A fault detection method for electric leg curling nursing instrument

Abstract

The present invention discloses a fault detection method for an electric leg curling nursing device, which relates to the technical field of electrical fault detection. The method comprises: obtaining K electrical components of a target nursing device and K operation monitoring data sets of the K electrical components within a preset monitoring window; obtaining an update history fault log set; determining K component deviation factors; obtaining Q aggregate electrical component sets and Q aggregate component deviation factor sets, and determining Q detection cycles for periodic component fault detection on the Q aggregate electrical component sets based on the size of the Q aggregate component deviation factor sets; and performing periodic electrical fault detection on the Q aggregate electrical component sets based on the Q detection cycles. The present invention solves the technical problems in the prior art that the electrical fault detection accuracy of the electric leg curling nursing device is low and the detection cycle has a large deviation from the actual operation of the nursing device, and achieves the technical effect of improving the fault detection reliability of the electric leg curling nursing device.

Description

Technical Field

[0001] The invention relates to the technical field of electrical fault detection, and in particular to a fault detection method for an electric leg curling nursing device. Background Art

[0002] At present, during the operation of the electric leg curling nursing device, the device often fails at a critical moment due to the failure of the electrical components being not identified in time, which affects the patient's recovery process. Traditional fault detection methods often rely on inspections at fixed time intervals and fail to combine actual operation data, which causes some potential fault hazards to be ignored. For example, the electrical components of the nursing device often have abnormal temperatures in actual use, but the existing detection system fails to capture these deviations in time, thus missing the best maintenance opportunity. In addition, due to the long detection cycle, some equipment is not repaired in time before the failure occurs, increasing the risk of equipment failure. This low accuracy and unreasonable detection cycle significantly affects the overall safety and reliability of the nursing device.

[0003] The prior art has the technical problems that the electrical fault detection accuracy of the electric leg curling nursing device is low and the detection cycle has a large deviation from the actual operation of the nursing device. Summary of the invention

[0004] The present application provides a fault detection method for an electric leg curling care device, which is used to solve the technical problems in the prior art that the electrical fault detection accuracy of the electric leg curling care device is low and the detection period has a large deviation from the actual operation of the care device.

[0005] In view of the above problems, the present application provides a fault detection method for an electric leg curling care device, the method comprising:

[0006] Acquire K electrical components of a target nursing device and K operation monitoring data sets of the K electrical components within a preset monitoring window, wherein the K electrical components include K component positions, and K is an integer greater than or equal to 1;

[0007] Retrieving the historical fault log set of the target nursing device, enumerating the historical fault logs in pairs on the historical fault log set, identifying the similarity of each enumeration combination, supplementing and updating the historical fault log set according to the identification result, and obtaining an updated historical fault log set;

[0008] Traversing the K operation monitoring data sets and the K tolerant operation data intervals to perform weighted deviation central trend analysis, and determining K component deviation factors;

[0009] Aggregate the K electrical components according to a preset tolerance factor threshold and the K component deviation factors to obtain Q aggregated electrical component sets and Q aggregated component deviation factor sets, and determine Q detection cycles for periodic component fault detection on the Q aggregated electrical component sets based on the sizes of the Q aggregated component deviation factor sets;

[0010] Periodic electrical fault detection is performed on the Q sets of aggregated electrical components based on the Q detection cycles.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] The present application obtains K electrical components of the target nursing device and K operation monitoring data sets of the K electrical components within a preset monitoring window, wherein the K electrical components include K component positions, and K is an integer greater than or equal to 1, then retrieves the historical fault log set of the target nursing device, and performs pairwise historical fault log enumeration on the historical fault log set, performs similarity recognition on each enumeration combination, supplements and updates the historical fault log set according to the recognition result, obtains an updated historical fault log set, and then traverses the K operation monitoring data sets and K tolerant operation data intervals to perform weighted deviation concentration trend analysis, determines K component deviation factors, and then aggregates the K electrical components according to the preset tolerance factor threshold and the K component deviation factors to obtain Q aggregated electrical component sets and Q aggregated component deviation factor sets, determines Q detection cycles for periodic component fault detection on the Q aggregated electrical component sets based on the size of the Q aggregated component deviation factor sets, and performs periodic electrical fault detection on the Q aggregated electrical component sets based on the Q detection cycles. The technical effects of improving the rationality of the fault detection cycle of the electric leg curling nursing device, improving maintenance efficiency, reducing the risk of faults, and improving the safety of the nursing device are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 A schematic diagram of a flow chart of a fault detection method for an electric leg curling nursing device provided in an embodiment of the present application;

[0015] Figure 2 A schematic diagram of a flow chart for obtaining K component deviation factors in a fault detection method for an electric leg curling care device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The present application provides a fault detection method for an electric leg curling care device, which is used to solve the technical problems in the prior art that the electrical fault detection accuracy of the electric leg curling care device is low and the deviation between the detection period and the actual operation of the care device is large.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0019] Embodiment 1, as Figure 1 As shown, the present application provides a fault detection method for an electric leg curling care device, wherein the method comprises:

[0020] S1: Acquire K electrical components of a target nursing device and K operation monitoring data sets of the K electrical components within a preset monitoring window, wherein the K electrical components include K component positions, and K is an integer greater than or equal to 1;

[0021] In a possible embodiment, the target care device is an electric leg curling care device, which is used to care for the user's legs. In order to improve the working quality of the electric leg curling care device, it is necessary to perform fault detection on the electrical components of the target care device according to a certain detection cycle, so as to find out the faulty electrical components. The preset monitoring window is a time period preset by a person skilled in the art to monitor the operating status of the target care device, which can be 3 days, 15 days, 30 days, etc.

[0022] Preferably, according to the design document or user manual of the target nursing device, K electrical components in the device are identified to ensure that K ≥ 1. The K electrical components include a control panel, a power module, a motor, a drive circuit, a sensor, and connecting cables, etc. According to the distribution positions of the K electrical components in the target nursing device, a unique identifier is assigned to each electrical component, and its position in the nursing device is recorded to form a set of K component positions.

[0023] In a preset monitoring window, the operation monitoring data of K electrical components are collected in real time to obtain the K operation monitoring data sets. Among them, the K operation monitoring data sets reflect the operating status of the K electrical components in the preset monitoring window. These data may include current, voltage, temperature, working status, etc., to ensure that the actual operating conditions of the components can be reflected. Exemplarily, in the preset monitoring window, a multimeter adjusted to the voltage block is used to continuously monitor the voltage at the input end of the power supply to obtain input voltage monitoring data, such as 220V, 218V, 205V, etc. The data fluctuates with the operating conditions of the input power supply. And another multimeter adjusted to the voltage block is used to continuously monitor the output voltage of the motor drive circuit to obtain output voltage monitoring data.

[0024] Through the above step S1, the K electrical components of the target nursing device and their operation monitoring data within the specified monitoring window can be effectively obtained, laying a foundation for subsequent fault detection.

[0025] S2: Retrieve the historical fault log set of the target nursing device, and perform pairwise historical fault log enumeration on the historical fault log set, perform similarity recognition on each enumeration combination, and supplement and update the historical fault log set according to the recognition result to obtain an updated historical fault log set;

[0026] Further, the historical fault log set of the target nursing device is retrieved, and the historical fault log set is enumerated in pairs, and similarity recognition is performed on each enumeration combination, and the historical fault log set is supplemented and updated according to the recognition result to obtain an updated historical fault log set. Step S2 of the embodiment of the present application also includes:

[0027] Enumerating the historical fault logs in pairs on the historical fault log set to obtain multiple enumeration combinations;

[0028] Traversing the multiple enumeration combinations to perform similarity identification and obtain multiple combination similarities;

[0029] Determine whether the number of combinations whose similarities exceed a preset similarity threshold is greater than a preset number. If so, obtain a supplementary update instruction, perform a cloud fault log search based on the supplementary update instruction and the model of the target care device, and supplement and update the historical fault log set according to the search results to obtain the updated historical fault log set.

[0030] In one embodiment, a historical fault log set is extracted from the storage system of the target nursing device, and each historical fault log includes information such as timestamp, fault type, fault description, and processing result. Each historical fault log reflects the electrical fault condition that occurred in the target nursing device during the historical time. When the target nursing device is used for a short time and the historical fault log set cannot fully reflect the common electrical fault condition that may occur in the target nursing device, it is necessary to retrieve cloud data for supplementary update, so as to obtain an updated historical fault log set with rich historical fault conditions.

[0031] Combine the retrieved historical fault log sets to generate all possible pairwise combinations. For example, if there are N log sets, N*(N-1) / 2 combinations can be generated. For each enumerated combination, use the cosine similarity calculation formula to analyze the similarity of the two logs in each enumerated combination. Record the similarity score of each combination. Set a preset similarity threshold (for example, 0.7), traverse all combinations, and count the number of combinations whose similarity exceeds the preset threshold.

[0032] It is determined whether the number of combinations whose similarity exceeds a preset threshold (the maximum similarity that meets the requirements set in advance by a person skilled in the art) is greater than a preset number (the maximum number of combinations whose similarity exceeds the preset threshold set in advance by a person skilled in the art). If so, it indicates that the requirements are not met, and a supplementary update instruction is generated according to the similarity recognition result, and the supplementary update instruction is a command for further retrieving more fault logs.

[0033] According to the model of the target nursing device, send a search request to the cloud fault log database to obtain relevant fault log information. It may be necessary to search based on specific keywords (such as fault type, time period, etc.). Merge the retrieved new fault log information with the original historical fault log collection. Preferably, clean up duplicate log entries to ensure the uniqueness and integrity of the collection. Record the timestamp and related information of the update operation for subsequent tracing.

[0034] By systematically retrieving, analyzing and updating the historical fault logs of the target nursing device, reliable data support is provided for the subsequent risk situations determined based on the updated historical fault log collection, thereby improving the efficiency and accuracy of fault management.

[0035] S3: traversing the K operation monitoring data sets and the K tolerant operation data intervals to perform weighted deviation central trend analysis, and determining K component deviation factors;

[0036] Further, such as Figure 2 As shown, step S3 of the embodiment of the present application also includes:

[0037] Obtaining K tolerant operation data intervals of the target nursing device;

[0038] Performing fault data retrieval on the update history fault log set, and performing interval risk edge identification in combination with the K tolerant operation data intervals to obtain K left-side risk operation data intervals and K right-side risk operation data intervals;

[0039] The K left-side risk operation data intervals, the K right-side risk operation data intervals and the K tolerant operation data intervals are used to perform a weighted deviation central trend analysis on the K operation monitoring data sets according to preset weight proportions to obtain the K component deviation factors.

[0040] Furthermore, step S3 of the embodiment of the present application also includes:

[0041] Extracting the update history fault operation data of K electrical components in the update history fault log set within the K tolerance operation data intervals to obtain K update history fault operation data sets;

[0042] Extracting the update history fault operation data with the top m fault type occurrence frequencies from the K update history fault operation data sets respectively to obtain K risk edge operation data sets, where m is an integer greater than or equal to 3;

[0043] Binary classification is performed on the K risk edge operation data sets based on the K interval centers of the K tolerant operation data intervals to obtain K interval left risk edge operation data sets and K interval right risk edge operation data sets;

[0044] Traversing the K interval left risk edge running data sets and the K interval right risk edge running data sets to perform mean calculation, and determining the mean of the K interval left risk edge running data and the mean of the K interval right risk edge running data;

[0045] The data interval from the mean of the left risk edge operation data of the K intervals to the left end point of the K tolerant operation data intervals is taken as the K left risk operation data intervals;

[0046] The data interval from the mean of the right risk edge operating data of the K intervals to the right end points of the K tolerant operating data intervals is taken as the K right risk operating data intervals.

[0047] In one possible embodiment, K tolerant operation data intervals of the target nursing instrument are retrieved from the system. These intervals are the performance data of K electrical components of the target nursing instrument under normal operating conditions, and the allowed deviation range of the component operation data is set. Due to the influence of various factors such as personnel operation and working environment in the specific use of the target nursing instrument, the operation status of the K electrical components will fluctuate. If it is within the allowable range, the target nursing instrument can work normally. However, when the deviation is more common, it indicates that the electrical components of the target nursing instrument are abnormal. Even if it can still operate normally at this time, there is a greater risk of electrical failure. It is necessary to perform fault detection in a shorter period of time, so as to grasp the operation of the electrical components and detect electrical failures in time. Therefore, by performing weighted deviation central trend analysis on K operation monitoring data sets and K tolerant operation data intervals, the operation deviation of K electrical components, that is, the K component deviation factors, can be obtained.

[0048] However, even within the K tolerance operation data intervals, electrical faults may occur. For example, when the input voltage tolerance operation data interval is 198V~242V, the frequency of electrical faults is high when the input voltage is 200V. At this time, there is a risk of electrical faults in the voltage tolerance operation data interval of 198V~200V. Therefore, it is necessary to further identify the risks of the K tolerance operation data intervals to determine the K left risk operation data intervals and the K right risk operation data intervals.

[0049] Optionally, the update history fault operation data of K electrical components in the update history fault log set within the K tolerance operation data intervals are extracted to obtain K update history fault operation data sets, wherein each update history fault operation data includes a fault type, occurrence time, maintenance result, and the like.

[0050] Each K update history fault operation data set is analyzed, the frequency of occurrence of each type of fault is counted, and the update history fault operation data corresponding to the m fault types with the occurrence frequency of the fault type in the top m positions are extracted to obtain K risk edge operation data sets, where m should be greater than or equal to 3. The K risk edge operation data sets are data with representative faults in the K update history fault operation data sets.

[0051] Then, the K risk edge operation data sets are classified into two categories based on the K interval centers of the K tolerant operation data intervals, that is, the center values ​​of the K tolerant operation data intervals are calculated. The center value is usually the midpoint of the interval, indicating the average level of normal operation. Based on the positions of the K risk edge operation data sets relative to the center values ​​of the K tolerant operation data intervals, the K risk edge operation data sets are classified into two categories to obtain the risk edge operation data sets on the left side of the K intervals and the risk edge operation data sets on the right side of the K intervals;

[0052] In order to determine the data intervals with electrical fault risks in the K left-side tolerant operating data intervals and the K right-side tolerant operating data intervals divided by the centers of the K intervals, the mean of the left-side risk edge operating data set and the right-side risk edge operating data set traversing the K intervals is calculated to determine the mean of the left-side risk edge operating data of the K intervals and the mean of the right-side risk edge operating data of the K intervals.

[0053] Furthermore, the data interval from the mean value of the left risk edge operation data of the K intervals to the left end point of the K tolerant operation data intervals is taken as the K left risk operation data intervals, and the data interval from the mean value of the right risk edge operation data of the K intervals to the right end point of the K tolerant operation data intervals is taken as the K right risk operation data intervals. The technical effect of determining the operation data interval with electrical fault risk among the K tolerant operation data intervals is achieved.

[0054] In one embodiment, the K left-side risk operation data intervals, the K right-side risk operation data intervals and the K tolerance operation data intervals are used to perform weighted deviation central trend analysis on the K operation monitoring data sets according to preset weight proportions to obtain the K component deviation factors. The preset weight proportions are the weight proportions of the operation monitoring data exceeding the K tolerance operation data intervals when calculating the component deviation factor, and the weight proportions of the operation monitoring data not exceeding the K tolerance operation data intervals but within the K left-side risk operation data intervals and the K right-side risk operation data intervals when calculating the component deviation factor.

[0055] Further, the K left-side risk operation data intervals, the K right-side risk operation data intervals and the K tolerance operation data intervals are used to perform a weighted deviation central trend analysis on the K operation monitoring data sets according to preset weight proportions to obtain the K component deviation factors. Step S3 of the embodiment of the present application further includes:

[0056] Based on the left end points and the right end points of the K tolerant operation data intervals, deviations of the K operation monitoring data sets are identified to obtain K data deviation value sets, wherein each data deviation value includes a positive flag or a negative flag;

[0057] Extracting K negative data deviation value sets with negative identifications from the K data deviation value sets, and using the K left-side risk operation data intervals and the K right-side risk operation data intervals to identify the K negative data deviation value sets, to obtain K risk negative data deviation value sets, wherein the risk negative data deviation value is a negative data deviation value that falls into the corresponding left-side risk operation data interval or the right-side risk operation data interval;

[0058] Extracting K positive data deviation value sets with positive identifications from the K data deviation value sets, and performing deviation concentration trend analysis in combination with the K risk negative data deviation value sets to obtain K concentrated positive data deviation value sets and K concentrated risk negative data deviation value sets;

[0059] The K concentrated positive data deviation value sets and the K concentrated risk negative data deviation value sets are weightedly calculated according to preset weight ratios, and the calculation results are used as the K component deviation factors.

[0060] Further, K positive data deviation value sets with positive identifications are extracted from the K data deviation value sets, and deviation concentration trend analysis is performed in combination with the K risk negative data deviation value sets to obtain K concentrated positive data deviation value sets and K concentrated risk negative data deviation value sets. Step S3 of the embodiment of the present application also includes:

[0061] Constructing K deviation central tendency analysis spaces based on the K positive data deviation value sets and the K risk negative data deviation value sets, wherein the K deviation central tendency analysis spaces include K analysis particle sets, each analysis particle is a positive data deviation value or a negative data deviation value;

[0062] Extracting the K center particles that deviate from the central trend analysis space, and taking the K center particles as iteration starting points, iterating the K center particles according to a preset centralized iteration bandwidth to obtain K iteration particles;

[0063] Determine whether the spatial aggregation amounts of the K iterative particles are greater than or equal to the spatial aggregation amounts of the K central particles respectively. If so, update the K iterative particles as iteration starting points, iterate in the K deviation central trend analysis spaces based on the preset centralized iteration bandwidth, and count the number of iterations. If the number of iterations meets the preset number of iterations, stop the iteration, and use the K iterative particles obtained in the last iteration as K target particles.

[0064] Taking the K target particles as the center and the preset concentrated iteration bandwidth as the space radius, K target subspaces are constructed, wherein the K target subspaces include K target subspace analysis particle sets;

[0065] According to the positive data deviation value or negative data deviation value corresponding to each target subspace analysis particle in the K target subspace analysis particle sets, the K target subspace analysis particle sets are distinguished to obtain the K concentrated positive data deviation value sets and the K concentrated risk negative data deviation value sets.

[0066] Furthermore, a positive sign indicates that the operation monitoring data is less than the left endpoint or greater than the right endpoint, and a negative sign indicates that the operation monitoring data is greater than the left endpoint and less than the right endpoint.

[0067] In one embodiment, the data deviation values ​​of the K operation monitoring data sets and the K tolerant operation data intervals are calculated, and deviation identification is performed according to the positions of the operation monitoring data in the K operation monitoring data sets relative to the left end point and the right end point of the K tolerant operation data intervals, so as to obtain K data deviation value sets, wherein each data deviation value includes a positive flag or a negative flag. A positive flag indicates that the operation monitoring data is less than the left end point or greater than the right end point, and a negative flag indicates that the operation monitoring data is greater than the left end point and less than the right end point.

[0068] Extracting K negative data deviation value sets with negative identifications from the K data deviation value sets, and using the K left-side risk operation data intervals and the K right-side risk operation data intervals to identify data with risks in the K negative data deviation value sets, to obtain K risk negative data deviation value sets, wherein the risk negative data deviation value is a negative data deviation value that falls into the corresponding left-side risk operation data interval or the right-side risk operation data interval;

[0069] K positive data deviation value sets with positive identifications are extracted from the K data deviation value sets, wherein the K positive data deviation value sets are data deviation values ​​with electrical failure risks that exceed the K tolerance operation data intervals.

[0070] K deviation central tendency analysis spaces are constructed based on the K positive data deviation value sets and the K risk negative data deviation value sets, wherein the K deviation central tendency analysis spaces include K analysis particle sets, each analysis particle being a positive data deviation value or a negative data deviation value. That is, each positive data deviation value or each risk negative data deviation value is taken as an analysis particle, and deviation prevalence analysis is performed in the K deviation central tendency analysis spaces.

[0071] In one embodiment, the K center particles that deviate from the central trend analysis space are extracted, and the K center particles are used as iteration starting points. The K center particles are iterated according to a preset centralized iteration bandwidth (the size of the data deviation value moved during a single iteration is preset by those skilled in the art) to obtain K iterated particles.

[0072] In one embodiment, K iterative subspaces are constructed with the K iterative particles as the space center and the preset concentrated iterative bandwidth as the space radius. The number of analysis particles in the K iterative subspaces is counted respectively to obtain the spatial aggregation amount of the K iterative particles. The spatial aggregation amount of the K iterative particles reflects the aggregation situation around the particles of the K iterative particles. The larger the spatial aggregation amount, the more representative the corresponding iterative particle is. Based on the same acquisition method as that for obtaining the spatial aggregation amount of the K iterative particles, the spatial aggregation amount of the K central particles is obtained.

[0073] It is determined whether the spatial aggregation amounts of the K iterative particles are greater than or equal to the spatial aggregation amounts of the K central particles. If so, it indicates that the K iterative particles are more representative than the K central particles. Therefore, the K iterative particles are updated as the iteration starting point, and iteration is performed in the K deviation from central trend analysis spaces based on the preset centralized iteration bandwidth, and the number of iterations is counted. If the number of iterations meets the preset number of iterations (the maximum number of iterations preset by those skilled in the art), the iteration is stopped, and the K iterative particles obtained in the last iteration are used as the K target particles.

[0074] Then, with the K target particles as the center and the preset concentrated iteration bandwidth as the space radius, K target subspaces are constructed, wherein the K target subspaces include K target subspace analysis particle sets. The K target subspaces reflect the situation that the data deviation values ​​of the K positive data deviation value sets and the K risk negative data deviation value sets are generally clustered.

[0075] Furthermore, according to the positive data deviation value or negative data deviation value corresponding to each target subspace analysis particle in the K target subspace analysis particle sets, the K target subspace analysis particle sets are distinguished to obtain the K concentrated positive data deviation value sets and the K concentrated risk negative data deviation value sets.

[0076] The means of the K concentrated positive data deviation value sets and the K concentrated risk negative data deviation value sets are calculated respectively to obtain the K concentrated positive data deviation means and the K concentrated risk negative data deviation means. Furthermore, the K concentrated positive data deviation means and the K concentrated risk negative data deviation means are weightedly calculated according to the preset weight ratios pre-set by a person skilled in the art to obtain the K component deviation factors. The larger the component deviation factor, the greater the risk of electrical failure of the corresponding electrical component.

[0077] By systematically analyzing and identifying deviations in the operating monitoring data of the electric leg curling care device, data support is provided for subsequent fault warning and maintenance. This analysis method helps to better understand the performance and risks of the device, thereby improving the safety and reliability of the device.

[0078] S4: Aggregating the K electrical components according to a preset tolerance factor threshold and the K component deviation factors to obtain Q aggregated electrical component sets and Q aggregated component deviation factor sets, and determining Q detection cycles for periodic component fault detection on the Q aggregated electrical component sets based on the sizes of the Q aggregated component deviation factor sets;

[0079] S5: Perform periodic electrical fault detection on the Q sets of aggregated electrical components based on the Q detection cycles.

[0080] Furthermore, step S4 of the embodiment of the present application also includes:

[0081] randomly extracting a first component deviation factor from the K component deviation factors without replacement, screening the K component deviation factors based on the preset tolerance factor threshold, and obtaining a first aggregated component deviation factor set;

[0082] Eliminating the first aggregated component deviation factor set from the K component deviation factors, and using the remaining plurality of component deviation factors as a first updated component deviation factor set;

[0083] randomly extracting a second component deviation factor from the first update component deviation factor set without replacement, screening the first update component deviation factor set based on the preset tolerance factor threshold, and obtaining a second aggregated component deviation factor set and a second update component deviation factor set;

[0084] After multiple screenings, the Q-1th element deviation factor is randomly extracted from the Q-2th update element deviation factor set without replacement, and the Q-2th update element deviation factor set is screened based on the preset tolerance factor threshold to obtain the Q-1th aggregation element deviation factor set and the Qth update element deviation factor set;

[0085] Taking the Qth updated element deviation factor set as the Qth aggregate element deviation factor set, and combining the first aggregate element deviation factor set, the second aggregate element deviation factor set, and the Q-1th aggregate element deviation factor set to obtain the Q aggregate element deviation factor sets;

[0086] According to a one-to-one mapping relationship between electronic components and component deviation factors, the Q aggregated electrical component sets are obtained according to the Q aggregated component deviation factor sets.

[0087] In one embodiment of the present application, K component deviation factors are aggregated according to a preset tolerance factor threshold, and electrical components with similar component deviation factors are aggregated into a group according to the aggregation result to form Q aggregate electrical component sets. Since the Q aggregate electrical component sets have similar component deviation factors, that is, the risk of electrical failure of the electrical components in each aggregate electrical component set is similar, at this time, Q detection cycles for periodic component failure detection of the Q aggregate electrical component sets can be determined according to the size of the Q aggregate component deviation factor sets.

[0088] Furthermore, Q detection cycles are used to perform periodic electrical fault detection on the Q aggregate electrical component sets, thereby achieving the technical effect of optimizing the configuration of detection resources while ensuring the reliability of electrical fault detection.

[0089] Optionally, the mean of the set of deviation factors of the Q aggregate components is calculated to obtain the mean of the deviation factors of the Q aggregate components. Furthermore, the mean of the deviation factors of the Q aggregate components is divided by the sum of the mean of the deviation factors of the Q aggregate components to obtain Q periodic coefficients. The difference between the Q periodic coefficients and 1 is calculated, and the calculation result is multiplied by a preset detection period pre-set by a person skilled in the art to obtain Q detection periods. Thus, the goal of using a smaller detection period for a set of Q aggregate electrical components with a larger mean of the deviation factors of the aggregate components to timely grasp the operating status of the electrical components is achieved, and the technical effect of using a larger detection period for a set of Q aggregate electrical components with a smaller mean of the deviation factors of the aggregate components to optimize the allocation of detection resources is achieved.

[0090] In a possible embodiment, a first component deviation factor is randomly extracted from the K component deviation factors without replacement, the K component deviation factors are screened based on the preset tolerance factor threshold (the difference between two component deviation factors that can be divided into a set pre-set by a person skilled in the art), and the component deviation factors among the K component deviation factors whose difference with the first component deviation factor is within the preset tolerance factor threshold are added to the first aggregated component deviation factor set.

[0091] Then, the first aggregated component deviation factor set is removed from the K component deviation factors, and the remaining multiple component deviation factors are used as the first updated component deviation factor set. Based on the same principle of obtaining the first aggregated component deviation factor set and the first updated component deviation factor set, a second component deviation factor is randomly extracted from the first updated component deviation factor set without replacement, and the first updated component deviation factor set is screened based on the preset tolerance factor threshold to obtain a second aggregated component deviation factor set and a second updated component deviation factor set.

[0092] After multiple screenings, the Q-1th element deviation factor is randomly extracted from the Q-2th update element deviation factor set without replacement, and the Q-2th update element deviation factor set is screened based on the preset tolerance factor threshold to obtain the Q-1th aggregate element deviation factor set and the Q-th update element deviation factor set. Then, the Q-th update element deviation factor set is used as the Q-th aggregate element deviation factor set, and the Q aggregate element deviation factor sets are obtained by combining the first aggregate element deviation factor set, the second aggregate element deviation factor set and the Q-1th aggregate element deviation factor set.

[0093] Due to the one-to-one mapping relationship between electronic components and component deviation factors, the K electrical components are searched with the Q aggregate component deviation factor sets as indexes to obtain the Q aggregate electrical component sets, thereby achieving the technical effect of analyzing the operating status of electrical components and improving fault detection efficiency.

[0094] In summary, the embodiments of the present application have at least the following technical effects:

[0095] The present application achieves the goal of mastering the operating status of electrical components by acquiring K electrical components of the target nursing device and their operating monitoring data, thereby timely discovering potential fault modes and supplementing and updating fault logs to enhance the accuracy and practicality of fault records. Then, by using weighted deviation central trend analysis, the deviation factors of K components can be accurately identified to achieve the goal of improving the sensitivity and accuracy of fault detection. The electrical components are aggregated according to the preset tolerance factor threshold to form a set of Q aggregated electrical components, thereby achieving the goal of optimizing monitoring objects and facilitating centralized management and fault analysis. By determining Q detection cycles, regular inspections of aggregated electrical components at different detection cycles are achieved, thereby improving the operating reliability and safety of the target nursing device, and achieving the technical effect of timely discovering and handling potential faults and reducing downtime.

[0096] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0097] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0098] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A fault detection method for an electric leg curling nursing device, characterized in that: The method comprises: Acquire K electrical components of a target nursing device and K operation monitoring data sets of the K electrical components within a preset monitoring window, wherein the K electrical components include K component positions, and K is an integer greater than or equal to 1; Retrieving the historical fault log set of the target nursing device, enumerating the historical fault logs in pairs on the historical fault log set, identifying the similarity of each enumeration combination, supplementing and updating the historical fault log set according to the identification result, and obtaining an updated historical fault log set; Traversing the K operation monitoring data sets and the K tolerant operation data intervals to perform weighted deviation central trend analysis, and determining K component deviation factors; Aggregate the K electrical components according to a preset tolerance factor threshold and the K component deviation factors to obtain Q aggregated electrical component sets and Q aggregated component deviation factor sets, and determine Q detection cycles for periodic component fault detection on the Q aggregated electrical component sets based on the sizes of the Q aggregated component deviation factor sets; Periodic electrical fault detection is performed on the Q sets of aggregated electrical components based on the Q detection cycles.

2. A fault detection method for an electric leg curling nursing device as claimed in claim 1, characterized in that: include: Obtaining K tolerant operation data intervals of the target nursing device; Performing fault data retrieval on the update history fault log set, and performing interval risk edge identification in combination with the K tolerant operation data intervals to obtain K left-side risk operation data intervals and K right-side risk operation data intervals; The K left-side risk operation data intervals, the K right-side risk operation data intervals and the K tolerant operation data intervals are used to perform a weighted deviation central trend analysis on the K operation monitoring data sets according to preset weight proportions to obtain the K component deviation factors.

3. A fault detection method for an electric leg curling nursing device as claimed in claim 2, characterized in that: include: Extracting the update history fault operation data of K electrical components in the update history fault log set within the K tolerance operation data intervals to obtain K update history fault operation data sets; Extracting the update history fault operation data with the top m fault type occurrence frequencies from the K update history fault operation data sets respectively to obtain K risk edge operation data sets, where m is an integer greater than or equal to 3; Binary classification is performed on the K risk edge operation data sets based on the K interval centers of the K tolerant operation data intervals to obtain K interval left risk edge operation data sets and K interval right risk edge operation data sets; Traversing the K interval left risk edge running data sets and the K interval right risk edge running data sets to perform mean calculation, and determining the mean of the K interval left risk edge running data and the mean of the K interval right risk edge running data; The data interval from the mean of the left risk edge operation data of the K intervals to the left end point of the K tolerant operation data intervals is taken as the K left risk operation data intervals; The data interval from the mean of the right risk edge operating data of the K intervals to the right end points of the K tolerant operating data intervals is taken as the K right risk operating data intervals.

4. A fault detection method for an electric leg curling nursing device as claimed in claim 2, characterized in that: The K left-side risk operation data intervals, the K right-side risk operation data intervals, and the K tolerance operation data intervals are used to perform a weighted deviation central trend analysis on the K operation monitoring data sets according to preset weight proportions to obtain the K component deviation factors, including: Based on the left end points and the right end points of the K tolerant operation data intervals, deviations of the K operation monitoring data sets are identified to obtain K data deviation value sets, wherein each data deviation value includes a positive flag or a negative flag; Extracting K negative data deviation value sets with negative identifications from the K data deviation value sets, and using the K left-side risk operation data intervals and the K right-side risk operation data intervals to identify the K negative data deviation value sets, to obtain K risk negative data deviation value sets, wherein the risk negative data deviation value is a negative data deviation value that falls into the corresponding left-side risk operation data interval or the right-side risk operation data interval; Extracting K positive data deviation value sets with positive identifications from the K data deviation value sets, and performing deviation concentration trend analysis in combination with the K risk negative data deviation value sets to obtain K concentrated positive data deviation value sets and K concentrated risk negative data deviation value sets; The K concentrated positive data deviation value sets and the K concentrated risk negative data deviation value sets are weightedly calculated according to preset weight ratios, and the calculation results are used as the K component deviation factors.

5. A fault detection method for an electric leg curling nursing device as claimed in claim 4, characterized in that: Extracting K positive data deviation value sets with positive identifications from the K data deviation value sets, and performing deviation concentration trend analysis in combination with the K risk negative data deviation value sets, to obtain K concentrated positive data deviation value sets and K concentrated risk negative data deviation value sets, including: Constructing K deviation central tendency analysis spaces based on the K positive data deviation value sets and the K risk negative data deviation value sets, wherein the K deviation central tendency analysis spaces include K analysis particle sets, each analysis particle is a positive data deviation value or a negative data deviation value; Extracting the K center particles that deviate from the central trend analysis space, and taking the K center particles as iteration starting points, iterating the K center particles according to a preset centralized iteration bandwidth to obtain K iteration particles; Determine whether the spatial aggregation amounts of the K iterative particles are greater than or equal to the spatial aggregation amounts of the K central particles respectively. If so, update the K iterative particles as iteration starting points, iterate in the K deviation central trend analysis spaces based on the preset centralized iteration bandwidth, and count the number of iterations. If the number of iterations meets the preset number of iterations, stop the iteration, and use the K iterative particles obtained in the last iteration as K target particles. Taking the K target particles as the center and the preset concentrated iteration bandwidth as the space radius, K target subspaces are constructed, wherein the K target subspaces include K target subspace analysis particle sets; According to the positive data deviation value or negative data deviation value corresponding to each target subspace analysis particle in the K target subspace analysis particle sets, the K target subspace analysis particle sets are distinguished to obtain the K concentrated positive data deviation value sets and the K concentrated risk negative data deviation value sets.

6. A fault detection method for an electric leg curling nursing device as claimed in claim 4, characterized in that: A positive sign indicates that the operation monitoring data is less than the left endpoint or greater than the right endpoint, and a negative sign indicates that the operation monitoring data is greater than the left endpoint and less than the right endpoint.

7. A fault detection method for an electric leg curling nursing device as claimed in claim 1, characterized in that: include: randomly extracting a first component deviation factor from the K component deviation factors without replacement, screening the K component deviation factors based on the preset tolerance factor threshold, and obtaining a first aggregated component deviation factor set; Eliminating the first aggregated component deviation factor set from the K component deviation factors, and using the remaining plurality of component deviation factors as a first updated component deviation factor set; randomly extracting a second component deviation factor from the first update component deviation factor set without replacement, screening the first update component deviation factor set based on the preset tolerance factor threshold, and obtaining a second aggregated component deviation factor set and a second update component deviation factor set; After multiple screenings, the Q-1th element deviation factor is randomly extracted from the Q-2th update element deviation factor set without replacement, and the Q-2th update element deviation factor set is screened based on the preset tolerance factor threshold to obtain the Q-1th aggregation element deviation factor set and the Qth update element deviation factor set; Taking the Qth updated element deviation factor set as the Qth aggregate element deviation factor set, and combining the first aggregate element deviation factor set, the second aggregate element deviation factor set, and the Q-1th aggregate element deviation factor set to obtain the Q aggregate element deviation factor sets; According to a one-to-one mapping relationship between electronic components and component deviation factors, the Q aggregated electrical component sets are obtained according to the Q aggregated component deviation factor sets.

8. A fault detection method for an electric leg curling nursing device as claimed in claim 1, characterized in that: Retrieving the historical fault log set of the target nursing device, enumerating the historical fault logs in pairs on the historical fault log set, identifying the similarity of each enumeration combination, supplementing and updating the historical fault log set according to the identification result, and obtaining an updated historical fault log set, including: Enumerating the historical fault logs in pairs on the historical fault log set to obtain multiple enumeration combinations; Traversing the multiple enumeration combinations to perform similarity identification and obtain multiple combination similarities; Determine whether the number of combinations whose similarities exceed a preset similarity threshold is greater than a preset number. If so, obtain a supplementary update instruction, perform a cloud fault log search based on the supplementary update instruction and the model of the target care device, and supplement and update the historical fault log set according to the search results to obtain the updated historical fault log set.

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