Cabinet control simulation method, equipment and system

Abstract

The invention relates to the technical field of cabinet detection, in particular to a cabinet control simulation method, device and system, and the method comprises the steps: obtaining a cabinet temperature sequence, and obtaining a local temperature change sequence; obtaining the non-central fluctuation degree of each element in the cabinet temperature sequence based on the difference distribution between adjacent elements in the local temperature change sequence; according to the difference distribution of each element and all adjacent elements in the cabinet temperature sequence, the data prominence of each element is obtained, and the noise prominence of each element is obtained; clustering elements in the cabinet temperature sequence to obtain a suspected electromagnetic interference set and an electromagnetic interference judgment set; obtaining the anomaly certainty degree of each element in the suspected electromagnetic interference set, and determining an anomaly certainty degree sequence; and filtering the cabinet temperature sequence to obtain a cabinet temperature simulation test result under each simulation temperature. The invention aims to accurately obtain the operation state of the cabinet and enhance the reliability of the control simulation result of the cabinet.

Description

Technical Field

[0001] The present application relates to the field of cabinet detection technology, and in particular 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 accuracy of the test. However, during the long-term use of the cabinet, since the cabinet equipment is powered on, the electricity will generate heat. When the heat accumulates, it will cause local overheating and may even cause a fire risk. Therefore, the temperature of the cabinet is usually monitored to determine whether there is any abnormality in the temperature during the cabinet control simulation.

[0003] For the temperature simulation test of cabinet control, it is usually judged whether the cabinet is operating normally by detecting whether the temperature in the cabinet exceeds the threshold. However, since the cabinet is under electrical load for a long time, the existence of electromagnetic field will cause the data collected by the temperature sensor to be interfered by a lot of noise, thereby reducing the accuracy of direct judgment through the threshold. The traditional method is generally to filter the cabinet temperature data and then make judgments based on the filtered data. However, the SG filter (Savitzky-Golay) with a fixed smoothing order will cause the cabinet temperature data to be processed. Due to the uncertainty of noise, the filtered data will have a problem of lack of accuracy. 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 comprising: Obtain the highest temperature value of the cabinet at each time under each simulation temperature to form a cabinet temperature sequence; A window of each element in the cabinet temperature sequence is preset to obtain a local temperature change sequence; based on the difference distribution between adjacent elements in the local temperature change sequence, a non-central fluctuation degree of each element in the cabinet temperature sequence is obtained; According to the difference distribution between each element in the cabinet temperature sequence and all its adjacent elements, the data prominence of each element is obtained, and the noise prominence of each element is obtained by combining the non-central fluctuation; Clustering 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; obtaining the abnormality certainty of each element in the suspected electromagnetic interference set based on the sequence number distribution of the elements in the suspected electromagnetic interference set and the electromagnetic interference judgment set in the cabinet temperature sequence, and determining the abnormality certainty sequence; Based on the distribution of each element in the abnormal certainty sequence, the cabinet temperature sequence is filtered to obtain the cabinet temperature simulation test results at each simulation temperature.

[0006] The non-central fluctuation degree of each element in the cabinet temperature sequence is obtained as follows: For each element in the cabinet temperature series, the average of the differences between all adjacent elements in the local variation series is taken as the non-central volatility of each element in the cabinet temperature series.

[0007] Wherein, obtaining the data prominence of each element includes: For each element in the cabinet temperature series, the result of forward fusion of the differences between each element and all its adjacent elements is taken as the data prominence of each element.

[0008] The noise prominence of each element is specifically the absolute value of the difference between the data prominence and the non-central volatility.

[0009] The obtaining of the suspected electromagnetic interference set and the electromagnetic interference judgment set is specifically as follows: The clusters are arranged from large to small according to the mean value of noise prominence, the first-ranked cluster is taken as the suspected electromagnetic interference set, and the second-ranked cluster is taken as the suspected electromagnetic interference judgment set.

[0010] The specific process of obtaining the abnormality certainty of each element of the suspected electromagnetic interference set is as follows: In the cabinet temperature sequence, based on the data corresponding to the serial number value of each element in the suspected electromagnetic interference set as the center, the number of elements belonging to the electromagnetic interference set and the number of elements belonging to the electromagnetic interference judgment set within the preset radius length are recorded as S and P respectively, and S and P are weightedly summed to obtain the abnormal certainty of each element in the suspected electromagnetic interference set.

[0011] The abnormality determination sequence is specifically: Replace the element with the position number corresponding to the element of the suspected electromagnetic interference set in the cabinet temperature sequence with the abnormality certainty of the position number; replace other elements in the cabinet temperature sequence with the minimum value of the abnormality certainty of all elements in the suspected electromagnetic interference set.

[0012] The process of filtering the cabinet temperature sequence based on the distribution of each element in the abnormal certainty sequence to obtain the cabinet temperature simulation test result at each simulation temperature includes: The smoothing order of each element in the cabinet temperature series is recorded as , the specific formula is: ; In the formula, represents the smoothing order of the yth element in the cabinet temperature series; Represents the value of the yth element in the abnormal certainty sequence; norm() represents the normalization function; represents the ceiling function; A filtering algorithm is used 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 has an abnormality in the simulated temperature test; otherwise, it is determined that the cabinet has no abnormality in the simulated temperature test.

[0013] According to another aspect of the present application, a cabinet control simulation device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0014] According to another aspect of the present application, a cabinet control simulation system is provided, wherein a computer program is stored in the system, and when the computer program is executed by a processor, any one of the above-mentioned simulation methods is implemented.

[0015] This application has at least the following beneficial effects: 1. This application accurately identifies data points affected by electromagnetic interference by calculating the non-central fluctuation and noise prominence of temperature data. Compared with traditional methods, it can effectively reduce the interference of noise on temperature data and make the collected cabinet temperature data more accurate. It provides a reliable foundation for subsequent simulation analysis and ensures the initial data quality of simulation testing.

[0016] 2. This application adopts a dynamically adjusted filtering method to apply different smoothing strategies to data with different noise levels. This adaptive filtering method can better preserve data details while removing noise interference. During the simulation process, this method can more accurately obtain the operating status 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.

[0017] 3. This application uses precise simulation tests to accurately determine whether the cabinet is working properly under a simulated temperature environment. In the actual operation of the cabinet, the temperature in 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 or 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

[0018] Figure 1 A flowchart of the steps of a cabinet control simulation method provided in this application; Figure 2A schematic diagram of the process of obtaining the abnormal certainty sequence provided in this application. DETAILED DESCRIPTION

[0019] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0021] It should also be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flowchart includes one or more steps for implementing the method. Without departing from the scope of the present application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0022] See also Figure 1 , which shows a step flow chart of a cabinet control simulation method provided by an embodiment of the present application, the method comprising the following steps: Step 1: Obtain the maximum temperature value of the cabinet at each time under each simulation temperature to form a cabinet temperature sequence.

[0023] For the control simulation modeling of the cabinet, firstly, the 3D simulation modeling is performed using 3ds max 3D modeling software based on the 2D structural drawings of the cabinet. Then, the temperature inside the cabinet is collected using a temperature sensor, and the temperature field of the cabinet is simulated and modeled using electromagnetic heat flow coupling based on the real-time collected data.

[0024] During the simulation test, simulation tests are performed at various temperatures. In this application, the simulation temperatures are ; The implementer can adjust it according to the actual situation. The control cabinet runs at full load. Then, through simulation modeling, the 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. The collected temperatures are arranged in the order of collection time, and recorded as the cabinet temperature sequence.

[0025] Step 2: Preset the window of 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 of each element in the cabinet temperature sequence.

[0026] The heat sources for the temperature rise in the cabinet include the temperature in the environment around the cabinet, which is transferred to the cabinet through heat transfer, and the heat generated by the equipment in the cabinet during operation, which causes the temperature rise in the environment. Therefore, the rise in cabinet temperature is a slow process and will not rise rapidly. Therefore, the greater the change in cabinet temperature in a short period of time, the greater the possibility of electromagnetic interference caused by the operation of cabinet equipment. By suppressing high-noise data, it can be determined whether the temperature is abnormal during the cabinet control simulation process.

[0027] Therefore, taking each element in the cabinet temperature sequence as the center, we can obtain a The window of m is generally in the range of , in this embodiment, the value is 7, and 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 can be used for filling. The mean filling is a well-known technology, and the specific calculation process will not be repeated here. Thus, the non-central fluctuation of each element in the cabinet temperature sequence is calculated: for each element in the cabinet temperature sequence, the average value of the difference between all adjacent elements in the local change sequence is used as the non-central fluctuation 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.

[0028] During the cabinet temperature simulation process, due to the presence 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 local differences of non-central elements to calculate the non-central fluctuation degree. This method can effectively suppress the impact of electromagnetic interference on the cabinet temperature data by analyzing the local change characteristics of the temperature data, thereby accurately calculating the local fluctuations of the cabinet temperature. This process provides a reliable data basis for subsequent simulation analysis, helps to accurately judge the operating status of the cabinet, and ensures the accuracy and reliability of the simulation results.

[0029] Step 3: According to the difference distribution between each element in the cabinet temperature sequence and all its adjacent elements, the data prominence of each element is obtained, and combined with the non-central volatility, the noise prominence of each element is obtained.

[0030] For the cabinet temperature data affected by the electromagnetic interference of the cabinet equipment, the noise data has a sudden change, which makes the cabinet temperature data with noise have a large difference between the cabinet temperature data on the left and right. This difference is very different from the non-central fluctuation of the cabinet temperature. The difference between the normal cabinet temperature and the temperature data of the cabinets on the left and right is smaller than the non-central fluctuation of the cabinet temperature data.

[0031] Thus, the noise prominence of each element in the cabinet temperature sequence is calculated: first, for each element in the cabinet temperature sequence, the difference between each element and all its adjacent elements is forward fused as the data prominence of each element. In this embodiment, the difference between elements is calculated using the absolute value of the difference; multiple variables are forward fused using the addition calculation method.

[0032] Furthermore, based on the data prominence and the non-central fluctuation, the noise prominence of each element in the cabinet temperature sequence is obtained: the absolute value of the difference between the data prominence and the non-central fluctuation is taken to obtain the noise prominence of each element in the cabinet temperature sequence.

[0033] It should be understood that in the cabinet temperature simulation, noise salience can effectively reflect abnormal conditions in the temperature data. The higher the noise salience, the greater the possibility that the data point is interfered with. In order to ensure the accuracy of the simulation results, it is necessary to apply stronger noise suppression capabilities to data points with high noise salience. 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 in actual operation.

[0034] 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 abnormality certainty of each element in the suspected electromagnetic interference set and determine the abnormality certainty sequence.

[0035] When the cabinet adjusts the temperature inside the cabinet, there is a certain fluctuation in the temperature inside the cabinet, but the actual temperature fluctuation of the cabinet should be in a smaller range. Therefore, for the collection of cabinet temperature data with noise, the noise prominence 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 is the cabinet temperature data affected by electromagnetic interference.

[0036] Therefore, the noise prominence of the cabinet temperature data is clustered by a clustering algorithm. The specific clustering algorithm is not limited in this application. The K-means clustering algorithm is used in this embodiment, where the value of the number of clusters K, a necessary parameter of the K-means clustering algorithm, is determined in this application according to the number of layers of the noise prominence of the cabinet temperature data, and K = 3. The calculation of the K-means clustering algorithm is a well-known technology, and the specific calculation process is not repeated here.

[0037] For the classified results, the cluster with the largest mean value of noise salience is selected as the suspected electromagnetic interference set; the cluster with the second largest mean value of noise salience is selected as the electromagnetic interference judgment set.

[0038] For the cabinet temperature data of electromagnetic interference, there are elements in the electromagnetic interference judgment set nearby. Therefore, the arrangement sequence number values ​​of the cabinet temperature in the cabinet temperature sequence of the elements in the suspected electromagnetic interference set and the electromagnetic interference judgment set are obtained and combined into a position set.

[0039] For the position value corresponding to the element of the suspected electromagnetic interference set, the number of elements in all suspected electromagnetic interference sets and the number of elements in the electromagnetic interference judgment set within the preset radius length are recorded as S and P respectively. In this embodiment, the radius value principle is as follows: , represents the floor function, and m represents the window length.

[0040] Thus, the abnormality certainty corresponding to each element in the suspected electromagnetic interference set is calculated: ; In the formula, Indicates the abnormality certainty of the xth 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 xth element in the suspected electromagnetic interference set; Represents the number of elements in the electromagnetic interference judgment set within the radius of the position value corresponding to the xth element in the suspected electromagnetic interference set; , Both represent preset weight adjustment coefficients. In this embodiment The value range is , ,in, The value is 0.7.

[0041] It should be understood that during the cabinet temperature simulation process, the data points affected by electromagnetic interference can be accurately identified through dynamic monitoring and analysis of the temperature data inside the cabinet. When calculating the abnormal certainty of an element in the suspected electromagnetic interference set, the simulation system will judge the interference intensity based on the distribution density of the suspected interference points around the element. The more suspected electromagnetic interference points there are around an element, the stronger the electromagnetic interference to the area during the simulation. At this time, the noise saliency of the element is significantly higher than the normal data, showing obvious electromagnetic interference characteristics. Therefore, the simulation system will give these elements higher weights to enhance their influence in abnormal 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 in simulated operation.

[0042] Next, since the elements of the suspected electromagnetic interference set correspond to a part of the elements in the cabinet temperature sequence, the abnormality certainty of the elements in the suspected electromagnetic interference set replaces the elements in the cabinet temperature sequence. For the unreplaced part of the cabinet temperature sequence, the element with the smallest abnormality certainty in the suspected electromagnetic interference set is selected for replacement to obtain the abnormality certainty sequence.

[0043] Among them, the schematic diagram of the process of obtaining the abnormal certainty sequence is as follows: Figure 2 shown.

[0044] Step 5: Based on the distribution of each element in the abnormal certainty sequence, the cabinet temperature sequence is filtered to obtain the cabinet temperature simulation test results at each simulation temperature.

[0045] When performing SG filtering and denoising on the cabinet temperature series, for those with a larger degree of abnormality, a smaller order should be used to smooth the data, and for those with a smaller degree of abnormality, a larger order should be maintained to ensure data details. The smoothing order of each element in the cabinet temperature series is calculated as follows: ; In the formula, represents the smoothing order of the yth element in the cabinet temperature series; norm() represents the normalization function; Represents the ceiling function.

[0046] An odd number is generally used for the calculation of SG filtering. Therefore, in this application, in order to limit the data order to three orders (3, 5, 7), the above formula can be used to limit the filtering order of the elements in the cabinet temperature sequence to (3, 5, 7) during the filtering process.

[0047] The smoothing order of each element in the cabinet temperature sequence calculated in the above step is replaced by the smoothing order in the SG filtering process to denoise the cabinet temperature sequence. The cabinet temperature sequences under the four simulated temperature environments are filtered through the above steps respectively. Then the denoised data is judged. 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 there is no problem with the operation of the cabinet in the corresponding simulated temperature environment. In this embodiment, the preset value is 45.

[0048] Based on the same concept as the method embodiment of the present 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, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0049] Based on the same concept as the method embodiment of the present application, a cabinet control simulation system is provided, in which a computer program is stored, and when the computer program is executed by a processor, any one of the above-mentioned simulation methods is implemented.

[0050] It should be noted that the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the system, method and computer program product according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0051] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A cabinet control simulation method, characterized in that: The method comprises the following steps: Obtain the highest temperature value of the cabinet at each time under each simulation temperature to form a cabinet temperature sequence; A window of each element in the cabinet temperature sequence is preset to obtain a local temperature change sequence; based on the difference distribution between adjacent elements in the local temperature change sequence, a non-central fluctuation degree of each element in the cabinet temperature sequence is obtained; According to the difference distribution between each element in the cabinet temperature sequence and all its adjacent elements, the data prominence of each element is obtained, and the noise prominence of each element is obtained by combining the non-central fluctuation; Clustering 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; obtaining the abnormality certainty of each element in the suspected electromagnetic interference set based on the sequence number distribution of the elements in the suspected electromagnetic interference set and the electromagnetic interference judgment set in the cabinet temperature sequence, and determining the abnormality certainty sequence; Based on the distribution of each element in the abnormal certainty sequence, the cabinet temperature sequence is filtered to obtain the cabinet temperature simulation test results at each simulation temperature.

2. A cabinet control simulation method according to claim 1, characterized in that: The non-central fluctuation degree of each element in the cabinet temperature sequence is obtained as follows: For each element in the cabinet temperature series, the average of the differences between all adjacent elements in the local variation series is taken as the non-central volatility of each element in the cabinet temperature series.

3. A cabinet control simulation method according to claim 1, characterized in that: The data prominence of each element is obtained, including: For each element in the cabinet temperature series, the result of forward fusion of the differences between each element and all its adjacent elements is taken as the data prominence of each element.

4. A cabinet control simulation method as claimed in claim 2, characterized in that: The noise prominence of each element is specifically the absolute value of the difference between the data prominence and the non-central volatility.

5. A cabinet control simulation method according to claim 1, characterized in that: The obtaining of the suspected electromagnetic interference set and the electromagnetic interference judgment set is specifically as follows: The clusters are arranged from large to small according to the mean value of noise prominence, the first-ranked cluster is taken as the suspected electromagnetic interference set, and the second-ranked cluster is taken as the suspected electromagnetic interference judgment set.

6. A cabinet control simulation method according to claim 1, characterized in that: The specific process of obtaining the abnormality certainty of each element of the suspected electromagnetic interference set is as follows: In the cabinet temperature sequence, based on the data corresponding to the serial number value of each element in the suspected electromagnetic interference set as the center, the number of elements belonging to the electromagnetic interference set and the number of elements belonging to the electromagnetic interference judgment set within the preset radius length are recorded as S and P respectively, and S and P are weightedly summed to obtain the abnormal certainty of each element in the suspected electromagnetic interference set.

7. A cabinet control simulation method according to claim 1, characterized in that: The abnormality determination sequence is specifically: Replace the element with the position number corresponding to the element of the suspected electromagnetic interference set in the cabinet temperature sequence with the abnormality certainty of the position number; replace other elements in the cabinet temperature sequence with the minimum value of the abnormality certainty of all elements in the suspected electromagnetic interference set.

8. A cabinet control simulation method according to claim 1, characterized in that: The process of filtering the cabinet temperature sequence based on the distribution of each element in the abnormal certainty sequence to obtain the cabinet temperature simulation test result at each simulation temperature includes: The smoothing order of each element in the cabinet temperature series is recorded as , the specific formula is: ; In the formula, represents the smoothing order of the yth element in the cabinet temperature series; Represents the value of the yth element in the abnormal certainty sequence; norm() represents the normalization function; represents the ceiling function; A filtering algorithm is used 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 has an abnormality in the simulated temperature test; otherwise, it is determined that the cabinet has no abnormality in the simulated temperature test.

9. 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, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A cabinet control simulation system, wherein a computer program is stored in the system, characterized in that: When the computer program is executed by a processor, the simulation method according to any one of claims 1 to 8 is implemented.

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