CN115818388A - Elevator operation condition adverse risk monitoring system - Google Patents
Elevator operation condition adverse risk monitoring system Download PDFInfo
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- CN115818388A CN115818388A CN202211649384.0A CN202211649384A CN115818388A CN 115818388 A CN115818388 A CN 115818388A CN 202211649384 A CN202211649384 A CN 202211649384A CN 115818388 A CN115818388 A CN 115818388A
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Abstract
The invention discloses an elevator operation condition adverse risk monitoring system.A lift control module collects risk assessment data in real time in the process of controlling the operation of a lift; the data uploading module uploads the risk assessment data to the database, the data processing module identifies whether adverse risks occur or not by using the risk assessment data in the database, pushes adverse risk prompt information to the maintenance terminal when the adverse risks exist, and identifies whether the adverse risks are eliminated or not according to maintenance operation information fed back by the maintenance terminal and the risk assessment data after maintenance is completed. The invention can reduce the manual intervention in the data acquisition stage, effectively obtain the improvement effect of the execution condition and the adverse risk of the maintenance operation, does not need to use an additional sensor, reduces the cost, and reduces the data processing amount and the data processing difficulty.
Description
Technical Field
The invention relates to the field of elevators, in particular to an elevator operation condition adverse risk monitoring system which is mainly based on analysis of elevator operation data.
Background
Elevators have become an integral part of the infrastructure in modern society and are the primary means of commuting between high-rise tower floors. Most elevators are frequently used, and many elevators are in an overload running state for a long time, so that once the elevators break down, inconvenience is brought to daily life and work of people, and even life safety is threatened. Therefore, the running state of the elevator is monitored, the abnormal condition can be found timely, and the serious fault of the elevator can be avoided.
In the existing elevator supervision system, as disclosed in the invention patent with publication number CN110921451A, a supervisor supervises each elevator operation terminal through a supervision server, and when the elevator operation terminal operates abnormally, the elevator operation terminal sends abnormal information (abnormal terminal operation data, elevator operation terminal 2ID data, and maintenance terminal 3ID data corresponding to the elevator operation terminal) to the supervision server, and a human-computer interaction module in the supervision server intervenes in uploading of the abnormal information according to instructions of the supervisor and only aims at data when the operation abnormality occurs. However, before the elevator reports the fault, the running state of the elevator has already been partially abnormal, and the method cannot find the problem in advance and prevent the problem in advance.
In another patent of the invention with publication number CN112390106A, data of each key part of the elevator is collected by data collecting equipment (various counters and sensors) and transmitted to a server for processing, and the key parts of the elevator and the car are monitored in real time by video monitoring equipment (internet monitoring equipment with behavior analysis function), so that the running state of each key part of the elevator and the running condition of the elevator are known in real time, and the elevator is subjected to preventive maintenance and fault early warning by comprehensively utilizing big data analysis. However, in the process of performing fault early warning on the elevator state, equipment such as video monitoring and the like needs to be used during data acquisition, so that the data acquisition cost is increased, and the data acquired by the camera is not the first-hand data of the elevator and is easily influenced by conditions such as illumination, visual field and the like. In addition, in the stage of verifying whether the maintenance personnel performs the maintenance operation, the method simply adopts the maintenance operation record of the maintenance personnel, but sometimes the maintenance personnel only checks the elevator and does not solve the cause of the risk, so that the risk monitoring event is not effectively solved.
Disclosure of Invention
The invention aims to provide an elevator operation condition adverse risk monitoring system, which can solve the problems that the data acquisition cost is high, the data is incomplete and the risk cannot be predicted in advance in the prior art.
In order to solve the technical problem, the invention provides an elevator operation condition bad risk monitoring system, which comprises:
the elevator control module is used for acquiring risk assessment data in real time in the process of controlling the operation of the elevator, wherein the risk assessment data are data samples which are required by identifying bad risks and are directly acquired from an elevator end, and the risk assessment data comprise at least one of elevator operation data and elevator component state data;
the data uploading module is used for uploading the risk assessment data acquired by the elevator control module to a server side;
the server side comprises a database, a data processing module and a data transmission module, wherein the database is used for storing the risk assessment data received by the data transmission module, the data processing module identifies whether adverse risks exist or not by using the risk assessment data in the database, pushes adverse risk prompt information to a maintenance terminal through the data transmission module when the adverse risks exist, and identifies whether the adverse risks are eliminated or not according to maintenance operation information fed back by the maintenance terminal and risk assessment data after maintenance is completed, and the adverse risk prompt information comprises risk levels and types of the adverse risks and corresponding improvement measures;
and the maintenance terminal is used for receiving the adverse risk prompt information by maintenance personnel, maintaining the elevator by the maintenance personnel according to the adverse risk prompt information received by the maintenance terminal, and feeding back the maintenance operation information to the server terminal after maintenance is finished.
Further, the data processing module comprises:
the data selecting unit is used for selecting risk evaluation data meeting the sample conditions;
and the data analysis unit identifies whether adverse risks occur or not based on the risk assessment data screened by the data selection unit, and generates adverse risk prompt information when the adverse risks exist.
Further, the database stores risk levels of the adverse risks, corresponding relations between the risk levels and the types of the adverse risks, corresponding relations between the types of the adverse risks and the risk assessment data, identification rules of each type of the adverse risks and corresponding improvement measures.
Further, the data processing module identifies the adverse risk according to the following steps:
step S1, the data selection unit judges whether the risk assessment data stored in the database meet sample conditions, if so, the step S2 is carried out, otherwise, the judgment is continued;
s2, screening out risk assessment data meeting sample conditions by the data selection unit;
s3, the data analysis unit judges whether adverse risks exist according to the screened risk assessment data, the corresponding relation between the adverse risk types stored in the database and the risk assessment data and the identification rule of each adverse risk, if so, the step S4 is carried out, otherwise, the step S1 is returned;
and S4, the data analysis unit generates the adverse risk prompt information according to the identified adverse risk, the corresponding relation between the risk grade and the category of the adverse risk stored in the database and the improvement measure corresponding to each adverse risk.
Further, the database also stores the corresponding relationship between the types of the bad risks and the risk levels.
Further, the adverse risk prompting message also comprises a risk grade of the adverse risk.
Further, the sample condition is that the accumulated online days of the elevator are greater than a set threshold of days and the data volume of the operation data uploaded every day is greater than a set threshold of data volume.
Further, the identification rule is to perform threshold comparison directly on the risk assessment data or perform statistical analysis on the risk assessment data or perform trend analysis on the risk assessment data.
Further, when the identification rule of the bad risk is to directly perform threshold comparison on the risk assessment data corresponding to the bad risk, comparing the maximum value in the screened risk assessment data corresponding to the bad risk with a set threshold, and when the maximum value exceeds the set threshold, judging that the bad risk exists in the current elevator.
Further, when the identification rule of the bad risk is to perform statistical analysis on the risk assessment data corresponding to the bad risk, comparing the screened risk assessment data corresponding to the bad risk with a set threshold, and counting the number of times that the risk assessment data exceeds the set threshold, and when the number of times that the risk assessment data exceeds the set threshold exceeds the number threshold, determining that the bad risk exists in the current elevator.
Further, when the identification rule of the adverse risk is to perform statistical analysis on the risk assessment data corresponding to the adverse risk, calculating a risk characterization index by using the screened risk assessment data corresponding to the adverse risk, comparing the risk characterization index with a set threshold value, and counting the number of times that the risk characterization index exceeds the set threshold value, when the number of times that the risk characterization index exceeds the set threshold value exceeds the number threshold value, determining that the adverse risk exists in the current elevator.
Further, when the identification rule of the adverse risk is to perform trend analysis on the risk assessment data corresponding to the adverse risk, calculating a risk characterization index by using the screened risk assessment data corresponding to the adverse risk, performing trend analysis on the risk characterization index, and when the trend analysis result meets the adverse risk occurrence condition, judging that the adverse risk exists in the current elevator.
Further, after the server receives the information of the maintenance operation, the data processing module judges whether the maintenance operation eliminates the adverse risk by using risk evaluation data stored in the database after the maintenance operation is completed for a set time.
Compared with the prior art, the invention has the beneficial technical effects that:
firstly, in the whole process from data acquisition to identification of whether the adverse risk exists and verification of whether the adverse risk is improved, manual intervention is only needed in a maintenance stage, so that manual intervention in the data acquisition stage can be reduced, field maintenance personnel do not need to interact with an elevator to acquire specified data, automatic data collection can be realized only according to logic preset by a program, the burden of the maintenance personnel is reduced, and the maintenance personnel do not need to perform tracking recording in the subsequent risk verification stage;
secondly, whether the maintenance personnel really perform effective maintenance operation can be seen from the data representation after the maintenance operation is completed, so that the improvement effects of the execution condition and the adverse risk of the maintenance operation can be effectively obtained, a single type verification channel relying on literal feedback information of the maintenance personnel in the prior art is optimized, the occurrence probability of risk events is effectively reduced, and the operation quality of the elevator is improved;
thirdly, the invention adopts a trigger type data collection strategy of the elevator operation stage, and the key information of each operation process of the elevator is uploaded to the database in real time, so that an additional sensor is not needed, the cost is reduced, and the data processing amount and the data processing difficulty are reduced.
Drawings
Fig. 1 is a schematic view of the elevator operation condition bad risk monitoring system of the present invention;
fig. 2 is a process flow diagram of an elevator operating condition unhealthy risk monitoring system of the present invention;
fig. 3a and 3b are schematic diagrams of trend results obtained by processing speed samples in the constant speed running stage of the elevator;
FIG. 4 is a schematic illustration of the identification of adverse risks using the present invention;
fig. 5a and 5b are schematic diagrams of non-trend adverse risks obtained by processing data samples in an elevator acceleration and deceleration running stage.
Detailed Description
Other advantages and effects of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein it is shown in the accompanying drawings, wherein the specific embodiments are by way of illustration. In the following description, specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced or applied in different embodiments, and the details may be based on different viewpoints and applications, and may be widely spread and replaced by those skilled in the art without departing from the spirit of the present invention.
Example one
The elevator operation condition bad risk monitoring system of the embodiment of the invention, as shown in figure 1, comprises:
the elevator control module is used for acquiring risk assessment data in real time in the process of controlling the operation of the elevator, wherein the risk assessment data are data samples which are required by identifying bad risks and are directly acquired from an elevator end, and the risk assessment data comprise at least one of elevator operation data and elevator component state data;
the data uploading module is used for uploading the risk assessment data acquired by the elevator control module to a server side;
the server side comprises a database, a data processing module and a data transmission module, wherein the database is used for storing the risk assessment data received by the data transmission module, the data processing module identifies whether adverse risks exist or not by using the risk assessment data in the database, pushes adverse risk prompt information to a maintenance terminal through the data transmission module when the adverse risks exist, and identifies whether the adverse risks are eliminated or not according to maintenance operation information fed back by the maintenance terminal and risk assessment data after maintenance is completed, and the adverse risk prompt information comprises the types of the adverse risks and corresponding improvement measures;
and the maintenance terminal is used for receiving the adverse risk prompt information by a maintenance worker, maintaining the elevator by the maintenance worker according to the adverse risk prompt information received by the maintenance terminal, and feeding back the maintenance operation information to the server after maintenance is completed.
Wherein the data processing module comprises:
the data selecting unit is used for selecting risk evaluation data meeting sample conditions;
and the data analysis unit identifies whether adverse risks occur or not based on the risk assessment data screened by the data selection unit, and generates adverse risk prompt information when the adverse risks exist.
In order to improve the reliability of the adverse risk evaluation index and the accuracy of the adverse risk prediction, the data selection unit screens accumulated data of the elevator running for a period of time, for example, screening according to the type of the guide shoe of the elevator and the online rate of the elevator, and the sample condition of the data sample is that the accumulated online days of the elevator are greater than a set threshold value of days and the data quantity of the running data uploaded every day is greater than a set threshold value of data quantity, so that the data sample depended on by the adverse risk evaluation result can be ensured to be sufficient and effective.
And after the server side receives the maintenance operation information, the data processing module judges whether the maintenance operation eliminates the adverse risk by using risk evaluation data which is stored in the database and has a set maintenance completion time length.
In the embodiment, the data acquisition stage, the identification of the bad risk and the verification of whether the bad risk is improved are carried out, and only the intermediate maintenance stage needs manual intervention, so that the manual intervention in the data acquisition stage is reduced, the field maintenance personnel is not required to interact with the elevator to acquire the specified data, the automatic data collection can be realized only according to the logic preset by the program, the burden of the maintenance personnel is reduced, and the tracking and recording of the maintenance personnel are not required in the subsequent risk verification stage. Moreover, in the embodiment, whether the maintenance personnel really perform the effective maintenance operation can be seen from the data representation after the maintenance operation is completed, so that the improvement effects of the execution condition and the adverse risk of the maintenance operation can be effectively obtained, a single type verification channel relying on literal feedback information of the maintenance personnel in the prior art is optimized, the occurrence probability of the risk event is effectively reduced, and the operation quality of the elevator is improved. In the embodiment, the key information of each operation process of the elevator is uploaded to the database of the server end in real time, an additional sensor is not needed, the cost is reduced, and the data processing amount and the data processing difficulty are reduced.
Example two
On the basis of the first embodiment, the present embodiment further describes how the data processing module identifies the adverse risk.
Specifically, the database further stores the types of the adverse risks, the corresponding relationship between the types of the adverse risks and the risk assessment data, the identification rule of each type of the adverse risks, and the corresponding improvement measures.
The elevator operation condition adverse risk monitoring system of the embodiment realizes the flow of adverse risk monitoring, as shown in fig. 2, that is, the step of identifying the adverse risk by the data processing module includes the following steps:
step S1, the data selection unit judges whether the risk assessment data stored in the database meet sample conditions, if so, the step S2 is carried out, otherwise, the judgment is continued;
s2, screening out risk assessment data meeting sample conditions by the data selection unit;
s3, the data analysis unit judges whether adverse risks exist according to the screened risk assessment data, the corresponding relation between the adverse risk types stored in the database and the risk assessment data and the identification rule of each adverse risk, if so, the step S4 is carried out, otherwise, the step S1 is returned;
and S4, the data analysis unit generates adverse risk prompt information according to the identified adverse risks and the improvement measures corresponding to each adverse risk stored in the database.
EXAMPLE III
On the basis of the second embodiment, the present embodiment mainly exemplifies how the data processing module processes the risk assessment data.
Wherein the identification rule of the bad risk is to directly perform threshold comparison on the risk assessment data. Specifically, the maximum value in the screened risk evaluation data corresponding to the adverse risk is compared with a set threshold value, and when the maximum value exceeds the set threshold value, the elevator is judged to have the adverse risk.
Elevators, in which an inverter is an important heat generating component in an elevator, necessarily generate heat during operation. When the elevator runs under a severe working condition, the inverter can generate a large amount of heat, if the heat cannot be dissipated in time, the elevator cannot be restarted, and therefore the risk of poor heat dissipation of the inverter exists in the running process of the elevator. At present, common reasons influencing the heat dissipation of an inverter in the elevator use field are faults of a heat dissipation fan and the like. The following describes an inverter in an elevator component in detail as an example.
In this embodiment, the elevator control module acquires the temperature of the inverter in the elevator running process in real time through the temperature sensor, and uploads the acquired inverter temperature data to the server side through the data uploading module, and the risk assessment data aiming at the adverse heat dissipation risk of the inverter at this time is the inverter temperature data. And when the inverter temperature data in the database meets the sample condition, the data processing module processes the inverter temperature data meeting the sample condition.
In this embodiment, there are two main types of identification rules for identifying the risk of poor heat dissipation of the inverter, specifically:
firstly, the data processing module selects the highest temperature from the inverter temperature data meeting the sample conditions, and when the highest temperature exceeds a set temperature threshold (the threshold can be set according to the product parameters of the inverter, can also be set according to the using environment of the elevator by manual experience, and can also be obtained by utilizing big data analysis), the elevator is judged to have the risk of poor heat dissipation of the inverter.
Secondly, the data processing module draws a temperature change curve by using inverter temperature data meeting sample conditions, performs gradient calculation according to the temperature change curve, and determines that the elevator has the risk of poor heat dissipation of the inverter when the difference value between the calculated gradient result and a set gradient threshold exceeds the set gradient difference threshold, wherein the gradient threshold and the gradient difference threshold can be set according to the product parameters of the inverter, can also be set according to the use environment of the elevator by manual experience, and can also be analyzed and set by using big data.
When the data processing module identifies that the inverter heat dissipation bad risk exists, the bad risk prompt information (including the inverter heat dissipation bad risk and corresponding improvement measures, such as checking and maintaining a heat dissipation fan) is pushed to a maintenance terminal of a maintenance worker. And the maintenance personnel operates according to the field condition and feeds back the maintenance operation information to the server side. The data processing module continuously monitors inverter temperature data within a period of time after maintenance operation is finished, judges whether the maximum temperature of the inverter and the gradient of a temperature change curve are improved (whether the related value is reduced within a certain range) again according to the two identification rules, and eliminates or continuously prompts the generated adverse risk according to the verification result. Of course, the data processing module may perform self-learning optimization on the set thresholds according to the verification result.
For another example, a traction machine brake is an important working component in an elevator, and the safety of the elevator is directly affected by the performance of the brake function. The contracting brake action response time can reflect the change condition of contracting brake performance to a certain extent, so that the contracting brake action response time is used as risk evaluation data of bad risks of contracting brake failure.
And the data processing module utilizes the contracting brake action response time of the elevator meeting the sample condition within a certain period of time and counts the maximum response time within the period of time. And comparing the maximum response time with a set response time threshold (obtained by analyzing the comprehensive industry standard and the big data), and if the maximum response time exceeds the set response time threshold, judging that the elevator has the bad risk of contracting brake failure.
When the bad risk of the brake failure occurs, the bad risk prompt information (including the bad risk of the brake failure and corresponding improvement measures, such as the maintenance of the brake) is pushed to a maintenance terminal of a maintenance worker. And the maintenance personnel operates according to the field condition and feeds back the maintenance operation information to the server side. And the data processing module continuously monitors the contracting brake response action time within a period of time after the maintenance operation is finished, judges whether the maximum response time of the contracting brake action is improved or not (whether the maximum response time is reduced to be below a threshold value or not), and eliminates or continuously prompts the generated adverse risk according to the result.
Example four
On the basis of the second embodiment, the present embodiment mainly exemplifies how the data processing module processes the risk assessment data.
Wherein the identification rule of the adverse risk is to perform statistical analysis on the risk assessment data. Specifically, the screened risk evaluation data corresponding to the adverse risk are compared with a set threshold, the number of times that the risk evaluation data exceed the set threshold is counted, and when the number of times that the risk evaluation data exceed the set threshold exceeds the number threshold, it is judged that the adverse risk exists in the current elevator. Or calculating a risk characterization index by using the screened risk evaluation data corresponding to the adverse risk, comparing the risk characterization index with a set threshold value, and counting the times of the risk characterization index exceeding the set threshold value, and when the times of the risk characterization index exceeding the set threshold value exceed a time threshold value, judging that the adverse risk exists in the current elevator.
The present embodiment further illustrates the identification of non-trending adverse risks (as shown in fig. 5a and 5 b).
In this embodiment, the elevator control module further collects speed data (acceleration data, current data, and position data) of the acceleration and deceleration stage in the elevator operation process, and calculates the acceleration and deceleration stagesThe speed data of the section is used as risk evaluation data, the data processing module processes the speed data meeting the sample condition, and a risk characterization index, namely a speed Root Mean Square Error (RMSE), is calculated according to the speed data spd . And the data processing module judges whether non-trend adverse risks exist according to the fluctuation condition of the risk characterization indexes, and when the non-trend adverse risks exist, the non-trend adverse risks and corresponding improvement measures are used as adverse risk prompt information to be pushed to the maintenance terminal.
Specifically, the risk characterization index is compared with a preset historical reference threshold, and when the fluctuation frequency of the risk characterization index exceeding the historical reference threshold in a set time period is greater than a set fluctuation threshold, it is determined that the elevator has a non-trend type bad risk.
Further, the database also stores the corresponding relationship between the types of the bad risks and the risk levels. Correspondingly, the adverse risk prompt message further comprises a risk grade of the adverse risk. For example, when the fluctuation times of the risk characterization indicator exceeding the historical reference threshold value in a set time period are larger than a set fluctuation threshold value, the non-trend type poor risk is judged to belong to a first type of poor risk, and when the fluctuation times of the risk characterization indicator exceeding the historical reference threshold value in the set time period are larger than zero but not larger than the set fluctuation threshold value, the non-trend type poor risk is judged to belong to a second type of poor risk.
EXAMPLE five
On the basis of the second embodiment, the present embodiment mainly exemplifies how the data processing module processes the risk assessment data.
Wherein the identification rule of the adverse risk is to perform trend analysis on the risk assessment data. Specifically, risk characterization indexes are calculated by utilizing the screened risk assessment data corresponding to the adverse risks, trend analysis is carried out on the risk characterization indexes, and when trend analysis results meet adverse risk occurrence conditions, the elevator is judged to have adverse risks.
The following describes the hoisting machine brake and the entire elevator in detail.
And as described in the third embodiment, the response time of the brake action is taken as risk assessment data of the bad risk of brake failure.
And the data processing module counts the average response time by utilizing the brake action response time of the elevator meeting the sample condition within a certain period of time. For example, an average response time is calculated according to response times of the brake operation for ten times, and a trend analysis method is used for performing trend judgment on the average response time of the brake operation. And when the trend result of the average response time of the brake action in the set time accords with the occurrence condition of the bad risk of brake failure (for example, the occurrence condition is the situations of irregular surge, rising of the response time trend and the like), judging that the elevator has the bad risk of brake failure.
When the bad risk of the brake failure occurs, the bad risk prompt information (including the bad risk of the brake failure and corresponding improvement measures, such as the maintenance of the brake) is pushed to a maintenance terminal of a maintenance worker. And the maintenance personnel operates according to the field condition and feeds back the maintenance operation information to the server side. The data processing module continuously monitors the contracting brake response action time within a period of time after the maintenance operation is finished, judges whether the average response time of the contracting brake action is improved (whether the average response time trend does not increase suddenly or slowly), and eliminates or continuously prompts the generated bad risks according to the result.
Aiming at the overall adverse risk of the elevator, the data processing module firstly calculates the risk characterization indexes of the uniform speed section according to the speed data and then carries out trend inspection on the risk characterization indexes of the uniform speed section. And the uniform speed section risk characterization index is a uniform speed section speed root mean square error.
Constant velocity segment risk characterization indicator (RMSE) spd ) There are three types of trend results, namely, an upward trend (as shown in fig. 3 a), a downward trend (as shown in fig. 3 b) and a no trend, and when the trend results are no trend, the elevator corresponding to the risk assessment data corresponding to the trend results is excluded from having a bad risk.
As shown in fig. 4, further, the data processing module further includes:
a seasonal factor comparison unit which determines a corresponding seasonal prediction trend from risk assessment data corresponding to the trend result when the trend result is an upward trend or a downward trend, and compares the trend result with the seasonal prediction trend;
and the data analysis unit identifies whether adverse risks exist according to the comparison result obtained by the seasonal factor comparison unit.
The seasonal factor comparison unit selects the month in which the number of digits in the day is located as a reference month in the risk assessment data corresponding to the trend result of the ascending trend or the descending trend, and takes the trend corresponding to the reference month as the seasonal prediction trend. The risk assessment indexes of the uniform speed section in each month have a preset monotonous trend, the seasonal prediction trend from 3 months to 9 months is a descending trend, and the seasonal prediction trend from 10 months to 2 months is an ascending trend.
When the trend result is an ascending trend and the trend result is inconsistent with the seasonal prediction trend corresponding to the risk assessment data, in order to ensure the reliability of the adverse risk assessment result and the rationality of the corresponding improvement measures, the elevator control module acquires the ambient temperature of the position where the elevator is located (for example, the ambient temperature in a hoistway or the ambient temperature in a region), and because the ventilation systems and the hoistway positions in different buildings are designed differently and the influence of the temperature control systems such as an air conditioner exists, the trend result may be increased. And the data processing module generates a third type of adverse risk (adverse risk A) and corresponding improvement measures according to the possible influence factors of the trend result caused by the environmental temperature analysis.
Similarly, when the trend result is an ascending trend and the trend result is consistent with the seasonal prediction trend corresponding to the risk assessment data, the elevator control module acquires the ambient temperature of the position where the elevator is located, and the data processing module analyzes possible influence factors causing the trend result according to the ambient temperature to generate a fourth type of adverse risk (adverse risk B) and corresponding improvement measures.
Similarly, when the trend result is a descending trend and the trend result is inconsistent with the seasonal predicted trend corresponding to the risk assessment data, the elevator control module acquires the ambient temperature of the position where the elevator is located, and the data processing module analyzes possible influence factors causing the trend result according to the ambient temperature to generate a fifth type of adverse risk (adverse risk C) and corresponding improvement measures.
When the trend result is a descending trend and the trend result is consistent with the seasonal prediction trend corresponding to the risk assessment data, the elevator corresponding to the data sample has no adverse risk.
Wherein the third type of adverse risk is ranked higher than the fourth type of adverse risk, and the second type of adverse risk is ranked higher than the fifth type of adverse risk.
After the maintenance personnel complete the maintenance operation aiming at the adverse risk, the data processing module recalculates the risk characterization index of the uniform velocity section, obtains a new trend result, and judges whether the maintenance operation effectively eliminates the adverse risk according to the new trend result, so that whether the maintenance operation is effectively completed can be judged. Compared with the prior art that the result evaluation cannot be carried out only by relying on the maintenance personnel to feed back the operation condition through the maintenance terminal, the method and the system can effectively reduce the occurrence probability of the risk event.
The present invention has been described in detail with reference to the specific embodiments, which are merely preferred embodiments of the present invention, and the present invention is not limited to the above embodiments. Equivalent substitutions and modifications of the type of risk assessment data, identification rules of adverse risk, etc., by those skilled in the art, without departing from the principles of the present invention, should be considered to be within the scope of the technology protected by the present invention.
Claims (13)
1. An elevator operating condition unhealthy risk monitoring system, comprising:
an elevator control module which collects risk assessment data in real time during the process of controlling the operation of the elevator, wherein the risk assessment data are data samples which are required for identifying adverse risks and are directly obtained from an elevator terminal, and the risk assessment data comprise at least one of elevator operation data and elevator component state data;
the data uploading module is used for uploading the risk assessment data acquired by the elevator control module to a server side;
the server side comprises a database, a data processing module and a data transmission module, wherein the database is used for storing the risk assessment data received by the data transmission module, the data processing module identifies whether adverse risks exist or not by using the risk assessment data in the database, pushes adverse risk prompt information to a maintenance terminal through the data transmission module when the adverse risks exist, and identifies whether the adverse risks are eliminated or not according to maintenance operation information fed back by the maintenance terminal and risk assessment data after maintenance is completed, and the adverse risk prompt information comprises the types of the adverse risks and corresponding improvement measures;
and the maintenance terminal is used for receiving the adverse risk prompt information by a maintenance worker, maintaining the elevator by the maintenance worker according to the adverse risk prompt information received by the maintenance terminal, and feeding back the maintenance operation information to the server after maintenance is completed.
2. The elevator operating condition unhealthy risk monitoring system according to claim 1, wherein said data processing module comprises:
the data selecting unit is used for selecting risk evaluation data meeting the sample conditions;
and the data analysis unit identifies whether adverse risks occur or not based on the risk assessment data screened by the data selection unit, and generates adverse risk prompt information when the adverse risks exist.
3. The system as claimed in claim 2, wherein the database further stores the type of the risk, the corresponding relationship between the type of the risk and the risk assessment data, the identification rule of each risk, and the corresponding improvement measure.
4. The elevator operating condition unhealthy risk monitoring system according to claim 3, wherein said data processing module identifies said unhealthy risk by:
step S1, the data selection unit judges whether the risk assessment data stored in the database meet sample conditions, if so, the step S2 is carried out, otherwise, the judgment is continued;
s2, screening out risk assessment data meeting sample conditions by the data selection unit;
s3, the data analysis unit judges whether adverse risks exist according to the screened risk assessment data, the corresponding relation between the adverse risk types stored in the database and the risk assessment data and the identification rule of each adverse risk, if so, the step S4 is carried out, otherwise, the step S1 is returned to;
and S4, the data analysis unit generates adverse risk prompt information according to the identified adverse risks and the improvement measures corresponding to each adverse risk stored in the database.
5. The system according to claim 3, wherein said database further stores a correspondence between a type of risk and a risk level.
6. The elevator operating condition adverse risk monitoring system of claim 5 wherein the adverse risk tips further include a risk level of adverse risk.
7. The elevator operating condition unhealthy risk monitoring system according to claim 2, wherein the sample condition is that an elevator cumulative number of online days is greater than a set threshold number of days and a data volume of the operational data uploaded daily is greater than a set threshold data volume.
8. The elevator operating condition poor risk monitoring system according to claim 3 or 4, wherein the identification rule is to perform a threshold comparison directly on the risk assessment data or to perform a statistical analysis on the risk assessment data or to perform a trend analysis on the risk assessment data.
9. The elevator operation condition adverse risk monitoring system according to claim 8, wherein when the identification rule of adverse risk is to directly perform threshold comparison on the risk assessment data corresponding to the adverse risk, the maximum value of the screened risk assessment data corresponding to the adverse risk is compared with a set threshold, and when the maximum value exceeds the set threshold, it is determined that the adverse risk exists in the current elevator.
10. The system according to claim 8, wherein when the identification rule of the bad risk is statistical analysis of the risk assessment data corresponding to the bad risk, the screened risk assessment data corresponding to the bad risk is compared with a set threshold, the number of times that the risk assessment data exceeds the set threshold is counted, and when the number of times that the risk assessment data exceeds the set threshold exceeds the number threshold, it is determined that the bad risk exists in the current elevator.
11. The system of claim 8, wherein when the identification rule of the bad risk is statistical analysis of the risk assessment data corresponding to the bad risk, the risk assessment data corresponding to the bad risk is used to calculate a risk characterization indicator, the risk characterization indicator is compared with a set threshold value, the number of times the risk characterization indicator exceeds the set threshold value is counted, and when the number of times the risk characterization indicator exceeds the set threshold value, the elevator is determined to have the bad risk.
12. The elevator operation condition adverse risk monitoring system according to claim 8, wherein when the identification rule of the adverse risk is to perform trend analysis on the risk assessment data corresponding to the adverse risk, the screened risk assessment data corresponding to the adverse risk is used to calculate a risk characterization index, the risk characterization index is subjected to trend analysis, and when the trend analysis result meets an adverse risk occurrence condition, it is determined that the adverse risk exists in the current elevator.
13. The elevator operation condition adverse risk monitoring system according to claim 1, wherein after the server receives the maintenance operation information, the data processing module determines whether the maintenance operation eliminates the adverse risk by using risk assessment data stored in the database after maintenance is completed for a set time.
Priority Applications (1)
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117032052A (en) * | 2023-10-07 | 2023-11-10 | 华能信息技术有限公司 | Security control method and system based on dynamic event |
| CN117032052B (en) * | 2023-10-07 | 2024-02-27 | 华能信息技术有限公司 | Security control method and system based on dynamic event |
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