CN109714725A - Environment of plant Safety self-inspection method based on fuzzy overall evaluation - Google Patents
Environment of plant Safety self-inspection method based on fuzzy overall evaluation Download PDFInfo
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- 238000011156 evaluation Methods 0.000 title claims abstract description 46
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Abstract
In wireless sensor network, generally require to determine the state of object by perception angled complimentary by multiple sensors, but there is sensors in the environment of plant it is very few, cost is excessively high and data are inaccurate and professional institution carries out environment of plant testing cost costly the problems such as.In the present invention; artificial intelligence approach can be used based on the environment of plant Safety self-inspection method of fuzzy overall evaluation to obtain jdgement matrix and weight calculation; it can assess to obtain accurate environment of plant evaluation and test using a small amount of sensor, so that workers ' health is protected, reduction environment measuring cost.
Description
Technical field
The invention belongs to internet of things field, and in particular to wireless sensor network environment monitoring, multi-sensor cooperation
The cross-applications such as perception and Fuzzy Comprehensive Evaluation.
Background technique
Wisdom environment of plant monitoring based on Internet of Things, will using access technologies such as wireless sensor network and radio frequency identifications
The material resources environment and internet of factory interconnect, and complete particular task by the convergence analysis to isomeric data.Internet of Things
Gradually evolving with wireless sensor monitoring network technology is the whole of integrated, micromation, networking, low cost and low-power consumption
Body wisdom system.By the flexible deployment of wireless sensor network make monitoring environmental data technology military affairs, traffic, transport and
The every field such as medical treatment are used widely, and are analyzed environmental data collecting, are achieved the purpose that monitoring and early warning, and complete to disaster
At self-healing control.
Wireless sensor network is one of the important content of wisdom plant environment data monitoring system research.Wireless sensor
Network synthesis sensor technology, embedded computer technology, distributed information processing and wireless communication technique, in military affairs
Scouting, environmental science, health care, industrial automation and business application etc. are with a wide range of applications.
On the basis of existing sensor network, research sensor synergism perception becomes wireless sensor network and Internet of Things
The new direction of fusion.Sensor synergism perception refers to and multiple sensors multiple to object or object disposition, by these not simultaneous interpretations
Sensor cooperates jointly completes detection perception task.Multi-sensor cooperation perceives it is possible to prevente effectively from perceiving angle by single-sensor
Error caused by unilateral can be reacted the same state analysis of object by counterweight physical parameter, perceive angle by multiple sensors
The complementary state raising to determine object of degree can be with perceived accuracy and objectivity.But number of sensors is directly proportional to cost
, although sensor excessively can guarantee that the accuracy of data, cost can also increase considerably.So how using suitable
Sensor obtains accurate and objectively data are just at a problem.It is known that Fuzzy Comprehensive Evaluation method is a kind of energy
Effectively solve the problems, such as therefore designing the tool that multifactor things is judged can sense with Fuzzy Comprehensive Evaluation method
One piece of accurate and objective data in region is obtained in the case that device quantity is certain.
Summary of the invention
The object of the present invention is to provide one kind can obtain inside plants with a small amount of sensor in wireless sensor network
The self checking method of environment.The monitoring data and country's air quality mark of factory's different location are obtained by a variety of, a small amount of sensor
Quasi-, labour law standard and hygienic standard limit compare, and form jdgement matrix, provide reasonable weight or are closed using the method for inversion
It manages weight and inside plants environment is provided according to the principle of maximum membership degree with one of four kinds of composite operators of fuzzy mathematics
Self-detection result, objectively understand inside plants environmental gap and make corresponding measure, optimize the environment of plant, protect worker's body
Body health.
To solve foregoing invention, whole process is divided into two stages: training stage and environment of plant self-test stage.One, it instructs
Practice the stage
The sensitivity of each sensor used when because detecting the environment of plant under various circumstances is different, and every kind
Placement position of the sensor in multiple factories may also be different, these situations can all cause confidence level error occur.It considers
These factors just need to take into account the confidence level of each case when carrying out the self-test of inside plants environment.Therefore, first
First need to carry out a training stage, with judge the environment using Fuzzy Comprehensive Evaluation method carry out environment of plant self-test can
Reliability.Here sensor is judged using fuzzy math integrated evaluation method in the credible of varying environment and different factory placement positions
Degree.Fuzzy model in response to this problem is made of three set: (1) set of factors U, and (2) judge collection V, (3) blurring mapping collection Γ.
Set of factors U includes all performance indicators, i.e. detection probability, false dismissal probability and empty inspection probability.Judging collection V includes the environment of plant
Judge measurement, i.e., it is excellent, good, in, it is poor, very poor.Blurring mapping collection Γ is that set of factors U is transformed to judge collection V, it is corresponding every
The sequence of values of a measurement.
It is as shown in table 1 that evaluation result is obtained in the training stage.
Table 1
Data in table are polls obtained from being judged according to judge measurement the performance of test result each time.Root
After all data in table is normalized according to the above results, available single factor evaluation matrix R,
Judge that subset can be provided according to the different attention degrees to different performance index in set of factors.Judge that subset is expressed
For
D=a1, a2, a3 (3)
Wherein a1 indicates detection probability weight, and a2 indicates false dismissal probability weight, and a3 indicates empty inspection probability right, and D expression is sentenced
Disconnected subset.
With operator M Λ, calculated result normalization can be obtained by result vector E by V
The calculation formula of operator M Λ, V are as follows:
Confidence level of the sensor under a certain environment of factory and placement position can be expressed as F
The size of sensor confidence level under a certain environment of factory and placement position is judged according to the value of result, so
It is whether suitable using the Fuzzy Comprehensive Evaluation method self-test environment of plant afterwards.Self-test will use multiple sensors, every kind of sensor
And other confidence levels in different placement positions can be obtained according to fuzzy overall evaluation process above.
Two, the environment of plant self-test stage
If the judgement for passing through the training stage, it was demonstrated that sensor can be adopted under a certain environment of factory and placement position
With the Fuzzy Comprehensive Evaluation method self-test environment of plant, then also need repeatedly training sensor in the confidence level of different location,
To select multiple positions with a high credibility to form matrix.
It thus can be carried out the self-test stage.Now provide fuzzy model (1), set of factors U (2) evaluate collection V.Factor
Collection U is the above-mentioned position with a high credibility selected, i.e. No.1 point, No. two points, No. three points ....Evaluate collection V is sensor detection
The metric of object, i.e., it is excellent, good, in, poor (metric is determined by state-set standard).
It is as shown in table 2 by set of factors U, evaluate collection V and the available evaluation result of real data.
Table 2
Data in table are polls obtained from being judged according to judge measurement the performance of test result each time.Root
After all data in table is normalized according to the above results, available single factor evaluation matrix R,
Judge that subset can be provided according to the different attention degrees to different performance index in set of factors.Judge that subset is expressed
For
D=a1, a2, a3 ..., an (8)
Wherein a1 indicates No.1 point weight, and a2 indicates No. two point weights, and a3 indicates No. three point weights ..., and D indicates judgement
Collection.
With operator M Λ, calculated result normalization can be obtained by result vector E by V
According to the available sensor of fuzzy mathematics maximum membership grade principle substance detected in inside plants environment
Fuzzy evaluation.This detection data is excellent in the fuzzy evaluation of inside plants environment if b1 maximum.
The self-test of inside plants environment obviously can not be realized by single-sensor, so the sensor of plurality of classes is logical
After spending the training stage, sensor substance detected can be obtained in inside plants environment by above-mentioned Field Using Fuzzy Comprehensive Assessment
Fuzzy evaluation and result vector.In view of this, it is desirable to obtain environment of plant self-detection result and two layers of fuzzy mathematics synthesis is needed to comment
Valence.The different classes of obtained result vector of sensor is now become set of factors V, and to obtain evaluation result as shown in table 3.
Table 3
After all data in table is normalized according to the above results, available two layers of fuzzy evaluating matrix R,
Judge that subset is expressed as D=a1, a2, a3 ..., an, a1 at this time is power of the detectable substance 1 in environment of plant judge
Weight, a2 are weight ... of the detectable substance 2 in environment of plant judge.
With operator M Λ, calculated result normalization can be obtained by result vector E by V
It can be obtained by the fuzzy evaluation result of environment of plant self-test according to fuzzy mathematics maximum membership grade principle.
Detailed description of the invention
Fig. 1: factory's noise No.1 point explanation views
Fig. 2: evaluation result explanation views
Specific embodiment
Assuming that having carried out 10 training to noise, temperature, the inhalable particles in the environment of plant in the training stage, performance is commented
Result is sentenced as shown in table 4, table 5, table 6.It is excellent=1, good=0.8, in=0.6, poor=0.4, very poor=0.2.
Table 4
Table 5
Table 6
Obtain single factor judgment matrix R (noise), R (temperature), R (inhalable particles)
The weight of detection probability, false dismissal probability and false-alarm probability is respectively 0.5,0.3 and 0.2
D=0.5,0.3,0.2 (15)
With operator M Λ, V, so that it may obtain
In B (noise) element and be 1, do not need to normalize.B (temperature) and B (inhalable particles) normalization is obtained
Confidence level under the environment and placement position is
This illustrates that the confidence level of noise and temperature is relatively high, and the Reliability ratio of inhalable particles is lower.Readjust inspection
The placement position for surveying inhalable particles sensor calculates the confidence level of inhalable particles again.
The inhalable particles evaluations matrix of readjustment is
After readjusting placement position, confidence level is significantly raised.Prove these three sensors putting in such a case
Position credibility is higher, is suitble to carry out environment of plant self-test using Fuzzy Comprehensive Evaluation method.
Second stage is carried out below, and the three higher positions of confidence level of every kind of sensor in the factory is allowed to put.
It is as shown in table 7 to obtain noise evaluation result.
Table 7
It obtains judging from evaluation result and puts to the proof R
No.1 point, No. two points, No. three point weights are respectively
D=0.4,0.4,0.2 (28)
Result vector E (noise) is obtained by operator M Λ, V
It can determine whether that noise is in excellent in the environment of plant by result vector E (noise).
Similarly obtain E (temperature), E (inhalable particles)
It can determine whether that temperature and inhalable particles are all in the environment of plant by result vector E (temperature) and E (inhalable particles)
In good.
The result vector of noise, temperature, inhalable particles is formed into evaluations matrix R from new
D=0.6,0.5,0.4 (33)
According to maximum membership grade principle, environment of plant self-detection result is obtained are as follows: good.
Claims (5)
1. the environment of plant Safety self-inspection method based on fuzzy overall evaluation, it is characterized in that utilizing Fuzzy Comprehensive Evaluation twice
Method is confidence level F of the determining sensor under the environment and the placement position for the first time, is for two layers of fuzzy synthesis for the second time
Evaluation, first layer determine that the result vector E of a variety of factory's detection datas, the second layer form evaluation by first layer result vector E
Matrix R, which is utilized, uses operator M Λ, V, finally obtains environment of plant self-detection result.
2. the environment of plant Safety self-inspection method based on fuzzy overall evaluation as described in claim 1, which is characterized in that realize
The detailed process of the Fuzzy Comprehensive Evaluation environment of plant are as follows:
Step 1: understanding sensor, quantification required for the environment of plant detects, setting blurring mapping collection is obtained in the training stage
To evaluations matrix of the sensor under the environment and the placement position；
Step 2: operator M Λ, V calculated result vector are used by evaluations matrix and weight；
Step 3: matrix of consequence and blurring mapping collection are obtained into confidence level as matrix multiplication；
Step 4: by judging that confidence level size decides whether to enter step 5, confidence level is larger to enter step 5, and confidence level is smaller
Sensor position is then put again enters step 1；
Step 5: starting collection plant data and obtain one layer of evaluations matrix of sensor of the same race；
Step 6: evaluations matrix and weight use operator M Λ, V calculated result vector；
Step 7: according to maximum membership grade principle, obtaining the metric of the detectable substance of the sensor in the factory；
Step 8: the result vector of all the sensors is formed into two layers of evaluations matrix；
Step 9: evaluations matrix and weight use operator M Λ, and V calculated result vector obtains entirely according to maximum membership grade principle
Environment of plant self-detection result.
3. the environment of plant Safety self-inspection method based on fuzzy overall evaluation as described in claim 1, which is characterized in that first
The secondary confidence level F for being determining sensor under the environment and the placement position using Fuzzy Comprehensive Evaluation method, obtains credible
Spend the specific practice of F are as follows:
It sets smear out effect collection Γ (level-one, second level, three-level, level Four, Pyatyi)
Sensing data is obtained after respective sensor is placed on the suitable position of factory, will test probability, false dismissal probability and empty inspection
Probability obtains evaluations matrix R in fine, good, general, poor, very poor this five number of votes obtained judged in measurement normalization
The weight D for judging subset i.e. detection probability, false dismissal probability and empty inspection probability is drafted,Calculate result vector E
Result vector E and blurring mapping collection Γ obtains confidence level F as matrix multiplication operation
Sensor position is varied multiple times and obtains the confidence level of different location.
4. the environment of plant Safety self-inspection method based on fuzzy overall evaluation as described in claim 1, which is characterized in that obtain
The result vector E of a variety of factory's detection datas, specific practice are determined in two layers of fuzzy overall evaluation in first layer are as follows:
The higher position of multiple sensors'credences is selected, state-set standard is divided into four etc. by collecting sensor data
Grade it is i.e. excellent, good, in, it is poor, by the sensor collection of different location to data it is excellent, good, in, the number of votes obtained in poor four grades
Normalization, obtains evaluations matrix R
Each point weight is drafted,Result vector E is calculated, other
Sensor way is consistent, the result vector E of available multiple sensors.
5. the environment of plant Safety self-inspection method based on fuzzy overall evaluation as described in claim 1, which is characterized in that obtain
The specific practice of environment of plant self-detection result are as follows:
The result vector E for the multiple sensors that first layer fuzzy overall evaluation is obtained forms jdgement matrix R, drafts every detectable substance
Weight D in the environment of plant,Calculate result vector E
Finally, wherein maximum value is corresponding according to the size of each element in fuzzy mathematics maximum membership grade principle comparison result vector E
Judge measurement (excellent, good, in, poor) be the environment of plant self-detection result.
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