A method for evaluating elevator health status based on fusion network model

Abstract

The present invention discloses a method for evaluating the health status of an elevator based on a fusion network model, which belongs to the technical field of special equipment evaluation. The method comprises: collecting multidimensional data of the elevator to be tested and performing preprocessing; constructing a BP neural network model, using the genetic algorithm GA as a strategy for optimizing the hyperparameters, weights and thresholds of the BP neural network model, obtaining a GA-BP neural network model, and obtaining a first evaluation result of the health status of the elevator based on the GA-BP neural network model; constructing a logical judgment model to obtain a second evaluation result of the health status of the elevator; fusing and grading the evaluation results of the GA-BP neural network model and the logical judgment model, and using the graded results as the final diagnosis result of the health status of the elevator. The present invention combines the learning and prediction capabilities of neural networks with the intuitiveness and accuracy of logical judgment to improve the accuracy and efficiency of elevator health status assessment and provide a strong guarantee for the safe operation of elevators.

Description

Technical Field

[0001] The present invention relates to the technical field of special equipment evaluation, and more particularly to an elevator health status evaluation method based on a fusion network model. Background Art

[0002] Elevators are important equipment in modern buildings, and their operating status directly affects the safety of passengers. In recent years, elevator safety accidents have occurred frequently. Traditional fault diagnosis methods rely on regular manual inspections and simple sensor alarms, and do not conduct multi-dimensional inspections of elevators. This is inefficient and not conducive to providing strong technical support for elevator maintenance and safe operation.

[0003] An analysis of closed accidents reveals that the main causes of elevator accidents include inadequate safety management and maintenance, illegal or improper operation, failure of safety components or protective devices, untimely inspections, improper emergency rescue, and other reasons. Based on the above data, inadequate safety management and maintenance, and untimely inspections account for a large proportion of accidents, while accidents caused by failure of safety components and protective devices are also high. If the health status and risks of elevators can be assessed and predicted before an accident occurs, unnecessary casualties can be reduced.

[0004] Therefore, how to provide an elevator health status assessment method based on a fusion network model that can solve the above problems is an issue that those skilled in the art urgently need to solve. Summary of the Invention

[0005] In view of this, the present invention provides an elevator health status assessment method based on a fusion network model, which is used to solve the technical problems existing in the above-mentioned prior art.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for evaluating the health status of an elevator based on a fusion network model includes the following steps:

[0008] S100: Collect multi-dimensional data of the elevator to be tested and perform pre-processing;

[0009] S200: constructing a BP neural network model, using a genetic algorithm (GA) as a strategy for optimizing hyperparameters, weights, and thresholds of the BP neural network model, obtaining a GA-BP neural network model, and obtaining a first evaluation result of the elevator health status based on the GA-BP neural network model;

[0010] S300: constructing a logic judgment model to obtain a second evaluation result of the elevator health status;

[0011] S400: Fusing the first evaluation result and the second evaluation result, grading the fusion result, and using the grading result as the final elevator health status diagnosis result.

[0012] Preferably, S100: collecting multi-dimensional data of the elevator to be tested and preprocessing it includes:

[0013] S110: Collecting elevator attribute information of the elevator to be tested;

[0014] S120: Setting a sampling period, and collecting multi-dimensional data of the elevator to be tested based on the sampling period, wherein the multi-dimensional data includes: IoT monitoring information, maintenance information, and inspection and testing information;

[0015] S130: Remove data records with missing values ​​or data that are obviously inconsistent with reality in the multidimensional data, and normalize the remaining data.

[0016] Preferably, the step S130 includes:

[0017] Plot data lists according to multidimensional data;

[0018] Perform column indexing on the data in the data list, traverse the missing values ​​in the row, determine the data integrity, and determine the integrity of the data in the data list;

[0019] retaining data whose integrity is greater than or equal to a first preset threshold, and removing data whose integrity is less than the first preset threshold;

[0020] Data records that are obviously inconsistent with reality are deleted from the data list by calculating a deviation value, wherein the deviation value is calculated based on an absolute value or a relative error. When the deviation value is greater than or equal to a second preset threshold, the data record is marked as inconsistent with reality and deleted;

[0021] The remaining data were normalized.

[0022] Preferably, the BP neural network model comprises: an input layer, a hidden layer and an output layer;

[0023] The number of nodes in the input layer is determined according to the number of features of the input data. The number of nodes in the output layer is 1, and the evaluation result of the elevator health status is output. The number of nodes in the hidden layer is obtained according to an empirical formula.

[0024] Preferably, the empirical formula is:

[0025] ;

[0026] in, is the number of hidden layer nodes, is the number of input layer nodes, is the number of output layer nodes, and a is a constant between 1 and 10.

[0027] Preferably, a genetic algorithm (GA) is used as a strategy to optimize the hyperparameters, weights, and thresholds of the BP neural network model, thereby obtaining a GA-BP neural network model, including:

[0028] Real number coding is used to establish the initial population, which consists of four parts: hidden layer threshold, connection weight between hidden layer and output layer, connection weight between input layer and hidden layer, and output layer threshold, as a set of chromosomes;

[0029] Perform selection, crossover and mutation operations of the genetic algorithm to generate the next generation population, calculate the individual fitness values ​​of the new population, and take the minimum value as the optimal value;

[0030] The optimized connection weights and thresholds are introduced into the BP neural network, and the normalized data are used as training samples for repeated training.

[0031] Update the optimized connection weights, thresholds and network structure obtained through training to obtain a GA-BP neural network model;

[0032] The first evaluation result of the elevator health status is obtained based on the GA-BP neural network model.

[0033] Preferably, the expression for calculating the individual fitness value of the new population is:

[0034] ;

[0035] Where n is the number of nodes in the network output layer, is the actual output of the i-th node; is the expected output of the i-th node.

[0036] Preferably, S300: constructing a logic judgment model to obtain a second evaluation result of the elevator health status includes:

[0037] Define logical relationships to determine the health status of elevators;

[0038] Among them, propositions of logical relations include:

[0039] The relationship between normal operation and fault conditions;

[0040] The relationship between maintenance operation and operating status;

[0041] The relationship between emergency electric operation and fault handling;

[0042] The relationship between docking operations and loading and unloading of cargo;

[0043] The relationship between fire operations and fire emergency response;

[0044] The defined logical relationships are organized into a rule base, and based on the rule base, a logical judgment model is obtained.

[0045] Preferably, the logical judgment model is expressed as P00N AND P00M;

[0046] Among them, P00N and P00M represent the true or false state of any two propositions of logical relationship.

[0047] Preferably, S400 includes:

[0048] The binary values ​​output by the logic judgment model are converted into continuous values ​​in the same range as those output by the GA-BP neural network model;

[0049] Calculate the fusion result, the expression is:

[0050]

[0051] Where, Indicates the final elevator health diagnosis result, represents the first evaluation result of the elevator health status output by the GA-BP neural network model, The second evaluation result of the elevator health status after the logic judgment model is converted, represents the weight of the GA-BP neural network model, Represents the weight of the logical judgment model;

[0052] The fusion results are graded and used as the final elevator health status diagnosis results.

[0053] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method for evaluating the health status of an elevator based on a fusion network model. In the process of training the model using a BP neural network, the weights are repeatedly corrected and the model is continuously optimized, so that the model can fit the training data with arbitrary precision. The dual identification of the health status of the elevator based on the single-even time series model of the GA-BP neural network and the group-even space model based on logical judgment combines the learning and prediction capabilities of the neural network with the intuitiveness and accuracy of logical judgment, so as to improve the accuracy and efficiency of the elevator health status assessment and provide a strong guarantee for the safe operation of the elevator. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0055] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] See also Figure 1 The embodiment of the present invention discloses a method for evaluating the health status of an elevator based on a fusion network model, comprising the following steps:

[0058] S100: Collect multi-dimensional data of the elevator to be tested and perform pre-processing;

[0059] S200: constructing a BP neural network model, using a genetic algorithm (GA) as a strategy for optimizing hyperparameters, weights, and thresholds of the BP neural network model, obtaining a GA-BP neural network model, and obtaining a first evaluation result of the elevator health status based on the GA-BP neural network model;

[0060] S300: constructing a logic judgment model to obtain a second evaluation result of the elevator health status;

[0061] S400: Fusing the first evaluation result and the second evaluation result, grading the fusion result, and using the grading result as the final elevator health status diagnosis result.

[0062] In a specific embodiment, S100: collecting multi-dimensional data of the elevator to be tested and performing pre-processing, including:

[0063] S110: Collecting elevator attribute information of the elevator to be tested;

[0064] S120: Setting a sampling period, and collecting multi-dimensional data of the elevator to be tested based on the sampling period, wherein the multi-dimensional data includes: IoT monitoring information, maintenance information, and inspection and testing information;

[0065] S130: Remove data records with missing values ​​or data that are obviously inconsistent with reality in the multidimensional data, and normalize the remaining data.

[0066] Specifically, elevator attribute information includes basic information such as the elevator model, specifications, manufacturer, installation date, and years of operation. IoT monitoring information includes real-time data collected on the elevator's operating status, fault information, passenger information, and operating environment. Maintenance information includes maintenance time, maintenance content, maintenance personnel, maintenance results, and troubleshooting records. Inspection and testing information includes regular or irregular inspection results, inspection reports, inspection findings, and solutions.

[0067] In a specific embodiment, the collected data may include elevator attribute information and IoT monitoring information from the following aspects:

[0068] The unit that manufactures (including renovates) the elevator: the main safety components, bridge cars, control cabinets, counterweights and main frames, etc., the distribution of the shaft and other influencing factors. The user unit: elevator safety management personnel, usage scenarios, the environment under the pit, the environment inside the machine room, elevator insurance, the location of the alarm device, the management of rental houses, rescue passages, usage registration information, management rules and regulations. The installation (including maintenance) unit: the license level of the installation unit, the original manufacturer's installation, the supervision and inspection pass rate and other data. The maintenance unit: maintenance contract, contract filing, the level of the maintenance unit, original factory maintenance, regular inspection pass rate, and the period for advance inspection. Inspection agency: statutory inspection items, major issues, safety hazard investigation, and safety assessment. Supervisory agency: elevator accidents, complaints, and safety risks and defects of elevators of the same model and type.

[0069] In a specific embodiment, S130 includes:

[0070] Plot data lists according to multidimensional data;

[0071] Perform column indexing on the data in the data list, traverse the missing values ​​in the row, determine the data integrity, and determine the integrity of the data in the data list;

[0072] retaining data whose integrity is greater than or equal to a first preset threshold, and removing data whose integrity is less than the first preset threshold;

[0073] Data records that are obviously inconsistent with reality are deleted from the data list by calculating a deviation value, wherein the deviation value is calculated based on an absolute value or a relative error. When the deviation value is greater than or equal to a second preset threshold, the data record is marked as inconsistent with reality and deleted;

[0074] The remaining data were normalized.

[0075] Through the above technical solutions, the accuracy and completeness of the data are ensured, and the data is converted into a unified format to facilitate subsequent analysis and modeling.

[0076] In a specific embodiment, constructing a BP neural network model includes: an input layer, a hidden layer, and an output layer;

[0077] The number of nodes in the input layer is determined according to the number of features of the input data. The number of nodes in the output layer is 1, and the evaluation result of the elevator health status is output. The number of nodes in the hidden layer is obtained according to an empirical formula.

[0078] Specifically, the empirical formula is:

[0079] ;

[0080] in, is the number of hidden layer nodes, is the number of input layer nodes, is the number of output layer nodes, and a is a constant between 1 and 10.

[0081] In a specific embodiment, an overly complex model may lead to overfitting. Therefore, when constructing a GA-BP neural network, it is necessary to select appropriate network structure and parameters to avoid overfitting. Therefore, a genetic algorithm (GA) is used as a strategy to optimize the hyperparameters, weights, and thresholds of the BP neural network model to obtain a GA-BP neural network model, specifically including:

[0082] Real number coding is used to establish the initial population, which consists of four parts: hidden layer threshold, connection weight between hidden layer and output layer, connection weight between input layer and hidden layer, and output layer threshold, as a set of chromosomes;

[0083] Perform selection, crossover and mutation operations of the genetic algorithm to generate the next generation population, calculate the individual fitness values ​​of the new population, and take the minimum value as the optimal value;

[0084] The optimized connection weights and thresholds are introduced into the BP neural network, and the normalized data are used as training samples for repeated training.

[0085] Update the optimized connection weights, thresholds and network structure obtained through training to obtain a GA-BP neural network model;

[0086] The first evaluation result of the elevator health status is obtained based on the GA-BP neural network model.

[0087] In a specific embodiment, the expression for calculating the individual fitness value of the new population is:

[0088] ;

[0089] Where n is the number of nodes in the network output layer, is the actual output of the i-th node; is the expected output of the i-th node.

[0090] By optimizing the initial weights and thresholds of the BP neural network through genetic algorithms, the training efficiency and prediction performance of the neural network can be improved.

[0091] In a specific embodiment, if there are few data samples for certain fault types, the model may have poor recognition ability for these fault types. Therefore, in S300, a logical judgment model is constructed to obtain a second evaluation result of the elevator health status. Based on the real-time collected elevator operation data and the defined logical relationship, a logical judgment is performed to conclude whether the elevator has an abnormality. Specifically, it includes:

[0092] Define logical relationships to determine the health status of elevators;

[0093] Among them, propositions of logical relations include:

[0094] The relationship between normal operation and fault conditions;

[0095] The relationship between maintenance operation and operating status;

[0096] The relationship between emergency electric operation and fault handling;

[0097] The relationship between docking operations and loading and unloading of cargo;

[0098] The relationship between fire operations and fire emergency response;

[0099] As shown in Table 1, the defined logical relationships are organized into a rule base. Based on the rule base, a logical judgment model is obtained. The following rule base can be established to judge the health status of the elevator.

[0100] Table 1 Rule base statistics

[0101]

[0102] Rule base application: Embed a rule base in the elevator health assessment, and automatically trigger corresponding response actions by monitoring the elevator's multi-dimensional information in real time.

[0103] Rule base update: With the continuous advancement of elevator technology and the diversification of fault types, the rule base needs to be updated and improved regularly to ensure its accuracy and effectiveness.

[0104] Through the above steps, a complete set of elevator health status judgment logic and rule library can be established, which is conducive to the assessment of elevator health status and provides strong support for the safe operation and maintenance of elevators.

[0105] In a specific embodiment, the logic judgment model is expressed as P00N AND P00M;

[0106] Among them, P00N and P00M represent the true or false state of any two propositions of logical relationship.

[0107] Specifically, a logical AND operation is used. If there are 0s, the result is 0, and if all 1s are 1s, the result is 1. P00N and P00M represent the truth or falsehood of any two logical relationships between P001-P007 in the table above, that is, logical values ​​of 0 or 1 (1 indicates normal, 0 indicates abnormal). For example, P00NP is P001 = 1, and P00M is P003 = 0. Therefore, if the elevator is operating normally but detects slight noise or vibration, 1AND0 = 0, indicating an abnormality in the elevator's health, it needs to be stopped and switched to emergency electric operation or maintenance mode.

[0108] The logical judgment model in this embodiment is a dual-space model of logical judgments. It is actually a combination of a series of logical rules and judgments used to determine the health of an elevator based on multiple elevator status parameters. These logical rules can be mathematically represented using logical expressions and Boolean algebra, making them easier to understand and calculate. In practical applications, the construction of logical expressions and the selection of parameter values ​​need to be determined based on the specific elevator system and health diagnosis requirements.

[0109] Specifically, the GA-BP neural network model outputs a probability vector and selects the category with the highest probability as .

[0110] The logical judgment model directly outputs the category label (assuming it is 0 or 1)

[0111] In a specific embodiment, S400 includes:

[0112] The binary values ​​output by the logic judgment model are converted into continuous values ​​in the same range as the output of the GA-BP neural network model, for example by mapping 0 to a low score (such as 0) and 1 to a high score (such as 1, or more commonly, a floating point number indicating high confidence, such as 0.9).

[0113] The fusion result is calculated as the final elevator health diagnosis result, and the expression is:

[0114]

[0115] Where, Indicates the final elevator health diagnosis result, represents the first evaluation result of the elevator health status output by the GA-BP neural network model, The second evaluation result of the elevator health status after the logic judgment model is converted, represents the weight of the GA-BP neural network model, Represents the weight of the logical judgment model;

[0116] The fusion results are graded and used as the final elevator health status diagnosis results.

[0117] In a specific embodiment, the grading includes scoring the elevators into five major levels: one, two, three, four, and five, and judging whether there are any problems with the health status of the elevator throughout its life cycle and predicting the risks based on the score levels.

[0118] Through the above steps, the present invention combines the learning and prediction capabilities of a neural network with the intuitiveness and precision of logical judgment to improve the accuracy and efficiency of elevator abnormality diagnosis.

[0119] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0120] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the health status of an elevator based on a fusion network model, characterized in that: The following steps are involved: S100: Collect multi-dimensional data of the elevator to be tested and perform pre-processing; S200: constructing a BP neural network model, using a genetic algorithm (GA) as a strategy for optimizing hyperparameters, weights, and thresholds of the BP neural network model, obtaining a GA-BP neural network model, and obtaining a first evaluation result of the elevator health status based on the GA-BP neural network model; S300: constructing a logic judgment model to obtain a second evaluation result of the elevator health status; S400: Fusing the first evaluation result and the second evaluation result, grading the fusion result, and using the grading result as the final elevator health status diagnosis result; S300: constructing a logic judgment model to obtain a second evaluation result of the elevator health status, including: Define logical relationships to determine the health status of elevators; Among them, propositions of logical relations include: The relationship between normal operation and fault conditions; The relationship between maintenance operation and operating status; The relationship between emergency electric operation and fault handling; The relationship between docking operations and loading and unloading of cargo; The relationship between fire operations and fire emergency response; Organize the defined logical relationships into a rule base, and obtain a logical judgment model based on the rule base; The logical judgment model is expressed as P00N AND P00M; Among them, P00N and P00M represent the true or false state of any two propositions of logical relationship.

2. The elevator health status assessment method based on the fusion network model according to claim 1 is characterized in that: S100: Collect multi-dimensional data of the elevator to be tested and perform pre-processing, including: S110: Collecting elevator attribute information of the elevator to be tested; S120: Setting a sampling period, and collecting multi-dimensional data of the elevator to be tested based on the sampling period, wherein the multi-dimensional data includes: IoT monitoring information, maintenance information, and inspection and testing information; S130: Remove data records with missing values ​​or data that are obviously inconsistent with reality in the multidimensional data, and normalize the remaining data.

3. The elevator health status assessment method based on the fusion network model according to claim 2 is characterized in that: The S130 includes: Plot data lists according to multidimensional data; Perform column indexing on the data in the data list, traverse the missing values ​​in the row, determine the data integrity, and determine the integrity of the data in the data list; retaining data whose integrity is greater than or equal to a first preset threshold, and removing data whose integrity is less than the first preset threshold; Data records that are obviously inconsistent with reality are deleted from the data list by calculating a deviation value, wherein the deviation value is calculated based on an absolute value or a relative error. When the deviation value is greater than or equal to a second preset threshold, the data record is marked as inconsistent with reality and deleted; The remaining data were normalized.

4. The elevator health status assessment method based on a fusion network model according to claim 1 is characterized in that: The BP neural network model comprises an input layer, a hidden layer and an output layer; The number of nodes in the input layer is determined according to the number of features of the input data. The number of nodes in the output layer is 1, and the evaluation result of the elevator health status is output. The number of nodes in the hidden layer is obtained according to an empirical formula.

5. The elevator health status assessment method based on the fusion network model according to claim 4 is characterized in that: The empirical formula is: ； in, is the number of hidden layer nodes, is the number of input layer nodes, is the number of output layer nodes, and a is a constant between 1 and 10.

6. The elevator health status assessment method based on a fusion network model according to claim 1 is characterized in that: The genetic algorithm GA is used as a strategy to optimize the hyperparameters, weights and thresholds of the BP neural network model, and the GA-BP neural network model is obtained, including: Real number coding is used to establish the initial population, which consists of four parts: hidden layer threshold, connection weight between hidden layer and output layer, connection weight between input layer and hidden layer, and output layer threshold, as a set of chromosomes; Perform selection, crossover and mutation operations of the genetic algorithm to generate the next generation population, calculate the individual fitness values ​​of the new population, and take the minimum value as the optimal value; The optimized connection weights and thresholds are introduced into the BP neural network, and the normalized data are used as training samples for repeated training. Update the optimized connection weights, thresholds and network structure obtained through training to obtain a GA-BP neural network model; The first evaluation result of the elevator health status is obtained based on the GA-BP neural network model.

7. The elevator health status assessment method based on the fusion network model according to claim 6 is characterized in that: The expression for calculating the individual fitness value of the new population is: ； Where n is the number of nodes in the network output layer, is the actual output of the i-th node; is the expected output of the i-th node.

8. The elevator health status assessment method based on a fusion network model according to claim 1 is characterized in that: S400, including: The binary values ​​output by the logic judgment model are converted into continuous values ​​in the same range as those output by the GA-BP neural network model; Calculate the fusion result, the expression is: Where, Indicates the final elevator health diagnosis result, represents the first evaluation result of the elevator health status output by the GA-BP neural network model, The second evaluation result of the elevator health status after the logic judgment model is converted, represents the weight of the GA-BP neural network model, Represents the weight of the logical judgment model; The fusion results are graded and used as the final elevator health status diagnosis results.

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