CN116465104A - Solar water heater temperature monitoring method based on big data - Google Patents
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Abstract
The invention relates to the technical field of data processing, in particular to a solar water heater temperature monitoring method based on big data, which comprises the following steps: acquiring a rising data set and a stable data set; obtaining a periodic average ambient temperature according to the ambient temperature data sequence; obtaining an ascending normal range value according to the periodic average ambient temperature; obtaining heat preservation efficiency according to the temperature data sequence; obtaining a stable normal range value according to the heat preservation efficiency; merging the temperature data sequences according to the rising normal range value and the stable normal range value; obtaining a first abnormal early warning result according to the redundant data; obtaining abnormal monitoring data according to the redundant data; obtaining a second abnormality early warning result according to the abnormality monitoring data; and carrying out final abnormal early warning according to the second abnormal early warning result, and carrying out temperature monitoring. The invention converts simple temperature monitoring into further temperature abnormality prediction and early warning, so that the abnormality early warning effect is more accurate.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a solar water heater temperature monitoring method based on big data.
Background
With the wide spread of solar water heaters, people are increasingly important to temperature monitoring of solar water heaters. The traditional temperature monitoring method can only monitor single temperature data, so that the thermodynamic state of the solar water heater cannot be comprehensively known, and the solar water heater temperature monitoring method based on big data can be used for carrying out data analysis and processing on a large amount of temperature data by collecting the temperature data, so that the thermodynamic state of the solar water heater can be comprehensively monitored and controlled. For temperature monitoring of a solar water heater, the main technology at the present stage is to put a temperature sensor in the solar water heater to monitor the temperature data of the water heater, but because of the singleness of the temperature data and the long-time measurement of the temperature data is not performed without acquisition and analysis, the temperature monitoring of the solar water heater cannot be comprehensively and effectively realized, so the traditional method is not suitable for the temperature monitoring of the solar water heater in the big data age, and the embodiment provides a solar water heater temperature monitoring method based on big data.
Disclosure of Invention
The invention provides a solar water heater temperature monitoring method based on big data, which aims to solve the existing problems.
The solar water heater temperature monitoring method based on big data adopts the following technical scheme:
one embodiment of the invention provides a solar water heater temperature monitoring method based on big data, which comprises the following steps:
acquiring the idle sunning temperature of a solar water heater, a temperature data sequence consisting of temperature data, an environment temperature data sequence consisting of environment temperature data, an illumination intensity data sequence consisting of illumination intensity data, and illumination times and no-illumination times;
threshold value screening is carried out on temperature data in the temperature data sequence according to the numerical value state to obtain an ascending data set and a stable data set of each acquisition period; obtaining the periodic average ambient temperature of each rising data group according to the ambient temperature data sequence; obtaining an ascending normal range value of each temperature data in each ascending data group according to the periodic average ambient temperature, the illumination intensity data sequence and the idle sunning temperature; obtaining the heat preservation efficiency of each stable data group according to the temperature data sequence; obtaining a stable normal range value of each temperature data of each stable data group according to the temperature data sequence and the heat preservation efficiency; combining the temperature data sequences according to the rising normal range value and the stable normal range value to obtain a combined temperature data sequence; obtaining redundant data of the merging temperature data sequence according to the merging temperature data sequence; obtaining a first abnormal early warning result according to the redundant data, the illumination times and the no illumination times; if a first abnormal early warning result exists, abnormal monitoring data of the temperature data sequence are obtained according to the redundant data of the temperature data sequence; obtaining a second abnormality early warning result according to the abnormality monitoring data;
and if the second abnormal early warning result exists, performing final abnormal early warning to realize the temperature monitoring of the solar water heater.
Preferably, the method for obtaining the rising normal range value is as follows:
in the middle ofA rising normal range value indicating the ith temperature data of the rising data group DaU; t1 represents the idle sunning temperature; t2 represents a cycle average ambient temperature; />Illumination intensity data representing the ith temperature data of DaU; c represents the specific heat capacity of water; m represents the mass of water; s represents the area of a light receiving heat collecting tube of the solar water heater.
Preferably, the method for obtaining the heat preservation efficiency comprises the following steps:
taking the last temperature data of the rising data set as the temperature data before heat preservation of the water heater, and recording the last temperature data as the first temperature; taking the last temperature data of the stable data set as the temperature data of the water heater after heat preservation, and recording the temperature data as a second temperature; the result of subtracting the first temperature from the second temperature is recorded as a first temperature difference; the result of subtracting the first temperature from the ambient temperature corresponding to the first temperature is recorded as a second temperature difference; and recording the ratio of the first temperature difference to the second temperature difference as the heat preservation efficiency.
Preferably, the expression for obtaining the stable normal range value is as follows:
in the middle ofA stable normal range value representing the jth temperature data of the stable data set DaS; />Representing a first temperature; />The heat preservation efficiency of the water heater is shown; t0 is the unit time of temperature data acquisition; t1 is the thermal efficiency per unit time.
Preferably, the method for acquiring the combined temperature data sequence is as follows:
the combination processing is carried out from the second temperature data of the rising data group to the last temperature data of the stable data group, and when the values of the front temperature data and the rear temperature data rise, if the ith temperature data is smaller than the value added by the rising normal range value corresponding to the ith-1 temperature data and the ith-1 temperature data, LZW combination is carried out; and when the value of the front and rear temperature data is reduced, if the result of the absolute value of the subtraction of the stable normal range value corresponding to the ith temperature data and the ith temperature data is larger than the value of the ith-1 th temperature data, LZW merging is carried out, so that a merging temperature data sequence after LZW merging processing is obtained.
Preferably, the method for acquiring the redundant data of the combined temperature data sequence is as follows:
acquiring an ascending group and a descending group in a combined temperature data sequence; if the absolute value of the difference value of two adjacent temperature data in the rising group is smaller than the rising normal range value of the next temperature data, the corresponding two temperature data are recorded as redundant data; if the absolute value of the difference value of the two adjacent temperature data in the descending group is smaller than the stable normal range value of the next temperature data, the corresponding two temperature data are recorded as redundant data.
Preferably, the specific process of acquiring the ascending group and the descending group in the combined temperature data sequence is as follows:
in the combined temperature data sequence, if the ith temperature data is larger than the (i-1) th temperature data, marking the corresponding two temperature data as an ascending group; if the ith temperature data is smaller than the (i-1) th temperature data, the corresponding two temperature data are marked as a descending group.
Preferably, the specific process of obtaining the second abnormality early warning result according to the abnormality monitoring data is as follows:
after all peaks and valleys of the abnormal monitoring data in the current acquisition period are obtained, all peaks are connected to obtain a broken line composed of peaks, namely a peak broken line, all valleys are connected to obtain a broken line composed of valleys, namely a valley broken line, curve fitting is carried out on the peak broken line and the valley broken line to respectively obtain a peak curve and a valley curve, then temperature data corresponding to each acquisition data in the peak curve and the valley curve are connected, an intermediate value T3 is taken as new temperature data corresponding to acquisition times, the new temperature data is recorded as second temperature data, and a second temperature data sequence composed of second temperature is obtained;
and taking the second temperature data sequence as input, judging whether the second temperature data sequence accords with the quantity of the ascending groups and the descending groups of the combined temperature data sequence, and if not, carrying out second abnormality early warning to realize second abnormality DF2 early warning and obtain a second abnormality early warning result.
Preferably, the specific process of performing the final abnormality pre-warning is as follows:
acquiring a combined temperature data sequence of all acquisition periods, and recording each temperature data in the combined temperature data sequence as a predicted value; the summation result of the predicted values in all the acquisition periods of each water heater is recorded as a first summation; the summation result of the number of the water heaters and all the collection times is recorded as a second summation; the ratio of the first sum to the second sum is recorded as the final predicted value of each water heater; and comparing whether the predicted value corresponding to the temperature data is matched with the final predicted value, and if so, carrying out final abnormal early warning to realize temperature monitoring of the solar water heater.
The technical scheme of the invention has the beneficial effects that: from temperature data acquisition to analysis to final abnormality prediction, the monitoring of the temperature of the solar water heater is reflected to a display screen from traditional single temperature data monitoring, and is converted into comprehensive analysis of multiple data, so that the abnormality temperature data prediction is realized, the temperature monitoring of the water heater is converted into further temperature abnormality prediction and early warning under the temperature monitoring from simple temperature monitoring, and the abnormality early warning effect is more accurate.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the solar water heater temperature monitoring method based on big data;
FIG. 2 is a schematic diagram of a temperature variation curve according to the present invention;
FIG. 3 is a schematic diagram of data points of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of the solar water heater temperature monitoring method based on big data according to the invention, and the detailed implementation, structure, characteristics and effects thereof are described in detail below with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the solar water heater temperature monitoring method based on big data provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for monitoring temperature of a solar water heater based on big data according to an embodiment of the invention is shown, the method includes the following steps:
step S001: and acquiring temperature data, environmental temperature data, illumination intensity, illumination times and no illumination times of the solar water heater to obtain a sequence consisting of corresponding data.
It should be noted that, with the widespread use of solar water heaters, temperature monitoring of solar water heaters is increasingly important. The traditional temperature monitoring method can only monitor single temperature data, so that the thermodynamic state of the solar water heater cannot be comprehensively known, and the solar water heater temperature monitoring method based on big data can be used for carrying out data analysis and processing on a large amount of temperature data by collecting the temperature data, so that the thermodynamic state of the solar water heater can be comprehensively monitored and controlled. For temperature monitoring of a solar water heater, the main technology at the present stage is to put a temperature sensor in the solar water heater to monitor the temperature data of the water heater, but because of the singleness of the temperature data and the long-time measurement of the temperature data is not performed without acquisition and analysis, the temperature monitoring of the solar water heater cannot be comprehensively and effectively realized, so the traditional method is not suitable for the temperature monitoring of the solar water heater in the big data age, and the embodiment provides a solar water heater temperature monitoring method based on big data.
The solar water heater temperature monitoring method based on big data firstly needs to collect temperature data of the solar water heater, wherein the implementation is not limited to the temperature sensor and the solar water heater, and the DS18B20 type digital temperature sensor and the flat plate type solar water heater are taken as examples for description.
Specifically, the process of collecting temperature data of the solar water heater comprises the following steps: selecting 100 flat-plate solar water heaters provided with DS18B20 type digital temperature sensors and CMOS sensors, firstly recording index parameters of the idle sunning temperature and the area of a light receiving heat collecting tube of each solar water heater, then recording temperature data displayed by the digital temperature sensors and illumination intensity data displayed by the CMOS sensors once at intervals of 20min, judging illumination conditions, and presetting an illumination intensity data threshold Y, wherein the embodiment is described by taking Y=30Lux as an example, the embodiment is not particularly limited, and Y can be determined according to specific implementation conditions. If the current illumination intensity data is larger than Y, recording the number of times of illumination once; if the current illumination intensity data is less than or equal to Y, recording the times of no illumination once, simultaneously recording the environmental temperature data once by using a mercury thermometer, and then collecting for 5 days with 1 day as one collecting period.
In addition, the highest water temperature which can be reached by the flat plate type solar water heater is as followsThe number of temperature data is +.>。
The acquisition process can obtain a temperature data sequence consisting of temperature data, an environment temperature data sequence consisting of environment temperature data, an illumination intensity data sequence consisting of illumination intensity data, and illumination times and no-illumination times.
Step S002: grouping according to temperature data in the temperature data sequence to obtain a grouping result, performing range LZW compression according to the grouping result to obtain a first abnormality early warning result, and obtaining a second abnormality early warning result according to the first abnormality early warning result.
It should be noted that, the collected temperature sequence is processed through temperature data compression, data approximate compression arrangement is performed through LZW, the fluctuation of the compression range is preset, the output result is recorded as abnormal monitoring data of the temperature data sequence, the prediction of the abnormal range of the temperature data can be realized through the decomposition of the abnormal monitoring data, the first abnormal DF1 early warning of the temperature data sequence is realized, finally, the predicted value is subjected to data splitting, a new temperature data sequence can be formed through the broken line fitting of the split data, the abnormal monitoring data in the temperature data sequence is further reflected by comparing with the normal temperature data in the temperature data sequence, and the second abnormal DF2 early warning of the temperature data sequence is realized.
1. And carrying out temperature data grouping on the temperature data in the temperature data sequence according to the numerical state to obtain a rising data group and a stable data group.
It should be noted that the numerical states of the temperature data are mainly divided into two types: the first is that the numerical value of the temperature data continuously rises, namely, the water temperature does not reach the highest water temperature which can be set by the water heater; the second is that the value of the temperature data is stable, and the water heater has a heat preservation function, so that when the water temperature reaches the highest water temperature, the value of the temperature data can be in a stable change state for a corresponding duration.
It should be further noted that, the abnormal conditions existing in the collecting process of the numerical states of the two corresponding temperature data also have two conditions: the first is that when the value of the temperature data continuously rises, the change speed of the value of the temperature data is abnormal; the second is that when the value change of the temperature data is stable, the temperature data is changed abnormally, for example, the value is rapidly increased or the value is rapidly decreased.
Specifically, grouping temperature data in the temperature data sequence, wherein the grouping specifically comprises the following steps:
in this embodiment, the temperature data of the first day is taken as an example, the temperature data of the water temperature reaching the predetermined maximum water temperature is taken as the demarcation point, the temperature data collected before the demarcation point is taken as the rising data group DaU, and the temperature data collected after the demarcation point is taken as the stable data group DaS.
Up data set DaU and stable data set DaS of the temperature data sequence acquired in the first day of the acquisition period can be obtained by the above method, so as to obtain up data set and stable data set of the temperature data sequence acquired in each acquisition period.
2. And obtaining a corresponding ascending normal range value and a corresponding stable normal range value according to the ascending data set and the stable data set, performing range LZW compression on temperature data of the temperature data sequence according to the ascending normal range value and the stable normal range value, and performing temperature data abnormal range prediction in combination with temperature data abnormality to realize first abnormal prediction of the temperature data.
Since the temperature data of the rising data group DaU changes approximately linearly with time, the compressed temperature data can better reflect each temperature data of the rising data group DaU, so that the rising data group DaU is subjected to LZW approximate compression with fluctuating range, and the approximate compression result is used as input of data analysis, and the first abnormal early warning range of the temperature data is obtained by quantifying the temperature data analysis.
It should be further noted that, under the condition of sunlight, the value of the temperature data in the normal state is in a general rising trend, and the rising trend has a certain rule; under the condition of no illumination, the numerical value of the temperature data basically stays in a stable state although fluctuating, but if the duration of the condition of no illumination is too long, the numerical value of the temperature data can be in an overall descending trend, and the descending trend can also have a certain rule, wherein the descending trend of the data is determined by the heat preservation performance of the water heater. In the process of collecting temperature data, the collected temperature data has certain regularity no matter in rising or falling trend, so that the rising data group DaU in the temperature data sequence can be subjected to a range LZW compression mode according to the temperature change rule to judge that the temperature data is abnormal.
The range LZW compression method refers to that data with the same value is combined and compressed for the existing LZW method. In this embodiment, referring to the existing LZW method, temperature data with values within a certain fluctuation range are combined and compressed, and the final compression ratio of the temperature data of the rising data group DaU and the stable data group DaS is calculated; the greater the compression ratio, the higher the stability of the temperature data of DaU and DaS, and the lower the information entropy of the temperature data of DaU and DaS; the smaller the compression ratio, the lower the stability of the temperature data of DaU and DaS, and the higher the information entropy of the temperature data of DaU and DaS. It can be determined whether there is an abnormality in the temperature data of DaU and DaS by the temperature data compression of DaU and DaS, and thus the stability of the temperature data of DaU and DaS can be inferred.
The temperature change rule in the presence of illumination generally rises, the temperature change is related to the local environment temperature, the heating time of the solar water heater and the light receiving area of the solar water heater, the temperature change is the product of the ratio of the difference value of the idle temperature and the average environment temperature to the solar irradiance and the measured time and the light receiving area in the unit time, namely the received heat, and the ratio of the specific heat capacity and the mass of the water is the unit value of the temperature rise.
Specifically, in the case of illumination, the collected temperature data belongs to the rising data set DaU, and the normal value of the ith temperature data of the rising data set in the normal fluctuation range is recorded as the rising normal range valueWherein the cycle averages ambient temperature andthe specific process of calculating the periodic average ambient temperature T1 is as follows:
the specific process of the periodic average ambient temperature T1 is as follows: the result of the summation of all the environmental temperature data in the current acquisition period is recorded as a first summation, and the ratio of the first summation to the number of the environmental temperature data in the current acquisition period is recorded as the period average environmental temperature.
Thus, the cycle average ambient temperature of each acquisition cycle can be obtained through the method.
Further, calculateThe specific process of (2) is as follows:
in the middle ofThe normal value of the ith temperature data in the normal fluctuation range, which represents the rising data group DaU, i.e. risingNormal range values; t1 represents the highest temperature reached when only air exists in the water heater, namely the idle sunning temperature; t2 represents a cycle average ambient temperature; />Illumination intensity data representing the ith temperature data of DaU; c represents the specific heat capacity of water; m represents the mass of water; s represents the area of a light receiving heat collecting tube of the solar water heater.
Up to this point, the up normal range value of the up data set DaU of the current collection period under the illumination condition can be obtained by the normal temperature data value formula, so as to obtain the up normal range value of the up data set DaU of each collection period under the illumination condition.
Specifically, under no illumination, the collected temperature data belongs to a stable data set DaS, and a normal value of the jth temperature data of the stable data set in a normal fluctuation range is recorded as a stable normal range valueWherein the change of temperature is related to the heat preservation efficiency of the water heater, and the specific process for calculating the heat preservation efficiency as W1 is as follows:
taking the last temperature data of DaU as the temperature data before heat preservation of the water heater, and recording the last temperature data as the first temperature; taking the last temperature data of DaS as the temperature data of the water heater after heat preservation, and recording the temperature data as a second temperature; the result of subtracting the first temperature from the second temperature is recorded as a first temperature difference; the result of subtracting the first temperature from the ambient temperature corresponding to the first temperature is recorded as a second temperature difference; the ratio of the first temperature difference to the second temperature difference is recorded as the heat preservation efficiency W1.
So far, the heat preservation efficiency of the stable data set in the current acquisition period can be obtained through the method.
Further, calculateThe specific process of (2) is as follows:
in the middle ofA normal value indicating that the jth temperature data of the stable data set DaS is in the normal fluctuation range, i.e., a stable normal range value; />Temperature data before heat preservation of the water heater is represented, namely a first temperature; />The heat preservation efficiency of the water heater is shown; t0 is the unit time of temperature data acquisition and is a fixed value of 20min; t1 is the thermal efficiency per unit time, wherein the present embodiment is given by +.>The embodiment is not specifically limited, and Y may be determined according to the specific implementation case.
Thus, the stable normal range value of the stable data set DaS of the current acquisition period under the condition of no illumination can be obtained through the normal temperature data value formula, so that the stable normal range value of the stable data set DaS of each acquisition period under the condition of no illumination is obtained.
In addition, after each ascending normal range value and the stable normal range value of the current acquisition period are acquired, LZW range merging compression can be performed according to the temperature data. For the collected temperature data, please refer to fig. 2, which shows the temperature data change relationship.
In fig. 2, under the condition that the fluctuation of the temperature data is normal, although the numerical value of the temperature data continuously changes, the numerical value of the temperature data changes within a normal range, and the corresponding temperature data is combined by taking the fact that the temperature data can be combined as a group of temperature data within the normal range as a standard.
Specifically, the specific process of temperature data combination is as follows: combining from the second temperature data of DaU to the last temperature data of DaS, wherein the ith temperature data is greater than the (i-1) th temperature data and the ith temperature data is less than the (i-1) th temperature dataAnd (3) withAdding the values, and then carrying out LZW combination; the ith temperature data is smaller than the (i-1) th temperature data, and the ith temperature data is equal to +.>And when the result of the absolute value of the subtraction is larger than the value of the i-1 temperature data, LZW combination is carried out.
The method can obtain the combined temperature data sequence of the current acquisition period after LZW combining treatment, and further can obtain the combined temperature data sequence of each current acquisition period after LZW combining treatment.
Further, the method for acquiring the ascending group and the descending group comprises the following steps: in the combined temperature data sequence, if the ith temperature data is larger than the (i-1) th temperature data, marking the corresponding two temperature data as an ascending group; if the ith temperature data is smaller than the (i-1) th temperature data, the corresponding two temperature data are marked as a descending group.
Thus, all ascending groups and descending groups in the combined temperature data sequence can be obtained through the method.
The method for acquiring the redundant data comprises the following steps: after two state groups of an ascending group and a descending group are obtained, if the absolute value of the difference value of two temperature data adjacent to each other in front and back in the ascending group is smaller than the ascending normal range value of the latter temperature data, the corresponding two temperature data are recorded as redundant data; if the absolute value of the difference value of the two adjacent temperature data in the descending group is smaller than the stable normal range value of the next temperature data, the corresponding two temperature data are recorded as redundant data.
So far, all redundant data in the combined temperature data sequence can be obtained through the method.
After all redundant data are removed, a final temperature data sequence formed by the remaining temperature data in the combined temperature data sequence can be obtained, the final temperature data sequence meets the rule that the temperature data alternately appear under the illumination time and the no illumination time, the sum of the illumination times L and the no illumination times NL in the temperature data sequence is recorded as a first sum, and the number N of the temperature data in the combined temperature data sequence is recorded as a first number. If the first sum is not equal to the first quantity, the first abnormity DF1 early warning is carried out.
Therefore, the first abnormal DF1 early warning can be realized through the method, and the first abnormal early warning DF1 result is obtained.
3. And obtaining abnormal monitoring data according to the early warning result of the first abnormal DF1, and carrying out second abnormal early warning on the temperature data according to the abnormal monitoring data.
It should be noted that, when the temperature data is abnormal, the abnormal monitoring data is extracted to perform early warning of the second abnormal DF2, where the abnormal monitoring data is the temperature data that does not conform to the temperature data change in the step 2, such temperature data is extracted to split the temperature data, each temperature data that does not conform to the temperature change is taken as a data point, and the abnormal monitoring data are all distributed in a point diagram, please refer to fig. 3, which shows a schematic diagram of the data points.
Specifically, if the first abnormality early warning DF1 result exists, performing a second abnormality early warning DF2 early warning specifically as follows: and referring to the acquisition mode of the redundant data, acquiring the redundant data of the temperature data sequence, and recording the redundant data as abnormal monitoring data of the temperature data sequence. In fig. 3, each anomaly-monitoring data is a data point, all peaks and valleys of the anomaly-monitoring data in the current acquisition period are obtained, the data point at the peak is regarded as a black point, the data point at the valley is regarded as a solid white point, and the remaining data points are regarded as dotted white points. After the abnormal monitoring data in the current acquisition period are divided into peaks and valleys, all the peaks are connected to obtain folding lines composed of the peaks, namely peak folding lines, and similarly, folding lines composed of the valleys, namely valley folding lines, can be obtained. And performing curve fitting on the peak value fold line and the valley value fold line to respectively obtain a peak value curve and a valley value curve. And then connecting the temperature data corresponding to the peak value curve and the valley value curve of each acquisition data, taking the intermediate value T3 as new temperature data corresponding to the acquisition times, and recording as second temperature data.
Thus, the second temperature data sequence composed of the second temperature data can be obtained through the method.
And (2) taking the second temperature data sequence as input, judging whether the second temperature data sequence accords with the quantity of the ascending group and the descending group in the step (2), and if not, carrying out second abnormality early warning, and realizing second abnormality DF2 early warning to obtain a second abnormality early warning result.
So far, the second abnormal early warning result can be realized by the method.
Step S003: and carrying out final abnormal early warning according to the second abnormal early warning result, and realizing intelligent temperature monitoring.
Specifically, if a second abnormality early warning DF2 result exists, final abnormality early warning is performed, specifically: and referring to the merging process of the temperature data, obtaining a temperature data sequence after merging all the acquisition periods, and recording each temperature data in the merged temperature data sequence as a predicted value. The summation result of the predicted values in all the acquisition periods of each water heater is recorded as a first summation; the summation result of the number of the water heaters and all the collection times is recorded as a second summation; the ratio of the first sum to the second sum is recorded as the final predicted value of each water heater. And comparing whether the predicted value corresponding to the temperature data is matched with the final predicted value, and if so, carrying out final abnormal early warning to realize temperature monitoring of the solar water heater.
This embodiment is completed.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (9)
1. The solar water heater temperature monitoring method based on big data is characterized by comprising the following steps of:
acquiring the idle sunning temperature of a solar water heater, a temperature data sequence consisting of temperature data, an environment temperature data sequence consisting of environment temperature data, an illumination intensity data sequence consisting of illumination intensity data, and illumination times and no-illumination times;
threshold value screening is carried out on temperature data in the temperature data sequence according to the numerical value state to obtain an ascending data set and a stable data set of each acquisition period; obtaining the periodic average ambient temperature of each rising data group according to the ambient temperature data sequence; obtaining an ascending normal range value of each temperature data in each ascending data group according to the periodic average ambient temperature, the illumination intensity data sequence and the idle sunning temperature; obtaining the heat preservation efficiency of each stable data group according to the temperature data sequence; obtaining a stable normal range value of each temperature data of each stable data group according to the temperature data sequence and the heat preservation efficiency; combining the temperature data sequences according to the rising normal range value and the stable normal range value to obtain a combined temperature data sequence; obtaining redundant data of the merging temperature data sequence according to the merging temperature data sequence; obtaining a first abnormal early warning result according to the redundant data, the illumination times and the no illumination times; if a first abnormal early warning result exists, abnormal monitoring data of the temperature data sequence are obtained according to the redundant data of the temperature data sequence; obtaining a second abnormality early warning result according to the abnormality monitoring data;
and if the second abnormal early warning result exists, performing final abnormal early warning to realize the temperature monitoring of the solar water heater.
2. The solar water heater temperature monitoring method based on big data according to claim 1, wherein the method for acquiring the rising normal range value is as follows:
in the middle ofA rising normal range value indicating the ith temperature data of the rising data group DaU; t1 represents the idle sunning temperature; t2 represents a cycle average ambient temperature; />Illumination intensity data representing the ith temperature data of DaU; c represents the specific heat capacity of water; m represents the mass of water; s represents the area of a light receiving heat collecting tube of the solar water heater.
3. The solar water heater temperature monitoring method based on big data according to claim 1, wherein the heat preservation efficiency obtaining method is as follows:
taking the last temperature data of the rising data set as the temperature data before heat preservation of the water heater, and recording the last temperature data as the first temperature; taking the last temperature data of the stable data set as the temperature data of the water heater after heat preservation, and recording the temperature data as a second temperature; the result of subtracting the first temperature from the second temperature is recorded as a first temperature difference; the result of subtracting the first temperature from the ambient temperature corresponding to the first temperature is recorded as a second temperature difference; and recording the ratio of the first temperature difference to the second temperature difference as the heat preservation efficiency.
4. The method for monitoring the temperature of the solar water heater based on big data according to claim 1, wherein the obtaining expression of the stable normal range value is as follows:
in the middle ofA stable normal range value representing the jth temperature data of the stable data set DaS; />Representing a first temperature;the heat preservation efficiency of the water heater is shown; t0 is the unit time of temperature data acquisition; t1 is the thermal efficiency per unit time.
5. The solar water heater temperature monitoring method based on big data according to claim 1, wherein the method for acquiring the combined temperature data sequence is as follows:
the combination processing is carried out from the second temperature data of the rising data group to the last temperature data of the stable data group, and when the values of the front temperature data and the rear temperature data rise, if the ith temperature data is smaller than the value added by the rising normal range value corresponding to the ith-1 temperature data and the ith-1 temperature data, LZW combination is carried out; and when the value of the front and rear temperature data is reduced, if the result of the absolute value of the subtraction of the stable normal range value corresponding to the ith temperature data and the ith temperature data is larger than the value of the ith-1 th temperature data, LZW merging is carried out, so that a merging temperature data sequence after LZW merging processing is obtained.
6. The solar water heater temperature monitoring method based on big data according to claim 1, wherein the method for acquiring redundant data of the combined temperature data sequence is as follows:
acquiring an ascending group and a descending group in a combined temperature data sequence; if the absolute value of the difference value of two adjacent temperature data in the rising group is smaller than the rising normal range value of the next temperature data, the corresponding two temperature data are recorded as redundant data; if the absolute value of the difference value of the two adjacent temperature data in the descending group is smaller than the stable normal range value of the next temperature data, the corresponding two temperature data are recorded as redundant data.
7. The method for monitoring the temperature of a solar water heater based on big data as set forth in claim 6, wherein the specific process of acquiring the ascending group and the descending group in the combined temperature data sequence is as follows:
in the combined temperature data sequence, if the ith temperature data is larger than the (i-1) th temperature data, marking the corresponding two temperature data as an ascending group; if the ith temperature data is smaller than the (i-1) th temperature data, the corresponding two temperature data are marked as a descending group.
8. The solar water heater temperature monitoring method based on big data according to claim 1, wherein the specific process of obtaining the second abnormal early warning result according to the abnormal monitoring data is as follows:
after all peaks and valleys of the abnormal monitoring data in the current acquisition period are obtained, all peaks are connected to obtain a broken line composed of peaks, namely a peak broken line, all valleys are connected to obtain a broken line composed of valleys, namely a valley broken line, curve fitting is carried out on the peak broken line and the valley broken line to respectively obtain a peak curve and a valley curve, then temperature data corresponding to each acquisition data in the peak curve and the valley curve are connected, an intermediate value T3 is taken as new temperature data corresponding to acquisition times, the new temperature data is recorded as second temperature data, and a second temperature data sequence composed of second temperature is obtained;
and taking the second temperature data sequence as input, judging whether the second temperature data sequence accords with the quantity of the ascending groups and the descending groups of the combined temperature data sequence, and if not, carrying out second abnormality early warning to realize second abnormality DF2 early warning and obtain a second abnormality early warning result.
9. The solar water heater temperature monitoring method based on big data according to claim 1, wherein the specific process of performing final abnormality pre-warning is as follows:
acquiring a combined temperature data sequence of all acquisition periods, and recording each temperature data in the combined temperature data sequence as a predicted value; the summation result of the predicted values in all the acquisition periods of each water heater is recorded as a first summation; the summation result of the number of the water heaters and all the collection times is recorded as a second summation; the ratio of the first sum to the second sum is recorded as the final predicted value of each water heater; and comparing whether the predicted value corresponding to the temperature data is matched with the final predicted value, and if so, carrying out final abnormal early warning to realize temperature monitoring of the solar water heater.
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| JPS6026247A (en) * | 1983-07-20 | 1985-02-09 | Sanyo Electric Co Ltd | Solar heat collector device |
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