CN111144717A - Method and device for determining equipment state, storage medium and electronic equipment - Google Patents
Method and device for determining equipment state, storage medium and electronic equipment Download PDFInfo
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Abstract
The disclosure relates to a method, a device, a storage medium and an electronic device for determining a device state, which are used for solving the problem of high health score of a fault device in the related art. The method comprises the following steps: acquiring state characteristic information of target equipment; acquiring state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment; according to state feature information of fault equipment in sample equipment, adjusting a state feature interval and/or a feature score corresponding to the state feature interval in a preset grading rule to reduce the feature score of the state feature interval corresponding to the fault equipment, wherein the preset grading rule comprises a plurality of state feature intervals and feature scores respectively corresponding to the state feature intervals; and determining the state score of the target equipment according to the adjusted preset scoring rule.
Description
Technical Field
The present disclosure relates to the field of device detection technologies, and in particular, to a method and an apparatus for determining a device state, a storage medium, and an electronic device.
Background
In the field of industrial internet of things, along with the acceleration of the industrial modernization process of China, the importance of electric power equipment is increasingly highlighted, and the reliability and safety of the electric power equipment are also higher in industry, so that the evaluation of the health condition of the equipment is very important. The related art equipment evaluation method is generally performed based on expert experience. Specifically, scoring rules for different state characteristics of the equipment are subjectively established mainly through expert experience, and then the health condition of the equipment is evaluated based on the scoring rules.
Disclosure of Invention
The invention aims to provide a method, a device, a storage medium and an electronic device for determining the state of the device, so as to provide a new way for evaluating the state of the device.
To achieve the above object, in a first aspect, the present disclosure provides a method of determining a device state, the method comprising:
acquiring state characteristic information of target equipment;
acquiring state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment;
according to the state feature information of the fault equipment in the sample equipment, adjusting the state feature intervals and/or the feature scores corresponding to the state feature intervals in the preset grading rule to reduce the feature scores of the state feature intervals corresponding to the fault equipment, wherein the preset grading rule comprises a plurality of state feature intervals and feature scores corresponding to the state feature intervals;
and determining the state score of the target equipment according to the adjusted preset scoring rule.
Optionally, the adjusting, according to the state feature information of the faulty device in the sample device, the state feature interval and/or the feature score corresponding to the state feature interval in the preset scoring rule includes:
clustering the sample equipment according to target state characteristic information of the sample equipment, wherein the target state characteristic information is any one of the state characteristic information of the sample equipment;
determining a target cluster comprising fault equipment in the clustered clusters;
adjusting a state feature interval in the preset scoring rule according to the target state feature information of the equipment in the target cluster;
and determining the adjusted characteristic score corresponding to the state characteristic interval according to the number of the fault equipment in the target cluster and the preset corresponding relation between the number of the fault equipment and the characteristic score.
Optionally, the adjusting, according to the target state feature information of the device in the target class cluster, the state feature interval in the preset scoring rule includes:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target cluster;
and taking the maximum value in the state characteristic values as the upper limit value of the interval, and taking the minimum value in the state characteristic values as the lower limit value of the interval to obtain a new state characteristic interval corresponding to the target state characteristic information.
Optionally, the adjusting, according to the target state feature information of the device in the target class cluster, the state feature interval in the preset scoring rule includes:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target cluster;
and determining a new state characteristic interval corresponding to the target state characteristic information according to the average value or the median value of the state characteristic values, so that the new state characteristic interval comprises the state characteristic values corresponding to the preset number of devices in the target cluster.
Optionally, the preset corresponding relationship is:
snew＝smax-a·(smax-smin)·nk/k！·e-n·P
wherein s isnewIndicating the adjusted feature score, smaxAnd sminRespectively representing the maximum characteristic score and the minimum characteristic score of the equipment in the target cluster under the target state characteristic, which are determined according to the preset grading rule, a representing a preset adjusting parameter, n representing the total number of the equipment in the target cluster, k representing the number of the fault equipment in the target cluster, and P representing the poisson distribution of the target state characteristic information of the equipment in the target cluster.
Optionally, before determining the status score of the target device according to the adjusted preset scoring rule, the method further includes:
determining an initial characteristic weight corresponding to target state characteristic information of the sample equipment, wherein the target state characteristic information is any one of the state characteristic information of the sample equipment;
determining feature scores respectively corresponding to the sample devices according to the preset scoring rules and the target state feature information of the sample devices, and determining low-score devices of which the feature scores are lower than preset feature scores in the sample devices;
if the number of the fault equipment in the low-score equipment is larger than the preset number, increasing the initial characteristic weight corresponding to the target state characteristic information;
determining the state score of the target device according to the adjusted preset scoring rule, wherein the determining the state score of the target device comprises:
and determining the state score of the target equipment according to the adjusted preset scoring rule and the increased initial feature weight.
Optionally, the increasing the initial feature weight corresponding to the target state feature information includes:
and increasing the initial characteristic weight corresponding to the target state characteristic information according to the following formula:
w＝w0·(1+mj/j！·e-m)
where w represents the increased feature weight, w0Representing the initial feature weights, m representing the number of failed devices in the sample device, and j representing the number of failed devices in the low scoring device.
In a second aspect, the present disclosure also provides an apparatus for determining a device state, the apparatus comprising:
the first acquisition module is used for acquiring state characteristic information of the target equipment;
the second acquisition module is used for acquiring state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment;
the adjusting module is used for adjusting the state feature intervals and/or the feature scores corresponding to the state feature intervals in the preset scoring rule according to the state feature information of the fault equipment in the sample equipment so as to reduce the feature scores of the state feature intervals corresponding to the fault equipment, and the preset scoring rule comprises a plurality of state feature intervals and feature scores respectively corresponding to the state feature intervals;
and the determining module is used for determining the state score of the target equipment according to the adjusted preset scoring rule.
In a third aspect, the present disclosure also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of any one of the first aspect.
In a fourth aspect, the present disclosure also provides an electronic device, including:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of any one of the first aspect.
By the technical scheme, the state feature interval in the preset scoring rule and/or the feature score corresponding to the state feature interval can be adjusted according to the state feature information of the fault equipment, so that the feature score of the state feature interval corresponding to the fault equipment is reduced, and the state score of the target equipment can be determined according to the reduced feature score subsequently. Compared with a mode of completely depending on expert experience to formulate a scoring rule in the related art, the method disclosed by the invention can avoid the condition that the health score of the fault equipment is too high, and obtain a scoring result which is more in line with the actual condition, so that the accuracy of evaluating the health condition of the equipment can be improved.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart illustrating a method of determining a device status according to an exemplary embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating a method of determining a device status according to another exemplary embodiment of the present disclosure;
FIG. 3 is a block diagram illustrating an apparatus for determining a device state according to an exemplary embodiment of the present disclosure;
fig. 4 is a block diagram illustrating an electronic device according to an exemplary embodiment of the present disclosure.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
In the field of industrial internet of things, along with the acceleration of the industrial modernization process of China, the importance of electric power equipment is increasingly highlighted, and the reliability and the safety of the electric power equipment are also higher in industry, so that the scientific evaluation of the health condition of the equipment is very important. The related art equipment evaluation method is generally performed based on expert experience. Specifically, scoring rules for different state characteristics of the equipment are subjectively formulated through expert experience, and then the health condition of the equipment is evaluated based on the scoring rules. If the expert experience is deviated, the formulated scoring rule may not be well adapted to the actual application scenario, so that errors may occur in the health score of the equipment, for example, an excessively high health score may be given to a faulty equipment, and the like.
In view of this, the embodiments of the present disclosure provide a method, an apparatus, a storage medium, and an electronic device for determining a device status, so as to solve the problem in the related art that a health score of a faulty device is too high, and improve accuracy of evaluating a health condition of the device.
Fig. 1 is a flow chart illustrating a method of determining a device status according to an exemplary embodiment of the present disclosure. Referring to fig. 1, the method may include:
And step 104, determining the state score of the target equipment according to the adjusted preset scoring rule.
By the above mode, the state feature interval in the preset scoring rule and/or the feature score corresponding to the state feature interval can be adjusted according to the state feature information of the faulty equipment, so that the feature score of the state feature interval corresponding to the faulty equipment is reduced, and the state score of the target equipment can be determined according to the reduced feature score. Compared with a mode of completely depending on expert experience to formulate a scoring rule in the related art, the method disclosed by the invention can avoid the condition that the health score of the fault equipment is too high, and obtain a scoring result which is more in line with the actual condition, so that the accuracy of evaluating the health condition of the equipment can be improved.
In order to make the method for determining the state of the device in the embodiments of the present disclosure more understandable to those skilled in the art, the above steps are exemplified in detail below.
In step 101, the target device may be a different type of device, such as an electrical device, which is not limited in this disclosure. The state characteristic information may be information that may be used to characterize a state of the device, such as a device production time, a device lifetime, a power system operating state, a transmission system operating state, and a historical failure frequency, which is not limited in this disclosure.
After obtaining the status characteristic information of the target device, status characteristic information of a sample device, which includes a malfunctioning device, may be obtained. For example, the sample device may be a plurality of devices belonging to the same batch and including a faulty device, and the sample device may include or not include a target device.
After the state feature information of the sample device is acquired, the state feature interval in the preset scoring rule and/or the feature score corresponding to the state feature interval can be adjusted according to the state feature information of the fault device in the sample device, so that the feature score of the state feature interval corresponding to the fault device is reduced.
The preset scoring rule may include a plurality of state feature intervals and feature scores corresponding to the plurality of state feature intervals respectively. For example, the plurality of state feature intervals may be different ranges of state feature values in a certain state feature information. For example, when the state feature information is the historical failure count, the state feature values may be the state feature sections corresponding to the historical failure count, where the failure count in the last year is less than 1, the failure count in the last year is 1 to 3, and the failure count in the last year is 3 or more, respectively.
For example, the feature score corresponding to each of the plurality of state feature intervals may be preset according to actual conditions, and the embodiment of the present disclosure does not limit this. For example, when the state feature information is the historical failure frequency, the feature score corresponding to the failure frequency of less than 1 time in the last year may be set to 100 points, the feature score corresponding to the failure frequency of 1 to 3 times in the last year may be set to 60 points, and the feature score corresponding to the failure frequency of 3 times or more in the last year may be set to 30 points, and so on.
For example, for the setting of the preset scoring rule, state feature intervals corresponding to the state feature information included in the scoring rule respectively may be determined first, and then a feature score corresponding to each state feature interval may be determined respectively. For example, the state feature information includes a device production time, a device service life, a power system operating state, a transmission system operating state, and a historical failure frequency, and in this case, a state feature interval corresponding to each state feature information and a feature score corresponding to each state feature interval may be set according to an actual situation, so as to obtain a preset scoring rule shown in table 1:
TABLE 1
If the target equipment is only scored according to the preset scoring rules, the health score of the fault equipment may be too high, and the fault equipment is mistakenly determined as the healthy equipment. For example, if the operating state of the transmission system of the device is faulty and other performances are good, when the health score is performed according to the preset scoring rule, the other performances of the faulty device are good, so that the characteristic scores of the faulty device may be all high except that the characteristic score corresponding to the operating state of the transmission system is low, which may result in that the overall state score of the faulty device obtained by performing the average value calculation or the weight calculation according to the characteristic scores is too high.
In order to solve the technical problem, in the embodiment of the present disclosure, according to the state feature information of the faulty device in the sample device, the state feature interval in the preset scoring rule and/or the feature score corresponding to the state feature interval may be adjusted to reduce the feature score of the state feature interval corresponding to the faulty device, and finally, according to the adjusted preset scoring rule, the state score of the target device is determined.
For example, as shown in table 1, the preset scoring rule indicates that the status feature value of the faulty device under the status feature information of "commissioning time and device life" is 0.7, that is, the ratio between the commissioning time and the device life of the faulty device is 0.7, and according to the preset scoring rule, the status feature value is included in the status feature interval of ≧ 0.5 and <0.8, so that the corresponding feature score can be determined to be 60 points.
In this case, in order to reduce the feature score of the status feature interval corresponding to the faulty device, the status feature interval in the preset scoring rule may be adjusted. For example, the state characteristic interval corresponding to the characteristic score of 60 is adjusted to be greater than or equal to 0.5 and less than 0.6, and the state characteristic interval corresponding to the characteristic score of 30 is adjusted to be greater than or equal to 0.6, so that the characteristic score of the characteristic interval corresponding to the faulty device with the value of 0.7 can be reduced to 30. Alternatively, the feature scores in the preset scoring rules may be adjusted. For example, the feature score corresponding to the state feature interval ≧ 0.5 and <0.8 is adjusted to 40, and so on. Or, the state adjustment interval and the feature score may be adjusted at the same time, for example, the original state feature interval corresponding to the feature score of 60 is adjusted to be greater than or equal to 0.5 and less than 0.6, and the original state feature interval corresponding to the feature score of 30 is adjusted to be greater than or equal to 0.6. Then, the state feature interval is adjusted to be more than or equal to 0.6, the corresponding feature score is reduced from 30 to 20, and the like.
It should be understood that, in a specific implementation of the present disclosure, an adjustment manner may be selected from the adjusting state feature interval, the adjusting feature score, and the adjusting state feature interval and the adjusting feature score according to an actual situation to achieve the purpose of reducing the feature score of the state feature interval corresponding to the faulty device, which is not limited in the embodiment of the present disclosure.
In a possible manner, for the adjustment manner of adjusting the state feature interval and the feature score, the adjustment manner may be: firstly, clustering is carried out on the sample equipment according to target state characteristic information of the sample equipment, wherein the target state characteristic information is any one of the state characteristic information of the sample equipment. Then, in the clustered clusters, a target cluster including the faulty device is determined. And then, adjusting a state characteristic interval in a preset scoring rule according to the target state characteristic information of the equipment in the target cluster. And finally, adjusting the characteristic score corresponding to the state characteristic interval in the preset scoring rule according to the number of the fault equipment in the target cluster and the preset corresponding relation between the number of the fault equipment and the characteristic score.
For example, the target state characteristic information is the production time and the device life, and the sample devices are clustered according to the target state characteristic information, so that a plurality of clusters can be obtained, and the ratio of the production time to the device life of the sample devices in each cluster is similar. For example, the ratio of the production time to the device life of the sample devices in a certain cluster may be 0.78, 0.79, 0.81, and 0.82, which are similar values.
After clustering the sample devices, a target class cluster including a failed device may be determined among the clustered class clusters. For example, the target class cluster may be a class cluster including at least one failed device, or may also be a class cluster including a number of failed devices greater than or equal to a preset threshold, which is not limited in this disclosure. It should be understood that, in order to avoid accidental errors, it is preferable to determine the target class cluster as a class cluster including the number of failed devices greater than or equal to a preset threshold. The preset threshold may be set according to actual conditions, which is not limited in the embodiments of the present disclosure.
After the target class cluster is determined, the state feature interval in the preset scoring rule can be adjusted according to the target state feature information of the device in the target class cluster.
In a possible manner, state feature values corresponding to the target state feature information of the devices in the target class cluster may be determined, and then a maximum value of the state feature values is used as an upper limit value of the interval, and a minimum value of the state feature values is used as a lower limit value of the interval, so as to obtain a new state feature interval.
For example, the target status feature information is a production time and a device lifetime, and the status feature value of the device in the target class cluster under the status feature information may be a ratio between the production time and the device lifetime, and is 0.78, 0.79, 0.81, and 0.82, respectively. If the preset scoring rule is shown in table 1, it may be determined that the status feature interval corresponding to the device includes two status feature intervals, which are greater than or equal to 0.5 and less than 0.8 and greater than or equal to 0.8. However, since the devices in the target class cluster have similar status feature values, it is obviously unreasonable to correspond them to different status feature intervals.
Therefore, in the embodiment of the present disclosure, the state characteristic value of the device under the target state characteristic information may be determined first, and then the maximum value of the state characteristic values is used as the upper limit value of the interval, and the minimum value of the state characteristic values is used as the lower limit value of the interval, so as to obtain a new state characteristic interval. For example, in the above example, the maximum state feature value of the device in the target class cluster in the target state feature information is 0.82, and the minimum state feature value is 0.78, so that it can be determined that the new state feature interval is 0.82 to 0.78.
In another possible manner, adjusting the state feature interval in the preset scoring rule may further be: the method comprises the steps of firstly determining state characteristic values corresponding to target state characteristic information of equipment in a target cluster, and then determining a new state characteristic interval according to the average value or the median value of the state characteristic values, so that the new state characteristic interval comprises the state characteristic values corresponding to the preset number of equipment in the target cluster.
For example, the preset number may be set according to actual situations, for example, the preset number may be set to 25% (rounded to an integer) of the total number of devices in the target class cluster, or the preset number may be set to the total number of devices in the target class cluster, and the like, and the embodiment of the present disclosure is not limited thereto. It should be understood that, in order to avoid dividing the devices in the target class cluster into different status feature intervals, it is preferable to set the preset number as the total number of devices in the target class cluster. In addition, each device has only one state feature value under the target feature state information, so the number of devices in the target cluster covered by the state feature interval can be determined according to the state feature values of the devices included in the state feature interval.
For example, the target status feature information is a production time and a device lifetime, and the status feature value of the device in the target class cluster under the status feature information may be a ratio between the production time and the device lifetime, and is 0.78, 0.79, 0.81, and 0.82, respectively. First, it may be determined that the average value of the state feature values of the above-mentioned devices is 0.795, then 0.795 may be used as a reference value to perform numerical expansion upward and downward, for example, in order to make the expanded numerical range include the state feature values of all the devices in the target cluster, 0.02 may be expanded upward and downward, respectively, to obtain a numerical range of 0.77 to 0.81, and then the numerical range is used as a new state feature interval corresponding to the target state feature information.
Or, it may be determined that the median of the state feature values of the above-mentioned devices is 0.79, then 0.79 may be used as a reference value, and the values may be expanded upward and downward, for example, in order to make the expanded value range include the state feature values of all the devices in the target class cluster, 0.02 may be expanded upward and downward, respectively, to obtain a value range of 0.77 to 0.81, and then the value range is used as a new state feature interval corresponding to the target state feature information.
After a new state feature interval corresponding to the target state feature information is obtained, a feature score corresponding to the new state feature interval can be determined. Specifically, the feature score corresponding to the new state feature interval may be determined according to the number of the faulty devices in the target class cluster and a preset corresponding relationship between the number of the faulty devices and the feature score.
For example, the preset corresponding relationship may be a corresponding relationship in which the number of the fault devices and the feature score are negatively related, so that the greater the number of the fault devices in the target class cluster, the lower the feature score corresponding to the new state feature interval is, and the purpose of reducing the feature score of the state feature interval corresponding to the fault device is further achieved.
In one possible approach, the preset correspondence may be:
snew＝smax-a·(smax-smin)·nk/k！·e-n·P (1)
wherein s isnewIndicating the adjusted feature score, smaxAnd sminRespectively representing the maximum characteristic score and the minimum characteristic score of equipment in the target cluster under the target state characteristic, which are determined according to a preset scoring rule, a representing a preset adjusting parameter, n representing the target clusterK denotes the number of failed devices in the target class cluster, and P denotes poisson distribution of target state characteristic information of the devices in the target class cluster.
For example, the target status feature information is a production time and a device lifetime, and the status feature value of the device in the target class cluster under the status feature information may be a ratio between the production time and the device lifetime, and is 0.78, 0.79, 0.81, and 0.82, respectively. According to a preset scoring rule, the maximum feature score of the device under the target state feature is determined to be 60 points, and the minimum feature score is determined to be 30 points. The total number of devices in the target class cluster and the number of failed devices may then be determined. Finally, the determined maximum characteristic score (60), minimum characteristic score (30), total equipment number in the target class cluster and the number of faulty equipment can be substituted into the formula (1) for calculation, so as to obtain the characteristic score corresponding to the new state characteristic interval.
After the new state feature interval and the feature score corresponding to the new state feature interval are obtained according to the above manner, the existing state feature interval in the preset scoring rule may be correspondingly adjusted to add the new state feature interval and the feature score corresponding to the new state feature interval to the preset scoring rule, so as to determine the state score of the target device according to the adjusted preset scoring rule.
In a possible mode, the feature scores of the target device under different state characteristics can be determined according to the adjusted preset scoring rule, and then the feature scores of the target device under different state characteristics are subjected to weighted summation, so that the state score for reflecting the health condition of the target device is obtained. In the related art, the weight values for weighted summation are subjectively determined mainly by expert experience. If the equipment fails, but the expert judges the importance of the failed state characteristic information to be deviated, the corresponding weight of the state characteristic information is possibly low, so that an excessively high state score is obtained, the failed equipment is identified as the equipment which does not fail, and the equipment state result which does not accord with the actual situation is obtained.
In order to solve the above problem, in the embodiment of the present disclosure, before determining the state score of the target device according to the adjusted preset scoring rule, an initial feature weight corresponding to target state feature information of the sample device is determined, where the target state feature information is any one of the state feature information of the sample device. And then, according to preset grading rules and target state characteristic information of the sample equipment, determining characteristic scores respectively corresponding to the sample equipment, and determining low-score equipment of which the characteristic score is lower than the preset score in the sample equipment. And if the number of the fault equipment in the low-score equipment is larger than the preset number, increasing the initial characteristic weight. Correspondingly, according to the adjusted preset scoring rule, determining the state score of the target device may be: and determining the state score of the target equipment according to the adjusted preset scoring rule and the increased initial feature weight.
For example, the initial feature weight may be determined in a manner in the related art, which is not limited by the embodiment of the present disclosure. For example, the sample device includes 6 devices, each of which includes four pieces of status feature information. In this case, the decision matrix may be constructed for the sample device first. Specifically, the value of each element in the decision matrix may be subjectively determined by an expert according to the rule shown in table 2, so as to obtain the value example shown in table 3.
TABLE 2
| Value taking | Means of | Value taking | Means of |
| 1 | The importance of the A element is the same as that of the B element | 1/3 | The B element is slightly more important than the A element |
| 3 | The A element is slightly more important than the B element | 1/5 | The B element is more important than the A element |
| 5 | The A element is more important than the B element | 1/7 | B element is significantly more important than A element |
| 7 | The A element is significantly more important than the B element | 1/9 | B element is more important than A element |
| 9 | The A element is more strongly important than the B element |
TABLE 3
For example, the preset score may be set according to actual conditions, and the embodiment of the present disclosure does not limit this. In a possible mode, the preset score can be determined according to the number of the state feature intervals corresponding to the target state feature information. Specifically, if the target state feature information corresponds to three state feature intervals, a score between two lower feature scores may be determined as a preset score. For example, the historical failure times shown in table 1 correspond to three status feature intervals, the lower two feature scores are 60 and 30, then the score between 60 and 30 may be determined as a predetermined score, such as 45 as a predetermined score, and so on. In this way, the device with the lowest feature score at the historical number of failures of the sample devices may be selected. If more fault equipment exist in the equipment with the lowest characteristic score, the weight of the state characteristic information of the historical fault times during weighting summation can be increased, and the overall state score of the fault equipment is further improved.
Alternatively, if the target state feature information corresponds to four or five state feature intervals, a score between two feature scores of the second largest and the third largest may be determined as a preset score, and so on, so that a device having a lower feature score under the target state feature information among the sample devices may be selected. If more fault equipment exist in the equipment with the lowest characteristic score, the weight of the target state characteristic information in the weighted summation can be increased, and the overall state score of the fault equipment is further improved.
In a possible manner, the initial feature weight corresponding to the target state feature information may be increased according to the following formula:
w＝w0·(1+mj/j！·e-m) (2)
where w represents the increased feature weight, w0Representing the initial feature weights, m representing the number of failed devices in the sample device, and j representing the number of failed devices in the low scoring device.
It should be understood that according to the formula (2), if the number of the faulty devices in the low-score device under the target state characteristic information is larger, the corresponding characteristic weight of the target state characteristic information is larger, so that the influence of the target state characteristic information on the overall state score can be increased, the too high state score is avoided, and the accuracy of the device score is improved.
For example, after the status score of the target device is obtained in the above manner, the operating status of the target device may be determined according to the status score. For example, it may be preset that the operating state of the target device is determined to be healthy if the state score is 80 or more (excluding 80), the operating state of the target device is determined to be good if the state score is 60 to 80, and the operating state of the target device is determined to be unhealthy if the state score is 60 or less, so that the operating state of the target device may be determined based on the state score. In addition, the situation that the state score of the fault equipment is too high can be avoided through the mode, the score result which is more in line with the actual situation is obtained, and therefore the accuracy of the equipment health condition evaluation is improved.
The method of determining the device status in the present disclosure is explained below by another exemplary embodiment.
Referring to fig. 2, the process includes:
In step 205, a new state feature interval corresponding to the target state feature information is obtained by using the maximum value of the state feature values as the upper limit value of the interval and the minimum value of the state feature values as the lower limit value of the interval.
and step 208, determining an initial characteristic weight corresponding to the target state characteristic information of the sample device.
In step 210, a low-score device with a feature score lower than a preset feature score is determined in the sample device.
In step 211, if the number of the faulty devices in the low-score devices is greater than the preset number, the initial feature weight corresponding to the target state feature information is increased.
And step 212, determining the state score of the target equipment according to the adjusted preset scoring rule and the increased initial feature weight.
The detailed description of the above steps is given above for illustrative purposes, and will not be repeated here. It will also be appreciated that for simplicity of explanation, the above-described method embodiments are all presented as a series of acts or combination of acts, but those skilled in the art will recognize that the present disclosure is not limited by the order of acts or combination of acts described above. Further, those skilled in the art will also appreciate that the embodiments described above are preferred embodiments and that the steps involved are not necessarily required for the present disclosure.
By the method, the feature score of the state feature interval corresponding to the fault equipment under the target state feature can be reduced, and the feature weight of the target state feature is increased, so that the state score of the target equipment can be determined according to the reduced feature score and the increased feature weight. Compared with a mode of completely depending on expert experience to formulate a scoring rule and a characteristic weight in the related art, the method disclosed by the invention can avoid the condition that the health score of the fault equipment is too high, and obtain a scoring result which is more in line with the actual condition, so that the accuracy of evaluating the health condition of the equipment can be improved.
Based on the same inventive concept, the disclosed embodiments also provide a device for determining the device status, which may be a part or all of an electronic device through software, hardware or a combination of both. Referring to fig. 3, the apparatus 300 for determining a device status may include:
a first obtaining module 301, configured to obtain status characteristic information of a target device;
a second obtaining module 302, configured to obtain status characteristic information of a sample device, where the sample device includes a faulty device;
the adjusting module 303 is configured to adjust a state feature interval and/or a feature score corresponding to the state feature interval in the preset scoring rule according to the state feature information of the faulty device in the sample device, so as to reduce the feature score of the state feature interval corresponding to the faulty device, where the preset scoring rule includes a plurality of state feature intervals and feature scores corresponding to the plurality of state feature intervals, respectively;
a determining module 304, configured to determine a status score of the target device according to the adjusted preset scoring rule.
Optionally, the adjusting module 303 is configured to:
clustering the sample equipment according to target state characteristic information of the sample equipment, wherein the target state characteristic information is any one of the state characteristic information of the sample equipment;
determining a target cluster comprising fault equipment in the clustered clusters;
adjusting a state feature interval in the preset scoring rule according to the target state feature information of the equipment in the target cluster;
and determining the adjusted characteristic score corresponding to the state characteristic interval according to the number of the fault equipment in the target cluster and the preset corresponding relation between the number of the fault equipment and the characteristic score.
Optionally, the adjusting module 303 is configured to:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target cluster;
and taking the maximum value in the state characteristic values as the upper limit value of the interval, and taking the minimum value in the state characteristic values as the lower limit value of the interval to obtain a new state characteristic interval corresponding to the target state characteristic information.
Optionally, the adjusting module 303 is configured to:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target cluster;
and determining a new state characteristic interval corresponding to the target state characteristic information according to the average value or the median value of the state characteristic values, so that the new state characteristic interval comprises the state characteristic values corresponding to the preset number of devices in the target cluster.
Optionally, the preset corresponding relationship is:
snew＝smax-a·(smax-smin)·nk/k！·e-n·P (1)
wherein s isnewIndicating the adjusted feature score, smaxAnd sminRespectively representing the maximum characteristic score and the minimum characteristic score of the equipment in the target cluster under the target state characteristic, which are determined according to the preset grading rule, a representing a preset adjusting parameter, n representing the total number of the equipment in the target cluster, k representing the number of the fault equipment in the target cluster, and P representing the poisson distribution of the target state characteristic information of the equipment in the target cluster.
Optionally, the apparatus 300 further comprises:
a first determining module, configured to determine an initial feature weight corresponding to target state feature information of the sample device before determining a state score of the target device according to the adjusted preset scoring rule, where the target state feature information is any one of the state feature information of the sample device;
the second determining module is used for determining the feature scores respectively corresponding to the sample equipment according to the preset scoring rule and the target state feature information of the sample equipment, and determining low-score equipment of which the feature score is lower than a preset feature score in the sample equipment;
the processing module is used for increasing the initial characteristic weight corresponding to the target state characteristic information when the number of the fault equipment in the low-score equipment is larger than the preset number;
the determining module 304 is configured to:
and determining the state score of the target equipment according to the adjusted preset scoring rule and the increased initial feature weight.
Optionally, the processing module is configured to:
and increasing the initial characteristic weight corresponding to the target state characteristic information according to the following formula:
w＝w0·(1+mj/j！·e-m) (2)
where w represents the increased feature weight, w0Representing the initial feature weights, m representing the number of failed devices in the sample device, and j representing the number of failed devices in the low scoring device.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Based on the same inventive concept, an embodiment of the present disclosure further provides an electronic device, including:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the above-described method of determining a device status.
In a possible approach, a block diagram of the electronic device may be as shown in fig. 4. Referring to fig. 4, the electronic device may include: a processor 401 and a memory 402. The electronic device 400 may also include one or more of a multimedia component 403, an input/output (I/O) interface 404, and a communications component 405.
The processor 401 is configured to control the overall operation of the electronic device 400, so as to complete all or part of the steps in the method for determining the device status. The memory 402 is used to store various types of data to support operations at the electronic device 400, such as instructions for any application or method operating on the electronic device 400 and application-related data, such as preset scoring rules, preset numbers, and so forth.
The Memory 402 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
The multimedia components 403 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 402 or transmitted through the communication component 405. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 404 provides an interface between the processor 401 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons.
The communication component 405 is used for wired or wireless communication between the electronic device 400 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. The corresponding communication component 405 may therefore include: Wi-Fi module, Bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic Device 400 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described method of determining the Device status.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the above-described method of determining a device state is also provided. For example, the computer readable storage medium may be the memory 402 described above comprising program instructions executable by the processor 401 of the electronic device 400 to perform the method of determining a device state described above.
In another exemplary embodiment, a computer program product is also provided, which comprises a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned method of determining a state of a device when executed by the programmable apparatus.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (10)
1. A method of determining a device state, the method comprising:
acquiring state characteristic information of target equipment;
acquiring state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment;
according to the state feature information of the fault equipment in the sample equipment, adjusting the state feature intervals and/or the feature scores corresponding to the state feature intervals in the preset grading rule to reduce the feature scores of the state feature intervals corresponding to the fault equipment, wherein the preset grading rule comprises a plurality of state feature intervals and feature scores corresponding to the state feature intervals;
and determining the state score of the target equipment according to the adjusted preset scoring rule.
2. The method according to claim 1, wherein the adjusting of the state feature interval in the preset scoring rule and the feature score corresponding to the state feature interval according to the state feature information of the faulty device in the sample device comprises:
clustering the sample equipment according to target state characteristic information of the sample equipment, wherein the target state characteristic information is any one of the state characteristic information of the sample equipment;
determining a target cluster comprising fault equipment in the clustered clusters;
adjusting a state feature interval in the preset scoring rule according to the target state feature information of the equipment in the target cluster;
and determining the adjusted characteristic score corresponding to the state characteristic interval according to the number of the fault equipment in the target cluster and the preset corresponding relation between the number of the fault equipment and the characteristic score.
3. The method according to claim 2, wherein the adjusting the state feature interval in the preset scoring rule according to the target state feature information of the device in the target cluster comprises:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target cluster;
and taking the maximum value in the state characteristic values as the upper limit value of the interval, and taking the minimum value in the state characteristic values as the lower limit value of the interval to obtain a new state characteristic interval corresponding to the target state characteristic information.
4. The method according to claim 2, wherein the adjusting the state feature interval in the preset scoring rule according to the target state feature information of the device in the target cluster comprises:
determining state characteristic values respectively corresponding to the target state characteristic information of the equipment in the target cluster;
and determining a new state characteristic interval corresponding to the target state characteristic information according to the average value or the median value of the state characteristic values, so that the new state characteristic interval comprises the state characteristic values corresponding to the preset number of devices in the target cluster.
5. The method according to any one of claims 2-4, wherein the predetermined correspondence is:
snew＝smax-a·(smax-smin)·nk/k！·e-n·P
wherein s isnewIndicating the adjusted feature score, smaxAnd sminRespectively representing the maximum characteristic score and the minimum characteristic score of the equipment in the target cluster under the target state characteristic, which are determined according to the preset grading rule, wherein a represents preset adjustment parameters, n represents the total number of the equipment in the target cluster, k represents the number of the fault equipment in the target cluster, and P representsAnd Poisson distribution of target state characteristic information of the equipment in the target class cluster.
6. The method according to claim 1, wherein before determining the status score of the target device according to the adjusted preset scoring rule, the method further comprises:
determining an initial characteristic weight corresponding to target state characteristic information of the sample equipment, wherein the target state characteristic information is any one of the state characteristic information of the sample equipment;
determining feature scores respectively corresponding to the sample devices according to the preset scoring rules and the target state feature information of the sample devices, and determining low-score devices of which the feature scores are lower than preset feature scores in the sample devices;
if the number of the fault equipment in the low-score equipment is larger than the preset number, increasing the initial characteristic weight corresponding to the target state characteristic information;
determining the state score of the target device according to the adjusted preset scoring rule, wherein the determining the state score of the target device comprises:
and determining the state score of the target equipment according to the adjusted preset scoring rule and the increased initial feature weight.
7. The method of claim 6, wherein the increasing the initial feature weight corresponding to the target state feature information comprises:
and increasing the initial characteristic weight corresponding to the target state characteristic information according to the following formula:
w＝w0·(1+mj/j！·e-m)
where w represents the increased feature weight, w0Representing the initial feature weights, m representing the number of failed devices in the sample device, and j representing the number of failed devices in the low scoring device.
8. An apparatus for determining a state of a device, the apparatus comprising:
the first acquisition module is used for acquiring state characteristic information of the target equipment;
the second acquisition module is used for acquiring state characteristic information of sample equipment, wherein the sample equipment comprises fault equipment;
the adjusting module is used for adjusting the state feature intervals and/or the feature scores corresponding to the state feature intervals in the preset scoring rule according to the state feature information of the fault equipment in the sample equipment so as to reduce the feature scores of the state feature intervals corresponding to the fault equipment, and the preset scoring rule comprises a plurality of state feature intervals and feature scores respectively corresponding to the state feature intervals;
and the determining module is used for determining the state score of the target equipment according to the adjusted preset scoring rule.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.
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