Lead screw perpendicularity detection method, device and equipment and storage medium

Abstract

The invention discloses a lead screw perpendicularity detection method and device, equipment and a storage medium. The method comprises the steps that vibration data collected by a vibration sensor installed on a working platform are acquired; processing the vibration data to obtain target data meeting a preset condition; performing feature extraction on the target data to obtain an acceleration vector of the screw rod; according to the acceleration vector, obtaining motion dominant direction vectors in two orthogonal directions; calculating a vector included angle between the two motion dominant direction vectors; and obtaining the perpendicularity deviation value according to the vector included angle. According to the method for detecting the perpendicularity of the lead screw, the perpendicularity deviation of the lead screw can be rapidly and accurately detected without disassembling a machine, the operation difficulty and the technical threshold can be reduced, and the detection efficiency and the normal utilization rate of equipment can be improved conveniently.

Description

Technical Field

[0001] This application relates to the field of testing methods, and more specifically, to a method, apparatus, equipment, and storage medium for testing the perpendicularity of a lead screw. Background Technology

[0002] XY-axis lead screws are typically key transmission components for precise positioning of a work platform, driving its movement along the X and Y axes. To ensure accurate platform movement, the XY-axis lead screws must maintain an ideal perpendicular relationship in space. If the perpendicularity is not met, the actual platform movement will deviate from the theoretical command, leading to out-of-tolerance machining of parts or assembly failures, resulting in significant waste of materials and time. Current methods for checking the perpendicularity of XY-axis lead screws usually require disassembling the equipment, which is cumbersome and time-consuming, affecting the normal operation of the equipment. Summary of the Invention

[0003] This application provides a method, apparatus, device, and storage medium for detecting the verticality of a lead screw to solve at least one of the aforementioned technical problems.

[0004] The method for detecting the verticality of the lead screw according to the embodiments of this application includes: Acquire the vibration data of the lead screw collected by the vibration sensor installed on the working platform; The vibration data is processed to obtain target data that meets preset conditions; Temporal features are extracted from the target data to obtain the acceleration vector of the lead screw; Based on the acceleration vector, obtain the dominant motion direction vectors in the two orthogonal directions, x and y; Calculate the vector angle between the two dominant motion direction vectors; The verticality deviation value of the lead screw is obtained based on the vector angle, and the verticality deviation value = |vector angle - 90°|.

[0005] The method for detecting the verticality of the lead screw provided in this application collects vibration data and analyzes the acceleration vector using a vibration sensor. Simultaneously, it quantifies the verticality deviation by calculating the vector angle. This method enables rapid and accurate detection of the lead screw verticality deviation without disassembling the machine, which helps reduce operational difficulty and technical barriers, and facilitates improved detection efficiency and normal equipment utilization.

[0006] In some embodiments, acquiring vibration data collected by a vibration sensor mounted on the work platform includes: The control inspection process is initiated, and an inspection signal acquisition command is sent to the vibration sensor temporarily installed on the work platform; Obtain the vibration data collected by the vibration sensor during point inspection.

[0007] In this way, by controlling the start of the inspection process and acquiring inspection vibration data, rapid data acquisition in temporary inspection scenarios is achieved, which helps to reduce the threshold and cost of inspection operations.

[0008] In some embodiments, acquiring vibration data collected by a vibration sensor mounted on the work platform includes: The long-term monitoring process is initiated, and a periodic signal acquisition command is sent to the vibration sensor fixedly installed on the working platform; The periodic vibration data within a preset period collected by the vibration sensor is acquired.

[0009] In this way, by controlling the long-term monitoring process and acquiring periodic vibration data, continuous monitoring of the equipment status is achieved, making it easier to detect changes in the verticality of the lead screw in a timely manner.

[0010] In some embodiments, processing the vibration data to obtain target data that meets preset conditions includes: The vibration data is subjected to integrity detection, and the integrity score of the vibration data is calculated based on the integrity detection results. The vibration data is evaluated for signal-to-noise ratio (SNR), and the vibration data is scored based on the SNR evaluation results. The vibration data is subjected to signal error detection, and the vibration data is scored based on the signal error detection results. Based on the integrity score, the signal-to-noise ratio score, and the signal error score, a comprehensive quality score is calculated by weighted averaging; vibration data with a comprehensive quality score greater than or equal to a preset score are identified as target data that meet the preset conditions.

[0011] In this way, through multi-dimensional quality inspection and weighted scoring mechanisms, a quantitative assessment of data quality is achieved, which helps to ensure the accuracy and reliability of subsequent analysis.

[0012] In some embodiments, the method includes: If the overall quality score is less than the preset score, the vibration data is deemed unqualified, and feedback for manual review is issued.

[0013] In this way, by setting quality thresholds and implementing a manual review mechanism, hierarchical control of data quality is achieved, which helps to prevent invalid data from entering subsequent processing procedures.

[0014] In some embodiments, the step of extracting features from the target data to obtain the acceleration vector of the lead screw includes: Based on the target data, obtain the acceleration vectors of the target data on the three coordinate axes x, y, and z, and obtain the composite acceleration amplitude based on the acceleration vectors of the three coordinate axes x, y, and z. The starting point of the motion of the working platform from rest is obtained based on the acceleration amplitude. When the acceleration amplitude is greater than a preset amplitude, the time node corresponding to the start of motion of the working platform is determined as the starting point of motion. The target data is divided into multiple motion segments based on the motion start point and preset amplitude. The acceleration vector is obtained by performing principal component analysis on the data of each motion segment.

[0015] Thus, by detecting motion segments and performing principal component analysis, the motion features are accurately extracted, which facilitates the accurate acquisition of the actual motion direction of the lead screw. In addition, by obtaining the motion starting point through acceleration amplitude and dividing the motion segments by judging the preset amplitude, the effective motion data is accurately extracted, which helps to eliminate the interference of invalid data segments.

[0016] In some embodiments, the principal component analysis includes: For each motion segment of the target data, the motion direction is extracted, and a data matrix is ​​constructed based on the x-direction acceleration sequence and the y-direction acceleration sequence; The data matrix is ​​centralized and the centralized data is obtained; Obtain the covariance matrix based on the centralized data; Calculate the eigenvalues ​​of the covariance matrix and the eigenvectors corresponding to the eigenvalues, wherein the eigenvector corresponding to the largest eigenvalue is the acceleration vector.

[0017] Thus, through the complete principal component analysis process, the dominant direction of motion is accurately extracted, making it easier to obtain the most representative acceleration vector.

[0018] Another embodiment of the lead screw verticality detection device of this application includes: The first acquisition module is used to acquire vibration data collected by vibration sensors installed on the working platform; The processing module is used to process the vibration data to obtain target data that meets preset conditions; The extraction module is used to extract features from the target data to obtain the acceleration vector of the lead screw; The second acquisition module is used to acquire the dominant motion direction vectors in the x and y orthogonal directions based on the acceleration vector; The first calculation module is used to calculate the vector angle between the two dominant motion direction vectors; The second calculation module is used to obtain the verticality deviation value based on the vector angle, where the verticality deviation value = |vector angle - 90°|.

[0019] Another embodiment of the lead screw perpendicularity detection device of this application includes a processor and a memory, the memory storing a computer program that, when executed by the processor, causes the processor to implement the instructions of the method described in any of the preceding claims.

[0020] Another embodiment of this application is a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the preceding claims.

[0021] Additional aspects and advantages of embodiments of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of this application. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating the method for detecting the verticality of the lead screw according to an embodiment of this application. Figure 2 This is a schematic diagram of the movement direction of the working platform of the screw verticality detection method according to the embodiments of this application; Figure 3 This is a flowchart illustrating a method for detecting the verticality of a lead screw according to one embodiment of this application. Figure 4 This is a flowchart illustrating a method for detecting the verticality of a lead screw according to one embodiment of this application. Figure 5 This is a flowchart illustrating a method for detecting the verticality of a lead screw according to one embodiment of this application. Figure 6 This is a flowchart illustrating a method for detecting the verticality of a lead screw according to one embodiment of this application. Figure 7 This is a flowchart illustrating a method for detecting the verticality of a lead screw according to one embodiment of this application. Figure 8 This is a flowchart illustrating a method for detecting the verticality of a lead screw according to one embodiment of this application. Figure 9This is a flowchart illustrating a method for detecting the verticality of a lead screw according to one embodiment of this application. Figure 10 This is a schematic diagram of the motion segment division of a screw verticality detection method according to a certain embodiment of this application; Figure 11 This is a flowchart illustrating a method for detecting the verticality of a lead screw according to one embodiment of this application. Figure 12 This is a flowchart illustrating a method for detecting the verticality of a lead screw according to one embodiment of this application. Figure 13 This is a schematic diagram of the module of the lead screw verticality detection device according to an embodiment of this application; Figure 14 This is a schematic diagram of the module of the lead screw verticality detection device according to the embodiments of this application.

[0023] Explanation of reference numerals in the attached drawings: 100 for detecting the verticality of the lead screw, 10 for detecting the verticality of the lead screw, 11 for the first acquisition module, 12 for the processing module, 13 for the extraction module, 14 for the second acquisition module, 15 for the first calculation module, 16 for the second calculation module, 20 for the processor, and 30 for the memory. Detailed Implementation

[0024] The embodiments of this application will be further described below with reference to the accompanying drawings. The same or similar reference numerals in the drawings denote the same or similar elements or elements having the same or similar functions throughout.

[0025] Furthermore, the embodiments of this application described below in conjunction with the accompanying drawings are exemplary and are only used to explain the embodiments of this application, and should not be construed as limiting this application.

[0026] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0027] This application provides a method, apparatus, device, and storage medium for detecting the perpendicularity of a lead screw. Specifically, the method for detecting the perpendicularity of a lead screw in this application can be executed by a lead screw perpendicularity detection device 100, which can be a terminal or a server. The terminal can be a smartphone, tablet, laptop, smart TV, wearable smart device, smart vehicle terminal, etc. The terminal can also include a client, which can be a game client, browser client, instant messaging client, or mini-program, etc. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0028] For example, when the method for detecting the verticality of the lead screw is used on a terminal device, the terminal device may include a display screen and a processor. The display screen is used to present game visuals and receive commands generated by the player interacting with the game visuals. The game visuals may include a portion of a virtual game scene, which is a virtual world where virtual characters interact. The processor is used to store the game application, run the game, generate game visuals, respond to commands, and control the display of the game visuals on the display screen. When the player interacts with the game visuals through the display screen, the game visuals can control the local content of the terminal device in response to the received operation commands. The terminal device can provide the graphical user interface to the player in various ways, such as rendering the display on the terminal device's screen or presenting the graphical user interface through holographic projection.

[0029] For example, when the method for detecting the perpendicularity of the lead screw runs on a server, it can be implemented and executed based on a cloud gaming system. A cloud gaming system refers to a gaming method based on cloud computing. A cloud gaming system includes servers and client devices. The main body running the game application and the main body displaying the game screen are separate. The storage and operation of the lead screw perpendicularity detection method are completed on the server. The game screen display is completed on the client, which is mainly used for receiving and sending game data and displaying the game screen. For example, the client can be a display device with data transmission capabilities close to the player, such as a mobile terminal, television, computer, PDA, personal digital assistant, head-mounted display device, etc. However, the terminal device for processing game data is the server in the cloud. During gameplay, the player operates the client to send commands to the server. The server controls the game operation according to the commands, encodes and compresses game screen data, returns it to the client via the network, and finally, the client decodes and outputs the game screen.

[0030] It should be noted that, in this embodiment, the execution entity of the lead screw perpendicularity detection method can be a terminal device or a server. The terminal device can be a local terminal device or a client device in the aforementioned cloud gaming. This embodiment does not limit the type of execution entity.

[0031] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for detecting the perpendicularity of a lead screw according to an embodiment of this application. It should be noted that the steps shown may be executed in a different logical order than that shown in the flowchart. The method may include the following steps: Step 110: Obtain vibration data collected by the vibration sensor installed on the working platform.

[0032] Specifically, vibration sensors can collect vibration data from the work platform in two ways: spot inspection mode and continuous monitoring mode. In some specific embodiments, in spot inspection mode, when collecting vibration data, the movement direction of the work platform is as follows: Figure 2As shown, the starting position of the working platform is O(0,0), located at the center of the xy-axis lead screw stroke to be tested. It performs reciprocating motions to eight target points (P1~P8), i.e., moving linearly from the origin O to P1 and then back to the origin O. The same applies to target points (P2~P8), for a total of eight reciprocating linear motions of the same path length. This includes independent x-axis motion segments (P1 / P5), independent y-axis motion segments (P3 / P7), and coordinated x and y-axis motion segments (P2 / P4 / P6 / P8). The verticality of the independent x-axis and independent y-axis motion segments, as well as the verticality of adjacent coordinated x and y-axis motion segments (e.g., P2&P4, P4&P6, P6&P8, P8&P2, a total of four groups), is then evaluated to deduce the overall verticality of the xy-axis lead screw motion. The target points for the coordinated x and y-axis motion segments can be flexibly set; four mutually perpendicular motion trajectories of the same path length can be selected. Based on the principal component analysis (PCA) and covariance matrix, the feature vectors of the target paths for each target point are obtained. The verticality deviation values ​​between adjacent target points in each independent motion segment and each coordinated motion segment are calculated based on these feature vectors. The average verticality deviations of each independent motion segment and each coordinated motion segment are then taken to obtain the average verticality deviation of the independent motion segment and the average verticality deviation of the coordinated motion segment. The overall verticality of the motion is inferred from the average verticality deviations of the independent motion segment, the average verticality deviation of the coordinated motion segment, and their corresponding preset weights. The preset weights can be set to an independent motion segment: coordinated motion segment ratio of 1:1. A higher weight for the independent motion segment results in a more lenient assessment of the overall verticality.

[0033] Specifically, the perpendicularity deviation between adjacent target points of each independent motion segment and each cooperative motion segment is calculated based on the target path feature vector. This is done by two theoretically perpendicular target paths. Specifically, the angles of the independent motion segments ∠P1P3, ∠P3P5, ∠P5P7, and ∠P7P1 are calculated pairwise and compared with the standard 90° perpendicularity. Similarly, the angles of the cooperative motion segments ∠P2P4, ∠P4P6, ∠P6P8, and ∠P48P2 are calculated pairwise and compared with the standard 90° perpendicularity.

[0034] like Figure 3 As shown, step 110 can achieve the acquisition of vibration data in the inspection mode through steps 111 to 112, specifically as follows: Step 111: Start the control inspection process and send an inspection signal acquisition command to the vibration sensor temporarily installed on the work platform.

[0035] Step 112: Obtain the vibration data collected by the vibration sensor.

[0036] In this way, by controlling the start of the inspection process and acquiring inspection vibration data, rapid data acquisition in temporary inspection scenarios is achieved, which helps to reduce the threshold and cost of inspection operations.

[0037] In some embodiments, the inspection mode can be a manual or semi-automatic inspection process. In the inspection mode, the vibration sensor can be temporarily installed on the equipment working platform. Specifically, the vibration sensor can be detachably installed on the working platform through fasteners, bolts, and other connecting parts.

[0038] like Figure 3 As shown, step 110 also includes step 113, specifically: Step 113: Temporarily store the vibration data from the inspection on a local storage device or upload it to a cloud server.

[0039] In this way, by temporarily storing the vibration data at the point of inspection on a local storage device or uploading it to a cloud server, flexible storage and remote access to the detection data are achieved, which is conducive to subsequent data traceability and centralized analysis, and facilitates the improvement of data management efficiency and convenience.

[0040] Specifically, due to the often unstable network conditions on-site, local storage ensures 100% successful data acquisition and avoids work interruptions caused by signal problems. Although the vibration data at this stage is a temporary inspection record, the sampling frequency is high and the original files are large. Uploading it in real time via cellular network would result in very high accumulated traffic costs over time. More importantly, the inspection work requires immediate feedback: if on-site personnel find data anomalies, they can immediately retest or investigate using the real-time analysis capabilities of local devices. This is much more efficient than waiting for the data to be uploaded to the cloud for analysis and then receiving the results. Therefore, inspection vibration data is usually temporarily stored on local storage devices.

[0041] Optional, such as Figure 4 As shown, step 110 can achieve the acquisition of vibration data in continuous monitoring mode through steps 114 to 115, specifically as follows: Step 114: Start the long-term monitoring process and send a periodic signal acquisition command to the vibration sensor fixedly installed on the working platform.

[0042] Step 115: Obtain periodic vibration data within a preset period collected by the vibration sensor.

[0043] In this way, by controlling the long-term monitoring process and acquiring periodic vibration data, continuous monitoring of the equipment status is achieved, making it easier to detect changes in the verticality of the lead screw in a timely manner.

[0044] In some embodiments, the continuous monitoring mode is suitable for long-term status tracking of production line equipment. In the continuous monitoring mode, the vibration sensor can be fixedly installed on the equipment working platform. Specifically, the vibration sensor can be installed on the working platform by means of bolt connection, riveting, etc.

[0045] like Figure 4 As shown, step 110 also includes step 116, specifically: Step 116: Temporarily store the periodic vibration data on a local storage device or upload it to a cloud server.

[0046] In this way, by temporarily storing the periodic vibration data on local storage devices or uploading it to cloud servers, effective storage and cloud backup of long-term monitoring data are achieved, which is conducive to the accumulation and in-depth mining of historical data and facilitates trend analysis and predictive maintenance of equipment status.

[0047] Specifically, the data stream from continuous monitoring is stable and uninterrupted, and the total amount of data grows exponentially over time. Local storage devices cannot handle this in terms of capacity and reliability, and data overwriting or loss is very likely to occur. Therefore, unlike the inspection scenario, the periodic vibration data obtained during continuous monitoring is more suitable for direct upload to the cloud server.

[0048] Step 120: Process the vibration data to obtain target data that meets the preset conditions.

[0049] In some embodiments, before calculating the verticality deviation value, the vibration data should first be processed such as quality inspection to ensure the data quality is good and the reliability of subsequent analysis.

[0050] In some embodiments, the data should be filtered during quality inspection, for example, by using a low-pass filter to extract the signal at 20 Hz from the target data.

[0051] In some embodiments, if the vibration data has been calibrated by software before acquisition, gravity component removal is not performed; if the vibration data has not been calibrated by software before acquisition, the mean gravity component when the working platform is stationary is subtracted from the filtered target data.

[0052] Thus, by using low-pass filtering, effective signal preprocessing is achieved, which helps to improve the accuracy of motion feature extraction.

[0053] Optional, such as Figure 5 As shown, step 120 can be achieved through steps 121 to 125, specifically as follows: Step 121: Perform integrity checks on the vibration data and score the integrity of the vibration data based on the results.

[0054] Integrity detection checks for null values, garbled text, and invalid characters in vibration data, and uses data timestamps to check whether the sampling frequency is uniform.

[0055] like Figure 3 As shown, step 121 can be implemented through steps 1211 to 1212, specifically as follows: Step 1211: Calculate the data missing rate of vibration data.

[0056] Step 1212: Check whether the continuous timestamps of the vibration data meet the expected sampling frequency.

[0057] In this way, by detecting the data missing rate and sampling frequency, an accurate assessment of data integrity can be achieved, which facilitates the rapid identification of collection anomalies.

[0058] Specifically, in this embodiment, the data missing rate = (actual number of sampling points / total expected number of sampling points) × 100%. Generally, when the data missing rate is >1%, it means that the quality of the current vibration data is questionable, and when the data missing rate is >5%, it can be determined that the current vibration data is abnormal.

[0059] In this embodiment, the standard deviation of the timestamps is typically used to determine whether the continuous timestamps of the vibration data meet the expected sampling frequency. Specifically: ; in, The standard deviation of the timestamp. N The total number of timestamp data. The i-th timestamp value This is the sample mean. Typically, When the deviation from the nearest point exceeds 1 / 10 of the sampling interval, the current vibration data is considered abnormal.

[0060] Step 122: Evaluate the signal-to-noise ratio of the vibration data and score the signal-to-noise ratio of the vibration data based on the results.

[0061] like Figure 7 As shown, step 122 can be implemented through steps 1221 to 1225, specifically as follows: Step 1221: Obtain the variance of the triaxial composite acceleration vector values ​​of the working platform during the dynamic activity period.

[0062] Step 1222: Calculate the signal power of the vibration data based on the variance of the triaxial composite acceleration vector.

[0063] Step 1223: Obtain the variance of the three-axis average data of the working platform during the static period.

[0064] Step 1224: Calculate the noise power of the vibration data based on the variance of the triaxial average data.

[0065] Step 1225: Calculate the signal-to-noise ratio of the vibration data based on the signal power and noise power.

[0066] Thus, by analyzing the signal characteristics during the dynamic and static periods, the signal-to-noise ratio can be accurately calculated, which is beneficial for objectively evaluating signal quality.

[0067] Specifically, signal-to-noise ratio SNR dB The calculation method is as follows: ; Where: P signal The signal power can be represented by the variance of the triaxial composite acceleration vector during the dynamic operation period of the working platform, P. noise The noise power can be taken as the variance of the triaxial composite acceleration vector during a known stationary period of the working platform (e.g., when the equipment is placed flat and stationary). In the embodiments of this application, SNR dB When the value is less than 15dB, the vibration data is considered abnormal.

[0068] Step 123: Detect signal error in the vibration data and score the signal error of the vibration data based on the results.

[0069] like Figure 8 As shown, step 123 can be implemented through steps 1231 to 1232, specifically as follows: Step 1231: When the work platform is stationary, determine whether the sum of the three-axis acceleration vectors of the work platform is approximately equal to the local gravitational acceleration.

[0070] Step 1232: After software calibration, determine whether the triaxial vector sum of the vibration data is 0.

[0071] In this way, through gravity acceleration calibration verification and zero-point check, the system error can be effectively detected, which helps to ensure the accuracy of sensor acquisition.

[0072] In this embodiment of the application, the sum of the three-axis vectors refers to the sum of the acceleration vectors of the working platform on the three coordinate axes. If so, then the vibration data is judged to be abnormal. , and These are the acceleration vectors of the working platform on the three coordinate axes, where g is the acceleration due to gravity.

[0073] Step 124: Calculate the overall quality score by weighted averaging based on the integrity score, signal-to-noise ratio score, and signal error score.

[0074] Step 125: Vibration data with a comprehensive quality score greater than or equal to a preset score are identified as target data that meet the preset conditions.

[0075] In this way, through multi-dimensional quality inspection and weighted scoring mechanisms, a quantitative assessment of data quality is achieved, which helps to ensure the accuracy and reliability of subsequent analysis.

[0076] Specifically, in this embodiment, a weight can be assigned to each of the integrity score, signal-to-noise ratio score, and signal error score. These weights can be derived from experience and multiple experimental verifications. A comprehensive quality score is obtained through a weighted average. The calculation method for the comprehensive quality score is as follows: Q total =w1·Q 完整性 +w2·Q 信噪比 +w3·Q 误差 ; Among them, Q total For the overall quality score, w1 is the weight of the completeness score, and Q is the weight of the completeness score. 完整性 For integrity scoring, w2 is the signal-to-noise ratio scoring weight, and Q is the score. 信噪比 For signal-to-noise ratio (SNR) score, w3 is the signal error score weight, and Q... 误差 Score the signal error.

[0077] Furthermore, by comparing the comprehensive quality score with the preset score, it is determined whether the vibration data meets the target data conditions. In this embodiment of the application, it is possible to select when Q... total When the vibration data is ≥60, the vibration data can be used as target data that meets the preset conditions.

[0078] Optional, such as Figure 5 As shown, step 120 may also include step 126, which is as follows: Step 126: If the overall quality score is less than the preset score, the vibration data is deemed unqualified, and feedback requiring manual review is issued.

[0079] In this way, by setting up preset scoring and manual review mechanisms, a hierarchical control of data quality is achieved, which helps to prevent invalid data from entering the subsequent processing flow.

[0080] In some embodiments, the overall quality score can be further refined, for example, when Q... total When the vibration data is ≥80, it can be used as target data and directly applied to subsequent analysis; when 60≤Q total When Q < 80, the vibration data may have minor issues, which should be considered during subsequent analysis and processing; when Q ≤ 40 total When Q < 60, the vibration data quality is questionable and requires manual review; when Q total When the vibration data is less than 40, the quality is substandard and it is recommended to discard it.

[0081] Step 130: Extract features from the target data to obtain the acceleration vector of the lead screw.

[0082] Optional, such as Figure 9 As shown, step 130 can be implemented through steps 131 to 134, specifically as follows: Step 131: Based on the target data, obtain the acceleration vectors of the target data on the three coordinate axes x, y, and z, and obtain the composite acceleration amplitude based on the acceleration vectors on the three coordinate axes x, y, and z.

[0083] Step 132: Obtain the starting point of the motion of the work platform from rest based on the acceleration amplitude. When the acceleration amplitude is greater than the preset amplitude, determine the time node corresponding to the start of the motion of the work platform as the starting point of the motion.

[0084] In some embodiments, segmentation is typically based on the synthetic acceleration amplitude and a preset amplitude determined according to the normal motion amplitude. The preset amplitude determined according to the normal motion amplitude is dynamically determined based on collected static / low-activity amplitude data. The specific steps are as follows: After acquiring the target data, filtering is performed to remove high-frequency noise, and the combined acceleration amplitude in the x, y, and z axes is calculated. Composite acceleration amplitude a 合成 The method for calculating [n] is as follows: ; in, a x [n]、 a y [n] and a z [n] represents the acceleration of the working platform on the x, y, and z axes at the nth sampling time. When... a 合成 If [n] > preset amplitude, then this sampling time is the start of motion; Select a static / low-activity data segment from the target data (usually select 5-10 seconds of static environmental data recorded at the beginning or end of data acquisition or extract the pause waiting period), and find the continuous segment with the smallest variation by using a sliding window (0.5~2s window length) to ensure that the signal segment can represent the normal state of the equipment and serve as the equipment reference segment; The preset amplitude is confirmed using the 6σ method for the signal of the equipment reference section. The following values ​​are calculated: the average value μ of the equipment reference section signal and the standard deviation σ of the equipment reference section. The upper preset amplitude (for positive acceleration) and the lower preset amplitude (for negative acceleration) are determined: μ ± 3σ.

[0085] The calculated upper preset amplitude is applied to the entire data sequence. When the combined acceleration value a continuously exceeds the upper preset amplitude (a minimum motion duration detection of 0.5 seconds needs to be set to avoid misjudgment caused by environmental noise or impact signals), it is marked as the start of motion. When the combined acceleration value a falls back to within the upper preset amplitude, it is marked as the end of motion.

[0086] The physical starting point of motion is the initial position of the working platform, located at the center of the stroke of the x and y axis lead screw to be measured. Optionally, the preset amplitude can be selected according to the model and size of the equipment. For example, when the equipment is a small precision lead screw device (such as a 3C machining tool), the preset amplitude can be set to 0.1~0.3g to accurately capture the start of minute movements; when the equipment is a large heavy-duty lead screw device (such as a heavy CNC machine tool), the preset amplitude can be set to 0.3~0.6g to avoid interference from environmental vibrations.

[0087] Step 133: Divide the target data into multiple motion segments based on the starting point of the motion and the preset amplitude; In the embodiments of this application, such as Figure 10 As shown, the filtered and denoised target data is divided into motion segments according to the starting point of the motion and the preset amplitude. For example, the target data is divided into a forward acceleration segment, a constant speed motion segment, a deceleration segment, an acceleration segment, a constant speed motion segment, and a reverse deceleration segment.

[0088] Step 134: Perform principal component analysis on the data from each motion segment to obtain the acceleration vector.

[0089] Thus, by segmenting motion segments and performing principal component analysis, the motion features are accurately extracted, facilitating the accurate acquisition of the actual motion direction of the lead screw. Furthermore, by obtaining the motion starting point through acceleration amplitude and segmenting motion segments based on preset amplitude, the effective motion data is accurately extracted, which helps to eliminate interference from invalid data segments.

[0090] Specifically, such as Figure 11 As shown, step 134 can be implemented through steps 1341 to 1344, wherein: Step 1341: Extract the motion direction for each motion segment of the target data, and construct a data matrix based on the x-direction acceleration sequence and the y-direction acceleration sequence.

[0091] In some embodiments, firstly, for each motion segment of the target data, the motion direction is extracted, specifically: ; in, This represents the change in velocity or the average velocity component over a certain time interval. `tend` and `tstart` represent the start and end times, respectively, and `Ti` represents the length of the time interval. tstart, This represents the acceleration vector that changes over time.

[0092] Furthermore, taking the horizontal movement of the work platform as an example, the acceleration sequence in the x-direction is ax(1), ax(2), ..., ax(N), ax(1), ax(2), ..., ax(N), and the acceleration sequence in the y-direction is ay(1), ay(2), ..., ay(N), ay(1), ay(2), ..., ay(N). A data matrix X is constructed based on the x-direction and y-direction acceleration sequences. ; Where N is the number of data points in the motion segment, and ax(i) and ay(i) are the accelerations of the i-th sampling point in the x and y directions, respectively.

[0093] Step 1342: Centralize the data matrix and obtain the centralized data.

[0094] In some embodiments, the centralized data matrix Xcentered is calculated as follows: ; Here, 1 is an N×N matrix with all elements being 1, used to expand the mean vector to match the dimension of X.

[0095] The mean of the acceleration sequence in the x-direction is calculated using the following formula: , This is the i-th sample of the acceleration sequence in the x-direction; The mean of the acceleration sequence in the y-direction is calculated using the following formula: ; Let be the i-th sample of the acceleration sequence in the y-direction.

[0096] Step 1343: Obtain the covariance matrix based on the centralized data.

[0097] In some embodiments, the centralized data matrix C is calculated as follows: ; is the variance of the acceleration sequence in the x-direction, representing the data distribution of the acceleration sequence in the x-direction, and it is calculated as follows: ; is the variance of the y-direction acceleration sequence, representing the data distribution of the y-direction acceleration sequence, and it is calculated as follows: ; and Covariance represents the linear relationship between the acceleration sequences in the x-direction and the acceleration sequences in the y-direction, and it is calculated as follows: .

[0098] Step 1344: Calculate the eigenvalues ​​and eigenvectors of the covariance matrix, where the eigenvector corresponding to the largest eigenvalue is the acceleration vector.

[0099] In some embodiments, eigenvalues ​​and eigenvectors can be calculated using the following formulas: Cv=λv; Where λ (λ1, λ2, ...) are eigenvalues ​​representing the variance of each principal component; the larger the eigenvalue, the greater the variation in the data analyzed by that principal component. v (v1, v2, ...) are eigenvectors, corresponding to the directions of each principal component. For two-dimensional data, the eigenvector is a unit vector, representing the projection direction of the data onto the corresponding principal component.

[0100] In some embodiments, the eigenvalues ​​can be arranged in descending order, i.e., λ1≥λ2≥……≥λn, and the corresponding eigenvectors are v1, v2,……,vn. Then the direction of the first principal component is v1, which is the main direction of the motion, i.e., the acceleration vector.

[0101] Step 140: Based on the acceleration vector, obtain the dominant motion direction vectors in the two orthogonal directions, x and y.

[0102] In some embodiments, since the acceleration vector v1 is two-dimensional data and is a unit vector, the motion direction angle θ is obtained by calculating the angle between it and the positive x-axis, i.e.: ; Thus, the dominant motion direction vector v in the two orthogonal directions is obtained. 1x and v 1y .

[0103] Step 150: Calculate the vector angle between the two dominant motion direction vectors.

[0104] In some embodiments, the vector angle θ between the two dominant motion direction vectors ortho The calculation method is as follows: ; Among them, v 1x and v 1y These are the dominant direction vectors of motion in two orthogonal directions.

[0105] Step 160: Obtain the perpendicularity deviation value based on the vector angle. Perpendicularity deviation value = |vector angle - 90°|.

[0106] The perpendicularity deviation value directly and sensitively reflects the angle error between the two lead screw axes in the projection plane. The larger the deviation value, the worse the perpendicularity. Zero value means perfect perpendicularity. Therefore, by calculating the perpendicularity deviation value, it can be determined that the xy axis lead screws must maintain an ideal perpendicular relationship in space.

[0107] Specifically, the verticality deviation value is calculated as follows: Verticality deviation value = |θ ortho -90°|.

[0108] All of the above technical solutions can be combined in any way to form optional embodiments of this application, and will not be described in detail here.

[0109] The method for detecting the verticality of the lead screw provided in this application uses a vibration sensor to collect vibration data and analyze the acceleration vector. Simultaneously, it calculates the vector angle and quantifies the verticality deviation, enabling rapid and accurate detection of the lead screw verticality deviation without disassembling the machine. This method helps reduce operational difficulty and technical barriers, and facilitates improved detection efficiency and normal equipment utilization.

[0110] To better illustrate the method for detecting the verticality of the lead screw provided in this application's embodiments, please refer to... Figure 12 The process of the lead screw perpendicularity detection method provided in this application embodiment can be summarized as follows: Acquire vibration data collected by vibration sensors installed on the work platform; Perform integrity checks on the vibration data and score the integrity of the vibration data based on the results; The signal-to-noise ratio (SNR) of the vibration data is evaluated, and the SNR score of the vibration data is given based on the results. Signal error detection is performed on the vibration data, and the signal error is scored based on the results. A comprehensive quality score is calculated by weighted averaging based on integrity score, signal-to-noise ratio score, and signal error score. Vibration data with a comprehensive quality score greater than or equal to the preset score are identified as target data that meet the preset conditions. Based on the target data, obtain the acceleration vectors of the target data on the three coordinate axes x, y, and z, and obtain the composite acceleration amplitude based on the acceleration vectors of the three coordinate axes x, y, and z. The starting point of the work platform's motion from rest is obtained based on the acceleration amplitude. When the acceleration amplitude is greater than the preset amplitude, the time node corresponding to the start of the work platform's motion is determined as the starting point of motion. The target data is divided into multiple motion segments based on the starting point of the motion and the preset amplitude. Principal component analysis is performed on the data from each motion segment to obtain the acceleration vector; Based on the acceleration vector, obtain the dominant motion direction vectors in the two orthogonal directions, x and y; Calculate the vector angle between the two dominant motion direction vectors; The perpendicularity deviation value is obtained from the vector angle. The perpendicularity deviation value = |vector angle - 90°|.

[0111] To facilitate better implementation of the lead screw perpendicularity detection method of this application embodiment, this application embodiment also provides a lead screw perpendicularity detection device. Please refer to... Figure 13 , Figure 13 This is a schematic diagram of a lead screw perpendicularity detection device provided in an embodiment of this application. The lead screw perpendicularity detection device 10 can provide a graphical user interface via a terminal device. The graphical user interface includes at least a partial virtual scene and at least one virtual character. The lead screw perpendicularity detection device 10 may include: The first acquisition module 11 is used to acquire vibration data collected by vibration sensors installed on the working platform; Processing module 12 is used to process vibration data and obtain target data that meets preset conditions; Extraction module 13 is used to extract features from the target data and obtain the acceleration vector of the lead screw; The second acquisition module 14 is used to acquire the dominant motion direction vectors in the two orthogonal directions of x and y based on the acceleration vector; The first calculation module 15 is used to calculate the vector angle between two dominant motion direction vectors; The second calculation module 16 is used to obtain the perpendicularity deviation value based on the vector angle, where the perpendicularity deviation value = |vector angle - 90°|.

[0112] Each unit in the aforementioned lead screw perpendicularity detection device can be implemented entirely or partially through software, hardware, or a combination thereof. Each unit can be embedded in the processor of the lead screw perpendicularity detection device 100 in hardware form, or stored in the memory of the lead screw perpendicularity detection device 100 in software form, so that the processor can call and execute the corresponding operations of each unit.

[0113] The lead screw verticality detection device 10 can be integrated into a terminal or server that has storage and a processor and thus computing power, or the lead screw verticality detection device 10 can be the terminal or server.

[0114] Optionally, this application also provides a screw perpendicularity detection device 100, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0115] Figure 14 This is a schematic diagram of a lead screw perpendicularity detection device 100 provided in an embodiment of this application. The lead screw perpendicularity detection device 100 can be a terminal or a server. Figure 14 As shown, the lead screw perpendicularity detection device 100 includes a processor 20 with one or more processing cores, a memory 30 with one or more computer-readable storage media, and a computer program stored in the memory 30 and executable on the processor. The processor 20 is electrically connected to the memory 30. Those skilled in the art will understand that the structure of the lead screw perpendicularity detection device 100 shown in the figure does not constitute a limitation on the device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0116] The processor 20 is the control center of the lead screw perpendicularity detection device 100. It connects various parts of the lead screw perpendicularity detection device 100 through various interfaces and lines. By running or loading software programs and / or modules stored in the memory 30, and calling data stored in the memory 30, it executes various functions of the lead screw perpendicularity detection device 100 and processes data, thereby performing overall processing of the lead screw perpendicularity detection device 100.

[0117] In this embodiment, the processor 20 in the lead screw perpendicularity detection device 100 loads the instructions corresponding to the processes of one or more computer programs into the memory 30 according to the following steps, and the processor 20 runs the computer programs stored in the memory 30 to realize various functions: Acquire vibration data collected by vibration sensors installed on the work platform; The vibration data is processed to obtain target data that meets the preset conditions; Feature extraction is performed on the target data to obtain the acceleration vector of the lead screw; Based on the acceleration vector, obtain the dominant motion direction vectors in the two orthogonal directions, x and y; Calculate the vector angle between the two dominant motion direction vectors; The perpendicularity deviation value is obtained from the vector angle. The perpendicularity deviation value = |vector angle - 90°|.

[0118] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0119] This application also provides a computer-readable storage medium for storing a computer program. This computer-readable storage medium can be applied to a lead screw perpendicularity detection device 100, and the computer program causes the lead screw perpendicularity detection device 100 to execute the corresponding process in the lead screw perpendicularity detection method of the embodiments of this application, which will not be elaborated further here for the sake of brevity.

[0120] This application also provides a computer program product including computer instructions stored in a computer-readable storage medium. The processor of the lead screw perpendicularity detection device 100 reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the lead screw perpendicularity detection device 100 to perform the corresponding process in the lead screw perpendicularity detection method of this application embodiment. For simplicity, further details are omitted here.

[0121] This application also provides a computer program that includes computer instructions stored in a computer-readable storage medium. The processor of the lead screw perpendicularity detection device 100 reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the lead screw perpendicularity detection device 100 to perform the corresponding process in the lead screw perpendicularity detection method of this application. For simplicity, further details are omitted here.

[0122] It should be understood that the processor in this application may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0123] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0124] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0125] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0126] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] In addition, the functional units in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0130] If the aforementioned function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a lead screw perpendicularity detection device 100 (which may be a personal computer or a server) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0131] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0132] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the stated features. In the description of this application, "multiple" means at least two, such as two or three, unless otherwise explicitly specified.

[0133] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for detecting the perpendicularity of a lead screw, characterized in that, include: Acquire the vibration data of the lead screw collected by the vibration sensor installed on the working platform; The vibration data is processed to obtain target data that meets preset conditions; Temporal features are extracted from the target data to obtain the acceleration vector of the lead screw; Based on the acceleration vector, obtain the dominant motion direction vectors in the two orthogonal directions, x and y; Calculate the vector angle between the two dominant motion direction vectors; The verticality deviation value of the lead screw is obtained based on the vector angle, and the verticality deviation value = |vector angle - 90°|.

2. The method according to claim 1, characterized in that, The acquisition of vibration data collected by vibration sensors installed on the working platform includes: The control inspection process is initiated, and an inspection signal acquisition command is sent to the vibration sensor temporarily installed on the work platform; Obtain the vibration data collected by the vibration sensor during point inspection.

3. The method according to claim 1, characterized in that, The acquisition of vibration data collected by vibration sensors installed on the working platform includes: The long-term monitoring process is initiated, and a periodic signal acquisition command is sent to the vibration sensor fixedly installed on the working platform; The periodic vibration data within a preset period collected by the vibration sensor is acquired.

4. The method according to claim 1, characterized in that, The process of processing the vibration data to obtain target data that meets preset conditions includes: The vibration data is subjected to integrity detection, and the integrity score of the vibration data is calculated based on the integrity detection results. The vibration data is evaluated for signal-to-noise ratio (SNR), and the vibration data is scored based on the SNR evaluation results. The vibration data is subjected to signal error detection, and the vibration data is scored based on the signal error detection results. Based on the integrity score, the signal-to-noise ratio score, and the signal error score, a comprehensive quality score is calculated by weighted averaging. Vibration data with a comprehensive quality score greater than or equal to a preset score are identified as target data that meet the preset conditions.

5. The method according to claim 4, characterized in that, The method includes: If the overall quality score is less than the preset score, the vibration data is deemed unqualified, and feedback for manual review is issued.

6. The method according to claim 1, characterized in that, The step of extracting features from the target data to obtain the acceleration vector of the lead screw includes: Based on the target data, obtain the acceleration vectors of the target data on the three coordinate axes x, y, and z, and obtain the composite acceleration amplitude based on the acceleration vectors of the three coordinate axes x, y, and z. The starting point of the motion of the working platform from rest is obtained based on the acceleration amplitude. When the acceleration amplitude is greater than a preset amplitude, the time node corresponding to the start of motion of the working platform is determined as the starting point of motion. The target data is divided into multiple motion segments based on the motion start point and preset amplitude. The acceleration vector is obtained by performing principal component analysis on the data of each motion segment.

7. The method according to claim 6, characterized in that, The principal component analysis includes: For each motion segment of the target data, the motion direction is extracted, and a data matrix is ​​constructed based on the x-direction acceleration sequence and the y-direction acceleration sequence; The data matrix is ​​centralized and the centralized data is obtained; Obtain the covariance matrix based on the centralized data; Calculate the eigenvalues ​​of the covariance matrix and the eigenvectors corresponding to the eigenvalues, wherein the eigenvector corresponding to the largest eigenvalue is the acceleration vector.

8. A device for detecting the perpendicularity of a lead screw, characterized in that, include: The first acquisition module is used to acquire vibration data collected by vibration sensors installed on the working platform; The processing module is used to process the vibration data to obtain target data that meets preset conditions; The extraction module is used to extract features from the target data to obtain the acceleration vector of the lead screw; The second acquisition module is used to acquire the dominant motion direction vectors in the x and y orthogonal directions based on the acceleration vector; The first calculation module is used to calculate the vector angle between the two dominant motion direction vectors; The second calculation module is used to obtain the verticality deviation value based on the vector angle, where the verticality deviation value = |vector angle - 90°|.

9. A device for detecting the perpendicularity of a lead screw, characterized in that, It includes a processor and a memory, the memory storing a computer program that, when executed by the processor, causes the processor to implement the instructions of the method as described in any one of claims 1-8.

10. A non-volatile computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-8.

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