CN111414999B - Method and device for monitoring running state of equipment - Google Patents
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Abstract
The application is applicable to the technical field of energy sources, and provides a method and a device for monitoring the running state of equipment, wherein the method comprises the following steps: acquiring measurement point data of the equipment according to the historical data of the equipment; processing the measurement point data by adopting a time sequence model to obtain a predicted value of the equipment at a preset moment; processing the measurement point data and the predicted value by adopting a reverse error neural network to obtain a residual predicted value; acquiring an evaluation reference value according to the predicted value and the residual error predicted value; determining a confidence interval of the equipment at a preset time according to the measuring point data and the evaluation reference value of the equipment; and determining the running state of the equipment according to the running data of the equipment at the preset time and the confidence interval. The application monitors the actual running state of the equipment in real time based on the time sequence model and the reverse error neural network algorithm, and determines the running state of the equipment by combining the confidence interval range, thereby ensuring the accuracy, convenience and rapidity of prediction.
Description
Technical Field
The application relates to the technical field of energy, in particular to a method and a device for monitoring the running state of equipment.
Background
Devices in an energy station can exhibit different operating states during operation due to different times of use, modes of use, and locations of use. At present, whether the equipment is in a normal running state or not is mainly discovered by monitoring the distance between certain measuring points of the equipment and a standard threshold value or a specified limit value.
However, the operation threshold or limit value of most devices is mainly derived from the rated parameters of the devices of the device manufacturer or provided by the professional technician according to experience, and since the threshold or limit value is usually a fixed value, the threshold or limit value cannot be adjusted in real time according to the operation state of the devices, so that the actual operation state of the devices cannot be accurately monitored, and the normal operation of the devices cannot be ensured.
Disclosure of Invention
In view of this, the embodiments of the present application provide a method, an apparatus, a terminal device, and a computer readable storage medium for monitoring an operating state of a device, so as to solve a technical problem that in the prior art, an accurate monitoring cannot be performed on an actual operating state of the device, and thus normal operation of the device cannot be guaranteed.
In a first aspect, the present application provides a method for monitoring an operating state of a device, including:
acquiring measurement point data of equipment according to historical data of the equipment;
processing the measurement point data by adopting a time sequence model to obtain a predicted value of the equipment at a preset moment;
processing the measurement point data and the predicted value by adopting a reverse error neural network to obtain a residual predicted value;
acquiring an evaluation reference value according to the predicted value and the residual error predicted value;
determining a confidence interval of the equipment at the preset time according to the measurement point data of the equipment and the evaluation reference value;
and determining the running state of the equipment according to the running data of the equipment at the preset time and the confidence interval.
Preferably, the acquiring the measurement point data of the device according to the historical data of the device includes:
preprocessing the acquired historical data of the equipment to acquire preprocessed time series data, wherein the preprocessing comprises removing repeated data and/or abnormal data in the historical data;
grouping the time series data according to a preset step length, and calculating the average value of each group of data to obtain the measuring point data of the equipment.
Preferably, the time series model comprises a differentially integrated moving average autoregressive model.
The processing the measurement point data and the predicted value by adopting a reverse error neural network to obtain a residual predicted value comprises the following steps:
acquiring an initial residual value according to the measurement point data and the predicted value;
and the reverse error neural network processes the initial residual value to obtain a residual prediction value.
Preferably, the obtaining an initial residual value according to the measurement point data and the predicted value includes:
acquiring operation data of the equipment at the preset moment according to the measuring point data;
and differencing the predicted value and the operation data to obtain the initial residual value.
Preferably, in the step of obtaining an evaluation reference value according to the predicted value and the residual predicted value, the evaluation reference value is a sum of the predicted value and the residual predicted value.
Preferably, the determining the confidence interval of the device at the preset time according to the measurement point data of the device and the evaluation reference value includes:
according to the measuring point data of the equipment, acquiring an average value of the measuring point data;
acquiring standard deviation of the measuring point data according to the average value of the measuring point data;
and determining a confidence interval of the equipment at the preset moment according to the evaluation reference value and the standard deviation of the measurement point data.
In a second aspect, the present application provides a device for monitoring an operating state of an apparatus, including:
the measuring point data acquisition module is used for acquiring measuring point data of the equipment according to the historical data of the equipment;
the predicted value acquisition module is used for processing the measurement point data by adopting a time sequence model to acquire a predicted value of the equipment at a preset moment;
the residual prediction value acquisition module is used for processing the measurement point data and the prediction value by adopting a reverse error neural network to acquire a residual prediction value;
the evaluation reference value acquisition module is used for acquiring an evaluation reference value according to the predicted value and the residual error predicted value;
the confidence interval determining module is used for determining a confidence interval of the equipment at the preset moment according to the measurement point data of the equipment and the evaluation reference value;
and the running state determining module is used for determining the running state of the equipment according to the running data of the equipment at the preset time and the confidence interval.
In a third aspect, the present application provides a readable medium comprising execution instructions which, when executed by a processor of an electronic device, perform a method of monitoring an operational state of the device according to any of the first aspects.
In a fourth aspect, the present application provides an electronic device, including a processor and a memory storing execution instructions, where when the processor executes the execution instructions stored in the memory, the processor performs a method for monitoring an operating state of the device according to any one of the first aspects.
The method for monitoring the running state of the equipment provided by the embodiment of the application has the beneficial effects that: the application combines big data technology, based on the combination of time sequence model and reverse error neural network algorithm, can monitor and acquire the actual running state of the target equipment in real time under the remote condition, and combines the reasonable confidence interval range of the equipment running, thereby effectively overcoming the defect of inaccurate prediction of the actual running state of the equipment in the prior art, and simultaneously being beneficial to the scheduling optimization of the follow-up work. The monitoring method provided by the embodiment is simple in integral operation, ensures accuracy, convenience and rapidness of prediction, improves integral processing speed, and saves processing resources.
Further effects of the above-described non-conventional preferred embodiments will be described below in connection with the detailed description.
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In order to more clearly illustrate the embodiments of the application or the prior art solutions, the drawings which are used in the description of the embodiments or the prior art will be briefly described below, it being obvious that the drawings in the description below are only some of the embodiments described in the present application, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for monitoring an operation state of a device according to an embodiment of the present application;
FIG. 2 is a flow chart of another method for monitoring an operation status of a device according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a device for monitoring an operation status of an apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the application, fall within the scope of protection of the application. The technical means used in the examples are conventional means well known to those skilled in the art unless otherwise indicated.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In order to illustrate the technical scheme of the application, the following description is made by specific examples.
The development of big data and internet of things technology makes the idea of remotely detecting the operation state of equipment more and more accepted, and when the operation data deviate from the operation standard (threshold or limit value) is found, the operation data is sometimes caused by abnormal operation of the equipment, but sometimes caused by the change of the operation standard due to the continuous change of the operation condition of the equipment.
The device operation standard generally complies with some fixed threshold or limit value set by the device manufacturer, but for some operation parameter standard, a relative evaluation standard or dynamic evaluation standard can be introduced, wherein the standard is aimed at the same monitoring amount of the same device under the same operation environment (geography, position and working condition), and the evaluation standard mainly comprises two parts, namely an evaluation reference value and a confidence interval.
The application provides a monitoring method of equipment operation state based on time sequence and neural network algorithm by combining big data technology.
Referring to fig. 1, a specific embodiment of a method for monitoring an operating state of a device according to the present application is shown. In this embodiment, the method specifically includes the following steps:
step 101: and acquiring measurement point data of the equipment according to the historical data of the equipment.
The device may be any device in the energy station that gathers all or a desired historical data information based on the determined target device.
The measurement point data related in the embodiment can be implemented by using a measurement point data acquisition module. The measuring point data acquisition module is mainly used for acquiring measuring point data of the equipment according to historical data of the equipment. The station data acquisition module may include a preprocessing unit and a station data acquisition unit, and the preprocessing unit and the station data acquisition unit may be connected in series.
Specifically, the preprocessing unit may remove repeated data and/or abnormal data that may occur, and then the measuring point data obtaining unit may perform preset step grouping on the time series data. Whereby station data of the device is obtained.
Step 102: and processing the measurement point data by adopting a time sequence model to obtain a predicted value of the equipment at a preset moment.
The time sequence algorithm is a relatively mature algorithm capable of realizing prediction aiming at time sequence data, and the data information adopted in the embodiment is ordered according to time, so that the time sequence algorithm is suitable for prediction by adopting a time sequence method. The time series model may include an autoregressive model (Autoregressive model, AR), an autoregressive moving average model (Autoregressive moving average model, ARMA), a differentially integrated moving average autoregressive model (Autoregressive integrated moving average, ARIMA), and the like. In this embodiment, it may be preferable to predict the predetermined time of the target device using a differentially integrated moving average autoregressive model (Autoregressive integrated moving average, ARIMA). The measurement point data involved in this step may be implemented using a predicted value acquisition module.
It should be understood that the differential integrated moving average autoregressive model is one of the preferred models of the present embodiment, and of course, other time series models or algorithms are also possible, and are not limited herein.
Step 103: and processing the measurement point data and the predicted value by adopting a reverse error neural network to obtain a residual predicted value.
The BP (back propagation) neural network is a multi-layer feedback neural network, is one of the most widely used neural networks, has strong nonlinear mapping capability and self-learning and self-adapting capability, and can include a multi-layer feedforward neural network and a reverse error neural network.
The residual prediction value involved in the present embodiment may be implemented by using a residual prediction value acquisition module. The residual prediction value obtaining module is used for carrying out operation processing on the difference value between the prediction value and the operation data, namely the initial residual value, so as to obtain a residual prediction value. The residual prediction value acquisition module may include an initial residual value acquisition unit and a residual prediction value acquisition unit.
Specifically, the initial residual value obtaining unit may obtain, according to the measurement point data, operation data of the device at the preset time, so as to obtain an initial residual value; the residual prediction value obtaining unit is used for processing the initial residual value by using a reverse error neural network to obtain a residual prediction value.
Step 104: and acquiring an evaluation reference value according to the predicted value and the residual error predicted value.
And based on the fact that the residual predicted value is real (including rational number and irrational number), the residual predicted value is signed, so that the predicted value and the residual predicted value are added to obtain an evaluation reference value.
The measurement point data involved in this step can be realized by an evaluation reference value acquisition module.
Step 105: and determining a confidence interval of the equipment at the preset time according to the measurement point data of the equipment and the evaluation reference value.
The evaluation reference value of the equipment operation is only a reference value, the reasonable operation of the equipment is theoretically an interval, the operation data in the interval are all reasonable ranges, and otherwise, the operation data are abnormal.
The measurement point data involved in this step may be implemented using a confidence interval determination module. The confidence interval determining module is mainly used for calculating the measurement point data and the evaluation reference value of the equipment to obtain the average value and standard deviation of the measurement point data so as to determine a confidence interval. The confidence interval determining module may include an average value acquiring unit, a standard deviation acquiring unit, and a confidence interval acquiring unit.
Specifically, the average value obtaining unit may obtain an average value based on the measurement point data, the standard deviation obtaining unit may obtain a standard deviation based on the average value, and the confidence interval obtaining unit may obtain a confidence interval based on the standard deviation and the evaluation reference value.
Step 106: and determining the running state of the equipment according to the running data of the equipment at the preset time and the confidence interval.
Based on the operational data (or referred to as actual values) and the confidence interval of the device at the preset time, the real-time operational state of the device can be judged and determined.
The measurement point data involved in this step may be implemented using an operational state determination module.
According to the technical scheme, the beneficial effects of the embodiment are as follows: the method combines the big data technology, is based on the combination of a time sequence model and a reverse error neural network algorithm, can monitor and acquire the actual running state of the target equipment in real time under a remote condition, and combines the reasonable confidence interval range of the running of the equipment, thereby effectively overcoming the defect of inaccurate prediction of the actual running state of the equipment in the prior art, and simultaneously being beneficial to the scheduling optimization of the follow-up work by an accurate prediction result. The monitoring method provided by the embodiment is simple in integral operation, ensures accuracy, convenience and rapidness of prediction, improves integral processing speed, and saves processing resources.
Fig. 1 shows only a basic embodiment of the method according to the application, on the basis of which certain optimizations and developments are made, but other preferred embodiments of the method can also be obtained.
Fig. 2 shows another embodiment of the method for monitoring the operation state of the device according to the present application. The present embodiment is further described with reference to specific application scenarios based on the foregoing embodiments. The method specifically comprises the following steps:
step 201: preprocessing the acquired historical data of the equipment to acquire preprocessed time series data, wherein the preprocessing comprises removing repeated data and/or abnormal data in the historical data.
When the historical data are arranged, the data can be collected as much as possible, so that the accuracy of prediction can be ensured.
The preprocessing involved in this step may be implemented using a preprocessing unit of the station data acquisition module.
Step 202: grouping the time series data according to a preset step length, and calculating the average value of each group of data to obtain the measuring point data of the equipment.
The historical data set refers to historical standard data of the operation of the equipment, and according to a statistical theory, the average value of the sample can be regarded as a theoretical average value, namely a theoretical standard value of the operation of the equipment. The measurement point data adopted in this embodiment may be understood as a group of data sequences of every 10 pieces of data according to time sequence, for example, the preset step length, and the average value of the data is calculated as new measurement point data. It should be understood that 5 pieces of data, 20 pieces of data, 99 pieces of data may be used as a set of data sequences, and are not limited herein.
The selection of the data measuring points in the historical data set is related to the attribute of the equipment to be monitored, and if the boiler exhaust gas temperature is to be observed to be normal or not, the boiler exhaust gas temperature data is required to be input. This can also be understood as follows: the measurement point data is the data of a certain attribute of the target equipment. It should be understood that the data measurement point data about the equipment attribute may be various attribute data information of the equipment such as the boiler exhaust gas temperature, exhaust gas waste heat, and the like, and is not limited herein.
The measurement point data involved in this step can be realized by using a measurement point data acquisition unit of the measurement point data acquisition module.
Step 203: and processing the measurement point data by adopting a differential integration moving average autoregressive model to obtain a predicted value of the equipment at a preset moment.
In this embodiment, it may be preferable to predict the predetermined time of the target device by using the differential integrated moving average autoregressive model. It should be understood that the differential integrated moving average autoregressive model is one of the preferred models of the present embodiment, and of course, other time series models or algorithms are also possible, and are not limited herein.
Step 204: and acquiring an initial residual value according to the measuring point data and the predicted value.
The initial residual value involved in this step may be implemented using an initial residual value acquisition unit of the residual prediction value acquisition module.
Specifically, according to the measurement point data, acquiring the operation data of the equipment at the preset moment; and differencing the predicted value and the operation data to obtain the initial residual value.
Step 205: and the reverse error neural network processes the initial residual value to obtain a residual prediction value.
The learning rule of the reverse error neural network is to continuously adjust the weight and the threshold value of the network by using the steepest descent method through back propagation, so that the square sum of errors of the network is minimized.
In order to further improve the prediction accuracy, a reverse error neural network algorithm is adopted to predict nonlinear residuals of measurement point data (actual values) in historical data and predicted values of the time sequence, so that the predicted values of the time sequence are corrected, and finally a final predicted result is output. It should be understood that the inverse error neural network algorithm is one of the preferred algorithms in this embodiment, and of course, other neural network algorithms are also possible, which is not limited herein.
And the predicted value is differenced with the operation data and is used as input data of the reverse error neural network, so that feedback of a differential integration moving average autoregressive model algorithm is realized, and a predicted result is more accurate.
The residual prediction value involved in this step may be implemented by a residual prediction value acquisition unit of the residual prediction value acquisition module.
Step 206: and in the step of acquiring an evaluation reference value according to the predicted value and the residual predicted value, the evaluation reference value is the sum of the predicted value and the residual predicted value.
Step 207: and obtaining the average value of the measuring point data according to the measuring point data of the equipment.
The average value of the measurement point data may be: every 10 data are used as a group of data sequences according to time sequence, and the average value is calculatedIt should be understood that 5 pieces of data, 20 pieces of data, 99 pieces of data may be used as a set of data sequences, and are not limited herein.
The average value involved in this step may be implemented using an average value acquisition unit of the confidence interval determination module.
Step 208: and acquiring the standard deviation of the measuring point data according to the average value of the measuring point data.
Based on the average value and the measurement point data, the standard deviation σ of the measurement point data can be obtained.
The standard deviation involved in this step may be implemented using a standard deviation acquisition unit of the confidence interval determination module.
Step 209: and determining a confidence interval of the equipment at the preset moment according to the evaluation reference value and the standard deviation of the measurement point data.
In this embodiment, a 3 sigma manner is adopted to determine a confidence interval, where the confidence interval is:
wherein ,for the reference value of the evaluation,sigma is the standard deviation of the measurement point data.
The confidence interval involved in this step may be implemented using a confidence interval acquisition unit of the confidence interval determination module.
Step 210: and determining the running state of the equipment according to the running data of the equipment at the preset time and the confidence interval.
The operating states involved in this step may be implemented using an operating state determination module.
According to the technical scheme, the embodiment has the following beneficial effects on the basis of the embodiment shown in fig. 1: the repeated data and/or abnormal data are removed by carrying out data preprocessing on the time sequence data, so that the basic data are tidier and cleaner; grouping the time series data to enable the processed data to be more similar to standard data, laying a foundation for further processing of data in the subsequent step, and improving prediction accuracy; meanwhile, according to the calculation result, the real-time running state of the equipment can be simply and rapidly calculated, and the reasonable running range can judge whether the running state of the equipment is abnormal or not.
As shown in fig. 3, an embodiment of the device for monitoring the operation state of the apparatus according to the present application is shown. The apparatus described in this embodiment is a physical apparatus for performing the method described in fig. 1-2. The technical solution is essentially identical to the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. The device in this embodiment includes:
the measuring point data acquisition module 301 is configured to acquire measuring point data of a device according to historical data of the device;
the predicted value obtaining module 302 is configured to process the measurement point data by using a time sequence model, and obtain a predicted value of the device at a preset time;
the residual prediction value obtaining module 303 is configured to process the measurement point data and the prediction value by using a reverse error neural network, so as to obtain a residual prediction value;
an evaluation reference value obtaining module 304, configured to obtain an evaluation reference value according to the predicted value and the residual predicted value;
a confidence interval determining module 305, configured to determine a confidence interval of the device at the preset time according to the measurement point data of the device and the evaluation reference value;
and the running state determining module 306 is configured to determine the running state of the device according to the running data of the device at the preset time and the confidence interval.
Additionally, based on the embodiment shown in fig. 3, preferably, the station data obtaining module 301 may include:
the preprocessing unit is used for preprocessing the acquired historical data of the equipment to acquire preprocessed time series data, and the preprocessing comprises the step of removing repeated data and/or abnormal data in the historical data.
The measuring point data acquisition unit is used for grouping the time series data according to a preset step length and calculating the average value of each group of data so as to acquire the measuring point data of the equipment.
Preferably, the residual prediction value obtaining module 303 may include:
and the initial residual value acquisition unit is used for acquiring an initial residual value according to the measurement point data and the predicted value.
Specifically, according to the measurement point data, acquiring the operation data of the equipment at the preset moment; and differencing the predicted value and the operation data to obtain the initial residual value.
The residual prediction value obtaining unit is used for processing the initial residual value by the reverse error neural network to obtain a residual prediction value.
Preferably, the confidence interval determination module 305 may include:
and the average value acquisition unit is used for acquiring the average value of the measuring point data according to the measuring point data of the equipment.
And the standard deviation acquisition unit is used for acquiring the standard deviation of the measuring point data according to the average value of the measuring point data.
And the confidence interval acquisition unit is used for determining the confidence interval of the equipment at the preset moment according to the evaluation reference value and the standard deviation of the measurement point data.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. At the hardware level, the electronic device comprises a processor, optionally an internal bus, a network interface, a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
And the memory is used for storing the execution instruction. In particular, a computer program that executes instructions may be executed. The memory may include memory and non-volatile storage and provide the processor with instructions and data for execution.
In one possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory into the memory and then runs the execution instruction, and may also acquire the corresponding execution instruction from other devices to form a monitoring device for the running state of the device on a logic level. The processor executes the execution instructions stored in the memory to implement the method for operating the device according to any of the embodiments of the present application by executing the execution instructions.
The method executed by the device for monitoring the operation state of the equipment provided by the embodiment of fig. 3 of the present application may be applied to a processor or implemented by the processor. 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 by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The embodiment of the application also provides a readable medium, wherein the readable storage medium stores execution instructions, and when the stored execution instructions are executed by a processor of electronic equipment, the electronic equipment can be enabled to execute the method for monitoring the running state of the equipment provided in any embodiment of the application, and the method is particularly used for executing the method shown in fig. 1-2.
The electronic device described in the foregoing embodiments may be a computer.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware aspects.
The embodiments of the present application are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
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 one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (6)
1. A method for monitoring the operational status of a device, comprising:
acquiring measurement point data of equipment according to historical data of the equipment;
processing the measurement point data by adopting a time sequence model to obtain a predicted value of the equipment at a preset moment;
processing the measurement point data and the predicted value by adopting a reverse error neural network to obtain a residual predicted value;
acquiring an evaluation reference value according to the predicted value and the residual predicted value, wherein the evaluation reference value is the sum of the predicted value and the residual predicted value;
determining a confidence interval of the equipment at the preset time according to the measurement point data of the equipment and the evaluation reference value;
determining the running state of the equipment according to the running data of the equipment at the preset time and the confidence interval;
the step of obtaining the measurement point data of the equipment according to the historical data of the equipment comprises the following steps:
preprocessing the acquired historical data of the equipment to acquire preprocessed time series data, wherein the preprocessing comprises removing repeated data and/or abnormal data in the historical data;
grouping the time series data according to a preset step length, and calculating the average value of each group of data to obtain the measuring point data of the equipment;
the processing the measurement point data and the predicted value by adopting a reverse error neural network to obtain a residual predicted value comprises the following steps:
acquiring an initial residual value according to the measurement point data and the predicted value;
the reverse error neural network processes the initial residual value to obtain a residual predicted value;
the determining the confidence interval of the equipment at the preset time according to the measurement point data of the equipment and the evaluation reference value comprises the following steps:
according to the measuring point data of the equipment, acquiring an average value of the measuring point data;
acquiring standard deviation of the measuring point data according to the average value of the measuring point data;
according to the evaluation reference value and the standard deviation of the measurement point data, determining a confidence interval of the equipment at the preset time, wherein the confidence interval is as follows:
wherein ,for the evaluation reference value, +_>And the standard deviation of the measurement point data.
2. The method of monitoring the operational state of a device of claim 1, wherein the time series model comprises a differentially integrated moving average autoregressive model.
3. The method for monitoring an operating state of an apparatus according to claim 1, wherein the obtaining an initial residual value based on the site data and the predicted value comprises:
acquiring operation data of the equipment at the preset moment according to the measuring point data;
and differencing the predicted value and the operation data to obtain the initial residual value.
4. A device for monitoring the operational status of an apparatus, comprising:
the measuring point data acquisition module is used for acquiring measuring point data of the equipment according to the historical data of the equipment;
the predicted value acquisition module is used for processing the measurement point data by adopting a time sequence model to acquire a predicted value of the equipment at a preset moment;
the residual prediction value acquisition module is used for processing the measurement point data and the prediction value by adopting a reverse error neural network to acquire a residual prediction value;
the evaluation reference value acquisition module is used for acquiring an evaluation reference value according to the predicted value and the residual predicted value, wherein the evaluation reference value is the sum of the predicted value and the residual predicted value;
the confidence interval determining module is used for determining a confidence interval of the equipment at the preset moment according to the measurement point data of the equipment and the evaluation reference value;
the running state determining module is used for determining the running state of the equipment according to the running data of the equipment at the preset time and the confidence interval;
the measuring point data acquisition module comprises:
a preprocessing unit, configured to preprocess acquired historical data of an apparatus to acquire preprocessed time-series data, where the preprocessing includes removing duplicate data and/or abnormal data in the historical data;
the measuring point data acquisition unit is used for grouping the time series data according to a preset step length and calculating the average value of each group of data so as to acquire measuring point data of the equipment;
the residual prediction value acquisition module comprises:
the initial residual value acquisition unit is used for acquiring an initial residual value according to the measurement point data and the predicted value;
the residual prediction value obtaining unit is used for processing the initial residual value through the reverse error neural network to obtain a residual prediction value;
the confidence interval determination module comprises:
the average value acquisition unit is used for acquiring the average value of the measuring point data according to the measuring point data of the equipment;
the standard deviation acquisition unit is used for acquiring the standard deviation of the measuring point data according to the average value of the measuring point data;
a confidence interval obtaining unit, configured to determine a confidence interval of the device at the preset time according to the evaluation reference value and the standard deviation of the measurement point data, where the confidence interval is:
wherein ,for the evaluation reference value, +_>And the standard deviation of the measurement point data.
5. A readable medium comprising execution instructions which, when executed by a processor of an electronic device, perform a method of monitoring the operational status of a device as claimed in any one of claims 1 to 3.
6. An electronic device comprising a processor and a memory storing execution instructions, which when executed by the processor, performs a method of monitoring the operating state of the device as claimed in any one of claims 1 to 3.
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| CN112115418B (en) * | 2020-08-13 | 2024-03-26 | 深圳市智物联网络有限公司 | Method, device and equipment for acquiring bias estimation information |
| CN112422376A (en) * | 2020-10-15 | 2021-02-26 | 招商华软信息有限公司 | Method for automatically accessing non-standardized equipment to cloud platform, electronic equipment and storage medium |
| CN112668887B (en) * | 2020-12-29 | 2024-07-12 | 新奥数能科技有限公司 | Service data acquisition method and device, readable storage medium and electronic equipment |
| CN113110971B (en) * | 2021-04-09 | 2024-07-16 | 上海众源网络有限公司 | Abnormal data detection method, device, computer equipment and storage medium |
| CN113300371B (en) * | 2021-05-31 | 2022-07-12 | 广东电网有限责任公司 | Method and device for determining real-time voltage of power distribution room |
| CN113190429B (en) * | 2021-06-03 | 2022-07-19 | 河北师范大学 | Server performance prediction method and device and terminal equipment |
| CN114066089A (en) * | 2021-11-25 | 2022-02-18 | 中国工商银行股份有限公司 | Batch job operation time-consuming interval determining method and device |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101771758A (en) * | 2008-12-31 | 2010-07-07 | 北京亿阳信通软件研究院有限公司 | Dynamic determine method for normal fluctuation range of performance index value and device thereof |
| CN104380739A (en) * | 2013-04-05 | 2015-02-25 | 索尼公司 | Image processing device and image processing method |
| CN106529145A (en) * | 2016-10-27 | 2017-03-22 | 浙江工业大学 | Bridge monitoring data prediction method based on ARIMA-BP neural network |
| CN106599271A (en) * | 2016-12-22 | 2017-04-26 | 江苏方天电力技术有限公司 | Emission monitoring time series data abnormal value detection method for coal-fired unit |
| CN107092582A (en) * | 2017-03-31 | 2017-08-25 | 江苏方天电力技术有限公司 | One kind is based on the posterior exceptional value on-line checking of residual error and method for evaluating confidence |
| CN107194034A (en) * | 2017-04-21 | 2017-09-22 | 广州明珞汽车装备有限公司 | A kind of equipment damage detection method and system based on GPR |
| CN107633331A (en) * | 2017-09-26 | 2018-01-26 | 北京福布罗科技有限公司 | Time series models method for building up and device |
| CN109948714A (en) * | 2019-03-22 | 2019-06-28 | 华南理工大学 | Chinese scene text row recognition methods based on residual error convolution sum recurrent neural network |
| CN110297712A (en) * | 2019-05-20 | 2019-10-01 | 贵州联科卫信科技有限公司 | A kind of ARIMA combination forecasting method towards block chain node load estimation |
| CN110400010A (en) * | 2019-07-11 | 2019-11-01 | 新华三大数据技术有限公司 | Prediction technique, device, electronic equipment and computer readable storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040236673A1 (en) * | 2000-10-17 | 2004-11-25 | Eder Jeff Scott | Collaborative risk transfer system |
| US20040102937A1 (en) * | 2002-11-21 | 2004-05-27 | Honeywell International Inc. | Energy forecasting using model parameter estimation |
-
2020
- 2020-04-27 CN CN202010341683.2A patent/CN111414999B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101771758A (en) * | 2008-12-31 | 2010-07-07 | 北京亿阳信通软件研究院有限公司 | Dynamic determine method for normal fluctuation range of performance index value and device thereof |
| CN104380739A (en) * | 2013-04-05 | 2015-02-25 | 索尼公司 | Image processing device and image processing method |
| CN106529145A (en) * | 2016-10-27 | 2017-03-22 | 浙江工业大学 | Bridge monitoring data prediction method based on ARIMA-BP neural network |
| CN106599271A (en) * | 2016-12-22 | 2017-04-26 | 江苏方天电力技术有限公司 | Emission monitoring time series data abnormal value detection method for coal-fired unit |
| CN107092582A (en) * | 2017-03-31 | 2017-08-25 | 江苏方天电力技术有限公司 | One kind is based on the posterior exceptional value on-line checking of residual error and method for evaluating confidence |
| CN107194034A (en) * | 2017-04-21 | 2017-09-22 | 广州明珞汽车装备有限公司 | A kind of equipment damage detection method and system based on GPR |
| CN107633331A (en) * | 2017-09-26 | 2018-01-26 | 北京福布罗科技有限公司 | Time series models method for building up and device |
| CN109948714A (en) * | 2019-03-22 | 2019-06-28 | 华南理工大学 | Chinese scene text row recognition methods based on residual error convolution sum recurrent neural network |
| CN110297712A (en) * | 2019-05-20 | 2019-10-01 | 贵州联科卫信科技有限公司 | A kind of ARIMA combination forecasting method towards block chain node load estimation |
| CN110400010A (en) * | 2019-07-11 | 2019-11-01 | 新华三大数据技术有限公司 | Prediction technique, device, electronic equipment and computer readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 潘轶彪，等.基于多层感知器的异常数据实时检测方法.《上海交通大学学报》.2011,第45卷(第8期),全文. * |
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