CN110807563A - Big data-based equipment life prediction system and method - Google Patents
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Abstract
The invention belongs to the technical field of big data, and particularly discloses a system and a method for predicting the service life of equipment based on big data, wherein the service life predicting system comprises a data acquisition module, a data processing module, a predicting and updating module and a display module, the data acquisition module is electrically connected with the data processing module, and the data processing module is electrically connected with the predicting and updating module and the display module. The method and the device can predict more fit actual use conditions, so that the prediction result gradually becomes accurate.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a system and a method for predicting the service life of equipment based on big data.
Background
With the continuous development of society and the continuous progress of science and technology, the big data era has gone towards us, and the conclusion is obtained by analyzing the big data, so that the big data era becomes the key point of utilization of people;
in the prior art, the service life of the equipment is usually a certain service life, and if a user still uses the equipment when the user cannot know that the service life of the equipment is up to the end, the equipment can cause faults and accidents, and normal plan and arrangement are affected, wherein the equipment comprises mechanical equipment, traffic equipment, communication equipment and the like, the use of the communication equipment is more extensive, almost human communication equipment is used in the modern society, one of the cores of the communication equipment is a communication equipment battery, and the prediction of the service life of the communication equipment battery has the following defects in the prior art:
1. after the service life of the communication equipment battery is predicted in the use process, the service life of the battery is further reduced due to improper operation in the use process, and the predicted service life cannot be reached, so that the prediction result is inaccurate;
2. in the prior art, only the power consumption rate of a dynamic state is considered for the prediction of a communication equipment battery, but the static power consumption rate and the dynamic power consumption rate are not comprehensively considered, so that the prediction result is not accurate.
Therefore, a system and a method for predicting the service life of equipment based on big data are urgently needed.
Disclosure of Invention
The invention aims to provide a system and a method for predicting the service life of equipment based on big data, which aim to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme: a life prediction system of equipment based on big data comprises a data acquisition module for acquiring relevant data, a data processing module for processing and calculating the acquired data, a prediction updating module for updating the processed data, a display module for displaying the prediction result, and a service life prediction module for updating the prediction updating module;
the data acquisition module is electrically connected with the data processing module, and the data processing module is electrically connected with the prediction updating module and the display module.
According to the technical scheme, the data acquisition module comprises a display detection unit, an electric quantity monitoring unit and a time recording unit;
the output ends of the display detection unit, the electric quantity monitoring unit and the time recording unit are electrically connected with the input end of the data processing module;
whether the display detection unit is used for lighting the display screen of the communication equipment and detecting, as the basis of judging static power consumption and dynamic power consumption, the service life prediction of the communication equipment battery can be more accurate, the power monitoring unit is used for monitoring the power of the communication equipment battery in real time, the power consumption rate is calculated and confirmed through the power variation of the communication equipment battery, the time recording unit is used for recording the time in real time, and the dynamic power consumption duration and the static power consumption duration are determined through the cooperation display detection unit.
According to the technical scheme, the data processing module comprises a processor, a service life prediction unit, a database and a data retrieval unit;
the output end of the data acquisition module is electrically connected with the input end of the processor, the output end of the processor is electrically connected with the input ends of the database, the service life prediction unit and the display module, the output end of the database is electrically connected with the input end of the data retrieval unit, and the output end of the data retrieval unit is electrically connected with the input end of the processor;
the processor is used for classifying and processing various data collected by the data collection module, the database is used for storing various data processed by the processor, the service life prediction unit is used for predicting the service life of the battery of the communication equipment according to the data processed by the processor, and the data calling unit is used for calling related historical data from the database and supplying the data to the processor for processing and reference.
According to the technical scheme, the prediction updating module comprises a data updating unit and a data replacing unit;
the output end of the service life prediction unit is electrically connected with the input end of the data updating unit, the output end of the data updating unit is electrically connected with the input end of the data replacing unit, and the output end of the data replacing unit is electrically connected with the input end of the database;
the data updating unit is used for comparing the actual use data and the predicted use data of the communication equipment battery, so that the real-time prediction of the communication equipment battery can be realized according to the actual use data, the prediction result is more accurate, and the data replacing unit is used for replacing the predicted use data in the database with the real-time use data, so that the real-time updating and replacing of the data are realized.
According to the technical scheme, the display module is used for displaying the data processed by the processor, the service life of the battery of the communication equipment predicted by the service life prediction unit and the data updated by the data updating unit.
A device life prediction method based on big data comprises the following steps:
s1, collecting various data by using a data collection module;
s2, processing the acquired data by using a data processing module;
s3, updating and predicting the predicted data in real time according to the actual use condition;
and S4, displaying each item of data by using a display module.
According to the above technical solution, in the steps S1-S2, the display detection unit is used to detect whether the display screen of the communication device is lighted, and determine whether the battery of the communication device is in a static power consumption state or a dynamic power consumption state, where the static power consumption state is denoted as J and the dynamic power consumption state is denoted as D, the power monitoring unit is used to monitor the power of the battery of the communication device in real time, especially the power of the battery of the communication device at a time point when the static power consumption is turned into the dynamic power consumption state and at a time point when the dynamic power consumption is turned into the static power consumption state, and the time recording unit is used to record the power consumption of the battery of the communication device in real time for time T, so as to form a power consumption distribution set of the battery of1,j2)，(j3,j4),(j5,j6),…,(jn-1,jn) The electricity consumption set of dynamic electricity consumption is D = { (D)1,d2),(d3,d4),(d5,d6),…,(dm-1,dm) The collection of the electricity consumption time of static electricity consumption is JT={(t1,t2),(t3,t4),(t5,t6),…,(tn-1,tn) D is the time set of power consumption for dynamic power consumptionT={(T1,T2),(T3,T4),(T5,T6),…,(Tm-1,Tm)}；
According to the formula:
wherein, P is the static average power consumption rate of the battery of the communication equipment;
according to the formula:
wherein Q is the dynamic average power consumption rate of the battery of the communication equipment;
when P is more than or equal to A and Q is more than or equal to B, the service life of the communication equipment battery is reached;
wherein, A represents the maximum threshold value of the static average power consumption rate of the battery of the communication equipment, and B represents the maximum threshold value of the dynamic average power consumption rate of the battery of the communication equipment.
According to the technical scheme, the data processing module predicts the variation of the static power consumption rate P and the dynamic power consumption rate Q of the battery of the communication equipment according to the data stored in the database;
the data retrieval unit retrieves data of static power consumption rate P and dynamic power consumption rate Q of each discharge of the communication equipment battery from the database to form a set P = { P =1,P2,P3,…,PxAnd Q = { Q =1,Q2,Q3,…,Qx}；
According to the formula:
wherein,
representing the variation of the static electricity consumption rate of the adjacent two discharges of the battery of the communication equipment,
representing the variation of the dynamic power consumption rate of the adjacent two-time discharging of the battery of the communication equipment;
according to the formula:
wherein,
representing the difference between the static electricity consumption rate change amounts of two adjacent times,
representing a difference between two adjacent dynamic power consumption rate change amounts;
obtaining a set M = &ofstatic power consumption rate variation difference values
,
,
,…,
}，N={
,,,…,
}；
Obtaining a formula Y of a static electricity consumption rate variation difference value according to the set M and the set NPFormula Y of difference value of dynamic power consumption rate variationQ；
When Y isPAnd YQWhen the current value is equal to A and B, the values of the static power consumption rate and the dynamic power consumption rate which are close to the latest discharge are obtained, and the values are the service life of the battery of the communication equipment, namely the times.
According to the above technical solution, in step S3, the data acquisition module is used to acquire real-time data of power consumption of the battery of the communication device in real time, the real-time data is used to calculate the predicted data, the data replacement unit is used to replace the predicted data in the original database, the data update unit is used to replace the actual data with the original predicted data, and the service life of the battery of the communication device is predicted again, so that the real-time update of the predicted result can be realized, and the predicted result is more accurate.
In step S4, the display module is used to display the data processed by the processor, the service life of the battery of the communication device predicted by the life prediction unit, and the data updated by the data update unit in real time, the processor processes the data to generate a graph, and the display module displays the graph, so that the actual service life of the battery of the communication device can be known more intuitively.
Compared with the prior art, the invention has the beneficial effects that:
1. the data acquisition module is arranged, whether a display screen of a communication equipment battery works can be detected, the dynamic power consumption and the static power consumption of the communication equipment can be distinguished, the service life of the communication equipment battery can be more accurately predicted, meanwhile, the electric quantity of the communication equipment battery is monitored by the electric quantity monitoring unit, the static power consumption rate and the dynamic power consumption rate of the communication equipment battery can be calculated, meanwhile, historical data stored in the database are used, the static power consumption rate curve and the dynamic power consumption rate curve of the battery are calculated and converted into a formula, the discharging times are calculated according to the service life termination power consumption rate threshold of the equipment, and the service life of the communication equipment battery can be more accurately known.
2. The data replacement unit and the data updating unit are arranged, so that the static power consumption rate and the dynamic power consumption rate of the original predicted time point can be calculated by utilizing the static power consumption rate and the dynamic power consumption rate in the actual use process of the communication equipment battery, the data can be updated, the phenomenon that the predicted result has larger deviation due to complete prediction is avoided, the predicted data is replaced by utilizing the actual use data, the more fit actual use condition can be predicted, and the predicted result can be gradually accurate.
Drawings
FIG. 1 is a schematic block diagram of a big data-based device life prediction system according to the present invention;
FIG. 2 is a schematic diagram of the module connections of a big data based device life prediction system according to the present invention;
FIG. 3 is a schematic diagram illustrating steps of a big data-based device life prediction method according to the present invention;
fig. 4 is a schematic flow chart of a method for predicting the service life of a device based on big data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
As shown in fig. 1-2, a life prediction system for a device based on big data includes a data acquisition module for acquiring relevant data, a data processing module for processing and calculating the acquired data, a prediction updating module for updating the processed data, so that the prediction data can be replaced by actual data according to the comparison between the actual data and the prediction data, so that the overall prediction result is more accurate, and a display module for displaying the prediction result;
the data acquisition module is electrically connected with the data processing module, and the data processing module is electrically connected with the prediction updating module and the display module.
The data acquisition module comprises a display detection unit, an electric quantity monitoring unit and a time recording unit;
the output ends of the display detection unit, the electric quantity monitoring unit and the time recording unit are electrically connected with the input end of the data processing module;
whether the display detection unit is used for lighting the display screen of the communication equipment and detecting, as the basis of judging static power consumption and dynamic power consumption, the service life prediction of the communication equipment battery can be more accurate, the power monitoring unit is used for monitoring the power of the communication equipment battery in real time, the power consumption rate is calculated and confirmed through the power variation of the communication equipment battery, the time recording unit is used for recording the time in real time, and the dynamic power consumption duration and the static power consumption duration are determined through the cooperation display detection unit.
The data processing module comprises a processor, a service life prediction unit, a database and a data retrieval unit;
the output end of the data acquisition module is electrically connected with the input end of the processor, the output end of the processor is electrically connected with the input ends of the database, the service life prediction unit and the display module, the output end of the database is electrically connected with the input end of the data retrieval unit, and the output end of the data retrieval unit is electrically connected with the input end of the processor;
the processor is used for classifying and processing various data collected by the data collection module, the database is used for storing various data processed by the processor, the service life prediction unit is used for predicting the service life of the battery of the communication equipment according to the data processed by the processor, and the data calling unit is used for calling related historical data from the database and supplying the data to the processor for processing and reference.
The prediction updating module comprises a data updating unit and a data replacing unit;
the output end of the service life prediction unit is electrically connected with the input end of the data updating unit, the output end of the data updating unit is electrically connected with the input end of the data replacing unit, and the output end of the data replacing unit is electrically connected with the input end of the database;
the data updating unit is used for comparing the actual use data and the predicted use data of the communication equipment battery, so that the real-time prediction of the communication equipment battery can be realized according to the actual use data, the prediction result is more accurate, and the data replacing unit is used for replacing the predicted use data in the database with the real-time use data, so that the real-time updating and replacing of the data are realized.
The display module is used for displaying the data processed by the processor, the service life of the battery of the communication equipment predicted by the service life prediction unit and the data updated by the data updating unit.
As shown in fig. 3-4, a big data based device life prediction method includes the following steps:
s1, collecting various data by using a data collection module;
s2, processing the acquired data by using a data processing module;
s3, updating and predicting the predicted data in real time according to the actual use condition;
and S4, displaying each item of data by using a display module.
In the steps S1-S2, the display detection unit is used to detect whether the display screen of the communication device is lit, determine whether the battery of the communication device is in a static power consumption state or a dynamic power consumption state, the static power consumption state is denoted as J, the dynamic power consumption state is denoted as D, the power consumption of the battery of the communication device is monitored in real time by the power monitoring unit, especially the power consumption of the battery of the communication device at a time point when the static power consumption is turned into the dynamic power consumption state and a time point when the dynamic power consumption is turned into the static power consumption state, the time recording unit is used to record the power consumption of the battery of the communication device in real time for time T, so as to form a power consumption distribution set of the battery of the communication device, and1,j2)，(j3,j4),(j5,j6),…,(jn-1,jn) The electricity consumption set of dynamic electricity consumption is D = { (D)1,d2),(d3,d4),(d5,d6),…,(dm-1,dm) The collection of the electricity consumption time of static electricity consumption is JT={(t1,t2),(t3,t4),(t5,t6),…,(tn-1,tn) D is the time set of power consumption for dynamic power consumptionT={(T1,T2),(T3,T4),(T5,T6),…,(Tm-1,Tm)}；
According to the formula:
wherein, P is the static average power consumption rate of the battery of the communication equipment;
according to the formula:
wherein Q is the dynamic average power consumption rate of the battery of the communication equipment;
when P is more than or equal to A and Q is more than or equal to B, the service life of the communication equipment battery is reached;
wherein, A represents the maximum threshold value of the static average power consumption rate of the battery of the communication equipment, and B represents the maximum threshold value of the dynamic average power consumption rate of the battery of the communication equipment.
The data processing module predicts the variation of the static power consumption rate P and the dynamic power consumption rate Q of the battery of the communication equipment according to the data stored in the database;
the data retrieval unit retrieves data of static power consumption rate P and dynamic power consumption rate Q of each discharge of the communication equipment battery from the database to form a set P = { P =1,P2,P3,…,PxAnd Q = { Q =1,Q2,Q3,…,Qx}；
According to the formula:
wherein,
representing the variation of the static electricity consumption rate of the adjacent two discharges of the battery of the communication equipment,
representing the variation of the dynamic power consumption rate of the adjacent two-time discharging of the battery of the communication equipment;
according to the formula:
wherein,
representing the difference between the static electricity consumption rate change amounts of two adjacent times,
representing a difference between two adjacent dynamic power consumption rate change amounts;
obtaining a set M = &ofstatic power consumption rate variation difference values
,
,,…,
}，N={
,
,,…,
}；
Obtaining a formula Y of a static electricity consumption rate variation difference value according to the set M and the set NPFormula Y of difference value of dynamic power consumption rate variationQ；
When Y isPAnd YQWhen the current value is equal to A and B, the values of the static power consumption rate and the dynamic power consumption rate which are close to the latest discharge are obtained, and the values are the service life of the battery of the communication equipment, namely the times.
In step S3, the data acquisition module is used to acquire real-time data of power consumption of the battery of the communication device in real time, the real-time data is used to calculate the predicted data, the data replacement unit is used to replace the predicted data in the original database, the data update unit is used to replace the actual data with the original predicted data, and the service life of the battery of the communication device is predicted again, so that the real-time update of the predicted result can be realized, and the predicted result is more accurate.
In step S4, the display module is used to display the data processed by the processor, the service life of the battery of the communication device predicted by the life prediction unit, and the data updated by the data update unit in real time, the processor processes the data to generate a graph, and the display module displays the graph, so that the actual service life of the battery of the communication device can be known more intuitively.
The first embodiment is as follows:
whether a display screen of the communication equipment is lightened is detected by using a display detection unit, whether the battery of the communication equipment is in a static power consumption state or a dynamic power consumption state is judged, the static power consumption state is marked as J, the dynamic power consumption state is marked as D, the battery power of the communication equipment is monitored in real time by using a power monitoring unit, particularly, the battery power of the communication equipment at a time point when the static power consumption rotates to be power consumption and a time point when the dynamic power consumption rotates to be static power consumption, a power consumption distribution set of the battery power of the communication equipment is formed by recording the power consumption of the communication equipment in real time at a time T by using a time recording unit, the power consumption of the battery of the communication equipment is integrated as J = { (100%,96%), (82%,79%), (55%,53%), (32%, 31%) }, and the power consumption of the dynamic power consumption is integrated as D = {, (53%,32%), (31%,20%) }, the electricity consumption time set of static electricity consumption is JT= { (8:30,9:10), (9:40,10:10), (11:00,11:30), (12:00,12:20) }, and the power consumption time set for dynamic power consumption is DT={(9:10,9:40),(10:10,11:00),(11:30,12:00), (12:20,12:50)}；
According to the formula:
wherein, P =0.083%/min is the static average power consumption rate of the battery of the communication equipment;
according to the formula:
wherein, Q =0.5%/min is the dynamic average power consumption rate of the battery of the communication equipment;
when P is more than or equal to A =0.2%/min and Q is more than or equal to B =1%/min, the service life of the communication equipment battery is reached;
wherein, A =0.2%/min represents the maximum threshold value of the static average power consumption rate of the battery of the communication equipment, and B =1%/min represents the maximum threshold value of the dynamic average power consumption rate of the battery of the communication equipment.
The data processing module predicts the variation of the static power consumption rate P and the dynamic power consumption rate Q of the battery of the communication equipment according to the data stored in the database;
the data retrieval unit retrieves data of static power consumption rate P and dynamic power consumption rate Q of each discharge of the battery of the communication equipment from the database to form sets P = {0.025,0.025,0.027,0.030,0.031,0.034,0.036, …,0.815} and Q = {0.12,0.123,0.125,0.127,0.131,0.135,0.139, …,0.483 };
according to the formula:
wherein,
representing the variation of the static electricity consumption rate of the adjacent two discharges of the battery of the communication equipment,
representing the variation of the dynamic power consumption rate of the adjacent two-time discharging of the battery of the communication equipment;
according to the formula:
wherein,representing the difference between the static electricity consumption rate change amounts of two adjacent times,
representing a difference between two adjacent dynamic power consumption rate change amounts;
obtaining a set M = &ofstatic power consumption rate variation difference values
,
,,0.001,
,0.002,…,0.004}，N={0.003,0.002,0.002,0.004,0.004,0.004,…,0.006}；
Obtaining a formula Y of a static electricity consumption rate variation difference value according to the set M and the set NPK =0.003 × k +0.083=0.2, and the formula Y of k =39 is obtained as the difference between the dynamic power consumption rate and the change amountQ=0.006 × k +0.5=2, yielding k = 250;
when Y isPAnd YQWhen the current is equal to A and B, the values of the static power consumption rate and the dynamic power consumption rate which are close to the latest discharge are obtained, namely the service life of the battery of the communication equipment, namely the frequency is 250.
In step S3, the data acquisition module is used to acquire real-time data of power consumption of the battery of the communication device in real time, the real-time data is used to calculate the predicted data, the data replacement unit is used to replace the predicted data in the original database, the data update unit is used to replace the actual data with the original predicted data, and the service life of the battery of the communication device is predicted again, so that the real-time update of the predicted result can be realized, and the predicted result is more accurate.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (7)
1. A big data based device life prediction system, characterized by: the life prediction system comprises a data acquisition module for acquiring related data, a data processing module for processing and calculating the acquired data, a prediction updating module for updating the processed data and a display module for displaying a prediction result;
the data acquisition module is electrically connected with the data processing module, and the data processing module is electrically connected with the prediction updating module and the display module;
the data acquisition module comprises a display detection unit, an electric quantity monitoring unit and a time recording unit;
the output ends of the display detection unit, the electric quantity monitoring unit and the time recording unit are electrically connected with the input end of the data processing module;
the display detection unit is used for detecting whether a display screen of the communication equipment is lighted or not, the electric quantity monitoring unit is used for monitoring the electric quantity of a battery of the communication equipment in real time, the time recording unit is used for recording time in real time, and the display detection unit is matched to determine dynamic power consumption time and static power consumption time.
2. The big-data based device life prediction system of claim 1, wherein: the data processing module comprises a processor, a service life prediction unit, a database and a data retrieval unit;
the output end of the data acquisition module is electrically connected with the input end of the processor, the output end of the processor is electrically connected with the input ends of the database, the service life prediction unit and the display module, the output end of the database is electrically connected with the input end of the data retrieval unit, and the output end of the data retrieval unit is electrically connected with the input end of the processor;
the processor is used for classifying and processing various data collected by the data collection module, the database is used for storing various data processed by the processor, the service life prediction unit is used for predicting the service life of the battery of the communication equipment according to the data processed by the processor, and the data calling unit is used for calling related historical data from the database and supplying the data to the processor for processing and reference.
3. The big-data based device life prediction system of claim 2, wherein: the prediction updating module comprises a data updating unit and a data replacing unit;
the output end of the service life prediction unit is electrically connected with the input end of the data updating unit, the output end of the data updating unit is electrically connected with the input end of the data replacing unit, and the output end of the data replacing unit is electrically connected with the input end of the database;
the data updating unit is used for comparing the actual use data of the battery of the communication equipment with the predicted use data, and the data replacing unit is used for replacing the predicted use data in the database with the real-time use data so as to realize real-time updating and replacing of the data.
4. The big-data based device life prediction system of claim 3, wherein: the display module is used for displaying the data processed by the processor, the service life of the battery of the communication equipment predicted by the service life prediction unit and the data updated by the data updating unit.
5. A device life prediction method based on big data is characterized in that: the method comprises the following steps:
s1, collecting various data by using a data collection module;
s2, processing the acquired data by using a data processing module;
s3, updating and predicting the predicted data in real time according to the actual use condition;
s4, displaying each item of data by using a display module;
in the steps S1-S2, the display detection unit is used to detect whether the display screen of the communication device is lit, determine whether the battery of the communication device is in a static power consumption state or a dynamic power consumption state, the static power consumption state is denoted as J, the dynamic power consumption state is denoted as D, the power consumption of the battery of the communication device is monitored in real time by the power monitoring unit, especially the power consumption of the battery of the communication device at a time point when the static power consumption is turned into the dynamic power consumption state and a time point when the dynamic power consumption is turned into the static power consumption state, the time recording unit is used to record the power consumption of the battery of the communication device in real time for time T, so as to form a power consumption distribution set of the battery of the communication device, and1,j2),(j3,j4),(j5,j6),…,(jn-1,jn) The electricity consumption set of dynamic electricity consumption is D = { (D)1,d2),(d3,d4),(d5,d6),…,(dm-1,dm) The collection of the electricity consumption time of static electricity consumption is JT={(t1,t2),(t3,t4),(t5,t6),…,(tn-1,tn) Is dynamically power consumingThe electricity consumption time is set to DT={(T1,T2),(T3,T4),(T5,T6),…,(Tm-1,Tm)}；
According to the formula:
wherein, P is the static average power consumption rate of the battery of the communication equipment;
according to the formula:
wherein Q is the dynamic average power consumption rate of the battery of the communication equipment;
when P is more than or equal to A and Q is more than or equal to B, the service life of the communication equipment battery is reached;
wherein, A represents the maximum threshold value of the static average power consumption rate of the battery of the communication equipment, and B represents the maximum threshold value of the dynamic average power consumption rate of the battery of the communication equipment.
6. The big data-based device life prediction method according to claim 5, wherein: the data processing module predicts the variation of the static power consumption rate P and the dynamic power consumption rate Q of the battery of the communication equipment according to the data stored in the database;
the data retrieval unit retrieves data of static power consumption rate P and dynamic power consumption rate Q of each discharge of the communication equipment battery from the database to form a set P = { P =1,P2,P3,…,PxAnd Q = { Q =1,Q2,Q3,…,Qx}；
According to the formula:
；
wherein,representing the variation of the static electricity consumption rate of the adjacent two discharges of the battery of the communication equipment,
representing the variation of the dynamic power consumption rate of the adjacent two-time discharging of the battery of the communication equipment;
according to the formula:
wherein,representing the difference between the static electricity consumption rate change amounts of two adjacent times,
representing a difference between two adjacent dynamic power consumption rate change amounts;
obtaining a set M = &ofstatic power consumption rate variation difference values
,
,
,…,
Andd, a set of static electricity consumption rate variation difference N = &
,
,
,…,
}；
Obtaining a formula Y of a static electricity consumption rate variation difference value according to the set M and the set NPFormula Y of difference value of dynamic power consumption rate variationQ；
When Y isPAnd YQWhen the current value is equal to A and B, the values of the static power consumption rate and the dynamic power consumption rate which are close to the latest discharge are obtained, and the values are the service life of the battery of the communication equipment, namely the times.
7. The big data-based device life prediction method according to claim 6, wherein: in step S3, the data acquisition module is used to acquire real-time data of battery power consumption of the communication device in real time, the real-time data is used to calculate the predicted data, the data replacement unit is used to replace the predicted data in the original database, the data update unit is used to replace the actual data with the original predicted data, and the service life of the battery of the communication device is predicted again;
in step S4, the display module is used to display the data processed by the processor, the service life of the battery of the communication device predicted by the service life prediction unit, and the data updated by the data update unit in real time, the processor processes the data to generate a graph, and the display module displays the graph.
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