Method for calculating load influence on line and related equipment thereof

Abstract

The invention discloses a method for calculating load influence on line and related equipment, and belongs to the field of software algorithms, and the method comprises the following steps: if an instruction for calculating the load influence is received, receiving and processing load data, and carrying out inflection point judgment on the processed load data; if judging that inflection point data exists in the processed load data, extracting the inflection point data, and storing the inflection point data into a data buffer; determining a first inflection point data set selected by the pointer; and extracting the first inflection point data set, and carrying out load influence calculation on inflection point data in the first inflection point data set through a preset load influence calculation algorithm. According to the method, the calculation amount of each iteration is controlled by introducing the pointer, so that the calculation amount of each iteration can be predicted, and the algorithm can be applied online in real time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of software algorithms, in particular to a method for calculating the influence of online load and a related device thereof. BACKGROUND

[0002] In the current technology, the rainflow counting method is often used to calculate the influence of load. The rainflow counting method is a statistical analysis method used for fatigue life assessment and prediction, mainly used for quantitative calculation of fatigue damage of materials or structures under alternating load. Implementing the rainflow counting method requires analyzing the load history and simplifying the entire load history into several load cycles for fatigue life estimation and preparation of fatigue test load spectrum. The scheme based on the rainflow counting method algorithm for load influence calculation depends on the complete load data history.

[0003] The current technology proposes a scheme using two buffers, the first buffer is used for real-time data inflection point determination, and the second buffer is used for caching inflection point data and checking load cycles from the last stored inflection point data backward to forward until the second buffer no longer contains any load cycle, and iterating the above process. The technical problem of the current technical solution is that if the real-time data currently participating in the load cycle check does not pass the check, all load data before the real-time data participating in the check will not pass. The current technical solution checks the load cycle from the last stored inflection point data backward to forward until the second buffer no longer contains any load cycle, which makes the operating system not until all substandard load data in the second buffer are checked once, the new real-time data is added to the check range, and then in a certain iteration, a large load change appears in the real-time data, which may check multiple load cycles at a time. The determination and processing of these multiple load cycles will consume a lot of computing time, significantly increasing the time consumption of this iteration. The current technical solution has high demand for computing power and storage resources, which ultimately makes the system's operating load rate unstable and unpredictable, and is not suitable for lightweight embedded system applications, so it is difficult to realize real-time online operation of load influence calculation.

[0004] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0005] The main purpose of the present application is to provide a method for realizing the rainflow counting method in real time and a related device thereof, aiming to solve the technical problem that the current technical solution has high demand for computing power and storage resources, which ultimately makes the system's operating load rate unstable and unpredictable, and is not suitable for lightweight embedded system applications.

[0006] To achieve the above object, the application provides a method for calculating load effect online, which is applied to a device for calculating load effect online, the device comprising a data buffer comprising a pointer, the method comprising the following steps:

[0007] If an instruction for calculating load effect is received, load data is received and processed, and inflection point judgment is performed on the processed load data;

[0008] If it is judged that there is inflection point data in the processed load data, the inflection point data is extracted and stored in the data buffer;

[0009] The first inflection point data group selected by the pointer is determined, wherein the first inflection point data group is composed of a preset number of inflection point data;

[0010] The first inflection point data group is extracted, and load effect calculation is performed on the inflection point data in the first inflection point data group by a preset load effect calculation algorithm.

[0011] Optionally, whether the inflection point data in the first inflection point data group can constitute a load cycle is judged by the rainflow counting method;

[0012] If the judgment result is no, the pointing position of the pointer is adjusted, the second inflection point data group selected by the pointer is determined and extracted, and whether the inflection point data in the second inflection point data group can constitute a load cycle is judged by the four-point rainflow counting method.

[0013] Optionally, the step of judging whether the inflection point data in the second inflection point data group can constitute a load cycle by the rainflow counting method comprises:

[0014] After the judgment of the inflection point data in the first inflection point data group is completed, if it is determined that the number of load cycle related judgments does not reach a preset upper limit of iteration number, whether the inflection point data in the second inflection point data group can constitute a load cycle is judged by the rainflow counting method.

[0015] Optionally, if the judgment result is yes, a third inflection point data group constituting a load cycle is extracted from the first inflection point data group;

[0016] The load effect of the inflection point data in the third inflection point data group is calculated.

[0017] Optionally, the number of times that whether the inflection point data in the first inflection point data group can constitute a load cycle is judged by the rainflow counting method is recorded;

[0018] It is determined whether the number of load cycle related judgments reaches a preset upper limit of iteration number;

[0019] If it is determined that the upper limit of the preset iteration number is not reached, the step of determining the first inflection point data set selected by the pointer is returned until the number of load cycle related determinations reaches the upper limit of the preset iteration number.

[0020] Optionally, load data is received and the load data is discretized to obtain processed load data.

[0021] Optionally, a preset number of processed load data is selected, a numerical monotonic relationship between the preset number of processed load data is determined, and if the numerical relationship of the preset number of processed load data does not have monotonicity (monotonically increasing or decreasing, but there is an inflection point), it is determined that there is an inflection point data in the preset number of processed load data.

[0022] In addition, to achieve the above object, the present application also provides an online load influence calculation device, which comprises:

[0023] An inflection point determination module is configured to receive and process load data, and determine the inflection point of the processed load data.

[0024] An inflection point management module is configured to extract the inflection point data and store the inflection point data in a data buffer.

[0025] A pointer management module is configured to determine the first inflection point data set selected by the pointer.

[0026] A load calculation module is configured to calculate the load influence of the inflection point data in the first inflection point data set by using a preset load influence calculation algorithm.

[0027] In addition, to achieve the above object, the present application also provides an online load influence calculation device, which comprises a memory, a processor, and an online load influence calculation program stored in the memory and executable on the processor, the online load influence calculation program being configured to implement the online load influence calculation steps as described above.

[0028] In addition, to achieve the above object, the present application also provides a computer readable storage medium having an online load influence calculation program stored thereon, the online load influence calculation program being executable by a processor to implement the steps of the online load influence calculation method as described above.

[0029] In addition, to achieve the above object, the present application also provides a computer program product comprising an online load influence calculation program, the online load influence calculation program being executable by a processor to implement the steps of the online load influence calculation method as described above.

[0030] The application provides an online load influence calculation method and a related device thereof. The online load influence calculation method is characterized in that a pointer is preset in a data buffer of an online load influence calculation device, a preset number of inflection point data selected by the pointer is extracted, and the load influence of the extracted inflection point data is calculated by using a preset load influence calculation algorithm. The method makes the calculation amount of the online load influence calculation device controllable during each load influence calculation, thereby solving the technical problem that the current technical solution has excessively high requirements for computing power and storage resources, and finally making the operation load rate of the system unstable and unpredictable, which is not suitable for lightweight embedded system applications and is difficult to realize real-time online operation of load influence calculation. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 The flowchart of the first embodiment of the online load influence calculation method of the application is shown in the figure.

[0032] Figure 2 The first case diagram of load cycle extraction related to the embodiment of the application is shown in the figure.

[0033] Figure 3 The second case diagram of load cycle extraction related to the embodiment of the application is shown in the figure.

[0034] Figure 4 The flowchart of the second embodiment of the online load influence calculation method of the application is shown in the figure.

[0035] Figure 5 The operation flowchart of the four-point rain flow counting method related to the embodiment of the application is shown in the figure.

[0036] Figure 6 The preset pointer diagram related to the embodiment of the application is shown in the figure.

[0037] Figure 7 The online load influence calculation device diagram related to the embodiment of the application is shown in the figure.

[0038] Figure 8 The device structure diagram of the hardware running environment related to the embodiment of the application is shown in the figure.

[0039] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0040] It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application.

[0041] REFERENCE Figure 1 , Figure 1A flowchart of a first embodiment of a method for implementing rainflow counting in real time for the present application.

[0042] The present embodiment proposes a method for online calculation of load influence, which is applied to an online load influence calculation device, the device comprising a data buffer comprising a pointer, the method comprising the following steps:

[0043] Step S10: If an instruction for calculating load influence is received, load data is received and processed, and inflection point judgment is performed on the processed load data.

[0044] Step S20: If it is judged that there is inflection point data in the processed load data, the inflection point data is extracted and stored in the data buffer.

[0045] Step S30: Determine the first inflection point data group selected by the pointer, wherein the first inflection point data group is composed of a preset number of inflection point data.

[0046] Step S40: Extract the first inflection point data group, and perform load influence calculation on the inflection point data in the first inflection point data group through a preset load influence calculation algorithm.

[0047] It should be noted that the online load influence calculation device is a device that can extract load cycles based on the rainflow counting method and calculate the influence of the extracted load cycles, which can be a stress analysis device. The load data refers to special points on the load-settlement (P-S) curve obtained through a plate load test (PLT), which can be stress data or a floating-point temperature. The data buffer is a computer hardware or software component used to store recently used data for fast access, which can be a hard disk cache (Disk Cache or Buffer Cache). The pointer is a special variable in computer science that stores the memory address of another variable. Through this memory address, the program can directly access and modify the data pointed to by the address. It can be understood that the real-time rainflow counting method calculation device caches the inflection point stress data to the hard disk cache and uses the pointer preset in the hard disk cache to quickly read and extract the stress data pointed to by the pointer.

[0048] It should be noted that in the data buffer, a plurality of data units are preset to store the inflection point data extracted from the load data (in actual application, there are multiple data units in the data buffer, for convenience of description, the present embodiment takes 9 data units in the data buffer as an example). A data unit stores an inflection point data put into the online load influence calculation device. As an example, in the present embodiment, the data units in the data buffer are arranged horizontally as a data unit row.

[0049] It should be noted that in the present embodiment, the preset load influence calculation algorithm is preferably a four-point rainflow counting method.

[0050] The following will be described in detail:

[0051] Step S10: If the instruction to calculate the load influence is received, the load data is received and processed, and the inflection point of the processed load data is judged.

[0052] It should be noted that in the present embodiment, if the device for online calculation of load influence receives an instruction to calculate the load influence, it starts to receive load data and performs discretization processing on the received load data, for example, discretizing the floating-point type temperature to 0-255 8-bit integer data type to reduce a large amount of storage space; and the discretized load data is subjected to inflection point judgment, preferably, the current real-time load data (which can be understood as the load data subjected to discretization processing most recently) and the load data of the previous two beats (which can be understood as the load data subjected to discretization processing twice previously) are subjected to inflection point judgment, the method being: if the numerical relationship of the real-time load data and the load data of the previous two beats changes with time and does not show a monotonous increasing or decreasing trend, it is determined that there is inflection point data, for example, the device for online calculation of load influence receives and discretizes load data A1, load data A2 and load data A3 in turn (wherein the load data A3 is the current real-time load data, and the load data A1 and the load data A2 are the load data of the previous two beats), if the numerical relationship of the load data A1, the load data A2 and the load data A3 is not monotonous increasing or monotonous decreasing, it is determined that there is inflection point data in the three load data.

[0053] Specifically, the stress analysis device receives an instruction to calculate the stress influence, starts to receive stress data, and performs discretization processing on each stress data in real time, and after receiving and discretizing three stress data, for example, stress data B1, stress data B2 and stress data B3, the numerical relationship of the discretized stress data B1, stress data B2 and stress data B3 is analyzed, if the numerical relationship between the stress data B1, stress data B2 and stress data B3 is not monotonous increasing or monotonous decreasing, it is determined that there is inflection point data in the stress data B1, stress data B2 and stress data B3, otherwise, it is determined that there is no inflection point data in the stress data B1, stress data B2 and stress data B3.

[0054] Step S20: If it is determined that there is inflection point data in the processed load data, the inflection point data is extracted and stored in the data buffer.

[0055] In the embodiment, if the device for online calculation of load influence judges that the load data A1, the load data A2 and the load data A3 have inflection point data, the inflection point data is extracted (for example, the load data A2), and the inflection point data is stored in a data unit in the data buffer.

[0056] It should be noted that in the embodiment, the inflection point data stored in the data buffer is arranged in sequence from front to back based on the storage order. Specifically, when the device for online calculation of load influence stores the inflection point data in the data buffer for the first time, the inflection point data is stored in the first data unit from front to back in the data buffer. Then, each time the inflection point data is stored, the inflection point data to be stored is stored in the adjacent data unit after the last inflection point data in the data buffer. In the embodiment, the inflection point data stored in the data buffer is preferably arranged in sequence from front to back based on the storage order. The specific storage method is described in detail with reference to Figure 6 As shown in Figure 6 , when the device for online calculation of load influence stores the inflection point data in the data buffer for the first time, the inflection point data X1 is stored in the first data unit from front to back in the data buffer. When the inflection point data X2 is stored, the inflection point data X2 is stored in the second data unit adjacent to the data unit where the inflection point data X1 is stored. For the subsequent inflection point data to be stored, the same storage method is adopted, which is not described here.

[0057] It can be understood that the preferred inflection point data storage method proposed in the embodiment improves the storage space utilization rate of the data buffer, and facilitates the device for online calculation of load influence to select the inflection point data in the subsequent steps.

[0058] Specifically, the stress analysis device extracts the inflection point data from the stress data judged to have inflection points. The extracted inflection point data is in sequence from the inflection point data X1 to the inflection point data X7. Based on the extraction order, the inflection point data X1 is stored in the first data unit, i.e., the data unit 1, in the hard disk buffer, the inflection point data X2 is stored in the data unit 2 adjacent to the data unit 1, and so on, and the inflection point data X7 is stored in the data unit 7.

[0059] Step S30: determining the first inflection point data group selected by the pointer, wherein the first inflection point data group is composed of a preset number of inflection point data.

[0060] It should be noted that in the embodiment, the device for online calculation of load influence adjusts the data pointer to a preset data unit to retrieve the inflection point data stored in the preset data unit. For example, in Figure 6 , the real-time rainflow counting method operation device sets the data pointer in the sixth data unit from front to back in the data buffer to retrieve the inflection point data X6.

[0061] It can be understood that, based on the four-point rainflow counting method selected in the embodiment, four inflection point data should be selected when extracting the load cycle each time, and the selected inflection point data is operated.

[0062] In the embodiment, the device for calculating the load effect online calls the inflection point data X6 stored in the sixth data unit pointed by the pointer, and preferably selects the inflection point data X5, the inflection point data X4 and the inflection point data X3, that is, when selecting the inflection point data, the inflection point data pointed by the pointer is selected, and three inflection point data before the inflection point data are selected, and it is ensured that the four inflection point data finally selected are adjacent to each other.

[0063] Specifically, the stress analysis device selects the corresponding inflection point data X6 based on the position pointed by the data pointer, and selects the inflection point data X5, the inflection point data X4 and the inflection point data X3.

[0064] Step S40: Extracting the first inflection point data group, and calculating the load effect of the inflection point data in the first inflection point data group by a preset load effect calculation algorithm.

[0065] It should be noted that, in the embodiment, the four-point rainflow counting method is selected as the algorithm basis for extracting the load cycle, and the specific operation mode is as follows:

[0066] Four data adjacent to each other are selected, and if the amplitude formed by the middle two data points is contained by the amplitude formed by the first and fourth points, the relatively smaller load cycle formed by the middle two points is counted as once. Specifically, referring to Figure 2 and Figure 3 X1, X2, X3 and X4 satisfy (X1-X2)(X2-X3)<0 and (X2-X3)(X3-X4)<0, and |X4-X1|≥|X2-X3|, then |X2-X3| is extracted as a load cycle.

[0067] In the embodiment, the device for calculating the load effect online determines whether the selected inflection point data X3 to X6 satisfy (X3-X4)(X4-X5)<0 and (X4-X5)(X5-X6)<0 and |X6-X3|≥|X4-X5| by the four-point rainflow counting method, that is, whether the selected inflection point data can form a load cycle. If yes, the inflection point data X4 and the inflection point data X5 are extracted (that is, two inflection point data that can form a load cycle), the load effect of the inflection point data X4 and the inflection point data X5 is calculated, and the inflection point data X4 and the inflection point data X5 are removed from the data buffer to save the buffer space.

[0068] Specifically, the stress analysis device judges and extracts the load cycle of the first inflection point data group through the rainflow counting method, and determines whether the selected inflection point data X3, the inflection point data X4, the inflection point data X5 and the inflection point data X6 satisfy (X3-X4)(X4-X5)<0 and (X4-X5)(X5-X6)<0 and |X6-X3|≥|X4-X5|, and extracts the load cycle based on the judgment result. If the conditions are met, the inflection point data X4 and the inflection point data X5 (i.e., two inflection point data that can constitute a load cycle) are extracted, the stress influence of the inflection point data X4 and the inflection point data X5 is calculated, and the inflection point data X4 and the inflection point data X5 are removed from the data buffer to save the buffer space.

[0069] The preset load influence calculation algorithm includes the rainflow counting method. The step of calculating the load influence of the inflection point data in the first inflection point data group through the preset load influence calculation algorithm includes:

[0070] The rainflow counting method is used to determine whether the inflection point data in the first inflection point data group can constitute a load cycle.

[0071] If the result is that the inflection point data cannot constitute a load cycle, the pointing position of the pointer is adjusted, the second inflection point data group selected by the pointer is determined and extracted, and the rainflow counting method is used to determine whether the inflection point data in the second inflection point data group can constitute a load cycle.

[0072] Specifically, in this embodiment, the rainflow counting method such as the three-point rainflow counting method or the four-point rainflow counting method is used to determine whether the inflection point data in the first inflection point data group can constitute a load cycle. If the result is that the inflection point data cannot constitute a load cycle, the pointing position of the pointer is adjusted, the second inflection point data group (which can be any other inflection point data group other than the first inflection point data group after the result is that the inflection point data cannot constitute a load cycle) selected by the pointer is determined and extracted, and the rainflow counting method is used to determine whether the inflection point data in the second inflection point data group can constitute a load cycle.

[0073] After the step of determining whether the inflection point data in the first inflection point data group can constitute a load cycle through the rainflow counting method, the method further includes:

[0074] If the result is that the inflection point data can constitute a load cycle, a third inflection point data group that constitutes a load cycle is extracted from the first inflection point data group.

[0075] The load influence of the inflection point data in the third inflection point data group is calculated.

[0076] The third inflection point data group can be any other inflection point data group other than the first inflection point data group after the result is that the inflection point data can constitute a load cycle.

[0077] Specifically, in the present embodiment, the step of determining whether the inflection point data in the second inflection point data set can constitute a load cycle by the rainflow counting method comprises:

[0078] After the determination of the inflection point data in the first inflection point data set is completed, if it is determined that the number of load cycle related determinations does not reach the preset upper limit of iteration number, then the inflection point data in the second inflection point data set is determined whether it can constitute a load cycle by the rainflow counting method.

[0079] In the present embodiment, after the determination of the inflection point data in the first inflection point data set is completed, if it is determined that the number of load cycle related determinations does not reach the preset upper limit of iteration number, then the inflection point data in the second inflection point data set is determined whether it can constitute a load cycle by the rainflow counting method. If the preset upper limit of iteration number has been reached, then the determination of whether the inflection point data in the second inflection point data set can constitute a load cycle by the rainflow counting method is not performed (specifically, how to determine whether the inflection point data can constitute a load cycle by the rainflow counting method belongs to the current technology, and mainly quantifies the fatigue damage of materials or structures under alternating loads).

[0080] The present embodiment proposes a method for online calculation of load influence and related equipment thereof. The method for online calculation of load influence presets a pointer in the data buffer of the online load influence calculation device, extracts a preset number of inflection point data selected by the pointer, and calculates the load influence of the extracted inflection point data by a preset load influence calculation algorithm. The method makes the operation amount of the online load influence calculation device controllable during each operation of the load influence, thereby solving the technical problem that the current technical solution has excessively high demand for computing power and storage resources, and finally making the operation load rate of the system unstable and unpredictable, which is not suitable for lightweight embedded system applications and is difficult to realize real-time online operation of load influence calculation.

[0081] Reference Figure 4 , Figure 4 The flowchart of the second embodiment of the method for real-time implementation of the rainflow counting method.

[0082] The method of the present embodiment comprises the following steps:

[0083] Step S50: record the number of times of determining whether the inflection point data in the first inflection point data set can constitute a load cycle by the rainflow counting method;

[0084] Step S60: determine whether the number of load cycle related determinations reaches the preset upper limit of iteration number;

[0085] Step S70: If it is determined that the preset iteration upper limit is not reached, return to the step of determining the first inflection point data group selected by the pointer until the number of load cycle related determinations reaches the preset iteration upper limit.

[0086] It should be noted that the device for online calculation of load effect in this embodiment limits the number of iterations of load cycle extraction and load effect calculation by the four-point rainflow counting method. Preferably, the number of times of load cycle extraction in each iteration is 2.

[0087] The steps will be described in detail below.

[0088] Step S50: Record the number of times that the inflection point data in the first inflection point data group is determined to be able to form a load cycle by the rainflow counting method.

[0089] In this embodiment, the device for online calculation of load effect records the number of determinations once after each determination of whether the selected four adjacent inflection point data can form a load cycle by the rainflow counting method (such as the four-point rainflow counting method) (regardless of the determination result), that is, the number of current load cycle related determinations is increased by 1.

[0090] Specifically, the stress analysis device increases the number of load cycle related determinations by 1 after extracting the inflection point data X3, the inflection point data X4, the inflection point data X5, and the inflection point data X6 and determining the selected inflection point data by the (four-point) rainflow counting method.

[0091] Step S60: Determine whether the number of load cycle related determinations reaches the preset iteration upper limit.

[0092] In this embodiment, the device for online calculation of load effect determines the total number of load cycle related determinations performed by itself after increasing the number of current load cycle related determinations by 1, and compares it with the preset upper limit of the number of load cycle related determinations (that is, compares it with the iteration upper limit of the determination of whether the selected inflection point data can form a load cycle by the (four-point) rainflow counting method and the calculation of load effect).

[0093] Specifically, the stress analysis device determines the total number of current load cycle related determinations after increasing the number of load cycle related determinations by 1, and determines whether the total number of current load cycle related determinations reaches 2.

[0094] Step S70: If it is determined that the preset iteration upper limit is not reached, return to the step of determining the first inflection point data group selected by the pointer until the number of load cycle related determinations reaches the preset iteration upper limit.

[0095] In the embodiment, if the device for online calculation of load effect determines that the total number of times of the current load cycle related judgment does not reach the preset upper limit of the number of times of the load cycle related judgment, the four-point rainflow counting method is used again to judge, extract and calculate the load effect of the selected inflection point data; if the device for online calculation of load effect determines that the total number of times of the current load cycle related judgment reaches the preset upper limit of the number of times of the load cycle related judgment, the calculation is ended.

[0096] Specifically, if the stress analysis device determines that the total number of times of the current load cycle related judgment is < 2 times, the four-point rainflow counting method is used again to judge, extract and calculate the stress effect of the selected inflection point data; if the stress analysis device determines that the total number of times of the current load cycle related judgment is ≥ 2 times, the calculation is ended.

[0097] The embodiment controls the total number of times of the load cycle related judgment for each iteration, and the upper limit is preferably 2 times. The stability problem of the calculation time for each iteration is avoided, and the space resource consumption is predictable. That is, the operation time is 2 times of the cycle time, and the space consumption can be calculated according to the theoretical maximum value of the discretization method, so that the calculation of the load effect can be realized in real time.

[0098] The following is a third embodiment of the method for realizing the rainflow counting method in real time according to the present application.

[0099] The embodiment details the overall operation process of the online load effect calculation system, which can be referred to Figure 5 , and the steps of the third embodiment will be described in detail below.

[0100] As shown in Figure 5 The operation process of the four-point rainflow counting method involved in the embodiment includes two necessary steps of inflection point judgment and load cycle extraction. After the load data is obtained in real time, the two steps of inflection point judgment and load cycle extraction are performed. In the inflection point judgment step, whether an inflection point is formed is judged according to the real-time data of the current shot and the real-time data of the last two shots. If an inflection point is formed, the real-time data is recorded at the end of the buffer.

[0101] In the load cycle extraction step, load cycle extraction judgment is performed at the data position indicated by the current pointer on the buffer. If the load cycle extraction is successful, the extracted load cycle data is removed on the buffer, and the data pointed to by the current pointer remains unchanged; if the load cycle extraction fails, the current pointer points to the next data position on the buffer (if the data exists).

[0102] Preferably, the effect of the extracted load cycle data is processed in real time, such as calculating the damage caused by the extracted load cycle.

[0103] Preferably, the method for determining whether a turning point is formed by the current real-time data and the data of the previous two shots is: if the real-time data and the data of the previous two shots are not monotonically increasing or decreasing over time, it is determined that a turning point is formed.

[0104] Preferably, the real-time data is discretized before the turning point is determined. For example, discretizing the floating-point type temperature to 8-bit integer data type of 0-255 can reduce a large amount of storage space.

[0105] Preferably, the load cycle extraction uses a four-point rainflow counting method, that is, if the amplitude formed by the middle two data points is contained by the amplitude formed by the first and fourth points, the relatively small load cycle formed by the middle two points is counted as one. For example, Figure 2 、 Figure 3 In the above, X1, X2, X3, X4 satisfy (X1-X2)(X2-X3) < 0 and (X2-X3)(X3-X4) < 0, and |X4-X1| ≥ |X2-X3|, then |X2-X3| is extracted as a load cycle.

[0106] Preferably, the load cycle extraction is performed N times in each iteration, and N ≥ 2. The advantage of this is that it can reduce the length of the saved data in the buffer and avoid storage space overflow or waste of storage space.

[0107] Preferably, the load cycle extraction is performed twice in each iteration. The advantage of this is that when the buffer data is discretized and the load cycle extraction is performed twice in each iteration, the data saved in the buffer has a theoretical maximum value, which can theoretically avoid storage space overflow.

[0108] In addition, the embodiment of the present application also proposes an online load influence calculation device, referring to Figure 7 , Figure 7 The online load influence calculation device involved in the embodiment of the present application is a schematic diagram of the online load influence calculation device, which comprises:

[0109] A turning point determination module 10 is configured to receive and process load data, and determine a turning point for the processed load data.

[0110] A turning point management module 20 is configured to extract the turning point data and store the turning point data in a data buffer.

[0111] A pointer management module 30 is configured to determine a first turning point data group selected by the pointer.

[0112] A load calculation module 40 is configured to calculate the load influence of the turning point data in the first turning point data group by using a preset load influence calculation algorithm.

[0113] The embodiment scheme provides an online load influence calculation method and a related device thereof. The online load influence calculation method is characterized in that a pointer is preset in a data buffer of an online load influence calculation device, a preset number of inflection point data selected by the pointer is extracted, and the load influence of the extracted inflection point data is calculated by using a preset load influence calculation algorithm. The method makes the calculation amount of the online load influence calculation device controllable during each calculation of the load influence, thereby solving the technical problem that the current technical scheme has excessively high requirements for computing power and storage resources, and finally making the operation load rate of the system unstable and unpredictable, which is not suitable for lightweight embedded system applications and is difficult to realize real-time online operation of load influence calculation.

[0114] It should be noted that each module in the device described above can be used to implement each step in the method described above, and the corresponding technical effects are achieved. The embodiment will not be described here.

[0115] Reference Figure 8 , Figure 8 The device structure diagram of the hardware running environment related to the embodiment scheme of the present application is shown in the figure.

[0116] As Figure 8 shown, the device can include a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 can include a display screen (Display) and an input unit such as a keyboard (Keyboard). The optional user interface 1003 can also include a standard wired interface, a wireless interface. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface). The memory 1005 can be a high-speed RAM memory, or a stable memory (non-volatile memory) such as a magnetic disk memory. The memory 1005 can also be an independent storage device from the aforementioned processor 1001.

[0117] Those skilled in the art can understand that Figure 8 the structure shown in the figure does not constitute a limitation on the device, and can include more or fewer components than the figure, or combine certain components, or different component arrangements.

[0118] As Figure 8 shown, the memory 1005, as a computer storage medium, can include an operating system, a network communication module, a user interface module, and a program for implementing the rain flow counting method in real time.

[0119] In Figure 8In the illustrated device, the network interface 1004 is mainly used for data communication with external network; the user interface 1003 is mainly used for receiving the input instruction of the user; the device calls the program for online calculation of load influence stored in the memory 1005 through the processor 1001, and performs the following operations:

[0120] If the instruction for calculating the load influence is received, the load data is received and processed, and the inflection point judgment is performed on the processed load data;

[0121] If it is judged that there is inflection point data in the processed load data, the inflection point data is extracted and stored in the data buffer;

[0122] The first inflection point data group selected by the pointer is determined, wherein the first inflection point data group is composed of a preset number of inflection point data;

[0123] The first inflection point data group is extracted, and the inflection point data in the first inflection point data group is calculated by a preset load influence calculation algorithm.

[0124] Further, the processor 1001 can call the program for online calculation of load influence stored in the memory 1005, and further perform the following operations:

[0125] It is judged by the rainflow counting method whether the inflection point data in the first inflection point data group can constitute a load cycle;

[0126] If the judgment result is no, the pointing position of the pointer is adjusted, the second inflection point data group selected by the pointer is determined and extracted, and it is judged by the four-point rainflow counting method whether the inflection point data in the second inflection point data group can constitute a load cycle.

[0127] Further, if the judgment result is yes, the third inflection point data group constituting the load cycle is extracted from the first inflection point data group;

[0128] The load influence of the inflection point data in the third inflection point data group is calculated.

[0129] Further, the processor 1001 can call the program for online calculation of load influence stored in the memory 1005, and further perform the following operations:

[0130] The number of times that the inflection point data in the first inflection point data group is judged by the rainflow counting method whether it can constitute a load cycle is recorded;

[0131] It is determined whether the number of times of the load cycle related judgment reaches a preset upper limit of iteration number;

[0132] If it is determined that the preset upper limit of the iteration number is not reached, the step of determining the first inflection point data set selected by the pointer is returned until the number of load cycle related determinations reaches the preset upper limit of the iteration number.

[0133] Further, the processor 1001 can invoke the program for online calculation of load impact stored in the memory 1005, and further perform the following operations:

[0134] Receiving load data and discretizing the load data to obtain processed load data.

[0135] Further, the processor 1001 can invoke the program for online calculation of load impact stored in the memory 1005, and further perform the following operations:

[0136] Selecting a preset number of processed load data, determining the numerical monotonicity between the preset number of processed load data, and if the numerical relationship of the preset number of processed load data does not have monotonicity, determining that there is an inflection point data in the preset number of processed load data.

[0137] The embodiment provides a method for online calculation of load impact and related equipment thereof. The method for online calculation of load impact is characterized in that a pointer is preset in a data buffer of an online load impact calculation device, and a preset number of inflection point data selected by the pointer is extracted. The load impact of the extracted inflection point data is calculated by using a preset load impact calculation algorithm. The method makes the calculation amount of the online load impact calculation device controllable during each calculation of load impact, thereby solving the technical problem that the current technical solution has excessively high requirements for computing power and storage resources, and finally making the operation load rate of the system unstable and unpredictable, which is not suitable for lightweight embedded system applications and is difficult to realize real-time online operation of load impact calculation.

[0138] In addition, the embodiment of the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a program for online calculation of load impact. When the program for online calculation of load impact is executed by a processor, the following operations are realized:

[0139] If a calculation load impact instruction is received, load data is received and processed, and inflection point determination is performed on the processed load data.

[0140] If it is determined that the processed load data contains inflection point data, the inflection point data is extracted and stored in the data buffer.

[0141] Determining a first inflection point data set selected by the pointer, wherein the first inflection point data set is composed of a preset number of inflection point data.

[0142] The first inflection point data set is extracted, and the inflection point data in the first inflection point data set is subjected to load influence calculation through a preset load influence calculation algorithm.

[0143] The embodiment provides an online load influence calculation method and a related device, the online load influence calculation method is characterized in that a pointer is preset in a data buffer of an online load influence calculation device, a preset number of inflection point data selected by the pointer is extracted, and load influence of the extracted inflection point data is calculated through a preset load influence calculation algorithm.

[0144] It should be noted that the computer readable storage medium described above can also implement each step in the above method when executed by the processor, and the corresponding technical effects are achieved, and the embodiment will not be described here.

[0145] In addition, the embodiment of the application further provides a computer program product, comprising an online load influence calculation program, the online load influence calculation program is executed by the processor to realize the steps of the online load influence calculation method described above.

[0146] The computer program product embodiment of the application is basically the same as the above-mentioned online load influence calculation method, and will not be described here.

[0147] It should be noted that in this document, the terms "comprise", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or system. Without more limitations, the element defined by the statement "comprises a" does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0148] The above-mentioned serial numbers of the embodiments of the application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0149] Those skilled in the art can clearly understand the above-mentioned embodiment method can be realized by means of software and the necessary general hardware platform, of course, can also be through hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application essentially or say the part of the prior art contribution can be embodied in the form of software products, the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disc, optical disc) as described above, including a number of instructions to make a terminal device (may be a mobile phone, computer, server, air conditioner, or network equipment, etc.) executes the method described in various embodiments of the present application.

[0150] The above is only the preferred embodiment of the present application, not therefore limit the patent scope of the present application, all the equivalent structure or equivalent process transformation, or directly or indirectly applied in other related technical fields, using the contents of the present application specification and drawings, are also included in the patent protection scope of the present application.

Claims

1. A method for online calculation of load effects, characterized in that, The method is applied to an online load impact calculation device, the device including a data buffer, the data buffer including pointers, and the method comprising the following steps: If an instruction to calculate the load impact is received, the load data is received and processed, and the inflection point is determined on the processed load data. If it is determined that there is inflection point data in the processed load data, then the inflection point data is extracted and stored in the data cache. Determine the first inflection point data group selected by the pointer, wherein the first inflection point data group consists of a preset number of inflection point data; Extract the first inflection point data group, and perform load impact calculation on the inflection point data in the first inflection point data group using a preset load impact calculation algorithm.

2. The method for online calculation of load influence as described in claim 1, characterized in that, The preset load impact calculation algorithm includes the rainflow counting method. The step of calculating the load impact on the inflection point data in the first inflection point data group using the preset load impact calculation algorithm includes: The rainflow counting method is used to determine whether the inflection point data in the first inflection point data group can form a load cycle. If the judgment result is no, then the pointer's pointing position is adjusted, the second inflection point data group selected by the pointer is determined and extracted, and the inflection point data in the second inflection point data group can be determined by the rainflow counting method to determine whether the inflection point data in the second inflection point data group can form a load cycle.

3. The method for online calculation of load influence as described in claim 2, characterized in that, The step of determining whether the inflection point data in the second inflection point data group can constitute a load cycle using the rainflow counting method includes: After determining the inflection point data in the first inflection point data group, if it is determined that the number of load cycle related determinations has not reached the preset upper limit of iterations, then the rainflow counting method is used to determine whether the inflection point data in the second inflection point data group can constitute a load cycle.

4. The method for online calculation of load influence as described in claim 2, characterized in that, After the step of determining whether the inflection point data in the first inflection point data group can constitute a load cycle using the rainflow counting method, the method further includes: If the judgment result is yes, then extract the third inflection point data group that constitutes the load cycle from the first inflection point data group; Calculate the load impact of the inflection point data in the third inflection point data group.

5. The method for online calculation of load influence as described in claim 1, characterized in that, After the step of calculating the load impact on the inflection point data in the first inflection point data group, the following steps are included: The number of times the inflection point data in the first inflection point data group can be determined by the rainflow counting method is recorded. Determine whether the number of load cycle-related judgments has reached the preset upper limit of iterations; If it is determined that the preset iteration count limit has not been reached, the process returns to the step of determining the first inflection point data group selected by the pointer, until the number of load cyclic correlation judgments reaches the preset iteration count limit.

6. The method for online calculation of load influence as described in claim 1, characterized in that, The step of receiving and processing payload data includes: The load data is received and discretized to obtain the processed load data.

7. The method for online calculation of load influence as described in claim 6, characterized in that, The step of determining the inflection point of the processed load data includes: Select a preset number of processed load data, determine the numerical monotonic relationship between the preset number of processed load data, and if the numerical relationship of the preset number of processed load data does not have monotonicity, then it is determined that there is inflection point data in the preset number of processed load data.

8. An apparatus for online calculation of load effects, characterized in that, The apparatus for online calculation of load impact includes: The inflection point judgment module is used to receive and process load data, and to judge the inflection points of the processed load data. The inflection point management module is used to extract the inflection point data and store the inflection point data into the data cache. The pointer management module is used to determine the first inflection point data group selected by the pointer; The load calculation module is used to perform load influence calculation on the inflection point data in the first inflection point data group using a preset load influence calculation algorithm.

9. A device for online calculation of load effects, characterized in that, The device for online calculation of load impact includes: a memory, a processor, and a program for online calculation of load impact stored in the memory and executable on the processor, the program for online calculation of load impact being configured to implement the steps of the method for online calculation of load impact as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a program for implementing an online method for calculating the load effect, and the program for implementing the online method for calculating the load effect is executed by a processor to implement the steps of the online method for calculating the load effect as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes a program for online calculation of load effects, which, when executed by a processor, implements the steps of the method for online calculation of load effects as described in any one of claims 1 to 7.

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