A method, apparatus, equipment and medium for processing drainage test data

Abstract

This application relates to the field of data processing technology, and discloses a method, apparatus, equipment, and medium for processing drainage test data. The method includes: acquiring target drainage test data of a preset drainage fluid; wherein the target drainage test data includes drainage data to be processed and multiple drainage condition data corresponding to the drainage data to be processed; under the condition that the drainage data to be processed meets preset data error conditions, predicting the drainage capacity of the preset drainage fluid based on the drainage data to be processed, and obtaining drainage capacity data of the preset drainage fluid; and constructing a drainage capacity model of the preset drainage fluid based on the drainage capacity data and multiple drainage condition data. Its beneficial effect is that by establishing a corresponding drainage capacity model for the preset drainage fluid, it is possible to quantitatively control the drainage of water tanks using the preset drainage fluid based on the drainage capacity model, thereby improving the accuracy of ship attitude adjustment and enhancing the safety and maneuverability of the ship.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, equipment and medium for processing drainage test data. Background Technology

[0002] Ship attitude directly impacts its safety and maneuverability. When a ship experiences heeling, rolling, or other similar situations, failure to stabilize its attitude promptly can lead to loss of balance and significantly affect navigation, potentially even causing accidents. Ship attitude is related to the buoyancy experienced by different parts of the ship. Therefore, to adjust ship attitude, multiple water tanks are typically installed at various locations within the ship. By controlling the filling and emptying of these tanks, the magnitude of gravity experienced by different parts of the ship can be adjusted, thus effectively regulating its attitude. However, the accuracy of using pneumatic control to precisely drain water from these tanks during ship attitude adjustment still needs improvement. Summary of the Invention

[0003] This application provides a drainage test data processing method, apparatus, equipment, and medium. Based on the target drainage test data of the preset drainage fluid, a corresponding drainage capacity model is established for the preset drainage fluid. Based on the drainage capacity model, the preset drainage fluid can be used to quantitatively control the drainage of the water storage tank, which improves the accuracy of ship attitude adjustment and enhances the safety and maneuverability of the ship.

[0004] To achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, embodiments of this application provide a method for processing drainage test data, the method comprising:

[0006] Obtain target drainage test data for a preset drainage fluid; wherein, the target drainage test data includes drainage data to be processed and multiple drainage condition data corresponding to the drainage data to be processed;

[0007] If the drainage data to be processed meets the preset data error condition, the drainage capacity of the preset drainage fluid is predicted based on the drainage data to be processed, and the drainage capacity data of the preset drainage fluid is obtained.

[0008] Based on the drainage capacity data and the plurality of drainage condition data, a drainage capacity model for the preset drainage fluid is constructed; wherein, the drainage capacity model is used to describe the drainage capacity data of the preset drainage fluid under target drainage conditions, and the target drainage conditions correspond to any one of the plurality of drainage condition data.

[0009] The drainage test data processing method proposed in this application obtains target drainage test data of a preset drainage fluid, predicts the drainage capacity of the preset drainage fluid based on the target drainage test data, and obtains the drainage capacity data of the preset drainage fluid. Based on the drainage capacity data and drainage condition data, a drainage capacity model of the preset drainage fluid is constructed, thereby enabling the determination of the drainage capacity of the preset drainage fluid under arbitrary drainage conditions using the drainage capacity model. Compared with related technologies, this application conducts prior tests on the preset drainage fluid and constructs a drainage capacity model based on the test results. This allows for the determination of the actual drainage capacity of the preset drainage fluid in real-world scenarios based on actual drainage condition data, achieving quantitative drainage control of the water storage tank, improving the accuracy of ship attitude adjustment, and enhancing ship safety and maneuverability. Furthermore, this application can effectively cope with complex actual drainage conditions, reduce interference from the actual environment on drainage control, and improve the reliability of drainage control.

[0010] Optionally, constructing the drainage capacity model of the preset drainage fluid based on the drainage capacity data and the multiple drainage condition data includes:

[0011] A correlation analysis is performed on the multiple drainage condition data, and a corresponding data fitting model is determined based on the correlation between the multiple drainage condition data.

[0012] Based on the target drainage test data, the optimal fitting number analysis is performed on the data fitting model to obtain the target fitting number;

[0013] The drainage capacity data and the multiple drainage condition data are fitted according to the target fitting number and the data fitting model to obtain the drainage capacity model.

[0014] Optionally, the step of performing optimal fitting number analysis on the data fitting model based on the target drainage test data to obtain the target fitting number includes:

[0015] Based on the initial fitting number and the data fitting model, the target drainage test data used as training data is trained and fitted to obtain the initial fitting model.

[0016] Error analysis is performed on the initial fitting model using the target drainage test data as test data, and the initial fitting number is updated based on the error analysis results;

[0017] Repeat the above steps of training and fitting the target drainage test data and updating the initial fitting number until the error analysis result meets the preset model error condition, and take the initial fitting number at this time as the target fitting number.

[0018] Optionally, the drainage data to be processed includes multiple simulated drainage data; whether the drainage data to be processed meets the preset data error condition is determined by the following method:

[0019] Error analysis is performed on any two simulated drainage data to obtain the drainage data error;

[0020] If all drainage data errors exceed the data error threshold, it is determined that the drainage data to be processed does not meet the preset data error condition.

[0021] If any drainage data error does not exceed the data error threshold, the drainage data to be processed is determined to meet the preset data error condition.

[0022] Optionally, predicting the drainage capacity of the preset drainage fluid based on the drainage data to be processed includes:

[0023] Valid test data are determined based on the numerical relationship between the drainage data error and the data error threshold; wherein, the valid test data is simulated drainage data corresponding to drainage data errors that do not exceed the data error threshold;

[0024] The drainage capacity data is obtained by performing an average analysis of the total drainage volume based on the valid test data.

[0025] Optionally, obtaining the target drainage test data of the preset drainage fluid includes:

[0026] Obtain initial drainage test data of the preset drainage fluid under the target drainage conditions;

[0027] The initial drainage test data is subjected to data synchronization preprocessing to obtain the target drainage test data.

[0028] Optionally, the initial drainage test data corresponds to a trigger timestamp indicating the data generation time and a reception timestamp indicating the data reception time, and the acquisition process of the initial drainage test data corresponds to a response delay and a communication delay; the step of performing data synchronization preprocessing on the initial drainage test data to obtain the target drainage test data includes:

[0029] The total delay is obtained by summing the response delay and the communication delay.

[0030] The initial drainage test data reception timestamp is compensated for based on the total delay, so that the reception timestamp is consistent with the trigger timestamp, thereby obtaining the target drainage test data.

[0031] Secondly, embodiments of this application provide a drainage test data processing device, the device comprising:

[0032] The drainage data test module is used to acquire target drainage test data of a preset drainage fluid; wherein, the target drainage test data includes drainage data to be processed and multiple drainage condition data corresponding to the drainage data to be processed;

[0033] The drainage capacity prediction module is used to predict the drainage capacity of the preset drainage fluid based on the drainage data to be processed, provided that the drainage data to be processed meets the preset data error conditions, so as to obtain the drainage capacity data of the preset drainage fluid.

[0034] The capacity model fitting module is used to construct a drainage capacity model of the preset drainage fluid based on the drainage capacity data and the plurality of drainage condition data; wherein, the drainage capacity model is used to describe the drainage capacity data of the preset drainage fluid under target drainage conditions; the target drainage conditions correspond to any one of the plurality of drainage condition data.

[0035] Thirdly, embodiments of this application provide a computer device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method described in any of the above embodiments.

[0036] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to perform the method described in any one of the above embodiments.

[0037] Fifthly, embodiments of this application provide a computer program product, including computer instructions, which are used to cause a computer to perform the method described in any of the above embodiments. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0039] Figure 1 A step diagram illustrating the drainage test data processing method provided in the embodiments of this application;

[0040] Figure 2 This is a structural diagram of the drainage capacity test system in an embodiment of this application;

[0041] Figure 3This is a diagram illustrating the steps involved in constructing the drainage capacity model in an embodiment of this application.

[0042] Figure 4 This is a diagram illustrating the steps involved in obtaining the target fitting number in an embodiment of this application.

[0043] Figure 5 This is a flowchart illustrating the steps in this application embodiment to determine whether the drainage data to be processed meets the preset data error conditions;

[0044] Figure 6 This is a diagram illustrating the steps involved in obtaining drainage capacity data in an embodiment of this application.

[0045] Figure 7 This is a flowchart illustrating the steps involved in obtaining the target drainage test data in an embodiment of this application.

[0046] Figure 8 This is a diagram illustrating the steps of data synchronization preprocessing for initial drainage test data in an embodiment of this application;

[0047] Figure 9 A block diagram of a drainage test data processing device provided in an embodiment of this application;

[0048] Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application.

[0049] The reference numerals in the accompanying drawings are as follows: 110. Fluid storage unit, 120. Drainage test unit, 130. Drainage receiving unit, 140. Drainage flow measurement unit. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] A ship's attitude directly impacts its safety and maneuverability. When a ship experiences heeling, rolling, or other similar situations, failure to stabilize its attitude in a timely manner can lead to loss of balance and significantly affect navigation, potentially even causing accidents. A ship's attitude is related to the buoyancy acting on different parts of the ship. Therefore, to adjust the ship's attitude, multiple water tanks are typically installed at various locations within the ship. By controlling the filling and emptying of these tanks, the magnitude of gravity acting on different parts of the ship can be adjusted, thus effectively regulating its attitude.

[0052] In related technologies, methods for controlling the filling and emptying of water tanks include pump-driven drainage and air-driven drainage. Pump-driven drainage refers to using pumps to extract water from the water tank and discharge it to the external environment. It offers advantages such as precise control and high drainage efficiency, but pumps typically have high energy consumption and maintenance requirements, and they also occupy significant space within the ship, increasing its internal complexity. Air-driven drainage, on the other hand, involves introducing a compressible fluid into the water tank, using the expansion of the fluid to expel water to the external environment. Compared to pump-driven drainage, it requires no additional power source and has a simple and reliable structure. The compressible fluids used in air-driven drainage can include air, nitrogen, and carbon dioxide.

[0053] However, in air-driven drainage, the volume of the expanded compressible fluid is affected by various drainage conditions, making it difficult to precisely control the drainage volume of the water tank. This reduces the effectiveness of adjusting the ship's attitude, affecting the ship's safety and maneuverability. Furthermore, the expansion process of the compressible fluid takes time, requiring continuous control and adjustment of its flow rate and pressure. The drainage conditions corresponding to the compressible fluid may also change in real time during expansion, further increasing the difficulty of quantitatively controlling the drainage of the water tank.

[0054] To address the aforementioned issues, this application provides a drainage test data processing method, apparatus, equipment, and medium. The method includes: acquiring target drainage test data for a preset drainage fluid; wherein the target drainage test data includes drainage data to be processed and multiple drainage condition data corresponding to the drainage data to be processed; under the condition that the drainage data to be processed meets preset data error conditions, predicting the drainage capacity of the preset drainage fluid based on the drainage data to be processed to obtain drainage capacity data of the preset drainage fluid; and constructing a drainage capacity model of the preset drainage fluid based on the drainage capacity data and multiple drainage condition data.

[0055] The drainage test data processing method provided in this application obtains target drainage test data of a preset drainage fluid, and predicts the drainage capacity of the preset drainage fluid based on the target drainage test data to obtain drainage capacity data of the preset drainage fluid; based on the drainage capacity data and drainage condition data, a drainage capacity model of the preset drainage fluid is constructed, thereby enabling the drainage capacity of the preset drainage fluid under any drainage conditions to be determined using the drainage capacity model.

[0056] Compared with related technologies, this application conducts prior experiments on a preset drainage fluid and constructs a drainage capacity model of the preset drainage fluid based on the experimental results. This allows for the determination of the actual drainage capacity of the preset drainage fluid in real-world scenarios using the drainage capacity model and actual drainage conditions, achieving quantitative drainage control of the water storage tanks. This improves the accuracy of ship attitude adjustments and enhances ship safety and maneuverability. Furthermore, this application can effectively handle complex actual drainage conditions, reduce interference from the actual environment on drainage control, and improve the reliability of drainage control.

[0057] The drainage test data processing method provided in this specification can be applied to compressible fluids capable of air-driven drainage to construct corresponding drainage capacity models and determine the drainage capacity data of a preset drainage fluid under different drainage conditions. Compressible fluids capable of air-driven drainage can include fluids such as air, nitrogen, and carbon dioxide. It is understood that, after adaptive modifications, the drainage test data processing method provided in this specification can also be applied to other compressible fluids besides the above-mentioned fluids, including but not limited to oils or other chemical liquids.

[0058] According to an embodiment of this application, a method for processing drainage test data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0059] This embodiment provides a drainage test data processing method, which can be used for the aforementioned compressible fluid capable of air-driven drainage. (Refer to...) Figure 1 As shown, the method includes:

[0060] S100. Obtain target drainage test data of the preset drainage fluid; wherein, the target drainage test data includes the drainage data to be treated and multiple drainage condition data corresponding to the drainage data to be treated.

[0061] S200. If the drainage data to be processed meets the preset data error conditions, the drainage capacity of the preset drainage fluid is predicted based on the drainage data to be processed, and the drainage capacity data of the preset drainage fluid is obtained.

[0062] S300. Based on drainage capacity data and multiple drainage condition data, construct a drainage capacity model for a preset drainage fluid; wherein, the drainage capacity model is used to describe the drainage capacity data of the preset drainage fluid under target drainage conditions, and the target drainage conditions correspond to any one of the multiple drainage condition data.

[0063] Reference Figure 2The drainage test data processing method provided in this application can be applied to Figure 2 The drainage capacity testing system shown obtains target drainage test data for a preset drainage fluid. The drainage capacity testing system includes a fluid storage unit 110, a drainage testing unit 120, and a drainage receiving unit 130. The fluid storage unit 110 and the drainage testing unit 120 are connected via a first delivery pipeline, which is equipped with a first delivery valve to control the opening and closing of the first delivery pipeline. The drainage testing unit 120 and the drainage receiving unit 130 are connected via a second delivery pipeline, which is equipped with a second delivery valve and a drainage flow measurement unit 140. The second delivery valve is used to control the opening and closing of the second delivery pipeline.

[0064] The fluid storage unit 110 stores a pre-compressed drainage fluid, which is a compressible fluid capable of performing air-driven drainage, including air, nitrogen, and carbon dioxide. The pre-compressed drainage fluid can be introduced into the drainage test unit 120 via a first delivery pipe. The drainage test unit 120 stores water, which is used to receive the pre-compressed drainage fluid via the first delivery pipe and to drain water, simulating the process of a ship's water tank being drained by the pre-compressed drainage fluid in a real-world scenario. The water discharged from the drainage test unit 120 is transported through a second delivery pipe and stored in the drainage receiving unit 130, and the water flow rate through the second delivery pipe can be measured by the drainage flow rate measuring unit 140. In some embodiments, the drainage receiving unit 130 is also connected to a pressure regulating unit for regulating the pressure in the drainage receiving unit 130 to modify the drainage condition data in the drainage receiving unit 130.

[0065] The drainage test unit 120 is equipped with a level gauge to measure the liquid level in the drainage test unit 120, thereby obtaining the total drainage volume caused by the preset drainage fluid in the drainage test unit 120 during the test. Similarly, the drainage receiving unit 130 is also equipped with a level gauge to measure the liquid level in the drainage receiving unit 130, thereby obtaining the total water volume discharged from the drainage test unit 120 during the test. It is understood that various types of sensors can be installed in the fluid storage unit 110, the drainage test unit 120, and the drainage receiving unit 130 to obtain drainage condition data corresponding to each unit. For example, the types of sensors may include temperature sensors and pressure sensors, etc.

[0066] Based on the aforementioned drainage capacity test system, the drainage data to be processed can include the total direct drainage volume, the total drainage received volume, and the total drainage transport volume. The total direct drainage volume can be the total drainage volume caused by the preset drainage fluid to the drainage test unit 120 during the test, and can be determined based on the changes in the liquid level in the drainage test unit 120. The total drainage received volume can be the total water volume discharged from the drainage test unit 120 during the test, and can be determined based on the changes in the liquid level in the drainage receiving unit 130. The total drainage transport volume can be the total water flow through the second transport pipe during the test, and can be obtained based on the water flow rate measured by the drainage flow rate measurement unit 140 and the duration of the test process.

[0067] The drainage condition data can be the unit condition data of the drainage test unit 120 and the drainage receiving unit 130, including but not limited to unit pressure data indicating the pressure magnitude in each unit, and unit temperature data indicating the temperature level in each unit. It can be understood that the drainage data to be processed and the drainage condition data are mutually corresponding, and the drainage condition data indicates the drainage conditions in the drainage capacity test system when the drainage data to be processed is generated.

[0068] Specifically, a drainage capacity testing system is used to obtain target drainage test data for a preset drainage fluid. During the test, the first and second delivery valves are opened, allowing the preset drainage fluid stored in the fluid storage unit 110 to enter the drainage test unit 120 through the first delivery pipe. The preset drainage fluid expands in the drainage test unit 120, applying pressure to the water in the drainage test unit 120, causing the water in the drainage test unit 120 to enter the drainage receiving unit 130 through the second delivery pipe, resulting in changes in the liquid levels in the drainage test unit 120 and the drainage receiving unit 130. After the test, the drainage data to be treated is obtained based on multiple measurement results from the drainage capacity testing system, including the total direct drainage volume, the total drainage receiving volume, and the total drainage transport volume. The total direct drainage volume is obtained from the liquid level change in the drainage test unit 120, the total drainage receiving volume is obtained from the liquid level change in the drainage receiving unit 130, and the total drainage transport volume is obtained from the measurement results of the drainage flow measurement unit 140. It is understood that the test process can be conducted under various drainage conditions to obtain drainage data to be treated under different drainage conditions. Different drainage conditions result in different drainage condition data. Target drainage test data can be obtained based on the drainage data to be treated and the corresponding drainage condition data.

[0069] Furthermore, the validity of the wastewater data to be processed is determined by judging whether it meets the preset data error conditions. If the wastewater data meets the preset data error conditions, the volume of wastewater caused by the preset wastewater fluid during the test is predicted based on the total direct wastewater volume, the total wastewater received volume, and the total wastewater transport volume, thus obtaining the wastewater capacity data of the preset wastewater fluid. It is understandable that by acquiring wastewater data from multiple locations in the wastewater capacity test system, and then performing error analysis based on the data from different locations, the validity and accuracy of the wastewater data to be processed are ensured, thereby improving the accuracy of the wastewater capacity data.

[0070] Furthermore, based on the drainage capacity data of the preset drainage fluid and its corresponding drainage condition data, data fitting is performed to construct a drainage capacity model for the preset drainage fluid. This model describes the drainage capacity data of the preset drainage fluid under target drainage conditions, where the target drainage condition can be any drainage condition, corresponding to any drainage condition data among multiple drainage condition data. Based on the drainage capacity model, the drainage capacity data of the preset drainage fluid under any drainage condition can be determined, thereby improving the control accuracy when using the preset drainage fluid for drainage in practical scenarios and achieving quantitative drainage control.

[0071] It should be noted that related technologies typically only utilize experimental data to determine the drainage capacity of a preset drainage fluid. However, experimental data is discrete and cannot accurately determine the drainage capacity of the preset drainage fluid under any given drainage condition. Furthermore, some related technologies utilize the state equation corresponding to the preset drainage fluid to directly calculate its drainage capacity. Compared to the aforementioned related technologies, the drainage capacity model in this application can represent the trend of continuous change in the drainage capacity of the preset drainage fluid with drainage conditions, improving the comprehensiveness and accuracy of the data. Simultaneously, in practical scenarios, the drainage capacity model can be used to determine the drainage capacity of the preset drainage fluid under current conditions based on the actual drainage conditions, thereby enabling accurate quantitative drainage control of the ship's water tanks and improving the ship's safety and maneuverability.

[0072] The drainage test data processing method provided in this embodiment obtains target drainage test data of a preset drainage fluid, and predicts the drainage capacity of the preset drainage fluid based on the target drainage test data to obtain drainage capacity data of the preset drainage fluid; based on the drainage capacity data and drainage condition data, a drainage capacity model of the preset drainage fluid is constructed, thereby enabling the drainage capacity of the preset drainage fluid under any drainage conditions to be determined using the drainage capacity model.

[0073] Compared with related technologies, this application conducts prior experiments on a preset drainage fluid and constructs a drainage capacity model of the preset drainage fluid based on the experimental results. This allows for the determination of the actual drainage capacity of the preset drainage fluid in real-world scenarios using the drainage capacity model and actual drainage conditions, achieving quantitative drainage control of the water storage tanks. This improves the accuracy of ship attitude adjustments and enhances ship safety and maneuverability. Furthermore, this application can effectively handle complex actual drainage conditions, reduce interference from the actual environment on drainage control, and improve the reliability of drainage control.

[0074] Reference Figure 3 As shown, in one embodiment of this application, a drainage capacity model for a preset drainage fluid is constructed based on drainage capacity data and multiple drainage condition data, including:

[0075] S310. Perform correlation analysis on multiple drainage condition data, and determine the corresponding data fitting model based on the correlation between the multiple drainage condition data.

[0076] S320. Analyze the optimal fitting number of the data fitting model based on the target drainage test data to obtain the target fitting number.

[0077] S330. Fit the drainage capacity data and multiple drainage condition data according to the target fitting number and the data fitting model to obtain the drainage capacity model.

[0078] Specifically, correlation analysis is performed on multiple drainage condition data to determine the correlation between them. The drainage condition data may include unit pressure data and test time data. The unit pressure data represents the pressure in the drainage receiving unit 130, and the test time data represents the duration of the test, i.e., the time from opening the first delivery valve and delivering the preset drainage medium to the end of drainage. Correlation analysis is performed on the unit pressure data and the test time data to obtain the correlation between them.

[0079] For example, correlation analysis methods can include image analysis or correlation coefficient calculation. Image analysis may involve plotting a scatter plot or a three-dimensional surface plot based on the trend of drainage capacity data changing with multiple drainage condition data, and determining the correlation between drainage condition data based on the distribution and trend of data points in the image. Correlation coefficient calculation may involve calculating a correlation coefficient using drainage condition data, and determining the correlation between drainage condition data based on the value of the correlation coefficient. The correlation coefficient used in the correlation coefficient calculation may be the Pearson correlation coefficient, which has the following form:

[0080]

[0081] in, The correlation coefficient; and Data for any drainage condition; and These are the sample means of the corresponding drainage condition data. If A value close to 1 or -1 indicates an approximately linear relationship between the drainage condition data; if... A value close to 0 indicates a non-linear relationship between the drainage condition data.

[0082] Furthermore, based on the correlation between multiple drainage condition data, a data fitting model corresponding to the correlation between the drainage condition data is determined for fitting the drainage capacity data and the multiple drainage condition data. For example, the data fitting model can be a linear regression model or a multinomial regression model, where the linear regression model corresponds to drainage condition data with an approximately linear correlation, and its form is as follows:

[0083]

[0084] in, This refers to drainage capacity data; and For drainage condition data, this section uses unit pressure data and test time data as examples. For unit pressure data, For test time data; , and These are the regression coefficients for the data fitting model. The multinomial regression model is used for drainage condition data where the correlation exhibits a non-linear relationship, and its form is as follows:

[0085]

[0086] in, , and These are the regression coefficients of the data fitting model.

[0087] Furthermore, before fitting the drainage capacity data and drainage condition data using the data fitting model, it is necessary to determine the target fitting number corresponding to the drainage capacity data and drainage condition data to reduce the probability of overfitting or underfitting during the fitting process. Based on the target drainage test data, an optimal fitting number analysis is performed on the selected data fitting model to obtain the target fitting number. The drainage capacity data and multiple drainage condition data are then fitted according to the target fitting number and the data fitting model to obtain the drainage capacity model.

[0088] Reference Figure 4As shown, in one embodiment of this application, the optimal fitting number analysis is performed on the data fitting model based on the target drainage test data to obtain the target fitting number, including:

[0089] S322. Based on the initial fitting number and the data fitting model, train and fit the target drainage test data used as training data to obtain the initial fitting model.

[0090] S324. Use the target drainage test data as test data to perform error analysis on the initial fitting model, and update the initial fitting number based on the error analysis results.

[0091] S326. Repeat the above steps of training and fitting the target drainage test data and updating the initial fitting number until the error analysis result meets the preset model error condition, and take the initial fitting number at this time as the target fitting number.

[0092] Specifically, based on a pre-set initial fitting count, all possible feature combinations are generated from multiple drainage condition data. Each feature combination is a combination of drainage condition data, and the number of times each feature combination is performed does not exceed the initial fitting count. For example, when the initial fitting count is 2, and the drainage condition data includes unit pressure data and test time data, the form of the feature combination is as follows:

[0093]

[0094] in, It is a combination of features.

[0095] Furthermore, the target drainage test data is divided into training data and test data in a proportional manner; for example, this ratio could be 7:3. Using the feature combination as input and the drainage capacity data as output, the target drainage test data (used as training data) is fitted using a corresponding regression model to obtain an initial fitted model.

[0096] Furthermore, error analysis is performed on the initial fitting model using the target drainage test data, which serves as the test data. The error index of the initial fitting model on the target drainage test data is calculated as the error analysis result, where the error index can be an index such as mean square error or coefficient of determination. If the error analysis result meets the preset model error condition, the initial fitting number at this time is taken as the target fitting number; if the error analysis result does not meet the preset model error condition, the initial fitting number is updated and adjusted according to the error analysis result, thereby obtaining the updated initial fitting number.

[0097] Furthermore, based on the updated initial fitting count, the above steps of training and fitting the target drainage test data and updating the initial fitting count are repeated until the error analysis results meet the preset model error conditions. The initial fitting count at this time is then used as the target fitting count.

[0098] Reference Figure 5 As shown in the embodiment of this application, the drainage data to be processed includes multiple simulated drainage data; the method for determining whether the drainage data to be processed meets the preset data error condition is as follows:

[0099] S202. Perform error analysis on any two simulated drainage data to obtain the drainage data error.

[0100] S204. If all drainage data errors exceed the data error threshold, it is determined that the drainage data to be processed does not meet the preset data error conditions.

[0101] S206. If any drainage data error does not exceed the data error threshold, the drainage data to be processed is determined to meet the preset data error condition.

[0102] Specifically, the simulated drainage data can be any one of the following: total direct drainage volume, total drainage reception volume, and total drainage transportation volume. It is understood that, assuming no error, the values ​​of the total direct drainage volume, total drainage reception volume, and total drainage transportation volume are the same. If the error between any two simulated drainage data sets exceeds a data error threshold, it indicates that one of the simulated drainage data sets corresponds to a sensor with an error. The error analysis process includes: calculating the difference between any two simulated drainage data sets, and then calculating the relative error between the two simulated drainage data sets based on the difference, which is taken as the drainage data error.

[0103] Furthermore, if all drainage data errors exceed the data error threshold, it indicates a significant error exists between the total direct drainage volume, the total drainage reception volume, and the total drainage transport volume. This error could originate from sensor errors or drift within each unit of the drainage capacity testing system. In this case, a warning can be issued to the testing personnel, reminding them to recalibrate the sensors in the drainage capacity testing system to reduce drainage data errors. Conversely, if any drainage data error does not exceed the data error threshold, it indicates that the error between the simulated drainage data corresponding to that error is small, and it can be considered valid simulated drainage data. In this case, the drainage data to be processed is determined to meet the preset data error condition, and the corresponding simulated drainage data is taken as valid test data.

[0104] Understandably, error analysis can also be performed on drainage condition data to determine whether the sensors used to acquire the data have errors or drift. Based on the error analysis results, warnings can be issued to the testing personnel to remind them to recalibrate the sensors in the drainage capacity testing system to improve the accuracy of the drainage condition data. For example, in the drainage capacity testing system, the pressure in the drainage test unit 120 and the pressure in the drainage receiving unit 130 are similar. If a large error occurs between the two, it indicates that the pressure sensor in the drainage test unit 120 or the drainage receiving unit 130 has an error or drift, requiring recalibration by the testing personnel.

[0105] Similarly, in the drainage capacity test system, temperature data inside the drainage receiving unit 130 can also be acquired. The temperature data inside the drainage receiving unit 130 is time-series data that changes over time, indicating the temperature change inside the drainage receiving unit 130 during the test. If the temperature change inside the drainage receiving unit 130 exceeds the preset temperature change range, it indicates that an abnormality has occurred in the test process, and the simulated drainage data obtained at this time cannot be used as valid test data.

[0106] Reference Figure 6 As shown, as one embodiment of this application, predicting the drainage capacity of a preset drainage fluid based on the drainage data to be processed includes:

[0107] S210. Determine the valid test data based on the numerical relationship between the drainage data error and the data error threshold; wherein, the valid test data is the simulated drainage data corresponding to the drainage data error that does not exceed the data error threshold.

[0108] S220. Analyze the average total drainage volume based on the valid test data to obtain drainage capacity data.

[0109] Specifically, if any drainage data error does not exceed the data error threshold, the simulated drainage data corresponding to that error is considered valid test data. In some embodiments, if multiple drainage data errors do not exceed the data error threshold, it indicates that the errors among the simulated drainage data corresponding to these multiple errors all meet the requirements, and in this case, all the simulated drainage data corresponding to these multiple errors can be considered valid test data.

[0110] Furthermore, based on the valid test data, an average analysis of the total drainage volume was performed to obtain drainage capacity data, which is in the following form:

[0111]

[0112] in, and These are valid experimental data. and These can be any one of the following: total direct drainage volume, total drainage reception volume, and total drainage transport volume. In some embodiments, if there are multiple valid test data points, the above formula can be adapted to perform an average analysis of the total drainage volume based on multiple valid test data points to obtain drainage capacity data.

[0113] Reference Figure 7 As shown, in one embodiment of this application, obtaining target drainage test data for a preset drainage fluid includes:

[0114] S110. Obtain initial drainage test data of the preset drainage fluid under target drainage conditions.

[0115] S120. Perform data synchronization preprocessing on the initial drainage test data to obtain the target drainage test data.

[0116] Specifically, under the set target drainage conditions, the drainage capacity of the preset drainage fluid is tested using a drainage capacity test system, and initial drainage test data is obtained based on the measurement results of the sensors in the drainage capacity test system, including the total amount of direct drainage without data synchronization preprocessing, the total amount of drainage received, and the total amount of drainage transported.

[0117] Furthermore, due to the individual response times and communication delays of each sensor in the drainage capacity testing system, there may be time asynchrony issues between the initial drainage test data, affecting the accuracy of the drainage capacity data and model. Therefore, it is necessary to perform data synchronization preprocessing on the initial drainage test data to align the data times of the initial drainage test data fed back from different sensors, reduce the deviation between the drainage capacity data and the drainage capacity model, and ensure the accuracy of both.

[0118] Furthermore, various data preprocessing techniques can be applied to the target drainage test data to further improve its accuracy and validity. Data preprocessing can include denoising filtering, outlier removal, and data interpolation. Denoising filtering can involve using filters to filter the sensors in the drainage capacity test system to reduce noise in the sensor measurement results. For example, the filter can be a Kalman filter.

[0119] Outlier removal can include identifying and removing outlier data points in the target drainage test data to reduce interference from sensor malfunctions or environmental factors on the test process. For example, outlier data points can be identified using statistical methods, including those based on the 3σ principle.

[0120] Data interpolation may include identifying and filling in missing data points in the target drainage test data. For example, missing data points may be filled in using methods such as linear interpolation or spline interpolation.

[0121] Reference Figure 8 As shown in one embodiment of this application, the initial drainage test data corresponds to a trigger timestamp indicating the data generation time and a reception timestamp indicating the data reception time. The acquisition process of the initial drainage test data corresponds to a response delay and a communication delay. Data synchronization preprocessing is performed on the initial drainage test data to obtain the target drainage test data, including:

[0122] S122. Sum the response delay and the communication delay to obtain the total delay.

[0123] S124. Perform delay compensation on the receiving timestamp of the initial drainage test data according to the total delay, so that the receiving timestamp is consistent with the trigger timestamp, and obtain the target drainage test data.

[0124] Specifically, the response delay can be the response time of the sensor in the drainage capacity test system, representing the time length between the generation time of the initial drainage test data and the time when the sensor measures the initial drainage test data. The communication delay can be the time length between the time when the sensor measures the initial drainage test data and the time when the initial drainage test data is transmitted to the data processing unit. Summing the response delay and communication delay yields the total delay, which represents the delay error between the receiving timestamp and the trigger timestamp of the initial drainage test data. Delay compensation is then applied to the receiving timestamp of the initial drainage test data based on the total delay, ensuring that the receiving timestamp matches the trigger timestamp, thus obtaining the target drainage test data. The form of delay compensation is as follows:

[0125]

[0126] in, The received timestamp after delay compensation; The timestamp of receipt before delay compensation; The total delay is in the following form:

[0127]

[0128] in, For response delay; This is due to communication delay.

[0129] Accordingly, please refer to Figure 9 This application provides a drainage test data processing device, the device comprising:

[0130] The drainage data test module 910 is used to acquire target drainage test data of a preset drainage fluid; wherein, the target drainage test data includes drainage data to be processed and multiple drainage condition data corresponding to the drainage data to be processed.

[0131] The drainage capacity prediction module 920 is used to predict the drainage capacity of a preset drainage fluid based on the drainage data to be processed, provided that the drainage data to be processed meets the preset data error conditions, and to obtain the drainage capacity data of the preset drainage fluid.

[0132] The capacity model fitting module 930 is used to construct a drainage capacity model of a preset drainage fluid based on drainage capacity data and multiple drainage condition data; wherein, the drainage capacity model is used to describe the drainage capacity data of the preset drainage fluid under target drainage conditions; the target drainage conditions correspond to any one of the multiple drainage condition data.

[0133] In some alternative implementations, the capability model fitting module 930 includes:

[0134] The correlation analysis unit is used to perform correlation analysis on multiple drainage condition data and determine the corresponding data fitting model based on the correlation between multiple drainage condition data.

[0135] The fitting number analysis unit is used to perform optimal fitting number analysis on the data fitting model based on the target drainage test data to obtain the target fitting number.

[0136] The model fitting unit is used to fit the drainage capacity data and multiple drainage condition data according to the target fitting number and the data fitting model to obtain the drainage capacity model.

[0137] In some optional implementations, the fitting number analysis unit includes:

[0138] The training fitting subunit is used to train and fit the target drainage test data, which is used as training data, based on the initial fitting number and data fitting model, to obtain the initial fitting model.

[0139] The error analysis subunit is used to perform error analysis on the initial fitted model using the target drainage test data as test data, and to update the initial fitting number based on the error analysis results.

[0140] Repeat the update sub-unit to repeat the above steps of training and fitting the target drainage test data and updating the initial fitting number until the error analysis result meets the preset model error condition. The initial fitting number at this time is taken as the target fitting number.

[0141] In some optional implementations, the drainage data to be processed includes multiple simulated drainage data; the drainage capacity prediction module 920 includes:

[0142] The drainage data error analysis unit is used to perform error analysis on any two simulated drainage data to obtain the drainage data error.

[0143] The first condition analysis unit is used to determine that the drainage data to be processed does not meet the preset data error conditions when all drainage data errors exceed the data error threshold.

[0144] The second condition analysis unit is used to determine whether the drainage data to be processed meets the preset data error conditions, provided that the error of any drainage data does not exceed the data error threshold.

[0145] In some optional implementations, the drainage capacity prediction module 920 further includes:

[0146] The effective data determination unit is used to determine the effective test data based on the numerical relationship between the drainage data error and the data error threshold; wherein, the effective test data is the simulated drainage data corresponding to the drainage data error that does not exceed the data error threshold.

[0147] The total drainage analysis unit is used to perform average analysis of total drainage based on valid test data to obtain drainage capacity data.

[0148] In some alternative implementations, the drainage data test module 910 includes:

[0149] The initial data acquisition unit is used to acquire initial drainage test data of the preset drainage fluid under target drainage conditions.

[0150] The data synchronization preprocessing unit is used to perform data synchronization preprocessing on the initial drainage test data to obtain the target drainage test data.

[0151] In some optional implementations, the initial drainage test data corresponds to a trigger timestamp indicating the time of data generation and a receive timestamp indicating the time of data reception; the acquisition process of the initial drainage test data corresponds to a response delay and a communication delay; the data synchronization preprocessing unit includes:

[0152] The delay calculation subunit is used to sum the response delay and communication delay to obtain the total delay.

[0153] The delay compensation subunit is used to compensate for the delay of the initial drainage test data receiving timestamp based on the total delay, so that the receiving timestamp is consistent with the trigger timestamp, and thus obtain the target drainage test data.

[0154] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0155] In this embodiment, the drainage test data processing device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0156] Please see Figure 10 , Figure 10 This is a schematic diagram of a computer device according to an embodiment of this application. As shown in the figure, the computer device includes one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components communicate with each other using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 10 Take a processor 10 as an example.

[0157] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0158] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0159] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0160] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0161] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0162] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0163] This application provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method of any embodiment of this application.

[0164] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

[0165] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0166] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0167] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0168] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0169] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0170] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0171] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0172] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0173] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

[0174] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for processing drainage test data, characterized in that, The method includes: Obtain target drainage test data for a preset drainage fluid; wherein, the target drainage test data includes drainage data to be processed and multiple drainage condition data corresponding to the drainage data to be processed; If the drainage data to be processed meets the preset data error condition, the drainage capacity of the preset drainage fluid is predicted based on the drainage data to be processed, and the drainage capacity data of the preset drainage fluid is obtained. A correlation analysis is performed on the multiple drainage condition data, and a corresponding data fitting model is determined based on the correlation between the multiple drainage condition data. An optimal fitting number analysis is performed on the data fitting model based on the target drainage test data to obtain the target fitting number. The drainage capacity data and the multiple drainage condition data are fitted based on the target fitting number and the data fitting model to obtain a drainage capacity model. The drainage capacity model is used to describe the drainage capacity data of the preset drainage fluid under target drainage conditions, and the target drainage conditions correspond to any one of the multiple drainage condition data.

2. The method according to claim 1, characterized in that, The step of performing optimal fitting number analysis on the data fitting model based on the target drainage test data to obtain the target fitting number includes: Based on the initial fitting number and the data fitting model, the target drainage test data used as training data is trained and fitted to obtain the initial fitting model. Error analysis is performed on the initial fitting model using the target drainage test data as test data, and the initial fitting number is updated based on the error analysis results; Repeat the above steps of training and fitting the target drainage test data as training data and updating the initial fitting number according to the error analysis results until the error analysis results meet the preset model error conditions, and take the initial fitting number at this time as the target fitting number.

3. The method according to claim 1, characterized in that, The drainage data to be processed includes multiple simulated drainage data; whether the drainage data to be processed meets the preset data error conditions is determined by the following method: Error analysis is performed on any two simulated drainage data to obtain the drainage data error; If all drainage data errors exceed the data error threshold, it is determined that the drainage data to be processed does not meet the preset data error condition. If any drainage data error does not exceed the data error threshold, the drainage data to be processed is determined to meet the preset data error condition.

4. The method according to claim 3, characterized in that, The prediction of the drainage capacity of the preset drainage fluid based on the drainage data to be processed includes: Valid test data are determined based on the numerical relationship between the drainage data error and the data error threshold; wherein, the valid test data is simulated drainage data corresponding to drainage data errors that do not exceed the data error threshold; The drainage capacity data is obtained by performing an average analysis of the total drainage volume based on the valid test data.

5. The method according to any one of claims 1 to 4, characterized in that, The acquisition of target drainage test data for the preset drainage fluid includes: Obtain initial drainage test data of the preset drainage fluid under the target drainage conditions; The initial drainage test data is subjected to data synchronization preprocessing to obtain the target drainage test data.

6. The method according to claim 5, characterized in that, The initial drainage test data corresponds to a trigger timestamp indicating the data generation time and a reception timestamp indicating the data reception time. The acquisition process of the initial drainage test data corresponds to response latency and communication latency. The step of performing data synchronization preprocessing on the initial drainage test data to obtain the target drainage test data includes: The total delay is obtained by summing the response delay and the communication delay. The initial drainage test data reception timestamp is compensated for based on the total delay, so that the reception timestamp is consistent with the trigger timestamp, thereby obtaining the target drainage test data.

7. A drainage test data processing device, characterized in that, The device includes: The drainage data test module is used to acquire target drainage test data of a preset drainage fluid; wherein, the target drainage test data includes drainage data to be processed and multiple drainage condition data corresponding to the drainage data to be processed; The drainage capacity prediction module is used to predict the drainage capacity of the preset drainage fluid based on the drainage data to be processed, provided that the drainage data to be processed meets the preset data error conditions, so as to obtain the drainage capacity data of the preset drainage fluid. The capacity model fitting module is used to perform correlation analysis on the multiple drainage condition data, determine the corresponding data fitting model based on the correlation between the multiple drainage condition data, perform optimal fitting number analysis on the data fitting model based on the target drainage test data to obtain the target fitting number, and fit the drainage capacity data and the multiple drainage condition data based on the target fitting number and the data fitting model to obtain the drainage capacity model; wherein, the drainage capacity model is used to describe the drainage capacity data of the preset drainage fluid under the target drainage conditions; the target drainage conditions correspond to any one of the multiple drainage condition data.

8. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1 to 6.

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