CN115906368A - Temperature signal real-time load spectrum calculation method and related equipment - Google Patents
Temperature signal real-time load spectrum calculation method and related equipment Download PDFInfo
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Abstract
The present disclosure provides a temperature signal real-time load spectrum calculation method and related equipment; the method comprises the following steps: firstly, carrying out data preprocessing on a temperature signal data set to obtain an available temperature signal data set; generating a temperature factor aiming at the available temperature signal data set and analyzing the temperature factor to obtain a temperature load spectrum; and finally, acquiring new temperature signal data and updating the temperature load spectrum according to the new temperature signal data. According to the scheme, the temperature signal load spectrum fully retains the characteristics in the actual operation process, and meanwhile, the real-time updating capability is achieved, so that the accuracy, the precision and the timeliness of data processing are improved.
Description
Technical Field
The disclosure relates to the technical field of environmental load spectrum conversion, in particular to a temperature signal real-time load spectrum calculation method and related equipment.
Background
When the operating condition of a workpiece is analyzed, a temperature signal load spectrum of the workpiece needs to be acquired as an analysis basis, and the load spectrum of the temperature signal changes correspondingly due to the change of working time and environment in the operating process. When some workpieces run, the operation parameters need to be adjusted in real time according to the change of the temperature signal load spectrum, so that a scheme capable of acquiring temperature signal data in real time to acquire the real-time load spectrum is needed.
Disclosure of Invention
In view of the above, an object of the present disclosure is to provide a method and related apparatus for calculating a real-time load spectrum of a temperature signal.
Based on the above purpose, the present disclosure provides a method for calculating a real-time load spectrum of a temperature signal, including:
preprocessing the temperature signal data set to obtain an available temperature signal data set;
generating a temperature factor from the available temperature signal data set;
obtaining a temperature load spectrum according to the temperature factor;
and acquiring new temperature signal data, and acquiring an updated temperature load spectrum according to the new temperature signal data.
Based on the same inventive concept, the present disclosure also provides a temperature signal real-time load spectrum calculation apparatus, comprising:
the preprocessing module is configured to preprocess the temperature signal data set to obtain an available temperature signal data set;
a generation module configured to generate a temperature factor from the available temperature signal dataset;
the calculation module is configured to obtain a temperature load spectrum according to the temperature factor;
and the updating module is configured to acquire new temperature signal data and obtain an updated temperature load spectrum according to the new temperature signal data.
Based on the same inventive concept, the present disclosure also provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method according to any one of the above aspects when executing the program.
From the above, according to the temperature signal real-time load spectrum calculation method and the related device provided by the disclosure, firstly, data preprocessing is performed on a temperature signal data set to obtain an available temperature signal data set; generating a temperature factor aiming at the available temperature signal data set and analyzing the temperature factor to obtain a temperature load spectrum; and finally, acquiring new temperature signal data and updating the temperature load spectrum according to the new temperature signal data. The temperature signal load spectrum fully retains the characteristics in the actual operation process, and meanwhile, the real-time updating capability is achieved, and the accuracy, precision and timeliness of data processing are improved.
Drawings
In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings needed to be used in the description of the embodiments or related technologies are briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for calculating a real-time load spectrum of a temperature signal according to an embodiment of the disclosure;
FIG. 2 is a schematic diagram of temperature signal data acquisition according to an embodiment of the disclosure;
FIG. 3 is a flow chart of initial high temperature load spectrum generation for an embodiment of the present disclosure;
FIG. 4 is a flowchart of an initial temperature cycle loading spectrum generation of an embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of a temperature signal real-time load spectrum calculation apparatus according to an embodiment of the disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that technical terms or scientific terms used in the embodiments of the present disclosure should have a general meaning as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items.
As discussed in the background section, in some situations where it is desirable to analyze the operation of a workpiece, a solution is needed that can acquire temperature signal data in real time to obtain a real-time load spectrum. In the process of implementing the present disclosure, the applicant finds that the existing temperature signal load spectrum has problems mainly in that: the drawn temperature signal load spectrum is a static result, the spectrum type is thick, on one hand, partial information of analysis data is lost, on the other hand, the real-time updating capability is not provided, the complete temperature environment of the workpiece service process cannot be represented, the application effect needs to be improved, and at present, a scheme for acquiring the real-time temperature signal load spectrum according to the real-time collected discontinuous temperature data does not exist.
In view of this, one or more embodiments of the present disclosure provide a temperature signal real-time load spectrum calculation scheme, and referring to fig. 1, a temperature signal real-time load spectrum calculation method according to an embodiment of the present disclosure includes the following steps:
step S101, preprocessing a temperature signal data set to obtain an available temperature signal data set;
in this step, since some missing values and abnormal values are often generated in the process of acquiring the temperature signal data, the temperature signal data set including all the temperature signal data needs to be preprocessed, the abnormal values are removed, and the missing values are filled. In the working process of the workpiece, the workpiece is in a closed state,referring to fig. 2, the real-time collected temperature signal data shows a discontinuous state with time due to the power-on and power-off process, and can be characterized as T = (T =) 1 ，T 1 ’,…,T n1 ,T n1+1 ,…T n2 ,…,T n2+1 ,…T n3 …), T represents temperature.
Step S102, generating a temperature factor according to the available temperature signal data set;
in this step, the temperature factor includes a high temperature factor T' and a temperature cycle factor. The high temperature factor includes all temperature signal data collected from the time when the product is powered on to reach thermal equilibrium to the time when the product is powered off, is a one-dimensional variable and can be characterized as T' = (T) 1 ’,…,T n1 ,T n1+1 ’,…,T n2 …). The temperature cycle factor needs to integrate high temperature factor, product power-on time, power-on temperature, day and night temperature difference and other factors, is multidimensional variable, and can be T cycle ＝(T max ,T min ,t max ,t min ,α up ,α down ) And (5) characterizing.
Wherein, T max In order to average the temperature signal data collected from the time of temperature equilibrium to the time of power failure, for the 1 st cycle of fig. 2, the number of sampling points from temperature equilibrium to power failure is counted as n 1 ', then
T min The lowest climate temperature on the day;
t max for high temperature dwell time, representing the duration of time to reach temperature equilibrium to the moment of power down, for cycle 1 of fig. 2:
t max ＝t n1 -t 1 '；
t min the low-temperature residence time is calculated by the following formula:
α up wen Bianlv, which is a temperature rise stage, has the following calculation formula:
α down in the stage Wen Bianlv of the cooling stage, no real-time temperature signal data is calculated, so that estimation is needed. According to the temperature signal data experience collected by the external sensor of the product, the temperature change difference between the temperature rise stage and the temperature drop stage is small, so that the temperature rise stage and the temperature drop stage can be assumed to be the same in the temperature cycle load spectrum, namely: alpha is alpha down ＝α up Thus, the temperature cycling factor can be reduced to T cycle ＝(T max ,T min ,t max ,t min α), where α = α) up ＝αd own 。
Step S103, obtaining a temperature load spectrum according to the temperature factor;
in this step, the temperature load spectrum includes an initial high-temperature load spectrum corresponding to the high-temperature factor and an initial temperature cycle load spectrum corresponding to the temperature cycle factor.
For the initial high temperature load spectrum, referring to fig. 3, it may further include the steps of:
step S201, rounding the temperature signal data in the high-temperature factors and arranging the data from small to large to obtain a new temperature sequence;
in the present embodiment, based on the one-dimensional variable T' = (T) 1 ’,…,T n1 ,T n1+1 ’,…,T n2 …) to obtain a new sequence T by arranging the variable observed values, i.e., the temperature signal data, from small to large new ：T new ＝(T new1 ,T new2 ,…,T newi ,T newi+1 ,…)。
Step S202, calculating the difference value between the next temperature signal data and the previous temperature signal data in the new temperature sequence, and inserting new temperature signal data by taking 1 as a step length in response to the fact that the difference value is larger than 1;
in this step, the number of the 2 adjacent temperature signals of the new sequence is calculatedBased on the difference between, e.g., the (i + 1) th temperature signal data and the (i) th temperature signal data, let T newi+1 -T newi And if m is larger than 1, inserting m-1 new observed values by taking 1 as a step size between two temperature signal data.
Step S203, calculating the frequency of the temperature signal data, wherein the frequency of the new temperature signal data is set to be 0;
in this step, the frequency of the temperature signal data indicates the frequency occurring in the high temperature factor.
And S204, obtaining an initial high-temperature load spectrum according to the temperature signal data and the frequency corresponding to the temperature signal data.
In this step, the temperature signal data and the frequencies corresponding to the temperature signal data are sorted according to the grades from small to large from 1 to k, and an initial high-temperature load spectrum is generated as follows:
wherein T is 1k Temperature at the level of rank k, p 1k Is the frequency at rank k level.
For the initial temperature cycle load spectrum, referring to fig. 4, it may further include the steps of:
s301, classifying data in the temperature cycle factors through clustering analysis;
in this step, because the temperature cycle factors are multidimensional variables, the data in the temperature cycle factors are firstly clustered and analyzed, and the data are classified, wherein the clustering analysis can adopt a k-means clustering method, a hierarchical clustering method and a clustering method based on a probability model. Obtaining l shapes as T cycle ＝(T max ,T min ,t max ,t min ,α up ,α down ) The category (2).
Step S302, taking the average value of each type of data as the temperature cycle grade of the type of data;
in this step, the average values of the data in each class are arranged from large to small, and the corresponding sequence of the class is used as the temperature cycle grade of the class, wherein the temperature cycle grade is from 1 to l.
Step S303, calculating the frequency of each type of data;
and S304, obtaining an initial temperature cycle load spectrum according to the temperature cycle grade and the frequency of each type of data.
In this step, an initial temperature cycle load spectrum is generated as follows:
wherein T is 1lmax Temperature at the temperature cycle level l, p 1l Is the frequency at the temperature cycling level l.
And step S104, acquiring new temperature signal data, and obtaining an updated temperature load spectrum according to the new temperature signal data.
In this step, in order to update the temperature load spectrum in real time, newly generated temperature signal data needs to be acquired, and the newly generated temperature signal data is updated to the generated temperature load spectrum, which specifically includes:
s401, acquiring new temperature signal data, and preprocessing a data set of the new temperature signal data to obtain a new available temperature signal data set;
s402, generating a new temperature factor according to the new available temperature signal data set and the temperature factor;
in this step, a new temperature signal needs to be inserted into the temperature factor to obtain a new temperature factor.
And S403, updating the temperature load spectrum according to the new temperature factor to obtain an updated temperature load spectrum.
In this step, for the new temperature factor, the operation is performed in the same manner in step S103, so as to obtain an updated temperature load spectrum. And updating the new temperature signal data in real time to obtain the real-time updated temperature load spectrum.
As can be seen, in this embodiment, first, data preprocessing is performed on the temperature signal data set to obtain an available temperature signal data set; generating a temperature factor aiming at the available temperature signal data set and analyzing the temperature factor to obtain a temperature load spectrum; and finally, acquiring new temperature signal data and updating the temperature load spectrum according to the new temperature signal data. The temperature signal load spectrum fully retains the characteristics in the actual operation process, and meanwhile, the real-time updating capability is achieved, and the accuracy, precision and timeliness of data processing are improved.
In some embodiments, missing value processing may be the replacement of outliers using interpolation; the abnormal value processing may be to identify an abnormal value of the temperature signal data in the temperature signal data set by a 3 σ rule and replace the abnormal value using an interpolation method.
It should be noted that the method of the embodiments of the present disclosure may be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the devices may interact with each other to complete the method.
It should be noted that the above describes some embodiments of the disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Based on the same inventive concept, corresponding to the method of any embodiment, the disclosure also provides a temperature signal real-time load spectrum calculating device.
Referring to fig. 5, the temperature signal real-time load spectrum calculation apparatus includes:
a preprocessing module 501 configured to preprocess the temperature signal data set to obtain an available temperature signal data set;
a generating module 502 configured to generate a temperature factor from the available temperature signal data set;
a calculating module 503 configured to obtain a temperature load spectrum according to the temperature factor;
an update module 504 configured to obtain new temperature signal data, and obtain an updated temperature load spectrum according to the new temperature signal data.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the various modules may be implemented in the same one or more pieces of software and/or hardware in practicing the present disclosure.
The device of the above embodiment is used to implement the corresponding temperature signal real-time load spectrum calculation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to the method of any embodiment described above, the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the method for calculating the real-time load spectrum of the temperature signal according to any embodiment described above is implemented.
Fig. 6 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static Memory device, a dynamic Memory device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The electronic device of the above embodiment is used to implement the corresponding temperature signal real-time load spectrum calculation method in any one of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to any of the above-described embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the temperature signal real-time load spectrum calculation method according to any of the above embodiments.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the temperature signal real-time load spectrum calculation method according to any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the concept of the present disclosure, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures for simplicity of illustration and discussion, and so as not to obscure the embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring embodiments of the present disclosure, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the present disclosure are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that the embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures, such as Dynamic RAM (DRAM), may use the discussed embodiments.
The disclosed embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalents, improvements, and the like that may be made within the spirit and principles of the embodiments of the disclosure are intended to be included within the scope of the disclosure.
Claims (10)
1. A temperature signal real-time load spectrum calculation method comprises the following steps:
preprocessing the temperature signal data set to obtain an available temperature signal data set;
generating a temperature factor from the available temperature signal data set;
obtaining a temperature load spectrum according to the temperature factor;
and acquiring new temperature signal data, and acquiring an updated temperature load spectrum according to the new temperature signal data.
2. The method of claim 1, wherein the preprocessing comprises missing value processing and outlier processing; the missing value processing includes replacing the outlier with an interpolation; the outlier processing includes identifying outliers of the temperature signal data in the temperature signal data set by 3 σ rule and replacing the outliers using interpolation.
3. The method of claim 1, wherein said obtaining new temperature signal data from which an updated temperature load spectrum is derived comprises:
acquiring new temperature signal data, and preprocessing a data set of the new temperature signal data to obtain a new available temperature signal data set;
generating a new temperature factor from the new available temperature signal data set and the temperature factor;
and updating the temperature load spectrum according to the new temperature factor to obtain an updated temperature load spectrum.
4. The method of claim 1, wherein the temperature factor comprises a high temperature factor and a temperature cycling factor, the temperature loading spectrum comprising an initial high temperature loading spectrum corresponding to the high temperature factor and an initial temperature cycling loading spectrum corresponding to the temperature cycling factor;
the high-temperature factor is a temperature sequence of all temperature signal data collected from the moment when a product is electrified to reach thermal equilibrium to the moment when the product is powered off; the temperature cycle factor is determined according to the high-temperature factor, the product power-on time, the temperature of the product during power-on and the day and night temperature difference.
5. The method of claim 4, wherein the deriving a temperature load spectrum from the temperature factor comprises:
the temperature signal data in the high-temperature factors are rounded and arranged from small to large to obtain a new temperature sequence;
calculating the difference between the latter and former temperature signal data in the new temperature sequence, and inserting new temperature signal data by taking 1 as a step length in response to determining that the difference is greater than 1;
calculating a frequency of the temperature signal data, wherein the frequency of the new temperature signal data is set to 0;
and obtaining an initial high-temperature load spectrum according to the temperature signal data and the frequency corresponding to the temperature signal data.
6. The method of claim 5, wherein said deriving a temperature load spectrum from said temperature factor further comprises:
classifying the data in the temperature cycle factors through clustering analysis;
taking the average value of each kind of data as the temperature cycle grade of the kind of data;
calculating the frequency of each type of data;
and obtaining an initial temperature cycle load spectrum according to the temperature cycle grade and the frequency of each type of data.
7. The method of claim 4, wherein the temperature cycling factor comprises: t is cycle ＝(T max ,T min ,t max ,t min ,α up ,α down ) (ii) a Wherein T is max The average value of the temperature collected from the time of reaching the temperature balance to the time of power failure; t is min The lowest temperature of the day; t is t max A high temperature residence time; t is t min Low temperature residence time; alpha is alpha up The temperature change rate in the temperature rise stage; alpha is alpha down Wen Bianlv is the cool down stage.
8. The method of claim 4, wherein the temperature cycling factor comprises: t is cycle ＝(T max ,T min ,t max ,t min α); wherein T is max To reach temperatureAverage temperature values collected from the moment of balancing to the moment of power failure; t is min The lowest temperature of the day; t is t max A high temperature residence time; t is t min Low temperature residence time; alpha is Wen Bianlv in the temperature rise stage and the temperature change stage.
9. A temperature signal real-time load spectrum calculation apparatus, comprising:
the preprocessing module is configured to preprocess the temperature signal data set to obtain an available temperature signal data set;
a generation module configured to generate a temperature factor from the available temperature signal dataset;
the calculation module is configured to obtain a temperature load spectrum according to the temperature factor;
and the updating module is configured to acquire new temperature signal data and obtain an updated temperature load spectrum according to the new temperature signal data.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 8 when the program is executed by the processor.
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