory graph where the operation safety status identifier indicates that the operation safety status of the to-be-detected collection tank is normal as operation safety trajectory points; Step S4027: Determine the area enclosed by several operation safety trajectory points as the operation safety area.

9. The method according to claim 8, wherein The step S400 includes: Step S410: Determine the first target operation data parameter value as the sum of the parameter values of several pieces of operation data corresponding to the first operation data group in the first time period, and determine the second target operation data parameter value as the sum of the parameter values of several pieces of operation data corresponding to the second operation data group in the first time period; Step S420: Determine the third target operation data parameter value as the sum of the parameter values of several pieces of operation data corresponding to the first operation data group in the second time period, and determine the fourth target operation data parameter value as the sum of the parameter values of several pieces of operation data corresponding to the second operation data group in the second time period; Step S430: Determine the fifth target operation data parameter value as the sum of the parameter values of several pieces of operation data corresponding to the first operation data group in the target time period, and determine the sixth target operation data parameter value as the sum of the parameter values of several pieces of operation data corresponding to the second operation data group in the target time period; Step S440: Input the first target operation data parameter value and the second target operation data parameter value into the data detection model to obtain the first trajectory point of the to-be-detected collection tank in the operation data trajectory graph in the first time period; Step S450: Input the third target operation data parameter value and the fourth target operation data parameter value into the data detection model to obtain the second trajectory point of the to-be-detected collection tank in the operation data trajectory graph in the second time period; Step S460: Input the fifth target operating data parameter value and the sixth target operating data parameter value into the data detection model to obtain the third trajectory point of the to-be-detected collection tank in the operating data trajectory diagram during the target time period; Step S470: If the first trajectory point, the second trajectory point, and the third trajectory point are all within the operating safety area, and the length between the third trajectory point and the nearest boundary point of the operating safety area is greater than the preset safety distance threshold, determine that the operating safety status of the to-be-detected collection tank during the third time period is normal; otherwise, determine that the operating safety status of the to-be-detected collection tank during the third time period is abnormal.

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