CN117928139B - Real-time monitoring system and method for running state of ice maker - Google Patents
Real-time monitoring system and method for running state of ice maker Download PDFInfo
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- CN117928139B CN117928139B CN202410313986.1A CN202410313986A CN117928139B CN 117928139 B CN117928139 B CN 117928139B CN 202410313986 A CN202410313986 A CN 202410313986A CN 117928139 B CN117928139 B CN 117928139B
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 title claims abstract description 37
- 230000002159 abnormal effect Effects 0.000 claims abstract description 78
- 238000012545 processing Methods 0.000 claims abstract description 25
- 238000004590 computer program Methods 0.000 claims description 24
- 230000005856 abnormality Effects 0.000 claims description 16
- 238000004891 communication Methods 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000013527 convolutional neural network Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 9
- 238000012423 maintenance Methods 0.000 description 9
- 230000006835 compression Effects 0.000 description 7
- 238000007906 compression Methods 0.000 description 7
- 238000011176 pooling Methods 0.000 description 6
- 239000013535 sea water Substances 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 238000012806 monitoring device Methods 0.000 description 4
- 230000003247 decreasing effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 239000002243 precursor Substances 0.000 description 2
- 238000005057 refrigeration Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25C—PRODUCING, WORKING OR HANDLING ICE
- F25C1/00—Producing ice
- F25C1/04—Producing ice by using stationary moulds
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25C—PRODUCING, WORKING OR HANDLING ICE
- F25C2600/00—Control issues
- F25C2600/04—Control means
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Abstract
The invention belongs to the technical field of monitoring and detecting of computers and ice making equipment. A system and method for monitoring the running state of ice maker in real time are provided. Wherein the method comprises the following steps: receiving a first real-time operation parameter of the ice machine, and comparing the first real-time operation parameter with a preset normal operation range to obtain a comparison result; if the comparison result is in the first range, judging that the running state of the ice machine is abnormal; if the comparison result is in the second range, judging that the running state of the ice machine is normal; if the comparison result is in the third range, analyzing and processing the first real-time operation parameters by using an abnormal prediction model to obtain an abnormal prediction result, and judging the operation state of the ice machine according to the abnormal prediction result; and outputting the running state of the ice maker on the monitoring equipment in real time.
Description
Technical Field
The invention relates to the technical field of monitoring and detecting of computers and ice making equipment, in particular to a real-time monitoring system and method for the running state of an ice maker.
Background
An ice maker is a refrigeration mechanical device that cools water through an evaporator by a refrigerant of a refrigeration system to produce ice. In order to ensure reliable operation of ice making operation, the operation state of the ice making machine needs to be comprehensively analyzed and output on the monitoring equipment in real time, and when abnormal conditions occur, maintenance personnel can process in time. The existing ice machine running state monitoring system generally compares the real-time running parameter of the ice machine with a preset threshold range, and judges that the ice machine is abnormal when the real-time running parameter exceeds the threshold range. However, ice machines also have a slight "anomaly" that may be a sign of a subsequent anomaly, as the ice machine fluctuates in normal operation due to external factors. How to accurately judge whether the slight abnormal situation really exists is a technical problem which needs to be solved at present.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method, a system, electronic equipment and a computer storage medium for monitoring the running state of an ice machine in real time.
The first aspect of the invention provides a method for monitoring the running state of an ice machine in real time, which comprises the following steps: receiving a first real-time operation parameter of the ice machine, and comparing the first real-time operation parameter with a preset normal operation range to obtain a comparison result; if the comparison result is in the first range, judging that the running state of the ice machine is abnormal; if the comparison result is in the second range, judging that the running state of the ice machine is normal; if the comparison result is in the third range, analyzing and processing the first real-time operation parameters by using an abnormal prediction model to obtain an abnormal prediction result, and judging the operation state of the ice machine according to the abnormal prediction result; outputting the running state of the ice maker on monitoring equipment in real time; wherein the first portion of the first range, the first portion of the third range, the second portion of the third range, and the second portion of the first range are contiguous in sequence.
In some embodiments, before the analyzing the first real-time operating parameter using the anomaly prediction model, further comprises: calculating a distance value between the first real-time operation parameter and an equivalent center point of the second range, and determining a first appointed duration according to the distance value; performing forward tracing according to the first appointed duration to obtain a second real-time operation parameter; wherein the second real-time operating parameter is used to input the anomaly prediction model.
In some embodiments, the equivalent center point is determined by: acquiring historical monitoring data of the ice machine in a second time period, and acquiring a trend of the ice machine, which is judged to be abnormal, in the running state according to the historical monitoring data; and determining positive adjustment deviation and negative adjustment deviation according to the positive trend and the negative trend in the trend, and respectively carrying out positive adjustment and negative adjustment on the actual center point of the second range according to the positive adjustment deviation and the negative adjustment deviation, wherein the point after adjustment is the equivalent center point.
In some embodiments, the method further comprises: acquiring ice making order data of the ice maker in a third time period, and analyzing according to the ice making order data to obtain the number of ice making order types; and determining and obtaining the updating frequency according to the quantity of the ice making order types, and updating the equivalent center point according to the updating frequency.
In some embodiments, the analyzing the first real-time operation parameter using an anomaly prediction model to obtain an anomaly prediction result includes: inputting the second real-time operation parameters and the related operation parameters into the abnormal prediction model, wherein the abnormal prediction model outputs a real-time operation parameter prediction data set which contains a plurality of prediction operation parameters, and each prediction operation parameter corresponds to different future time; wherein the associated operating parameter refers to an operating parameter of an associated component of the component corresponding to the second real-time operating parameter.
In some embodiments, the anomaly prediction model outputs a real-time operating parameter prediction dataset comprising: the anomaly prediction model outputs a first number of the predicted operating parameters; and deleting other predicted operation parameters except the first second number of predicted operation parameters according to the prediction accuracy of the abnormal prediction model, and constructing the real-time operation parameter prediction data set.
In some embodiments, determining an operational state of the ice maker based on the anomaly prediction result comprises: comparing each predicted operation parameter contained in the abnormal prediction result with a preset normal operation range to obtain a comparison result; if any comparison result is in the first range, judging that the running state of the ice machine is abnormal; if all the comparison results are in the second range, judging that the running state of the ice machine is normal; and if the comparison result smaller than the first proportion is in the third range and the rest comparison result is in the second range, judging that the running state of the ice machine is normal.
The invention provides a real-time monitoring system for the running state of an ice machine, which comprises a communication device, a processing device, a storage device and a monitoring device, wherein the processing device is electrically connected with the storage device, the communication device and the monitoring device respectively; the communication equipment is used for acquiring real-time operation parameters of the ice maker and transmitting the real-time operation parameters to the processing equipment; the storage device is used for storing a computer program; the processing device is used for retrieving and executing the computer program in the storage device to execute the method according to any one of the previous claims so as to realize the judgment of the running state of the ice maker; and the monitoring equipment is used for outputting the judging result of the running state of the ice maker generated by the processing equipment in real time.
The third aspect of the present invention also discloses an electronic device, including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor executing the computer program to implement the method of any of the preceding claims.
A fourth aspect of the invention provides a computer storage medium storing a computer program for execution by a processor to implement a method as claimed in any preceding claim.
A fifth aspect of the invention provides a computer program product which, when run on a terminal, causes the terminal to perform the method as claimed in any preceding claim.
The invention has the beneficial effects that: compared with the traditional monitoring mode of the running state of the ice machine, the invention can realize accurate prediction analysis on the slight abnormal condition of the ice machine, not only can assist maintenance personnel to timely treat the abnormal running state, but also can assist the maintenance personnel to treat the potential abnormal condition in advance, thereby realizing better state monitoring and running maintenance of the ice machine.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for monitoring the running state of an ice maker in real time according to an embodiment of the invention.
Fig. 2 is a schematic structural diagram of a convolutional neural network (convolution kernel 3) disclosed in an embodiment of the present invention.
Fig. 3 is a schematic diagram of a data transmission flow of a convolutional neural network according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an ice maker operation state real-time monitoring system according to an embodiment of the present invention.
Detailed Description
Other advantages and advantages of the present application will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In addition, the technical features of the different embodiments of the present application described below may be combined with each other as long as they do not collide with each other.
As shown in fig. 1, the embodiment of the invention discloses a method for monitoring the running state of an ice machine in real time, which comprises the following steps: receiving a first real-time operation parameter of the ice machine, and comparing the first real-time operation parameter with a preset normal operation range to obtain a comparison result; if the comparison result is in the first range, judging that the running state of the ice machine is abnormal; if the comparison result is in the second range, judging that the running state of the ice machine is normal; if the comparison result is in the third range, analyzing and processing the first real-time operation parameters by using an abnormal prediction model to obtain an abnormal prediction result, and judging the operation state of the ice machine according to the abnormal prediction result; outputting the running state of the ice maker on monitoring equipment in real time; wherein the first portion of the first range, the first portion of the third range, the second portion of the third range, and the second portion of the first range are contiguous in sequence.
In the embodiment of the invention, three operation ranges, namely a first range, a second range and a third range, wherein the second range is an absolute normal range for judging the real-time operation parameters of the ice machine, the first range is an absolute abnormal range for judging the real-time operation parameters of the ice machine, and the third range is a part of the adjacent area between the first range and the second range and represents the slight abnormal operation state of the ice machine. Therefore, the invention adopts different abnormal judgment modes aiming at the three real-time operation parameters, specifically, the real-time operation parameters can be directly judged to be abnormal in operation when being in a first range, and the real-time operation parameters can be directly judged to be normal in operation when being in a second range; and when the temperature is in the third range, the abnormal running condition of the ice machine cannot be judged directly according to the fixed threshold, so that the invention adopts the abnormal prediction model to analyze the real-time running parameters of the ice machine, thereby outputting an abnormal prediction result and then determining whether the running state of the ice machine is abnormal or not. Compared with the traditional monitoring mode of the running state of the ice machine, the invention can realize accurate prediction analysis on the slight abnormal condition of the ice machine, not only can assist maintenance personnel to timely treat the abnormal running state, but also can assist the maintenance personnel to treat the potential abnormal condition in advance, thereby realizing better state monitoring and running maintenance of the ice machine.
The anomaly prediction model in the present invention is preferably constructed using a convolutional neural network. The basic structure of the constructed anomaly prediction model is shown in fig. 2-3. Specifically, the anomaly prediction model in the invention sequentially comprises a C1 convolution layer, an S2 pooling layer, a C3 convolution layer, an S4 pooling layer, a C5 convolution layer, an F6 full connection layer and an O7 output layer from input. Wherein the convolution kernel width of the convolution layer is 3; the pooling layer adopts average pooling (average pooling). Each basic unit in the fully connected layer receives data from the upper layer, forward data is output to a fully connected first layer F1 by a convolution layer and a pooling layer, and the first layer F1 comprises k1 basic modules which are instantiated; the F1 layer calculates the received data and then transmits the data to the next layer of full connection F2, and the second layer F2 comprises k2 basic modules which are instantiated. According to the above sequence, the data is transmitted to the Fn layer of the last full-connection layer, the Fn layer comprises kn basic modules, and the data calculated by the Fn layer is transmitted to the output layer.
After the anomaly prediction model is built, the anomaly prediction model needs to be trained by using a well-arranged data set, wherein the data set contains data marked by a man/machine [ A i,Bi],Ai is an operation parameter matrix related to the slight anomaly condition of the seawater ice maker, and B i is information marked by the man/machine about anomaly or not. For the training process, no further description is given.
Wherein, the first range, the second range and the third range are exemplified as follows: a1 = [ a1, A2], [ a5, a6], a2= [ A3, a4], a3= [ A2, A3], [ a4, a5], a1< a2< a3< a4< a5< a6. That is, the third range is actually a transition region between the first range and the second and third ranges, and when the real-time operation parameter of the ice maker is located in the region, it cannot be uniquely determined whether the operation state is abnormal, and then the above-mentioned abnormality prediction model is used for comprehensive analysis.
The following are illustrated: the compressor is a key component of the seawater ice machine, the received first real-time operation parameters are compression ratio, air inlet pressure, exhaust pressure, a compressor current fluctuation value, a compressor voltage fluctuation value and the like, and the compression ratio in the first real-time operation parameters is compared with a preset compression ratio normal operation range. If the compression ratio is in the first range, judging that the running state of the ice machine is abnormal; if the compression ratio is in the second range, the operation state of the ice maker is judged to be normal. If the compression ratio is in the third range, it indicates that the compressor is in a slight abnormal state at present, and at this time, the abnormal operation condition of the compressor cannot be determined directly according to the fixed threshold, so that all the compression ratio, the intake pressure, the exhaust pressure, the compressor current fluctuation value, the compressor voltage fluctuation value and the like in the first real-time operation parameters are input into the abnormal prediction model, and the fully trained abnormal prediction model is used for analyzing whether the slight abnormality at this time is caused by the self-failure of the compressor (i.e., the compressor is in an abnormal state) or is caused by the normal fluctuation of the input voltage/current (i.e., the compressor is in a normal state).
In some embodiments, before the analyzing the first real-time operating parameter using the anomaly prediction model, further comprises: calculating a distance value between the first real-time operation parameter and an equivalent center point of the second range, and determining a first appointed duration according to the distance value; performing forward tracing according to the first appointed duration to obtain a second real-time operation parameter; wherein the second real-time operating parameter is used to input the anomaly prediction model.
In the embodiment of the invention, for the slightly abnormal running state, it is insufficient to use the first real-time running parameter at a single moment, more data is needed to be used as a prediction basis, otherwise, the prediction accuracy cannot be ensured. Therefore, the present invention determines real-time operational parameter data (i.e., historical data of real-time operational parameters) of an ice machine for how long to screen forward based on the distance value of the first real-time operational parameter from the equivalent center point of the second range. The distance value can be used for representing the amplitude of the deviation of the real-time operation parameter of the ice machine from the normal operation range, and the distance value and the first appointed duration are in a negative association relationship, namely, the greater the distance value is, the more obvious the abnormal tendency is, and only the historical data of the real-time operation parameter in a smaller period is needed to be determined at the moment, so that the prediction rate can be improved; otherwise, the less obvious the tendency of abnormality is, the more historical data of the real-time operation parameters in a larger period needs to be determined, so that the prediction accuracy can be improved. By doing so, a balance between prediction accuracy and prediction rate can be achieved.
In some embodiments, the equivalent center point is determined by: acquiring historical monitoring data of the ice machine in a second time period, and acquiring a trend of the ice machine, which is judged to be abnormal, according to the historical monitoring data; and determining positive adjustment deviation and negative adjustment deviation according to the positive trend and the negative trend in the trend, and respectively carrying out positive adjustment and negative adjustment on the actual center point of the second range according to the positive adjustment deviation and the negative adjustment deviation, wherein the point after adjustment is the equivalent center point.
In the embodiment of the present invention, the equivalent center point of the second range may be the actual center point of the second range, i.e., (a3+a4)/2. However, due to the influence of factors such as the accumulated operating year and the maintenance state, the normal operating state of the ice maker has a large uncertainty, and at this time, the actual center point of the second range is used as the calculation reference of the distance value, so that the calculation result of the slightly abnormal deviation amplitude is easily inaccurate. In this regard, the present invention extracts statistical data of which the operation state of the ice maker is judged to be abnormal from the history monitoring data of the ice maker, and further analyzes the trend thereof judged to be abnormal, the trend including a plurality of trend subsections, each trend subsection having a respective abnormal increasing trend or abnormal decreasing trend, and the abnormal increasing trend, the abnormal decreasing trend being further configured with an adjustment deviation value corresponding to the increasing or decreasing amplitude thereof, the adjustment deviation value also corresponding to the positive adjustment deviation and the negative adjustment deviation. And finally, respectively carrying out positive and negative deviation adjustment on the actual center points of the second range according to the positive adjustment deviation or the negative adjustment deviation corresponding to all the trend subsections, so as to obtain the final equivalent center point. After the deviation is regulated, the obtained equivalent center point is not located at the actual center point, but has a left-right deviation with a certain amplitude, the deviation is related to the recent abnormal condition of the ice machine, and the calculated distance value can more accurately reflect the amplitude of the current real-time operation parameter of the ice machine deviating from the normal operation range.
The trend refers to the number of times, time interval, frequency, etc. that the operation state of the ice maker is determined to be abnormal.
In some embodiments, the method further comprises: acquiring ice making order data of the ice maker in a third time period, and analyzing according to the ice making order data to obtain the number of ice making order types; and determining and obtaining the updating frequency according to the quantity of the ice making order types, and updating the equivalent center point according to the updating frequency.
In the embodiment of the invention, the equivalent center point needs to be updated according to a certain frequency along with the factors such as the accumulated operation year, the maintenance state and the like of the ice maker, so that the accuracy of the equivalent center point can be ensured. Moreover, the greater the number of ice making order types, the more automatic or manual switching of the ice making operating parameters the ice making machine will need to be made, and accordingly the greater the probability of anomalies occurring. In contrast, the invention acquires and analyzes the ice making order data of the ice making machine in the near term (namely, the third time length), and determines the updating frequency according to the quantity of the ice making order types involved in the data, namely, the larger the quantity of the ice making order types is, the larger the abnormal probability of the ice making machine is, and at the moment, the updating frequency is improved, namely, the updating of the equivalent center point is faster.
The ice making order type of the ice maker comprises pure water ice making and seawater ice making, and the seawater ice making also comprises seawater ice making with various concentrations (for example, the salinity is 15% -40%). The invention can obtain the quantity of the ice making order types by analyzing and counting the recent ice making order data.
In some embodiments, the analyzing the first real-time operation parameter using an anomaly prediction model to obtain an anomaly prediction result includes: inputting the second real-time operation parameters and the related operation parameters into the abnormal prediction model, wherein the abnormal prediction model outputs a real-time operation parameter prediction data set which contains a plurality of prediction operation parameters, and each prediction operation parameter corresponds to different future time; wherein the associated operating parameter refers to an operating parameter of an associated component of the component corresponding to the second real-time operating parameter.
In the embodiment of the invention, in order to realize more accurate judgment on whether the real-time operation parameter in the slight abnormal condition is abnormal or not, the abnormal prediction model is used for predicting the predicted operation parameter of the component corresponding to the real-time operation parameter in each future moment, and the predicted operation parameter in the future moment is analyzed to help judge whether the slight abnormality of the ice machine is normal fluctuation or abnormal precursor.
In order to improve the prediction accuracy of the anomaly prediction model, besides the determined second real-time operation parameter, the invention inputs the operation parameter of the related component of the component corresponding to the second real-time operation parameter, namely the related operation parameter, into the anomaly prediction model, wherein the related component of the component refers to the component which is electrically and mechanically connected with the component, and the connection can be direct connection or indirect connection.
For the associated components, the following is illustrated: the ice making module and the power supply module are related components, and the power supply state of the power supply module can influence the operation state of the ice making module, for example, when the voltage of the supplied current of the power supply module is insufficient, the ice making power of the ice making module can not be completely output, and at the moment, the slight abnormality of the ice making module is not self abnormality, but the power supply abnormality of the power supply module is caused. Therefore, the invention realizes more accurate prediction of the predicted operation parameters of each component by comprehensively analyzing the operation parameters of each component and the related components, and is beneficial to the subsequent accurate judgment of whether each component is abnormal under the condition of slight abnormality.
In addition, the association relationship between the components may be specified in advance, and details are not described in detail.
In some embodiments, the anomaly prediction model outputs a real-time operating parameter prediction dataset comprising: the anomaly prediction model outputs a first number of the predicted operating parameters; and deleting other predicted operation parameters except the first second number of predicted operation parameters according to the prediction accuracy of the abnormal prediction model, and constructing the real-time operation parameter prediction data set.
In the embodiment of the invention, after the abnormal prediction model predicts the operation parameters, a larger number of predicted operation parameters can be obtained, and the predicted operation parameters span a larger time span, and obviously, the reliability of the predicted operation parameters which are more backward (namely, are far from the current moment) is lower. In this regard, the present invention screens a certain number of predicted operating parameters located in front (i.e., nearer to the current time) according to the prediction accuracy of the anomaly prediction model to construct a real-time operating parameter prediction data set, so that the accuracy of the subsequent judgment of whether the operating state of the ice machine is anomalous can be improved.
In addition, the prediction accuracy of the abnormal prediction model can be obtained by secondary verification and statistical analysis of the prediction data output by the model in the training process and the subsequent prediction use process, and the method is not particularly limited. The prediction accuracy and the second number should be in a forward correlation.
In some embodiments, determining an operational state of the ice maker based on the anomaly prediction result comprises: comparing each predicted operation parameter contained in the abnormal prediction result with a preset normal operation range to obtain a comparison result; if any comparison result is in the first range, judging that the running state of the ice machine is abnormal; if all the comparison results are in the second range, judging that the running state of the ice machine is normal; and if the comparison result smaller than the first proportion is in the third range and the rest comparison result is in the second range, judging that the running state of the ice machine is normal.
In the embodiment of the invention, the depth analysis of the slight abnormality can be realized through the abnormality prediction model, and if any predicted operation parameter of future time obtained through prediction is in a first range as a result of the depth analysis, the current slight abnormality of the ice machine is judged to belong to a precursor of the subsequent abnormality, and the ice machine is judged to be in an abnormal state; and when the predicted operation parameters are all in the second range, judging that the current slight abnormality of the ice machine belongs to normal fluctuation, and judging that the ice machine is in a normal state; and when the predicted operation parameter is only in a small proportion and is in the third range, judging that the current slight abnormality of the ice machine belongs to normal fluctuation with high probability, and judging that the ice machine is in a normal state.
As shown in fig. 4, the embodiment of the invention also discloses a real-time monitoring system for the running state of the ice machine, which comprises a communication device, a processing device, a storage device and a monitoring device, wherein the processing device is electrically connected with the storage device, the communication device and the monitoring device respectively; the communication equipment is used for acquiring real-time operation parameters of the ice maker and transmitting the real-time operation parameters to the processing equipment; the storage device is used for storing a computer program; the processing device is used for retrieving and executing the computer program in the storage device to execute the method according to the previous embodiment so as to judge the running state of the ice maker; and the monitoring equipment is used for outputting the judging result of the running state of the ice maker generated by the processing equipment in real time.
The embodiment of the invention also discloses an electronic device, which comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor executing the computer program to perform the method as described in the previous embodiments.
The embodiment of the invention also discloses a computer storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method according to the previous embodiment.
The embodiment of the invention also discloses a computer program product which, when run on a terminal, causes the terminal to execute to implement the method as described in the previous embodiment.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention.
Claims (6)
1. The method for monitoring the running state of the ice machine in real time is characterized by comprising the following steps:
receiving a first real-time operation parameter of the ice machine, and comparing the first real-time operation parameter with a preset normal operation range to obtain a comparison result;
if the comparison result is in the first range, judging that the running state of the ice machine is abnormal; if the comparison result is in the second range, judging that the running state of the ice machine is normal;
If the comparison result is in the third range, analyzing and processing the first real-time operation parameters by using an abnormal prediction model to obtain an abnormal prediction result, and judging the operation state of the ice machine according to the abnormal prediction result; the anomaly prediction model is constructed by using a convolutional neural network;
outputting the running state of the ice maker on monitoring equipment in real time;
Wherein a first portion of the first range, a first portion of the third range, the second range, a second portion of the third range, and a second portion of the first range are contiguous in sequence;
before the analysis processing is performed on the first real-time operation parameter by using the anomaly prediction model, the method further comprises:
Calculating a distance value between the first real-time operation parameter and an equivalent center point of the second range, and determining a first appointed duration according to the distance value;
Performing forward tracing according to the first appointed duration to obtain a second real-time operation parameter; the second real-time operation parameter is used for inputting the abnormality prediction model;
Analyzing and processing the first real-time operation parameter by using an anomaly prediction model to obtain an anomaly prediction result, wherein the method comprises the following steps:
Inputting the second real-time operation parameters and the related operation parameters into the abnormal prediction model, wherein the abnormal prediction model outputs a real-time operation parameter prediction data set which contains a plurality of prediction operation parameters, and each prediction operation parameter corresponds to different future time;
Wherein the associated operating parameter refers to an operating parameter of an associated component of the component corresponding to the second real-time operating parameter;
the anomaly prediction model outputs a real-time operational parameter prediction dataset comprising:
the anomaly prediction model outputs a first number of the predicted operating parameters;
Deleting other predicted operation parameters except the first second number of predicted operation parameters according to the prediction accuracy of the abnormal prediction model, and constructing a real-time operation parameter prediction data set;
determining an operating state of the ice maker according to the abnormality prediction result, including:
Comparing each predicted operation parameter contained in the abnormal prediction result with a preset normal operation range to obtain a comparison result;
if any comparison result is in the first range, judging that the running state of the ice machine is abnormal;
If all the comparison results are in the second range, judging that the running state of the ice machine is normal;
And if the comparison result smaller than the first proportion is in the third range and the rest comparison result is in the second range, judging that the running state of the ice machine is normal.
2. The method for monitoring the running state of an ice maker in real time according to claim 1, wherein: the equivalent center point is determined by:
Acquiring historical monitoring data of the ice machine in a second time period, and acquiring a trend of the ice machine, which is judged to be abnormal, according to the historical monitoring data;
And determining positive adjustment deviation and negative adjustment deviation according to the positive trend and the negative trend in the trend, and respectively carrying out positive adjustment and negative adjustment on the actual center point of the second range according to the positive adjustment deviation and the negative adjustment deviation, wherein the point after adjustment is the equivalent center point.
3. The method for monitoring the running state of the ice maker in real time according to claim 2, wherein the method comprises the following steps: the method further comprises the steps of:
Acquiring ice making order data of the ice maker in a third time period, and analyzing according to the ice making order data to obtain the number of ice making order types;
And determining and obtaining the updating frequency according to the quantity of the ice making order types, and updating the equivalent center point according to the updating frequency.
4. The system comprises communication equipment, processing equipment, storage equipment and monitoring equipment, wherein the processing equipment is electrically connected with the storage equipment, the communication equipment and the monitoring equipment respectively;
the communication equipment is used for acquiring real-time operation parameters of the ice maker and transmitting the real-time operation parameters to the processing equipment;
the storage device is used for storing a computer program;
The method is characterized in that: the processing device is used for retrieving and executing the computer program in the storage device to execute the method as set forth in any one of claims 1-3 so as to realize the judgment of the running state of the ice maker;
And the monitoring equipment is used for outputting the judging result of the running state of the ice maker generated by the processing equipment in real time.
5. An electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, characterized by: the processor executes the computer program to implement the method of any one of claims 1-3.
6. A computer storage medium storing a computer program, characterized in that: the computer program being executed by a processor to implement the method of any of claims 1-3.
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