CN111929586A - Charging state evaluation method and device of passive wireless monitoring device - Google Patents
Charging state evaluation method and device of passive wireless monitoring device Download PDFInfo
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Abstract
The application discloses a charging state evaluation method and charging state evaluation equipment of a passive wireless monitoring device, which are used for solving the technical problems that a large amount of manpower and material resources are wasted and the evaluation process is complicated in the conventional charging state evaluation method. The method comprises the following steps: preprocessing log data uploaded by the passive wireless monitoring device to obtain microclimate data and charging data corresponding to the passive wireless monitoring device; determining the output power of the solar panel of the passive wireless monitoring device according to the corresponding relation between the pre-stored microclimate data and the output power of the solar panel; calculating the charging power of the passive wireless monitoring device according to the charging data; and determining the charging state evaluation value of the passive wireless monitoring device according to the relation between the output power and the charging power and a preset evaluation coefficient. According to the method, the charging state is quantitatively evaluated, the abnormal charging device can be processed in advance, and the efficient and simple charging state evaluation method is realized.
Description
Technical Field
The present disclosure relates to the field of passive wireless surveillance devices, and particularly, to a charging state evaluation method and apparatus for a passive wireless surveillance device.
Background
The passive wireless monitoring device can only adopt a solar panel and other passive modes to charge due to the particularity of the charging mode. However, the efficiency of charging by using solar energy is closely related to the weather conditions of the location of the passive wireless monitoring device, and when the sunlight is insufficient, the charging efficiency is easily low. The collection state of the solar panel determines whether the storage battery of the passive wireless monitoring device can obtain sufficient electric quantity, and then the monitoring function of the monitoring device is influenced. Therefore, it is very important to evaluate the charging state of the passive wireless monitoring device.
However, in the prior art, a mode of manually inquiring logs is usually adopted for the passive wireless monitoring device in the charging state evaluation, so that a large amount of manpower, time and energy are wasted, and the evaluation process is complicated and complicated.
Disclosure of Invention
The embodiment of the application provides a charging state evaluation method and charging state evaluation equipment of a passive wireless monitoring device, which are used for solving the technical problems that a large amount of labor and time are wasted and the evaluation process is complicated when the charging state of the passive wireless monitoring device is evaluated in the prior art.
In one aspect, an embodiment of the present application provides a charging state evaluation method for a passive wireless monitoring device, including: preprocessing log data uploaded by the passive wireless monitoring device to obtain microclimate data and charging data corresponding to the passive wireless monitoring device; wherein the microclimate data at least comprises any one of: temperature, light radiation illuminance; the charging data includes at least any one of: charging current, charging voltage; determining the output power of the solar panel of the passive wireless monitoring device according to the corresponding relation between the pre-stored microclimate data and the output power of the solar panel of the passive wireless monitoring device; calculating the charging power of the passive wireless monitoring device according to the charging data; and determining the charging state evaluation value of the passive wireless monitoring device according to the relation between the output power and the charging power and a preset evaluation coefficient.
According to the charging state evaluation method provided by the embodiment of the application, log data of the passive wireless monitoring device are cleaned and screened to obtain corresponding microclimate data and charging data; and the charging power of the passive wireless monitoring device and the output power of the solar cell panel are obtained based on the microclimate data and the charging data, and then the relation between the charging power and the output power is obtained. And an evaluation value of the state of charge is calculated based on a preset evaluation coefficient. The charge state is evaluated in a quantification mode, the quantification dynamically changes along with factors such as the environment where the monitoring device is located, the real evaluation of the charge state of the monitoring device can be given in real time, and therefore the device which is concerned about the charge state abnormity in a targeted mode can achieve the purposes of pre-estimation and early diagnosis. The charging state evaluation value is determined through the relation between the output power and the charging power and the preset evaluation coefficient, a simple and efficient evaluation mode is provided, manual participation is reduced, and a rapid and efficient evaluation process is realized.
In an implementation manner of the present application, the log data uploaded by the passive wireless monitoring device is preprocessed, which specifically includes: filtering and cleaning the log data, and removing abnormal log data to obtain first log data; and screening the content of the first log data, and extracting microclimate data and the charging data contained in the first log data.
In one implementation of the present application, the method further comprises: determining output power respectively corresponding to different moments in each hour in a first preset mode on the basis of microclimate data of different moments in each hour by taking the hour as a minimum unit; determining charging power respectively corresponding to different moments in each hour in a second preset mode based on the charging data of different moments in each hour; and calculating the mean square error between the output power and the charging power respectively corresponding to each hour according to the output power and the charging power respectively corresponding to different moments in each hour.
In one implementation of the present application, the method further comprises: screening the content of the first log data, and extracting geographical position data and season-related data contained in the first log data; and acquiring a preset evaluation coefficient of the passive wireless monitoring device from the evaluation coefficient database according to the geographic position data and the season related data.
In one implementation manner of the present application, the geographic location data includes longitude and latitude of a location where the passive wireless monitoring device is located; the seasonal related data may include any one or more of: season, sunrise time, sunset time.
In one implementation of the present application, the method further comprises: the mean square error and the preset evaluation coefficient are both in a matrix form; multiplying the mean square error matrix and a preset evaluation coefficient matrix to obtain a result matrix; and calculating determinant values corresponding to the result matrix to obtain the charging state evaluation value of the passive wireless monitoring device. By using the mode of combining the mean square error and the preset evaluation coefficient, a method for obtaining the evaluation value in a simplified and efficient manner is realized, and an effective method is further provided for the evaluation process of the charging state of the passive wireless monitoring device.
In one implementation of the present application, the preset evaluation coefficient matrix is a matrix of 24 × 1; wherein, 24 elements are respectively in one-to-one correspondence with 24 hours; the values of the 1 st to 9 th and 16 th to 24 th elements in the 24 elements are rounded to zero. The charging process of the solar panel is mainly concentrated in the time period from 10 hours to 15 hours, so that the rest elements except the 10 th to 15 th elements in the preset evaluation coefficient are set to be close to zero values, namely the zero values are rounded, the actual situation is fully combined, and the estimated value of the charging state obtained by the method provided by the embodiment of the application is ensured to be closer to the real state.
In one implementation manner of the present application, after determining the charge state evaluation value of the passive wireless monitoring apparatus, the method further includes: and calculating a difference value between the charging state evaluation value and the mean square error standard value, determining that the passive wireless monitoring device is abnormal in charging under the condition that the difference value is greater than a preset threshold value, and sending charging abnormal information to the mobile terminal. And sending abnormal information when the charging state evaluation value is abnormal to the mobile terminal in time, so as to achieve the technical effects of pre-estimating and pre-judging the monitoring device of the charging state abnormality in advance.
In one implementation of the present application, the method further comprises: under the condition that the passive wireless monitoring device normally operates, calculating to obtain the mean square error between the output power and the charging power respectively corresponding to each hour within the preset number of days; and averaging a plurality of mean square errors to obtain a mean square error standard value.
On the other hand, the embodiment of the present application further provides a charging state evaluation device of a passive wireless monitoring device, including: a processor; and a memory having executable code stored thereon, the executable code, when executed, causing the processor to perform a method of state of charge assessment for a passive wireless surveillance device as described above.
According to the charging state evaluation equipment of the passive wireless monitoring device, the mean square error and the preset evaluation coefficient between the charging power of the passive wireless monitoring device and the output power of the solar cell panel are determined through the microclimate data, the charging data, the geographic position information data and the season related data obtained after the log data are processed, and then the evaluation value of the charging state is obtained. The quantitative evaluation of the charging state is realized, the quantification dynamically changes along with the change of factors such as the environment where the passive wireless monitoring device is located, the real evaluation of the charging state of the monitoring device can be given in real time, and therefore the device which is pertinently concerned about the abnormal charging state can achieve the purposes of pre-estimation and early diagnosis. The difference value and the preset evaluation coefficient are combined to provide a simple and efficient evaluation mode, so that the artificial participation is reduced, and the rapid and efficient evaluation process is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of a charging state evaluation method of a passive wireless surveillance device according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of an internal structure of a charging state evaluation device of a passive wireless monitoring apparatus according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The passive wireless monitoring device can only adopt a solar panel and other passive modes for charging due to the particularity of the power supply mode, and the efficiency of the power supply mode is closely related to the meteorological conditions at the position of the passive wireless monitoring device. The collection state of the solar cell panel determines whether the storage battery can obtain sufficient electric quantity, and then the monitoring function of the passive wireless monitoring device is influenced.
The passive wireless monitoring device has no good solution on the charging state evaluation, and in most cases, the running state of the monitoring device is found to be abnormal only when the monitoring device takes a picture abnormally or no log is uploaded. In the running state anomaly analysis and statistics process, the charging state anomaly accounts for a large proportion. In the process of tracing, much more, the charging state is evaluated by looking up a large amount of past logs manually to find out the abnormal reason. This method not only wastes a lot of manpower and material resources, but also has a complicated and complicated evaluation process of the charging state.
The embodiment of the application provides a charging state evaluation method and charging state evaluation equipment of a passive wireless monitoring device. And then, calculating an evaluation value of the charging state according to the relationship between the two and a preset evaluation coefficient, and carrying out quantitative evaluation on the charging state. The technical problems that the existing assessment method wastes a large amount of labor and time cost, and the assessment process is complicated and complex are solved.
The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a charging state evaluation method of a passive wireless surveillance device according to an embodiment of the present disclosure. As shown in fig. 1, the evaluation method provided in the embodiment of the present application mainly includes the following steps:
And the server receives log data uploaded by the passive wireless monitoring device in real time and preprocesses the log data. The preprocessing of the log data mainly comprises the following processes:
firstly, log data are filtered and cleaned, and abnormal log data are removed.
Here, the abnormal log data mainly includes any one of: the log content is incomplete, and obviously does not accord with the actual situation.
Then, the log data content after the filtering cleaning is filtered. Extracting microclimate data and charging data in the step 2; and geographic location data, season-related data in step 4.
And 102, acquiring microclimate data and charging data corresponding to the passive wireless monitoring device.
And extracting the content in the log data to obtain microclimate data and charging data corresponding to the passive wireless monitoring device.
The microclimate data mainly comprises ambient temperature T of the passive wireless monitoring device and light radiation illuminance L of the passive wireless monitoring device; the charging data mainly comprises battery charging current I and battery charging voltage V of the passive wireless monitoring device.
And 103, calculating to obtain the mean square error between the output power of the solar panel and the charging power of the passive wireless monitoring device according to the microclimate data and the charging data.
Inquiring an ideal output power reference value table of the solar cell panel according to the temperature T and the light radiation illumination L to obtain the output power P of the solar cell panel under the conditions of the temperature T and the light radiation illumination L1。
Then, according to the charging current I and the charging voltage V, the charging power P of the passive wireless monitoring device is calculated through the following formula2：
P2＝I*V
In one embodiment of the present application, the output power P corresponding to different times in each hour is determined by taking the hour as the minimum unit1And charging power P2Calculating the mean square error corresponding to each hour through the following formula:
wherein n is the number of times at different times in each hour.
And 104, obtaining the geographic position related data and the season related data corresponding to the passive wireless monitoring device.
And screening the geographical position data and the season-related data while screening the log data to obtain microclimate data and charging data.
The geographic position data includes, but is not limited to, longitude and latitude of the current position of the passive wireless monitoring device. It can be understood by those skilled in the art that the geographic position related data can be obtained by a GPS unit built in the passive wireless monitoring device, and the details of the embodiments of the present application are not described herein. The method can also be obtained by other relevant modules or units for obtaining the position, and the embodiment of the application is not limited by comparison.
The seasonal related data includes any one or more of: season, sunrise time, sunset time.
The passive wireless monitoring device in the embodiment of the application charges through the solar cell panel, and the charging capacity of the solar cell panel is closely related to data such as illumination time and illumination intensity. Therefore, the sunrise time and the sunset time contained in the log data are obtained, and the illumination time length in one day is further obtained; the method comprises the steps of obtaining season data so as to obtain a time period with the strongest illumination intensity in a day corresponding to the current season through the current season, and further determining the power taking capacity of the solar panel in the day in the current season.
And 105, determining a preset evaluation coefficient according to the geographic position data and the season-related data.
According to the season-related data and the geographic position-related data acquired in step 104, the battery charging capacity of the passive wireless monitoring device at different time periods is comprehensively considered, and a preset evaluation coefficient is obtained.
In one embodiment of the application, the preset evaluation coefficients are a series of preset values in the form of a matrix of 24 x 1. I.e. in the form of a matrix of 24 rows and 1 column, where each element corresponds to an hour point in 24 hours.
In another embodiment of the present application, the time period during which the intensity of the sunlight is strongest is 10 hours to 15 hours in a day, that is, the time period during which the charging capability of the battery is strongest is 10 hours to 15 hours. Therefore, the specific gravity of the charging power is mainly concentrated on the time period of 10 hours to 15 hours, and therefore, the sum of 10 th to 15 th elements in the matrix of the preset evaluation coefficient is approximately equal to 1, and the 1 st to 9 th and 16 th to 24 th elements are lower in proportion, close to zero. Therefore, the values of elements 1-9, 16-24 are rounded to zero.
In the actual charging state evaluation process, a preset evaluation coefficient corresponding to the current environmental data is acquired in the evaluation coefficient database according to the current longitude and latitude information, the current season information, and the like of the passive wireless monitoring device. That is to say, the preset evaluation coefficient matrix in the embodiment of the present application is not always constant, but is changed correspondingly according to different geographic location data and season-related data, so as to ensure that the evaluation value finally obtained for the charging state of the passive wireless monitoring device can truly reflect the charging state.
It should be further noted that the obtained preset evaluation coefficient matrix is different according to different geographical position data of different passive wireless monitoring devices. And the corresponding preset evaluation coefficient matrixes of the same passive wireless monitoring device are different under different seasons and illumination conditions.
And step 106, calculating to obtain a charging state evaluation value according to a preset evaluation coefficient and a mean square error.
And calculating to obtain an evaluation value of the charging state after obtaining a mean square error between the output power of the solar panel and the charging power of the passive wireless monitoring device and a preset evaluation coefficient.
In one embodiment of the present application, the preset evaluation coefficient matrix is in the form of a 24 × 1 matrix; the mean square error is also in the form of a matrix, which is specifically in the form of a 1 x 24 matrix.
Specifically, 24 elements in the mean square error matrix correspond one-to-one to 24 hours of the day. For example, the 10 th element in the mean square error matrix corresponds to the mean square error between the charging power of the passive wireless surveillance device at 10 hours and the output power of the solar panel at 10 hours.
And further, multiplying the mean square error matrix by a preset evaluation coefficient matrix, and calculating a determinant value of a multiplication result matrix to obtain a charging state evaluation value of the passive wireless monitoring device.
And step 107, judging whether the evaluation value is larger than a preset threshold value.
After the evaluation value of the state of charge is obtained, the magnitude relation between the evaluation value and a preset evaluation threshold value is determined.
Specifically, the size situation between the two is determined by a method which is relatively large and small. And determining whether the charging state of the passive wireless monitoring device is abnormal or not according to the comparison result.
In one embodiment of the present application, each element in the preset evaluation coefficient matrix is given in the form of a percentage.
Therefore, a method for determining whether the charging state of the passive wireless monitoring device is abnormal is as follows:
determining mean square error standard value MSE between output power of solar panel and charging power of passive wireless monitoring devicestd。
Wherein, the mean square error standard value MSEstdDetermined by the following procedure:
firstly, a plurality of log data of the passive wireless monitoring device in a normal operation state are obtained.
The passive wireless monitoring device is also in a normal state according to microclimate data and charging data of each hour in a plurality of days determined by a plurality of log data under the condition of normal operation, namely under the condition that the charging state of the passive wireless monitoring device is not abnormal.
Then, according to the microclimate data and the charging data, determining mean square error MSE of different time periods of multiple dayswThen for a plurality of MSEswTaking the mean value to obtain the standard value MSE of the mean square errorstd。
Further, it is determined whether the evaluation value of the state of charge is larger than a mean square error criterion value MSEstdAnd 60% of the total amount of the active wireless monitoring device, and further determining whether the charging state of the passive wireless monitoring device is normal.
And step 108, determining that the charging state of the passive wireless monitoring device is not abnormal.
In the case where the evaluation value of the state of charge is smaller than the preset evaluation value, or the evaluation value of the state of charge is smaller than the mean square error criterion value MSEstdAnd (4) determining that the charging state evaluation value belongs to a normal range, and further determining that the charging state of the passive wireless monitoring device is abnormal.
And step 109, determining that the evaluation value is abnormal, and sending charging state abnormal information.
In the case where the evaluation value of the state of charge is greater than or equal to the preset evaluation value, orThe estimated value of the state of charge is greater than or equal to the mean square error standard value MSEstdAnd (4) determining that the state of charge evaluation value is abnormal, and further determining that the state of charge of the passive wireless monitoring device is abnormal.
In one embodiment of the application, after the charging state is determined to be abnormal, charging abnormal information is sent to a mobile terminal of a field inspection worker, so that the inspection worker can timely process the passive wireless monitoring device.
Wherein, patrol and examine the mobile terminal of staff and include any one of the following at least: cell-phone, flat board, handheld terminal.
The following is a realization process of evaluating a comprehensive charging state of a certain string of 860723043349881 devices at 3/27/2020 and 11 am by using the charging state evaluation method of the passive wireless monitoring device provided by the embodiment of the application.
Firstly, cleaning a real-time log, and screening microclimate data, charging data, geographical position data and season-related data contained in log data. Meanwhile, the current temperature T is obtained by inquiring an ideal output power reference value table of the solar cell paneldevIlluminance L of light radiationdevOutput power value f of the caseW(Tdev，Ldev)。
Charging current I obtained according to log dataiCharging voltage ViMicroclimate data, and output power value f determined from the microclimate dataW(Tdev，Ldev) The results are shown in the following table.
TABLE 1
Secondly, calculating Mean Square Error (MSE) through microclimate data and charging dataW。
Thus, MSEWThe formed matrix U ═ 0000000000.310.02 …]。
Thirdly, acquiring a preset evaluation coefficient matrix WW。
Acquiring a preset evaluation coefficient matrix W according to the geographical position data and the season-related data obtained from the matrix dataW。
Wherein, the longitude and latitude of the geographic position data are as follows: (36.27,114.54);
the sunrise time: 06:17: 00; the sunset time: 18:39: 00;
season: spring;
according to the condition information, a preset evaluation coefficient matrix is obtained from a plurality of prestored evaluation coefficient matrixes as follows:
WW＝[0 0 0 0 0 0 0 0 0 0.18 0.24 …]T
wherein, the mean square error matrix U and the preset evaluation coefficient matrix WWEach of which contains 24 elements.
And fourthly, calculating a charge state evaluation value.
The mean square error matrix U and a preset evaluation coefficient matrix W are combinedWMultiplying to obtain a result matrix; and taking the determinant value to obtain an evaluation value e of the charging state.
e＝|U*WW|＝0.0606
The fifth step, judging whether the estimated value e of the charging state is larger than the mean square error standard value MSEstd60% of (1), wherein the mean square error criterion value MSEstdIs 0.121.
e＝0.0606<0.121*0.6＝0.0726
Thus, the state of charge evaluation value of the passive wireless monitoring device at 11 can be determined to be smaller than the mean square error standard value MSEstd60% of the total.
Therefore, it can be determined through the above calculation process that the charging state of the passive wireless monitoring device is not abnormal at 11 am.
The above is an embodiment of the method of the present application, and based on the same inventive concept, the embodiment of the present application further provides a charging state evaluation device of a passive wireless monitoring device, and an internal structure of the charging state evaluation device is shown in fig. 2.
Fig. 2 is a schematic diagram of an internal structure of a charging state evaluation device of a passive wireless monitoring apparatus according to an embodiment of the present disclosure. As shown in fig. 2, the apparatus includes a processor 201; and a memory 201 having executable code stored thereon, which when executed, causes the processor 201 to perform a charging state evaluation method of a passive wireless monitoring apparatus as described above.
In an embodiment of the present application, the processor 201 is configured to perform preprocessing on log data uploaded by the passive wireless monitoring device, so as to obtain microclimate data and charging data corresponding to the passive wireless monitoring device; wherein the microclimate data at least comprises any one of: temperature, light radiation illuminance; the charging data includes at least any one of: charging current, charging voltage; the passive wireless monitoring device is used for determining the output power of the solar panel of the passive wireless monitoring device according to the corresponding relation between the pre-stored microclimate data and the output power of the solar panel of the passive wireless monitoring device; according to the charging data, calculating the charging power of the passive wireless monitoring device; and the passive wireless monitoring device is also used for determining the charging state evaluation value of the passive wireless monitoring device according to the relation between the output power and the charging power and a preset evaluation coefficient.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A charging state evaluation method of a passive wireless monitoring device is characterized by comprising the following steps:
preprocessing log data uploaded by a passive wireless monitoring device to obtain microclimate data and charging data corresponding to the passive wireless monitoring device; wherein the microclimate data includes at least any one of: temperature, light radiation illuminance; the charging data includes at least any one of: charging current, charging voltage;
determining the output power of the solar panel of the passive wireless monitoring device according to the corresponding relation between the pre-stored microclimate data and the output power of the solar panel of the passive wireless monitoring device; calculating the charging power of the passive wireless monitoring device according to the charging data;
and determining the charging state evaluation value of the passive wireless monitoring device according to the relation between the output power and the charging power and a preset evaluation coefficient.
2. The charging state evaluation method of the passive wireless monitoring device according to claim 1, wherein the preprocessing the log data uploaded by the passive wireless monitoring device specifically includes:
filtering and cleaning the log data, and removing abnormal log data to obtain first log data;
and screening the content of the first log data, and extracting the microclimate data and the charging data contained in the first log data.
3. The charging state evaluation method of the passive wireless monitoring device according to claim 1, further comprising:
determining output power respectively corresponding to different moments in each hour in a first preset mode on the basis of the microclimate data at different moments in each hour by taking the hour as a minimum unit; determining charging power respectively corresponding to different moments in each hour in a second preset mode based on the charging data at different moments in each hour;
and calculating the mean square error between the output power and the charging power respectively corresponding to each hour according to the output power and the charging power respectively corresponding to different moments in each hour.
4. The charging state evaluation method of the passive wireless monitoring device according to claim 2, further comprising:
screening the content of the first log data, and extracting geographic position data and season-related data contained in the first log data;
and acquiring a preset evaluation coefficient of the passive wireless monitoring device from an evaluation coefficient database according to the geographic position data and the season-related data.
5. The charging state evaluation method of the passive wireless monitoring device according to claim 4, wherein the geographic position data includes longitude and latitude of a position where the passive wireless monitoring device is located;
the season-related data may include substantially any one or more of: season, sunrise time, sunset time.
6. The charging state evaluation method of the passive wireless monitoring device according to claim 3, further comprising:
the mean square error and the preset evaluation coefficient are both in a matrix form;
multiplying the mean square error matrix and a preset evaluation coefficient matrix to obtain a result matrix;
and calculating a determinant value corresponding to the result matrix to obtain a charging state evaluation value of the passive wireless monitoring device.
7. The charging state evaluation method of the passive wireless monitoring device according to claim 6, wherein the preset evaluation coefficient matrix is a matrix of 24 x 1; wherein, the 24 elements are respectively in one-to-one correspondence with 24 hours;
the values of the 1 st to 9 th and 16 th to 24 th elements in the 24 elements are rounded to zero.
8. The charging state evaluation method of a passive wireless monitoring device according to claim 1, wherein after determining the charging state evaluation value of the passive wireless monitoring device, the method further comprises:
and calculating a difference value between the charging state evaluation value and a mean square error standard value, determining that the passive wireless monitoring device is abnormal in charging under the condition that the difference value is greater than a preset threshold value, and sending abnormal charging information to the mobile terminal.
9. The charging state evaluation method of the passive wireless monitoring device according to claim 8, further comprising:
under the condition that the passive wireless monitoring device normally operates, calculating to obtain the mean square error between the output power and the charging power respectively corresponding to each hour in preset days;
and averaging a plurality of mean square errors to obtain the mean square error standard value.
10. A charging state evaluation apparatus of a passive wireless monitoring device, the apparatus comprising: a processor;
and a memory having executable code stored thereon, which when executed causes the processor to perform a method of charge state assessment for a passive wireless surveillance device as claimed in any one of claims 1-9.
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| CN115560864A (en) * | 2022-11-18 | 2023-01-03 | 杭州集联科技有限公司 | Wireless temperature measurement sensor temperature measurement method based on solar power supply |
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