An intelligent supervision and risk monitoring and early warning system for elevators

Abstract

The embodiment of the application relates to the elevator operation and maintenance technical field, and discloses an elevator intelligent supervision and risk monitoring and early warning system, the system comprises: an elevator intelligent supervision platform composed of a data cloud processing module and a maintenance quality examination module, and an elevator risk monitoring platform composed of an early warning and prevention module and a linkage disposal module; the data cloud processing module is used for regularly collecting basic information, basic operation data and historical fault data of all elevators accessed to the system, and performing data mining, big data cloud computing and statistical analysis to obtain analysis indexes; the maintenance quality examination module is used for performing maintenance quality examination based on the analysis indexes; the early warning and prevention module is used for performing early warning analysis on each elevator based on the examination results, and judging whether to perform early warning publishing; and the linkage disposal module is used for publishing early warning information to the elevators needing to perform early warning publishing, and contacting corresponding management departments to perform linkage disposal. The system greatly reduces the occurrence probability of elevator accidents.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of elevator operation and maintenance, and particularly relate to an elevator intelligent supervision and risk monitoring and early warning system. BACKGROUND

[0002] China's elevator ownership, annual output and annual growth are the largest in the world, and among all special equipment, the proportion of elevators is the highest, and is even rising year by year. Combined with China's population base and density, as well as the proportion of 10,000 special equipment A-class supervision personnel certificates, special equipment supervision personnel are severely insufficient, so it is necessary to establish a forward-looking supervision system that monitors risks, prevents early warning, and prevents problems from occurring to reduce safety hazards and avoid accidents.

[0003] In the direction of elevator supervision, the current solution focuses more on how to improve the Internet of Things high-tech sensing technology and how to achieve more efficient rescue in technology. For the field of risk monitoring and early warning prevention, although the general direction is pointed out, there is no specific implementation plan, quantitative indicators, indicative data and detailed measures.

[0004] In the direction of risk monitoring, the current mature elevator risk monitoring system mainly relies on Internet of Things intelligent terminal equipment, which can indeed achieve 24-hour sensing of elevator failure risk state, but the installation cost of Internet of Things equipment is relatively high, and it can only be popularized in a small range. In this way, elevator risk monitoring is only in a small range and does not have macro comprehensive reference value. SUMMARY

[0005] Therefore, embodiments of the present application propose an elevator intelligent supervision and risk monitoring and early warning system, which aims to form a four-level risk monitoring, risk research and judgment, early warning push, risk response and linkage disposal supervision mode, eliminate safety hazards and illegal behaviors in the bud, achieve the purpose of early warning prevention and risk control, and maximize the reduction of failures and accidents.

[0006] In order to achieve the above object, the embodiment of the present application proposes an elevator intelligent supervision and risk monitoring and early warning system, the system comprising an elevator intelligent supervision platform and an elevator risk monitoring platform, the elevator intelligent supervision platform consisting of a data cloud processing module and a maintenance quality assessment module, the elevator risk monitoring platform consisting of a warning and prevention module and a linkage disposal module; the data cloud processing module is used for regularly collecting basic information, basic operation data and historical failure data of all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system, performing data mining, big data cloud computing and statistical analysis based on the basic information, basic operation data and historical failure data of all elevators, and obtaining overall analysis indexes and all secondary classification analysis indexes; wherein the basic information of the elevator includes elevator brand, service life, elevator scale and working scene, the secondary classification is determined based on the basic information of the elevator, and the analysis indexes at least include emergency response rate, emergency arrival rate, average emergency arrival time, average failure rate, average person-trapping rate and average emergency rescue station quantity; the maintenance quality assessment module is used for performing maintenance quality assessment based on the overall analysis indexes and all secondary classification analysis indexes corresponding to each elevator, and obtaining assessment results corresponding to each elevator; the warning and prevention module is used for performing multi-dimensional four-level warning analysis on each elevator based on the assessment results corresponding to each elevator, and judging whether to issue a warning to the elevator; the linkage disposal module is used for issuing warning information to a target elevator needing to issue a warning, and contacting all management departments corresponding to the target elevator for linkage disposal.

[0007] In order to achieve the above object, the embodiment of the present application also proposes an elevator intelligent supervision and risk monitoring and early warning method, which is realized based on the above-mentioned elevator intelligent supervision and risk monitoring and early warning system, the method comprising: regularly collecting basic information, basic operation data and historical failure data of all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system, performing data mining, big data cloud computing and statistical analysis based on the basic information, basic operation data and historical failure data of all elevators, and obtaining overall analysis indexes and all secondary classification analysis indexes; wherein the basic information of the elevator includes elevator brand, service life, elevator scale and working scene, the secondary classification is determined based on the basic information of the elevator, and the analysis indexes at least include emergency response rate, emergency arrival rate, average emergency arrival time, average failure rate, average person-trapping rate and average emergency rescue station quantity; performing maintenance quality assessment based on the overall analysis indexes and all secondary classification analysis indexes corresponding to each elevator, and obtaining assessment results corresponding to each elevator; performing multi-dimensional four-level warning analysis on each elevator based on the assessment results corresponding to each elevator, and judging whether to issue a warning to the elevator; issuing warning information to a target elevator needing to issue a warning, and contacting all management departments corresponding to the target elevator for linkage disposal.

[0008] In order to achieve the above-mentioned purpose, the embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions can be executed by the at least one processor to enable the at least one processor to execute the elevator intelligent supervision and risk monitoring and early warning method as described above.

[0009] In order to achieve the above-mentioned purpose, the embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program can implement the elevator intelligent supervision and risk monitoring and early warning method as described above when executed by a processor.

[0010] The elevator intelligent supervision and risk monitoring and early warning system provided by the embodiment of the present application is equipped with an elevator intelligent supervision platform and an elevator risk monitoring platform, and integrates intelligent macro supervision, risk monitoring and early warning, statistical analysis and judgment, and subject evaluation. Through the use of data mining, big data cloud computing, statistical analysis, secondary classification, maintenance quality evaluation, and four-level early warning analysis, safety hazards, failure risks, and illegal behaviors can be eliminated in the bud, the purpose of early warning and prevention and risk control can be achieved, and the occurrence of elevator failures and accidents can be minimized. When performing maintenance quality evaluation, not only the overall analysis index is considered, but also all secondary classification analysis indexes of the elevator are considered. This design is closer to the actual use of the elevator, so that the subsequent four-level early warning analysis is more scientific and reasonable, and the linkage disposal can better solve actual problems. The system effectively improves the elevator safety management capability, strengthens the safety first consciousness of each enterprise subject, reduces the elevator accident rate, improves the failure disposal efficiency, effectively realizes the city large-scale elevator whole life cycle management, and effectively guarantees the safety of people's lives and the safety of enterprise production. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0012] Figure 1 is a structural schematic diagram of an elevator intelligent supervision and risk monitoring and early warning system provided in an embodiment of the present application;

[0013] Figure 2 is a structural schematic diagram of a data cloud processing module provided in an embodiment of the present application;

[0014] Figure 3 is a schematic diagram of the basis for maintenance quality assessment provided in an embodiment of the present application;

[0015] Figure 4 is a schematic diagram of the risk interval provided in an embodiment of the present application;

[0016] Figure 5 is a schematic diagram of the management department in linkage disposal provided in an embodiment of the present application;

[0017] Figure 6 is a flowchart of an elevator intelligent supervision and risk monitoring and early warning method provided in another embodiment of the present application;

[0018] Figure 7 is a structural schematic diagram of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0019] To make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below with reference to the drawings. However, those skilled in the art can understand that in the embodiments of the present application, many technical details are proposed in order to make the readers better understand the present application. However, the technical solutions claimed by the present application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the following embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present application, and the embodiments can be combined and referred to each other without contradiction.

[0020] An embodiment of the present application proposes an elevator intelligent supervision and risk monitoring and early warning system, and the implementation details of the elevator intelligent supervision and risk monitoring and early warning system proposed in the embodiment will be described in detail below. The following content only provides implementation details for the convenience of understanding, and is not necessary for implementing the present solution.

[0021] The specific structure of the elevator intelligent supervision and risk monitoring and early warning system proposed in the embodiment can be as shown in Figure 1 , which includes an elevator intelligent supervision platform 10 and an elevator risk monitoring platform 20. The elevator intelligent supervision platform 10 is composed of a data cloud processing module 11 and a maintenance quality assessment module 12, and the elevator risk monitoring platform 20 is composed of an early warning and prevention module 21 and a linkage disposal module 22.

[0022] The data cloud processing module 11 is configured to periodically collect basic information, basic operation data and historical failure data of all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system, and perform data mining, big data cloud computing and statistical analysis based on the basic information, basic operation data and historical failure data of all elevators to obtain overall analysis indexes and all secondary classification analysis indexes. The basic information of the elevator includes elevator brand, service life, elevator size and working scene, and the secondary classification is determined based on the basic information of the elevator. The analysis indexes at least include emergency response rate, emergency arrival rate, average emergency arrival time, average failure rate, average person-trapping rate and average number of emergency rescue stations.

[0023] The maintenance quality assessment module 12 is configured to perform maintenance quality assessment based on the overall analysis indexes and all secondary classification analysis indexes corresponding to each elevator to obtain an assessment result corresponding to each elevator.

[0024] The early warning and prevention module 21 is configured to perform multi-dimensional four-level early warning analysis on each elevator based on the assessment result corresponding to the elevator to determine whether to issue an early warning to the elevator.

[0025] The linkage disposal module 22 is configured to issue early warning information to a target elevator that needs to be issued with an early warning, and contact all management departments corresponding to the target elevator for linkage disposal.

[0026] The functions and components of the data cloud processing module 11, the maintenance quality assessment module 12, the early warning and prevention module 21 and the linkage disposal module 22 will be described in detail below.

[0027] The data cloud processing module 11 can be composed as shown in Figure 2 and includes a data collection unit 111, a data mining unit 112 and a big data cloud computing and statistical analysis unit 113.

[0028] The interface authentication and data collection unit 111 is configured to perform identity authentication on a new elevator that needs to be connected to the elevator intelligent supervision and risk monitoring and early warning system, allow the new elevator to be connected to the elevator intelligent supervision and risk monitoring and early warning system after the identity authentication is passed, and periodically collect basic information, basic operation data and historical failure data of all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system. It is necessary to perform identity authentication on a new elevator that needs to be connected to the elevator intelligent supervision and risk monitoring and early warning system, which can avoid intrusion of the elevator intelligent supervision and risk monitoring and early warning system by illegal persons, thereby providing intelligent, timely and safe services for all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system.

[0029] The elevator failure and the elevator accident are small probability events. For cost consideration, the interface authentication and data collection unit 111 collects the basic information, the basic operation data and the historical failure data of all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system according to a preset period. The preset period is generally set to one month, that is, the elevator intelligent supervision and risk monitoring and early warning system performs routine maintenance on all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system once a month.

[0030] The data mining unit 112 is used to classify all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system into two levels based on the basic information of the elevators. Then, the collected basic information, basic operation data and historical failure data of the elevators are cleaned by using regular expressions and other technologies, so as to output the basic information, basic operation data and historical failure data of the elevators in different formats into an elevator information table in a unified format.

[0031] It can be understood that the formats of the recorded basic information, basic operation data and historical failure data of elevators of different brands are likely to be different. Therefore, the collected data is not directly used, but is normalized in format by data cleaning, so as to store the basic information, basic operation data and historical failure data of all elevators in an elevator information table, facilitating subsequent big data cloud computing and statistical analysis.

[0032] It should be noted that the data mining unit 112 classifies the elevators into new elevators, healthy life time elevators and old elevators (for example, less than 1 year is classified as a new elevator, more than 1 year and less than 10 years is classified as a healthy life time elevator, and more than 10 years is classified as an old elevator) based on the service life, classifies the elevators into small elevators, medium elevators and large elevators (for example, less than 600 kg is classified as a small elevator, more than 600 kg and less than 1300 kg is classified as a medium elevator, and more than 1300 kg is classified as a large elevator) based on the elevator size, and classifies the elevators into commercial elevators, industrial elevators, sightseeing elevators, living elevators, office elevators and medical elevators based on the working scene. In addition, each brand corresponds to a two-level classification.

[0033] The big data cloud computing and statistical analysis unit 113 is used to perform big data cloud computing and statistical analysis based on the elevator information table, to obtain overall analysis indicators and all two-level classification analysis indicators. The analysis indicators at least include the emergency response rate, the emergency arrival rate, the average emergency arrival time, the average failure rate, the average person trapping rate and the average emergency rescue station quantity. As shown in Figure 3 These analysis indicators are also the basis for maintenance quality assessment.

[0034] The overall emergency response rate and the emergency response rate of each secondary classification are calculated by the following formula:

[0035] P JA =(N JTA / N HA )×100%;

[0036] P JB =(N JTB / N HB )×100%;

[0037] Wherein, P JA represents the overall emergency response rate, N HA represents the total number of emergency calls initiated by all elevators to the elevator intelligent supervision and risk monitoring and early warning system, N JTA represents the total number of emergency calls initiated by all elevators to the elevator intelligent supervision and risk monitoring and early warning system within 3 minutes, P JB represents the emergency response rate of the Bth secondary classification, N HB represents the total number of emergency calls initiated by all elevators in the Bth secondary classification to the elevator intelligent supervision and risk monitoring and early warning system, N JTB represents the total number of emergency calls initiated by all elevators in the Bth secondary classification to the elevator intelligent supervision and risk monitoring and early warning system within 3 minutes.

[0038] The overall emergency arrival rate and the emergency arrival rate of each secondary classification are calculated by the following formula:

[0039] P DA =(N DCA / N HA )×100%;

[0040] P DB =(N DCB / N HB )×100%;

[0041] Wherein, P DA represents the overall arrival rate, N DCA represents the number of emergency rescues arriving at the rescue site within 30 minutes for all elevators, P DB represents the arrival rate of the Bth secondary classification, N DCB represents the number of emergency rescues arriving at the rescue site within 30 minutes for all elevators in the Bth secondary classification.

[0042] The overall average emergency arrival time and the average emergency arrival time of each secondary classification are calculated by the following formula:

[0043]

[0044] T Di = T Xi -T Yi ;

[0045]

[0046] T Dk = T Xk -T Yk ;

[0047] wherein, T DA represents the average arrival time of the whole, T Di represents the time of arrival at the rescue site corresponding to the i-th emergency call for all elevators, T Xi represents the time of arrival at the rescue site corresponding to the i-th emergency call for all elevators, T Yi represents the time of occurrence of the i-th emergency call for all elevators, T DB represents the average arrival time of the B-th secondary classification, T Dk represents the time of arrival at the rescue site corresponding to the k-th emergency call for all elevators in the B-th secondary classification, T Xk represents the time of arrival at the rescue site corresponding to the k-th emergency call for all elevators in the B-th secondary classification, T Yi represents the time of occurrence of the k-th emergency call for all elevators in the B-th secondary classification.

[0048] The average failure rate of the whole and the average failure rate of each secondary classification are calculated by the following formula:

[0049] P WA = (N HA / N A ) x 100%;

[0050] P WB = (N HB / N B ) x 100%;

[0051] wherein, P WA represents the average failure rate of the whole, N A represents the total number of elevators accessing the intelligent supervision and risk monitoring and early warning system of elevators, P WB represents the average failure rate of the B-th secondary classification, N B represents the total number of elevators in the B-th secondary classification.

[0052] The average person-trapping rate of the whole and the average person-trapping rate of each secondary classification are calculated by the following formula:

[0053] PRA = (N HRA / N A ) x 100%;

[0054] P RB = (N HRB / N B ) x 100%;

[0055] wherein P RA represents the average rate of trapped people of the whole, N HRA represents the total number of emergency calls initiated by all elevators to the elevator intelligent supervision and risk monitoring and early warning system which are trapped people accident emergency calls, P RB represents the average rate of trapped people of the Bth secondary classification, N HRB represents the total number of emergency calls initiated by all elevators in the Bth secondary classification to the elevator intelligent supervision and risk monitoring and early warning system which are trapped people accident emergency calls.

[0056] The average number of emergency rescue stations of the whole and the average number of emergency rescue stations of each secondary classification are calculated by the following formula:

[0057]

[0058] wherein N RSA represents the average number of emergency rescue stations of the whole, N RSa represents the number of emergency rescue stations corresponding to the ath elevator, N RsB represents the average number of emergency rescue stations of the Bth secondary classification, N RSb represents the number of emergency rescue stations corresponding to the bth elevator in the Bth secondary classification.

[0059] Each elevator has four different dimension secondary classifications corresponding to elevator brand, service life, elevator size and working scene, which are respectively recorded as the first secondary classification, the second secondary classification, the third secondary classification and the fourth secondary classification. After the elevator intelligent supervision and risk monitoring and early warning system is built, the maintenance quality assessment module 12 needs to assign the first comprehensive weight, the second comprehensive weight, the third comprehensive weight and the fourth comprehensive weight to the four different dimension secondary classifications based on the importance of elevator brand, service life, elevator size and working scene, and assign the fifth comprehensive weight to the overall analysis index. It should be noted that the sum of the first comprehensive weight to the fourth comprehensive weight is not greater than the fifth comprehensive weight.

[0060] In addition to the comprehensive weight, the maintenance quality assessment module 12 also needs to assign the first reference weight, the second reference weight, the third reference weight, the fourth reference weight, the fifth reference weight and the sixth reference weight to the emergency response rate, the emergency arrival rate, the average emergency arrival time, the average failure rate, the average rate of trapped people and the average number of emergency rescue stations, respectively.

[0061] The maintenance quality assessment module 12 scores each overall analysis index and each secondary classification analysis index after receiving the overall analysis index and all secondary classification analysis indexes sent by the data cloud processing module 11, traverses each elevator, and based on the overall analysis index and all secondary classification analysis indexes to which the current elevator belongs, in combination with the first comprehensive weight, the second comprehensive weight, the third comprehensive weight, the fourth comprehensive weight, the fifth comprehensive weight, the first reference weight, the second reference weight, the third reference weight, the fourth reference weight, the fifth reference weight, and the sixth reference weight, performs maintenance quality assessment to obtain an assessment result of the current elevator, thereby obtaining an assessment result of each elevator.

[0062] Based on the overall analysis index and all secondary classification analysis indexes to which the current elevator belongs, in combination with the first comprehensive weight, the second comprehensive weight, the third comprehensive weight, the fourth comprehensive weight, the fifth comprehensive weight, the first reference weight, the second reference weight, the third reference weight, the fourth reference weight, the fifth reference weight, and the sixth reference weight, the maintenance quality assessment is performed to obtain the assessment result of the current elevator, which can be realized by the following formula:

[0063] S totat = η1S B1 + η2S B2 + η3S B3 + η4S B4 + η5S A ;

[0064] S A = ξ1S(P JA ) + ξ2S(P DA ) + ξ3S(T DA ) + ξ4S(P WA ) + ξ5S(P RA ) + ξ6S(N RSA ) ;

[0065] S B1 = ξ1S(P JB1 ) + ξ2S(P DB1 ) + ξ3S(T DB1 ) + ξ4S(P WB1 ) + ξ5S(P RB1 ) + ξ6S(N RSB1 ) ;

[0066] S B2 = ξ1S(P JB2 ) + ξ2S(P DB2 ) + ξ3S(T DB2 ) + ξ4S(P WB2 ) + ξ5S(PRB2 )+ξ6S(N RSB2 );

[0067] S B3 =ξ1S(P JB3 )+ξ2S(P DB3 )+ξ3S(T DB3 )+ξ4S(P WB3 )+ξ5S(P RB3 )+ξ6S(N RSB3 );

[0068] S B4 =ξ1S(P JB4 )+ξ2S(P DB4 )+ξ3S(T DB4 )+ξ4S(P WB4 )+ξ5S(P RB4 )+ξ6S(N RSB4 );

[0069] η1+η2+η3+η4+η5=1;

[0070] η1+η2+η3+η4≤η5;

[0071] ξ1+ξ2+ξ3+ξ4+ξ5+ξ6=1;

[0072] Among them, η1, η2, η3, η A η5 and η5 represent the first comprehensive weight, the second comprehensive weight, the third comprehensive weight, the fourth comprehensive weight, and the fifth comprehensive weight, respectively. B1 S B2 S B3 S B4 and S A S represents the scores for the first and second-level categories, the second and third-level categories, the third and fourth-level categories, and the overall score, respectively. total This represents the current assessment result of the elevator. ξ1, ξ2, ξ3, ξ4, ξ5 and ξ6 represent the first reference weight, the second reference weight, the third reference weight, the fourth reference weight, the fifth reference weight and the sixth reference weight, respectively. S(·) represents the scoring function.

[0073] It should be noted that the maintenance quality assessment module 12 needs to refer to the results of the secondary classification when scoring. For example, for medical elevators, the requirements for emergency response rate and emergency arrival rate are very high, while the requirements for emergency response rate and emergency arrival rate are lower for industrial elevators.

[0074] like Figure 4As shown, the early warning and prevention module 21 can divide the examination results of the elevators into five intervals, namely, green safety interval, blue risk interval, yellow risk interval, orange risk interval and red risk interval, based on the preset division threshold after the elevator intelligent supervision and risk monitoring early warning system is built. The interval into which the target elevator falls is determined based on S total whether to issue an early warning to the target elevator, and the level of the early warning issued to the target elevator. The level of the early warning is determined according to S total The intervals into which the target elevator falls are divided into blue early warning, yellow early warning, orange early warning and red early warning. The red early warning is the most serious, the orange early warning is the second, the yellow early warning is the third, and the blue early warning is the least serious early warning.

[0075] The linkage disposal module 22, when working, needs to issue corresponding early warning information to the target elevator that needs to be issued an early warning based on the early warning mode and the issue frequency corresponding to the level of the early warning, and contact all the management departments corresponding to the level of the early warning for linkage disposal. For the red early warning, the early warning mode is the most, the issue frequency is the highest, and the management departments that need to be disposed of are the highest. The specific management departments in the linkage disposal can be as shown in Figure 5 The city-level supervision department, the district and county-level supervision department, the local supervision department and the right ownership and use unit. For the blue early warning, only the right ownership and use unit needs to be linked.

[0076] The elevator intelligent supervision and risk monitoring early warning system proposed in this embodiment carries an elevator intelligent supervision platform and an elevator risk monitoring platform, integrates intelligent macro supervision, risk monitoring and early warning, statistical analysis and judgment, and main body examination and evaluation, and uses data mining, big data cloud computing, statistical analysis, secondary classification, maintenance quality examination, four-level early warning analysis and other technologies to eliminate safety hazards, fault risks and illegal behaviors in the bud, achieve the purpose of early warning and prevention, risk control, and maximize the reduction of elevator failures and accidents. When performing maintenance quality examination, not only the overall analysis index is considered, but also all the secondary classification analysis indexes of the elevator are considered. This design is closer to the actual use of the elevator, makes the subsequent four-level early warning analysis more scientific and reasonable, and the linkage disposal can better solve the actual problems. The system effectively improves the elevator safety management capability, strengthens the safety first consciousness of each enterprise subject, reduces the elevator accident rate, improves the fault disposal efficiency, effectively realizes the city large-scale elevator whole life cycle management, and effectively guarantees the safety of people's lives and the safety of enterprise production.

[0077] It is worth mentioning that each module and module involved in the embodiment is a logical module. In actual application, one logical unit can be one physical unit, or a part of one physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present application, units not closely related to solving the technical problems proposed in the present application are not introduced in the embodiment, but this does not mean that there are no other units in the embodiment.

[0078] Another embodiment of the present application proposes an elevator intelligent supervision and risk monitoring and early warning method, which is realized based on an elevator intelligent supervision and risk monitoring and early warning system as described in the above system embodiment. The implementation details of the elevator intelligent supervision and risk monitoring and early warning method proposed in the embodiment are described below. The following details are provided for easy understanding and are not essential for implementing the present solution.

[0079] The specific process of the elevator intelligent supervision and risk monitoring and early warning method proposed in the embodiment can be as shown in Figure 6

[0080] Step 31, periodically collect the basic information, basic operation data and historical failure data of all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system, and based on the basic information, basic operation data and historical failure data of all elevators, perform data mining, big data cloud computing and statistical analysis to obtain overall analysis indicators and all secondary classification analysis indicators.

[0081] In specific implementation, the basic information of the elevator includes elevator brand, service life, elevator size and working scene, the secondary classification is determined based on the basic information of the elevator, and the analysis indicators at least include emergency response rate, emergency arrival rate, average emergency arrival time, average failure rate, average person-trapping rate and average number of emergency rescue stations.

[0082] Step 32, based on the overall analysis indicators and all secondary classification analysis indicators corresponding to each elevator, perform maintenance quality assessment to obtain the assessment result corresponding to each elevator.

[0083] Step 33, based on the assessment result corresponding to each elevator, perform multi-dimensional four-level early warning analysis on each elevator respectively to determine whether to issue early warning to the elevator.

[0084] Step 34, issue early warning information to the target elevator that needs to be early warned, and contact all management departments corresponding to the target elevator for joint disposal.

[0085] ​The step division of the above various methods is only for the purpose of clear description, and can be combined into one step or split into multiple steps in implementation, as long as the same logical relationship is included, and the irrelevant modifications or irrelevant designs added to the algorithm or flow are within the protection scope of the present application, and do not change the core design of the algorithm and flow.

[0086] It can be found that the embodiment is a method embodiment corresponding to the above system embodiment, and the embodiment can be implemented in cooperation with the above system embodiment. The related technical details and technical effects mentioned in the above system embodiment are still valid in the embodiment. In order to reduce repetition, they will not be described again. Correspondingly, the related technical details mentioned in the embodiment can also be applied to the above system embodiment.

[0087] Another embodiment of the present application provides an electronic device, as shown in the figure, comprising: at least one processor 41; and a memory 42 connected with the at least one processor 41; wherein the memory 42 stores instructions executable by the at least one processor 41, and the instructions can be executed by the at least one processor 41 to enable the at least one processor 41 to perform an elevator intelligent supervision and risk monitoring and early warning method as described in the above method embodiment. Figure 7

[0088] Wherein the memory and the processor are connected in a bus mode, the bus can include any number of interconnected buses and bridges, and the bus connects one or more processors and memories and various circuits together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art, and therefore will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, which provide units for communicating with various other devices on the transmission medium.

[0089] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. And the memory can be used to store the data used by the processor in the execution of the operation.

[0090] Another embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement an elevator intelligent supervision and risk monitoring and early warning method as described in the above method embodiment.

[0091] ​That is, a person skilled in the art can understand that all or part of the steps in the above method embodiments can be completed by programs instructing related hardware, the programs are stored in a storage medium, and the programs include a plurality of instructions for causing an apparatus (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the elevator intelligent supervision and risk monitoring and early warning method described in the above method embodiments. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and various media that can store program codes.

[0092] A person of ordinary skill in the art can understand that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes can be made in form and details without departing from the spirit and scope of the present application.

Claims

1. An intelligent elevator supervision and risk monitoring and early warning system, characterized in that, include: The elevator intelligent supervision platform consists of a data cloud processing module and a maintenance quality assessment module, while the elevator risk monitoring platform consists of an early warning and prevention module and a joint response module. The data cloud processing module is used to periodically collect basic information, basic operating data, and historical fault data of all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system. Based on the basic information, basic operating data, and historical fault data of all elevators, it performs data mining, big data cloud computing, and statistical analysis to obtain overall analytical indicators and analytical indicators for all secondary categories. Among them, the basic information of the elevator includes elevator brand, service life, elevator size, and working scenario. The secondary categories are determined based on the basic information of the elevator. The analytical indicators include at least emergency response rate, emergency arrival rate, average emergency arrival time, average failure rate, average entrapment rate, and average number of emergency rescue stations. The maintenance quality assessment module is used to conduct maintenance quality assessments based on overall analysis indicators and analysis indicators for all secondary categories corresponding to each elevator, and to obtain the assessment results for each elevator. The early warning and prevention module is used to perform multi-dimensional, four-level early warning analysis on each elevator based on the assessment results of each elevator, and to determine whether to issue an early warning for the elevator. The coordinated response module is used to issue warning information to the target elevator that needs to be warned, and to contact all management departments corresponding to the target elevator for coordinated response. The data cloud processing module consists of an interface authentication and data collection unit, a data mining unit, and a big data cloud computing and statistical analysis unit. The docking authentication and data collection unit is used to authenticate the identity of new elevators that need to be connected to the elevator intelligent supervision and risk monitoring and early warning system. After the identity authentication is passed, the new elevator is allowed to connect to the elevator intelligent supervision and risk monitoring and early warning system, and the basic information, basic operation data and historical fault data of all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system are collected regularly. The data mining unit is used to perform secondary classification of all elevators connected to the intelligent elevator supervision and risk monitoring and early warning system based on the basic information of the elevators. It also uses regular expressions to clean the collected basic information, basic operation data and historical fault data of the elevators, and outputs the basic information, basic operation data and historical fault data of the elevators in different formats into a unified format elevator information table. Specifically, the data mining unit classifies elevators into new elevators, elevators with healthy service life and old elevators based on their service life, into small elevators, medium elevators and large elevators based on their size, and into commercial elevators, industrial elevators, sightseeing elevators, residential elevators, office elevators and medical elevators based on their work scenario. The Big Data, Cloud Computing and Statistical Analysis Unit is used to perform big data, cloud computing and statistical analysis based on elevator information tables to obtain overall analytical indicators and analytical indicators for all secondary categories.

2. The elevator intelligent supervision and risk monitoring and early warning system according to claim 1, characterized in that, The overall emergency response rate and the emergency response rates for each secondary category are calculated using the following formula: ； ； in, This indicates the overall emergency response rate. This represents the total number of emergency calls initiated by all elevators to the elevator intelligent supervision and risk monitoring and early warning system. This indicates the total number of emergency calls initiated by all elevators to the elevator intelligent supervision and risk monitoring and early warning system that were successfully connected and responded to within 3 minutes. Indicates the first Emergency response rate for each secondary category Indicates the first The total number of emergency calls initiated by all elevators in each secondary category to the elevator intelligent supervision and risk monitoring and early warning system. Indicates the first The total number of emergency calls initiated by all elevators in each secondary category to the elevator intelligent supervision and risk monitoring and early warning system that were connected and successfully responded to within 3 minutes; The overall emergency response rate and the emergency response rate for each secondary category are calculated using the following formula: ； ； in, Indicates the overall arrival rate. This indicates the number of emergency rescues that can reach the scene within 30 minutes for all elevators. Indicates the first Reach rate of each secondary category Indicates for the first The number of emergency rescues that arrive at the rescue site within 30 minutes for all elevators in each secondary category; The overall average emergency response time and the average emergency response time for each sub-category are calculated using the following formula: ； ； ； ； in, This represents the overall average arrival time. This indicates that for all elevators, the first... The arrival time at the rescue site corresponding to each emergency call This indicates that for all elevators, the first... The arrival time at the rescue site corresponds to each emergency call. This indicates that for all elevators, the first... The moment an emergency call occurs Indicates the first Average arrival time for each secondary category Indicates for the first For all elevators in the second-level category, the first The arrival time at the rescue site corresponding to each emergency call Indicates for the first For all elevators in the second-level category, the first The arrival time at the rescue site corresponds to each emergency call. Indicates for the first For all elevators in the second-level category, the first The moment an emergency call occurs; The overall average failure rate and the average failure rate for each secondary category are calculated using the following formula: ； ； in, This represents the overall average failure rate. This indicates the total number of elevators connected to the elevator intelligent supervision and risk monitoring and early warning system. Indicates the first Average failure rate of each secondary category Indicates the first The total number of elevators in each secondary category; The overall average entrapment rate and the average entrapment rate for each secondary category are calculated using the following formula: ； ； in, This represents the overall average slump rate. This indicates the total number of emergency calls initiated by all elevators to the elevator intelligent supervision and risk monitoring and early warning system for entrapment accidents. Indicates the first The average poverty rate in each secondary category Indicates the first The total number of emergency calls initiated by all elevators in each secondary category to the elevator intelligent supervision and risk monitoring and early warning system is the total number of emergency calls for entrapment accidents. The overall average number of emergency response stations and the average number of emergency response stations in each secondary category are calculated using the following formula: ； ； in, This indicates the overall average number of emergency response stations. Indicates the first Number of emergency rescue stations per elevator Indicates the first The average number of emergency rescue stations in each secondary category Indicates the first The first of the second-level categories The number of emergency rescue stations corresponding to each elevator.

3. The elevator intelligent supervision and risk monitoring and early warning system according to claim 2, characterized in that, Each elevator is categorized into four secondary categories based on its brand, service life, size, and operating scenario, referred to as the first, second, third, and fourth secondary categories, respectively. After the elevator intelligent supervision and risk monitoring and early warning system is established, the maintenance quality assessment module assigns a first, second, third, and fourth comprehensive weight to each of the four secondary categories based on the importance of elevator brand, service life, size, and operating scenario. A fifth comprehensive weight is assigned to the overall analysis indicators, which are then assigned a first reference weight, second reference weight, third reference weight, fourth reference weight, fifth reference weight, and sixth reference weight, respectively, for emergency response rate, emergency arrival rate, average emergency arrival time, average failure rate, average entrapment rate, and average number of emergency rescue stations. The sum of the first to fourth comprehensive weights does not exceed the fifth comprehensive weight. After receiving the overall analysis indicators and the analysis indicators of all secondary categories, the maintenance quality assessment module scores each overall analysis indicator and each secondary category analysis indicator. It then iterates through each elevator, and based on the overall analysis indicators and the analysis indicators of all secondary categories to which the current elevator belongs, it combines the first comprehensive weight, the second comprehensive weight, the third comprehensive weight, the fourth comprehensive weight, the fifth comprehensive weight, the first reference weight, the second reference weight, the third reference weight, the fourth reference weight, the fifth reference weight, and the sixth reference weight to conduct a maintenance quality assessment, thereby obtaining the assessment result for the current elevator, and thus obtaining the assessment result for each elevator.

4. The elevator intelligent supervision and risk monitoring and early warning system according to claim 3, characterized in that, Based on the overall analysis indicators and the analysis indicators of all secondary categories to which the current elevator belongs, and combined with the first comprehensive weight, second comprehensive weight, third comprehensive weight, fourth comprehensive weight, fifth comprehensive weight, first reference weight, second reference weight, third reference weight, fourth reference weight, fifth reference weight, and sixth reference weight, the maintenance quality assessment is carried out to obtain the assessment result of the current elevator, which is achieved through the following formula: ； ； ； ； ； ； ； ； ； in, , , , and These represent the first, second, third, fourth, and fifth comprehensive weights, respectively. , , , and These represent the scores for the first and second-level categories, the second and third-level categories, the third and fourth-level categories, and the overall score, respectively. This indicates the current assessment results for the elevator. , , , , and These represent the first reference weight, second reference weight, third reference weight, fourth reference weight, fifth reference weight, and sixth reference weight, respectively. This represents the scoring function.

5. The elevator intelligent supervision and risk monitoring and early warning system according to claim 4, characterized in that, After the elevator intelligent supervision and risk monitoring and early warning system is built, the early warning and prevention module divides the elevator's assessment results into five intervals based on preset thresholds: green safety interval, blue risk interval, yellow risk interval, orange risk interval, and red risk interval. The system determines whether to issue a warning for the target elevator based on the range it falls into, and then determines the warning level for the target elevator. The warning level is determined according to... The affected area is divided into blue, yellow, orange, and red alert levels.

6. The elevator intelligent supervision and risk monitoring and early warning system according to claim 5, characterized in that, The coordinated response module is used to issue corresponding warning information to the target elevator that needs to issue a warning, based on the warning method and issuance frequency corresponding to the warning level, and to contact all management departments of the target elevator corresponding to the warning level for coordinated response.

7. A method for intelligent supervision and risk monitoring and early warning of elevators, characterized in that, Based on the elevator intelligent supervision and risk monitoring and early warning system as described in any one of claims 1 to 6, the method includes: Regularly collect basic information, basic operating data, and historical fault data of all elevators connected to the elevator intelligent supervision and risk monitoring and early warning system. Based on the basic information, basic operating data, and historical fault data of all elevators, conduct data mining, big data cloud computing, and statistical analysis to obtain overall analytical indicators and analytical indicators for all secondary categories. Among them, the basic information of the elevator includes elevator brand, service life, elevator size, and working scenario. The secondary categories are determined based on the basic information of the elevator. The analytical indicators include at least the emergency response rate, emergency arrival rate, average emergency arrival time, average failure rate, average entrapment rate, and average number of emergency rescue stations. Based on the overall analysis indicators and the analysis indicators of all secondary categories corresponding to each elevator, the maintenance quality is assessed to obtain the assessment results for each elevator. Based on the assessment results for each elevator, a multi-dimensional, four-level early warning analysis is conducted for each elevator to determine whether an early warning should be issued. Issue warning information to the target elevator that requires warning, and contact all relevant management departments of the target elevator for joint handling.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which enable the at least one processor to perform the elevator intelligent supervision and risk monitoring and early warning method as described in claim 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it can realize the elevator intelligent supervision and risk monitoring and early warning method as described in claim 7.

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