Engine cycle test data processing method, device and equipment and storage medium
By calculating the integral characteristic value difference of engine cycle test data, the engine cycle test data is automatically aligned, solving the problems of low efficiency and poor real-time performance in the existing technology, and realizing efficient and accurate data processing and analysis.
Patent Information
- Application Number
- CN202511511327.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-22
- Publication Date
- 2026-02-17
AI Technical Summary
Existing technologies suffer from low efficiency and poor real-time performance in processing engine cycle test data, making it difficult to achieve real-time, dynamic monitoring and early warning of performance trends, and failing to support timely test decisions.
By acquiring the benchmark data sequence and the data sequence to be processed of the engine at different times, the first integral feature value within the preset integral window and the second integral feature value of the candidate integral window are calculated, the difference degree is calculated, and the data sequence under the candidate integral window corresponding to the minimum difference degree is aligned with the data sequence under the preset integral window.
It has achieved automated and precise alignment of engine cycle test data, improved data processing efficiency and real-time performance, and enhanced the efficiency and accuracy of performance analysis and fault diagnosis.
Smart Images
Figure CN121542592A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, specifically to a method, apparatus, equipment, and storage medium for processing engine cycle test data. Background Technology
[0002] In engine reliability testing, alignment analysis of multiple test data segments is required to assess performance stability.
[0003] In related technologies, engine cycle test data processing typically relies on engineers manually adjusting the time and visually observing the waveform overlap of the cycle test data. This involves periodically (e.g., every 100 hours of operation) manually searching for and extracting data segments collected at specific operating conditions from the engine cycle test data, comparing these segments to roughly determine if performance has changed. However, manually searching and filtering data segments for target operating conditions from massive amounts of engine cycle test data is time-consuming, labor-intensive, and highly dependent on the experience of analysts, resulting in extremely low automation. Furthermore, this method is usually a post-hoc analysis performed after a certain stage of the test, making it difficult to achieve real-time, dynamic monitoring and early warning of performance trends during the test, and failing to provide timely support for test decisions. This leads to low efficiency and poor real-time performance in engine cycle test data processing. Summary of the Invention
[0004] In view of this, it is necessary to provide a method, apparatus, device and storage medium for processing engine cycle test data, so as to solve the technical problems of low efficiency and poor real-time performance of engine cycle test data processing in the prior art.
[0005] To address the aforementioned technical problems, in a first aspect, the present invention provides a method for processing engine cycle test data, comprising: Acquire the baseline data sequence and the data sequence to be processed collected by the engine in different periods and the same cycle test; Calculate the first integral characteristic value of the reference data sequence within a preset integration window; Calculate the second integral feature value of the data sequence to be processed in multiple candidate integral windows, wherein the duration of the candidate integral window is equal to the duration of the preset integral window; Calculate the degree of difference between each second integral eigenvalue and the first integral eigenvalue; Align the data sequence to be processed in the candidate integration window corresponding to the smallest difference with the benchmark data sequence in the preset integration window.
[0006] In one possible implementation, calculating the difference between each of the second integral feature values and the first integral feature value includes: Calculate the absolute value of the difference between the second integral eigenvalue and the first integral eigenvalue; The degree of difference is determined by the ratio of the absolute value of the difference to the first integral feature value.
[0007] In one possible implementation, calculating the first integral feature value of the reference data sequence within a preset integration window includes: The preset integration window is divided into N preset sub-windows, where N is a natural number greater than 1; Calculate the N first integral sub-feature values of the benchmark data sequence within N preset integration windows; The first integral feature value is determined based on N first integral sub-feature values.
[0008] In one possible implementation, calculating the second integral feature value of the data sequence to be processed within a plurality of candidate integration windows includes: Calculate the N second integral sub-feature values of the data sequence to be processed within N candidate sub-windows, where the duration of each candidate sub-window is equal to the duration of the corresponding preset sub-integral window; The second integral feature value is determined based on N second integral sub-feature values.
[0009] In one possible implementation, calculating the difference between each of the second integral feature values and the first integral feature value includes: Calculate the absolute value of the difference between each second integral sub-eigenvalue in the second integral eigenvalue and each first integral sub-eigenvalue in the first integral eigenvalue, and obtain N absolute values of sub-differences; Calculate the ratio between the absolute value of the sub-difference and the corresponding first integral sub-feature value to obtain N sub-ratios; The degree of difference is determined based on N of the sub-ratios.
[0010] In one possible implementation, the step of determining the preset integration window includes: The target valid time point interval in the reference data sequence is identified to obtain the preset integration window.
[0011] In one possible implementation, after aligning the data sequence to be processed under the candidate integration window corresponding to the minimum difference with the reference data sequence under the preset integration window, the method further includes: The dynamic chart generation module is invoked to compare the data sequence to be processed under the candidate integration window corresponding to the smallest difference with the benchmark data sequence under the preset integration window in a visual chart.
[0012] Secondly, the present invention also provides an engine cycle test data processing device, comprising: The acquisition unit is used to acquire the baseline data sequence and the data sequence to be processed collected by the engine in different periods and the same cycle test; The first integration unit is used to calculate the first integral feature value of the reference data sequence within a preset integration window; The second integration unit is used to calculate the second integral feature value of the data sequence to be processed in multiple candidate integration windows, wherein the duration of the candidate integration window is equal to the duration of the preset integration window. The calculation unit is used to calculate the difference between each second integral feature value and the first integral feature value; The alignment unit is used to align the data sequence to be processed under the candidate integration window corresponding to the smallest difference with the reference data sequence under the preset integration window.
[0013] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the engine cycle test data processing method described in any of the above implementations.
[0014] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instructions, which, when executed by a processor, can implement the steps in the engine cycle test data processing method described in any of the above implementations.
[0015] The beneficial effects of this invention are: The engine cycle test data processing method provided by this invention acquires a reference data sequence and a data sequence to be processed collected from the engine in the same cycle test at different times; calculates the first integral feature value of the reference data sequence within a preset integration window, thereby reflecting the overall shape and key point values of the data in the reference data sequence within the time interval corresponding to the preset integration window, providing an accurate and quantifiable comparison benchmark for finding the most similar waveform segment in the data sequence to be processed; and calculates the second integral feature value of the data sequence to be processed in multiple candidate integration windows, wherein the duration of the candidate integration windows is equal to the duration of the preset integration window, realizing a global scan of the data points in the data sequence to be processed, so that each possible position in the data sequence to be processed can be automatically evaluated subsequently. The matching degree between the candidate integration window and the preset integration window of the reference data sequence is determined; the difference between each second integral feature value and the first integral feature value is calculated, realizing the quantitative analysis of the difference between the waveform of each candidate integration window and the preset integration window; the data sequence to be processed under the candidate integration window corresponding to the smallest difference degree is aligned with the reference data sequence under the preset integration window, ensuring that the alignment operation is based on the optimal candidate integration window, which improves the positioning accuracy and alignment accuracy of the data sequence to be processed. Moreover, the entire calculation is fully automated. Compared with manual visual search and manual dragging, it greatly improves the positioning calculation efficiency of the optimal candidate integration window and the positioning alignment accuracy and efficiency of the data sequence under the optimal candidate integration window, thereby improving the efficiency and real-time performance of engine cycle test data processing. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 A schematic flowchart of an embodiment of the engine cycle test data processing method provided by the present invention; Figure 2 A schematic diagram of the data sequence corresponding to the engine speed provided by the present invention; Figure 3 The difference provided by the present invention A curve graph; Figure 4 A schematic diagram of the aligned data sequences A and B provided by the present invention; Figure 5 A schematic diagram of an embodiment of the engine cycle test data processing device provided by the present invention; Figure 6A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0019] In the description of the embodiments of the present invention, unless otherwise stated, "a plurality of" means two or more.
[0020] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.
[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0022] This invention provides a method, apparatus, device, and storage medium for processing engine cycle test data, which will be described below.
[0023] The engine cycle test data processing method provided in this application embodiment is applicable to engine bench reliability testing, vehicle durability testing, and remote big data platforms. It can automatically align multiple segments of incomplete cycle data that are misaligned due to start-stop and operating condition fluctuations to the same time axis with one click, replacing manual visual translation, quickly completing performance degradation and comparative analysis, and significantly improving the efficiency and accuracy of subsequent performance analysis and fault diagnosis.
[0024] The execution subject of the engine cycle test data processing method in this application embodiment can be the engine cycle test data processing device provided in this application embodiment, or different types of electronic devices such as server equipment, physical host, or user equipment (UE) that integrate the engine cycle test data processing device. The engine cycle test data processing device can be implemented in hardware or software. The UE can be a terminal device such as a smartphone, tablet computer, laptop computer, handheld computer, desktop computer, or personal digital assistant (PDA).
[0025] Figure 1 This is a schematic flowchart of an embodiment of the engine cycle test data processing method provided by the present invention, as shown below. Figure 1 As shown, the engine cycle test data processing method includes: S101. Obtain the baseline data sequence and the data sequence to be processed collected by the engine in different periods and the same cycle test.
[0026] Among them, the cyclic test can be an engine reliability cyclic test, such as an engine reliability thermal shock test of 5,000 cycles or an engine reliability thermal shock test of 10,000 cycles.
[0027] A data sequence refers to a sequence of test data collected during a reliability cycle test. It includes the test data under that reliability cycle test and the corresponding timestamps, where the timestamps represent the acquisition time of each test data point. Test data can be engine performance data, such as engine speed, torque, oil temperature, and coolant temperature. A baseline data sequence is a sequence of engine test data from multiple complete test cycles, taken when the engine is in its initial healthy state after break-in, and used as a benchmark for test data analysis and comparison. A data sequence to be processed is a sequence of engine test data that requires analysis and processing. This data sequence shares the same cyclic test conditions as the baseline data sequence, but the test periods differ.
[0028] Specifically, test data of the engine can be collected in advance during different periods and the same cycle of testing, forming a text file, such as a txt file, from which the baseline data sequence and the data sequence to be processed can be obtained.
[0029] It is worth noting that, in order to improve the efficiency of alignment processing of the data sequence to be processed, invalid data cleaning can be performed on the test data collected by the engine in different periods and the same cycle test, such as removing test data during the time period when the engine was not running.
[0030] S102. Calculate the first integral feature value of the reference data sequence within a preset integration window.
[0031] The preset integration window refers to a pre-defined time period within the reference data sequence. The first integral eigenvalue refers to the integral value of the reference data sequence within the preset integration window.
[0032] Specifically, the values of all data points in the benchmark data sequence within a preset integration window can be summed and calculated to obtain the first integral characteristic value. Alternatively, a benchmark sequence function corresponding to the benchmark data sequence can be constructed, for example, by fitting all data points in the benchmark data sequence. Then, the integral value of the benchmark sequence function within the preset integration window is calculated to obtain the first integral characteristic value. The first integral characteristic value characterizes the overall intensity or area of the benchmark data sequence under the preset integration window, thereby reflecting the overall shape and key point values of the data in the benchmark data sequence within the time interval corresponding to the preset integration window. This provides an accurate and quantifiable comparison benchmark for finding the most similar waveform segment in the data sequence to be processed.
[0033] S103. Calculate the second integral feature value of the data sequence to be processed in multiple candidate integral windows, wherein the duration of the candidate integral window is equal to the duration of the preset integral window.
[0034] The candidate integration window refers to multiple time intervals selected in the data sequence to be processed, and the duration of the candidate integration window is equal to the duration of the preset integration window.
[0035] Specifically, the data sequence to be processed can be slid within a candidate integration window. That is, starting from the beginning time of each candidate integration window, the second integral feature value of each candidate integration window is calculated, resulting in multiple second integral feature values. Based on all data points of the data sequence within each candidate integration window, the second integral feature value within that candidate integration window is calculated using the same algorithm as for calculating the first integral feature value. Understandably, in this embodiment, by using a sliding window and integration algorithm to calculate multiple second integral feature values, a global scan of the data points of the data sequence to be processed is achieved. This allows for the automatic evaluation of the matching degree between each possible position of the data sequence and the preset integration window of the benchmark data sequence.
[0036] S104. Calculate the degree of difference between each second integral eigenvalue and the first integral eigenvalue.
[0037] The difference degree is a quantitative value that characterizes the degree of difference between the waveform of each candidate integration window and the preset integration window. The smaller the difference degree, the higher the similarity between the waveform of the candidate integration window and the preset integration window.
[0038] Specifically, the difference between each second integral eigenvalue and the first integral eigenvalue can be calculated, and the degree of difference can be determined based on the difference. In this embodiment, by calculating the degree of difference between each second integral eigenvalue and the first integral eigenvalue, a quantitative analysis of the degree of difference between the waveform of each candidate integral window and the preset integral window is achieved. This allows for the rapid determination of the candidate integral window that best matches the waveform of the preset integral window based on the degree of difference, effectively improving the accuracy and robustness of determining the candidate integral window that best matches the waveform of the preset integral window from the data sequence to be processed.
[0039] S105. Align the data sequence to be processed in the candidate integration window corresponding to the smallest difference with the reference data sequence in the preset integration window.
[0040] Among them, the candidate integration window corresponding to the smallest difference is the candidate integration window that best matches the preset integration window. That is, the waveform of the data sequence to be processed under the candidate integration window corresponding to the smallest difference is closest to the waveform of the reference data sequence under the preset integration window.
[0041] Specifically, the candidate integration window corresponding to the minimum difference is obtained as the optimal window. The starting time point of the optimal window is obtained, and the starting time point of the optimal window can be used as the optimal alignment time point. Then, the data points of the data sequence to be processed are moved as a whole so that the data points of the optimal alignment time point are aligned with the data points of the starting time point of the preset integration window of the reference data sequence on the time axis, thereby achieving the alignment of the data sequence to be processed under the optimal window with the reference data sequence under the preset integration window.
[0042] Understandably, in this embodiment, the data sequence to be processed under the candidate integration window corresponding to the minimum difference degree is aligned with the reference data sequence under the preset integration window to ensure that the alignment operation is based on the optimal candidate integration window. This improves the positioning accuracy and alignment accuracy of the data sequence to be processed. Moreover, the entire calculation is fully automated. Compared with manual visual search and manual dragging, it greatly improves the positioning calculation efficiency of the optimal candidate integration window and the positioning alignment accuracy and efficiency of the data sequence under the optimal candidate integration window, thereby improving the efficiency and real-time performance of engine cycle test data processing.
[0043] In summary, the engine cycle test data processing method provided by this invention acquires a reference data sequence and a data sequence to be processed collected from the engine in different periods and the same cycle test; calculates the first integral feature value of the reference data sequence within a preset integration window, thereby reflecting the overall shape and key point values of the data in the reference data sequence within the time interval corresponding to the preset integration window, providing an accurate and quantifiable comparison benchmark for finding the most similar waveform segment in the data sequence to be processed; and calculates the second integral feature value of the data sequence to be processed in multiple candidate integration windows, wherein the duration of the candidate integration windows is equal to the duration of the preset integration window, realizing a global scan of the data points in the data sequence to be processed, so that each data point in the data sequence to be processed can be automatically evaluated in the subsequent processing. The system calculates the degree of matching between the possible location and the preset integration window of the reference data sequence; it calculates the difference between each second integral feature value and the first integral feature value, realizing a quantitative analysis of the difference between the waveform of each candidate integration window and the preset integration window; it aligns the data sequence to be processed under the candidate integration window corresponding to the smallest difference with the reference data sequence under the preset integration window, ensuring that the alignment operation is based on the optimal candidate integration window, thus improving the positioning accuracy and alignment accuracy of the data sequence to be processed. Moreover, the entire calculation is fully automated. Compared with manual visual search and manual dragging, it greatly improves the positioning calculation efficiency of the optimal candidate integration window and the positioning alignment accuracy and efficiency of the data sequence under the optimal candidate integration window, thereby improving the efficiency and real-time performance of engine cycle test data processing.
[0044] In some embodiments of the present invention, step S104 includes: S201. Calculate the absolute value of the difference between the second integral eigenvalue and the first integral eigenvalue; S202. Determine the degree of difference by the ratio of the absolute value of the difference to the first integral feature value.
[0045] Specifically, the absolute value of the difference between the second integral eigenvalue and the first integral eigenvalue is calculated, quantifying the difference between the candidate window and the reference window in the total integral area. The absolute difference is then compared with the first integral eigenvalue to determine the final degree of difference, thus obtaining the percentage deviation of the candidate integral window relative to the preset integral window. Understandably, in this embodiment, using the ratio of the absolute value of the difference to the first integral eigenvalue as the degree of difference is simple and quick to calculate. Furthermore, this degree of difference is a relative degree of difference, eliminating the influence of dimensions, unifying the alignment judgment criteria for different parameters, and enhancing the adaptability of the degree of difference calculation method to different energy level operating conditions of the engine.
[0046] In one specific implementation, such as Figure 2The diagram shows the data sequence corresponding to engine speed, including a reference data sequence A and a data sequence B to be processed. Due to periodic fluctuations and interruptions during the experiment, data sequences A and B are not aligned on the time axis (X-axis). Therefore, an integral comparison algorithm is used to automatically and accurately calculate the optimal alignment offset of data sequence B relative to A. The specific calculation process is as follows: S1: Determine the characteristic interval of the baseline data sequence A, which is defined by the start time aX0, the end time aX1, and an intermediate reference time aXm. This characteristic interval should contain typical characteristic waveforms representing the current cycle (e.g., a rapid acceleration process), and the corresponding row numbers. , and line spacing , .
[0047] S2: Extract data within the feature interval and construct an integral function. in, i ∈ ( i 0, i 1) Calculate the integral to obtain the integral value of the data sequence A. Find the integral function over the data sequence B. Calculate the integral value of B under the pre-read n sets of data, i.e., n candidate integration windows. F(Y j+di ) F(Y j ) Calculate the absolute value of the difference between the integral values of data sequence B and A, and then obtain the comprehensive difference function based on the ratio of the absolute value of the difference to the integral value of data sequence A. That is, the degree of difference: H(Y j ) =|( F ( Y j+di ) F ( Y j ) aY | / aY ( j =0~ n - di ) like Figure 3 As shown, this represents the degree of difference. The curve graph, find Minimum value The corresponding row number i and time bXi value indicate the time that coincides with the feature interval of the data sequence. bXi is the alignment position where the feature intervals of data segment B and data segment A best match.
[0048] S3: Based on the calculated optimal alignment time bXi, perform translation correction on the entire data sequence B to ensure precise alignment with data sequence A on the X-axis. For example... Figure 4 The figure shows a schematic diagram of the aligned data sequences A and B.
[0049] In some embodiments of the present invention, step S102 includes: S301. Divide the preset integration window into N preset sub-windows, where N is a natural number greater than 1; S302. Calculate the N first integral sub-feature values of the reference data sequence within N preset integration windows; S303. Determine the first integral feature value based on N first integral sub-feature values.
[0050] Specifically, the preset integration window can be randomly divided into multiple preset sub-windows, or it can be evenly divided into multiple preset sub-windows. For each preset sub-window, the corresponding first integral sub-feature value is calculated according to step S102. The N first integral sub-feature values are accumulated and summed to obtain the first integral feature value. Understandably, in this embodiment, by comparing multiple local features, the details of the waveform can be captured more precisely, avoiding misalignment caused by local matching of the waveform but overall deviation, thereby finding the segment with the most similar waveform shape and improving the accuracy of subsequent alignment and positioning.
[0051] In one specific implementation, after determining the feature interval (from aX0 to aX1) of the baseline data sequence A, the feature interval is divided into two preset sub-windows on the time axis, uniformly or non-uniformly, [aX0, aXm] and [aXm, aX1], where aXm1 is an equal division or a specific ratio division point within the feature interval, the first integral sub-feature value corresponding to [aX0, aXm] is aY1, and the first integral sub-feature value corresponding to [aXm, aX1] is aY2.
[0052] In some embodiments of the present invention, step S103 includes: S401. Calculate the N second integral sub-feature values of the data sequence to be processed in N candidate sub-windows, wherein the duration of each candidate sub-window is equal to the duration of the corresponding preset sub-integral window; S402. Determine the second integral feature value based on N second integral sub-feature values.
[0053] Specifically, for each candidate sub-window, the corresponding second integral sub-feature value is calculated according to step S102. The N second integral sub-feature values are accumulated and summed to obtain the second integral feature value of the data sequence to be processed under a single candidate integral window.
[0054] In one specific implementation, the second integral sub-feature value under the candidate sub-window corresponding to [aX0, aXm] is The second integral eigenvalue corresponding to [aXm, aX1] is .
[0055] In some embodiments of the invention, step 103 includes: S501. Calculate the absolute value of the difference between each second integral sub-feature value in the second integral feature value and each first integral sub-feature value in the first integral feature value, to obtain N sub-difference absolute values; S502. Calculate the ratio between the absolute value of the sub-difference and the corresponding first integral sub-feature value to obtain N sub-ratios; S503. Determine the degree of difference based on the N sub-ratios.
[0056] Specifically, the difference degree is obtained by summing the N sub-ratios. Intuitively, by calculating the sub-ratios of multiple sub-windows, the waveform differences between corresponding intervals of each sub-window can be compared, improving the accuracy of the difference degree and facilitating improved positioning accuracy for alignment time.
[0057] In one specific implementation, taking N=2 as an example, the sub-ratios corresponding to [aX0, aXm] are further explained. The sub-ratios corresponding to [aXm, aX1] Difference That is ,Right now .
[0058] In some embodiments of the present invention, the step of determining the preset integration window includes: S601. Identify the target valid time point interval in the reference data sequence to obtain the preset integration window. The target valid time point interval refers to the set of continuous or discontinuous data points in the reference data sequence that can uniquely and stably represent the key operating condition characteristics of the cycle. This interval can be determined as the preset integration window.
[0059] Specifically, based on the requirements of cyclic testing, target valid conditions can be set, and the time intervals in the baseline data sequence that meet the target valid conditions can be determined as target valid time point intervals, thereby obtaining a preset integration window. This embodiment, by identifying the target valid time point intervals, facilitates the rapid and accurate anchoring and alignment of the position from massive non-strictly cyclic data, greatly accelerating the processing speed of engine cyclic test data.
[0060] In some embodiments of the present invention, after step S105, the method further includes: S701. Call the dynamic chart generation module to compare the data sequence to be processed under the candidate integration window corresponding to the smallest difference with the benchmark data sequence under the preset integration window in a visual chart.
[0061] Specifically, in this embodiment, by calling the dynamic chart generation module, the data sequence to be processed under the candidate integration window corresponding to the smallest difference degree is compared side by side with the benchmark data sequence under the preset integration window in the visualization chart. That is, the aligned data sequence to be processed and the benchmark data sequence are visually compared, which improves the intuitiveness of engine cycle test data comparison and processing effect.
[0062] To better implement the engine cycle test data processing method in the embodiments of the present invention, based on the engine cycle test data processing method, correspondingly, as follows: Figure 5 As shown, this embodiment of the invention also provides an engine cycle test data processing device, the engine cycle test data processing device 500 comprising: The acquisition unit 501 is used to acquire the baseline data sequence and the data sequence to be processed collected by the engine in different periods and the same cycle test; The first integration unit 502 is used to calculate the first integral feature value of the reference data sequence within a preset integration window; The second integration unit 503 is used to calculate the second integral feature value of the data sequence to be processed in multiple candidate integration windows, wherein the duration of the candidate integration window is equal to the duration of the preset integration window. Calculation unit 504 is used to calculate the degree of difference between each second integral feature value and the first integral feature value; Alignment unit 505 is used to align the data sequence to be processed under the candidate integration window corresponding to the smallest difference with the reference data sequence under the preset integration window.
[0063] The engine cycle test data processing device 500 provided in the above embodiments can realize the technical solutions described in the above engine cycle test data processing method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above engine cycle test data processing method embodiments, and will not be repeated here.
[0064] like Figure 6 As shown, the present invention also provides an electronic device 600. The electronic device 600 includes a processor 601, a memory 602, and a display 603. Figure 6 Only some components of the electronic device 600 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.
[0065] In some embodiments, processor 601 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 602 or process data, such as the engine cycle test data processing method of the present invention.
[0066] In some embodiments, processor 601 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 601 may be local or remote. In some embodiments, processor 601 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, intranet, multi-cloud, etc., or any combination thereof.
[0067] In some embodiments, memory 602 may be an internal storage unit of electronic device 600, such as a hard disk or memory of electronic device 600. In other embodiments, memory 602 may also be an external storage device of electronic device 600, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 600.
[0068] Furthermore, the memory 602 may include both internal storage units of the electronic device 600 and external storage devices. The memory 602 is used to store application software and various types of data installed on the electronic device 600.
[0069] In some embodiments, display 603 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 603 is used to display information from electronic device 600 and to display a visual user interface. Components 601-603 of electronic device 600 communicate with each other via a system bus.
[0070] In one embodiment, when processor 601 executes the engine cycle test data processing program in memory 602, the following steps can be implemented: Acquire the baseline data sequence and the data sequence to be processed collected by the engine in different periods and the same cycle test; Calculate the first integral characteristic value of the reference data sequence within a preset integration window; Calculate the second integral feature value of the data sequence to be processed in multiple candidate integral windows, wherein the duration of the candidate integral window is equal to the duration of the preset integral window; Calculate the degree of difference between each second integral eigenvalue and the first integral eigenvalue; Align the data sequence to be processed in the candidate integration window corresponding to the smallest difference with the benchmark data sequence in the preset integration window.
[0071] It should be understood that when the processor 601 executes the engine cycle test data processing program in the memory 602, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.
[0072] Furthermore, this embodiment of the invention does not specifically limit the type of electronic device 600 mentioned. Electronic device 600 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the invention, electronic device 600 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).
[0073] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the engine cycle test data processing methods provided in the above-described method embodiments.
[0074] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.
[0075] The above provides a detailed description of the engine cycle test data processing method, apparatus, equipment, and storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for processing engine cycle test data, characterized in that, include: Acquire the baseline data sequence and the data sequence to be processed collected by the engine in different periods and the same cycle test; Calculate the first integral characteristic value of the reference data sequence within a preset integration window; Calculate the second integral feature value of the data sequence to be processed in multiple candidate integral windows, wherein the duration of the candidate integral window is equal to the duration of the preset integral window; Calculate the degree of difference between each second integral eigenvalue and the first integral eigenvalue; Align the data sequence to be processed in the candidate integration window corresponding to the smallest difference with the benchmark data sequence in the preset integration window.
2. The engine cycle test data processing method according to claim 1, characterized in that, The calculation of the difference between each second integral feature value and the first integral feature value includes: Calculate the absolute value of the difference between the second integral eigenvalue and the first integral eigenvalue; The degree of difference is determined by the ratio of the absolute value of the difference to the first integral feature value.
3. The engine cycle test data processing method according to claim 1, characterized in that, The calculation of the first integral feature value of the benchmark data sequence within a preset integration window includes: The preset integration window is divided into N preset sub-windows, where N is a natural number greater than 1; Calculate the N first integral sub-feature values of the benchmark data sequence within N preset integration windows; The first integral feature value is determined based on N first integral sub-feature values.
4. The engine cycle test data processing method according to claim 3, characterized in that, The calculation of the second integral feature value of the data sequence to be processed in multiple candidate integral windows includes: Calculate the N second integral sub-feature values of the data sequence to be processed within N candidate sub-windows, where the duration of each candidate sub-window is equal to the duration of the corresponding preset sub-integral window; The second integral feature value is determined based on N second integral sub-feature values.
5. The engine cycle test data processing method according to claim 4, characterized in that, The calculation of the difference between each second integral feature value and the first integral feature value includes: Calculate the absolute value of the difference between each second integral sub-eigenvalue in the second integral eigenvalue and each first integral sub-eigenvalue in the first integral eigenvalue, and obtain N absolute values of sub-differences; Calculate the ratio between the absolute value of the sub-difference and the corresponding first integral sub-feature value to obtain N sub-ratios; The degree of difference is determined based on N of the sub-ratios.
6. The engine cycle test data processing method according to claim 1, characterized in that, The step of determining the preset integration window includes: The target valid time point interval in the reference data sequence is identified to obtain the preset integration window.
7. The engine cycle test data processing method according to claim 1, characterized in that, After aligning the data sequence to be processed within the candidate integration window corresponding to the minimum difference with the reference data sequence within the preset integration window, the process further includes: The dynamic chart generation module is invoked to compare the data sequence to be processed under the candidate integration window corresponding to the smallest difference with the benchmark data sequence under the preset integration window in a visual chart.
8. An engine cycle test data processing device, characterized in that, include: The acquisition unit is used to acquire the baseline data sequence and the data sequence to be processed collected by the engine in different periods and the same cycle test; The first integration unit is used to calculate the first integral feature value of the reference data sequence within a preset integration window; The second integration unit is used to calculate the second integral feature value of the data sequence to be processed in multiple candidate integration windows, wherein the duration of the candidate integration window is equal to the duration of the preset integration window. The calculation unit is used to calculate the difference between each second integral feature value and the first integral feature value; The alignment unit is used to align the data sequence to be processed under the candidate integration window corresponding to the smallest difference with the reference data sequence under the preset integration window.
9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the engine cycle test data processing method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the engine cycle test data processing method according to any one of claims 1 to 7.