Temperature signal real-time load spectrum calculation method and related device

Abstract

The present disclosure provides a temperature signal real-time load spectrum calculation method and related equipment; the method comprises the following steps: firstly, data preprocessing is performed on a temperature signal data set to obtain a usable temperature signal data set; for the usable temperature signal data set, a temperature factor is generated and the temperature factor is analyzed to obtain a temperature load spectrum; finally, new temperature signal data is acquired and the temperature load spectrum is updated according to the new temperature signal data. The scheme of the present disclosure makes the temperature signal load spectrum fully retain the characteristics in the actual operation process, while having the real-time updating capability, thereby improving the accuracy, precision and timeliness of data processing.

Description

Technical Field

[0001] This disclosure relates to the field of environmental load spectrum conversion technology, and in particular to a method and related equipment for real-time load spectrum calculation of temperature signals. Background Technology

[0002] In analyzing the operational status of workpieces, it is necessary to collect the workpiece's temperature signal load spectrum as a basis for analysis. During operation, the temperature signal load spectrum will change accordingly due to variations in working time and environment. For some workpieces, it is necessary to adjust operating parameters in real time based on changes in the temperature signal load spectrum. Therefore, a solution is needed that can acquire temperature signal data and obtain real-time load spectra. Summary of the Invention

[0003] In view of this, the purpose of this disclosure is to propose a method and related equipment for real-time load spectrum calculation of temperature signals.

[0004] To achieve the above objectives, this disclosure provides a method for calculating the real-time load spectrum of a temperature signal, including:

[0005] Preprocess the temperature signal dataset to obtain a usable temperature signal dataset;

[0006] Based on the available temperature signal dataset, generate a temperature factor;

[0007] Based on the temperature factor, the temperature load spectrum is obtained;

[0008] Acquire new temperature signal data, and obtain an updated temperature load spectrum based on the new temperature signal data.

[0009] Based on the same inventive concept, this disclosure also provides a real-time load spectrum calculation device for temperature signals, comprising:

[0010] The preprocessing module is configured to preprocess the temperature signal dataset to obtain a usable temperature signal dataset;

[0011] The generation module is configured to generate a temperature factor based on the available temperature signal dataset;

[0012] The calculation module is configured to obtain the temperature load spectrum based on the temperature factor;

[0013] The update module is configured to acquire new temperature signal data and obtain an updated temperature load spectrum based on the new temperature signal data.

[0014] Based on the same inventive concept, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any of the above.

[0015] As can be seen from the above, the real-time temperature signal load spectrum calculation method and related equipment provided in this disclosure first preprocess the temperature signal dataset to obtain a usable temperature signal dataset; then, for the usable temperature signal dataset, temperature factors are generated and analyzed to obtain the temperature load spectrum; finally, new temperature signal data is acquired and the temperature load spectrum is updated based on the new temperature signal data. This ensures that the temperature signal load spectrum fully retains the characteristics of the actual operation process, while also possessing real-time update capabilities, thus improving the accuracy, precision, and timeliness of data processing. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this disclosure or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the real-time load spectrum calculation method for temperature signals according to an embodiment of the present disclosure;

[0018] Figure 2 This is a schematic diagram of temperature signal data acquisition according to an embodiment of the present disclosure;

[0019] Figure 3 This is a flowchart illustrating the initial high-temperature load spectrum generation process of an embodiment of this disclosure;

[0020] Figure 4 This is a flowchart illustrating the initial temperature cyclic load spectrum generation process of an embodiment of this disclosure;

[0021] Figure 5 This is a schematic diagram of the structure of the real-time load spectrum calculation device for temperature signals according to an embodiment of this disclosure;

[0022] Figure 6 This is a schematic diagram of the electronic device structure according to an embodiment of the present disclosure. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0024] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. Words such as "comprising" or "including" mean that an element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, but do not exclude other elements or objects.

[0025] As described in the background section, when analyzing the operation of a workpiece, a solution is needed to acquire real-time temperature signal data and obtain a real-time load spectrum. In developing this disclosure, the applicant discovered that existing temperature signal load spectra have the following main problems: the plotted temperature signal load spectra are all static results, and the spectrum is relatively coarse. On the one hand, some information from the analysis data is lost; on the other hand, they lack real-time updating capabilities and cannot characterize the complete temperature environment during the workpiece's service process. The application effect needs improvement. Currently, there is no solution for obtaining a real-time temperature signal load spectrum based on real-time acquired discontinuous temperature data.

[0026] In view of this, one or more embodiments of this disclosure provide a scheme for calculating the real-time load spectrum of a temperature signal, with reference to Figure 1 This disclosure discloses a method for calculating the real-time load spectrum of a temperature signal according to an embodiment, which includes the following steps:

[0027] Step S101: Preprocess the temperature signal dataset to obtain a usable temperature signal dataset;

[0028] In this step, because missing and outlier values ​​often occur during the acquisition of temperature signal data, it is first necessary to preprocess the temperature signal dataset containing all temperature signal data to remove outliers and fill in missing values. During the workpiece's operation, refer to... Figure 2 Because of the power-on and power-off process, the real-time temperature signal data varies discontinuously over time, which can be represented as T = (T1, T1', ..., T...). n1 ,T n1+1 ,…T n2 ,…,T n2+1 ,…T n3 …), where T represents temperature.

[0029] Step S102: Generate a temperature factor based on the available temperature signal dataset;

[0030] In this step, the temperature factor includes the high-temperature factor T' and the temperature cycling factor. The high-temperature factor contains all temperature signal data collected from the moment the product is powered on and reaches thermal equilibrium until the moment it is powered off. It is a one-dimensional variable and can be represented as T' = (T1', ..., T...). n1 ,T n1+1 ',…,Tn2 (,...). The temperature cycling factor needs to consider factors such as the high-temperature factor, the product's power-on time and temperature at power-on, and the diurnal temperature range. It is a multidimensional variable and can be represented by T. cycle =(T max ,T min ,t max ,t min ,α up ,α down ) representation.

[0031] Among them, T max To achieve the average value of the temperature signal data collected from the point of temperature equilibrium until the power is cut off, for Figure 2 In the first loop, let n1' be the number of sampling points from temperature equilibrium to power-off.

[0032]

[0033] T min This is the lowest temperature of the day.

[0034] t max High-temperature residence time refers to the duration from reaching temperature equilibrium to the moment of power-off. Figure 2 The first cycle:

[0035] t max =t n1 -t1';

[0036] t min The low-temperature residence time is calculated using the following formula:

[0037]

[0038] α up The rate of temperature change during the heating phase is calculated using the following formula:

[0039]

[0040] α down The temperature change rate during the cooling phase needs to be estimated since real-time temperature signal data is unavailable for calculation. Based on experience with temperature signal data collected from external sensors, the temperature change rate differs little between the heating and cooling phases. Therefore, it can be assumed that they are the same in the temperature cycling load spectrum, i.e.: α down =α up Therefore, the temperature cycling factor can be simplified to T cycle =(T max ,T min ,t max ,t min ,α), where α=α up =αd own .

[0041] Step S103: Obtain the temperature load spectrum based on the temperature factor;

[0042] In this step, the temperature load spectrum includes the initial high-temperature load spectrum corresponding to the high-temperature factor and the initial temperature cycle load spectrum corresponding to the temperature cycle factor.

[0043] For the initial high-temperature load spectrum, reference Figure 3 It may also include the following steps:

[0044] Step S201: Round the temperature signal data in the high temperature factor and arrange them in ascending order to obtain a new temperature sequence;

[0045] In this embodiment, based on the one-dimensional variable T'=(T1',…,T n1 ,T n1+1 ',…,T n2 Arrange the observed variable values, i.e., the temperature signal data, from smallest to largest to obtain a new sequence T. new :T new =(T new1 ,T new2 ,…,T newi ,T newi+1 ,…).

[0046] Step S202: Calculate the difference between the next and previous temperature signal data in the new temperature sequence; in response to determining that the difference is greater than 1, insert new temperature signal data with a step size of 1.

[0047] In this step, the difference between two adjacent temperature signal data points in the new sequence is calculated. For example, the (i+1)th temperature signal data point and the ith temperature signal data point, assuming T... newi+1 -T newi = m, if m>1, then insert m-1 new observations between the two temperature signal data with a step size of 1.

[0048] Step S203: Calculate the frequency of the temperature signal data, wherein the frequency of the new temperature signal data is set to 0;

[0049] In this step, the frequency of the temperature signal data represents the frequency of occurrence in the high-temperature factor.

[0050] Step S204: Obtain the initial high-temperature load spectrum based on the temperature signal data and the frequency corresponding to the temperature signal data.

[0051] In this step, the temperature signal data and the corresponding frequencies are sorted according to levels 1 to k from smallest to largest to generate the initial high-temperature load spectrum as follows:

[0052]

[0053] Where T 1k p represents the temperature at level k. 1k The frequency is at level k.

[0054] For the initial temperature cyclic load spectrum, reference Figure 4 It may also include the following steps:

[0055] Step S301: Classify the data in the temperature cycling factor through cluster analysis;

[0056] In this step, because the temperature cycle factor is a multidimensional variable, cluster analysis must first be performed on the data in the temperature cycle factor to classify the data. Cluster analysis can use the k-means clustering method, hierarchical clustering, or probability-based clustering methods. This will result in l clusters of the form T. cycle =(T max ,T min ,t max ,t min ,α up ,α down () category.

[0057] Step S302: Use the average value of each type of data as the temperature cycle level for that type of data;

[0058] In this step, the average value of the data in each class is arranged from largest to smallest, and the order corresponding to the class is used as the temperature cycle level of the class, with the temperature cycle level ranging from 1 to 1.

[0059] Step S303: Calculate the frequency of each type of data;

[0060] Step S304: Obtain the initial temperature cycle load spectrum based on the temperature cycle level and the frequency of each type of data.

[0061] In this step, the initial temperature cyclic load spectrum is generated as follows:

[0062]

[0063] Where T 1lmax p is the temperature at temperature cycle level l. 1l The frequency at temperature cycling level l.

[0064] Step S104: Obtain new temperature signal data, and obtain the updated temperature load spectrum based on the new temperature signal data.

[0065] In this step, in order to update the temperature load spectrum in real time, it is necessary to acquire newly generated temperature signal data and update the already generated temperature load spectrum with the newly generated temperature signal data. Specifically, this includes:

[0066] S401. Acquire new temperature signal data, preprocess the dataset of the new temperature signal data to obtain a new usable temperature signal dataset.

[0067] S402. Generate a new temperature factor based on the new available temperature signal dataset and the temperature factor;

[0068] In this step, the new available temperature signal needs to be inserted into the temperature factor to obtain a new temperature factor.

[0069] S403. Update the temperature load spectrum according to the new temperature factor to obtain the updated temperature load spectrum.

[0070] In this step, for the new temperature factor, the same operation as described in step S103 is performed to obtain the updated temperature load spectrum. By updating the new temperature signal data in real time, the real-time updated temperature load spectrum can be obtained.

[0071] As can be seen, in this embodiment, the temperature signal dataset is first preprocessed to obtain a usable temperature signal dataset; for the usable temperature signal dataset, temperature factors are generated and analyzed to obtain a temperature load spectrum; finally, new temperature signal data is acquired and the temperature load spectrum is updated based on the new temperature signal data. This ensures that the temperature signal load spectrum fully retains the characteristics of the actual operation process, while also having real-time update capabilities, thus improving the accuracy, precision, and timeliness of data processing.

[0072] In some embodiments, missing value handling may involve replacing outliers using interpolation; outlier handling may involve identifying outliers in the temperature signal data in the temperature signal dataset using the 3σ rule and replacing them using interpolation.

[0073] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0074] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] Based on the same inventive concept, corresponding to any of the above embodiments, this disclosure also provides a real-time load spectrum calculation device for temperature signals.

[0076] refer to Figure 5 The real-time load spectrum calculation device for the temperature signal includes:

[0077] Preprocessing module 501 is configured to preprocess the temperature signal dataset to obtain a usable temperature signal dataset.

[0078] Generation module 502 is configured to generate a temperature factor based on the available temperature signal dataset;

[0079] The calculation module 503 is configured to obtain a temperature load spectrum based on the temperature factor;

[0080] The update module 504 is configured to acquire new temperature signal data and obtain an updated temperature load spectrum based on the new temperature signal data.

[0081] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, in implementing this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0082] The apparatus described above is used to implement the corresponding real-time load spectrum calculation method for temperature signals in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0083] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the real-time load spectrum calculation method for temperature signals as described in any of the above embodiments.

[0084] Figure 6This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0085] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0086] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0087] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0088] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0089] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0090] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0091] The electronic devices described above are used to implement the corresponding real-time load spectrum calculation method for temperature signals in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0092] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the real-time load spectrum calculation method for temperature signals as described in any of the above embodiments.

[0093] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0094] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the real-time load spectrum calculation method of temperature signal as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0095] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure (including the claims) is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this disclosure as described above, which are not provided in detail for the sake of brevity.

[0096] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this disclosure, the provided drawings may or may not show well-known power / ground connections to integrated circuit (IC) chips and other components. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this disclosure, and this also takes into account the fact that the details of implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this disclosure will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuitry) have been set forth to describe exemplary embodiments of this disclosure, it will be apparent to those skilled in the art that the embodiments of this disclosure may be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0097] Although this disclosure has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0098] This disclosure is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for real-time load spectrum calculation of temperature signals, comprising: Preprocess the temperature signal dataset to obtain a usable temperature signal dataset; Based on the available temperature signal dataset, generate a temperature factor; The temperature factor includes a high-temperature factor and a temperature cycling factor; the temperature load spectrum includes an initial high-temperature load spectrum corresponding to the high-temperature factor and an initial temperature cycling load spectrum corresponding to the temperature cycling factor; the high-temperature factor is a temperature sequence of all temperature signal data collected from the moment the product is powered on and reaches thermal equilibrium until the moment it is powered off. The temperature cycling factor is determined based on the high temperature factor, product power-on time, product power-on temperature, and diurnal temperature range; the temperature cycling factor includes: T cycle = (T max , T min , t max , t min ,α up , α down ); where T max The average temperature collected from the point of temperature equilibrium until the moment of power failure; T min The lowest temperature of the day; t max The time spent in high-temperature environments; t min The low-temperature residence time; α up The rate of temperature change during the heating phase; α down The temperature change rate during the cooling phase; The temperature load spectrum is obtained based on the temperature factor, including: rounding the temperature signal data in the high-temperature factor and arranging them in ascending order to obtain a new temperature sequence; calculating the difference between the next and previous temperature signal data in the new temperature sequence, and inserting new temperature signal data with a step size of 1 in response to determining that the difference is greater than 1; calculating the frequency of the temperature signal data, wherein the frequency of the new temperature signal data is set to 0; obtaining an initial high-temperature load spectrum based on the temperature signal data and the corresponding frequencies; classifying the data in the temperature cycling factor through cluster analysis; using the average value of each class of data as the temperature cycling level of that class of data; calculating the frequency of each class of data; and obtaining an initial temperature cycling load spectrum based on the temperature cycling level and the frequency of each class of data. Acquire new temperature signal data, and obtain an updated temperature load spectrum based on the new temperature signal data.

2. The method according to claim 1, wherein, The preprocessing includes missing value processing and outlier processing; the missing value processing includes replacing the outlier values ​​using interpolation; the outlier processing includes identifying outliers in the temperature signal data in the temperature signal dataset using the 3σ rule, and replacing the outliers using interpolation.

3. The method according to claim 1, wherein, The step of acquiring new temperature signal data and obtaining an updated temperature load spectrum based on the new temperature signal data includes: Acquire new temperature signal data, preprocess the dataset of the new temperature signal data, and obtain a new usable temperature signal dataset. Based on the new available temperature signal dataset and the temperature factor, a new temperature factor is generated; The temperature load spectrum is updated based on the new temperature factor to obtain the updated temperature load spectrum.

4. A real-time load spectrum calculation device for temperature signals, comprising: The preprocessing module is configured to preprocess the temperature signal dataset to obtain a usable temperature signal dataset; The generation module is configured to generate a temperature factor based on the available temperature signal dataset; the temperature factor includes a high-temperature factor and a temperature cycling factor, and the temperature load spectrum includes an initial high-temperature load spectrum corresponding to the high-temperature factor and an initial temperature cycling load spectrum corresponding to the temperature cycling factor; the high-temperature factor is a temperature sequence of all temperature signal data collected from the time the product reaches thermal equilibrium after being powered on until the time of power-off. The temperature cycling factor is determined based on the high temperature factor, product power-on time, product power-on temperature, and diurnal temperature range; the temperature cycling factor includes: T cycle = (T max , T min , t max , t min , α up , α down ); where T max The average temperature collected from the point of temperature equilibrium until the moment of power failure; T min The lowest temperature of the day; t max The time spent in high-temperature environments; t min The low-temperature residence time; α up The rate of temperature change during the heating phase; α down The temperature change rate during the cooling phase; The calculation module is configured to obtain a temperature load spectrum based on the temperature factor, including: rounding the temperature signal data in the high-temperature factor and arranging them in ascending order to obtain a new temperature sequence; calculating the difference between the next and previous temperature signal data in the new temperature sequence, and inserting new temperature signal data with a step size of 1 in response to determining that the difference is greater than 1; calculating the frequency of the temperature signal data, wherein the frequency of the new temperature signal data is set to 0; obtaining an initial high-temperature load spectrum based on the temperature signal data and the corresponding frequencies; classifying the data in the temperature cycling factor through cluster analysis; using the average value of each class of data as the temperature cycling level of that class of data; calculating the frequency of each class of data; and obtaining an initial temperature cycling load spectrum based on the temperature cycling level and the frequency of each class of data. The update module is configured to acquire new temperature signal data and obtain an updated temperature load spectrum based on the new temperature signal data.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as claimed in any one of claims 1 to 3.

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