CN111624906A - Equipment operation state determination method and device, readable medium and electronic equipment - Google Patents
Equipment operation state determination method and device, readable medium and electronic equipment Download PDFInfo
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- CN111624906A CN111624906A CN202010392312.7A CN202010392312A CN111624906A CN 111624906 A CN111624906 A CN 111624906A CN 202010392312 A CN202010392312 A CN 202010392312A CN 111624906 A CN111624906 A CN 111624906A
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
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Abstract
The invention discloses a method and a device for determining the running state of equipment, a computer readable storage medium and electronic equipment, wherein the method comprises the following steps: acquiring equipment operation data of industrial equipment; performing feature selection on the equipment operation data, and determining feature data from the equipment operation data; and predicting the running state of the equipment according to the characteristic data, and determining the running state of the equipment corresponding to the industrial equipment. By the technical scheme, the data size and data dimension for determining the running state of the equipment can be reduced, the difficulty of analyzing the running state of the equipment is reduced, and the winning features are extracted, so that the running state of the industrial equipment can be determined quickly and accurately.
Description
Technical Field
The invention relates to the technical field of energy, in particular to a method and a device for determining an equipment running state, a readable medium and electronic equipment.
Background
At present, industrial equipment (such as steam boilers, gas boilers, generators and the like) is generally applied in various fields, and meanwhile, the operation state of the industrial equipment is monitored, and the timely discovery and elimination of equipment faults are important work in the operation process of the industrial equipment.
At present, for analyzing the equipment running state of the industrial equipment, the data collected by the field sensor of the industrial equipment is analyzed by expert knowledge, so as to determine the equipment running state.
However, the data volume collected by the field sensor of the industrial equipment is large, the data dimension is large, the state of the equipment is difficult to analyze only by using expert knowledge, and the accuracy of the determined running state of the equipment can be directly influenced.
Disclosure of Invention
The invention provides a method and a device for determining the running state of equipment, a computer readable storage medium and electronic equipment, which can reduce the data volume and data dimension for determining the running state of the equipment, reduce the difficulty of analyzing the running state of the equipment and extract the winning features, thereby being convenient for rapidly and accurately determining the running state of the industrial equipment.
In a first aspect, the present invention provides a method for determining an operating state of a device, including:
acquiring equipment operation data of industrial equipment;
performing feature selection on the equipment operation data, and determining feature data from the equipment operation data;
and predicting the running state of the equipment according to the characteristic data, and determining the running state of the equipment corresponding to the industrial equipment.
Preferably, the performing feature selection on the device operation data and determining feature data from the device operation data includes:
and based on the equipment monitoring requirement corresponding to the industrial equipment, utilizing a preset ensemble learning algorithm to select the characteristics so as to determine characteristic data from the equipment operation data.
Preferably, the preset ensemble learning algorithm comprises a random forest algorithm.
Preferably, the predicting the device operating state according to the characteristic data and determining the device operating state corresponding to the industrial device includes:
and based on the characteristic data, predicting the running state of the equipment by using a preset time prediction algorithm so as to determine the running state of the equipment corresponding to the industrial equipment.
Preferably, the preset time prediction algorithm comprises a long-short term memory network.
In a second aspect, the present invention provides an apparatus for determining an operating status of a device, including:
the acquisition module is used for acquiring equipment operation data of the industrial equipment;
the selection module is used for performing characteristic selection on the equipment operation data and determining characteristic data from the equipment operation data;
and the prediction module is used for predicting the running state of the equipment according to the characteristic data and determining the running state of the equipment corresponding to the industrial equipment.
Preferably, the selection module is configured to perform feature extraction by using a preset ensemble learning algorithm based on the device monitoring requirement corresponding to the industrial device, so as to determine feature data corresponding to the industrial device from the device operation data.
Preferably, the preset ensemble learning algorithm comprises a random forest algorithm.
Preferably, the prediction module is configured to perform device operation state prediction by using a preset time prediction algorithm based on the feature data, so as to determine a device operation state corresponding to the industrial device.
Preferably, the preset time prediction algorithm comprises a long-short term memory network.
In a third aspect, the invention provides a computer-readable storage medium comprising executable instructions which, when executed by a processor of an electronic device, cause the processor to perform the method according to any one of the first aspect.
In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
The invention provides a method, a device, a computer readable storage medium and an electronic device for determining an equipment running state, wherein the method comprises the steps of obtaining equipment running data of industrial equipment, then, carrying out feature selection on the equipment running data, extracting out a superior feature, determining the feature data from the equipment running data, then, predicting the equipment running state by using the feature data, and determining the equipment running state corresponding to the industrial equipment. In summary, according to the technical scheme of the invention, the data size and data dimension for determining the operation state of the equipment can be reduced, the difficulty of analyzing the operation state of the equipment is reduced, and the winning features are extracted, so that the operation state of the industrial equipment can be determined quickly and accurately.
Further effects of the above-mentioned unconventional preferred modes will be described below in conjunction with specific embodiments.
Drawings
In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic flowchart of a method for determining an operating state of a device according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of another method for determining an operating state of a device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an apparatus operation state determining device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for determining an operating state of a device, including the following steps:
The device operating data refers in particular to data generated by the industrial device during operation. The device operation data in the embodiment of the invention is a multi-dimensional time data set. It should be noted that the equipment operation data includes, but is not limited to, operation data of the industrial equipment under different conditions and states.
In some feasible embodiments, in the operation process of the equipment, the data of the key nodes are acquired through the sensors, the acquired data are preprocessed and standardized, null values, abnormal values and data of different dimensions are eliminated, the influence on the algorithm model is reduced, and the equipment operation data for feature extraction is obtained.
In some feasible implementation manners, the application scenarios of the embodiment of the invention can be applied to the scenarios of node monitoring, equipment running state monitoring and the like of an energy network. Wherein, the energy network can be a universal energy microgrid. The industrial equipment is specifically used for industrial production, for example, in the universal energy microgrid, the industrial equipment can be a gas internal combustion engine, a waste heat boiler, a steam boiler, a bromine cooler, photovoltaic equipment, a ground source heat pump, wind energy equipment, energy storage equipment and the like.
And 102, performing feature selection on the equipment operation data, and determining feature data from the equipment operation data.
The characteristic data refers to at least one operation data in the equipment operation data.
In some feasible embodiments, based on the device monitoring requirement of the industrial device, feature selection is performed by using an ensemble learning algorithm, and feature data is determined from device operation data and used for predicting the device operation state, so that the device operation state of the industrial device is judged. In other words, in an actual scene, there may be a plurality of indexes indicating the operation state of the device, and the device monitoring requirement indicates one or more indexes indicating the operation state of the device. The device running states of the industrial device under different device monitoring requirements are determined by determining the characteristic data of the different device monitoring requirements, and a prediction model does not need to be respectively established for the different device monitoring requirements, so that the device running states of the industrial device can be determined more quickly and accurately.
Optionally, the feature data may be determined based on a random forest algorithm, and a decision tree is established to screen out several types of superior data from the device operation data as the feature data. The principles of random forest algorithms and decision trees are well known in the art and will not be described in detail herein. The characteristic data obtained by screening is considered to be equipment operation data which has relatively obvious influence on equipment monitoring requirements; in other words, the device operation data can better represent the current operation condition of the industrial device. Of course, the feature data may also be determined by other ensemble learning algorithms in the prior art, which is not limited in the embodiment of the present invention.
And 103, predicting the running state of the equipment according to the characteristic data, and determining the running state of the equipment corresponding to the industrial equipment.
In some possible embodiments, the device operation state prediction is performed by using a preset time prediction algorithm based on the characteristic data, so as to predict the device operation state corresponding to the industrial device.
Alternatively, the predetermined time prediction algorithm may be a long-short term memory network, and the principle of the long-short term memory network is well known in the art and is not described herein. Of course, the operation state of the device may also be determined by other time prediction algorithms in the prior art, which is not limited in the embodiment of the present invention.
According to the technical scheme, the embodiment of the invention has the following beneficial effects: the data size and data dimensionality for determining the equipment running state prediction are reduced through the feature selection, the difficulty of equipment running state analysis is reduced, and the winning features are extracted, so that the equipment running state of the industrial equipment can be determined quickly and accurately.
Fig. 1 shows only a basic embodiment of the method of the present invention, and based on this, certain optimization and expansion can be performed, and other preferred embodiments of the method can also be obtained.
Fig. 2 shows another embodiment of the method for determining the operating state of the device according to the present invention. On the basis of the foregoing embodiments, the embodiments of the present invention are described in more detail with reference to application scenarios. The method specifically comprises the following steps:
In the embodiment of the present invention, it is assumed that the industrial equipment is a gas boiler. In the process of operating the industrial equipment, information and equipment static information in the equipment operation process corresponding to a plurality of moments respectively can be collected, and then, for data at each moment, preprocessing (including null value processing and data normalization) is performed on each data as a feature, so that a feature item X is obtained [ X ═ X0，X1，X2，…，XM]The characteristic items include information during the operation of the equipment, such as gas consumption, power generation amount, cylinder liner water temperature, power generation efficiency and the like, and also include static information of the equipment, such as a manufacturer, a date of generation, unit price of the equipment, rated parameters of the equipment and the like. It should be noted that the device operation data should be time series data. The embodiment of the invention aims to select at least one item of equipment operation data as characteristic data, and the equipment operation state can be predicted more quickly and accurately through the characteristic data.
And 202, based on the equipment monitoring requirement corresponding to the industrial equipment, utilizing a preset ensemble learning algorithm to perform characteristic selection so as to determine characteristic data from the equipment operation data.
In the embodiment of the present invention, it is assumed that the device monitoring requirement is energy consumption prediction, and of course, the embodiment of the present invention is not intended to limit the device monitoring requirement at all, and may be other monitoring tasks in practice. Specifically, the above feature term X ═ X is calculated by using a random forest algorithm0，X1，X2，…，XM]Performing feature extraction to obtain a feature item X ═ X'0，X′1，X′2，…，X′N]The training method of the random forest algorithm is supervised training, the sample labels are data corresponding to equipment monitoring requirements, namely the energy consumption states, and the sample labels are high, moderate and low if the energy consumption states are respectively high, moderate and low. Here, the N features that are superior are selected from the M features, and the size of N needs to be adjusted and selected according to the data dimension of the device operation data. The dominant standard is the importance of the features in the random forest algorithm, the importance of the features can be determined through the prior art and the future technology, the importance is not limited in the embodiment of the invention, and then the N features are selected according to the ordering of the importance of the features. Optionally, calculating a certain characteristic X'iThe specific steps of importance are as follows:
1. for each decision tree, select feature X'iCorresponding out-of-bag data (out of bag, OOB) calculated characteristic X'iThe corresponding out-of-bag data error for each sample is denoted as errOOB 1.
By out-of-bag data is meant that each time a decision tree is built, one datum is obtained by repeated sampling for training the decision tree, and about 1/3 of data is not utilized and is not involved in the decision tree building. The part of data which does not participate in the establishment of the decision tree can be used for evaluating the performance of the decision tree, and the prediction error rate of the model is calculated and is called as out-of-bag data error. This has proven to be an unbiased estimate, so no further cross-validation or separate test set is required in the random forest algorithm to obtain an unbiased estimate of the test set error.
2. Random pair of feature X 'in out-of-bag data OOB'iAdding noise interference to all samples (e.g. it can be randomly changed)Varying the value of the sample at a feature), the out-of-bag data error for each sample is again calculated and denoted as errOOB 2.
3. Assuming there are N trees in the forest, the feature XiThe importance of (A) is ∑ (errOOB2-errOOB1)/N
It should be noted that the feature data determined by the embodiment of the present invention is feature dimension data with relatively large correlation with the energy consumption state prediction result.
And 203, predicting the running state of the equipment by using a preset time prediction algorithm based on the characteristic data so as to determine the running state of the equipment corresponding to the industrial equipment.
In the embodiment of the invention, the characteristic term X ═ X'0，X′1，X′2，…，X′N]The sample set is input into the long-short term memory neural network in a time sequence form, the input is a three-dimensional array, the dimensionality of the three-dimensional array is (1, T, N), and the three-dimensional array represents 1 sample at each time step, T time steps and N characteristics. The value of T is related to the time interval and the change rate of the sample set time sequence, and the output of the long-term and short-term neural network is the state value of the energy consumption state monitoring task in the example. Optionally, in the prediction of the energy consumption of the gas boiler by taking days as a unit, the value T is 360, N is the number of the characteristic numbers selected in the previous step, optionally, the number of the long-term and short-term neural networks is 4, and the number of the nodes of the hidden layer is the number of the characteristic numbers N multiplied by 3.
The embodiment of the invention exemplarily provides energy consumption prediction results of a gas boiler at two moments, wherein the two moments are respectively 1 and 2, the advantages of the energy consumption prediction results are characterized by generating efficiency, power factor, gas consumption, generating capacity and cylinder liner water temperature, the energy consumption states are respectively high, moderate and low, and the results are shown in table 1:
TABLE 1
According to the technical scheme, the embodiment of the invention has the following beneficial effects: based on the equipment monitoring requirement, the random forest algorithm is used for feature selection, feature data which obviously affect the equipment monitoring requirement are determined, and based on the feature data, the equipment running state of the industrial equipment corresponding to the equipment monitoring requirement can be determined more quickly and accurately.
Based on the same concept as the method embodiment of the present invention, referring to fig. 3, an embodiment of the present invention further provides an apparatus for determining an operating state of a device, including:
an obtaining module 301, configured to obtain device operation data of an industrial device;
a selecting module 302, configured to perform feature selection on the device operation data, and determine feature data from the device operation data;
and the prediction module 303 is configured to perform device operation state prediction according to the feature data, and determine a device operation state corresponding to the industrial device.
In an embodiment of the present invention, the selecting module 302 is configured to perform feature extraction by using a preset ensemble learning algorithm based on an equipment monitoring requirement corresponding to the industrial equipment, so as to determine feature data corresponding to the industrial equipment from the equipment operation data.
In an embodiment of the invention, the preset ensemble learning algorithm comprises a random forest algorithm.
In an embodiment of the present invention, the predicting module 303 is configured to predict an apparatus operating state by using a preset time prediction algorithm based on the feature data, so as to determine an apparatus operating state corresponding to the industrial apparatus.
In one embodiment of the present invention, the predetermined time prediction algorithm comprises a long-term and short-term memory network.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device includes a processor 401 and a memory 402 storing execution instructions, and optionally an internal bus 403 and a network interface 404. The memory 402 may include a memory 4021, such as a Random-access memory (RAM), and may further include a non-volatile memory 4022 (e.g., at least 1 disk memory); the processor 401, the network interface 404, and the memory 402 may be connected to each other by an internal bus 403, and the internal bus 403 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (extended Industry Standard Architecture) bus, or the like; the internal bus 403 may be divided into an address bus, a data bus, a control bus, etc., which is indicated by only one double-headed arrow in fig. 4 for convenience of illustration, but does not indicate only one bus or one type of bus. Of course, the electronic device may also include hardware required for other services. When the processor 401 executes execution instructions stored by the memory 402, the processor 401 performs the method in any of the embodiments of the present invention and at least is used to perform the method as shown in fig. 1 or fig. 2.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory to the memory and then executes the execution instruction, and may also obtain the corresponding execution instruction from other devices, so as to form a device operation state determination apparatus on a logic level. The processor executes the execution instructions stored in the memory, so that the executed execution instructions realize the device operation state determination method provided by any embodiment of the invention.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Embodiments of the present invention further provide a computer-readable storage medium, which includes an execution instruction, and when a processor of an electronic device executes the execution instruction, the processor executes a method provided in any one of the embodiments of the present invention. The electronic device may specifically be the electronic device shown in fig. 4; the execution instruction is a computer program corresponding to the device operation state determination device.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
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 boiler 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 boiler. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or boiler that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. An apparatus operation state determination method, characterized by comprising:
acquiring equipment operation data of industrial equipment;
performing feature selection on the equipment operation data, and determining feature data from the equipment operation data;
and predicting the running state of the equipment according to the characteristic data, and determining the running state of the equipment corresponding to the industrial equipment.
2. The method of claim 1, wherein the performing feature selection on the device operating data and determining feature data from the device operating data comprises:
and based on the equipment monitoring requirement corresponding to the industrial equipment, utilizing a preset ensemble learning algorithm to select the characteristics so as to determine characteristic data from the equipment operation data.
3. The method of claim 2, wherein the pre-set ensemble learning algorithm comprises a random forest algorithm.
4. The method according to claim 1, wherein the predicting the operation state of the equipment according to the characteristic data to determine the operation state of the equipment corresponding to the industrial equipment comprises:
and based on the characteristic data, predicting the running state of the equipment by using a preset time prediction algorithm so as to determine the running state of the equipment corresponding to the industrial equipment.
5. The method of claim 4, wherein the pre-set time prediction algorithm comprises a long-short term memory network.
6. An apparatus operating state determining device, comprising:
the acquisition module is used for acquiring equipment operation data of the industrial equipment;
the selection module is used for performing characteristic selection on the equipment operation data and determining characteristic data from the equipment operation data;
and the prediction module is used for predicting the running state of the equipment according to the characteristic data and determining the running state of the equipment corresponding to the industrial equipment.
7. The apparatus according to claim 6, wherein the selecting module is configured to perform feature extraction by using a preset ensemble learning algorithm based on the device monitoring requirement corresponding to the industrial device, so as to determine feature data corresponding to the industrial device from the device operation data.
8. The apparatus of claim 6, wherein the prediction module is configured to perform device operation state prediction by using a preset time prediction algorithm based on the characteristic data to determine the device operation state corresponding to the industrial device.
9. A computer-readable storage medium comprising executable instructions that, when executed by a processor of an electronic device, cause the processor to perform the method of any of claims 1 to 5.
10. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-5 when the processor executes the execution instructions stored by the memory.
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