CN116136979A - Prediction method and system based on big data - Google Patents
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Abstract
The application relates to the field of big data processing, in particular to a big data-based prediction method and a big data-based prediction system, comprising the following steps: collecting various influencing factors influencing the reliability of the electronic equipment in a monitoring time period; matching reliability prediction weights for each type of collected influence factors; acquiring a reliability evaluation value of the electronic equipment according to various influence factors acquired in a monitoring time period and reliability prediction weights matched with each acquired influence factor; comparing the obtained reliability evaluation value of the electronic equipment with a preset reliability threshold value; and if the obtained reliability evaluation value of the electronic equipment is not greater than the preset reliability threshold value, closing the electronic equipment. The reliability of the electronic equipment can be predicted, so that the operation safety of the electronic equipment is practically improved.
Description
Technical Field
The present disclosure relates to the field of big data processing, and in particular, to a big data-based prediction method and system.
Background
With the continuous development of science and technology, the reliability of electronic devices is receiving more and more attention. However, many factors affect the reliability of electronic devices, including natural environmental factors (e.g., temperature, humidity, salt mist, and air pollution particles), mechanical structural factors (e.g., strong mechanical vibrations, collisions, etc.), electromagnetic environmental factors, and assembly process factors.
Under the comprehensive actions of different factors, the reliability of the electronic equipment inevitably shows a declining trend, and when the reliability of the electronic equipment declines to a certain extent, the electronic equipment cannot complete normal tasks and functions, so that economic loss and resource waste which are difficult to recover are caused.
Therefore, how to predict the reliability of the electronic device to actually increase the operation safety of the electronic device is a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
The application provides a prediction method and a prediction system based on big data, so as to predict the reliability of electronic equipment, thereby practically increasing the operation safety of the electronic equipment.
In order to solve the technical problems, the application provides the following technical scheme:
a prediction method based on big data comprises the following steps: step S110, various influencing factors influencing the reliability of the electronic equipment are collected in a monitoring time period; step S120, matching reliability prediction weights for each type of collected influence factors; step S130, obtaining a reliability evaluation value of the electronic equipment according to various influence factors collected in a monitoring time period and reliability prediction weights matched with each kind of influence factors; step S140, comparing the obtained reliability evaluation value of the electronic equipment with a preset reliability threshold value to judge the current reliability of the electronic equipment; step S150, if the obtained reliability evaluation value of the electronic equipment is larger than a preset reliability threshold value, returning to step S110.
The big data based prediction method as described above, wherein preferably, further comprising the steps of: and step S160, if the obtained reliability evaluation value of the electronic equipment is not greater than a preset reliability threshold value, closing the electronic equipment.
The big data-based prediction method as described above, wherein it is preferable that all the sub-influence factors in the influence factors of the same class collected at the same time are collected together to form a sub-influence factor feature vector set of each influence factor of the same class at the same time; obtaining the comprehensive characteristic value of each same type of influence factors at the moment according to the sub-influence factors of each same type of influence factors at the moment; and collecting all the comprehensive characteristic values of each same type of influence factors obtained in the monitoring time period together to form a comprehensive characteristic value set.
The big data-based prediction method as described above, wherein it is preferable to calculate the similarity of the collected category characteristics of each type of influence factors with the category characteristics of the preset influence factors; classifying the collected categories of each type of influence factors into the categories of preset influence factors with the maximum similarity, and taking the reliability prediction weight corresponding to the classified categories of the preset influence factors as the reliability prediction weight of each type of collected influence factors.
The prediction method based on big data as described above, wherein it is preferable to extract category characteristics contained in the comprehensive characteristic value of each category of influence factors and form a category characteristic set for collecting the influence factors; and obtaining the similarity between the acquired category characteristics of each type of influence factors and the category characteristics of the influence factors preset in the prediction server according to the category characteristic set of the preset influence factors and the category characteristic set of the acquired influence factors.
A big data based prediction system, comprising: the system comprises a factor acquisition unit, a weight matching unit, an evaluation value calculation unit and a comparison prediction unit; the factor acquisition unit acquires various influencing factors influencing the reliability of the electronic equipment in a monitoring time period; the weight matching unit is used for matching reliability prediction weights for each type of collected influence factors; the evaluation value calculation unit obtains a reliability evaluation value of the electronic equipment according to various influence factors collected in the monitoring time period and reliability prediction weights matched with each kind of influence factors collected; the comparison and prediction unit compares the obtained reliability evaluation value of the electronic equipment with a preset reliability threshold value to judge the current reliability of the electronic equipment; if the obtained reliability evaluation value of the electronic equipment is larger than a preset reliability threshold, the factor acquisition unit continuously acquires various influence factors influencing the reliability of the electronic equipment in the next monitoring time period.
The big data based prediction system as described above, wherein preferably, further comprising: a state stopping unit; if the obtained reliability evaluation value of the electronic equipment is not greater than the preset reliability threshold value, the state stopping unit closes the electronic equipment.
The big data-based prediction system as described above, wherein it is preferable to aggregate all the sub-influence factors in the influence factors of the same class collected at the same time to form a sub-influence factor feature vector set of each influence factor of the same class at the same time; obtaining the comprehensive characteristic value of each same type of influence factors at the moment according to the sub-influence factors of each same type of influence factors at the moment; and collecting all the comprehensive characteristic values of each same type of influence factors obtained in the monitoring time period together to form a comprehensive characteristic value set.
The big data-based prediction system as described above, wherein it is preferable to calculate similarity of the collected category characteristics of each type of influence factors with the category characteristics of preset influence factors; classifying the collected categories of each type of influence factors into the categories of preset influence factors with the maximum similarity, and taking the reliability prediction weight corresponding to the classified categories of the preset influence factors as the reliability prediction weight of each type of collected influence factors.
The big data-based prediction system as described above, wherein it is preferable to extract class features contained in the comprehensive feature value of each class of influence factors and form a class feature set for collecting the influence factors; and obtaining the similarity between the acquired category characteristics of each type of influence factors and the category characteristics of the influence factors preset in the prediction server according to the category characteristic set of the preset influence factors and the category characteristic set of the acquired influence factors.
Compared with the background art, the prediction method and the prediction system based on big data can predict the reliability of the electronic equipment, so that the operation safety of the electronic equipment is practically improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of a big data based prediction method provided by an embodiment of the present application;
fig. 2 is a schematic diagram of a big data based prediction system provided by an embodiment of the present application.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
Example 1
Referring to fig. 1, fig. 1 is a flowchart of a big data based prediction method according to an embodiment of the present application.
The application provides a prediction method based on big data, which comprises the following steps:
step S110, various influencing factors influencing the reliability of the electronic equipment are collected in a monitoring time period;
there are many kinds of influencing factors that influence the reliability of an electronic device, for example: natural environmental factors (temperature, humidity, salt mist, air pollution particles, etc.), mechanical structural factors (intense mechanical vibrations, collisions, etc.), electromagnetic environmental factors, assembly process factors, etc. Natural environmental factors such as temperature, humidity, salt mist, air pollution particles and the like can influence the normal operation of components of the electronic equipment, so that the electrical performance of the electronic equipment is reduced, even the components are damaged, and faults are caused; strong mechanical structural factors such as mechanical vibration, collision and the like can cause damage deformation of a mechanical structure of the electronic equipment, even cause physical damage or failure of components of the electronic equipment, and cause the electronic equipment to fail to operate normally; under the influence of electromagnetic environment factors, the noise of a circuit of the electronic equipment can become large, the stability can become poor, and even the operation failure of the electronic equipment can be caused; the assembly process factors can directly influence the connection firmness, the tightness, the environment corrosion resistance and the like, so that the quality and the reliability of the electronic equipment are further influenced.
Different numbers of sub-influence factors exist in different kinds of influence factors, so that all the sub-influence factors in the same kind of influence factors collected at the same moment are integrated to form a sub-influence factor characteristic vector set of each same kind of influence factors at the moment
And obtaining the comprehensive characteristic value of each same type of influence factor at the moment according to the sub-influence factors of each same type of influence factor at the moment. Specifically, according to the formula
Calculating to obtain->
Time->
Complex eigenvalue of class influencing factors +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Is->
Time->
Class influence factor->
Seed influencing factors, ->
Is->
Class influence factor->
Influence weight of seed influence factor on comprehensive characteristic value, < ->
，/>
Is->
Time->
The number of sub-influencing factors of the class influencing factors.
All the comprehensive characteristic values of each same kind of influence factors obtained in the monitoring time period are integrated together to form a comprehensive characteristic value set
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Is the integrated characteristic value of the type 1 influencing factors at time 1, +>
Is->
The integrated characteristic value of the influence factors of the 1 st class of the moment,/->
Is the 1 st time ∈>
Comprehensive characteristic value of class influence factors, +.>
Is->
Time->
Comprehensive characteristic value of class influence factors, +.>
Is the 1 st time ∈>
Comprehensive characteristic value of class influence factors, +.>
Is->
Time->
And (5) the comprehensive characteristic value of the class influence factors.
Step S120, matching reliability prediction weights for each type of collected influence factors;
in the prediction server, the category characteristics of a plurality of influence factors and the reliability prediction weights corresponding to each category are preset, so that after various influence factors influencing the reliability of the electronic equipment are collected, the similarity between the category characteristics of each collected influence factor and the category characteristics of the influence factors preset in the prediction server is calculated, the category of each collected influence factor is classified into the category of the preset influence factor with the maximum similarity, and the reliability prediction weights corresponding to the classified categories of the preset influence factors are used as the reliability prediction weights of each collected influence factor.
As an example, in the prediction server, a category feature set of a preset influence factor is pre-stored
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
For the category characteristic which is the 1 st preset influencing factor, +.>
Is->
Category characteristics of individual preset influencing factors, +.>
Is->
Category characteristics of individual preset influencing factors, +.>
. In addition, the comprehensive characteristic value of each type of influence factors influencing the reliability of the electronic equipment contains the same category characteristics to represent the category to which the type of influence factors belong, so that the comprehensive characteristic value set influencing the reliability of the electronic equipment is obtained>
Then extracting the category characteristics contained in the comprehensive characteristic values of each category of influence factors, and forming a category characteristic set for collecting the influence factors
Wherein->
Is the category characteristic of the 1 st acquisition influencing factor, and +.>
Correspondingly (I)>
Is->
Category characteristics of individual acquisition influencing factors, and +.>
Correspondingly (I)>
Is->
Category characteristics of individual acquisition influencing factors, and +.>
Correspondingly (I)>
。
And obtaining the similarity between the acquired category characteristics of each type of influence factors and the category characteristics of the influence factors preset in the prediction server according to the category characteristic set of the preset influence factors and the category characteristic set of the acquired influence factors. Specifically, according to the formula
Calculating the similarity between the acquired category characteristics of each type of influence factors and the category characteristics of the influence factors preset in the prediction server; wherein (1)>
Is->
And->
Is a function of the similarity of the sequences,
is->
And->
The size of the intersection of all sub-features, +.>
Is->
And->
The size of the union of all the sub-features of (a).
For example: if it is calculated to obtain
And->
The similarity of (2) is greatest, then +.>
The corresponding reliability prediction weight is used as the comprehensive characteristic value of the influence factors of the class +.>
Reliability prediction weight of +.>
。
Step S130, obtaining a reliability evaluation value of the electronic equipment according to various influence factors collected in a monitoring time period and reliability prediction weights matched with each kind of influence factors;
specifically, according to the formula
Calculating to obtain reliability evaluation value +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Is the influence weight of the type of electronic device on the reliability evaluation value (+)>
Is an empirical value, according to the kind, < > according to the kind>
Different values of (a)>
Is the->
Complex eigenvalue of class influencing factors +.>
Reliability prediction weights of (c).
Step S140, comparing the obtained reliability evaluation value of the electronic equipment with a preset reliability threshold value to judge the current reliability of the electronic equipment;
the prediction server is also pre-stored with a preset reliability threshold (the preset reliability threshold can be an empirical value or data obtained according to neural network learning), and after the reliability evaluation value of the electronic equipment is obtained, the obtained reliability evaluation value of the electronic equipment is compared with the reliability threshold preset in the prediction server so as to judge the current reliability of the electronic equipment.
Step S150, if the obtained reliability evaluation value of the electronic equipment is larger than a preset reliability threshold, returning to step S110, and continuously collecting various influence factors influencing the reliability of the electronic equipment in the next monitoring time period;
if the reliability evaluation value of the obtained electronic equipment is greater than the reliability threshold value preset in the prediction server, the state of the electronic equipment is reliable and the electronic equipment can continue to work, so that the step S110 is returned, and various influencing factors influencing the reliability of the electronic equipment are continuously collected in the next monitoring time period.
And step S160, if the obtained reliability evaluation value of the electronic equipment is not greater than a preset reliability threshold value, closing the electronic equipment.
If the obtained reliability evaluation value of the electronic equipment is not greater than the reliability threshold value preset in the prediction server, the state of the electronic equipment is unreliable, potential safety hazards exist, the electronic equipment cannot continue to work, and therefore the electronic equipment is closed, so that the safety of the electronic equipment is improved.
Example two
Referring to fig. 2, fig. 2 is a schematic diagram of a big data based prediction system according to an embodiment of the present application.
The present application provides a big data based prediction system 200, comprising: factor acquisition unit 210, weight matching unit 220, evaluation value calculation unit 230, comparison prediction unit 240, and state stop unit 250.
The factor collection unit 210 collects various kinds of influencing factors affecting the reliability of the electronic device during the monitoring period.
There are many kinds of influencing factors that influence the reliability of an electronic device, for example: natural environmental factors (temperature, humidity, salt mist, air pollution particles, etc.), mechanical structural factors (intense mechanical vibrations, collisions, etc.), electromagnetic environmental factors, assembly process factors, etc. Natural environmental factors such as temperature, humidity, salt mist, air pollution particles and the like can influence the normal operation of components of the electronic equipment, so that the electrical performance of the electronic equipment is reduced, even the components are damaged, and faults are caused; strong mechanical structural factors such as mechanical vibration, collision and the like can cause damage deformation of a mechanical structure of the electronic equipment, even cause physical damage or failure of components of the electronic equipment, and cause the electronic equipment to fail to operate normally; under the influence of electromagnetic environment factors, the noise of a circuit of the electronic equipment can become large, the stability can become poor, and even the operation failure of the electronic equipment can be caused; the assembly process factors can directly influence the connection firmness, the tightness, the environment corrosion resistance and the like, so that the quality and the reliability of the electronic equipment are further influenced.
Different numbers of sub-influence factors exist in different kinds of influence factors, so that all the sub-influence factors in the same kind of influence factors collected at the same moment are integrated to form a sub-influence factor characteristic vector set of each same kind of influence factors at the moment
And obtaining the comprehensive characteristic value of each same type of influence factor at the moment according to the sub-influence factors of each same type of influence factor at the moment. Specifically, according to the formula
Calculating to obtain->
Time of day,/->
Complex eigenvalue of class influencing factors +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Is->
Time->
Class influence factor->
Seed influencing factors, ->
Is->
Class influence factor->
Influence weight of seed influence factor on comprehensive characteristic value, < ->
，/>
Is->
Time->
The number of sub-influencing factors of the class influencing factors.
All the comprehensive characteristic values of each same kind of influence factors obtained in the monitoring time period are integrated together to form a comprehensive characteristic value set
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Is the integrated characteristic value of the type 1 influencing factors at time 1, +>
Is->
The integrated characteristic value of the influence factors of the 1 st class of the moment,/->
Is the 1 st time ∈>
Comprehensive characteristic value of class influence factors, +.>
Is->
Time->
Comprehensive characteristic value of class influence factors, +.>
Is the 1 st time ∈>
Comprehensive characteristic value of class influence factors, +.>
Is->
The integrated characteristic value of the time-of-day type influence factors.
The weight matching unit 220 matches reliability prediction weights for each type of influence factor acquired.
In the prediction server, the category characteristics of a plurality of influence factors and the reliability prediction weights corresponding to each category are preset, so that after various influence factors influencing the reliability of the electronic equipment are collected, the similarity between the category characteristics of each collected influence factor and the category characteristics of the influence factors preset in the prediction server is calculated, the category of each collected influence factor is classified into the category of the preset influence factor with the maximum similarity, and the reliability prediction weights corresponding to the classified categories of the preset influence factors are used as the reliability prediction weights of each collected influence factor.
As an example, in the prediction server, a category feature set of a preset influence factor is pre-stored
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
For the category characteristic which is the 1 st preset influencing factor, +.>
Is->
Category characteristics of individual preset influencing factors, +.>
Is->
Category characteristics of individual preset influencing factors, +.>
. In addition, the comprehensive characteristic value of each type of influence factors influencing the reliability of the electronic equipment contains the same category characteristics to represent the category to which the type of influence factors belong, so that the comprehensive characteristic value set influencing the reliability of the electronic equipment is obtained/>
Then extracting the category characteristics contained in the comprehensive characteristic values of each category of influence factors, and forming a category characteristic set for collecting the influence factors +.>
Wherein->
Is the category characteristic of the 1 st acquisition influencing factor, and +.>
Correspondingly (I)>
Is->
Category characteristics of individual acquisition influencing factors, and +.>
Correspondingly (I)>
Is->
Category characteristics of individual acquisition influencing factors, and +.>
Correspondingly (I)>
。
And obtaining the similarity between the acquired category characteristics of each type of influence factors and the category characteristics of the influence factors preset in the prediction server according to the category characteristic set of the preset influence factors and the category characteristic set of the acquired influence factors. Specifically, according to the formula
Calculate the acquiredSimilarity between the category characteristics of each type of influence factors and the category characteristics of the influence factors preset in the prediction server; wherein (1)>
Is->
And->
Similarity of->
Is that
And->
The size of the intersection of all sub-features, +.>
Is->
And->
The size of the union of all the sub-features of (a).
For example: if it is calculated to obtain
And->
The similarity of (2) is greatest, then +.>
The corresponding reliability prediction weight is used as the comprehensive characteristic value of the influence factors of the class +.>
Reliability prediction weight of +.>
。
The evaluation value calculating unit 230 obtains a reliability evaluation value of the electronic device according to various influence factors collected in the monitoring period and reliability prediction weights matched for each kind of influence factors collected.
Specifically, according to the formula
Calculating to obtain reliability evaluation value +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Is the influence weight of the type of electronic device on the reliability evaluation value (+)>
Is an empirical value, according to the kind, < > according to the kind>
Different values of (a)>
Is the->
Complex eigenvalue of class influencing factors +.>
Reliability prediction weights of (c).
The comparison and prediction unit 240 compares the obtained reliability evaluation value of the electronic device with a preset reliability threshold value to determine the current reliability of the electronic device.
The prediction server is also pre-stored with a preset reliability threshold (the preset reliability threshold can be an empirical value or data obtained according to neural network learning), and after the reliability evaluation value of the electronic equipment is obtained, the obtained reliability evaluation value of the electronic equipment is compared with the reliability threshold preset in the prediction server so as to judge the current reliability of the electronic equipment.
If the obtained reliability evaluation value of the electronic device is greater than the preset reliability threshold, the factor collection unit 210 continues to collect various influencing factors influencing the reliability of the electronic device in the next monitoring time period;
if the reliability evaluation value of the obtained electronic equipment is larger than the reliability threshold value preset in the prediction server, the state of the electronic equipment is reliable, the electronic equipment can continue to work, and various influencing factors influencing the reliability of the electronic equipment can be continuously collected in the next monitoring time period.
If the obtained reliability evaluation value of the electronic device is not greater than the preset reliability threshold, the state stopping unit 250 turns off the electronic device.
If the obtained reliability evaluation value of the electronic equipment is not greater than the reliability threshold value preset in the prediction server, the state of the electronic equipment is unreliable, potential safety hazards exist, the electronic equipment cannot continue to work, and therefore the electronic equipment is closed, so that the safety of the electronic equipment is improved.
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 characteristics 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.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (10)
1. The prediction method based on big data is characterized by comprising the following steps:
step S110, various influencing factors influencing the reliability of the electronic equipment are collected in a monitoring time period;
step S120, matching reliability prediction weights for each type of collected influence factors;
step S130, obtaining a reliability evaluation value of the electronic equipment according to various influence factors collected in a monitoring time period and reliability prediction weights matched with each kind of influence factors;
step S140, comparing the obtained reliability evaluation value of the electronic equipment with a preset reliability threshold value to judge the current reliability of the electronic equipment;
step S150, if the obtained reliability evaluation value of the electronic equipment is larger than a preset reliability threshold value, returning to step S110.
2. The big data based prediction method according to claim 1, further comprising the steps of:
and step S160, if the obtained reliability evaluation value of the electronic equipment is not greater than a preset reliability threshold value, closing the electronic equipment.
3. The big data based prediction method according to claim 1 or 2, wherein all sub-influence factors in the same kind of influence factors collected at the same moment are gathered together to form a sub-influence factor feature vector set of each same kind of influence factors at the moment;
obtaining the comprehensive characteristic value of each same type of influence factors at the moment according to the sub-influence factors of each same type of influence factors at the moment;
and collecting all the comprehensive characteristic values of each same type of influence factors obtained in the monitoring time period together to form a comprehensive characteristic value set.
4. The big data-based prediction method according to claim 3, wherein similarity between the collected category characteristics of each type of influence factors and the category characteristics of preset influence factors is calculated;
classifying the collected categories of each type of influence factors into the categories of preset influence factors with the maximum similarity, and taking the reliability prediction weight corresponding to the classified categories of the preset influence factors as the reliability prediction weight of each type of collected influence factors.
5. The big data based prediction method according to claim 4, wherein class features contained in the comprehensive feature values of each class of influence factors are extracted, and a class feature set for collecting the influence factors is formed;
and obtaining the similarity between the acquired category characteristics of each type of influence factors and the category characteristics of the influence factors preset in the prediction server according to the category characteristic set of the preset influence factors and the category characteristic set of the acquired influence factors.
6. A big data based prediction system, comprising: the system comprises a factor acquisition unit, a weight matching unit, an evaluation value calculation unit and a comparison prediction unit;
the factor acquisition unit acquires various influencing factors influencing the reliability of the electronic equipment in a monitoring time period;
the weight matching unit is used for matching reliability prediction weights for each type of collected influence factors;
the evaluation value calculation unit obtains a reliability evaluation value of the electronic equipment according to various influence factors collected in the monitoring time period and reliability prediction weights matched with each kind of influence factors collected;
the comparison and prediction unit compares the obtained reliability evaluation value of the electronic equipment with a preset reliability threshold value to judge the current reliability of the electronic equipment;
if the obtained reliability evaluation value of the electronic equipment is larger than a preset reliability threshold, the factor acquisition unit continuously acquires various influence factors influencing the reliability of the electronic equipment in the next monitoring time period.
7. The big data based prediction system of claim 6, further comprising: a state stopping unit;
if the obtained reliability evaluation value of the electronic equipment is not greater than the preset reliability threshold value, the state stopping unit closes the electronic equipment.
8. The big data based prediction system of claim 6 or 7, wherein all sub-influencing factors in the same class of influencing factors collected at the same moment are grouped together to form a sub-influencing factor feature vector set for each same class of influencing factors at the moment;
obtaining the comprehensive characteristic value of each same type of influence factors at the moment according to the sub-influence factors of each same type of influence factors at the moment;
and collecting all the comprehensive characteristic values of each same type of influence factors obtained in the monitoring time period together to form a comprehensive characteristic value set.
9. The big data based prediction system of claim 8, wherein the similarity between the collected category characteristics of each type of influence factors and the category characteristics of the preset influence factors is calculated;
classifying the collected categories of each type of influence factors into the categories of preset influence factors with the maximum similarity, and taking the reliability prediction weight corresponding to the classified categories of the preset influence factors as the reliability prediction weight of each type of collected influence factors.
10. The big data based prediction system of claim 9, wherein the class features included in the integrated feature values of each class of influencing factors are extracted and a class feature set for collecting influencing factors is formed;
and obtaining the similarity between the acquired category characteristics of each type of influence factors and the category characteristics of the influence factors preset in the prediction server according to the category characteristic set of the preset influence factors and the category characteristic set of the acquired influence factors.
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