CN110526065B - Method and system for acquiring maintenance strategy of elevator according to needs - Google Patents

Abstract

The invention relates to the field of elevator maintenance, in particular to a method and a system for acquiring an elevator maintenance-on-demand strategy; the method comprises the steps of firstly, determining the maintenance time of the elevator based on an analytic hierarchy process, then obtaining a maintenance position of the elevator needing maintenance based on an association rule algorithm of front and rear constraint rules, and finally obtaining the maintenance strategy of the elevator by combining the maintenance time and the maintenance position of the elevator. The maintenance cost is reduced, and the maintenance resource allocation is more reasonable. Wherein determining the maintenance time based on the analytic hierarchy process comprises: the group weight corresponding to each risk index is obtained by combining a plurality of judgment matrixes obtained by comparing the risk indexes with a plurality of experts, then the risk indexes are graded one by one, the scores of the risk indexes are combined with the corresponding group weight to obtain the risk grade of the elevator, and finally the maintenance time of the elevator is determined according to the risk grade of the elevator. The elevator fine management is realized.

Description

Method and system for acquiring maintenance strategy of elevator according to needs

Technical Field

The invention relates to the technical field of elevator maintenance, in particular to a method and a system for acquiring an elevator maintenance-on-demand strategy.

Background

In recent years, during the rapid construction period when cities enter high-rise buildings, elevators play an important role in production and life of people as vertical transportation means for the entrance and exit of the high-rise buildings. The traditional maintenance mode has the phenomena of man-machine mismatching, vicious competition, unreal and bad maintenance work, and brings great threat to safe and reliable operation of the elevator.

The elevator maintenance plan is arranged by means of unified time calculation, the management is extensive, fine management cannot be performed on a single elevator, the phenomenon that the maintenance is performed on some elevators with good quality or infrequently used elevators and the running conditions of the elevators are good is caused, but resources are arranged for maintenance, and the scarce maintenance resources are occupied. At present, the use amount of elevators in China is increased year by year, the flow of maintenance personnel is large, the reserve amount of technical personnel of elevator maintenance companies cannot meet the actual requirement of maintenance work, and newly-recruited maintenance personnel cannot meet the actual requirement of elevator maintenance in a short time. If the elevator in the break-in period and the old elevator are not scientifically and reasonably maintained, the failure rate in the running process can be obviously increased.

Disclosure of Invention

Technical problem to be solved

The invention provides a method and a system for acquiring an on-demand maintenance strategy of an elevator, which are used for solving the problems that the elevator cannot be subjected to fine management and the allocation of maintenance resources is unreasonable in the prior art.

(II) technical scheme

In order to solve the problems, the invention provides an elevator on-demand maintenance strategy acquisition method, which comprises the following steps:

x1: determining maintenance time of the elevator based on an analytic hierarchy process;

x2: obtaining a maintenance position of the elevator needing maintenance based on an association rule algorithm of front and rear constraint rules;

x3: obtaining an elevator maintenance strategy according to the maintenance time of the elevator and the maintenance position of the elevator needing maintenance;

the step X1 specifically includes:

s1: establishing a judgment matrix A according to comparison results obtained by comparing r experts based on the relative importance degrees of the two risk indexes^k(ii) a Wherein A is^kA judgment matrix, k ∈ {1,2,3.. r }, r } constructed after the k expert compares the risk indexes is represented>2；

S2: for the judgment matrix A^kProcessing to obtain individual weight vector

And the consistency ratio CR^kWherein

Individual weights corresponding to the risk indicators respectively;

s3: according to the consistency ratio CR^kTo determine the matrix A^kIf the consistency requirement is met, judging the matrix A^kIf all the consistency requirements are met, the step is switched to step S4;

s4: assigning a specific weight to the kth expert:

then, a population weight vector is calculated:

group weight:

wherein W₁ ^G,W₂ ^G,W₃ ^G...W_n ^GThe group weight corresponding to each risk indicator respectively;

s5: and when the elevator actually runs, acquiring a real-time numerical value of the risk index, scoring the risk index one by one according to the real-time numerical value, multiplying the score of each risk index by the corresponding group weight to obtain a total score, evaluating the risk grade of the elevator according to the total score, and determining the maintenance time according to the risk grade.

Preferably, in step S1:

the risk indexes comprise the service life of the elevator, the service place of the elevator, the area where the elevator is located, the temperature of a machine room, the humidity of the machine room, the failure times of the elevator, the maintenance unit of the elevator, the average maintenance time of the elevator and the reasonable reporting and complaint times of a user.

Preferably, the step S1 is specifically:

firstly, obtaining a comparison result of a k-th expert comparing a plurality of risk indexes in the risk indexes pairwise;

then, the judgment matrix is constructed according to the comparison result, and the judgment matrix A^k：

Wherein

Representing the comparison of the l-th one of the risk indicators with respect to the m-th one of the risk indicators, and l ∈ {1,2,3.. n }, m ∈ {1,2,3.. n };

preferably, the step S2 specifically includes the following steps:

s21: solving the judgment matrix A^kMaximum eigenvalue λ of_maxAnd a feature vector;

s22: carrying out normalization processing on the feature vector to obtain an individual weight vector W^kWherein the sum of all of the individual weights in the individual weight vector equals 1, i.e.:

s23: according to the maximum eigenvalue lambda_maxCalculating the consistency ratio according to the formula:

n is the judgment matrix A^kThe order of (a); RI is the average random consistency index, the value of which is equal to the judgment matrix A^kCorresponding to the order of (a).

Preferably, in the step S3, when the judgment matrix a is^kIf the consistency requirement is not met, the new comparison result of the kth expert is obtained again, and the judgment matrix A is updated according to the new comparison result^kAnd returns to the step S2;

wherein the CR is based on the consistency ratio^kJudging the judgment matrix A according to the size of the judgment matrix^kWhether the consistency requirements are met includes:

when the consistency ratio CR^k<0.1, i.e. the decision matrix A^kMeeting the consistency requirement;

when the consistency ratio isCR^kWhen the value is more than or equal to 0.1, namely the judgment matrix A^kThe consistency requirement is not satisfied.

Preferably, in the step S5, the required maintenance time is different for different risk levels.

Preferably, the step X2: obtaining a maintenance position of an elevator needing maintenance based on an association rule algorithm of front and rear constraint of a rule comprises the following steps:

b1: setting an operation interval for the operation data according to the type of the elevator operation data;

b2: collecting the operation data of an elevator, judging whether the operation data is in the operation interval, dividing the operation data in the operation interval into normal data, and dividing the operation data not in the operation interval into maintenance data;

b3: obtaining the maintenance position of the elevator needing maintenance according to the specific type of the maintenance data and an association rule;

the method for acquiring the association rule specifically comprises the following steps:

p1: establishing a transaction database D, wherein the transaction database D comprises a plurality of maintenance transactions, a single transaction item is the primary maintenance of the elevator, and the maintenance transactions comprise the maintenance position when the elevator is maintained and the operation data causing the maintenance of the elevator;

p2: placing the operational data into a front set of items F; placing the dimension part into a rear item set R;

p3: finding all non-empty subsets of the F, finding subset support degrees of the subsets in the transaction database D, and putting the subsets with the subset support degrees being greater than or equal to a preset minimum support degree minS1 into a set F1; finding all non-empty subsets of said R and placing in set R1;

p4: and performing Cartesian product on the F1 and the R1 to obtain all item sets, putting the obtained all item sets into a set V, solving the item set support degree of all the item sets in the transaction database D in the set V, putting the item sets of which the item set support degree is greater than or equal to a preset minimum support degree minS2 into a frequent item set, wherein the corresponding relation between the running data and the maintenance part in the item sets in the frequent item set is the association rule.

Preferably, in the step B1, the operation data includes operation acceleration (x, y, z axis), noise in the compartment, noise in the machine room, speed of opening and closing the door, noise during opening and closing the door, temperature of the machine room, operation speed, leveling accuracy, and brake temperature.

Preferably, in the step P3, the subset support degree is the number of times the subset appears in the F; in the step P4, the item set support is the number of times the item set appears in the F.

Preferably, the present invention further provides an elevator maintenance policy acquisition system, including: the system comprises a risk grade evaluation module, an on-demand maintenance module and a strategy module;

the risk level evaluation module is used for determining maintenance time of the elevator based on an analytic hierarchy process;

the on-demand maintenance module obtains maintenance positions of the elevator needing maintenance based on an association rule algorithm for front and rear constraint of the rule;

the strategy module obtains an elevator maintenance strategy according to the maintenance time of the elevator and the maintenance position of the elevator needing maintenance;

the risk level evaluation module comprises: the device comprises a judgment matrix unit, a matrix processing unit, a consistency judgment unit, a group weight unit and a maintenance time unit;

the judgment matrix unit is used for establishing a judgment matrix A according to comparison results obtained by comparing r experts based on the relative importance degrees of the two risk indexes^k；r>2, wherein A^kA judgment matrix which is constructed after the k-th expert compares the risk indexes, k ∈ {1,2,3.. r }, and a matrix processing unit which is used for judging the judgment matrix A^kProcessing to obtain individual weight vector

And the consistency ratio CR^kWherein

Individual weights corresponding to the risk indicators respectively;

a consistency judging unit for judging the consistency ratio CR according to the consistency^kTo determine the matrix A^kIf the consistency requirement is met, judging the matrix A^kIf all the consistency requirements are met, switching to the group weight unit;

the group weight unit is used for giving a specific gravity to the kth expert:

then, a population weight vector is calculated:

group weight:

wherein W₁ ^G,W₂ ^G,W₃ ^G...W_n ^GThe group weight corresponding to each risk indicator respectively;

and the maintenance time unit is used for acquiring real-time numerical values of the risk indexes during the actual running of the elevator, scoring the risk indexes one by one according to the real-time numerical values, obtaining a total score by the sum of the multiplication of the scores of the risk indexes and the corresponding group weight, evaluating the risk grade of the elevator according to the total score, and determining the maintenance time according to the risk grade.

(III) advantageous effects

The invention provides a method and a system for obtaining an elevator maintenance strategy according to needs, wherein an elevator risk evaluation system is established based on an analytic hierarchy process, the risk indexes of an elevator are compared and scored pairwise by multiple experts to obtain the weights of the risk indexes during the evaluation of the risk grades, the collected numerical values of the risk indexes are scored, and the risk grades of the elevator are judged by the sum of the multiplication of all the risk index scores and the corresponding weights, so that the maintenance time of the elevator is determined; meanwhile, obtaining a maintenance position of the elevator needing maintenance through an association rule algorithm of front and rear constraint rules; the elevator fine management and the optimization of maintenance resources are realized.

Drawings

FIG. 1 is a flow chart of a method of obtaining an on-demand maintenance policy for an elevator;

fig. 2 is a flow chart for determining the maintenance time of an elevator based on an analytic hierarchy process;

fig. 3 is a diagram of an elevator risk assessment system;

fig. 4 is a graph of elevator risk level versus failure rate;

FIG. 5 is a flow chart of obtaining a maintenance location of an elevator requiring maintenance based on an association rule algorithm of front and rear rule constraints;

FIG. 6 is a flow chart of a method of obtaining association rules;

FIG. 7 is a block diagram of an acquisition system for an on-demand maintenance policy for an elevator;

fig. 8 is a structural diagram of a risk level evaluation module.

[ description of reference ]

1: a risk level evaluation module; 11: judging a matrix unit; 12: a matrix processing unit; 13: a consistency judgment unit; 14: a group weight unit; 15: a maintenance time unit; 2: maintaining the module as required; 3: and a policy module.

Detailed Description

For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.

It should be noted that all the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.

In addition, the descriptions related to "first", "second", etc. in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

The invention provides a method for acquiring an elevator maintenance strategy according to needs, which comprises the following steps: as shown in FIG. 1: the flow chart of the method for acquiring the elevator maintenance-on-demand strategy is shown as follows:

x1: the maintenance time of the elevator is determined based on an analytic hierarchy process.

X2: and obtaining the maintenance position of the elevator needing maintenance based on an association rule algorithm of front and rear constraint of the rule.

X3: and obtaining an elevator maintenance strategy according to the maintenance time of the elevator and the maintenance position of the elevator needing maintenance.

In a preferred embodiment, as in fig. 2: as shown in the flowchart of determining the maintenance time of the elevator based on the analytic hierarchy process, step X1 specifically includes:

s01: the elevator risk level is used as a target layer, the elevator service condition, the elevator fault condition and the elevator maintenance condition are used as criterion layers, and the risk index is used as an index layer, so that an elevator risk evaluation system is established, as shown in fig. 3: elevator risk evaluation architecture picture.

S1: establishing a judgment matrix A according to comparison results obtained by comparing r experts based on the relative importance degrees of the two risk indexes^k(ii) a Wherein A is^kThe judgment matrix k ∈ {1,2,3.. r }, r } is constructed after the k expert compares the risk indexes>2。

In a more preferred embodiment, the risk indicators include the age of the elevator, the location of the elevator, the area where the elevator is located, the temperature of the machine room, the humidity of the machine room, the number of elevator failures, the maintenance unit of the elevator, the average maintenance time of the elevator, and the number of reasonable reports and complaints of the user.

Step S1 specifically includes:

firstly, a plurality of risk indexes in the k-th expert are obtained and compared with each other pairwise to obtain a comparison result.

Then, a judgment matrix is constructed according to the comparison result, and a judgment matrix A is constructed^k：

Wherein

Represents the comparison result of the ith risk indicator in the risk indicators relative to the mth risk indicator in the risk indicators, and l ∈ {1,2,3.. n }, m ∈ {1,2,3.. n }.

In particular A^kThe representation is shown in table 1:

table 1:

wherein, a₁,a₂,...a_nIn order to be an indicator of risk,

denotes a_lRelative to a_mThe comparison result of (1);

and matrix a is scaled to 1-9 or its corresponding inverse^kElement (1) of

The values are assigned, and the corresponding meanings of the scale values are shown in the table 2:

table 2:

scale	Means of
		1	alRatio amOf equal importance
3	alRatio amOf slight importance
		5	alRatio amOf obvious importance
7	alRatio amOf strong importance
		9	alRatio amOf absolute importance
2,4,6,8	Intermediate values of two adjacent judgments
		Reciprocal of the	If alRatio amTaking the above certain value, then amRatio alTaking the reciprocal of the value

Wherein a is_lIs the first risk indicator in the risk indicators, a_mThe m-th risk index of the risk indexes, i ∈ {1,2,3.. n }, m ∈ {1,2,3.. n }.

Finally, the maximum eigenvalue lambda of the judgment matrix is calculated_maxAnd a feature vector.

S2: to judgment matrixA^kProcessing to obtain individual weight vector

And the consistency ratio CR^kWherein

The individual weights respectively correspond to the risk indicators.

In a preferred embodiment, step S2 specifically includes:

first, the judgment matrix A is obtained^kMaximum eigenvalue λ of_maxAnd a feature vector;

then, normalization processing is carried out on the feature vectors to obtain individual weight vectors W^kWherein the sum of all of the individual weights in the individual weight vector equals 1, i.e.:

finally, according to the maximum eigenvalue lambda_maxCalculating the consistency ratio according to the formula:

n is the judgment matrix A^kThe order of (a); RI is the average random consistency index, the value of which is equal to the judgment matrix A^kCorresponding to the order of (a).

When the order of the matrix is judged to be greater than 2, it is often difficult to construct a matrix that satisfies consistency. However, the judgment matrix should have a degree of deviation from the consistency condition, and for this reason, it is necessary to identify whether the judgment matrix is acceptable, which is the meaning of consistency check.

Can be based on λ_maxChecking the consistency of the matrix if n is true, if λ_maxThe larger the ratio n, the larger A^kThe more severe the degree of non-uniformity.

Thus, a consistency index is defined:

the closer the CI is to 0, the better the consistency; the larger the CI, the more severe the inconsistency. To measure the magnitude of CI, a random consistency index RI was introduced. The value of RI is obtained by: the numbers are randomly extracted from 1 to 9 and the reciprocal thereof to construct a positive and reciprocal matrix of 1000 samples, the judgment matrices thus constructed are the most inconsistent, and the consistency indexes are averaged, as shown in table 3:

table 3:

n	1	2	3	4	5	6	7	8	9	10
											RI	0	0	0.52	0.89	1.12	1.26	1.36	1.41	1.46	1.49

s3: according to the consistency ratio CR^kDetermines the matrix A according to the size of the matrix A^kIf the consistency requirement is met, judging the matrix A^kIf all the consistency requirements are met, the step S4 is executed;

if the matrix A is judged^kIf the consistency requirement is not met, the new comparison result of the kth expert is obtained again, and the judgment matrix A is updated according to the new comparison result^kAnd returns to step S2;

wherein CR is based on the consistency ratio^kIs judged by the size of the judgment matrix A^kWhether the consistency requirements are met includes:

when the consistency ratio CR^k<0.1, i.e. the decision matrix A^kThe requirement of consistency is met;

when the consistency ratio CR^kWhen the value is more than or equal to 0.1, the matrix A is judged^kDoes not meet the requirement of consistency.

S4: assigning a specific weight to the kth expert:

then, a population weight vector is calculated:

group weight:

wherein W₁ ^G,W₂ ^G,W₃ ^G...W_n ^GAnd the group weights respectively correspond to the risk indexes.

Judgment matrix A^kThe better the consistency of the risk indexes is, the stronger the logic of the expert in comparing the risk indexes is, so that the expert is given a higher proportion, and due to the cognitive difference of different experts on the same problem, the preference difference, the level of the expert and other factors, the judgment given by the experts may be greatly different, so that the comparison results of multiple experts are introduced for comprehensive balance.

S5: and when the elevator actually runs, acquiring real-time numerical values of the risk indexes, and according to comparison between the real-time numerical values and reference values of the corresponding risk indexes, scoring the risk indexes one by one, multiplying the scores of all the risk indexes by the corresponding group weight to obtain a total score, evaluating the risk grade of the elevator according to the total score, and determining the maintenance time according to the risk grade.

In a preferred embodiment: a 10-point score is taken for the risk indicators in the indicator levels of the elevator. Comparing the elevator data collected actually with the standard data, so as to score the risk indexes one by one, wherein the higher the score is, the greater the influence on the elevator risk grade is, and the score obtained by the risk indexes and the corresponding group weight W are compared_i ^GMultiplying the two components respectively, wherein the final total score is within the range of 0-10. The evaluation was rated as 3 risk ratings as shown in table 4:

table 4:

total score	Less than 6	6～8	Greater than 8
				Risk rating	Ⅰ	Ⅱ	Ⅲ

As shown in table 4: the smaller the total score of the elevator, the lower the corresponding risk level, and in a preferred embodiment the risk level of the elevator is divided into three levels, and the risk level can also be divided into more levels depending on the size of the total score.

As shown in fig. 4: as shown in the graph of the relationship between the elevator risk level and the failure rate, the higher the elevator risk level is, the higher the corresponding elevator failure rate is, and maintenance needs to be performed as soon as possible, so that the maintenance time required for different risk levels is different, and the specific conditions are shown in table 5:

table 5:

risk rating	Required maintenance time
		Ⅰ	The elevator is represented with a low risk grade and can be maintained according to a conventional period
Ⅱ	Indicating that the risk grade is higher and the maintenance is required within 48 hours
		Ⅲ	Indicating that the risk level is high and needs to be done immediatelyMaintenance protector

As shown in table 5: the higher the elevator risk level is, the higher the emergency degree of the elevator needing maintenance is, and in the same preferred embodiment, the maintenance time of the elevator in the three risk level pairs is described, and if the elevator risk level is divided into more, the maintenance time can be further subdivided to correspond to the corresponding risk level.

Step X2: obtaining a maintenance position of the elevator needing maintenance based on an association rule algorithm of front and rear constraint of the rule, as shown in fig. 5: the flow chart of the maintenance position of the elevator needing maintenance is obtained based on the association rule algorithm of the front and rear constraint of the rule, which specifically comprises the following steps:

b1: different operation intervals are set for different operation data of the elevator.

B2: the method comprises the steps of collecting operation data of the elevator, judging whether the operation data are in an operation interval, dividing the operation data in the operation interval into normal data, and dividing the operation data which are not in the operation interval into maintenance data.

The range of the operating interval can be referred to according to the specified range of the technical parameters of the elevator.

In a preferred embodiment, the operational data includes operational acceleration (x, y, z axes), noise in the car, machine room noise, speed of opening and closing the door, noise during opening and closing the door, machine room temperature, operational speed, leveling accuracy, brake temperature.

B3: and obtaining the maintenance position of the elevator needing maintenance according to the specific type of the maintenance data and the association rule.

As shown in fig. 5: as shown in the flowchart of the method for acquiring association rules, the method for acquiring association rules specifically includes:

p1: and establishing a transaction database D, wherein the transaction database D comprises a plurality of maintenance transactions, a single transaction item is the maintenance of the elevator, and the maintenance transactions comprise maintenance positions when the elevator is maintained and operation data causing the elevator to be maintained.

P2: putting the operation data into a front item set F; the dimension bit is placed in the back set of items R.

P3: all non-empty subsets of F are calculated, the subset support degree of the subsets in the transaction database D is calculated, and the subsets with the subset support degree being greater than or equal to a preset minimum support degree minS1 are placed in a set F1; all non-empty subsets of R are solved and put into the set R1.

In a preferred embodiment, the subset support is the number of times the subset occurs in F.

P4: and F1 and R1 do Cartesian product, all the obtained item sets are put into a set V, the item set support degree of all the item sets in the set V in a transaction database D is solved, the item sets with the item set support degree being greater than or equal to a preset minimum support degree minS2 are put into a frequent item set, and the corresponding relation between the running data and the maintenance position in the item sets in the frequent item set is a correlation rule.

In the algorithm, the subset in the front item set F is screened by using the minimum support degree, and the item set in the set V is screened by using the minimum support degree minS2, so that the generation of redundant item sets is reduced, the memory overhead and the execution time are reduced, and the effective ratio of the association rule is improved compared with other constraint association rule algorithms.

The values of the minS1 and the minS2 can be adjusted for many times in simulation, so that the values reach a reasonable range.

The invention realizes the risk grade evaluation and maintenance as required of the elevator by using the acquired risk indexes and elevator operation data and combining basic data of the elevator and the like. Aiming at the elevator with low safety performance and high fault risk, a maintenance company timely arranges maintenance personnel to go to the door for maintenance and maintenance, converts the maintenance based on time and projects into an on-demand maintenance mode based on dynamic and static multi-source heterogeneous data, preliminarily establishes a mathematical model for maintaining the elevator according to risk levels and risk positions, provides data support for maintenance innovation, is favorable for reducing the labor intensity and maintenance cost of the maintenance personnel, and improves the maintenance efficiency and operation safety performance of the elevator.

As shown in fig. 6: the structure diagram of the elevator maintenance-on-demand strategy acquisition system is shown, the invention also provides an elevator maintenance-on-demand strategy acquisition system, which is characterized in that the elevator maintenance strategy acquisition system comprises: a risk level evaluation module 1, an on-demand maintenance module 2 and a strategy module 3.

The risk level evaluation module 1 is used for determining the maintenance time of the elevator based on an analytic hierarchy process;

the on-demand maintenance module 2 obtains the maintenance position of the elevator needing maintenance based on the association rule algorithm for the front and rear constraint of the rule.

And the strategy module 3 obtains an elevator maintenance strategy according to the maintenance time of the elevator and the maintenance position of the elevator needing maintenance.

As shown in fig. 7: as shown in the structure diagram of the risk level evaluation module, the risk level evaluation module 1 includes: a judgment matrix unit 11, a matrix processing unit 12, a consistency judgment unit 13, a group weight unit 14, and a maintenance time unit 15.

The judgment matrix unit 11 is used for constructing a judgment matrix A according to comparison results obtained by comparing r experts based on the relative importance degrees of the two risk indexes^k；r>2, wherein A^kAnd k ∈ {1,2,3.. r }.

The matrix processing unit 12 is used for determining the matrix A^kProcessing to obtain individual weight vector

And the consistency ratio CR^kWherein

The individual weights respectively correspond to the risk indicators.

The consistency judging unit 13 is used for judging the consistency according to the consistency ratio CR^kDetermines the matrix A according to the size of the matrix A^kIf the consistency requirement is met, judging the matrix A^kAll meeting the consistency requirement is passed to the population weight unit 14.

The population weighting unit 14 is used to assign a specific weight to the kth expert:

then, a population weight vector is calculated:

group weight:

wherein W₁ ^G,W₂ ^G,W₃ ^G...W_n ^GAnd the group weights respectively correspond to the risk indexes.

The maintenance time unit 15 is used for collecting real-time values of the risk indexes during the actual operation of the elevator, scoring the risk indexes one by one according to the real-time values, multiplying the scores of the risk indexes by the corresponding group weights to obtain a total score, evaluating the risk grade of the elevator according to the total score, and determining the maintenance time according to the risk grade.

It should be understood that the above description of specific embodiments of the present invention is only for the purpose of illustrating the technical lines and features of the present invention, and is intended to enable those skilled in the art to understand the contents of the present invention and to implement the present invention, but the present invention is not limited to the above specific embodiments. It is intended that all such changes and modifications as fall within the scope of the appended claims be embraced therein.

Claims (7)

1. The method for acquiring the elevator maintenance-on-demand strategy is characterized by comprising the following steps of:

x1: determining maintenance time of the elevator based on an analytic hierarchy process;

x2: obtaining a maintenance position of the elevator needing maintenance based on an association rule algorithm of front and rear constraint rules;

x3: obtaining an elevator maintenance strategy according to the maintenance time of the elevator and the maintenance position of the elevator needing maintenance;

the step X1 specifically includes:

s1: establishing a judgment matrix A according to comparison results obtained by comparing r experts based on the relative importance degrees of the two risk indexes^k(ii) a Wherein A is^kA judgment matrix, k ∈ {1,2,3.. r }, r } constructed after the k expert compares the risk indexes is represented>2；

Wherein, step S1 specifically includes:

firstly, obtaining a comparison result of a k-th expert comparing a plurality of risk indexes in the risk indexes pairwise;

then, the judgment matrix is constructed according to the comparison result, and the judgment matrix A^k：

Wherein

Representing the comparison of the l-th one of the risk indicators with respect to the m-th one of the risk indicators, and l ∈ {1,2,3.. n }, m ∈ {1,2,3.. n };

s2: for the judgment matrix A^kProcessing to obtain individual weight vector

And the consistency ratio CR^kWherein

Individual weights corresponding to the risk indicators respectively;

wherein, step S2 specifically includes the following steps:

s21: solving the judgment matrix A^kMaximum eigenvalue λ of_maxAnd a feature vector;

s22: normalizing the feature vectorThen obtaining individual weight vector W^kWherein the sum of all of the individual weights in the individual weight vector equals 1, i.e.:

s23: according to the maximum eigenvalue lambda_maxCalculating the consistency ratio according to the formula:

n is the judgment matrix A^kThe order of (a); RI is the average random consistency index, the value of which is equal to the judgment matrix A^kThe orders of (A) correspond to (B);

s3: according to the consistency ratio CR^kTo determine the matrix A^kIf the consistency requirement is met, judging the matrix A^kIf all the consistency requirements are met, the step is switched to step S4;

s4: assigning a specific weight to the kth expert:

then, a population weight vector is calculated:

group weight:

wherein

The group weight corresponding to each risk indicator respectively;

s5: collecting real-time numerical values of the risk indexes during the actual running of the elevator, scoring the risk indexes one by one according to the real-time numerical values, multiplying the scores of the risk indexes by the corresponding group weights to obtain total scores, evaluating the risk grade of the elevator according to the total scores, and determining the maintenance time according to the risk grade;

the step X2 includes:

b1: setting an operation interval for the operation data according to the type of the elevator operation data;

b2: collecting the operation data of an elevator, judging whether the operation data is in the operation interval, dividing the operation data in the operation interval into normal data, and dividing the operation data not in the operation interval into maintenance data;

b3: obtaining the maintenance position of the elevator needing maintenance according to the specific type of the maintenance data and an association rule;

the method for acquiring the association rule specifically comprises the following steps:

p1: establishing a transaction database D, wherein the transaction database D comprises a plurality of maintenance transactions, a single transaction item is the primary maintenance of the elevator, and the maintenance transactions comprise the maintenance position when the elevator is maintained and the operation data causing the maintenance of the elevator;

p2: placing the operational data into a front set of items F; placing the dimension part into a rear item set R;

p3: finding all non-empty subsets of the F, finding subset support degrees of the subsets in the transaction database D, and putting the subsets with the subset support degrees being greater than or equal to a preset minimum support degree minS1 into a set F1; finding all non-empty subsets of said R and placing in set R1;

p4: and performing Cartesian product on the F1 and the R1 to obtain all item sets, putting the obtained all item sets into a set V, solving the item set support degree of all the item sets in the transaction database D in the set V, putting the item sets of which the item set support degree is greater than or equal to a preset minimum support degree minS2 into a frequent item set, wherein the corresponding relation between the running data and the maintenance part in the item sets in the frequent item set is the association rule.

2. The method for acquiring an on-demand maintenance policy for elevators according to claim 1, wherein in the step S1:

the risk indexes comprise the service life of the elevator, the service place of the elevator, the area where the elevator is located, the temperature of a machine room, the humidity of the machine room, the failure times of the elevator, the maintenance unit of the elevator, the average maintenance time of the elevator and the reasonable reporting and complaint times of a user.

3. The method for acquiring an on-demand maintenance strategy for elevators according to claim 2, wherein in the step S3, when the judgment matrix a is^kIf the consistency requirement is not met, the new comparison result of the kth expert is obtained again, and the judgment matrix A is updated according to the new comparison result^kAnd returns to the step S2;

wherein the CR is based on the consistency ratio^kJudging the judgment matrix A according to the size of the judgment matrix^kWhether the consistency requirements are met includes:

when the consistency ratio CR^k<0.1, i.e. the decision matrix A^kMeeting the consistency requirement;

when the consistency ratio CR^kWhen the value is more than or equal to 0.1, namely the judgment matrix A^kThe consistency requirement is not satisfied.

4. The method for acquiring an on-demand maintenance policy for elevators according to claim 1, wherein in the step S5, required maintenance time is different for different risk levels.

5. The method for acquiring an elevator on-demand maintenance strategy according to claim 1, wherein in the step B1, the operation data comprises operation acceleration, car noise, machine room noise, door opening and closing speed, door opening and closing noise, machine room temperature, operation speed, leveling accuracy and brake temperature of the elevator on x, y and z axes respectively.

6. The method for acquiring an on-demand maintenance policy for elevators according to claim 1, wherein in the step P3, the subset support degree is the number of times the subset appears in the F; in the step P4, the item set support is the number of times the item set appears in the F.

7. An elevator maintenance policy acquisition system, characterized by comprising: the system comprises a risk grade evaluation module, an on-demand maintenance module and a strategy module;

the risk level evaluation module is used for determining maintenance time of the elevator based on an analytic hierarchy process;

the on-demand maintenance module obtains maintenance positions of the elevator needing maintenance based on an association rule algorithm for front and rear constraint of the rule;

the strategy module obtains an elevator maintenance strategy according to the maintenance time of the elevator and the maintenance position of the elevator needing maintenance;

the risk level evaluation module comprises: the device comprises a judgment matrix unit, a matrix processing unit, a consistency judgment unit, a group weight unit and a maintenance time unit;

the judgment matrix unit is used for establishing a judgment matrix A according to comparison results obtained by comparing r experts based on the relative importance degrees of the two risk indexes^k；r>2, wherein A^kA judgment matrix constructed by comparing the risk indexes by the kth expert is represented, and k ∈ {1,2,3.. r };

wherein the judgment matrix unit is specifically configured to:

firstly, obtaining a comparison result of a k-th expert comparing a plurality of risk indexes in the risk indexes pairwise;

then, the judgment matrix is constructed according to the comparison result, and the judgment matrix A^k：

Wherein

Representing the comparison of the l-th one of the risk indicators with respect to the m-th one of the risk indicators, and l ∈ {1,2,3.. n }, m ∈ {1,2,3.. n };

the matrix processing unit is used for judging the matrix A^kProcessing to obtain individual weight vector

And the consistency ratio CR^kWherein

Individual weights corresponding to the risk indicators respectively;

wherein the matrix processing unit is specifically configured to:

firstly: solving the judgment matrix A^kMaximum eigenvalue λ of_maxAnd a feature vector;

then: carrying out normalization processing on the feature vector to obtain an individual weight vector W^kWherein the sum of all of the individual weights in the individual weight vector equals 1, i.e.:

and finally: according to the maximum eigenvalue lambda_maxCalculating the consistency ratio according to the formula:

n is the judgment matrix A^kThe order of (a); RI is the average random consistency index, the value of which is equal to the judgment matrix A^kThe orders of (A) correspond to (B);

a consistency judging unit for judging the consistency ratio CR according to the consistency^kTo determine the matrix A^kIf the consistency requirement is met, judging the matrix A^kIf all the consistency requirements are met, switching to the group weight unit;

the group weight unit is used for giving a specific gravity to the kth expert:

then, a population weight vector is calculated:

group weight:

wherein

The group weight corresponding to each risk indicator respectively;

the maintenance time unit is used for acquiring real-time numerical values of the risk indexes during the actual running of the elevator, scoring the risk indexes one by one according to the real-time numerical values, obtaining total scores by the sum of the scores of the risk indexes multiplied by the corresponding group weights, evaluating the risk grade of the elevator according to the total scores, and determining the maintenance time according to the risk grade;

the on-demand maintenance module is specifically configured to:

b1: setting an operation interval for the operation data according to the type of the elevator operation data;

b2: collecting the operation data of an elevator, judging whether the operation data is in the operation interval, dividing the operation data in the operation interval into normal data, and dividing the operation data not in the operation interval into maintenance data;

b3: obtaining the maintenance position of the elevator needing maintenance according to the specific type of the maintenance data and an association rule;

the method for acquiring the association rule specifically comprises the following steps:

p1: establishing a transaction database D, wherein the transaction database D comprises a plurality of maintenance transactions, a single transaction item is the primary maintenance of the elevator, and the maintenance transactions comprise the maintenance position when the elevator is maintained and the operation data causing the maintenance of the elevator;

p2: placing the operational data into a front set of items F; placing the dimension part into a rear item set R;

p3: finding all non-empty subsets of the F, finding subset support degrees of the subsets in the transaction database D, and putting the subsets with the subset support degrees being greater than or equal to a preset minimum support degree minS1 into a set F1; finding all non-empty subsets of said R and placing in set R1;

p4: and performing Cartesian product on the F1 and the R1 to obtain all item sets, putting the obtained all item sets into a set V, solving the item set support degree of all the item sets in the transaction database D in the set V, putting the item sets of which the item set support degree is greater than or equal to a preset minimum support degree minS2 into a frequent item set, wherein the corresponding relation between the running data and the maintenance part in the item sets in the frequent item set is the association rule.

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