CN114936207A - Method for evaluating sensing data quality of sensing equipment of Internet of things - Google Patents

Method for evaluating sensing data quality of sensing equipment of Internet of things Download PDF

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CN114936207A
CN114936207A CN202210875069.3A CN202210875069A CN114936207A CN 114936207 A CN114936207 A CN 114936207A CN 202210875069 A CN202210875069 A CN 202210875069A CN 114936207 A CN114936207 A CN 114936207A
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CN114936207B (en
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林涛
张兵
邹忱
许华杰
吕国林
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Shenzhen Urban Transport Planning Center Co Ltd
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Abstract

The invention provides a method for evaluating the quality of sensing data of sensing equipment of the Internet of things, and belongs to the technical field of evaluation of the quality of sensing data of sensing equipment of the Internet of things. The method comprises the steps that real-time sensing data of the sensing equipment of the Internet of things are acquired by the acquisition and docking equipment and are stored in a database; the method comprises the steps of obtaining sensing data of the sensing equipment of the Internet of things from a database, wherein the sensing data of the sensing equipment of the Internet of things comprises sensing data of sensing equipment of the discrete Internet of things and sensing data of sensing equipment of the continuous Internet of things, respectively carrying out data quality evaluation on the sensing data of the sensing equipment of the discrete Internet of things and the sensing data of the sensing equipment of the continuous Internet of things to obtain a process sigma value, determining data quality according to the process sigma value, and creating a linear regression model to predict the sensing data quality of the sensing equipment of the Internet of things. The method and the system effectively filter the defective equipment data and mark the problematic equipment in time, thereby improving the correctness of service decision. The technical problem of low data quality accuracy rate in the prior art is solved.

Description

Method for evaluating quality of sensing data of sensing equipment of Internet of things
Technical Field
The application relates to a quality evaluation method, in particular to a quality evaluation method for sensing data of sensing equipment of the Internet of things, and belongs to the technical field of quality evaluation of sensing data of sensing equipment of the Internet of things.
Background
The internet of things is also called as a sensing network. The internet of things is a network which connects any article with the internet according to an agreed protocol through information sensing equipment such as Radio Frequency Identification (RFID), an infrared sensor, a global positioning system, a laser scanner and the like to exchange and communicate information so as to realize intelligent identification, positioning, tracking, monitoring and management.
The concept of the internet of things and related technical products have widely penetrated into various fields of social and economic lives and play a key role in more and more industrial innovation. The internet of things plays an important role in promoting transformation and upgrading, improving social services, improving service lives, promoting efficiency and energy conservation and the like by virtue of deep integration and comprehensive application of a new generation of information technology, and brings real 'intelligent' application in partial fields.
With the further development and breakthrough of the technology in the related field and the gradual improvement of the cognition degree of the client, the internet of things can be more widely applied to various fields such as industry, agriculture, electric power, building, traffic, logistics, environmental protection, medical treatment, security, home furnishing and the like. The market of the internet of things has great potential and can be developed at high speed in the coming years, and the good development prospect of the market also brings opportunities and challenges for numerous manufacturers.
The Internet of things equipment serves as a sensing terminal and feeds back accurate and effective sensing information in time based on different needs of services. The condition of the quality of the sensing data can seriously affect the analysis and calculation of the final result, the quality of the sensing data is ensured, and the invalid equipment and the sensing data are necessary to be checked and removed in time. The current data quality inspection algorithm usually counts the qualified rate by the integral qualified rate, but judges the qualified rate by the deviation condition of single data, which causes the qualified rate of partial equipment within the specified deviation to be high, but the data quality accuracy rate caused by the deviation is low.
Disclosure of Invention
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. It should be understood that this summary is not an exhaustive overview of the invention. It is not intended to determine the key or important part of the present invention, nor is it intended to limit the scope of the present invention. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.
In view of this, in order to solve the technical problem of low data quality accuracy rate in the prior art, the invention provides a method for evaluating the sensing data quality of sensing equipment of the internet of things.
The first scheme is as follows: the method for evaluating the quality of the sensing data of the sensing equipment of the Internet of things is characterized by comprising the steps of acquiring real-time sensing data of the sensing equipment of the Internet of things obtained by butt joint equipment and storing the sensing data into a database; the method comprises the steps of obtaining sensing data of the sensing equipment of the Internet of things from a database, wherein the sensing data of the sensing equipment of the Internet of things comprises sensing data of sensing equipment of the discrete Internet of things and sensing data of sensing equipment of the continuous Internet of things, respectively carrying out data quality evaluation on the sensing data of the sensing equipment of the discrete Internet of things and the sensing data of the sensing equipment of the continuous Internet of things to obtain a process sigma value, determining data quality according to the process sigma value, and creating a linear regression model to predict the sensing data quality of the sensing equipment of the Internet of things.
Preferably, the quality evaluation method of the sensing data of the discrete internet of things sensing device comprises the following steps:
s1, selecting N pieces of sensing data of the discrete Internet of things sensing equipment in a certain time period from the database, and recording the data of each piece of Internet of things sensing equipment as i 1 ,i 2 ,i 3 .....i n (ii) a Respectively receiving six data of each Internet of things sensing deviceChecking the dimensions to obtain W pieces of defect data;
s2, calculating the probability of the computer fault DPO;
s3, calculating the DPMO (number of million opportunity defects);
s4, inquiring a corresponding relation table of DMPO and sigma to obtain a process sigma value Z of sensing data of the sensing equipment of the current Internet of things;
and S5, evaluating the quality of the sensing data of the discrete Internet of things sensing equipment according to the flow sigma value Z.
Preferably, the six dimensions include completeness, normalization, consistency, accuracy, uniqueness and relevance; the sensing data of the sensing equipment of the Internet of things which does not conform to one of six dimensions is the sensing equipment data of the defective Internet of things, and each sensing data of the sensing equipment of the Internet of things has six defect opportunities.
Preferably, the calculation method of the opportunistic defect rate DPO is as follows:
DPO = number of defects/(number of products × number of opportunities for defects);
the calculation method of the DPMO with the million chance defect number is as follows:
DPMO=DPO*10^6
the number of the products is the number of sensing data of the sensing equipment of the Internet of things, the number of the defects is the number of the defects of the sensing data of the sensing equipment of the Internet of things, and the number of the opportunities of the defects is the proportion of the number of the defects of the sensing data of each sensing equipment of the Internet of things to the number of the defects of the sensing data of all sensing equipment of the Internet of things.
Preferably, the method for evaluating the quality of the sensing data of the discrete internet of things sensing device according to the process sigma value Z is that the larger the process sigma value Z is, the better the quality of the sensing data of the internet of things sensing device is.
Preferably, the quality evaluation method for sensing data of the continuous internet of things sensing equipment comprises the following steps:
step one, selecting N pieces of sensing data of continuous Internet of things sensing equipment in a certain time period from a database, and respectively recording the sensing data result of each piece of Internet of things sensing equipment as i 1 ,i 2 ,i 3 .....i n Obtaining sensing number of continuous Internet of things sensing equipmentThe average value according to;
step two, variance calculation is carried out on sensing data and the average value of the continuous Internet of things sensing equipment;
thirdly, calculating probability density according to the average value and the variance to obtain the perception data qualification rate P of the continuous Internet of things sensing equipment good Thereby obtaining the sensing data opportunity defect rate 1-P of the continuous Internet of things sensing equipment good
Step four, calculating the DPMO with the million chance defect number, wherein the DPMO = DPO 10^6, and the DPO value is 1-P good Inquiring a corresponding relation table of DMPO and sigma to obtain a process sigma value Z of sensing data of the current sensing equipment of the Internet of things;
and fifthly, evaluating the quality of the sensing data of the continuous Internet of things sensing equipment according to the flow sigma value Z.
Preferably, the specific method for calculating the probability density according to the mean and the variance to obtain the yield is as follows:
function of probability density
Figure 966793DEST_PATH_IMAGE001
=
Figure 132195DEST_PATH_IMAGE002
exp(-
Figure 839251DEST_PATH_IMAGE003
)
When in use
Figure 406499DEST_PATH_IMAGE004
=0,
Figure 298231DEST_PATH_IMAGE005
When =1, the normal distribution is a standard normal distribution, and the probability density function is simplified as follows:
Figure 75563DEST_PATH_IMAGE006
=
Figure 445365DEST_PATH_IMAGE007
exp
Figure 867119DEST_PATH_IMAGE008
the cumulative probability area function is: p (X) = ∅ (X) =
Figure 929753DEST_PATH_IMAGE009
=
Figure 679534DEST_PATH_IMAGE010
=1
According to the formula, the fraction defective under the tolerance lower bound LSL is calculated as follows:
P(X<LSL)=
Figure 118606DEST_PATH_IMAGE011
=
Figure 394866DEST_PATH_IMAGE012
the result of calculating the reject ratio above the tolerance upper bound USL is as follows:
P(X>USL)=
Figure 18614DEST_PATH_IMAGE013
=
Figure 380326DEST_PATH_IMAGE014
the cumulative probability area of the region between the lower tolerance bound and the upper tolerance bound is:
P(LSL≤
Figure 91930DEST_PATH_IMAGE015
≤USL)=
Figure 488276DEST_PATH_IMAGE016
=
Figure 768079DEST_PATH_IMAGE017
-
Figure 617086DEST_PATH_IMAGE011
therefore, the yield is P good =P(LSL≤
Figure 132381DEST_PATH_IMAGE015
≤USL)。
Preferably, the method for predicting the sensing data quality of the sensing equipment of the internet of things by establishing the linear regression model comprises the following steps: the linear regression model is: y =
Figure 773447DEST_PATH_IMAGE018
(x)=
Figure 348785DEST_PATH_IMAGE019
0 +
Figure 685088DEST_PATH_IMAGE019
1 x, wherein
Figure 4074DEST_PATH_IMAGE018
(x) Representing a functional mapping from x to y,
Figure 984799DEST_PATH_IMAGE020
0 and
Figure 731038DEST_PATH_IMAGE020
1 the method comprises the steps that regression parameters are adopted, x is an independent variable, corresponding time T and y are target output variables, the target output variables are inquired through a corresponding relation table of DMPO and sigma to obtain a process sigma value Z of sensing data of current sensing equipment, and the larger the process sigma value Z is, the better the sensing data quality of the sensing equipment of the Internet of things is.
The second scheme is that the electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the first scheme when executing the computer program.
Solution three, a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of solution one.
The invention has the following beneficial effects: the method and the device can effectively filter out defective equipment data, mark out problem equipment in time and improve the correctness of service decision. The method and the device can analyze the data quality of the equipment in a segmented manner, dynamically evaluate the perceived data quality condition of the equipment, and give an alarm in time when the perceived data quality condition is lower than a set quality standard condition.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of a normal distribution curve according to the present invention.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Embodiment 1, this embodiment is described with reference to fig. 1 to 2, and a method for evaluating sensing data quality of internet of things sensing equipment includes acquiring real-time sensing data of the internet of things sensing equipment acquired by docking equipment and storing the data in a database; the method comprises the steps of obtaining sensing data of the sensing equipment of the Internet of things from a database, wherein the sensing data of the sensing equipment of the Internet of things comprises sensing data of sensing equipment of the discrete Internet of things and sensing data of sensing equipment of the continuous Internet of things, respectively carrying out data quality evaluation on the sensing data of the sensing equipment of the discrete Internet of things and the sensing data of the sensing equipment of the continuous Internet of things to obtain a process sigma value, determining data quality according to the process sigma value, and creating a linear regression model to predict the sensing data quality of the sensing equipment of the Internet of things.
Specifically, acquiring real-time device sensing data acquired by the docking device includes the following contents: firstly, introducing a sensing result parameter standard deviation value (tolerance upper bound (USL)/tolerance lower bound (LSL)) of the obtained equipment through national standards and an Internet of things sensing equipment product; and secondly, connecting the sensing equipment of the Internet of things to a power grid, verifying whether the equipment is normal, checking and confirming whether the voltage and the network bandwidth of the sensing equipment of the Internet of things are normal, checking whether the voltage and the network bandwidth of the sensing equipment of the Internet of things meet the requirements of the normal working environment of the equipment, continuing to perform the next step if the voltage and the network bandwidth meet the requirements, otherwise, giving an alarm and notifying, and stopping detection. And finally, after the normal work of the equipment is confirmed, acquiring the perception information of the equipment for modeling analysis, and evaluating the quality condition of the perception data of the equipment.
Specifically, the quality evaluation method for sensing data of the discrete internet of things sensing equipment comprises the following steps:
s1, selecting N discrete Internet of things sensing equipment sensing data in a certain time period from the database, and recording the sensing data of each discrete Internet of things sensing equipment as i 1 ,i 2 ,i 3 .....i n (ii) a And verifying the sensing data of each discrete Internet of things sensing device from six dimensions of integrity, normalization, consistency, accuracy, uniqueness and relevance to obtain W pieces of defect data, wherein the six dimensions are six defect opportunities, and the sensing data of each Internet of things sensing device has six defect opportunities as long as the sensing data of the Internet of things sensing device does not conform to one of the six dimensions, namely the defect data.
S2, calculating the chance defect rate DPO by using the chance defect rate DPO calculation formula, i.e. the ratio of the defect occurrence rate in each chance represents the proportion of the defect number in each sample amount to the total chance number, and therefore, calculating the chance defect rate DPO as follows:
DPO = number of defects/(number of products × number of opportunities for defects); i.e. DPO = W/(6 × N).
S3, calculating the DPMO (number of million opportunity defects); the calculation method is as follows:
DPMO = DPO 10^ 6; namely DMPO = (10^6 × W)/(6 × N).
The product number is the number of sensing data of the sensing equipment of the Internet of things, the defect number is the defect number of the sensing data of the sensing equipment of the Internet of things, and the defect opportunity number is the proportion of the defect number of the sensing data of each sensing equipment of the Internet of things to the defect number of all sensing data of the sensing equipment of the Internet of things.
S4, obtaining a flow sigma value Z of the sensing data of the current discrete type Internet of things sensing equipment through query of a corresponding relation table (DMPO and sigma) of DMPO and sigma;
table-DMPO and sigma correspondence table
Figure 289059DEST_PATH_IMAGE021
And S5, evaluating the quality of the sensing data of the discrete Internet of things sensing equipment according to the flow sigma value Z, wherein the larger the flow sigma value Z is, the better the quality of the sensing data of the Internet of things sensing equipment is.
Specifically, the quality evaluation method for sensing data of the continuous internet of things sensing equipment comprises the following steps:
step one, selecting N pieces of sensing data of continuous Internet of things sensing equipment in a certain time period from a database, and respectively recording the sensing data result of each piece of Internet of things sensing equipment as i 1 ,i 2 ,i 3 .....i n And acquiring the average value of the sensing data of the continuous Internet of things sensing equipment.
The method for obtaining the average value is realized by the following formula: μ = g
Figure 801949DEST_PATH_IMAGE022
And μ represents the average value of the sensing data of the N pieces of sensing equipment of the Internet of things.
Step two, variance calculation is carried out on the sensing data and the average value of the continuous Internet of things sensing equipment, and the specific calculation method is as follows:
σ 2 =(1/N)
Figure 761814DEST_PATH_IMAGE023
wherein i n Values, σ, representing each successive datum 2 The variance obtained for N data is shown.
Thirdly, calculating probability density according to the average value and the variance to obtain the perception data qualification rate P of the continuous Internet of things sensing equipment good So as to obtain the sensing data opportunity defect rate 1-P of the continuous Internet of things sensing equipment good
Step four, calculating the DPMO with the million chance defect number, wherein the DPMO = DPO 10^6, and the DPO value is 1-P good Inquiring a flow sigma value Z of the data of the current sensing equipment through a corresponding relation table of DMPO and sigma;
and step five, evaluating the quality of the continuous sensing data according to the flow sigma value Z.
Specifically, the normal distribution data N to (. mu.,. sigma.) will be described with reference to FIG. 2 2 ) The symmetry axis of the normal distribution curve is the average of the normal samples, the average of the samples increases, the curve translates to the right, the average of the samples decreases, and the curve translates to the left. The larger the standard deviation of a normal sample, the flatter the normal distribution curve and the smaller the peak, with a total probability Area under the distribution curve of 1 (Rejected Area being a bad Area).
Specifically, the method for calculating the probability density according to the mean and the variance to obtain the yield is that,
function of probability density
Figure 678955DEST_PATH_IMAGE024
=
Figure 989850DEST_PATH_IMAGE025
exp(-
Figure 526005DEST_PATH_IMAGE026
)
When in use
Figure 340377DEST_PATH_IMAGE004
=0,
Figure 693998DEST_PATH_IMAGE027
When =1, the normal distribution is a standard normal distribution, and the probability density function is simplified as follows:
Figure 226611DEST_PATH_IMAGE028
=
Figure 815724DEST_PATH_IMAGE029
exp(-
Figure 750182DEST_PATH_IMAGE030
) The cumulative probability area function is: p (X) = ∅ (X) =
Figure 274704DEST_PATH_IMAGE031
=
Figure 904400DEST_PATH_IMAGE032
=1
According to the formula, we calculate the reject ratio below the lower tolerance bound LSL as:
P(X<LSL)=
Figure 172570DEST_PATH_IMAGE011
=
Figure 961534DEST_PATH_IMAGE012
the result of calculating the fraction defective above the tolerance upper bound USL is as follows:
P(X>USL)=
Figure 781592DEST_PATH_IMAGE013
=
Figure 288796DEST_PATH_IMAGE033
the cumulative probability area of the region between the lower tolerance bound and the upper tolerance bound is:
P(LSL≤
Figure 95078DEST_PATH_IMAGE015
≤USL)=
Figure 738549DEST_PATH_IMAGE016
=
Figure 480240DEST_PATH_IMAGE017
-
Figure 474741DEST_PATH_IMAGE011
therefore, the yield is P good =P(LSL≤
Figure 84714DEST_PATH_IMAGE015
≤USL);
Therefore, DPMO = (1-P) in million chance defect number good )*
Figure 972905DEST_PATH_IMAGE034
And inquiring a flow sigma value Z of the data of the current sensing equipment through a corresponding relation table I of DMPO and sigma, and determining whether the sensing data quality of the current sensing equipment of the Internet of things is good or bad according to the Z value, wherein the larger the Z value is, the better the data quality is.
Specifically, the method for establishing the linear regression model to predict the sensing data quality of the sensing equipment of the internet of things comprises the following steps: the linear regression model is: y =
Figure 744552DEST_PATH_IMAGE018
(x)=
Figure 226348DEST_PATH_IMAGE020
0 +
Figure 374433DEST_PATH_IMAGE020
1 x, wherein
Figure 867862DEST_PATH_IMAGE018
(x) Representing a functional mapping from x to y,
Figure 810411DEST_PATH_IMAGE020
0 and
Figure 513924DEST_PATH_IMAGE020
1 the regression parameter is x is independent variable, corresponding time T and y are target output variables, and the target output variables are inquired through a corresponding relation table of DMPO and sigma to obtain the number of the current sensing equipmentAccording to the flow sigma value Z.
Specifically, the detailed process of the method for establishing the model to predict the quality of the sensing data of the sensing equipment of the internet of things is as follows:
a. the corresponding sigma level Z values are calculated for the data of different time periods T, i.e. assuming the data set S = { (T) 1 ,Z 1 ),(T 2 ,Z 2 ),...(T 3 ,Z 3 )};
b. Assuming a linear regression model as: y =
Figure 855913DEST_PATH_IMAGE018
(x)=
Figure 328483DEST_PATH_IMAGE020
0 +
Figure 707511DEST_PATH_IMAGE020
1 x, wherein
Figure 898321DEST_PATH_IMAGE018
(x) Representing a functional mapping from x to y,
Figure 263575DEST_PATH_IMAGE020
0 and
Figure 818687DEST_PATH_IMAGE020
1 is a regression parameter, x is an independent variable, corresponding to time T, y is a target output variable, corresponding to sigma level Z;
c. quantifying a loss function such that the regression parameters
Figure 514197DEST_PATH_IMAGE020
0 And
Figure 722958DEST_PATH_IMAGE020
1 the optimal regression parameters required to be calculated in the step a can be continuously optimized in the solving process
Figure 554648DEST_PATH_IMAGE020
0 And
Figure 259802DEST_PATH_IMAGE020
1 can be converted into min (
Figure 895182DEST_PATH_IMAGE018
(x)-y);
Assuming the loss function is: j (J)
Figure 316936DEST_PATH_IMAGE019
)=
Figure 520516DEST_PATH_IMAGE037
Where m represents the number of instances in the training set,
Figure 394931DEST_PATH_IMAGE038
Figure 568423DEST_PATH_IMAGE039
represents the ith observation instance;
find out a group
Figure 844684DEST_PATH_IMAGE020
0 And
Figure 202853DEST_PATH_IMAGE020
1 minimizing the value of the loss function, the solution J (b) can be
Figure 564564DEST_PATH_IMAGE020
) The partial derivative is 0, and the calculation is derived as follows:
Figure 541747DEST_PATH_IMAGE040
J(
Figure 938094DEST_PATH_IMAGE020
)=
Figure 217896DEST_PATH_IMAGE041
=0 (formula 1.3)
Handle
Figure 66904DEST_PATH_IMAGE020
0 And
Figure 847778DEST_PATH_IMAGE020
1 the generation yields a system of equations as follows:
Figure 223264DEST_PATH_IMAGE042
J(
Figure 798602DEST_PATH_IMAGE020
)=
Figure 869326DEST_PATH_IMAGE043
= 0
Figure 188312DEST_PATH_IMAGE044
J(
Figure 434617DEST_PATH_IMAGE020
)=
Figure 180856DEST_PATH_IMAGE045
= 0
it can be solved that:
Figure 4456DEST_PATH_IMAGE046
=
Figure 986187DEST_PATH_IMAGE047
Figure 211632DEST_PATH_IMAGE048
=
Figure 128772DEST_PATH_IMAGE049
to obtain
Figure 174089DEST_PATH_IMAGE020
Figure 975823DEST_PATH_IMAGE019
0 And
Figure 55774DEST_PATH_IMAGE020
Figure 143816DEST_PATH_IMAGE019
1 values of two parameters, so the linear regression model y =
Figure 801062DEST_PATH_IMAGE018
(x)=
Figure 265542DEST_PATH_IMAGE020
0 +
Figure 200000DEST_PATH_IMAGE020
1 x can be used for predicting a sigma horizontal Z value corresponding to the time period T, the quality of the sensing data quality of the sensing equipment of the Internet of things can be predicted according to the Z value, and the larger the Z value of the sigma value of the process is, the better the sensing data quality of the sensing equipment of the Internet of things is.
Specifically, in different time periods, the prediction model can predict sigma level values corresponding to different time periods.
In embodiment 2, the computer device of the present invention may be a device including a processor, a memory, and the like, for example, a single chip microcomputer including a central processing unit, and the like. And the processor is used for implementing the steps of the recommendation method capable of modifying the relationship-driven recommendation data based on the CREO software when executing the computer program stored in the memory.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Embodiment 3 computer-readable storage Medium embodiment
The computer readable storage medium of the present invention may be any form of storage medium that can be read by a processor of a computer device, including but not limited to non-volatile memory, ferroelectric memory, etc., and the computer readable storage medium has stored thereon a computer program that, when the computer program stored in the memory is read and executed by the processor of the computer device, can implement the above-mentioned steps of the CREO-based software that can modify the modeling method of the relationship-driven modeling data.
The computer program comprises computer program code which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.

Claims (10)

1. The method for evaluating the quality of the sensing data of the sensing equipment of the Internet of things is characterized by comprising the steps of acquiring real-time sensing data of the sensing equipment of the Internet of things obtained by butt joint equipment and storing the sensing data into a database; the method comprises the steps of obtaining sensing data of the sensing equipment of the Internet of things from a database, wherein the sensing data of the sensing equipment of the Internet of things comprises sensing data of sensing equipment of the discrete Internet of things and sensing data of sensing equipment of the continuous Internet of things, respectively carrying out data quality evaluation on the sensing data of the sensing equipment of the discrete Internet of things and the sensing data of the sensing equipment of the continuous Internet of things to obtain a process sigma value, determining data quality according to the process sigma value, and creating a linear regression model to predict the sensing data quality of the sensing equipment of the Internet of things.
2. The method for evaluating the quality of the sensing data of the sensing equipment of the internet of things according to claim 1, wherein the method for evaluating the quality of the sensing data of the sensing equipment of the discrete type internet of things comprises the following steps:
s1, selecting N pieces of sensing data of the discrete Internet of things sensing equipment in a certain time period from the database, and recording the data of each piece of Internet of things sensing equipment as i 1 ,i 2 ,i 3 .....i n (ii) a Verifying each piece of data of the sensing equipment of the Internet of things from six dimensions respectively to obtain W pieces of defect data;
s2, calculating the probability of defect DPO;
s3, calculating the DPMO of million opportunity defects;
s4, inquiring a corresponding relation table of DMPO and sigma to obtain a process sigma value Z of sensing data of the sensing equipment of the current Internet of things;
and S5, evaluating the quality of the sensing data of the discrete Internet of things sensing equipment according to the flow sigma value Z.
3. The method for evaluating the quality of the perception data of the sensing equipment of the internet of things according to claim 2, wherein the six dimensions comprise integrity, normalization, consistency, accuracy, uniqueness and relevance; the sensing data of the IOT sensing equipment which does not conform to one of the six dimensions is defect IOT sensing equipment data, and each sensing data of the IOT sensing equipment has six defect opportunities.
4. The method for evaluating the perception data quality of the sensing equipment of the Internet of things according to claim 3,
the calculation method of the opportunistic defect rate DPO is as follows:
DPO = number of defects/(number of products × number of opportunities for defects);
the calculation method of the DPMO with the million chance defect number is as follows:
DPMO=DPO*10^6
the number of the products is the number of sensing data of the sensing equipment of the Internet of things, the number of the defects is the number of the defects of the sensing data of the sensing equipment of the Internet of things, and the number of the opportunities of the defects is the proportion of the number of the defects of the sensing data of each sensing equipment of the Internet of things to the number of the defects of the sensing data of all sensing equipment of the Internet of things.
5. The method for evaluating the quality of the sensing data of the IOT sensing equipment according to claim 4, wherein the method for evaluating the quality of the sensing data of the discrete type IOT sensing equipment according to the process sigma value Z is that the greater the process sigma value Z is, the better the quality of the sensing data of the IOT sensing equipment is.
6. The method for evaluating the quality of the sensing data of the sensing equipment of the internet of things according to claim 5, wherein the method for evaluating the quality of the sensing data of the sensing equipment of the continuous internet of things comprises the following steps:
step one, selecting N pieces of sensing data of continuous Internet of things sensing equipment in a certain time period from a database, and respectively recording the sensing data result of each piece of Internet of things sensing equipment as i 1 ,i 2 ,i 3 .....i n Acquiring an average value of sensing data of continuous Internet of things sensing equipment;
secondly, calculating the variance of the sensing data and the average value of the continuous Internet of things sensing equipment;
thirdly, calculating probability density according to the average value and the variance to obtain the perception data qualification rate P of the continuous Internet of things sensing equipment good Thereby obtaining the sensing data opportunity defect rate 1-P of the continuous Internet of things sensing equipment good
Step four, calculating the DPMO of million chance defects, wherein the DPMO = DPO 10^6, and the DPO value is 1-P good Inquiring a corresponding relation table of DMPO and sigma to obtain a process sigma value Z of sensing data of the current sensing equipment of the Internet of things;
and fifthly, evaluating the quality of the sensing data of the continuous Internet of things sensing equipment according to the flow sigma value Z.
7. The method for evaluating the quality of the sensing data of the sensing equipment of the internet of things according to claim 6, wherein the specific method for calculating the probability density according to the mean value and the variance to obtain the qualification rate is as follows:
function of probability density
Figure 19333DEST_PATH_IMAGE001
=
Figure 139736DEST_PATH_IMAGE002
exp(-
Figure 857156DEST_PATH_IMAGE003
)
When in use
Figure 721207DEST_PATH_IMAGE004
=0,
Figure 4421DEST_PATH_IMAGE005
When =1, the normal distribution is a standard normal distribution, and the probability density function is simplified as follows:
Figure 561304DEST_PATH_IMAGE001
=
Figure 500441DEST_PATH_IMAGE006
exp(-
Figure 168183DEST_PATH_IMAGE007
) The cumulative probability area function is: p (X) = ∅ (X) =
Figure 40324DEST_PATH_IMAGE008
=
Figure 33688DEST_PATH_IMAGE009
=1
According to the formula, the fraction defective under the tolerance lower bound LSL is calculated as follows:
P(X<LSL)=
Figure 256859DEST_PATH_IMAGE010
=
Figure 657185DEST_PATH_IMAGE011
the result of calculating the fraction defective above the tolerance upper bound USL is as follows:
P(X>USL)=
Figure 914991DEST_PATH_IMAGE012
=
Figure 813677DEST_PATH_IMAGE013
the cumulative probability area of the region between the lower tolerance bound and the upper tolerance bound is:
P(LSL≤
Figure 258565DEST_PATH_IMAGE014
≤USL)=
Figure 471372DEST_PATH_IMAGE015
=
Figure 583684DEST_PATH_IMAGE016
-
Figure 387692DEST_PATH_IMAGE010
therefore, the yield is P good =P(LSL≤
Figure 585455DEST_PATH_IMAGE014
≤USL)。
8. The method for evaluating the quality of the sensing data of the sensing equipment of the internet of things according to claim 7, wherein a linear regression model is created for predicting the quality of the sensing data of the sensing equipment of the internet of things: the linear regression model is: y =
Figure 133111DEST_PATH_IMAGE017
(x)=
Figure 365509DEST_PATH_IMAGE018
0 +
Figure 340419DEST_PATH_IMAGE018
1 x, wherein
Figure 25478DEST_PATH_IMAGE017
(x) Representing a functional mapping from x to y,
Figure 376825DEST_PATH_IMAGE018
0 and
Figure 398483DEST_PATH_IMAGE018
1 and inquiring the target output variable through a corresponding relation table of DMPO and sigma to obtain a process sigma value Z of the sensing data of the current sensing equipment, wherein the larger the process sigma value Z is, the better the sensing data quality of the sensing equipment of the internet of things is.
9. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for evaluating the perceptual data quality of the internet of things sensing device according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for assessing the perceptual data quality of the internet of things sensing device according to any one of claims 1 to 8.
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