CN115563095A - Data reconstruction method and system based on time sequence - Google Patents
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Abstract
The application provides a data reconstruction method and a data reconstruction system based on a time sequence, relates to the technical field of computers, is applied to a diesel engine set of target equipment, and comprises the following steps: acquiring a detection image of a component to be detected; and acquiring a sample data set of the component to be detected, wherein the sample data set comprises first working data and second working data, the first working data is used for representing data of the component to be detected working at different times, and the second working data is used for representing data of the component to be detected working at different space coordinates. The method and the device can improve the efficiency and accuracy of determining the abnormal data and reconstructing the data of the components in the diesel engine set.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a data reconstruction method and system based on a time sequence.
Background
In a state judgment system of a main propulsion diesel engine set in a traditional device such as a ship, an image of a component to be detected is generally acquired, and the state of the component to be detected, whether abnormal data exist, abnormal data reconstruction and the like are judged and operated by combining human experience.
However, this method too depends on the experience and ability of the detector, and it is difficult to ensure the efficiency and accuracy of fault detection and abnormal data reconstruction of the main propulsion diesel engine set. Therefore, a time sequence-based data reconstruction method and system applied to a main propulsion diesel engine set are needed to solve the technical problem.
Disclosure of Invention
The embodiment of the invention aims to provide a data reconstruction method and system based on a time sequence. The specific technical scheme is as follows:
in a first aspect of an embodiment of the present invention, a data reconstruction method based on a time sequence is provided, and is applied to a diesel engine set of a target device, where the method includes:
acquiring a detection image of a component to be detected;
and acquiring a sample data set of the component to be detected, wherein the sample data set comprises first working data and second working data, the first working data is used for representing data of the component to be detected working at different times, and the second working data is used for representing data of the component to be detected working at different space coordinates.
Determining abnormal data and target data corresponding to the abnormal data based on a data reconstruction model and by combining the detection image, the first working data and the second data;
and performing data reconstruction on the abnormal data according to the target data.
Optionally, the detection image carries detection marks of the component to be detected, and the detection marks include time marks and space marks.
3. The time series sequence-based data reconstruction method of claim 1, wherein before the determining abnormal data based on the data reconstruction model combining the inspection image, the first working data and the second data, the method further comprises:
obtaining an average value of a plurality of sample data in the sample data set;
and processing the plurality of sample data according to the average value to obtain sample processing data.
4. The method of claim 3, further comprising:
encoding the sample processing data to obtain first low-dimensional sample data;
decoding the first sample data to obtain second sample data;
and constructing a cost function related to the abnormal data according to the second sample data.
5. The method of time series sequence based data reconstruction according to claim 1, further comprising:
acquiring a first time sequence and a first position coordinate of the component to be detected;
determining the type of the unit where the part to be detected is located according to the first time sequence and the first position coordinate;
and determining standard working data corresponding to the sample data in the sample data set according to the unit type, the first time sequence and the first position coordinate.
6. The method according to claim 5, wherein the determining abnormal data and the target data corresponding to the abnormal data based on the data reconstruction model by combining the detection image, the first working data and the second working data comprises:
inputting the detection image, the first working data and the second working data into the data reconstruction model, and acquiring the abnormal score of each sample data in the sample data set by combining the standard working data and the abnormal score function in the data reconstruction model;
determining the sample data with the abnormal score meeting a preset threshold value as the abnormal data;
performing inverse normalization on the abnormal data to obtain a second time sequence and a second position coordinate corresponding to the target abnormal data;
and determining the target data from the standard working data according to the second time sequence and the second position coordinate.
7. The method for time series sequence based data reconstruction according to any one of claims 1-6, wherein the method further comprises:
evaluating the target data to obtain a data evaluation result;
and replacing the abnormal data by the target data meeting the preset condition.
In another aspect of the embodiments of the present invention, a data reconstruction system based on a time sequence is provided, which is applied to a diesel engine set of a target device, and the system includes:
the detection image acquisition module is used for acquiring a detection image of the component to be detected;
the working data acquisition module is used for acquiring first working data and second working data of the part to be detected, wherein the first working data is used for representing data of the part to be detected working at different time, and the second working data is used for representing data of the part to be detected working at different space coordinates;
the data determining module is used for determining abnormal data and target data corresponding to the abnormal data by combining the detection image, the first working data and the second working data based on a data reconstruction model;
and the data reconstruction module is used for reconstructing data of the abnormal data according to the target data.
In a further aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed, performs the steps of the method as described above.
In a further aspect of the embodiments of the present invention, there is provided a computer device, including a processor, a memory and a computer program stored on the memory, the processor implementing the steps of the method as described above when executing the computer program.
Therefore, in the process of determining the abnormal data and reconstructing the data of the component to be detected of the diesel engine set, the working data of the component to be detected in the time sequence and the space coordinate sequence are fully considered, so that the abnormal data and the target data for data reconstruction are accurately and efficiently determined through the trained data reconstruction model based on the data, and the accuracy and the efficiency of reconstructing the data after the abnormal data of the component to be detected occur can be improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic application scenario of a time series sequence-based data reconstruction system provided in an embodiment of the present application;
FIG. 2 is a schematic flow chart of a data reconstruction method based on a time series sequence according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a data reconstruction system based on a time series sequence according to an embodiment of the present application;
fig. 4 is an internal structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system," "unit," and/or "module" as used herein is a method for distinguishing different components, elements, components, parts, or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" are intended to cover only the explicitly identified steps or elements as not constituting an exclusive list and that the method or apparatus may comprise further steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to or removed from these processes.
FIG. 1 is a schematic diagram of an exemplary time series sequence based data reconstruction system 100 associated with a vessel (FIG. 1 illustrates a vessel diesel bank) according to some embodiments of the present application. In some embodiments, the time series sequence based data reconstruction system 100 may include a server 110, a network 120, a vessel 130, and a memory 140.
In some embodiments, the server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system). In some embodiments, the server 110 may be local or remote. For example, server 110 may access information and/or data stored in vessel 130 and/or memory 140 via network 120. As another example, server 110 may be directly connected to vessel 130 and/or memory 140 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform or on-board computer. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof. In some embodiments, server 110 may execute on a computing device 200 described in FIG. 2 herein that includes one or more components.
In some embodiments, the server 110 may include a processing engine 112. The processing engine 112 may process information and/or data associated with the travel information of the vessel 130 to perform one or more functions described herein. For example, processing engine 112 may obtain travel information for vessel 130 and determine control parameters that may be used to control vessel 130 based on the travel information. In some embodiments, the processing engine 112 may comprise one or more processing engines (e.g., a single chip processing engine or a multi-chip processing engine). By way of example only, processing engine 112 may include a Central Processing Unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a Graphics Processing Unit (GPU), a physical computing processor (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic device (pld), a controller, a microcontroller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.
In some embodiments, the server 110 may be connected to the network 120 to communicate with one or more components of the time series sequence based data reconstruction system 100 (e.g., the vessel 130 and the memory 140). In some embodiments, the server 110 may be directly connected to or in communication with one or more components in the time series sequence based data reconstruction system 100 (e.g., the vessel 130 and the memory 140). In some embodiments, the server 110 may be integrated in the vessel 130.
The vessel 130 may include conventional structures such as a chassis, suspension, steering wheel, drive train components, engines, and the like. The vessel 130 may also include at least two sensors (e.g., a distance sensor 131, a speed sensor 132, a position sensor 133, etc.), a brake device 134, an accelerator (not shown), and the like. In some embodiments, the at least two sensors may detect travel information of the vessel 130. For example, the position sensor 133 may periodically (e.g., every 20 ms) detect the current position of the vessel 130. As another example, the distance sensor 131 may detect a distance between the current location of the vessel 130 and a defined location (e.g., the destination 150). As another example, the distance sensor 131 may detect a distance between the current position of the vessel 130 and other vessels in the vicinity. As yet another example, the speed sensor 132 may detect the instantaneous speed of the vessel 130.
In some embodiments, the distance sensor 131 may include a radar, lidar, infrared sensor, or the like, or a combination thereof. The speed sensor 132 may include a hall sensor. In some embodiments, the at least two sensors may also include an acceleration sensor (e.g., an accelerometer), a steering angle sensor (e.g., an inclination sensor), a traction-related sensor (e.g., a force sensor), and/or any sensor configured to detect information associated with a dynamic condition of the vessel 130.
The braking device 134 may be configured for controlling a braking process of the vessel 130. For example, brake device 134 may adjust the actual acceleration of the vessel based on instructions including a target acceleration obtained from processing engine 112. The accelerator may be configured to control an acceleration process of the vessel 130.
In some embodiments, the memory 140 may be connected to the network 120 to communicate with one or more components of the time series sequence based data reconstruction system 100 (e.g., the server 110 and the vessel 130). One or more components in the time-sequential sequence-based data reconstruction system 100 may access data or instructions stored in the memory 140 via the network 120. In some embodiments, the memory 140 may be directly connected to or in communication with one or more components in the time series sequence based data reconstruction system 100 (e.g., the server 110 and the vessel 130). In some embodiments, the memory 140 may be part of the server 110.
Fig. 2 shows a schematic flowchart of a data reconstruction method and system based on a time sequence according to an embodiment of the present application, and as shown in fig. 2, the data reconstruction method and system based on the time sequence includes the following steps:
The component to be detected refers to a component to be subjected to abnormal data detection in the diesel engine set. The detection image can carry detection marks of the part to be detected, and the detection marks comprise a time mark and a space mark. The time mark and the space mark respectively carry first working data and second working data of the component to be detected.
202, acquiring a sample data set of the component to be detected, wherein the sample data set comprises first working data and second working data.
The first working data are used for representing data of the part to be detected working at different time, and the second working data are used for representing data of the part to be detected working at different space coordinates.
For example only, the operational data may be operational data relating to the operation of the component to be tested, for example, assuming that the component to be tested is a diesel engine in a main propulsion diesel engine set, the operational data may be data generated by the crankshaft mounting angle, the connecting rod temperature, the cooling system water injection amount, and the like, when the component to be tested is in operation.
It should be noted that the data values in the first working data and the second working data do not only include the above data values, but also need to combine the corresponding time stamp and the spatial coordinate stamp to form complete first working data and second working data. For example, the first operation data may be that the connecting rod corresponds to a connecting rod temperature of 150 ℃ at time t, and the second operation data may be that the connecting rod corresponds to a connecting rod temperature of 150 ℃ at relative coordinates (a, b) of a center point of the diesel engine. That is, the first working data may carry a time sequence { t, t1, t2 \8230 { } and the second working sequence may carry a control coordinate sequence { (a, b), (b, c) } 8230 { (a, b), (b, c) }.
Optionally, the embodiment of the present application may further include the following steps:
acquiring a first time sequence and a first position coordinate of the component to be detected;
determining the type of the unit where the part to be detected is located according to the first time sequence and the first position coordinate;
and determining standard working data corresponding to the sample data in the sample data set according to the unit type, the first time sequence and the first position coordinate.
The unit types can include a common unit, a standby unit and an emergency unit. The common unit is a target device (for example, a ship or the like) carrying a main propulsion diesel unit and uses the diesel unit under normal working conditions. The standby scene refers to the situation that the target equipment uses the diesel engine set under the limited working condition, such as electricity limitation, power limitation and the like. The emergency scene refers to that the target equipment uses the diesel engine set under the fault working condition.
Specifically, after the first time sequence and the spatial coordinate sequence of the first position coordinate are obtained, the standard position coordinate of the unit type corresponding to the component to be detected can be searched from the database, and the candidate unit type of the component to be detected is determined according to the proximity degree of the standard position coordinate and the first position coordinate. And calculating the detection working time length of the component to be detected according to the first time and the last time of the first time sequence, and judging whether the candidate unit type is reasonable according to the first working data under the detection working time length and the standard working data which can be generated by the component to be detected under the candidate unit type under the detection working time length so as to determine the unit type finally corresponding to the component to be detected. For example, in the common unit, the standard working temperature generated by the connecting rod in the working time of 1 hour is 150 ℃, and the first working data corresponds to the connecting rod temperature of 150 ℃ in the detection working time of 50min, so that the two are relatively close to each other, and the candidate unit type can be determined as the final unit type.
optionally, before step 203, the present application further includes the following steps:
obtaining an average value of a plurality of sample data in the sample data set;
and processing the plurality of sample data according to the average value to obtain sample processing data.
It can be understood that, because the sample data in the sample data set has a difference in magnitude between different sample data in the data set, the difficulty of model prediction is increased, and the accuracy is difficult to guarantee, so that a plurality of sample data in the sample data set need to be standardized.
Specifically, the formula for obtaining the sample processing data can be expressed as:
wherein x' is the normalized sample processing data; x is initial sample data, average (x) is the average value, sigma is the standard deviation, the obtained data average value is 0, and the standard deviation is 1. According to the method and the device, standardScaler operation in Scikit-leann is used for standardization processing, so that the defect of overlarge difference among a plurality of sample data is integrated by eliminating the influence of overlarge variance, a new data set is obtained, and accurate prediction of a subsequent model on target data is facilitated.
Optionally, the embodiment of the present application further includes the following steps:
encoding the sample processing data to obtain first low-dimensional sample data;
decoding the first sample data to obtain second sample data;
and constructing a cost function related to the abnormal data according to the second sample data.
Wherein the sample data may be encoded by an auto-encoder. The self-encoder may include an encoding network and a decoding network, where the encoding network encodes input sample data x 'to obtain a low-dimensional intermediate feature z, where z = f (wx' + b), w is a weight parameter of the hidden layer, and b is a bias parameter of the hidden layer. The resulting z is then decoded into y' by decoding the trellis, and the resulting cost function can be expressed as:
wherein J (y) is a cost function for subsequent prediction of abnormal data, and N is the number of sample data.
Specifically, after the cost function J (y) is constructed, the data reconstruction model may be trained by using the cost function, so as to obtain a trained data reconstruction model.
And step 204, performing data reconstruction on the abnormal data according to the target data.
Optionally, step 204 may further include the steps of:
inputting the detection image, the first working data and the second working data into the data reconstruction model, and acquiring the abnormal score of each sample data in the sample data set by combining the standard working data and the abnormal score function in the data reconstruction model;
determining the sample data with the abnormal score meeting a preset threshold value as the abnormal data;
performing inverse normalization on the abnormal data to obtain a second time sequence and a second position coordinate corresponding to the target abnormal data;
and determining the target data from the standard working data according to the second time sequence and the second position coordinate.
Specifically, the anomaly score function may be configured to calculate a feature similarity between each sample data and the standard working data, and the smaller the similarity between the two is, the higher the anomaly score corresponding to the sample data is, and the sample data with the anomaly score greater than a preset threshold value is determined as the anomalous data. For example, sample data is still taken as the connecting rod temperature for example, assuming that the standard working data is still 150 ℃, the connecting rod temperature corresponding to the sample data is 200 ℃, the standard value is exceeded 30%, the obtained abnormal score is higher, for example, the corresponding abnormal score is 80 points, and the preset threshold value is exceeded 60 points, the sample data is determined as abnormal data.
Further, the data reconstruction model may perform inverse normalization on the abnormal data to obtain a second time sequence and a second position coordinate corresponding to the target abnormal data, and determine the standard working data with the closest time and space coordinates from the time sequence and the space coordinate sequence corresponding to the standard working data as the target data according to the second time sequence and the second position coordinate.
Optionally, the embodiments of the present application may further include the following steps:
evaluating the target data to obtain a data evaluation result;
and replacing the abnormal data by the target data meeting the preset condition.
It can be understood that after the target data is obtained, because the target data is data predicted by the model, a certain difference may exist between the target data and the real ideal reconstructed data. Therefore, an evaluation standard of the target data can be formulated by combining the standard working data, the target data is evaluated, the data meeting the preset condition and having a difference of less than 1% with the data value of the standard working data is used as the target data meeting the requirement, and therefore the accuracy of data reconstruction can be further improved.
Therefore, in the process of determining the abnormal data and reconstructing the data of the component to be detected of the diesel engine set, the working data of the component to be detected in the time sequence and the space coordinate sequence are fully considered, so that the abnormal data and the target data for data reconstruction are accurately and efficiently determined through the trained data reconstruction model based on the data, and the accuracy and the efficiency of reconstructing the data after the abnormal data of the component to be detected occur can be improved.
In order to implement the foregoing method class embodiments, an embodiment of the present application further provides a data reconstruction system based on a time sequence, and fig. 3 shows a schematic structural diagram of the data reconstruction system based on the time sequence provided in the embodiment of the present application, which is applied to a diesel engine set of a target device, where the system includes:
a detection image acquisition module 301, configured to acquire a detection image of a component to be detected;
a working data obtaining module 302, configured to obtain first working data and second working data of the component to be detected, where the first working data is used to represent data that the component to be detected works at different times, and the second working data is used to represent data that the component to be detected works at different spatial coordinates;
a data determining module 303, configured to determine, based on a data reconstruction model, abnormal data and target data corresponding to the abnormal data by combining the detected image, the first working data, and the second working data;
and the data reconstruction module 304 is configured to perform data reconstruction on the abnormal data according to the target data.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the modules/units/sub-units/components in the above-described system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Therefore, in the process of determining the abnormal data and reconstructing the data of the component to be detected of the diesel engine set, the working data of the component to be detected in the time sequence and the space coordinate sequence are fully considered, so that the abnormal data and the target data for data reconstruction are accurately and efficiently determined through the trained data reconstruction model based on the data, and the accuracy and the efficiency of data reconstruction after the abnormal data of the component to be detected occur can be improved.
In some embodiments, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing relevant data of the image acquisition device. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a data reconstruction method and system based on a time sequence.
In some embodiments, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 4. The computer device includes a processor, a memory, a communication interface, a display screen, and an input system connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method and system for time series sequence based data reconstruction. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In some embodiments, there is further provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above method embodiments when executing the computer program.
In some embodiments, a computer-readable storage medium is provided, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RA M may take a variety of forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.
In summary, the data reconstruction method based on the time sequence provided by the present application includes:
acquiring a detection image of a component to be detected;
and acquiring a sample data set of the component to be detected, wherein the sample data set comprises first working data and second working data, the first working data is used for representing data of the component to be detected working at different time, and the second working data is used for representing data of the component to be detected working at different space coordinates.
Determining abnormal data and target data corresponding to the abnormal data based on a data reconstruction model and by combining the detection image, the first working data and the second data;
and performing data reconstruction on the abnormal data according to the target data.
In the embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and there may be other divisions in actual implementation, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of systems or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit the technical solutions, and the scope of the present application is not limited to the above-mentioned embodiments, although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: those skilled in the art can still make modifications or changes to the embodiments described in the foregoing embodiments, or make equivalent substitutions for some features, within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A data reconstruction method based on a time sequence is characterized in that the data reconstruction method is applied to a diesel set of target equipment, and the method comprises the following steps:
acquiring a detection image of a component to be detected;
and acquiring a sample data set of the component to be detected, wherein the sample data set comprises first working data and second working data, the first working data is used for representing data of the component to be detected working at different time, and the second working data is used for representing data of the component to be detected working at different space coordinates.
Determining abnormal data and target data corresponding to the abnormal data based on a data reconstruction model and by combining the detection image, the first working data and the second data;
and performing data reconstruction on the abnormal data according to the target data.
2. The time-series-sequence-based data reconstruction method according to claim 1, wherein the detection image carries detection marks of the component to be detected, and the detection marks comprise a time mark and a space mark.
3. The time series sequence-based data reconstruction method of claim 1, wherein before the determining abnormal data based on the data reconstruction model combining the inspection image, the first working data and the second data, the method further comprises:
obtaining an average value of a plurality of sample data in the sample data set;
and processing the plurality of sample data according to the average value to obtain sample processing data.
4. The method of claim 3, further comprising:
encoding the sample processing data to obtain first low-dimensional sample data;
decoding the first sample data to obtain second sample data;
and constructing a cost function related to the abnormal data according to the second sample data.
5. The method of time series sequence based data reconstruction according to claim 1, further comprising:
acquiring a first time sequence and a first position coordinate of the component to be detected;
determining the type of the unit where the part to be detected is located according to the first time sequence and the first position coordinate;
and determining standard working data corresponding to the sample data in the sample data set according to the unit type, the first time sequence and the first position coordinate.
6. The method according to claim 5, wherein the determining abnormal data and the target data corresponding to the abnormal data based on the data reconstruction model by combining the detection image, the first working data and the second working data comprises:
inputting the detection image, the first working data and the second working data into the data reconstruction model, and combining the standard working data and an abnormal score function in the data reconstruction model to obtain the abnormal score of each sample data in the sample data set;
determining the sample data with the abnormal score meeting a preset threshold value as the abnormal data;
performing inverse normalization on the abnormal data to obtain a second time sequence and a second position coordinate corresponding to the target abnormal data;
and determining the target data from the standard working data according to the second time sequence and the second position coordinate.
7. The method for time series sequence based data reconstruction according to any one of claims 1-6, wherein the method further comprises:
evaluating the target data to obtain a data evaluation result;
and replacing the abnormal data by the target data meeting the preset condition.
8. A time series sequence-based data reconstruction system, applied to a diesel engine set of a target device, the system comprising:
the detection image acquisition module is used for acquiring a detection image of the component to be detected;
the working data acquisition module is used for acquiring first working data and second working data of the component to be detected, wherein the first working data is used for representing data of the component to be detected working at different time, and the second working data is used for representing data of the component to be detected working at different space coordinates;
the data determining module is used for determining abnormal data and target data corresponding to the abnormal data by combining the detection image, the first working data and the second working data based on a data reconstruction model;
and the data reconstruction module is used for reconstructing data of the abnormal data according to the target data.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed, implements the steps of the method according to any one of claims 1 to 7.
10. A computer arrangement comprising a processor, a memory and a computer program stored on the memory, characterized in that the processor, when executing the computer program, carries out the steps of the method according to any of claims 1-7.
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