CN112508457B - Data processing method and device, industrial equipment and storage medium - Google Patents
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Abstract
The embodiment of the application provides a data processing method and device, industrial equipment and a storage medium, and relates to the technical field of data processing. The data processing method comprises the following steps: firstly, obtaining structural operation data of industrial equipment; secondly, inputting the structured operation data into a preset classification model to obtain at least one operation stage of the industrial equipment; and then, calculating according to the structured operation data to obtain performance indexes of each operation stage, and carrying out anomaly analysis according to the performance indexes. By the method, the processes of data classification, index calculation and anomaly analysis can be automatically realized, and the problems of manual processing, data classification and index calculation on a large amount of test data generated during operation of industrial equipment and low efficiency of data processing caused by the problems of positioning equipment in the prior art are solved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data processing method and apparatus, an industrial device, and a storage medium.
Background
In the industrial field, how to quickly locate equipment in mass operation data of the equipment is a problem, so that the acceleration of the iteration speed of the equipment is always an important part of industrial equipment manufacturers to promote the core competitiveness.
However, the inventor researches and discovers that in the prior art, the industrial equipment production enterprises mainly adopt a mode based on manual analysis for identifying the problems of the multi-operation-step equipment, and the problems of the positioning equipment exist by manually processing, classifying data and calculating indexes of a large amount of test data generated during the operation of the industrial equipment. In the face of a large amount of equipment operation data, the manual data processing mode is not only very low in efficiency, but also influenced by human subjectivity and experience, and the speed of product production and iterative optimization is seriously influenced.
Disclosure of Invention
Accordingly, an object of the present application is to provide a data processing method and apparatus, an industrial device and a storage medium, so as to solve the problems in the prior art.
In order to achieve the above purpose, the embodiment of the present application adopts the following technical scheme:
In a first aspect, the present invention provides a data processing method, including:
obtaining structured job data of industrial equipment;
Inputting the structured job data into a preset classification model to obtain at least one job stage of the industrial equipment;
and calculating the performance index of each operation stage according to the structured operation data, and carrying out anomaly analysis according to the performance index.
In an optional embodiment, the structured job data includes time data, the performance index includes a job duration, the step of calculating a performance index of each of the job phases according to the structured job data, and performing anomaly analysis according to the performance index includes:
for each operation stage, calculating the operation duration of the operation stage according to the time data;
and carrying out exception analysis on the operation stage according to the operation duration.
In an alternative embodiment, the step of performing anomaly analysis on the working stage according to the working duration includes:
Judging whether the operation duration is longer than the preset operation duration of the operation stage;
if yes, an alarm signal is sent out.
In an optional embodiment, the structured job data includes pressure data and time data of at least one pressure sensor, the performance index includes a duration corresponding to an on state of the at least one pressure sensor, the step of calculating a performance index of each working stage according to the structured job data, and performing anomaly analysis according to the performance index includes:
for each working stage, calculating to obtain the duration corresponding to the opening state of the at least one pressure sensor in the working stage according to the pressure data and the time data of the at least one pressure sensor;
And carrying out exception analysis on the operation stage according to the duration corresponding to the opening state.
In an optional embodiment, the step of calculating, for each working stage, a duration corresponding to an on state of the at least one pressure sensor according to the pressure data and the time data of the at least one pressure sensor includes:
For each working stage, calculating the opening state of the at least one pressure sensor according to the pressure data of the at least one pressure sensor;
And calculating the duration corresponding to the opening state according to the opening state and the time data.
In an optional embodiment, the step of performing anomaly analysis on the working stage according to the duration corresponding to the on state includes:
judging whether the duration corresponding to the opening state meets the preset opening duration of the operation stage or not;
If not, an alarm signal is sent out.
In an alternative embodiment, the step of obtaining structured job data of an industrial device includes:
acquiring operation data of industrial equipment;
and preprocessing the operation data to obtain structured operation data.
In a second aspect, the present invention provides a data processing apparatus comprising:
the data acquisition module is used for acquiring the structured operation data of the industrial equipment;
the operation stage classification module is used for inputting the structured operation data into a preset classification model to obtain at least one operation stage of the industrial equipment;
and the analysis module is used for calculating the performance index of each working stage according to the structured working data and carrying out exception analysis according to the performance index.
In a third aspect, the present invention provides an industrial device comprising a memory and a processor for executing an executable computer program stored in the memory to implement the data processing method of any of the previous embodiments.
In a fourth aspect, the present invention provides a storage medium having stored thereon a computer program which, when executed, performs the steps of the data processing method according to any of the preceding embodiments.
According to the data processing method and device, the industrial equipment and the storage medium, the obtained structured operation data are input into the preset classification model to obtain the operation stages, the performance indexes of each operation stage are obtained through calculation to conduct abnormal analysis, the processes of automatic data classification, index calculation and abnormal analysis are achieved, and the problems of low data processing efficiency caused by the fact that a large amount of test data generated during operation of the industrial equipment are manually processed, data classification and index calculation in the prior art are solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of an industrial apparatus according to an embodiment of the present application.
Fig. 2 is a flow chart of a data processing method according to an embodiment of the present application.
Fig. 3 is another flow chart of a data processing method according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of a preset classification model according to an embodiment of the present application.
Fig. 5 is another flow chart of a data processing method according to an embodiment of the present application.
Fig. 6 is another flow chart of a data processing method according to an embodiment of the present application.
Fig. 7 is another flow chart of a data processing method according to an embodiment of the present application.
Fig. 8 is another flow chart of a data processing method according to an embodiment of the present application.
Fig. 9 is another flow chart of a data processing method according to an embodiment of the present application.
Fig. 10 is a block diagram of a data processing apparatus according to an embodiment of the present application.
Icon: 100-industrial equipment; 110-network ports; 120-a first processor; 130-a communication bus; 140-a first storage medium; 150-interface; 1000-a data processing device; 1010-a data acquisition module; 1020-a job stage classification module; 1030-analysis module.
Detailed Description
The present application is directed to a method for manufacturing a semiconductor device, and a semiconductor device manufactured by the method.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be described in detail with reference to the accompanying drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for the purpose of illustration and description only and are not intended to limit the scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. A flowchart, as used in this disclosure, illustrates operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be implemented out of order and that steps without logical context may be performed in reverse order or concurrently. Moreover, one or more other operations may be added to or removed from the flow diagrams by those skilled in the art under the direction of the present disclosure.
In addition, the described embodiments are only some, but not all, embodiments of the application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
In order to enable one skilled in the art to make use of this disclosure, the following embodiments are presented. It will be apparent to those having ordinary skill in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. Applications of the system or method of the present application may include web pages, plug-ins to a browser, client terminals, customization systems, internal analysis systems, or artificial intelligence robots, etc., or any combination thereof.
It should be noted that the term "comprising" will be used in embodiments of the application to indicate the presence of the features stated hereafter, but not to exclude the addition of other features.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
FIG. 1 shows a schematic diagram of exemplary hardware and software components of an industrial device 100 in which the concepts of the present application may be implemented, according to some embodiments of the present application. The industrial device 100 can include a network port 110 connected to a network, one or more first processors 120 for executing program instructions, a communication bus 130, and a first storage medium 140 of different form, such as a disk, ROM, or RAM, or any combination thereof. Illustratively, the industrial device 100 can also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented in accordance with these program instructions. The industrial device 100 can also include an Input/Output (I/O) interface 150 with other Input/Output devices (e.g., keyboard, display screen).
In some embodiments, the first processor 120 may process information and/or data related to data processing to perform one or more functions described herein. In some embodiments, the first processor 120 may include one or more processing cores (e.g., a single core processor (S) or a multi-core processor (S)). By way of example only, the first Processor 120 may include a central processing unit (Central Processing Unit, CPU), an Application SPECIFIC INTEGRATED Circuit (ASIC), a special purpose instruction set Processor (Application Specific Instruction-set Processor, ASIP), a graphics processing unit (Graphics Processing Unit, GPU), a physical processing unit (Physics Processing Unit, PPU), a digital signal Processor (DIGITAL SIGNAL Processor, DSP), a field programmable gate array (Field Programmable GATE ARRAY, FPGA), a programmable logic device (Programmable Logic Device, PLD), a controller, a microcontroller unit, a reduced instruction set computer (Reduced Instruction Set Computing, RISC), a microprocessor, or the like, or any combination thereof.
The first processor 120 in the industrial device 100 can be a general purpose computer or a set purpose computer, both of which can be used to implement the data processing method of the present application. Although only one computer is shown, the functionality described herein may be implemented in a distributed fashion across multiple similar platforms for convenience to balance processing loads.
For ease of illustration, only one processor is depicted in the industrial device 100. It should be noted, however, that the industrial device 100 of the present application may also include multiple processors, and thus, the steps performed by one processor described in the present application may also be performed jointly by multiple processors or separately. For example, if the processors of the industrial device 100 perform steps a and B, it should be understood that steps a and B may also be performed by two different processors together or performed separately in one processor. For example, the first processor performs step a, the second processor performs step B, or the first processor and the second processor together perform steps a and B.
The network may be used for the exchange of information and/or data. In some embodiments, one or more components in the industrial device 100 can send information and/or data to other components. For example, the industrial device 100 can acquire signals via a network. By way of example only, the network may include a wireless network, a telecommunications network, an intranet, the internet, a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), a wireless local area network (Wireless Local Area Networks, WLAN), a metropolitan area network (Metropolitan Area Network, MAN), a wide area network (Wide Area Network, WAN), a bluetooth network, a ZigBee network, or a near field Communication (NEAR FIELD Communication) network, or the like, or any combination thereof.
In some embodiments, the network may include one or more network access points. For example, the network may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the industrial device 100 can connect to the network to exchange data and/or information.
Alternatively, the specific type of the industrial device 100 is not limited, and may be set according to actual application requirements. For example, industrial equipment 100 may include, but is not limited to, various types of work machines, hoisting machines, power machines, and the like having multi-stage operating properties.
Fig. 2 shows one of flowcharts of a data processing method according to an embodiment of the present application, which is applicable to the industrial apparatus 100 shown in fig. 1, and is executed by the industrial apparatus 100 in fig. 1. It should be understood that, in other embodiments, the order of some steps in the data processing method of the present embodiment may be interchanged according to actual needs, or some steps therein may be omitted or deleted. The flow of the data processing method shown in fig. 2 is described in detail below.
Step S210, obtaining structured job data of the industrial device 100.
Step S220, inputting the structured job data into a preset classification model to obtain at least one working stage of the industrial equipment 100.
Step S230, calculating the performance index of each operation stage according to the structured operation data, and performing anomaly analysis according to the performance index.
According to the method, the obtained structured operation data are input into the preset classification model to obtain the operation stages, the performance indexes of the operation stages are obtained through calculation to conduct abnormal analysis, the processes of data classification, index calculation and abnormal analysis are automatically achieved, and the problems of low data processing efficiency caused by the fact that a large amount of test data generated during operation of the industrial equipment 100 are manually processed, data classification and index calculation are conducted in the prior art are solved.
For step S210, it should be noted that the specific manner of obtaining the structured job data is not limited, and may be set according to the actual application requirement. For example, in an alternative example, step S210 may include a step of performing preprocessing. Therefore, on the basis of fig. 2, fig. 3 is a flow chart of another data processing method according to an embodiment of the present application, referring to fig. 3, step S210 may include:
step S211, acquiring job data of the industrial equipment 100.
In detail, in an alternative example, the industrial device 100 may directly collect massive time-series industrial big data that may represent the working state of the industrial device 100 in the working process through a set sensor or the like, and in the process of accessing the working data, the working data source may be that the sensor of the industrial device 100 collects data of each part in the running process. In another alternative example, the job data may be imported from a file class data source (dat, r32, csv), a database class data source (mysql), and an API class data source, and the imported data may be parsed and stored in a normalized manner to obtain the job data.
Step S212, preprocessing the job data to obtain structured job data.
The data preprocessing may include preprocessing steps such as checking the integrity of the job data, data filtering, data normalization, automatic data slicing, etc., to convert the job data into structured job data that can be input to the neural network.
In detail, the integrity of the data can be checked, and abnormal prompt can be carried out on the missing channel data; the data filtering can adopt median filtering to carry out smoothing treatment on the data and filter abnormal data; the data normalization can be performed by adopting a normalization algorithm, so that the influence of dimension on model training and prediction is avoided; the data slicing may perform reasonable equal length slicing on the data based on a working cycle of the industrial apparatus 100, and perform a structuring process on the data.
For step S220, it should be noted that, the preset classification model may perform effective modeling according to the current massive operation data, so as to accurately implement intelligent classification of time series data, and may construct a deep learning network of gru+u_net+ DEPTHWISE SEPARABLE CONVOLUTION, and implement accurate identification of which operation stage the data generated by the industrial equipment 100 is in according to the structured operation data training model.
In detail, the embodiment of the application designs a deep learning network structure of GRU+U_net+ DEPTHWISE SEPARABLE CONVOLUTION, the model uses GRU to replace a convolution layer in the original U_net downsampling process to capture time sequence characteristics in data, the model classification accuracy is improved, and the original spliced convolution layer is replaced by separable convolution (DEPTHWISE SEPARABLE CONVOLUTION) in the upper adoption process, so that training parameters are greatly reduced.
With reference to fig. 4, the overall model adopts a u_net network structure, and the batch_ pooling and the batch_upsampling modules in the u_net structure are improved, so that the effects of reducing the parameter number and improving the accuracy of the model are achieved. The batch_ pooling module replaces the original CNN+CNN module by combining the bidirectional GRU and the CNN module, so that the network pays more attention to the time sequence characteristic of the data, and the classification accuracy is improved. The batch_upsampling module replaces the original CNN convolution module by adopting separable convolution separableCNN, the separable convolution is that features are extracted by convolution of 1*1 after convolution channel by channel, the function of reducing the model parameter is achieved, the model size is reduced from 79.5M to 58.9M, and 26% is reduced.
For step S230, it should be noted that the specific manner of performing the anomaly analysis is not limited, and may be set according to the actual application requirement. For example, in an alternative example, the structured job data includes time data, the performance index includes a job duration, and step S230 may include a step of performing anomaly analysis according to the job duration. Therefore, on the basis of fig. 2, fig. 5 is a flow chart of another data processing method according to an embodiment of the present application, referring to fig. 5, step S230 may include:
step S231, for each operation stage, calculating the operation duration of the operation stage according to the time data.
In detail, the type of the operation stage is not limited, and may be set according to actual application requirements. For example, the working stages in the embodiment of the application may include four working stages of digging, boom lifting+turning, unloading and resetting, and after classifying the working stages, the working duration of each working stage can be obtained according to time data statistics.
Step S232, carrying out exception analysis on the operation stage according to the operation duration.
For step S232, it should be noted that the specific manner of performing the anomaly analysis is not limited, and may be set according to the actual application requirement. For example, in an alternative example, step S232 may include a step of comparing the job duration with a preset job duration. Therefore, on the basis of fig. 5, fig. 6 is a flow chart of another data processing method according to an embodiment of the present application, referring to fig. 6, step S232 may include:
step S2321, judging whether the operation duration is greater than the preset operation duration of the operation stage.
In the embodiment of the present application, when the working time period is longer than the preset working time period of the working phase, it is determined that the industrial equipment 100 is in an abnormal state, and step S2322 is executed; when the operation duration is not greater than the preset operation duration of the operation stage, it is determined that the industrial equipment 100 is in a normal state.
Step S2322, an alarm signal is sent out.
For example, the job duration of each job stage is obtained according to the time data statistics: the excavating operation stage is 4.93 seconds, the movable arm lifting and rotating operation stage is 3.39 seconds, the unloading operation stage is 1.83 seconds and the resetting operation stage is 4.13 seconds, when the preset operation time length of the resetting operation stage is 4 seconds, the actual operation time length is longer than the preset operation time length, and the working efficiency of the resetting operation stage is low.
For step S230, it should be noted that the specific manner of performing the anomaly analysis is not limited, and may be set according to the actual application requirement. For another alternative example, the structured job data may include pressure data and time data of at least one pressure sensor, the performance index may include a duration corresponding to an on state of the at least one pressure sensor, and the step S230 may include a step of performing anomaly analysis according to the duration corresponding to the on state. Therefore, based on fig. 2, fig. 7 is a flow chart of another data processing method according to an embodiment of the present application, referring to fig. 7, step S230 may include:
step S233, for each working stage, calculating a duration corresponding to the on state of at least one pressure sensor in the working stage according to the pressure data and the time data of the at least one pressure sensor.
In detail, the specific number and positions of the pressure sensors are not limited, and may be set according to actual application requirements. For example, in an alternative example, the number of pressure sensors may be four, and when the industrial apparatus 100 is an excavator, the pressure sensors may be pilot pressure sensors provided to a boom, an arm, a bucket, and a swing mechanism during an excavating operation of the excavator.
Step S234, performing exception analysis on the operation stage according to the duration corresponding to the opening state.
For step S233, it should be noted that the specific manner of calculating the duration corresponding to the on state is not limited, and may be set according to the actual application requirement. For example, in an alternative example, step S233 may include a step of calculating a duration corresponding to the on state from the on state and the time data. Therefore, on the basis of fig. 7, fig. 8 is a flow chart of another data processing method according to an embodiment of the present application, referring to fig. 8, step S233 may include:
Step S2331, for each working stage, calculating the on state of at least one pressure sensor according to the pressure data of the at least one pressure sensor.
In detail, whether the pressure data of the pressure sensor is larger than a preset pressure value can be judged, if so, the pressure sensor is in an open state, and the pilot pressure is opened, so that a state that a plurality of pilot pressures are simultaneously opened in the working stage can be obtained.
Step S2332, calculating the duration corresponding to the on state according to the on state and the time data.
In detail, the duration of each opening state can be calculated according to the time data, and the ratio of the duration of each opening state to the total duration can be obtained. For example, the total duration of the unloading phase may be 10 seconds, the duration of two pressure sensors in the on state may be 1 second (10% duty cycle), the duration of three pressure sensors in the on state may be 3 seconds (30% duty cycle), and the duration of four pressure sensors in the on state may be 6 seconds (60% duty cycle).
In step S234, it should be noted that the specific manner of performing the anomaly analysis is not limited, and may be set according to the actual application requirement. For example, in an alternative example, step S234 may include a step of comparing a duration corresponding to the on state with a preset on duration. Therefore, on the basis of fig. 7, fig. 9 is a flowchart of another data processing method according to an embodiment of the present application, referring to fig. 9, step S234 may include:
Step S2341, judging whether the duration corresponding to the on state meets the preset on duration of the operation stage.
In the embodiment of the present application, when the duration corresponding to the on state does not satisfy the preset on duration of the operation stage, it is determined that the industrial equipment 100 is in an abnormal state, and step S2342 is performed; when the duration corresponding to the on state satisfies the preset on duration of the operation stage, it is determined that the industrial equipment 100 is in a normal state.
Step S2342, an alarm signal is sent.
In detail, the preset opening duration may include durations corresponding to different opening states, and first, it may be determined whether the number of opening states included in the opening state and the preset opening duration is the same, and if not, it is determined that the industrial device 100 is in an abnormal state. For example, the total duration of the excavation phase may be 10 seconds, the duration of one pressure sensor in the on state may be 1 second (10% by weight), the duration of two pressure sensors in the on state may be 2 seconds (20% by weight), the duration of three pressure sensors in the on state may be 3 seconds (30% by weight), and the duration of four pressure sensors in the on state may be 4 seconds (40% by weight). When the preset opening time length only comprises the time length corresponding to the opening states of the two, three and four pressure sensors, the excavator is indicated to have excessive actions (the opening state of one pressure sensor).
Further, when the number of open states included in the open state and the preset open time period is the same, it may also be determined whether the time periods of the open states are the same, and if not, it is determined that the industrial device 100 is in an abnormal state. For example, when the preset opening time period includes 2 seconds of the corresponding time periods of one pressure sensor opening state, 1 second of the corresponding time periods of two pressure sensors opening state, 3 seconds of the corresponding time periods of three pressure sensors opening state and 4 seconds of the corresponding time periods of four pressure sensors opening state, the excavator is indicated to have too few actions (the time period is less than the preset time period) in the opening state of one pressure sensor, and has too many actions (the time period is greater than the preset time period) in the opening state of the two pressure sensors, so that whether the problems of high single action composite number occupation ratio (the high single pilot pressure opening time period occupation ratio), uncoordinated actions and the like exist in the working stage is judged.
It should be noted that, the structured operation data in the embodiment of the present application may further include oil quantity, the performance index may include oil consumption data, the oil consumption data of each operation stage is obtained by calculating the oil quantity, whether the oil consumption data is greater than the preset oil consumption data is determined, if yes, it is determined that the oil consumption of the industrial equipment 100 is too high, and the abnormal index is rapidly located, so that the problem stage is located.
With reference to fig. 10, an embodiment of the present application further provides a data processing apparatus 1000, where a function implemented by the data processing apparatus 1000 corresponds to a step performed by the above method. The data processing apparatus 1000 may be understood as a processor of the industrial device 100 described above, or may be understood as a component that performs the functions of the present application under the control of the industrial device 100, independent of the industrial device 100 or the processor described above. The data processing apparatus 1000 may include, among other things, a data acquisition module 1010, a job stage classification module 1020, and an analysis module 1030.
The data acquisition module 1010 is configured to acquire structured job data of the industrial device 100. In an embodiment of the present application, the data acquisition module 1010 may be used to perform step S210 shown in fig. 2, and the description of step S210 may be referred to above for the relevant content of the data acquisition module 1010.
The job phase classification module 1020 is configured to input the structured job data into a predetermined classification model to obtain at least one job phase of the industrial equipment 100. In an embodiment of the present application, the job phase classification module 1020 may be used to perform step S220 shown in fig. 2, and the description of step S220 may be referred to above for the relevant content of the job phase classification module 1020.
The analysis module 1030 is configured to calculate performance indexes of each working stage according to the structured working data, and perform anomaly analysis according to the performance indexes. In an embodiment of the present application, the analysis module 1030 may be used to perform step S230 shown in fig. 2, and the description of step S230 may be referred to above for the relevant content of the analysis module 1030.
Furthermore, the embodiment of the present application also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, performs the steps of the data processing method described above.
The computer program product of the data processing method provided in the embodiment of the present application includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute steps of the data processing method in the method embodiment, and specifically, reference may be made to the method embodiment, and details are not repeated herein.
In summary, the data processing method and apparatus, the industrial device and the storage medium provided in the embodiments of the present application input the obtained structured job data into the preset classification model to obtain the job stage, calculate the performance index of each job stage to perform the anomaly analysis, so as to implement the processes of automatic data classification, index calculation and anomaly analysis, and improve the problems of the prior art that a large amount of test data generated during the operation of the industrial device are manually processed, data classification and index calculation, and the positioning device has low data processing efficiency.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The above is only a preferred embodiment of the present application, and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (9)
1. A method of data processing, comprising:
obtaining structured job data of industrial equipment;
Inputting the structured job data into a preset classification model to obtain at least one job stage of the industrial equipment;
calculating performance indexes of each operation stage according to the structured operation data, and carrying out anomaly analysis according to the performance indexes;
the step of obtaining structured job data for an industrial device includes:
acquiring operation data of industrial equipment, wherein the operation data comprises time sequence industrial big data;
preprocessing the operation data to obtain structured operation data;
The preset classification model comprises a deep learning network structure of GRU+U_net+ DEPTHWISE SEPARABLE CONVOLUTION, the model uses GRU to replace a convolution layer in the original U_net downsampling process to capture time sequence characteristics in data, the original spliced convolution layer is replaced by separable convolution in the process of adopting the GRU, the whole model adopts the U_net network structure, the batch_ pooling and the batch_upsampling module in the U_net structure are improved, the batch_ pooling module replaces the original CNN+CNN module by combining a bidirectional GRU and a CNN module, the batch_upsampling module replaces the original CNN convolution module by adopting the separable convolution separableCNN, and the separable convolution achieves the effect of reducing the number of model parameters by adopting the 1*1 convolution extraction characteristics after channel-by-channel convolution.
2. The data processing method according to claim 1, wherein the structured job data includes time data, the performance index includes a job duration, the performance index of each of the job phases is calculated from the structured job data, and the step of performing anomaly analysis based on the performance index includes:
for each operation stage, calculating the operation duration of the operation stage according to the time data;
and carrying out exception analysis on the operation stage according to the operation duration.
3. The data processing method according to claim 2, wherein the step of performing anomaly analysis on the job phase according to the job duration includes:
Judging whether the operation duration is longer than the preset operation duration of the operation stage;
if yes, an alarm signal is sent out.
4. The data processing method according to claim 1, wherein the structured job data includes pressure data and time data of at least one pressure sensor, the performance index includes a duration corresponding to an on state of the at least one pressure sensor, the performance index of each of the job phases is calculated from the structured job data, and the step of performing anomaly analysis based on the performance index includes:
for each working stage, calculating to obtain the duration corresponding to the opening state of the at least one pressure sensor in the working stage according to the pressure data and the time data of the at least one pressure sensor;
And carrying out exception analysis on the operation stage according to the duration corresponding to the opening state.
5. The data processing method according to claim 4, wherein the step of calculating, for each working stage, a duration corresponding to an on state of the at least one pressure sensor according to the pressure data and the time data of the at least one pressure sensor includes:
For each working stage, calculating the opening state of the at least one pressure sensor according to the pressure data of the at least one pressure sensor;
And calculating the duration corresponding to the opening state according to the opening state and the time data.
6. The data processing method according to claim 4, wherein the step of performing anomaly analysis on the job phase according to the duration corresponding to the on state includes:
judging whether the duration corresponding to the opening state meets the preset opening duration of the operation stage or not;
If not, an alarm signal is sent out.
7. A data processing apparatus, comprising:
the data acquisition module is used for acquiring the structured operation data of the industrial equipment;
the operation stage classification module is used for inputting the structured operation data into a preset classification model to obtain at least one operation stage of the industrial equipment;
the analysis module is used for calculating the performance index of each operation stage according to the structured operation data and carrying out exception analysis according to the performance index;
the step of obtaining structured job data for an industrial device includes:
acquiring operation data of industrial equipment, wherein the operation data comprises time sequence industrial big data;
preprocessing the operation data to obtain structured operation data;
The preset classification model comprises a deep learning network structure of GRU+U_net+ DEPTHWISE SEPARABLE CONVOLUTION, the model uses GRU to replace a convolution layer in the original U_net downsampling process to capture time sequence characteristics in data, the original spliced convolution layer is replaced by separable convolution in the process of adopting the GRU, the whole model adopts the U_net network structure, the batch_ pooling and the batch_upsampling module in the U_net structure are improved, the batch_ pooling module replaces the original CNN+CNN module by combining a bidirectional GRU and a CNN module, the batch_upsampling module replaces the original CNN convolution module by adopting the separable convolution separableCNN, and the separable convolution achieves the effect of reducing the number of model parameters by adopting the 1*1 convolution extraction characteristics after channel-by-channel convolution.
8. An industrial device comprising a memory and a processor for executing an executable computer program stored in the memory to implement the data processing method of any one of claims 1-6.
9. A storage medium having stored thereon a computer program which when executed performs the steps of the data processing method of any of claims 1-6.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107544464A (en) * | 2017-09-11 | 2018-01-05 | 上海斐讯数据通信技术有限公司 | A kind of detection method and system of industrial failure |
| CN109635958A (en) * | 2018-12-12 | 2019-04-16 | 成都航天科工大数据研究院有限公司 | A kind of predictive industrial equipment maintaining method and maintenance system based on edge calculations |
| CN109933500A (en) * | 2019-03-27 | 2019-06-25 | 新奥数能科技有限公司 | Equipment fault alarm method, device, readable medium and electronic equipment |
| US10430949B1 (en) * | 2018-04-24 | 2019-10-01 | Shenzhen Keya Medical Technology Corporation | Automatic method and system for vessel refine segmentation in biomedical images using tree structure based deep learning model |
| CN110320892A (en) * | 2019-07-15 | 2019-10-11 | 重庆邮电大学 | The sewage disposal device fault diagnosis system and method returned based on Lasso |
| CN110334816A (en) * | 2019-07-12 | 2019-10-15 | 深圳市智物联网络有限公司 | A kind of industrial equipment detection method, device, equipment and readable storage medium storing program for executing |
| CN110782370A (en) * | 2019-10-31 | 2020-02-11 | 国网重庆市电力公司北碚供电分公司 | Comprehensive operation and maintenance management platform for power dispatching data network |
| CN111353911A (en) * | 2020-04-28 | 2020-06-30 | 深圳力维智联技术有限公司 | Power equipment operation and maintenance method, system, equipment and storage medium |
| CN111461925A (en) * | 2020-04-24 | 2020-07-28 | 广东电网有限责任公司东莞供电局 | Power distribution network fault analysis method, device, equipment and storage medium |
| CN111866031A (en) * | 2020-09-23 | 2020-10-30 | 天津中新智冠信息技术有限公司 | Equipment monitoring and analyzing method and device, server and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110569695B (en) * | 2018-08-31 | 2021-07-09 | 创新先进技术有限公司 | Image processing method and device based on loss assessment image judgment model |
| US11210554B2 (en) * | 2019-03-21 | 2021-12-28 | Illumina, Inc. | Artificial intelligence-based generation of sequencing metadata |
-
2020
- 2020-12-25 CN CN202011559545.8A patent/CN112508457B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107544464A (en) * | 2017-09-11 | 2018-01-05 | 上海斐讯数据通信技术有限公司 | A kind of detection method and system of industrial failure |
| US10430949B1 (en) * | 2018-04-24 | 2019-10-01 | Shenzhen Keya Medical Technology Corporation | Automatic method and system for vessel refine segmentation in biomedical images using tree structure based deep learning model |
| CN109635958A (en) * | 2018-12-12 | 2019-04-16 | 成都航天科工大数据研究院有限公司 | A kind of predictive industrial equipment maintaining method and maintenance system based on edge calculations |
| CN109933500A (en) * | 2019-03-27 | 2019-06-25 | 新奥数能科技有限公司 | Equipment fault alarm method, device, readable medium and electronic equipment |
| CN110334816A (en) * | 2019-07-12 | 2019-10-15 | 深圳市智物联网络有限公司 | A kind of industrial equipment detection method, device, equipment and readable storage medium storing program for executing |
| CN110320892A (en) * | 2019-07-15 | 2019-10-11 | 重庆邮电大学 | The sewage disposal device fault diagnosis system and method returned based on Lasso |
| CN110782370A (en) * | 2019-10-31 | 2020-02-11 | 国网重庆市电力公司北碚供电分公司 | Comprehensive operation and maintenance management platform for power dispatching data network |
| CN111461925A (en) * | 2020-04-24 | 2020-07-28 | 广东电网有限责任公司东莞供电局 | Power distribution network fault analysis method, device, equipment and storage medium |
| CN111353911A (en) * | 2020-04-28 | 2020-06-30 | 深圳力维智联技术有限公司 | Power equipment operation and maintenance method, system, equipment and storage medium |
| CN111866031A (en) * | 2020-09-23 | 2020-10-30 | 天津中新智冠信息技术有限公司 | Equipment monitoring and analyzing method and device, server and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 全卷积神经网络研究综述;章琳;袁非牛;张文睿;曾夏玲;;计算机工程与应用;20191108(第01期);全文 * |
| 基于工业大数据的三一重工设备工作状态分类研究;庞健;硕士电子期刊;20220315;全文 * |
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