CN111582636A - Elevator maintenance unit credit evaluation method and system - Google Patents
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Abstract
The invention provides a credit evaluation method for an elevator maintenance unit, which evaluates each unit by combining an evaluation model with the operation condition of an elevator accepted by each unit and software and hardware conditions of the unit. According to the credit evaluation method for the elevator maintenance unit, the evaluation model is obtained through big data training, and the unit is automatically evaluated through the evaluation model and the data, so that the intellectualization and the high efficiency of credit evaluation of the maintenance unit are realized.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a method and a system for evaluating credit of an elevator maintenance unit.
Background
At present, the elevator plays an increasingly important role in public trip, and the trip safety of the public is directly influenced by the running quality state of the elevator. At present, the safety quality of an elevator is influenced most by maintenance, the maintenance process of the elevator is implemented by a maintenance unit, and the implementation result becomes the most core element of the safety of the elevator. At present, star-level evaluation methods for maintenance units are various and have different standards, and the regional and random evaluation methods are very strong, so that scientific, complete and universal evaluation is difficult to achieve, unified evaluation cannot be carried out, and the final evaluation result of the maintenance units is influenced.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a method and a system for evaluating the credit of an elevator maintenance unit.
The invention provides a credit evaluation method for elevator maintenance units, which evaluates each unit by combining an evaluation model with the operation condition of an elevator accepted by each unit and the software and hardware conditions of the unit;
the obtaining of the evaluation model comprises the following steps:
s11, establishing a grading database, wherein the grading database stores evaluation information of each unit, the evaluation information comprises the grading value of each elevator accepted by the unit and software and hardware conditions of the unit, and the software and hardware conditions comprise: basic information and public scores;
s12, selecting partial units from the grading database as samples, and carrying out expert grading on each sample as a label;
s13, selecting a part of samples as training samples, and training an evaluation model according to the evaluation information and the labels of the training samples;
and S14, taking the residual sample as a correction sample, and outputting the evaluation model after carrying out cyclic training on the evaluation model according to the evaluation information and the label of the correction sample.
Preferably, the basic information includes: the service range, the enterprise qualification, the operation age, the team scale, the personnel qualification and the number of maintenance elevators.
Preferably, the public score is obtained by: obtaining evaluation information on the Internet, and obtaining netizen scores through information processing; obtaining a rating for an industry association; and calculating the public score according to the preset weight by combining the netizen score and the industry association score.
Preferably, the software and hardware conditions further include: business situation, management means and project situation; the operation conditions comprise: corporate/stockholder integrity information, credit liability, tax payment status, and business disputes; the management mode includes: management mechanisms and management means; the project conditions include: the maintenance overdue record, violation record, per capita maintenance amount, maintenance price and annual inspection condition of the elevator in the items in the operation history.
Preferably, step S14 specifically includes:
s141, dividing the residual samples into a plurality of groups of corrected samples;
s142, selecting a group of correction samples to evaluate through an evaluation model, comparing the evaluation with corresponding expert scores through model evaluation, and judging whether the evaluation model is qualified; if yes, outputting an evaluation model;
s143, if not, a group of correction samples is reselected and the step S142 is returned to.
Preferably, in step S142, the specific way of comparing the model evaluation with the corresponding expert score to determine whether the evaluation model is qualified is as follows: setting a floating difference value and a fault tolerance rate; and obtaining the ratio of the unit quantity of which the absolute value of the difference between the model evaluation and the corresponding expert score is greater than the floating difference value to the total quantity of the corrected samples, and judging that the evaluation model is qualified if the ratio is less than or equal to the fault tolerance rate.
Preferably, the fault tolerance is less than or equal to 0.5%.
Preferably, in step S11, the score value of each elevator is calculated by an elevator scoring model in combination with elevator data, and the elevator data includes: at least two of a frequency of service, a frequency of cleaning, a frequency of failure, and a frequency of use.
Preferably, the training of the elevator scoring model comprises the steps of:
s21, establishing an elevator database to store elevator data;
s22, selecting a part of elevators from an elevator database as samples, and carrying out expert scoring on each sample as a label;
and S23, training the elevator scoring model according to the partial samples and the corresponding labels, and outputting the elevator scoring model after circularly training the elevator scoring model according to the rest samples and the labels.
An elevator maintenance unit credit evaluation system comprising:
elevator database for obtain and store elevator data, elevator data includes: at least two of the frequency of maintenance, the frequency of cleaning, the frequency of failure and the frequency of use;
the elevator scoring module is connected with the elevator database; an elevator scoring model is preset in the elevator scoring module and used for scoring each elevator according to elevator data stored in an elevator database;
the unit information database is used for acquiring and storing software and hardware conditions of each unit; the software and hardware conditions include: basic information and public scores;
the unit scoring module is respectively connected with the unit information database and the elevator scoring module; and an evaluation model is preset in the unit scoring module and is used for obtaining the score of each unit by combining the software and hardware conditions of each unit and the received scoring value of each elevator through the evaluation model.
According to the credit evaluation method for the elevator maintenance unit, the evaluation model is obtained through big data training, and the unit is automatically evaluated through the evaluation model and the data, so that the intellectualization and the high efficiency of credit evaluation of the maintenance unit are realized.
According to the elevator maintenance unit credit evaluation method provided by the invention, when the maintenance unit is evaluated, the unit is macroscopically evaluated through unit software and hardware conditions; the objective evaluation of the units from the maintenance result is realized through the operation condition of the elevator accepted by the units. Therefore, the comprehensiveness and reliability of unit evaluation are ensured by combining the two evaluation modes.
Drawings
FIG. 1 is a flow chart of a method for evaluating credit of an elevator maintenance unit according to the present invention;
fig. 2 is a block diagram of a credit evaluation system of an elevator maintenance unit according to the present invention.
Detailed Description
Referring to fig. 1, the elevator maintenance unit credit evaluation method provided by the invention evaluates each unit by combining the operation condition of the elevator accepted by each unit and the software and hardware conditions of the unit through an evaluation model.
In this way, in the embodiment, when the maintenance unit is evaluated, the unit is macroscopically evaluated according to the software and hardware conditions of the unit; the objective evaluation of the units from the maintenance result is realized through the operation condition of the elevator accepted by the units. Therefore, the comprehensiveness and reliability of unit evaluation are ensured by combining the two evaluation modes.
Specifically, in the present embodiment, the obtaining of the evaluation model includes the steps of:
and S11, establishing a grading database, wherein the grading database stores evaluation information of each unit, and the evaluation information comprises the grading value of each elevator accepted by the unit and software and hardware conditions of the unit.
In this embodiment, the score value of each elevator is calculated by combining an elevator score model with elevator data, and the elevator data includes: at least two of a frequency of service, a frequency of cleaning, a frequency of failure, and a frequency of use. The elevator scoring model can be obtained through data training.
In this embodiment, the software and hardware conditions include: basic information and public scores. The basic information includes: the service range, the enterprise qualification, the operation age, the team scale, the personnel qualification and the number of maintenance elevators.
The public score is obtained in the following mode: obtaining evaluation information on the Internet, and obtaining netizen scores through information processing; obtaining a rating for an industry association; and calculating the public score according to the preset weight by combining the netizen score and the industry association score. Specifically, in the embodiment, the scores of the industry association are supplemented by netizen scores, so that the representativeness of the finally obtained public scores is ensured; the professional and effective public scoring of the final public scoring is guaranteed by the industry association. In the specific implementation, the evaluation information can be obtained from chat group and unit service evaluation information of the cell where the elevator is located. And the netizen score can be obtained by calculating according to the proportion of the number of the positive information and the number of the negative information in the obtained evaluation information.
In specific implementation, in order to ensure the reliability of the evaluation of the unit through the evaluation model, the software and hardware conditions of the unit can be further enriched. For example, the software and hardware conditions may also include: business situation, management means and project situation. Wherein, the business conditions include: corporate/stockholder integrity information, credit liability, tax payment status, and business disputes; the management mode includes: management mechanisms and management means; the project conditions include: the maintenance overdue record, violation record, per capita maintenance amount, maintenance price and annual inspection condition of the elevator in the items in the operation history.
Specifically, in the present embodiment, the number of times of the business dispute or the amount of money related to the business dispute may be specifically selected as the business dispute. The management mechanism can be combined with the conditions that whether a unit establishes a standardized flow system and whether unit employees buy insurance according to the regulations and the like to assign points; similarly, the management means can be assigned according to the situations of whether the unit uses the information platform, whether equipment risk is purchased, whether the Internet of things is installed and the like.
And S12, selecting partial units from the grading database as samples, and carrying out expert grading on each sample as a label. In specific implementation, 0.1% to 1% of units can be selected from the scoring database as samples, and the units can be determined according to the total number of the units so as to balance the reliability and the training difficulty of the finally obtained evaluation model.
And S13, selecting a part of samples as training samples, and training an evaluation model according to the evaluation information and the labels of the training samples. Specifically, in the present embodiment, the evaluation model may be trained based on a deep neural network model. In this step, in order to ensure the training efficiency of the evaluation model, a maximum of 100 training samples may be selected.
And S141, dividing the residual samples into a plurality of groups of corrected samples.
S142, selecting a group of correction samples to evaluate through an evaluation model, comparing the evaluation with corresponding expert scores through model evaluation, and judging whether the evaluation model is qualified; if yes, the evaluation model is output.
Specifically, in this step, the specific way of judging whether the evaluation model is qualified by comparing the model evaluation with the corresponding expert score is as follows: setting a floating difference value and a fault tolerance rate; and obtaining the ratio of the unit quantity of which the absolute value of the difference between the model evaluation and the corresponding expert score is greater than the floating difference value to the total quantity of the corrected samples, and judging that the evaluation model is qualified if the ratio is less than or equal to the fault tolerance rate. In specific implementation, the floating difference value is less than or equal to 2% of the maximum score, and the fault tolerance rate is less than or equal to 0.5%.
S143, if not, a group of correction samples is reselected and the step S142 is returned to.
In this way, in combination with steps S141 to S143, the remaining samples are used as correction samples, and the evaluation model is output after being cyclically trained according to the evaluation information of the correction samples and the corresponding labels, so that repeated training and cyclic correction of the evaluation model are realized, and the reliability of the evaluation model is ensured.
In this embodiment, the training of the elevator scoring model may be performed with reference to the training of the evaluation model, and specifically includes the following steps:
and S21, establishing an elevator database to store elevator data.
And S22, selecting partial elevators from the elevator database as samples, and carrying out expert scoring on each sample as a label.
And S23, training the elevator scoring model according to the partial samples and the corresponding labels, and outputting the elevator scoring model after circularly training the elevator scoring model according to the rest samples and the labels.
Referring to fig. 2, the invention also provides a credit evaluation system for an elevator maintenance unit, comprising: the system comprises an elevator database, an elevator scoring module, a unit information database and a unit scoring module.
Elevator database for obtain and store elevator data, elevator data includes: at least two of a frequency of service, a frequency of cleaning, a frequency of failure, and a frequency of use. In particular, the elevator data can be extracted directly from the history data. In the embodiment, the maintenance frequency and the cleaning frequency represent the execution performance of the maintenance unit, the fault frequency represents the execution effect of the maintenance unit, and the use frequency represents the recognition degree of the public on the maintenance effect of the elevator. In the concrete implementation, the elevator can be comprehensively evaluated by combining the four elevators.
The elevator scoring module is connected with the elevator database; an elevator scoring model is preset in the elevator scoring module and used for scoring each elevator according to elevator data stored in an elevator database. Specifically, the elevator scoring model is obtained by referring to steps S21 to S23 in the above method.
And the unit information database is used for acquiring and storing the software and hardware conditions of each unit. The software and hardware conditions include: the basic information and the public score can further comprise one or more of business situation, management means and project situation.
And the unit scoring module is respectively connected with the unit information database and the elevator scoring module. And an evaluation model is preset in the unit scoring module and is used for obtaining the score of each unit by combining the software and hardware conditions of each unit and the received scoring value of each elevator through the evaluation model. Specifically, the evaluation model may be obtained by referring to steps S11 to S14 in the above method.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention are equivalent to or changed within the technical scope of the present invention.
Claims (10)
1. A credit evaluation method for elevator maintenance units is characterized in that each unit is evaluated by combining an evaluation model with the operation condition of an elevator accepted by each unit and the software and hardware conditions of the unit;
the obtaining of the evaluation model comprises the following steps:
s11, establishing a grading database, wherein the grading database stores evaluation information of each unit, the evaluation information comprises the grading value of each elevator accepted by the unit and software and hardware conditions of the unit, and the software and hardware conditions comprise: basic information and public scores;
s12, selecting partial units from the grading database as samples, and carrying out expert grading on each sample as a label;
s13, selecting a part of samples as training samples, and training an evaluation model according to the evaluation information and the labels of the training samples;
and S14, taking the residual sample as a correction sample, and outputting the evaluation model after carrying out cyclic training on the evaluation model according to the evaluation information and the label of the correction sample.
2. The elevator maintenance unit credit evaluation method of claim 1, wherein the basic information comprises: the service range, the enterprise qualification, the operation age, the team scale, the personnel qualification and the number of maintenance elevators.
3. The method for evaluating the credit of an elevator maintenance unit according to claim 1, wherein the public score is obtained by: obtaining evaluation information on the Internet, and obtaining netizen scores through information processing; obtaining a rating for an industry association; and calculating the public score according to the preset weight by combining the netizen score and the industry association score.
4. The method of claim 1, wherein the software and hardware conditions further comprise: business situation, management means and project situation; the operation conditions comprise: corporate/stockholder integrity information, credit liability, tax payment status, and business disputes; the management mode includes: management mechanisms and management means; the project conditions include: the maintenance overdue record, violation record, per capita maintenance amount, maintenance price and annual inspection condition of the elevator in the items in the operation history.
5. The method for evaluating the credit of an elevator maintenance unit according to claim 1, wherein the step S14 specifically comprises:
s141, dividing the residual samples into a plurality of groups of corrected samples;
s142, selecting a group of correction samples to evaluate through an evaluation model, comparing the evaluation with corresponding expert scores through model evaluation, and judging whether the evaluation model is qualified; if yes, outputting an evaluation model;
s143, if not, a group of correction samples is reselected and the step S142 is returned to.
6. The method for evaluating the credit of the elevator maintenance unit according to claim 5, wherein in the step S142, the specific way of judging whether the evaluation model is qualified by comparing the model evaluation with the corresponding expert score is as follows: setting a floating difference value and a fault tolerance rate; and obtaining the ratio of the unit quantity of which the absolute value of the difference between the model evaluation and the corresponding expert score is greater than the floating difference value to the total quantity of the corrected samples, and judging that the evaluation model is qualified if the ratio is less than or equal to the fault tolerance rate.
7. The elevator maintenance unit credit evaluation method of claim 6, wherein the fault tolerance rate is less than or equal to 0.5%.
8. The method for evaluating credit of an elevator maintenance unit according to claim 1, wherein in step S11, the score value of each elevator is calculated by an elevator scoring model in combination with elevator data, and the elevator data comprises: at least two of a frequency of service, a frequency of cleaning, a frequency of failure, and a frequency of use.
9. The elevator maintenance unit credit evaluation method of claim 8, wherein the training of the elevator scoring model comprises the steps of:
s21, establishing an elevator database to store elevator data;
s22, selecting a part of elevators from an elevator database as samples, and carrying out expert scoring on each sample as a label;
and S23, training the elevator scoring model according to the partial samples and the corresponding labels, and outputting the elevator scoring model after circularly training the elevator scoring model according to the rest samples and the labels.
10. An elevator maintenance unit credit evaluation system, comprising:
elevator database for obtain and store elevator data, elevator data includes: at least two of the frequency of maintenance, the frequency of cleaning, the frequency of failure and the frequency of use;
the elevator scoring module is connected with the elevator database; an elevator scoring model is preset in the elevator scoring module and used for scoring each elevator according to elevator data stored in an elevator database;
the unit information database is used for acquiring and storing software and hardware conditions of each unit; the software and hardware conditions include: basic information and public scores;
the unit scoring module is respectively connected with the unit information database and the elevator scoring module; and an evaluation model is preset in the unit scoring module and is used for obtaining the score of each unit by combining the software and hardware conditions of each unit and the received scoring value of each elevator through the evaluation model.
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| CN113869750A (en) * | 2021-09-30 | 2021-12-31 | 中国计量大学 | Automatic elevator maintenance enterprise rating system based on big data |
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