A cabinet control simulation method, device and system

Abstract

This application relates to the technical field of cabinet detection, and specifically relates to a cabinet control simulation method, device, and system. The method includes: obtaining a cabinet temperature sequence to obtain a local temperature change sequence; based on the difference distribution between adjacent elements in the local temperature change sequence, obtaining the non-central fluctuation degree of each element in the cabinet temperature sequence; according to the difference distribution between each element in the cabinet temperature sequence and all its adjacent elements, obtaining the data prominence of each element to obtain the noise prominence of each element; clustering the elements in the cabinet temperature sequence to obtain a suspected electromagnetic interference set and an electromagnetic interference judgment set; obtaining the anomaly determination degree of each element in the suspected electromagnetic interference set to determine the anomaly determination degree sequence; filtering the cabinet temperature sequence to obtain the cabinet temperature simulation test results under each simulation temperature. This application aims to accurately obtain the operating state of the cabinet and enhance the reliability of the cabinet control simulation results.

Description

Technical Field

[0001] This application relates to the technical field of cabinet detection, and specifically relates to a cabinet control simulation method, device, and system. Background Art

[0002] In the simulation test of cabinet control, the internal temperature of the cabinet is one of the important conditions for judging the test accuracy. However, during the long-term use of the cabinet, since the cabinet equipment is powered on, electricity will generate heat. When the heat accumulates, it will cause local overheating and may even pose a fire risk. Therefore, usually, the temperature of the cabinet is monitored to determine whether there is an abnormality in the temperature during the cabinet control simulation.

[0003] For the temperature simulation test of cabinet control, it is usually determined whether the cabinet is operating normally by detecting whether the temperature in the cabinet exceeds the threshold. However, since the cabinet is in an electrical load for a long time, the presence of the electromagnetic field will cause a large amount of noise interference to the data collected by the temperature sensor, thereby reducing the accuracy of directly judging by the threshold. The traditional method is generally to filter the cabinet temperature data and then make a judgment based on the filtered data. However, the SG filter (Savitzky-Golay) with a fixed smoothing order will cause a problem of accuracy loss in the filtered data due to the uncertainty of the noise when processing the cabinet temperature data. Summary of the Invention

[0004] In view of the above, it is necessary to provide a cabinet control simulation method, device, and system to solve the above problems.

[0005] According to one aspect of the present application, a cabinet control simulation method is provided. The method includes:

[0006] Obtain the highest temperature value of each moment of the cabinet at each simulation temperature, and form a cabinet temperature sequence;

[0007] Preset a window for each element in the cabinet temperature sequence to obtain a local temperature change sequence; based on the difference distribution between adjacent elements in the local temperature change sequence, obtain the non-central fluctuation degree of each element in the cabinet temperature sequence;

[0008] According to the difference distribution between each element in the cabinet temperature sequence and all its adjacent elements, obtain the data prominence of each element, and combine the non-central fluctuation degree to obtain the noise prominence of each element;

[0009] Cluster the elements in the cabinet temperature sequence based on the noise prominence to obtain a suspected electromagnetic interference set and an electromagnetic interference judgment set; based on the serial number distribution of the elements in the suspected electromagnetic interference set and the electromagnetic interference judgment set in the cabinet temperature sequence, obtain the abnormality determination degree of each element in the suspected electromagnetic interference set, and determine the abnormality determination degree sequence;

[0010] Based on the distribution of each element in the anomaly determination degree sequence, filter the cabinet temperature sequence to obtain the cabinet temperature simulation test results at each simulation temperature.

[0011] Among them, obtaining the non-central fluctuation degree of each element in the cabinet temperature sequence specifically includes:

[0012] For each element in the cabinet temperature sequence, take the average value of the differences between all adjacent elements in the local change sequence as the non-central fluctuation degree of each element in the cabinet temperature sequence.

[0013] Among them, obtaining the data prominence of each element includes:

[0014] For each element in the cabinet temperature sequence, take the result of positively fusing the difference between each element and all its adjacent elements as the data prominence of each element.

[0015] Among them, the noise prominence of each element is specifically the absolute value of the difference between the data prominence and the non-central fluctuation degree.

[0016] Among them, obtaining the suspected electromagnetic interference set and the electromagnetic interference judgment set specifically includes:

[0017] Arrange the clustering clusters in descending order of the average noise prominence, take the clustering cluster ranked first as the suspected electromagnetic interference set, and take the clustering cluster ranked second as the suspected electromagnetic interference judgment set.

[0018] Among them, the specific process of obtaining the anomaly determination degree of each element in the suspected electromagnetic interference set is:

[0019] Based on the data corresponding to the serial number value of each element in the suspected electromagnetic interference set in the cabinet temperature sequence as the center, within the preset radius length, record the number of elements belonging to the electromagnetic interference set and the number of elements belonging to the electromagnetic interference judgment set as S and P respectively, and perform weighted summation on S and P to obtain the anomaly determination degree of each element in the suspected electromagnetic interference set.

[0020] Among them, determining the anomaly determination degree sequence specifically includes:

[0021] Replace the element at the serial number corresponding to the element in the suspected electromagnetic interference set in the cabinet temperature sequence with the anomaly determination degree of this serial number; replace other elements in the cabinet temperature sequence with the minimum value of the anomaly determination degrees of all elements in the suspected electromagnetic interference set.

[0022] Among them, the process of filtering the cabinet temperature sequence based on the distribution of each element in the anomaly determination degree sequence to obtain the cabinet temperature simulation test results at each simulation temperature includes:

[0023] Denote the smoothing order of each element in the cabinet temperature sequence as , and the specific formula form is: ; where, represents the smoothing order of the y-th element in the cabinet temperature sequence; represents the value of the y-th element in the anomaly determination degree sequence; norm() represents the normalization function; represents the ceiling function;

[0024] Adopt a filtering algorithm to filter the cabinet temperature sequence based on the smoothing order. If there is data greater than the preset value after filtering, it is determined that the cabinet is abnormal in the simulation temperature test; otherwise, it is determined that the cabinet is not abnormal in the simulation temperature test.

[0025] According to another aspect of the present application, there is provided a cabinet control simulation device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0026] According to still another aspect of the present application, there is provided a cabinet control simulation system. A computer program is stored in the system, and when the computer program is executed by a processor, the simulation method described in any one of the above is implemented.

[0027] The present application has at least the following beneficial effects:

[0028] 1. By calculating the non-central fluctuation degree and noise saliency of temperature data, the present application accurately identifies data points affected by electromagnetic interference. Compared with traditional methods, it can effectively reduce the interference of noise on temperature data, making the collected cabinet temperature data more accurate. It provides a reliable basis for subsequent simulation analysis and ensures the quality of the initial data for simulation tests.

[0029] 2. The present application adopts a dynamically adjusted filtering method, applying different smoothing strategies to data with different noise levels. This adaptive filtering method can better retain data details while removing noise interference. During the simulation process, this method can more accurately obtain the operating state of the temperature in the cabinet, thereby enhancing the reliability of the cabinet control simulation results and providing strong support for the optimal design of the cabinet.

[0030] 3. Through precise simulation tests, this application can accurately determine whether the cabinet operates normally under the simulated temperature environment. During the actual operation of the cabinet, the temperature inside the cabinet is directly related to the safety of the equipment. Through the simulation method of this application, potential problems of abnormal temperature during the use of the cabinet can be discovered in advance, avoiding equipment failures and even fire risks caused by abnormal temperature, thereby ensuring the safe operation of the cabinet and related equipment and reducing operation and maintenance costs and risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flowchart of the steps of a cabinet control simulation method provided by this application;

[0032] Figure 2 is a schematic diagram of the acquisition process of the anomaly determination degree sequence provided by this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In the description of the embodiments of this application, words such as "exemplary", "or", "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "exemplary", "or", "for example" is intended to present related concepts in a specific manner.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used in the description of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0035] In addition, it should be noted that the terms "first" and "second" in this application and the drawings are used to distinguish similar objects and are not used to describe a specific order or sequence. The methods disclosed in the embodiments of this application or shown in the flowcharts of the methods include one or more steps for implementing the methods. Without departing from the scope of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0036] Please refer to Figure 1 , which shows a flowchart of the steps of a cabinet control simulation method provided by an embodiment of this application. The method includes the following steps:

[0037] Step 1: Obtain the highest temperature value of each moment of the cabinet at each simulated temperature and form a cabinet temperature sequence.

[0038] For the control simulation modeling of the cabinet, first, three-dimensional simulation modeling is carried out using the 3ds max three-dimensional modeling software based on the two-dimensional structure drawing of the cabinet. Then, temperature sensors are used to collect the temperature inside the cabinet, and the temperature field of the cabinet is simulated and modeled by electromagnetic-thermal flow coupling using the real-time collected data.

[0039] During the simulation test process, simulation tests will be carried out at various temperatures. In this application, the simulation temperatures are respectively ; the implementer can adjust it according to the actual situation. Control the cabinet to operate at full load. Then, through simulation modeling, a simulation model is obtained, and the highest temperature value in the cabinet temperature field is collected. In this embodiment, the collection frequency is 1Hz, and the collection time length is 10min. The implementer can adjust it by himself. Arrange the collected temperatures in the order of the collection time, and record it as the cabinet temperature sequence.

[0040] Step 2: Preset a window for each element in the cabinet temperature sequence to obtain a local temperature change sequence; based on the difference distribution between adjacent elements in the local temperature change sequence, obtain the non-central fluctuation degree of each element in the cabinet temperature sequence.

[0041] For the heat sources with rising temperature in the cabinet, one is the temperature in the surrounding environment of the cabinet, which transfers the environmental temperature to the cabinet through heat transfer, and the other is the heat generated by the operation of the equipment in the cabinet during the operation of the cabinet, causing the temperature in the environment to rise. Therefore, the rise of the cabinet temperature is a slow process and will not rise rapidly. So, the greater the change in the cabinet temperature in a short period of time, the greater the possibility of being affected by electromagnetic interference during the operation of the cabinet equipment. By suppressing high-noise data, it can be determined whether the temperature is abnormal during the cabinet control simulation process.

[0042] Thus, with each element in the cabinet temperature sequence as the center, a window is obtained. The value range of m is generally , and in this embodiment, the value is 7. Then, the central element in the window is removed to obtain the local temperature change sequence. It should be noted that when the number of elements in the window is insufficient, the mean value can be used for filling. Mean value filling is a well-known technology, and the specific calculation process will not be elaborated here. Thus, calculate the non-central fluctuation degree of each element in the cabinet temperature sequence: for each element in the cabinet temperature sequence, take the average value of the differences between all adjacent elements in the local change sequence as the non-central fluctuation degree of each element in the cabinet temperature sequence. In this embodiment, the difference between two variables is calculated by the absolute value of the difference.

[0043] During the cabinet temperature simulation process, due to the existence of electromagnetic interference, the collected temperature data often deviates from the normal value, resulting in increased data fluctuations. In order to accurately evaluate the local fluctuations of the cabinet temperature, this application uses the method of non-central element local difference to calculate the non-central fluctuation degree. This method can effectively suppress the influence of electromagnetic interference on the cabinet temperature data by analyzing the local change characteristics of the temperature data, so as to accurately calculate the local fluctuation situation of the cabinet temperature. This process provides a reliable data basis for subsequent simulation analysis, helps to accurately judge the operating state of the cabinet, and ensures the accuracy and reliability of the simulation results.

[0044] Step 3: According to the difference distribution between each element in the cabinet temperature sequence and all its adjacent elements, obtain the data prominence of each element, and combine the non-central fluctuation degree to obtain the noise prominence of each element.

[0045] For the cabinet temperature data affected by the electromagnetic interference of cabinet equipment, the noise data has mutability, resulting in a large difference between the cabinet temperature data on the left and right adjacent sides of the noisy cabinet temperature data. There is a large difference between this part of the difference and the non-central fluctuation degree of the cabinet temperature. The difference between the normal cabinet temperature and the cabinet temperature data on the left and right adjacent sides is smaller than the difference in the non-central fluctuation degree of this cabinet temperature data.

[0046] Therefore, calculate the noise prominence of each element in the cabinet temperature sequence: First, for each element in the cabinet temperature sequence, use the result of positively integrating the difference between each element and all its adjacent elements as the data prominence of each element. In this embodiment, the difference between elements is calculated using the absolute value of the difference; the positive integration of multiple variables uses the calculation method of addition.

[0047] Furthermore, based on the data prominence and the non-central fluctuation degree, obtain the noise prominence of each element in the cabinet temperature sequence: Take the absolute value of the difference between the data prominence and the non-central fluctuation degree to obtain the noise prominence of each element in the cabinet temperature sequence.

[0048] It should be understood that in the cabinet temperature simulation, the noise prominence can effectively reflect the abnormal conditions in the temperature data. The higher the noise prominence, the greater the possibility that the data point is affected by interference. In order to ensure the accuracy of the simulation results, it is necessary to apply stronger noise suppression capabilities to the data points with high noise prominence. By dynamically adjusting the noise suppression strategy, interference can be effectively removed, and the detailed features of the temperature data can be retained, thereby improving the accuracy of the data. This not only provides a reliable basis for the optimal design of the cabinet simulation system, but also improves the overall reliability of the cabinet control simulation, ensuring that the simulation results can truly reflect the safety of the cabinet during actual operation.

[0049] Step 4: Cluster the elements in the cabinet temperature sequence based on the noise saliency to obtain a suspected electromagnetic interference set and an electromagnetic interference judgment set; based on the serial number distribution of the elements in the suspected electromagnetic interference set and the electromagnetic interference judgment set in the cabinet temperature sequence, obtain the anomaly determination degree of each element in the suspected electromagnetic interference set, and determine the anomaly determination degree sequence.

[0050] When the cabinet adjusts the temperature inside the cabinet, there is a certain fluctuation in the temperature inside the cabinet. However, the actual temperature fluctuation of the cabinet should be within a relatively small range. Therefore, for the collected cabinet temperature data with noise, the noise saliency of the cabinet temperature data has three obvious layers: one is the cabinet temperature data near the noise, one is the normal cabinet data, and the last one is the cabinet temperature data affected by electromagnetic interference.

[0051] Therefore, through the clustering algorithm to cluster the noise saliency of the cabinet temperature data, the specific clustering algorithm is not limited in this application. In this embodiment, the K-means clustering algorithm is used. Among them, the value of the necessary parameter clustering number K of the K-means clustering algorithm is determined according to the number of layers of the noise saliency of the cabinet temperature data in this application, and K = 3. Among them, the calculation of the K-means clustering algorithm is a well-known technology, and the specific calculation process will not be elaborated here.

[0052] For the classification result, select the clustering cluster with the largest average noise saliency as the suspected electromagnetic interference set; and take the clustering cluster with the second largest average noise saliency as the electromagnetic interference judgment set.

[0053] For the cabinet temperature data with electromagnetic interference, there are elements in the electromagnetic interference judgment set near it. Therefore, obtain the serial number values of the elements in the suspected electromagnetic interference set and the electromagnetic interference judgment set for the cabinet temperature in the cabinet temperature sequence, and form a position set with them.

[0054] For the position value corresponding to the element in the suspected electromagnetic interference set, with its value as the center, record the number of elements in the suspected electromagnetic interference set and the number of elements in the electromagnetic interference judgment set within the preset radius length as S and P respectively. In this embodiment, the radius value principle is , represents the floor function, and m represents the window length.

[0055] Therefore, calculate the anomaly determination degree corresponding to each element in the suspected electromagnetic interference set: ; In the formula, represents the anomaly determination degree of the x-th element in the suspected electromagnetic interference set; represents the number of elements in the suspected electromagnetic interference set within the radius of the position value corresponding to the x-th element in the suspected electromagnetic interference set; The number of elements in the electromagnetic interference judgment set within the radius corresponding to the position value of the x-th element in the suspected electromagnetic interference set; 、 Both represent the preset weight adjustment coefficients. In this embodiment The value range of is , where The value is 0.7.

[0056] It should be understood that during the cabinet temperature simulation process, by dynamically monitoring and analyzing the internal temperature data of the cabinet, data points affected by electromagnetic interference can be accurately identified. When calculating the anomaly determination degree of elements in the suspected electromagnetic interference set, the simulation system will judge the interference intensity according to the distribution density of suspected interference points around the elements. The more suspected electromagnetic interference points around a certain element, the stronger the electromagnetic interference in this area during the simulation process. At this time, the noise saliency of this element is significantly higher than that of normal data, showing obvious electromagnetic interference characteristics. Therefore, the simulation system will assign higher weight values to these elements to enhance their influence in anomaly judgment. This value can provide a more reliable basis for the simulation optimization and fault warning of the cabinet, ensuring the safety and reliability of the cabinet during simulated operation.

[0057] Next, since the elements in the suspected electromagnetic interference set correspond to a part of the elements in the cabinet temperature sequence, replace the elements in the cabinet temperature sequence with the anomaly determination degrees of the elements in the suspected electromagnetic interference set. For the part of the cabinet temperature sequence that is not replaced, select the smallest anomaly determination degree of the elements in the suspected electromagnetic interference set for replacement to obtain the anomaly determination degree sequence.

[0058] Among them, the schematic diagram of the acquisition process of the anomaly determination degree sequence is as shown in Figure 2 shown.

[0059] Step Five: Based on the distribution of each element in the anomaly determination degree sequence, filter the cabinet temperature sequence to obtain the cabinet temperature simulation test results at each simulation temperature.

[0060] When performing SG filtering and denoising on the cabinet temperature sequence, for those with a larger anomaly determination degree, a smaller order should be used to smooth the data, and for those with a smaller anomaly determination degree, a larger order should be maintained to ensure data details. Thus, calculate the smoothing order of each element in the cabinet temperature sequence: ; In the formula, represents the smoothing order of the y-th element in the cabinet temperature sequence; norm() represents the normalization function; represents the ceiling function.

[0061] For the calculation of SG filtering, an odd number is generally used. Therefore, in this application, in order to limit the data order to three orders of (3, 5, 7), through the above formula, it is possible to limit the order of filtering to (3, 5, 7) for the elements in the cabinet temperature sequence during the filtering process.

[0062] Replace the smoothing order of each element in the cabinet temperature sequence in the above steps with the smoothing order in the SG filtering process to denoise the cabinet temperature sequence. Filter the cabinet temperature sequences in 4 kinds of simulated temperature environments respectively through the above steps. Then, judge the denoised data. If there is data greater than the preset value after filtering, it is judged that there is a problem with the operation of the cabinet in the corresponding simulated temperature environment, and the cabinet temperature control system needs to be optimized; otherwise, it is judged that the operation of the cabinet in the corresponding simulated temperature environment is normal. In this embodiment, the preset value is 45.

[0063] Based on the same concept as the method embodiment of this application, a cabinet control simulation device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0064] Based on the same concept as the method embodiment of this application, a cabinet control simulation system is provided. A computer program is stored in the system, and when the computer program is executed by a processor, the simulation method described in any one of the above is implemented.

[0065] It should be noted that the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the systems, methods, and computer program products according to the embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, which may depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the descriptions. Sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, which may depend on the functions involved. Each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0066] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A cabinet control simulation method, characterized in that, The method includes the following steps: Obtain the highest temperature value of each moment of the cabinet at each simulation temperature, and form a cabinet temperature sequence; Preset a window for each element in the cabinet temperature sequence to obtain a local temperature change sequence; for each element in the cabinet temperature sequence, take the average value of the differences between all adjacent elements in the local change sequence as the non-central fluctuation degree of each element in the cabinet temperature sequence; For each element in the cabinet temperature sequence, take the result of positive fusion of the difference between each element and all its adjacent elements as the data prominence of each element, and take the absolute value of the difference between the data prominence and the non-central fluctuation degree as the noise prominence of each element; Cluster the elements in the cabinet temperature sequence based on the noise prominence to obtain a suspected electromagnetic interference set and an electromagnetic interference judgment set; based on the serial number distribution of the elements in the suspected electromagnetic interference set and the electromagnetic interference judgment set in the cabinet temperature sequence, obtain the abnormality determination degree of each element in the suspected electromagnetic interference set, and determine an abnormality determination degree sequence; Based on the distribution of the elements in the abnormality determination degree sequence, filter the corresponding cabinet temperature sequence to obtain the cabinet temperature simulation test result at each simulation temperature.

2. The cabinet control simulation method according to claim 1, wherein The obtaining of the suspected electromagnetic interference set and the electromagnetic interference judgment set is specifically as follows: Arrange the clustering clusters in descending order of the average noise prominence, take the clustering cluster ranked first as the suspected electromagnetic interference set, and take the clustering cluster ranked second as the electromagnetic interference judgment set.

3. The cabinet control simulation method according to claim 1, wherein The specific process of obtaining the abnormality determination degree of each element in the suspected electromagnetic interference set is as follows: Based on the data corresponding to the serial number value of each element in the suspected electromagnetic interference set as the center in the cabinet temperature sequence, obtain within a preset radius length, record the number of elements belonging to the electromagnetic interference set and the number of elements belonging to the electromagnetic interference judgment set as S and P respectively, and perform weighted summation on S and P to obtain the abnormality determination degree of each element in the suspected electromagnetic interference set.

4. The cabinet control simulation method according to claim 1, wherein The determination of the abnormality determination degree sequence is specifically as follows: Replace the element at the serial number position corresponding to the element in the suspected electromagnetic interference set in the cabinet temperature sequence with the abnormality determination degree of this position serial number; replace other elements in the cabinet temperature sequence with the minimum value of the abnormality determination degrees of all elements in the suspected electromagnetic interference set.

5. The cabinet control simulation method according to claim 1, characterized in that The process of filtering the corresponding cabinet temperature sequence based on the distribution of the elements in the abnormality determination degree sequence to obtain the cabinet temperature simulation test result at each simulation temperature includes: Denote the smoothing order of each element in the cabinet temperature sequence as , and the specific formula form is: ; where represents the smoothing order of the y-th element in the cabinet temperature sequence; represents the value of the y-th element in the anomaly determination degree sequence; norm() represents the normalization function; represents the ceiling function; Use a filtering algorithm to filter the cabinet temperature sequence based on the smoothing order. If there is data greater than a preset value after filtering, it is determined that the cabinet is abnormal in the simulation temperature test; otherwise, it is determined that the cabinet is not abnormal in the simulation temperature test.

6. A cabinet control simulation device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1-5.

7. A cabinet control simulation system, in which a computer program is stored, characterized in that, When the computer program is executed by the processor, it implements the simulation method described in any one of claims 1-5.

Images