CN116451014A - Real-time monitoring method for temperature of movable part in wireless passive equipment - Google Patents
Real-time monitoring method for temperature of movable part in wireless passive equipment Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 69
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000011156 evaluation Methods 0.000 claims abstract description 25
- 238000010606 normalization Methods 0.000 claims description 13
- 230000001174 ascending effect Effects 0.000 claims description 3
- 230000032683 aging Effects 0.000 abstract description 11
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
- G01K11/006—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using measurement of the effect of a material on microwaves or longer electromagnetic waves, e.g. measuring temperature via microwaves emitted by the object
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
- G01K13/04—Thermometers specially adapted for specific purposes for measuring temperature of moving solid bodies
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K3/00—Thermometers giving results other than momentary value of temperature
- G01K3/08—Thermometers giving results other than momentary value of temperature giving differences of values; giving differentiated values
- G01K3/10—Thermometers giving results other than momentary value of temperature giving differences of values; giving differentiated values in respect of time, e.g. reacting only to a quick change of temperature
Abstract
The invention relates to the technical field of temperature monitoring, in particular to a real-time monitoring method for the temperature of a movable part in wireless passive equipment. The method comprises the following steps: acquiring equipment temperature, equipment temperature and equipment actual power, and acquiring a time period to be analyzed; determining a change weight of each time point according to the time period to be analyzed; determining a first error coefficient according to the change weight of all time points and the temperature difference of equipment at adjacent time points; determining an error weight according to the first error coefficient and the time span; determining a second error coefficient according to the first error coefficient and the error weight, and determining an error evaluation coefficient according to the first error coefficient and the second error coefficient; and obtaining a monitoring section, and determining the monitoring temperature of the time point according to the equipment temperature of any time point and the error evaluation coefficient of the time period to be analyzed contained in the monitoring section of the time point. The invention can analyze the aging condition and effectively improve the accuracy and reliability of monitoring the temperature of the inner moving part.
Description
Technical Field
The invention relates to the technical field of temperature monitoring, in particular to a real-time monitoring method for the temperature of a movable part in wireless passive equipment.
Background
Under automatic industrial scene, most interior moving parts all need to carry out temperature monitoring to its inside mechanical motion condition to guarantee that its operation is normal, and wireless temperature monitoring equipment is the wireless passive equipment of a monitoring temperature commonly used, including data acquisition module and data conversion module and wireless signal transmission module in the wireless passive equipment, wherein, in the in-process that wireless temperature monitoring equipment carries out real-time supervision to the temperature, can lead to the load increase because of the ageing of in-process equipment, and then make the temperature data that data conversion module handled and obtain produce the error, and then make the precision to the temperature of transmission produce the influence.
In the related art, the temperature of the internal moving part is monitored through a plurality of wireless temperature monitoring devices, the monitoring values of all the wireless temperature monitoring devices are integrated to fit the final temperature value, and in this way, the temperature value error of final transmission is still larger due to different ageing conditions of different devices, so that the accuracy of monitoring the temperature of the internal moving part is insufficient.
Disclosure of Invention
In order to solve the technical problem of insufficient accuracy in monitoring the temperature of an internal moving part, the invention provides a real-time monitoring method for the temperature of the internal moving part of wireless passive equipment, which adopts the following technical scheme:
the invention provides a real-time monitoring method for the temperature of an internal moving part of wireless passive equipment, which is used for detecting the temperature of the internal moving part and detecting the temperature of the internal moving part, and comprises the following steps:
periodically acquiring the temperature of a component, the temperature of equipment and the actual power of the equipment, and combining adjacent time points with the equipment temperature in an ascending trend according to the equipment temperatures of the adjacent time points to serve as a time period to be analyzed;
determining a change weight of any time point in a time period to be analyzed according to the equipment temperature, the actual power of the equipment and the component temperature of the time point; determining a first error coefficient of the time period to be analyzed according to the change weights of all time points in the time period to be analyzed and the equipment temperature differences of adjacent time points;
determining an error weight of the time period to be analyzed according to the time span and the first error coefficient of the time period to be analyzed and the first error coefficient of the time period adjacent to the time period to be analyzed; determining a second error coefficient according to the first error coefficients and the error weights of all the time periods to be analyzed, and determining an error evaluation coefficient according to the first error coefficient and the second error coefficient;
and determining a monitoring section according to a time point between two adjacent time periods to be analyzed, and determining the monitoring temperature of any time point according to the component temperature of the time point and an error evaluation coefficient of the time period to be analyzed contained in the monitoring section to which the time point belongs.
Further, the determining the change weight of the time point according to the device temperature, the device actual power and the component temperature of any time point in the time period to be analyzed includes:
calculating an inverse proportion normalization value of the actual power of the equipment as a load influence factor; calculating a difference normalized value of the equipment temperature and the component temperature at the same time point as a temperature confidence coefficient;
and determining the change weight according to the load influence factor, the temperature confidence coefficient and the equipment temperature, wherein the load influence factor, the temperature confidence coefficient and the equipment temperature are in positive correlation with the change weight, and the value of the change weight is a normalized value.
Further, the determining the first error coefficient of the time period to be analyzed according to the change weights of all time points in the time period to be analyzed and the equipment temperature differences of adjacent time points includes:
calculating the absolute value of the difference value of the equipment temperatures of adjacent time points in the time period to be analyzed as the equipment temperature difference of the later time point in the adjacent time points, wherein the equipment temperature difference of the first time point in the time period to be analyzed is 0;
and calculating the average value of the products of the variation weights and the equipment temperature differences of all time points in the time period to be analyzed as a first error coefficient of the time period to be analyzed.
Further, the determining the error weight of the time period to be analyzed according to the time span and the first error coefficient of the time period to be analyzed and the first error coefficient of the time period adjacent to the time period to be analyzed includes:
taking the time interval from the first time point to the last time point of each time period to be analyzed as the time span of the time period to be analyzed;
determining two time periods to be analyzed adjacent to any time period to be analyzed as adjacent time periods of the time periods to be analyzed, calculating the average value of first error coefficients in the adjacent time periods to obtain an error coefficient average value, and taking the absolute value of the difference between the first error coefficients of the time periods to be analyzed and the error coefficient average value as an adjacent error influence factor;
and obtaining an error weight according to the time span and the adjacent error influence factor, wherein the time span and the error weight are in positive correlation, the adjacent error influence factor and the error weight are in positive correlation, and the value of the error weight is a normalized value.
Further, the determining a second error coefficient according to the first error coefficients and the error weights of all the time periods to be analyzed includes:
calculating the product of the first error coefficient and the error weight as an error adjustment coefficient;
and taking the average value of the error adjustment coefficients of all the time periods to be analyzed as a second error coefficient.
Further, the determining an error estimation coefficient according to the first error coefficient and the second error coefficient includes:
and calculating the average value of the first error coefficient and the second error coefficient as an error evaluation coefficient of the first error coefficient corresponding to the time period to be analyzed.
Further, the determining the monitored temperature at any time point according to the component temperature at the time point and the error evaluation coefficient of the time period to be analyzed included in the monitored period to which the time point belongs, includes:
calculating the ratio of the error evaluation coefficient to a preset maximum error evaluation coefficient as a temperature influence weight, calculating the product of the temperature influence weight and the component temperature as an error temperature, and calculating the difference between the component temperature and the error temperature at the time point as a monitoring temperature at the time point.
Further, the determining the monitoring segment according to the time point between the two adjacent time segments to be analyzed includes:
when a time point which is not included in the time periods to be analyzed exists between two adjacent time periods to be analyzed, combining the time point between the two adjacent time periods to be analyzed with the previous time period to be analyzed as a monitoring period;
when no time point which is not included in the time periods to be analyzed exists between two adjacent time periods to be analyzed, the former time period to be analyzed is taken as a monitoring period.
The invention has the following beneficial effects:
according to the invention, by acquiring the temperature of the component, the temperature of the equipment and the actual power of the equipment, determining the time period to be analyzed, segmenting the time according to the change of the temperature of the equipment, avoiding the error influence caused by analyzing all the time, reducing unnecessary data calculation, improving the data analysis speed, obtaining the change weight through the temperature of the equipment, the actual power of the equipment and the temperature of the component at any time point, effectively analyzing the operation condition of the wireless temperature monitoring equipment at each time point and the temperature of the equipment and the temperature of the component at each time point, ensuring that the change weight can represent the load and the temperature influence of the time point, determining the first error coefficient according to the change weight of all the time points in the time period to be analyzed and the temperature difference of the equipment at adjacent time points, analyzing all the time points in the time period as a whole, ensuring that the first error coefficient can effectively represent the whole error condition of the time period to be analyzed, determining the error weight of the time period through the time and the first error coefficient, and the error weight of the time period to be analyzed, and the error weight can represent the error confidence coefficient of the time period to be analyzed, thereby ensuring that the first error coefficient is more accurate according to the weight of the first time point and the first error coefficient to be analyzed, and the temperature coefficient to be more accurately evaluated, and the temperature coefficient to be more accurately estimated, and the temperature coefficient is more accurately estimated because the temperature coefficient is more estimated and the temperature coefficient is more accurately estimated, and the temperature coefficient is more accurately estimated, the temperature monitoring device has the advantages that the temperature condition of the corresponding internal moving part can be more accurately represented by the monitored temperature, so that the accuracy and the reliability of monitoring the temperature of the internal moving part are improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for monitoring the temperature of a moving part in a wireless passive device in real time according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of the specific implementation, structure, characteristics and effects of the method for monitoring the temperature of the moving parts in the wireless passive equipment according to the invention in real time with reference to the attached drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a specific scheme of a real-time monitoring method for the temperature of a moving part in wireless passive equipment, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of a method for monitoring temperature of a moving part in a wireless passive device in real time according to an embodiment of the invention is shown, and the method includes:
s101: periodically acquiring the component temperature, the equipment temperature and the actual power of the equipment, and combining adjacent time points with the equipment temperature in an ascending trend according to the equipment temperatures of the adjacent time points to serve as a time period to be analyzed.
It can be understood that, in the process of monitoring the temperature of the moving part in the device in real time, the wireless temperature monitoring device is usually placed beside the moving part or is closely attached to the moving part, the temperature obtained by the wireless temperature monitoring device is used as the temperature of the moving part, that is, the temperature of the moving part, and meanwhile, the wireless temperature sensor can also be used for monitoring the temperature of the moving part, that is, the temperature of the device. In this case, due to the complexity of the internal structure of the wireless temperature monitoring device and the long-time operation, the device is easy to age, resulting in the change of the load of the device, and the wireless temperature monitoring device can change the obtained component temperature in the process of internal transmission and conversion of the wireless temperature monitoring device due to the change of the load, that is, the transmitted component temperature value generates an error, so that the aging condition of the device is analyzed by combining the device temperature, the component temperature and the actual power of the device, and the invention is particularly referred to in the following embodiments.
The acquisition period of the component temperature, the equipment temperature and the actual power of the equipment in the embodiment of the invention can be specifically one month, or can be 10 days, for example, and can be adjusted according to the actual monitoring requirement, so that the method is not limited.
In the embodiment of the invention, the power value of the wireless temperature monitoring equipment can be monitored in real time by using the power monitoring equipment to obtain the actual power of the equipment, and it can be understood that the equipment aging mainly affects the load of the equipment, and the load of the equipment is mainly represented by the actual power of the equipment, wherein the power of the wireless temperature monitoring equipment is the power consumption of the load of the wireless temperature monitoring equipment, such as a resistor and the like, which can consume power, and the resistor generates heat during operation, and the equipment aging causes the resistor to generate more heat, so that the temperature of the wireless temperature monitoring equipment is increased, namely, the equipment aging is usually accompanied with the temperature increase of the wireless temperature monitoring equipment.
S102: determining a change weight of a time point according to the equipment temperature, the actual power of the equipment and the component temperature at any time point in the time period to be analyzed; and determining a first error coefficient of the time period to be analyzed according to the change weights of all time points in the time period to be analyzed and the equipment temperature differences of the adjacent time points.
In the embodiment of the invention, after the equipment temperature, the actual power of the equipment and the component temperature are determined, the change weight of the time point in the time period to be analyzed can be determined according to the difference between the equipment temperature and the component temperature and the influence corresponding to the actual power of the equipment.
Further, in some embodiments of the present invention, determining the change weight of the time point according to the device temperature, the device actual power and the component temperature at any time point in the time period to be analyzed includes: calculating an inverse proportion normalization value of the actual power of the equipment as a load influence factor; calculating a difference normalized value of the equipment temperature and the component temperature at the same time point as a temperature confidence coefficient; and determining a change weight according to the load influence factor, the temperature confidence coefficient and the equipment temperature, wherein the load influence factor, the temperature confidence coefficient and the equipment temperature are in positive correlation with the change weight, and the change weight is normalized.
In the embodiment of the invention, the actual power of the device can be specifically the actual power of the wireless temperature monitoring device at the corresponding time point, and the actual power is subjected to inverse proportion normalization processing to obtain a load influence factor.
In one embodiment of the present invention, the normalization process may be specifically, for example, a maximum-minimum linear normalization process, and the normalization in the subsequent steps may be performed by using the maximum-minimum normalization process, and in other embodiments of the present invention, other normalization methods may be selected according to a specific range of values, which will not be described herein.
In the embodiment of the present invention, the calculation formula of the variation weight may specifically be, for example:
in the method, in the process of the invention,indicating the weight of the change at the t-th time point in the nth time period to be analyzed,/->Load influencing factors representing the t-th time point in the nth time period to be analyzed, +.>Indicating the device temperature, c indicating the wireless temperature monitoring device,/->Indicating temperature confidence,/-, and%>Representing normalization processing, t representing the index of the time point, and n representing the index of the time period to be analyzed.
In the embodiment of the invention, the positive correlation relationship indicates that the dependent variable increases along with the increase of the independent variable, the dependent variable decreases along with the decrease of the independent variable, and the specific relationship can be multiplication relationship, addition relationship, power of exponential function and the like, and is determined by practical application; the negative correlation indicates that the dependent variable decreases with increasing independent variable, and the dependent variable increases with decreasing independent variable, which may be a subtraction relationship, a division relationship, or the like, and is determined by the actual application. Therefore, as the load influence factor, the temperature confidence coefficient and the equipment temperature are in positive correlation with the change weight, the product normalization value of the load influence factor, the temperature confidence coefficient and the equipment temperature can be calculated to be used as the change weight.
In the embodiment of the invention, the working environment of the wireless temperature monitoring equipment is in a constant pressure state, that is, when the resistance value is increased due to aging, the corresponding actual power is reduced, and the embodiment of the invention performs inverse proportion normalization processing on the actual power to obtain a load influence factor, wherein the load influence factor and the aging degree are in positive correlation.
In the embodiment of the invention, when the resistor of the wireless temperature monitoring device generates heat, a certain upper limit temperature value exists, and meanwhile, the more the temperature is difficult to increase under the same heat generation power consumption, namely, the smaller the temperature change speed is, so that the higher the temperature of the wireless temperature monitoring device is, namely, the higher the temperature of the device is, the larger the influence of the temperature change speed can be represented, and the higher the change weight obtained according to the actual power is indicated.
The difficulty of temperature increase under different temperature conditions is different, and when the equipment temperature is higher, the temperature increase is more difficult, and at the moment, the corresponding weight of the temperature increase is larger, namely the influence of temperature change is larger, and the change weight is larger.
For the stability of monitoring, the wireless temperature monitoring device is usually closely attached to the monitored internal moving part, so that when the temperature of the internal moving part is higher than that of the wireless temperature monitoring device, the corresponding part temperature is higher, the influence of the device temperature is larger, the device temperature cannot fully represent the aging condition of the device, the confidence is lower, when the part temperature of the internal moving part is lower than that of the wireless temperature monitoring device, the temperature of the wireless temperature monitoring device is higher than that of the internal moving part, namely that the power consumption temperature generated by the corresponding wireless temperature monitoring device in operation is higher, the confidence is higher, therefore, by calculating the temperature difference between the device temperature and the part temperature, the confidence of the value calculated by the sensor according to the two weight values in front is positive, positive and 0, when the device temperature is higher than that of the part temperature, the difference between the device temperature and the part temperature is positive, the influence of the aging of the corresponding device temperature monitoring device can be represented, the confidence is higher, and therefore, the confidence value of the relationship between the normalized difference of the device temperature and the part temperature is positive and negative, and the confidence value of the temperature is obtained.
Further, in some embodiments of the present invention, determining a first error coefficient of a time period to be analyzed according to the change weights of all time points in the time period to be analyzed and the device temperature differences of adjacent time points includes: calculating the absolute value of the difference value of the device temperatures of adjacent time points in the time period to be analyzed as the device temperature difference of the later time point in the adjacent time points, wherein the device temperature difference of the first time point in the time period to be analyzed is 0; and calculating the average value of the products of the variation weights of all time points in the time period to be analyzed and the equipment temperature difference as a first error coefficient of the time period to be analyzed.
In the embodiment of the present invention, the absolute value of the difference between the temperatures of the devices at adjacent time points may be calculated as the difference between the temperatures of the devices at the later time points in the adjacent time points, for example, when the number of time points is 3, the absolute value of the difference between the temperatures of the devices at the first time point and the second time point may be used as the difference between the temperatures of the devices at the second time point and the third time point, the absolute value of the difference between the temperatures of the devices at the second time point and the third time point may be used as the difference between the temperatures of the devices at the third time point, and the difference between the temperatures of the devices at the first time point is 0.
In the embodiment of the present invention, the average value of the products of the variation weights of all time points and the device temperature difference in the time period to be analyzed is calculated as the first error coefficient of the time period to be analyzed, and the corresponding calculation formula may specifically be, for example:
in the method, in the process of the invention,indicating the weight of the change at the t-th time point in the nth time period to be analyzed,/->Indicating the difference in device temperature at the t-th time point in the nth time period to be analyzed,/-, for the n-th time period to be analyzed>Represents the total number of time points in the nth time period to be analyzed, t represents the index of the time point, n represents the index of the time period to be analyzed, +.>A first error coefficient representing an nth time period to be analyzed.
In the embodiment of the invention, the first error coefficient characterizes the error influence degree in the time period to be analyzed, that is, the first error coefficient is determined by the change weight and the equipment temperature difference, so that the influence values of the temperature and the load change at corresponding moments and the temperature change condition at adjacent moments can be effectively combined, the error is analyzed, and the more severe the temperature change at the adjacent moments is, the greater the error possibility corresponding to the moment is, and the equipment temperature difference and the first error coefficient are in positive correlation.
S103: determining an error weight of the time period to be analyzed according to the time span and the first error coefficient of the time period to be analyzed and the first error coefficient of the time period adjacent to the time period to be analyzed; and determining a second error coefficient according to the first error coefficients and the error weights of all the time periods to be analyzed, and determining an error evaluation coefficient according to the first error coefficients and the second error coefficients.
In the embodiment of the invention, after the first error coefficients of the time periods to be analyzed are determined, the overall situation can be analyzed according to the first error coefficients of all the time periods to be analyzed.
Further, in some embodiments of the present invention, determining the error weight of the time period to be analyzed according to the time span and the first error coefficient of the time period to be analyzed and the first error coefficient of the time period adjacent to the time period to be analyzed includes: taking the time interval from the first time point to the last time point of each time period to be analyzed as the time span of the time period to be analyzed; determining two time periods to be analyzed adjacent to any time period to be analyzed as adjacent time periods of the time periods to be analyzed, calculating the average value of the first error coefficients in the adjacent time periods to obtain an error coefficient average value, and taking the absolute value of the difference value of the first error coefficients and the error coefficient average value of the time periods to be analyzed as an adjacent error influence factor; and obtaining error weight according to the time span and the adjacent error influence factor, wherein the time span and the error weight are in positive correlation, the adjacent error influence factor and the error weight are in positive correlation, and the value of the error weight is a normalized numerical value.
According to the embodiment of the invention, through the calculation of the first error coefficient, although the temperature error condition of the time period to be analyzed can be obtained through direct analysis, the data can possibly cause that part of the temperature data is inaccurate due to the problems of part of environment factors, signal interference and the like existing in wireless passive equipment, and a certain deviation exists in each time period to be analyzed, so that the error coefficient of the time period to be analyzed needs to be predicted by combining the adjacent time periods to be analyzed corresponding to the time period to be analyzed, the deviation caused by the inaccuracy of local data is reduced through the whole, and the error coefficient to be predicted and the first error coefficient calculated corresponding to the time period to be analyzed are mutually corrected, so that the error coefficient with higher robustness and higher accuracy is obtained.
In some embodiments of the present invention, the calculation formula of the error weight may specifically be, for example:
in the method, in the process of the invention,error weight representing the nth time period to be analyzed, +.>Representing the time span of the nth time period to be analyzed, i.e. the time interval from the first time point to the last time point in the nth time period to be analyzed,/-)>A first error coefficient representing the nth time period to be analyzed,>、/>a first error coefficient representing the adjacent time period to the nth time period to be analyzed, +.>Representing the mean value of error coefficients,/">Representing adjacent error influencing factors, +.>The normalization process is represented.
In the embodiment of the invention, the larger the time span of the time period to be analyzed is, the longer the corresponding temperature increase time is, namely the greater the importance of the time period to be analyzed is; the mean value of the error coefficient represents the mean value of the first error coefficient corresponding to the adjacent time period, when the absolute value of the difference value between the first error coefficient and the mean value of the error coefficient in the time period to be analyzed is larger, namely, the adjacent error influence factor is larger, the mean value can represent the temperature trend that the time period to be analyzed and the adjacent time period cannot be maintained to be monotonically increased, namely, the higher the possibility that the data is error data is, the greater the importance is as the corresponding error data in the time period to be analyzed can influence the accuracy of final temperature analysis. Therefore, in the embodiment of the invention, the time span and the error weight are in positive correlation, and the adjacent error influence factors and the error weight are in positive correlation.
Further, in some embodiments of the present invention, determining the second error coefficient according to the first error coefficients and the error weights for all the time periods to be analyzed includes: calculating the product of the first error coefficient and the error weight as an error adjustment coefficient; and taking the average value of the error adjustment coefficients of all the time periods to be analyzed as a second error coefficient.
In the embodiment of the present invention, the calculation formula of the second error coefficient may specifically be, for example:
in the method, in the process of the invention,represents a second error coefficient, N represents the total number of time periods to be analyzed, N represents the index of the time periods to be analyzed, +.>A first error coefficient representing the nth time period to be analyzed,>representing the error weight of the nth time period to be analyzed,representing the error adjustment coefficient of the nth time period to be analyzed.
In the embodiment of the invention, the first error coefficient is weighted through the error weight to obtain the error adjustment coefficient, and the error weight analyzes the interval time of each time period to be analyzed and the error condition of the adjacent time period, so that the error adjustment coefficient can more effectively represent the error condition of the corresponding time period to be analyzed, and the error adjustment coefficient of all the time periods to be analyzed is combined, thereby determining the second error coefficient, analyzing all the time periods to be analyzed, and further ensuring the accuracy of the second error coefficient.
Further, in some embodiments of the present invention, determining the error estimation coefficient based on the first error coefficient and the second error coefficient includes: and calculating the average value of the first error coefficient and the second error coefficient as an error evaluation coefficient of the first error coefficient corresponding to the time period to be analyzed.
In the embodiment of the invention, the average value of the first error coefficient and the second error coefficient can be calculated as the error evaluation coefficient of the first error coefficient corresponding to the time period to be analyzed, and it can be understood that the first error coefficient and the second error coefficient can be effectively corrected by calculating the average value of the first error coefficient and the second error coefficient, and that the calculated first error coefficient has some randomness and insufficient robustness due to the complexity of a scene, and the second error coefficient has the effect of error weight, so that the obtained second error coefficient is slightly smaller than the actual error coefficient, but has stronger robustness, so that the more stable error evaluation coefficient is obtained after the averaging.
S104: and determining a monitoring section according to the time points between two adjacent time periods to be analyzed, and determining the monitoring temperature of the time points according to the component temperature of any time point and the error evaluation coefficient of the time period to be analyzed contained in the monitoring section to which the time point belongs.
Further, the determining the monitoring segment according to the time point between the two adjacent time segments to be analyzed includes: when a time point which is not included in the time periods to be analyzed exists between two adjacent time periods to be analyzed, combining the time point between the two adjacent time periods to be analyzed with the previous time period to be analyzed as a monitoring period; when no time point which is not included in the time periods to be analyzed exists between two adjacent time periods to be analyzed, the former time period to be analyzed is taken as a monitoring period.
In the embodiment of the invention, in the middle of two adjacent time periods to be analyzed, a plurality of time points may be included, and the temperatures of equipment acquired by the time points may be the same or decrease, so that the time points cannot be integrated into any one of the two adjacent time periods to be analyzed, therefore, analysis needs to be performed on the part of time points, and firstly, the part of time points and the previous time period to be analyzed are combined to form a monitoring period, so that the part of time points and the previous time period to be analyzed are analyzed as a whole.
Further, in some embodiments of the present invention, determining the monitored temperature at a time point according to the component temperature at any time point and the error evaluation coefficient of the time period to be analyzed included in the monitored period to which the time point belongs includes: calculating the ratio of the error evaluation coefficient to the preset maximum error evaluation coefficient as a temperature influence weight, calculating the product of the temperature influence weight and the component temperature as an error temperature, and calculating the difference between the component temperature and the error temperature at the time point as a monitoring temperature at the time point.
In the embodiment of the invention, the ratio of the error evaluation coefficient to the preset maximum error evaluation coefficient is taken as the temperature influence weight, and it can be understood that the larger the temperature influence weight is, the larger the corresponding error influence of the corresponding temperature can be represented, therefore, the product of the temperature influence weight and the component temperature is calculated as the error temperature, and the difference value of the component temperature and the error temperature is taken as the monitoring temperature of the time point, therefore, the heat generated when the load generates power can be analyzed, and the heat is removed from the component temperature as the error, thereby ensuring that the monitoring temperature can accurately and effectively represent the actual temperature of the movable component in the wireless passive equipment.
According to the invention, by acquiring the temperature of the component, the temperature of the equipment and the actual power of the equipment, determining the time period to be analyzed, segmenting the time according to the change of the temperature of the equipment, avoiding the error influence caused by analyzing all the time, reducing unnecessary data calculation, improving the data analysis speed, obtaining the change weight through the temperature of the equipment, the actual power of the equipment and the temperature of the component at any time point, effectively analyzing the operation condition of the wireless temperature monitoring equipment at each time point and the temperature of the equipment and the temperature of the component at each time point, ensuring that the change weight can represent the load and the temperature influence of the time point, determining the first error coefficient according to the change weight of all the time points in the time period to be analyzed and the temperature difference of the equipment at adjacent time points, analyzing all the time points in the time period as a whole, ensuring that the first error coefficient can effectively represent the whole error condition of the time period to be analyzed, determining the error weight of the time period through the time and the first error coefficient, and the error weight of the time period to be analyzed, and the error weight can represent the error confidence coefficient of the time period to be analyzed, thereby ensuring that the first error coefficient is more accurate according to the weight of the first time point and the first error coefficient to be analyzed, and the temperature coefficient to be more accurately evaluated, and the temperature coefficient to be more accurately estimated, and the temperature coefficient is more accurately estimated because the temperature coefficient is more estimated and the temperature coefficient is more accurately estimated, and the temperature coefficient is more accurately estimated, the temperature monitoring device has the advantages that the temperature condition of the corresponding internal moving part can be more accurately represented by the monitored temperature, so that the accuracy and the reliability of monitoring the temperature of the internal moving part are improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (8)
1. The utility model provides a wireless passive equipment internal moving part temperature real-time supervision method which is characterized in that, wireless passive equipment is used for detecting the temperature of internal moving part and obtains the part temperature, detects self temperature simultaneously and obtains equipment temperature, and the method includes:
periodically acquiring the temperature of a component, the temperature of equipment and the actual power of the equipment, and combining adjacent time points with the equipment temperature in an ascending trend according to the equipment temperatures of the adjacent time points to serve as a time period to be analyzed;
determining a change weight of any time point in a time period to be analyzed according to the equipment temperature, the actual power of the equipment and the component temperature of the time point; determining a first error coefficient of the time period to be analyzed according to the change weights of all time points in the time period to be analyzed and the equipment temperature differences of adjacent time points;
determining an error weight of the time period to be analyzed according to the time span and the first error coefficient of the time period to be analyzed and the first error coefficient of the time period adjacent to the time period to be analyzed; determining a second error coefficient according to the first error coefficients and the error weights of all the time periods to be analyzed, and determining an error evaluation coefficient according to the first error coefficient and the second error coefficient;
and determining a monitoring section according to a time point between two adjacent time periods to be analyzed, and determining the monitoring temperature of any time point according to the component temperature of the time point and an error evaluation coefficient of the time period to be analyzed contained in the monitoring section to which the time point belongs.
2. The method for monitoring the temperature of a moving part in a wireless passive device in real time according to claim 1, wherein the determining the change weight of the time point according to the device temperature, the device actual power and the part temperature at any time point in the time period to be analyzed comprises:
calculating an inverse proportion normalization value of the actual power of the equipment as a load influence factor; calculating a difference normalized value of the equipment temperature and the component temperature at the same time point as a temperature confidence coefficient;
and determining the change weight according to the load influence factor, the temperature confidence coefficient and the equipment temperature, wherein the load influence factor, the temperature confidence coefficient and the equipment temperature are in positive correlation with the change weight, and the value of the change weight is a normalized value.
3. The method for monitoring the temperature of a moving part in a wireless passive device in real time according to claim 1, wherein the determining the first error coefficient of the time period to be analyzed according to the change weights of all time points in the time period to be analyzed and the device temperature differences of adjacent time points comprises:
calculating the absolute value of the difference value of the equipment temperatures of adjacent time points in the time period to be analyzed as the equipment temperature difference of the later time point in the adjacent time points, wherein the equipment temperature difference of the first time point in the time period to be analyzed is 0;
and calculating the average value of the products of the variation weights and the equipment temperature differences of all time points in the time period to be analyzed as a first error coefficient of the time period to be analyzed.
4. The method for monitoring the temperature of a moving part in a wireless passive device in real time according to claim 1, wherein the determining the error weight of the time period to be analyzed according to the time span and the first error coefficient of the time period to be analyzed and the first error coefficient of the time period adjacent to the time period to be analyzed comprises:
taking the time interval from the first time point to the last time point of each time period to be analyzed as the time span of the time period to be analyzed;
determining two time periods to be analyzed adjacent to any time period to be analyzed as adjacent time periods of the time periods to be analyzed, calculating the average value of first error coefficients in the adjacent time periods to obtain an error coefficient average value, and taking the absolute value of the difference between the first error coefficients of the time periods to be analyzed and the error coefficient average value as an adjacent error influence factor;
and obtaining an error weight according to the time span and the adjacent error influence factor, wherein the time span and the error weight are in positive correlation, the adjacent error influence factor and the error weight are in positive correlation, and the value of the error weight is a normalized value.
5. The method for monitoring the temperature of a moving part in a wireless passive device in real time according to claim 1, wherein said determining a second error coefficient based on the first error coefficients and the error weights for all the time periods to be analyzed comprises:
calculating the product of the first error coefficient and the error weight as an error adjustment coefficient;
and taking the average value of the error adjustment coefficients of all the time periods to be analyzed as a second error coefficient.
6. The method for real-time monitoring of temperature of a moving part in a wireless passive device according to claim 1, wherein said determining an error evaluation coefficient based on said first error coefficient and said second error coefficient comprises:
and calculating the average value of the first error coefficient and the second error coefficient as an error evaluation coefficient of the first error coefficient corresponding to the time period to be analyzed.
7. The method for monitoring the temperature of a moving part in a wireless passive device in real time according to claim 1, wherein the determining the monitored temperature at any time point according to the temperature of the part at the time point and the error evaluation coefficient of the time period to be analyzed included in the monitored period to which the time point belongs comprises:
calculating the ratio of the error evaluation coefficient to a preset maximum error evaluation coefficient as a temperature influence weight, calculating the product of the temperature influence weight and the component temperature as an error temperature, and calculating the difference between the component temperature and the error temperature at the time point as a monitoring temperature at the time point.
8. The method for monitoring the temperature of a moving part in a wireless passive device in real time according to claim 1, wherein the determining the monitoring segment according to the time point between two adjacent time segments to be analyzed comprises:
when a time point which is not included in the time periods to be analyzed exists between two adjacent time periods to be analyzed, combining the time point between the two adjacent time periods to be analyzed with the previous time period to be analyzed as a monitoring period;
when no time point which is not included in the time periods to be analyzed exists between two adjacent time periods to be analyzed, the former time period to be analyzed is taken as a monitoring period.
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