CN119125741B - Fault detection method for electric leg bending nursing instrument - Google Patents
Fault detection method for electric leg bending nursing instrument Download PDFInfo
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Abstract
本发明公开了一种电动屈腿护理仪故障检测方法，涉及电气故障检测技术领域，该方法包括：获取目标护理仪的K个电气元件和K个电气元件在预设监测窗口内的K个运行监测数据集合；获得更新历史故障日志集合；确定K个元件偏离因子；获得Q个聚合电气元件集合和Q个聚合元件偏离因子集合，基于Q个聚合元件偏离因子集合的大小确定对Q个聚合电气元件集合进行元件故障周期性检测的Q个检测周期；基于Q个检测周期对Q个聚合电气元件集合进行周期性电气故障检测。本发明解决了现有技术中电动屈腿护理仪的电气故障检测准确性低，检测周期与护理仪实际运行情况偏差较大的技术问题，达到了提高电动屈腿护理仪的故障检测可靠性的技术效果。
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 deviation between the detection cycle and the actual operation of the nursing device is large, and achieves the technical effect of improving the fault detection reliability of the electric leg curling nursing device.
Description
Technical Field
The invention relates to the technical field of electric fault detection, in particular to a fault detection method of an electric leg bending nursing instrument.
Background
At present, in the operation process of the electric leg bending nursing instrument, the failure of an electric element can not be recognized in time, so that the equipment fails at a key moment, and the situation of affecting the rehabilitation progress of a patient occurs sometimes. Conventional fault detection methods often rely on fixed time intervals to check, failing to incorporate actual operational data, which allows some potential fault hazards to be ignored. For example, the electrical components of the care apparatus often have abnormal temperature during actual use, and the existing detection system cannot capture the deviation in time, so that the best maintenance time is missed. In addition, due to overlong detection period, some equipment cannot be overhauled in time before the fault occurs, and the risk of the equipment fault is increased. Such low accuracy and unreasonable detection periods significantly affect the overall safety and reliability of the care instrument.
The prior art has the technical problems that the electric fault detection accuracy of the electric leg bending nursing instrument is low, and the deviation between the detection period and the actual running condition of the nursing instrument is large.
Disclosure of Invention
The application provides a fault detection method of an electric leg-bending nursing instrument, which is used for solving the technical problems of low accuracy of electric fault detection of the electric leg-bending nursing instrument and larger deviation between the detection period and the actual running condition of the nursing instrument in the prior art.
In view of the above problems, the present application provides a fault detection method for an electric leg-bending nursing apparatus, the method comprising:
acquiring K electrical elements of a target care instrument and K operation monitoring data sets of the K electrical elements in a preset monitoring window, wherein the K electrical elements comprise K element positions, and K is an integer greater than or equal to 1;
The method comprises the steps of calling a historical fault log set of the target care instrument, performing pairwise historical fault log enumeration on the historical fault log set, performing similarity recognition on each enumeration combination, and performing supplementary update on the historical fault log set according to a recognition result to obtain an updated historical fault log set;
traversing the K operation monitoring data sets and the K tolerant operation data intervals to perform weighted deviation centralized trend analysis, and determining K element deviation factors;
The K electrical elements are aggregated according to a preset tolerance factor threshold value and the K element deviation factors to obtain Q aggregation electrical element sets and Q aggregation element deviation factor sets, and Q detection periods for periodically detecting element faults of the Q aggregation electrical element sets are determined based on the sizes of the Q aggregation element deviation factor sets;
and carrying out periodic electric fault detection on the Q aggregation electric element sets based on the Q detection periods.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
According to the application, K electric elements of a target care instrument and K operation monitoring data sets of the K electric elements in a preset monitoring window are obtained, wherein the K electric elements comprise K element positions, K is an integer greater than or equal to 1, then a historical fault log set of the target care instrument is obtained, the historical fault log sets are enumerated in pairs, similarity identification is carried out on each enumeration combination, the historical fault log sets are updated in a supplementing mode according to identification results, updated historical fault log sets are obtained, further weighted deviation concentrated trend analysis is carried out on the K operation monitoring data sets and the K tolerant operation data intervals, K element deviation factors are determined, then the K electric elements are aggregated according to preset tolerance factor thresholds and the K element deviation factors, Q aggregation electric element deviation factor sets are obtained, Q detection periods for periodically detecting element faults of the Q aggregation electric element sets are determined based on the sizes of the Q aggregation element deviation factor sets, and periodic electric fault detection is carried out on the Q aggregation electric element sets based on the Q detection periods. The technical effects of improving the rationality of the fault detection period of the electric leg bending nursing instrument, improving the maintenance efficiency, reducing the risk of fault occurrence and improving the safety of the nursing instrument are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a fault detection method of an electric leg-bending nursing instrument according to an embodiment of the present application;
fig. 2 is a schematic flow chart of obtaining K element deviation factors in a fault detection method of an electric leg-bending nursing instrument according to an embodiment of the present application.
Detailed Description
The application provides a fault detection method of an electric leg bending nursing instrument, which is used for solving the technical problems of low accuracy of electric fault detection of the electric leg bending nursing instrument and larger deviation between the detection period and the actual running condition of the nursing instrument in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "comprises" and "comprising" are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
In a first embodiment, as shown in fig. 1, the present application provides a fault detection method of an electric leg-bending nursing apparatus, where the method includes:
s1, acquiring K electrical elements of a target care instrument and K operation monitoring data sets of the K electrical elements in a preset monitoring window, wherein the K electrical elements comprise K element positions, and K is an integer greater than or equal to 1;
In one possible embodiment, the target care instrument is an electric leg-bending care instrument for caring for the legs of the user. In order to improve the working quality of the electric leg-bending nursing instrument, the electric elements of the target nursing instrument need to be subjected to fault detection according to a certain detection period, so that the electric elements with problems are detected. The preset monitoring window is a preset time period for monitoring the running state of the target care instrument by a person skilled in the art, and the time period can be 3 days, 15 days, 30 days and the like.
Preferably, K electrical elements in the target care instrument are identified according to a design document or a user manual of the instrument, and K is ensured to be more than or equal to 1. The K electrical elements comprise a control panel, a power supply module, a motor, a driving circuit, a sensor, a connecting cable and the like. And according to the distribution positions of the K electrical elements in the target nursing instrument, distributing a unique identifier for each electrical element, and recording the positions of the electrical elements in the nursing instrument to form K element position sets.
And in a preset monitoring window, acquiring operation monitoring data of K electrical elements in real time, and obtaining K operation monitoring data sets. The K operation monitoring data sets reflect the operation states of the K electrical elements in a preset monitoring window. Such data may include current, voltage, temperature, operating conditions, etc., ensuring that the actual operating conditions of the element are reflected. In an exemplary embodiment, in a preset monitoring window, the voltage at the input end of the power supply is continuously monitored by using a multimeter adjusted to a voltage level, so as to obtain input voltage monitoring data, such as 220V, 218V, 205V, etc., where the data fluctuates with the operation condition of the input power supply. And continuously monitoring the output voltage of the driving circuit of the motor by using another universal meter which is adjusted to the voltage level, so as to obtain output voltage monitoring data.
Through the step S1, K electrical elements of the target care instrument and operation monitoring data of the K electrical elements in the appointed monitoring window can be effectively obtained, and a foundation is laid for subsequent fault detection.
S2, calling a historical fault log set of the target care instrument, performing pairwise historical fault log enumeration on the historical fault log set, performing similarity recognition on each enumeration combination, and performing supplementary update on the historical fault log set according to a recognition result to obtain an updated historical fault log set;
Further, the historical fault log set of the target care instrument is called, the historical fault log sets are enumerated in pairs, similarity recognition is conducted on each enumeration combination, the historical fault log set is updated in a supplementary mode according to recognition results, and an updated historical fault log set is obtained, and step S2 of the embodiment of the application further comprises the following steps:
Performing pairwise historical fault log enumeration on the historical fault log set to obtain a plurality of enumeration combinations;
Traversing the plurality of enumeration combinations to perform similarity recognition to obtain a plurality of combination similarities;
And judging whether the number of the plurality of combined similarities exceeds a preset similarity threshold value or not, if so, obtaining a supplementary update instruction, carrying out cloud fault log retrieval on the model of the target care instrument based on the supplementary update instruction, and carrying out supplementary update on the historical fault log set according to a retrieval result to obtain the updated historical fault log set.
In one embodiment, a set of historical fault logs is extracted from a storage system of the target care appliance, each of the historical fault logs including information of a time stamp, a fault type, a fault description, a processing result, and the like. Each historical fault log reflects electrical fault conditions occurring at the target care instrument over a historical time. When the service time of the target nursing instrument is short, and the history fault log set cannot fully reflect the possible general electrical fault conditions of the target nursing instrument, the cloud data is required to be called for supplementary update, so that the updated history fault log set with rich history fault conditions is obtained.
The retrieved sets of historical fault logs are combined to generate all possible pairwise combinations. For example, if the log set has N pieces, then N (N-1)/2 combinations may be generated. For each enumeration combination, a cosine similarity calculation formula is used to analyze the similarity of the two logs within each enumeration combination. The similarity score for each combination is recorded. A preset similarity threshold (e.g., 0.7) is set, all combinations are traversed, and the number of combinations with similarity exceeding the preset threshold is counted.
Judging whether the number of combinations with the similarity exceeding a preset threshold (the maximum similarity meeting the requirements preset by a person skilled in the art) is larger than the preset number (the maximum number of combinations with the similarity meeting the requirements of the historical fault log set preset by a person skilled in the art) or not. If yes, the requirement is not met, and a supplementary update instruction is generated according to the similarity recognition result, wherein the supplementary update instruction is a command for further searching more fault logs.
And sending a search request to a cloud fault log database according to the model of the target care instrument, and acquiring related fault log information. Retrieval may need to be based on specific keywords (e.g., type of failure, time period, etc.). And merging the retrieved new fault log information with the original historical fault log set, and preferably cleaning repeated log entries to ensure the uniqueness and the integrity of the set. The time stamp of the update operation and related information are recorded for subsequent trace back.
By systematically retrieving, analyzing and updating the historical fault log of the target nursing instrument, reliable data support is provided for subsequent risk conditions determined based on the updated historical fault log set, and further the efficiency and accuracy of fault management are improved.
S3, traversing the K operation monitoring data sets and the K tolerant operation data intervals to perform weighted deviation centralized trend analysis, and determining K element deviation factors;
Further, as shown in fig. 2, step S3 of the embodiment of the present application further includes:
obtaining K tolerant operation data intervals of the target care instrument;
Performing fault data retrieval on the updated historical fault log set, and performing interval risk edge recognition by combining the K tolerant operation data intervals to obtain K left-side risk operation data intervals and K right-side risk operation data intervals;
And carrying out weighted deviation centralized trend analysis on the K operation monitoring data sets according to a preset weight ratio by utilizing the K left risk operation data intervals, the K right risk operation data intervals and the K tolerant operation data intervals to obtain the K element deviation factors.
Further, step S3 of the embodiment of the present application further includes:
Extracting updated historical fault operation data of K electrical elements in the K tolerant operation data intervals in the updated historical fault log set to obtain K updated historical fault operation data sets;
Respectively extracting updated historical fault operation data with the fault type occurrence frequency at the first m bits in the K updated historical fault operation data sets to obtain K risk edge operation data sets, wherein m is an integer greater than or equal to 3;
Performing two classification on the K risk edge operation data sets by using 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 K interval left risk edge operation data sets and K interval right risk edge operation data sets to perform average value calculation, and determining K interval left risk edge operation data average values and K interval right risk edge operation data average values;
Taking the data interval from the average value of the running data of the left risk edges of the K intervals to the left end point of the K tolerant running data intervals as K left risk running data intervals;
And taking the data interval from the right risk edge operation data average value of the K intervals to the right end point of the K tolerant operation data intervals as K right risk operation data intervals.
In one possible embodiment, K tolerant operating data intervals of the target care instrument are retrieved from the system, and these intervals are the performance data of K electrical components of the target care instrument under normal operating conditions, and the allowable deviation range of the component operating data is set. Because the target nursing instrument is influenced by various factors such as personnel operation, working environment and the like in specific use, the running states of the K electrical elements can fluctuate, and if the running states are within the allowable range, the target nursing instrument can work normally. However, when the deviation situation is more common, the electric element of the target nursing instrument works abnormally, even if the electric element still can normally operate at the moment, the electric fault risk is high, and the fault detection in a short period is needed, so that the operation situation of the electric element is mastered, and the electric fault is found in time. Therefore, by performing weighted deviation centralized trend analysis on the K operation monitoring data sets and the K tolerant operation data sections, operation deviation conditions of the K electrical components, that is, the K component deviation factors, can be obtained.
However, even in the K tolerant operation data intervals, there may be a case where an electrical fault occurs, for example, the input voltage tolerant operation data interval is 198v to 242V, and when the frequency of the electrical fault occurs when the input voltage is 200V, the voltage tolerant operation data interval of 198v to 200V has an electrical fault risk. Therefore, further risk identification is required for the K tolerant operation data intervals, and the K left side risk operation data intervals and the K right side risk operation data intervals are determined.
Optionally, the updated historical fault operation data of the K electrical elements in the K tolerant operation data intervals in the updated historical fault log set are extracted, so as to obtain K updated historical fault operation data sets. Wherein each updated historical fault operation data includes fault type, time of occurrence, maintenance results, etc.
Analyzing each K updated historical fault operation data set, counting the occurrence frequency of various fault types, extracting updated historical fault operation data corresponding to m fault types with the occurrence frequency of the fault types being in the first m bits, and obtaining K risk edge operation data sets, wherein m is greater than or equal to 3. The K risk edge operation data sets are data with fault representatives in the K updated historical fault operation data sets.
Further, the K risk edge operation data sets are classified into two with K interval centers of the K tolerant operation data intervals, that is, center values of the K tolerant operation data intervals are calculated. The center value is typically the mid-point of the interval, representing the average level of normal operation. According to the positions of the K risk edge operation data sets relative to the central values of the K tolerant operation data intervals, carrying out two classification on the K risk edge operation data sets to obtain K interval left side risk edge operation data sets and K interval right side risk edge operation data sets;
In order to determine data intervals with electrical fault risks in K left-side broadside operation data intervals and K right-side broadside operation data intervals which are divided by K interval centers, mean value calculation is carried out on the traversing K interval left-side risk edge operation data sets and K interval right-side risk edge operation data sets, and K interval left-side risk edge operation data mean values and K interval right-side risk edge operation data mean values are determined.
And taking the data interval from the left risk edge operation data average value of the K intervals to the left end point of the K tolerant operation data intervals as K left risk operation data intervals, and taking the data interval from the right risk edge operation data average value of the K intervals to the right end point of the K tolerant operation data intervals as K right risk operation data intervals. The technical effect of determining the operation data interval with the electrical fault risk in the K tolerant operation data intervals is achieved.
In one embodiment, the K element deviation factors are obtained by performing weighted deviation centralized trend analysis on the K operation monitoring data sets according to a preset weight ratio by using the K left risk operation data intervals, the K right risk operation data intervals and the K tolerant operation data intervals. The preset weight ratio is a weight ratio which is preset by a person skilled in the art and occupied by operation monitoring data exceeding the K tolerant operation data intervals when element deviation factor calculation is performed, and a weight ratio which is occupied by operation monitoring data within the K left side risk operation data intervals and the K right side risk operation data intervals when element deviation factor calculation is performed when the K tolerant operation data intervals are not exceeded.
Further, the K left risk operation data intervals, the K right risk operation data intervals and the K tolerant operation data intervals are utilized to perform weighted deviation centralized trend analysis on the K operation monitoring data sets according to a preset weight ratio to obtain the K element deviation factors, and step S3 of the embodiment of the present application further includes:
Performing deviation recognition on the K operation monitoring data sets based on the left end point and the right end point of the K tolerant operation data intervals to obtain K data deviation value sets, wherein each data deviation value comprises a positive identification or a negative identification;
Extracting K negative data deviation value sets with negative identifications from the K data deviation value sets, and identifying the K negative data deviation value sets by utilizing the K left risk operation data intervals and the K right risk operation data intervals to obtain K risk negative data deviation value sets, wherein the risk negative data deviation values are negative data deviation values falling into corresponding left risk operation data intervals or right risk operation data intervals;
Extracting K positive data deviation value sets with positive marks from the K data deviation value sets, and carrying out deviation centralized trend analysis by combining the K risk negative data deviation value sets to obtain K centralized positive data deviation value sets and K centralized risk negative data deviation value sets;
And respectively carrying out weighted calculation on the K concentrated positive data deviation value sets and the K concentrated risk negative data deviation value sets according to a preset weight duty ratio, and taking the calculation result as the K element deviation factors.
Further, K positive data deviation value sets with positive identification are extracted from the K data deviation value sets, deviation centralized trend analysis is performed in combination with the K risk negative data deviation value sets, and K centralized positive data deviation value sets and K centralized risk negative data deviation value sets are obtained, and step S3 of the embodiment of the present application further includes:
constructing K deviation concentrated trend analysis spaces based on the K positive data deviation value sets and the K risk negative data deviation value sets, wherein the K deviation concentrated trend analysis spaces comprise K analysis particle sets, and each analysis particle is a positive data deviation value or a negative data deviation value;
Extracting K central particles deviating from a central trend analysis space, and iterating the K central particles according to a preset central iteration bandwidth by taking the K central particles as iteration starting points to obtain K iterative particles;
Respectively judging whether the space aggregation amount of the K iterative particles is larger than or equal to the space aggregation amount of the K central particles, if so, updating the K iterative particles as iteration starting points, carrying out iteration in the K deviation centralized trend analysis spaces based on the preset centralized iteration bandwidth, counting iteration times, if the iteration times meet the preset iteration times, stopping iteration, and taking K iterative particles obtained in the last iteration as K target particles;
taking the K target particles as the center and taking a preset concentrated iteration bandwidth as a space radius, and constructing K target subspaces, wherein the K target subspaces comprise K target subspace analysis particle sets;
And distinguishing the K target subspace analysis particle sets according to positive data deviation values or negative data deviation values corresponding to each target subspace analysis particle in the K target subspace analysis particle sets to obtain the K concentrated positive data deviation value sets and the K concentrated risk negative data deviation value sets.
Further, positive indicia indicates that the operational monitoring data is less than the left endpoint or greater than the right endpoint, and negative indicia indicates that the operational monitoring data is greater than the left endpoint and less than the right endpoint.
In one embodiment, data deviation values of the K operation monitoring data sets and the K tolerant operation data intervals are calculated, deviation recognition is performed on operation monitoring data in the K operation monitoring data sets according to positions of left end points and right end points of the K tolerant operation data intervals, and K data deviation value sets are obtained, wherein each data deviation value comprises a positive identification or a negative identification. The positive sign indicates that the operational monitoring data is less than the left endpoint or greater than the right endpoint, and the negative sign indicates that the operational monitoring data is greater than the left endpoint and less than the right endpoint.
Extracting K negative data deviation value sets with negative identifications from the K data deviation value sets, and identifying data with risks in the K negative data deviation value sets by utilizing the K left risk operation data intervals and the K right risk operation data intervals to obtain K risk negative data deviation value sets, wherein the risk negative data deviation values are negative data deviation values falling into corresponding left risk operation data intervals or right risk operation data intervals;
And extracting K forward data deviation value sets with forward identification from the K data deviation value sets, wherein the K forward data deviation value sets are data deviation values with electrical fault risks exceeding the K tolerant operation data intervals.
And constructing K deviation concentrated trend analysis spaces based on the K positive data deviation value sets and the K risk negative data deviation value sets, wherein the K deviation concentrated trend analysis spaces comprise K analysis particle sets, and each analysis particle is a positive data deviation value or a negative data deviation value. That is, the deviation prevalence analysis is performed in the K deviation concentration trend analysis spaces with each positive-direction data deviation value or each risk negative-direction data deviation value as one analysis particle.
In one embodiment, K central particles of the K offset central trend analysis spaces are extracted, and the K central particles are used as iteration starting points, and iterated according to a preset central iteration bandwidth (the size of a data offset value moved when a single iteration is preset by a person skilled in the art), so as to obtain K iterated particles.
In one embodiment, K iteration subspaces are constructed with the K iteration particles as spatial centers and the preset concentrated iteration bandwidth as spatial radius. And respectively counting the number of the analysis particles in the K iteration subspaces to obtain the space aggregation quantity of the K iteration particles. Wherein the spatial aggregation amounts of the K iterative particles reflect the particle periphery aggregation conditions of the K iterative particles. The larger the amount of spatial aggregation, the more representative the corresponding iterative particles. The spatial aggregation amounts of the K center particles are obtained based on the same obtaining method as that of the K iterative particles.
And respectively judging whether the space aggregation amount of the K iterative particles is larger than or equal to the space aggregation amount of the K central particles, if so, indicating that the K iterative particles are more representative than the K central particles, updating the K iterative particles as iteration starting points, carrying out iteration in the K deviation concentrated trend analysis spaces based on the preset concentrated iteration bandwidth, counting the iteration times, and stopping iteration if the iteration times meet the preset iteration times (the maximum iteration times preset by a person skilled in the art), wherein the K iterative particles obtained in the last iteration are taken as K target particles.
And constructing K target subspaces by taking the K target particles as a center and taking a preset concentrated iteration bandwidth as a space radius, wherein the K target subspaces comprise K target subspace analysis particle sets. The K target subspaces reflect the situation that data deviation values of the K positive data deviation value sets and the K risk negative data deviation value sets are commonly aggregated.
And distinguishing the K target subspace analysis particle sets according to positive data deviation values or negative data deviation values corresponding to each target subspace analysis particle in the K target subspace analysis particle sets to obtain the K concentrated positive data deviation value sets and the K concentrated risk negative data deviation value sets.
And respectively calculating the average values of the K concentrated positive data deviation value sets and the K concentrated risk negative data deviation value sets to obtain K concentrated positive data deviation average values and K concentrated risk negative data deviation average values. And respectively carrying out weighted calculation on the K concentrated positive data deviation average values and the K concentrated risk negative data deviation average values according to the preset weight ratio preset by a person skilled in the art to obtain the K element deviation factors. The greater the element deviation factor, the greater the risk of an electrical failure of the corresponding electrical element.
The deviation condition of the operation monitoring data of the electric leg bending nursing instrument is systematically analyzed and identified, so that data support is provided for subsequent fault early warning and maintenance. Such an analysis method helps to better understand the performance and risk of the device, thereby improving the safety and reliability of the device.
S4, aggregating the K electrical elements according to a preset tolerance factor threshold and the K element deviation factors to obtain Q aggregate electrical element sets and Q aggregate element deviation factor sets, and determining Q detection periods for periodically detecting element faults of the Q aggregate electrical element sets based on the Q aggregate element deviation factor sets;
and S5, carrying out periodic electric fault detection on the Q aggregation electric element sets based on the Q detection periods.
Further, step S4 of the embodiment of the present application further includes:
Randomly not replacing and extracting a first element deviation factor from the K element deviation factors, and screening the K element deviation factors based on the preset tolerance factor threshold value to obtain a first aggregate element deviation factor set;
Removing the first aggregate element deviation factor set from the K element deviation factors, and taking the rest element deviation factors as a first updated element deviation factor set;
Randomly not replacing the second element deviation factor from the first updated element deviation factor set again, and screening the first updated element deviation factor set based on the preset tolerance factor threshold value to obtain a second aggregate element deviation factor set and a second updated element deviation factor set;
The Q-1 element deviation factor is extracted from the Q-2 updated element deviation factor set without being replaced randomly after multiple times of screening, and the Q-2 updated element deviation factor set is screened based on the preset tolerance factor threshold value to obtain a Q-1 aggregate element deviation factor set and a Q updated element deviation factor set;
Taking the Q updating element deviation factor set as a Q aggregation element deviation factor set, and combining the first aggregation element deviation factor set, the second aggregation element deviation factor set and the Q-1 aggregation element deviation factor set to obtain Q aggregation element deviation factor sets;
And obtaining the Q aggregation electrical element sets according to the Q aggregation element deviation factor sets according to a one-to-one mapping relation between the electronic elements and the element deviation factors.
In one embodiment of the present application, K element deviation factors are aggregated according to a preset tolerance factor threshold, and electrical elements having similar element deviation factors are aggregated into a group according to an aggregation result to form Q aggregate electrical element sets. Since the Q sets of aggregated electrical components have similar component deviation factors, that is, the risk of electrical faults occurring in the electrical components in each set of aggregated electrical components is similar, Q detection periods for periodically detecting component faults of the Q sets of aggregated electrical components may be determined according to the magnitudes of the Q sets of aggregated component deviation factors.
And then, the Q aggregation electrical element sets are subjected to periodic electrical fault detection by utilizing Q detection periods, so that the technical effect of optimizing detection resource allocation on the basis of ensuring electrical fault detection reliability is achieved.
Optionally, calculating the average value of the Q aggregate element deviation factor sets to obtain the average value of the Q aggregate element deviation factors. And further, respectively summing the average values of the Q aggregate element deviation factors on the average value ratio of the Q aggregate element deviation factors to obtain Q periodic coefficients. And calculating the difference value between the Q period coefficients and 1, and multiplying the calculation result by a preset detection period preset by a person skilled in the art to obtain Q detection periods. Therefore, the method realizes that Q aggregation electrical element sets with larger aggregation element deviation factor mean values adopt smaller detection periods, timely grasps the object of the operation state of the electrical element, and achieves the technical effect of optimizing the detection resource allocation by adopting larger detection periods for Q aggregation electrical element sets with smaller aggregation element deviation factor mean values.
In one possible embodiment, the first element deviation factor is randomly not replaced from the K element deviation factors, the K element deviation factors are filtered based on the preset tolerance factor threshold (the difference between two element deviation factors that can be divided into a set preset by a person skilled in the art), and element deviation factors of the K element deviation factors, whose difference from the first element deviation factor is within the preset tolerance factor threshold range, are added to the first aggregate element deviation factor set.
Further, the first aggregate element deviation factor set is eliminated from the K element deviation factors, and the remaining element deviation factors are used as a first updated element deviation factor set. And extracting a second element deviation factor again from the first updated element deviation factor set without randomly replacing the second element deviation factor based on the same obtaining principle as the first aggregate element deviation factor set and the first updated element deviation factor set, and screening the first updated element deviation factor set based on the preset tolerance factor threshold value to obtain a second aggregate element deviation factor set and a second updated element deviation factor set.
And after multiple screening, randomly not replacing the Q-1 element deviation factor from the Q-2 updated element deviation factor set again, and screening the Q-2 updated element deviation factor set based on the preset tolerance factor threshold value to obtain a Q-1 aggregate element deviation factor set and a Q updated element deviation factor set. And the Q-th updated element deviation factor set is taken as a Q-th aggregation element deviation factor set, and the Q aggregation element deviation factor sets are obtained by combining the first aggregation element deviation factor set, the second aggregation element deviation factor set and the Q-1-th aggregation element deviation factor set.
And because of the one-to-one mapping relation between the electronic elements and the element deviation factors, the Q aggregation element deviation factor sets are used as indexes to search the K electrical elements, so that the Q aggregation electrical element sets are obtained. The technical effect of analyzing the running state of the electric element and improving the fault detection efficiency is achieved.
In summary, the embodiment of the application has at least the following technical effects:
According to the application, K electrical elements and operation monitoring data thereof of the target nursing instrument are obtained, the purpose of grasping the operation state of the electrical elements is achieved, then a potential fault mode is found in time, fault logs are updated in a complementary mode, the accuracy and the practicability of fault records are enhanced, then weighted deviation centralized trend analysis is applied, the deviation factors of the K elements can be accurately identified, the purpose of improving the sensitivity and the accuracy of fault detection is achieved, Q aggregate electrical element sets are formed according to preset tolerance factor threshold values, the purpose of optimizing monitoring objects and facilitating centralized management and fault analysis is achieved, the purpose of periodically detecting the aggregate electrical elements in different detection periods is achieved by determining Q detection periods, the operation reliability and the safety of the target nursing instrument are improved, potential faults are found and processed in time, and the technical effect of reducing the downtime is achieved.
It should be noted that the 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 foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
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| CN204072401U (en) * | 2014-09-05 | 2015-01-07 | 商登霞 | Fully automatic leg fracture rehabilitation nursing instrument |
| CN113297046A (en) * | 2020-08-03 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Early warning method and device for memory fault |
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| CN204072401U (en) * | 2014-09-05 | 2015-01-07 | 商登霞 | Fully automatic leg fracture rehabilitation nursing instrument |
| CN113297046A (en) * | 2020-08-03 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Early warning method and device for memory fault |
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