An elevator operation and maintenance quality evaluation method and system based on artificial intelligence

Abstract

The present invention discloses an elevator operation and maintenance quality evaluation method and system based on artificial intelligence, the method comprising: obtaining elevator maintenance information, wherein the elevator maintenance information comprises: elevator operation failure rate, elevator passenger entrapment rate, average elevator rescue arrival time, elevator regular inspection pass rate, elevator due maintenance execution rate and user complaint rate; setting a first elevator maintenance quality evaluation model, and calculating a first quality evaluation value of elevator maintenance according to the elevator maintenance information, and setting a quality evaluation threshold, comparing the first quality evaluation value with the quality evaluation threshold, when exceeding the quality evaluation threshold, the elevator maintenance quality evaluation is qualified, and when lower than the quality evaluation threshold, the elevator maintenance quality evaluation is unqualified, and feeding back this information to the safety management department as soon as possible, and the relevant personnel of the safety management department urge the maintenance unit to make timely rectification according to the result, and impose punishment if the circumstance is serious.

Description

Technical Field

[0001] The present invention belongs to the technical field of elevator maintenance quality evaluation, and more specifically, relates to an elevator operation and maintenance quality evaluation method and system based on artificial intelligence. Background Art

[0002] The traditional evaluation method for elevator maintenance quality is that the Market Supervision and Administration Bureau organizes manpower to conduct spot checks on elevator projects to find out the arrival time of the elevator maintenance personnel, the completeness of the elevator safety technical files, the elevator safety performance, the elevator hygiene conditions, the elevator user experience, etc. This method requires a lot of manpower, and the spot check data has certain human and accidental factors, and cannot fully objectively and impartially evaluate the elevator maintenance quality.

[0003] Therefore, there is an urgent need for a technical solution that can intelligently and automatically evaluate the quality of elevator maintenance. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes an elevator operation and maintenance quality evaluation method based on artificial intelligence, comprising:

[0005] Obtaining elevator maintenance information, wherein the elevator maintenance information includes: elevator operation failure rate, elevator passenger entrapment rate, average elevator rescue arrival time, elevator regular inspection pass rate, elevator due maintenance execution rate and user complaint rate;

[0006] A first model for evaluating the quality of elevator maintenance is set, and a first quality evaluation value of the elevator maintenance is calculated based on the elevator maintenance information, and a quality evaluation threshold is set. The first quality evaluation value is compared with the quality evaluation threshold. When the quality evaluation threshold is exceeded, the elevator maintenance quality is evaluated as qualified; when the quality evaluation threshold is lower than the quality evaluation threshold, the elevator maintenance quality is evaluated as unqualified. This information is fed back to the safety management department as soon as possible, and the relevant personnel of the safety management department urge the maintenance unit to make timely rectifications based on the results, and impose penalties if the circumstances are serious.

[0007] Furthermore, the first elevator maintenance quality evaluation model includes:

[0008] ,

[0009] in, It is the first quality evaluation value of elevator maintenance. is the failure rate weight, is the elevator operation failure rate, is the trapped rate weight, is the elevator passenger trapped rate, is the arrival time weight, is the average arrival time of elevator rescue. is the pass rate weight, Regular inspection pass rate for elevators, is the elevator due maintenance execution rate, is the complaint rate weight, The user complaint rate.

[0010] Further, the first elevator maintenance quality evaluation model is optimized to generate a second elevator maintenance quality evaluation model, and the second quality evaluation value of elevator maintenance is calculated. and through the second quality evaluation value of the elevator maintenance The elevator maintenance quality is evaluated, and the second elevator maintenance quality evaluation model includes:

[0011] ,

[0012] in, is the random disturbance term of the elevator operation failure rate, It is the adjustment factor for the average arrival time of elevator rescue.

[0013] Furthermore, the random disturbance term of the elevator operation failure rate is include:

[0014] ,

[0015] in, is the first adjustment factor of the elevator operation failure rate, is the second adjustment factor for the elevator operation failure rate, It is the third adjustment factor for the elevator operation failure rate.

[0016] The present invention also proposes an elevator operation and maintenance quality evaluation system based on artificial intelligence, comprising:

[0017] An information acquisition module is used to acquire elevator maintenance information, wherein the elevator maintenance information includes: elevator operation failure rate, elevator passenger entrapment rate, average elevator rescue arrival time, elevator regular inspection pass rate, elevator due maintenance execution rate and user complaint rate;

[0018] The evaluation module is used to set a first model for evaluating the quality of elevator maintenance, and calculate a first quality evaluation value of the elevator maintenance based on the elevator maintenance information, and set a quality evaluation threshold, and compare the first quality evaluation value with the quality evaluation threshold. When the quality evaluation threshold is exceeded, the elevator maintenance quality is evaluated as qualified, and when the quality evaluation threshold is lower than the quality evaluation threshold, the elevator maintenance quality is evaluated as unqualified, and this information is fed back to the safety management department as soon as possible. The relevant personnel of the safety management department urge the maintenance unit to make timely rectifications based on the results, and impose penalties if the circumstances are serious.

[0019] Furthermore, the first elevator maintenance quality evaluation model includes:

[0020] ,

[0021] in, It is the first quality evaluation value of elevator maintenance. is the failure rate weight, is the elevator operation failure rate, is the trapped rate weight, is the elevator passenger trapped rate, is the arrival time weight, is the average arrival time of elevator rescue. is the pass rate weight, Regular inspection pass rate for elevators, is the elevator due maintenance execution rate, is the complaint rate weight, The user complaint rate.

[0022] Further, the first elevator maintenance quality evaluation model is optimized to generate a second elevator maintenance quality evaluation model, and the second quality evaluation value of elevator maintenance is calculated. and through the second quality evaluation value of the elevator maintenance The elevator maintenance quality is evaluated, and the second elevator maintenance quality evaluation model includes:

[0023] ,

[0024] in, is the random disturbance term of the elevator operation failure rate, It is the adjustment factor for the average arrival time of elevator rescue.

[0025] Furthermore, the random disturbance term of the elevator operation failure rate is include:

[0026] ,

[0027] in, is the first adjustment factor of the elevator operation failure rate, is the second adjustment factor for the elevator operation failure rate, It is the third adjustment factor for the elevator operation failure rate.

[0028] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0029] The present invention obtains elevator maintenance information, wherein the elevator maintenance information includes: elevator operation failure rate, elevator passenger entrapment rate, average arrival time of elevator rescue, elevator periodic inspection pass rate, elevator due maintenance execution rate and user complaint rate; sets a first model for elevator maintenance quality evaluation, and calculates a first quality evaluation value of elevator maintenance based on the elevator maintenance information, and sets a quality evaluation threshold, compares the first quality evaluation value with the quality evaluation threshold, and when the quality evaluation threshold is exceeded, the elevator maintenance quality is evaluated as qualified. The present invention can evaluate the elevator maintenance quality in multiple dimensions according to the above technical solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0031] Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0032] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0033] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0034] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0035] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0036] The display is used to show the interactive cross-section of each application.

[0037] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0038] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0039] Example 1

[0040] like Figure 1 As shown, an embodiment of the present invention provides an elevator operation and maintenance quality evaluation method based on artificial intelligence, comprising:

[0041] Step 101, obtaining elevator maintenance information, wherein the elevator maintenance information includes: elevator operation failure rate, elevator passenger entrapment rate, average elevator rescue arrival time, elevator regular inspection pass rate, elevator due maintenance execution rate and user complaint rate;

[0042] Step 102, setting a first model for evaluating the quality of elevator maintenance, and calculating a first quality evaluation value of elevator maintenance according to the elevator maintenance information, and setting a quality evaluation threshold, comparing the first quality evaluation value with the quality evaluation threshold, and when the first quality evaluation value exceeds the quality evaluation threshold, the elevator maintenance quality evaluation is qualified;

[0043] Specifically, the first model for evaluating the quality of elevator maintenance includes:

[0044] ,

[0045] in, It is the first quality evaluation value of elevator maintenance. is the failure rate weight, is the elevator operation failure rate, is the trapped rate weight, is the elevator passenger trapped rate, is the arrival time weight, is the average arrival time of elevator rescue. is the pass rate weight, Regular inspection pass rate for elevators, is the elevator due maintenance execution rate, is the complaint rate weight, The user complaint rate.

[0046] Specifically, the first elevator maintenance quality evaluation model is optimized to generate a second elevator maintenance quality evaluation model, and the second quality evaluation value of elevator maintenance is calculated. and through the second quality evaluation value of the elevator maintenance The elevator maintenance quality is evaluated, and the second elevator maintenance quality evaluation model includes:

[0047] ,

[0048] in, is the random disturbance term of the elevator operation failure rate, It is the adjustment factor for the average arrival time of elevator rescue.

[0049] Specifically, the random disturbance term of the elevator operation failure rate is include:

[0050] ,

[0051] in, is the first adjustment factor of the elevator operation failure rate, is the second adjustment factor for the elevator operation failure rate, It is the third adjustment factor for the elevator operation failure rate.

[0052] Step 103, when the quality is lower than the quality evaluation threshold, the elevator maintenance quality evaluation is unqualified, and this information is fed back to the safety management department as soon as possible. The relevant personnel of the safety management department urge the maintenance unit to make timely rectifications based on the results, and impose penalties if the circumstances are serious.

[0053] Specifically, all weights and adjustment factors are fitted by gradient descent method or ant colony algorithm.

[0054] Specifically, when the quality evaluation threshold is exceeded, the elevator maintenance quality is evaluated as qualified and recorded in the database;

[0055] When it is less than the quality evaluation threshold, the elevator maintenance quality evaluation is unqualified, and an alarm message is issued and recorded in the database.

[0056] Example 2

[0057] like Figure 2 As shown, the embodiment of the present invention also proposes an elevator operation and maintenance quality evaluation system based on artificial intelligence, including:

[0058] An information acquisition module is used to acquire elevator maintenance information, wherein the elevator maintenance information includes: elevator operation failure rate, elevator passenger entrapment rate, average elevator rescue arrival time, elevator regular inspection pass rate, elevator due maintenance execution rate and user complaint rate;

[0059] An evaluation module is used to set a first model for evaluating the quality of elevator maintenance, and calculate a first quality evaluation value of elevator maintenance according to the elevator maintenance information, and set a quality evaluation threshold, and compare the first quality evaluation value with the quality evaluation threshold. When the first quality evaluation value exceeds the quality evaluation threshold, the elevator maintenance quality is evaluated as qualified;

[0060] Specifically, the first model for evaluating the quality of elevator maintenance includes:

[0061] ,

[0062] in, It is the first quality evaluation value of elevator maintenance. is the failure rate weight, is the elevator operation failure rate, is the trapped rate weight, is the elevator passenger trapped rate, is the arrival time weight, is the average arrival time of elevator rescue. is the pass rate weight, Regular inspection pass rate for elevators, is the elevator due maintenance execution rate, is the complaint rate weight, The user complaint rate.

[0063] Specifically, the first elevator maintenance quality evaluation model is optimized to generate a second elevator maintenance quality evaluation model, and the second quality evaluation value of elevator maintenance is calculated. and through the second quality evaluation value of the elevator maintenance The elevator maintenance quality is evaluated, and the second elevator maintenance quality evaluation model includes:

[0064] ,

[0065] in, is the random disturbance term of the elevator operation failure rate, It is the adjustment factor for the average arrival time of elevator rescue.

[0066] Specifically, the random disturbance term of the elevator operation failure rate is include:

[0067] ,

[0068] in, is the first adjustment factor of the elevator operation failure rate, is the second adjustment factor for the elevator operation failure rate, It is the third adjustment factor for the elevator operation failure rate.

[0069] The assigned maintenance module is used to evaluate the elevator maintenance quality as unqualified when the quality is below the quality evaluation threshold, and to feed back this information to the safety management department as soon as possible. The relevant personnel of the safety management department urge the maintenance unit to make timely rectifications based on the results, and impose penalties if the circumstances are serious.

[0070] Example 3

[0071] An embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the elevator operation and maintenance quality evaluation method based on artificial intelligence.

[0072] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0073] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining elevator maintenance information, wherein the elevator maintenance information includes: elevator operation failure rate, elevator passenger trapped rate, average elevator rescue arrival time, elevator regular inspection pass rate, elevator due maintenance execution rate and user complaint rate;

[0074] Step 102, setting a first model for evaluating the quality of elevator maintenance, and calculating a first quality evaluation value of elevator maintenance according to the elevator maintenance information, and setting a quality evaluation threshold, comparing the first quality evaluation value with the quality evaluation threshold, and when the first quality evaluation value exceeds the quality evaluation threshold, the elevator maintenance quality evaluation is qualified;

[0075] Specifically, the first model for evaluating the quality of elevator maintenance includes:

[0076] ,

[0077] in, It is the first quality evaluation value of elevator maintenance. is the failure rate weight, is the elevator operation failure rate, is the trapped rate weight, is the elevator passenger trapped rate, is the arrival time weight, is the average arrival time of elevator rescue. is the pass rate weight, Regular inspection pass rate for elevators, is the elevator due maintenance execution rate, is the complaint rate weight, The user complaint rate.

[0078] Specifically, the first elevator maintenance quality evaluation model is optimized to generate a second elevator maintenance quality evaluation model, and the second quality evaluation value of elevator maintenance is calculated. and through the second quality evaluation value of the elevator maintenance The elevator maintenance quality is evaluated, and the second elevator maintenance quality evaluation model includes:

[0079] ,

[0080] in, is the random disturbance term of the elevator operation failure rate, It is the adjustment factor for the average arrival time of elevator rescue.

[0081] Specifically, the random disturbance term of the elevator operation failure rate is include:

[0082] ,

[0083] in, is the first adjustment factor of the elevator operation failure rate, is the second adjustment factor for the elevator operation failure rate, It is the third adjustment factor for the elevator operation failure rate.

[0084] Step 103, when the quality is lower than the quality evaluation threshold, the elevator maintenance quality evaluation is unqualified, and this information is fed back to the safety management department as soon as possible. The relevant personnel of the safety management department urge the maintenance unit to make timely rectifications based on the results, and impose penalties if the circumstances are serious.

[0085] Example 4

[0086] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, which can be loaded and executed by the processor so that the processor can execute an elevator operation and maintenance quality evaluation method based on artificial intelligence.

[0087] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0088] Among them, the storage medium can be used to store software programs and modules, such as an elevator operation and maintenance quality evaluation method based on artificial intelligence in an embodiment of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned elevator operation and maintenance quality evaluation method based on artificial intelligence. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0089] The processor can call the information and application program stored in the storage medium through the transmission system to execute the steps: Step 101, obtaining elevator maintenance information, wherein the elevator maintenance information includes: elevator operation failure rate, elevator passenger trapped rate, average elevator rescue arrival time, elevator regular inspection pass rate, elevator due maintenance execution rate and user complaint rate;

[0090] Step 102, setting a first model for evaluating the quality of elevator maintenance, and calculating a first quality evaluation value of elevator maintenance according to the elevator maintenance information, and setting a quality evaluation threshold, comparing the first quality evaluation value with the quality evaluation threshold, and when the first quality evaluation value exceeds the quality evaluation threshold, the elevator maintenance quality evaluation is qualified;

[0091] Specifically, the first model for evaluating the quality of elevator maintenance includes:

[0092] ,

[0093] in, It is the first quality evaluation value of elevator maintenance. is the failure rate weight, is the elevator operation failure rate, is the trapped rate weight, is the elevator passenger trapped rate, is the arrival time weight, is the average arrival time of elevator rescue. is the pass rate weight, Regular inspection pass rate for elevators, is the elevator due maintenance execution rate, is the complaint rate weight, The user complaint rate.

[0094] Specifically, the first elevator maintenance quality evaluation model is optimized to generate a second elevator maintenance quality evaluation model, and the second quality evaluation value of elevator maintenance is calculated. and through the second quality evaluation value of the elevator maintenance The elevator maintenance quality is evaluated, and the second elevator maintenance quality evaluation model includes:

[0095] ,

[0096] in, is the random disturbance term of the elevator operation failure rate, It is the adjustment factor for the average arrival time of elevator rescue.

[0097] Specifically, the random disturbance term of the elevator operation failure rate is include:

[0098] ,

[0099] in, is the first adjustment factor of the elevator operation failure rate, is the second adjustment factor for the elevator operation failure rate, It is the third adjustment factor for the elevator operation failure rate.

[0100] Step 103, when the quality is lower than the quality evaluation threshold, the elevator maintenance quality evaluation is unqualified, and this information is fed back to the safety management department as soon as possible. The relevant personnel of the safety management department urge the maintenance unit to make timely rectifications based on the results, and impose penalties if the circumstances are serious.

[0101] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0102] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0103] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0107] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. An elevator operation and maintenance quality evaluation method based on artificial intelligence, characterized in that: include: Obtaining elevator maintenance information, wherein the elevator maintenance information includes: elevator operation failure rate, elevator passenger entrapment rate, average elevator rescue arrival time, elevator regular inspection pass rate, elevator due maintenance execution rate and user complaint rate; A first elevator maintenance quality evaluation model is set, and according to the elevator maintenance information, a first quality evaluation value of the elevator maintenance is calculated, and a quality evaluation threshold is set, and the first quality evaluation value is compared with the quality evaluation threshold. When the first quality evaluation value exceeds the quality evaluation threshold, the elevator maintenance quality is evaluated as qualified; the first elevator maintenance quality evaluation model includes: ， in, It is the first quality evaluation value of elevator maintenance. is the failure rate weight, is the elevator operation failure rate, is the trapped rate weight, is the elevator passenger trapped rate, is the arrival time weight, is the average arrival time of elevator rescue. is the pass rate weight, Regular inspection pass rate for elevators, is the elevator due maintenance execution rate, is the complaint rate weight, User complaint rate; Optimize the first elevator maintenance quality evaluation model to generate a second elevator maintenance quality evaluation model, and calculate the second quality evaluation value of elevator maintenance and through the second quality evaluation value of the elevator maintenance The elevator maintenance quality is evaluated, and the second elevator maintenance quality evaluation model includes: ， in, is the random disturbance term of the elevator operation failure rate, The adjustment factor for the average arrival time of elevator rescue; Specifically, the random disturbance term of the elevator operation failure rate is include: ， in, is the first adjustment factor of the elevator operation failure rate, is the second adjustment factor for the elevator operation failure rate, It is the third adjustment factor for the elevator operation failure rate.

2. The elevator operation and maintenance quality evaluation method based on artificial intelligence according to claim 1, characterized in that: All weights and adjustment factors are fitted by gradient descent method or ant colony algorithm.

3. The elevator operation and maintenance quality evaluation method based on artificial intelligence according to claim 2, characterized in that: The first quality evaluation value of elevator maintenance and the second quality evaluation value of elevator maintenance Perform weighted average as the final quality evaluation value of elevator maintenance. When the final quality evaluation value of elevator maintenance exceeds the quality evaluation threshold, the elevator maintenance quality evaluation is qualified and recorded in the database. When the final quality evaluation value of the elevator maintenance is less than the quality evaluation threshold, the elevator maintenance quality evaluation is unqualified, and an alarm message is issued and recorded in the database.

4. An elevator operation and maintenance quality evaluation system based on artificial intelligence, characterized in that: include: An information acquisition module is used to acquire elevator maintenance information, wherein the elevator maintenance information includes: elevator operation failure rate, elevator passenger entrapment rate, average elevator rescue arrival time, elevator regular inspection pass rate, elevator due maintenance execution rate and user complaint rate; An evaluation module is used to set a first model for evaluating the quality of elevator maintenance, and calculate a first quality evaluation value of elevator maintenance according to the elevator maintenance information, and set a quality evaluation threshold, and compare the first quality evaluation value with the quality evaluation threshold. When the first quality evaluation value exceeds the quality evaluation threshold, the elevator maintenance quality is evaluated as qualified; The first model for evaluating the quality of elevator maintenance includes: ， in, It is the first quality evaluation value of elevator maintenance. is the failure rate weight, is the elevator operation failure rate, is the trapped rate weight, is the elevator passenger trapped rate, is the arrival time weight, is the average arrival time of elevator rescue. is the pass rate weight, Regular inspection pass rate for elevators, is the elevator due maintenance execution rate, is the complaint rate weight, User complaint rate; Optimize the first elevator maintenance quality evaluation model to generate a second elevator maintenance quality evaluation model, and calculate the second quality evaluation value of elevator maintenance and through the second quality evaluation value of the elevator maintenance The elevator maintenance quality is evaluated, and the second elevator maintenance quality evaluation model includes: ， in, is the random disturbance term of the elevator operation failure rate, The adjustment factor for the average arrival time of elevator rescue; Specifically, the random disturbance term of the elevator operation failure rate is include: ， in, is the first adjustment factor of the elevator operation failure rate, is the second adjustment factor for the elevator operation failure rate, It is the third adjustment factor for the elevator operation failure rate.

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