CN113723723A - Operation parameter fluctuation path extraction method and device, storage medium and processor - Google Patents
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Abstract
The embodiment of the invention provides a method and a device for extracting a running parameter fluctuation path, a storage medium and a processor, and belongs to the technical field of chemical engineering. The method comprises the following steps: determining at least one fluctuation time of the operating data of each of the plurality of operating parameters over a first time period; determining fluctuation relevance of any two operation parameters in the plurality of operation parameters according to at least one fluctuation moment of the operation data of each operation parameter; connecting the operating parameters with fluctuation relevance in the plurality of operating parameters to form at least one fluctuation path; and extracting the fluctuation paths of which the connection times are greater than a threshold value in the at least one fluctuation path. The invention can extract frequently-occurring fluctuation path information, provide clear and clear abnormal condition monitoring data for field personnel, and improve the effectiveness and the intelligent level of the alarm system.
Description
Technical Field
The invention relates to the technical field of chemical industry, in particular to a method and a device for extracting a running parameter fluctuation path, a storage medium and a processor.
Background
The chemical system is formed by connecting various basic units (equipment, devices, systems and the like) together through various pipelines and electric wires, and the interaction relationship among the basic units forms a complex network system to realize the exchange and transmission of substances, energy and signals. Particularly, in the production process of a chemical plant, operation fluctuation occurs due to changes in product characteristics, production load, raw material composition, and the like. And multiple variables interact during system fluctuations. The existing operation monitoring system can only give an alarm and an early warning according to the independent operation fluctuation condition of the site, but cannot analyze the fluctuation correlation among different sites, and the data comprises the internal relation of the operation of the device and the propagation rule of abnormal fluctuation. If the operation fluctuation is not processed in time, further operation abnormity can be caused, and even the device can be stopped, so that serious loss is caused.
Disclosure of Invention
The invention aims to provide an operation parameter fluctuation path extraction method, an operation parameter fluctuation path extraction device, a storage medium and a processor, which can extract frequently-occurring fluctuation path information, provide clear and clear abnormal condition monitoring data for field personnel and improve the effectiveness and the intelligent level of an alarm system.
In order to achieve the above object, the present invention provides a method for extracting a fluctuation path of an operating parameter of a chemical plant, the method comprising: determining at least one fluctuation time of the operating data of each of the plurality of operating parameters over a first time period; determining fluctuation relevance of any two operation parameters in the plurality of operation parameters according to at least one fluctuation moment of the operation data of each operation parameter; connecting the operating parameters with fluctuation relevance in the plurality of operating parameters to form at least one fluctuation path; and extracting the fluctuation paths of which the connection times are greater than a threshold value in the at least one fluctuation path.
Preferably, the determining at least one fluctuation time of the operating data of each of the plurality of operating parameters within the first time period comprises: determining a standard deviation and a mean of the operating data of each operating parameter over a first time period; for each operating parameter, determining an operating time of the operating data which is greater than the sum of the mean value and a standard deviation of a preset multiple as the at least one fluctuation time.
Preferably, the determining the fluctuation correlation of any two of the plurality of operating parameters according to the at least one fluctuation time of each operating parameter includes: obtaining membership degrees according to the number of times that the first operation parameter has fluctuation time within preset time after each fluctuation time of the second operation parameter and the number of the fluctuation times of the second operation parameter; obtaining a confidence coefficient according to the sum of the number of fluctuation moments of the second operation parameter in a preset time before each fluctuation moment of the first operation parameter and the number of fluctuation moments of the first operation parameter; and judging the fluctuation relevance according to the membership degree and the confidence degree.
Preferably, the determining the fluctuation correlation according to the membership degree and the confidence further includes: determining that the second operating parameter and the first operating parameter have a fluctuating correlation when the confidence is greater than a confidence threshold and the membership is greater than a membership threshold; and when the confidence coefficient is less than or equal to a confidence coefficient threshold value and/or the membership degree is less than or equal to a membership degree threshold value, determining that the second operating parameter and the first operating parameter do not have fluctuation correlation.
Preferably, the obtaining the membership degree according to the number of times that the first operation parameter has a fluctuation time within a preset time after each fluctuation time of the second operation parameter and the number of fluctuation times of the second operation parameter includes: dividing the number of times that the first operating parameter also has fluctuation time within a preset time after each fluctuation time of the second operating parameter by the number of fluctuation times of the second operating parameter to obtain the membership degree.
Preferably, the obtaining a confidence level according to a sum of a number of fluctuation times of the second operation parameter within a preset time before each fluctuation time of the first operation parameter and a number of fluctuation times of the first operation parameter includes: dividing the sum of the number of fluctuation times of the second operating parameter within a preset time before each fluctuation time of the first operating parameter by the number of fluctuation times of the first operating parameter to obtain a confidence.
The invention also provides a device for extracting the operation parameter fluctuation path of the chemical device, which comprises: the device comprises a fluctuation time determining unit, a fluctuation relevance determining unit, a fluctuation path determining unit and an extracting unit, wherein the fluctuation time determining unit is used for determining at least one fluctuation time of the operation data of each of a plurality of operation parameters in a first time period; the fluctuation relevance determining unit is used for determining the fluctuation relevance of any two operating parameters in the plurality of operating parameters according to at least one fluctuation moment of the operating data of each operating parameter; the fluctuation path determination unit is used for connecting the operation parameters with fluctuation relevance in the plurality of operation parameters to form at least one fluctuation path; the extraction unit is used for extracting the fluctuation path of which the connection times are larger than a threshold value in the at least one fluctuation path.
Preferably, the fluctuation time determination unit is further configured to: determining a standard deviation and a mean of the operating data of each operating parameter over a first time period; for each operating parameter, determining an operating time of the operating data which is greater than the sum of the mean value and a standard deviation of a preset multiple as the at least one fluctuation time.
Preferably, the fluctuation correlation determination unit is further configured to: obtaining membership degrees according to the number of times that the first operation parameter has fluctuation time within preset time after each fluctuation time of the second operation parameter and the number of the fluctuation times of the second operation parameter; obtaining a confidence coefficient according to the sum of the number of fluctuation moments of the second operation parameter in a preset time before each fluctuation moment of the first operation parameter and the number of fluctuation moments of the first operation parameter; and judging the fluctuation relevance according to the membership degree and the confidence degree.
Preferably, the fluctuation correlation determination unit is further configured to: determining that the second operating parameter and the first operating parameter have a fluctuating correlation when the confidence is greater than a confidence threshold and the membership is greater than a membership threshold; and when the confidence coefficient is less than or equal to a confidence coefficient threshold value and/or the membership degree is less than or equal to a membership degree threshold value, determining that the second operating parameter and the first operating parameter do not have fluctuation correlation.
Preferably, the fluctuation correlation determination unit is further configured to: dividing the number of times that the first operating parameter also has fluctuation time within a preset time after each fluctuation time of the second operating parameter by the number of fluctuation times of the second operating parameter to obtain the membership degree.
Preferably, the fluctuation correlation determination unit is further configured to: dividing the sum of the number of fluctuation times of the second operating parameter within a preset time before each fluctuation time of the first operating parameter by the number of fluctuation times of the first operating parameter to obtain a confidence.
The present invention also provides a machine-readable storage medium having stored thereon instructions for causing a machine to execute the chemical plant operation parameter fluctuation path extraction method described above.
The present invention also provides a processor for executing a program, wherein the program is executed for performing: the method for extracting the operation parameter fluctuation path of the chemical plant is described above.
By adopting the technical scheme, the operation parameter fluctuation path extraction method, the device, the storage medium and the processor provided by the invention firstly determine at least one fluctuation moment of the operation data of each operation parameter in a plurality of operation parameters in a first time period; then determining the fluctuation relevance of any two operation parameters in the plurality of operation parameters according to at least one fluctuation moment of the operation data of each operation parameter; then connecting the operation parameters with fluctuation relevance in the plurality of operation parameters to form at least one fluctuation path; and finally, extracting the fluctuation paths of which the connection times are greater than a threshold value in the at least one fluctuation path. The extracted path is frequently-occurring fluctuation path information, clear and clear abnormal condition monitoring data are provided for field personnel, and the effectiveness and the intelligent level of the alarm system are improved.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
fig. 1 is a flowchart of a method for extracting a fluctuation path of an operation parameter of a chemical device according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for determining a surge time according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for determining a relevance of a fluctuation according to an embodiment of the present invention;
FIG. 4 is a flow chart of a method for determining a relevance of a fluctuation according to another embodiment of the present invention;
fig. 5 is a block diagram of a structure of a chemical plant operation parameter fluctuation path extraction apparatus according to an embodiment of the present invention.
Description of the reference numerals
1 fluctuation time determination unit 2 fluctuation relevance determination unit
3 fluctuation path determining unit 4 extracting unit
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
Example one
Fig. 1 is a flowchart of a method for extracting a fluctuation path of an operation parameter of a chemical plant according to an embodiment of the present invention. As shown in fig. 1, the method includes:
step S11 of determining at least one fluctuation time of the operation data of each of the plurality of operation parameters in the first period of time;
specifically, the operation data of a measuring point in a chemical device system is monitored on line, and corresponding historical data in a period of time is recorded. And collecting operation real-time data from a control system of the device or an enterprise real-time database by using an OPC DA server framework according to a data collection interface provided by an enterprise, refreshing a real-time data table of the server, and simultaneously storing the real-time data into a historical data table of each parameter in a time sequence. 10000 data are stored in a server real-time data table by each variable, and the real-time database is updated in a rolling mode once every 1000 running moments, namely, if the time for generating 1000 data is 10 days, the former 1000 data are abandoned every 10 days, and the newly generated 1000 data are stored.
The generation time of 1000 pieces of data for each operation parameter may be set as a first time period, and then it is determined which pieces of data of the 1000 pieces of data are fluctuation data, and the operation time of the fluctuation data is the fluctuation time.
Step S12, determining the fluctuation relevance of any two operation parameters in the plurality of operation parameters according to at least one fluctuation moment of the operation data of each operation parameter;
specifically, after determining the fluctuation time of the fluctuation data for each operating parameter, the fluctuation time can be used to determine the fluctuation relevance of any two parameters, such as whether the fluctuation situations of the two parameters are similar (e.g., whether the fluctuation times are close to each other or even the same), and so on.
Step S13, connecting the operation parameters with fluctuation relevance in the plurality of operation parameters to form at least one fluctuation path;
specifically, if the above steps determine, for example, that 6 operating parameters A, B, C, D, E, F are in fluctuating relationship, that is, A and B, B and C, C are in fluctuating relationship with D, D and E, A and F, then A and B, B are connected with C, C and D, D are connected with E, A and F, thus forming paths A-B-C-D-E and A-F. The parameters of the preferred path connection have a causal relationship, and the path can be formed only in one direction, i.e. if A-B-C is formed into a path, even if A and C have fluctuating correlation, A-C-B cannot be formed into another path.
And step S14, extracting the fluctuation path of which the connection times are greater than a threshold value in the at least one fluctuation path.
Specifically, the threshold value may be preferably set to 3, for example, but is not limited thereto. If the threshold is 3, then paths A-B-C-D-E and A-F, paths A-B-C-D-E are connected 4 times and the path can be extracted.
Example two
The present embodiment provides a specific method for determining at least one fluctuation time of the operation data of each operation parameter, and other embodiments are similar to the embodiments and are not described herein again. The method for determining the fluctuation time comprises the following steps:
step S21, determining a standard deviation and a mean of the operating data of each of the operating parameters in a first time period;
specifically, the standard deviation can be calculated by the following formula:
Step S22, determining, for each of the operation parameters, an operation time of the operation data that is greater than a sum of the mean value and a standard deviation of a preset multiple as the at least one fluctuation time.
Specifically, the preset multiple is preferably 3, but the present invention is not limited thereto. If the operation data exceeds the mean value by 3 σ, the operation time of the operation data is the fluctuation time. Therefore, the fluctuation time of each parameter can be recorded, the fluctuation time sequence of the operation data is established, and all sequence lists are updated regularly. Data structure of fluctuation time series: each measuring point is assigned a list of unfixed length, and the fluctuation time is recorded in the list.
EXAMPLE III
The present embodiment provides a specific method for determining the fluctuation correlation between any two operating parameters, and other embodiments are similar to the first and second embodiments, and are not described herein again. The method for determining the relevance of the fluctuation comprises the following steps:
step S31, obtaining membership degree according to the number of times that the first operation parameter has fluctuation time in the preset time after each fluctuation time of the second operation parameter and the number of the fluctuation times of the second operation parameter;
specifically, assuming that the second operation parameter has fluctuation time in 700 th and 400 th operation data, and the preset time is preferably (but not limited to) the time when 100 operation data are generated, it is determined whether the first operation parameter has fluctuation time in the operation time corresponding to the 700 and 800 operation data of the second operation parameter, and if the fluctuation time is present, 1 is counted. Similarly, if the 400 th operation data of the second operation parameter also has the fluctuation time, it is determined whether the first operation parameter has the fluctuation time within the operation time corresponding to the 400-500 operation data, and if the fluctuation time is present, the count is counted again by 1. Thus, adding all the counts gives a total number of times, i.e. 1+1 gives 2, which is used together with the number of times of fluctuation of the second operating parameter (which as assumed above is also 2), it can be seen whether the times of fluctuation of the first operating parameter and the second operating parameter are similar. Assuming that both are 2 as above, it can be seen that the fluctuation time moments of both are very similar and the membership is high.
Step S32, obtaining a confidence level according to the sum of the number of fluctuation times of the second operation parameter within a preset time before each fluctuation time of the first operation parameter and the number of fluctuation times of the first operation parameter;
specifically, assuming that the first operation parameter has fluctuation time points in 800 th and 500 th operation data, it is determined how many fluctuation time points the second operation parameter has in the operation time points of the 700 th and 800 th operation data of the first operation parameter, assuming that there are 2 fluctuation time points. Then, it is determined how many fluctuation moments the second operation parameter has in the operation moments of the 400 th and 500 th operation data of the first operation parameter, and it is assumed that there are 3 fluctuation moments. By similarly judging each fluctuation time of the first operating parameter in this manner, it is possible to obtain how many fluctuation times of the second operating parameter are within the preset time before each fluctuation time corresponding to the first operating parameter, and by adding the number of fluctuation times of the second operating parameter obtained by these judgments, that is, by assuming that 2+3 is 5 as described above, and by using this number and the number of fluctuation times of the first operating parameter (assuming that 2 as described above), it is possible to see that many fluctuation times of the second operating parameter are before the fluctuation time of the first operating parameter, and the reliability is high.
Step S33, judging the fluctuation relevance according to the membership degree and the confidence coefficient;
specifically, it is assumed that, as described above, when the degree of membership and the degree of reliability are both high, it can be determined that the first operating parameter and the second operating parameter have a fluctuation correlation.
Example four
The present embodiment provides a specific method for determining a membership degree and a confidence degree and determining a fluctuation correlation using the membership degree and the confidence degree, and other embodiments are similar to the first, second, and third embodiments, and are not described herein again.
And step S41, dividing the number of times that the first operating parameter also has fluctuation time within a preset time after each fluctuation time of the second operating parameter by the number of fluctuation times of the second operating parameter to obtain a membership degree. As assumed above, dividing 2 by 2 yields 1. The degree of membership is 1.
Step S42, dividing the sum of the number of fluctuation times of the second operating parameter within a preset time before each fluctuation time of the first operating parameter by the number of fluctuation times of the first operating parameter to obtain a confidence. As assumed above, dividing 5 by 2 yields 2.5. The confidence is 2.5.
Step S43, judging whether the confidence coefficient is larger than a confidence coefficient threshold value and whether the membership degree is larger than a membership degree threshold value;
specifically, the confidence threshold is preferably 0.7, and the membership threshold is preferably 0.75, but the present invention is not limited thereto. Assume, as above, that membership 1 is greater than 0.75 and confidence 2.5 is greater than 0.7.
Step S44, when the confidence coefficient is greater than a confidence coefficient threshold value and the membership degree is greater than a membership degree threshold value, determining that the second operation parameter and the first operation parameter have fluctuation relevance;
specifically, assuming that the degree of membership 1 is greater than 0.75 and the confidence level 2.5 is greater than 0.7 as described above, the second operating parameter and the first operating parameter have a fluctuating correlation.
And step S45, when the confidence coefficient is less than or equal to a confidence coefficient threshold value and/or the membership degree is less than or equal to a membership degree threshold value, determining that the second operating parameter and the first operating parameter have no fluctuation correlation.
Specifically, as long as one of the confidence and membership is less than their corresponding threshold, neither the second operating parameter nor the first operating parameter has a fluctuating correlation.
EXAMPLE five
The present embodiment provides a chemical plant operation parameter fluctuation path extraction device corresponding to the chemical plant operation parameter fluctuation path extraction method in the first to fourth embodiments, as shown in fig. 5, the device includes: a fluctuation time determination unit 1, a fluctuation relevance determination unit 2, a fluctuation path determination unit 3, and an extraction unit 4, wherein the fluctuation time determination unit 1 is configured to determine at least one fluctuation time of the operation data of each of the plurality of operation parameters in the first time period; the fluctuation relevance determining unit 2 is configured to determine fluctuation relevance of any two operating parameters of the plurality of operating parameters according to at least one fluctuation time of the operating data of each operating parameter; the fluctuation path determination unit 3 is configured to connect the operation parameters with fluctuation relevance in the plurality of operation parameters to form at least one fluctuation path; the extracting unit 4 is configured to extract a fluctuation path of the at least one fluctuation path, where the number of connections is greater than a threshold.
Preferably, the fluctuation time determination unit 1 is further configured to: determining a standard deviation and a mean of the operating data of each operating parameter over a first time period; for each operating parameter, determining an operating time of the operating data which is greater than the sum of the mean value and a standard deviation of a preset multiple as the at least one fluctuation time.
Preferably, the fluctuation correlation determination unit 2 is further configured to: obtaining membership degrees according to the number of times that the first operation parameter has fluctuation time within preset time after each fluctuation time of the second operation parameter and the number of the fluctuation times of the second operation parameter; obtaining a confidence coefficient according to the sum of the number of fluctuation moments of the second operation parameter in a preset time before each fluctuation moment of the first operation parameter and the number of fluctuation moments of the first operation parameter; and judging the fluctuation relevance according to the membership degree and the confidence degree.
Preferably, the fluctuation correlation determination unit 2 is further configured to: determining that the second operating parameter and the first operating parameter have a fluctuating correlation when the confidence is greater than a confidence threshold and the membership is greater than a membership threshold; and when the confidence coefficient is less than or equal to a confidence coefficient threshold value and/or the membership degree is less than or equal to a membership degree threshold value, determining that the second operating parameter and the first operating parameter do not have fluctuation correlation.
Preferably, the fluctuation correlation determination unit 2 is further configured to: dividing the number of times that the first operating parameter also has fluctuation time within a preset time after each fluctuation time of the second operating parameter by the number of fluctuation times of the second operating parameter to obtain the membership degree.
Preferably, the fluctuation correlation determination unit 2 is further configured to: dividing the sum of the number of fluctuation times of the second operating parameter within a preset time before each fluctuation time of the first operating parameter by the number of fluctuation times of the first operating parameter to obtain a confidence.
The specific implementation manner of this embodiment is similar to the first to fourth embodiments described above, and is not described herein again.
By adopting the technical scheme, the operation parameter fluctuation path extraction method, the device, the storage medium and the processor provided by the invention firstly determine at least one fluctuation moment of the operation data of each operation parameter in a plurality of operation parameters in a first time period; then determining the fluctuation relevance of any two operation parameters in the plurality of operation parameters according to at least one fluctuation moment of the operation data of each operation parameter; then connecting the operation parameters with fluctuation relevance in the plurality of operation parameters to form at least one fluctuation path; and finally, extracting the fluctuation paths of which the connection times are greater than a threshold value in the at least one fluctuation path. The extracted path is frequently-occurring fluctuation path information, clear and clear abnormal condition monitoring data are provided for field personnel, and the effectiveness and the intelligent level of the alarm system are improved.
The operation parameter fluctuation path extraction device comprises a processor and a memory, wherein the fluctuation time determination unit, the fluctuation relevance determination unit, the fluctuation path determination unit, the extraction unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can set one or more, and the fluctuation path is extracted by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
An embodiment of the present invention provides a storage medium, on which a program is stored, and when the program is executed by a processor, the method for extracting a fluctuation path of an operating parameter is implemented.
The embodiment of the invention provides a processor, which is used for running a program, wherein the running parameter fluctuation path extraction method is executed when the program runs.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein the processor executes the program and realizes the following steps:
determining at least one fluctuation time of the operating data of each of the plurality of operating parameters over a first time period; determining fluctuation relevance of any two operation parameters in the plurality of operation parameters according to at least one fluctuation moment of the operation data of each operation parameter; connecting the operating parameters with fluctuation relevance in the plurality of operating parameters to form at least one fluctuation path; and extracting the fluctuation paths of which the connection times are greater than a threshold value in the at least one fluctuation path.
Preferably, the determining at least one fluctuation time of the operating data of each of the plurality of operating parameters within the first time period comprises: determining a standard deviation and a mean of the operating data of each operating parameter over a first time period; for each operating parameter, determining an operating time of the operating data which is greater than the sum of the mean value and a standard deviation of a preset multiple as the at least one fluctuation time.
Preferably, the determining the fluctuation correlation of any two of the plurality of operating parameters according to the at least one fluctuation time of each operating parameter includes: obtaining membership degrees according to the number of times that the first operation parameter has fluctuation time within preset time after each fluctuation time of the second operation parameter and the number of the fluctuation times of the second operation parameter; obtaining a confidence coefficient according to the sum of the number of fluctuation moments of the second operation parameter in a preset time before each fluctuation moment of the first operation parameter and the number of fluctuation moments of the first operation parameter; and judging the fluctuation relevance according to the membership degree and the confidence degree.
Preferably, the determining the fluctuation correlation according to the membership degree and the confidence further includes: determining that the second operating parameter and the first operating parameter have a fluctuating correlation when the confidence is greater than a confidence threshold and the membership is greater than a membership threshold; and when the confidence coefficient is less than or equal to a confidence coefficient threshold value and/or the membership degree is less than or equal to a membership degree threshold value, determining that the second operating parameter and the first operating parameter do not have fluctuation correlation.
Preferably, the obtaining the membership degree according to the number of times that the first operation parameter has a fluctuation time within a preset time after each fluctuation time of the second operation parameter and the number of fluctuation times of the second operation parameter includes: dividing the number of times that the first operating parameter also has fluctuation time within a preset time after each fluctuation time of the second operating parameter by the number of fluctuation times of the second operating parameter to obtain the membership degree.
Preferably, the obtaining a confidence level according to a sum of a number of fluctuation times of the second operation parameter within a preset time before each fluctuation time of the first operation parameter and a number of fluctuation times of the first operation parameter includes: dividing the sum of the number of fluctuation times of the second operating parameter within a preset time before each fluctuation time of the first operating parameter by the number of fluctuation times of the first operating parameter to obtain a confidence.
The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device:
determining at least one fluctuation time of the operating data of each of the plurality of operating parameters over a first time period; determining fluctuation relevance of any two operation parameters in the plurality of operation parameters according to at least one fluctuation moment of the operation data of each operation parameter; connecting the operating parameters with fluctuation relevance in the plurality of operating parameters to form at least one fluctuation path; and extracting the fluctuation paths of which the connection times are greater than a threshold value in the at least one fluctuation path.
Preferably, the determining at least one fluctuation time of the operating data of each of the plurality of operating parameters within the first time period comprises: determining a standard deviation and a mean of the operating data of each operating parameter over a first time period; for each operating parameter, determining an operating time of the operating data which is greater than the sum of the mean value and a standard deviation of a preset multiple as the at least one fluctuation time.
Preferably, the determining the fluctuation correlation of any two of the plurality of operating parameters according to the at least one fluctuation time of each operating parameter includes: obtaining membership degrees according to the number of times that the first operation parameter has fluctuation time within preset time after each fluctuation time of the second operation parameter and the number of the fluctuation times of the second operation parameter; obtaining a confidence coefficient according to the sum of the number of fluctuation moments of the second operation parameter in a preset time before each fluctuation moment of the first operation parameter and the number of fluctuation moments of the first operation parameter; and judging the fluctuation relevance according to the membership degree and the confidence degree.
Preferably, the determining the fluctuation correlation according to the membership degree and the confidence further includes: determining that the second operating parameter and the first operating parameter have a fluctuating correlation when the confidence is greater than a confidence threshold and the membership is greater than a membership threshold; and when the confidence coefficient is less than or equal to a confidence coefficient threshold value and/or the membership degree is less than or equal to a membership degree threshold value, determining that the second operating parameter and the first operating parameter do not have fluctuation correlation.
Preferably, the obtaining the membership degree according to the number of times that the first operation parameter has a fluctuation time within a preset time after each fluctuation time of the second operation parameter and the number of fluctuation times of the second operation parameter includes: dividing the number of times that the first operating parameter also has fluctuation time within a preset time after each fluctuation time of the second operating parameter by the number of fluctuation times of the second operating parameter to obtain the membership degree.
Preferably, the obtaining a confidence level according to a sum of a number of fluctuation times of the second operation parameter within a preset time before each fluctuation time of the first operation parameter and a number of fluctuation times of the first operation parameter includes: dividing the sum of the number of fluctuation times of the second operating parameter within a preset time before each fluctuation time of the first operating parameter by the number of fluctuation times of the first operating parameter to obtain a confidence.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that 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 an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to 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 scope of the claims of the present application.
Claims (14)
1. A method for extracting operation parameter fluctuation paths of chemical engineering devices is characterized by comprising the following steps:
determining at least one fluctuation time of the operating data of each of the plurality of operating parameters over a first time period;
determining fluctuation relevance of any two operation parameters in the plurality of operation parameters according to at least one fluctuation moment of the operation data of each operation parameter;
connecting the operating parameters with fluctuation relevance in the plurality of operating parameters to form at least one fluctuation path;
and extracting the fluctuation paths of which the connection times are greater than a threshold value in the at least one fluctuation path.
2. The method according to claim 1, wherein the determining at least one fluctuation time of the operation data of each of the plurality of operation parameters in the first time period comprises:
determining a standard deviation and a mean of the operating data of each operating parameter over a first time period;
for each operating parameter, determining an operating time of the operating data which is greater than the sum of the mean value and a standard deviation of a preset multiple as the at least one fluctuation time.
3. The method for extracting fluctuation paths of operating parameters of a chemical plant according to claim 1, wherein the determining of the fluctuation relevance of any two operating parameters of the plurality of operating parameters according to at least one fluctuation time of each operating parameter comprises:
obtaining membership degrees according to the number of times that the first operation parameter has fluctuation time within preset time after each fluctuation time of the second operation parameter and the number of the fluctuation times of the second operation parameter;
obtaining a confidence coefficient according to the sum of the number of fluctuation moments of the second operation parameter in a preset time before each fluctuation moment of the first operation parameter and the number of fluctuation moments of the first operation parameter;
and judging the fluctuation relevance according to the membership degree and the confidence degree.
4. The method for extracting fluctuation paths of operating parameters of a chemical plant according to claim 3, wherein the determining the fluctuation correlation according to the membership degree and the confidence degree further comprises:
determining that the second operating parameter and the first operating parameter have a fluctuating correlation when the confidence is greater than a confidence threshold and the membership is greater than a membership threshold;
and when the confidence coefficient is less than or equal to a confidence coefficient threshold value and/or the membership degree is less than or equal to a membership degree threshold value, determining that the second operating parameter and the first operating parameter do not have fluctuation correlation.
5. The method for extracting fluctuation paths of operating parameters of a chemical plant according to claim 3, wherein the obtaining of the membership degree according to the number of times that the first operating parameter has fluctuation time within a preset time after each fluctuation time of the second operating parameter and the number of fluctuation times of the second operating parameter comprises:
dividing the number of times that the first operating parameter also has fluctuation time within a preset time after each fluctuation time of the second operating parameter by the number of fluctuation times of the second operating parameter to obtain the membership degree.
6. The chemical plant operation parameter fluctuation path extraction method according to claim 3, wherein the obtaining of the confidence degree according to a sum of the number of fluctuation times that the second operation parameter has within a preset time before each fluctuation time of the first operation parameter and the number of fluctuation times of the first operation parameter includes:
dividing the sum of the number of fluctuation times of the second operating parameter within a preset time before each fluctuation time of the first operating parameter by the number of fluctuation times of the first operating parameter to obtain a confidence.
7. The utility model provides a chemical plant device operation parameter fluctuation path extraction element which characterized in that, the device includes:
a fluctuation time determination unit, a fluctuation relevance determination unit, a fluctuation path determination unit, and an extraction unit, wherein,
the fluctuation time determining unit is used for determining at least one fluctuation time of the operation data of each of the plurality of operation parameters in a first time period;
the fluctuation relevance determining unit is used for determining the fluctuation relevance of any two operating parameters in the plurality of operating parameters according to at least one fluctuation moment of the operating data of each operating parameter;
the fluctuation path determination unit is used for connecting the operation parameters with fluctuation relevance in the plurality of operation parameters to form at least one fluctuation path;
the extraction unit is used for extracting the fluctuation path of which the connection times are larger than a threshold value in the at least one fluctuation path.
8. The chemical plant operation parameter fluctuation path extraction device according to claim 7, wherein the fluctuation time determination unit is further configured to:
determining a standard deviation and a mean of the operating data of each operating parameter over a first time period;
for each operating parameter, determining an operating time of the operating data which is greater than the sum of the mean value and a standard deviation of a preset multiple as the at least one fluctuation time.
9. The chemical plant operation parameter fluctuation path extraction device according to claim 7, wherein the fluctuation correlation determination unit is further configured to:
obtaining membership degrees according to the number of times that the first operation parameter has fluctuation time within preset time after each fluctuation time of the second operation parameter and the number of the fluctuation times of the second operation parameter;
obtaining a confidence coefficient according to the sum of the number of fluctuation moments of the second operation parameter in a preset time before each fluctuation moment of the first operation parameter and the number of fluctuation moments of the first operation parameter;
and judging the fluctuation relevance according to the membership degree and the confidence degree.
10. The chemical plant operation parameter fluctuation path extraction device according to claim 9, wherein the fluctuation correlation determination unit is further configured to:
determining that the second operating parameter and the first operating parameter have a fluctuating correlation when the confidence is greater than a confidence threshold and the membership is greater than a membership threshold;
and when the confidence coefficient is less than or equal to a confidence coefficient threshold value and/or the membership degree is less than or equal to a membership degree threshold value, determining that the second operating parameter and the first operating parameter do not have fluctuation correlation.
11. The chemical plant operation parameter fluctuation path extraction device according to claim 9, wherein the fluctuation correlation determination unit is further configured to:
dividing the number of times that the first operating parameter also has fluctuation time within a preset time after each fluctuation time of the second operating parameter by the number of fluctuation times of the second operating parameter to obtain the membership degree.
12. The chemical plant operation parameter fluctuation path extraction device according to claim 9, wherein the fluctuation correlation determination unit is further configured to:
dividing the sum of the number of fluctuation times of the second operating parameter within a preset time before each fluctuation time of the first operating parameter by the number of fluctuation times of the first operating parameter to obtain a confidence.
13. A machine-readable storage medium having stored thereon instructions for causing a machine to execute the chemical plant operating parameter fluctuation path extraction method of any one of claims 1 to 6.
14. A processor configured to execute a program, wherein the program is configured to perform: the method for extracting the fluctuation path of the operation parameter of the chemical plant according to any one of claims 1 to 6.
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