Elevator maintenance system with camera maintenance record query function

Abstract

The invention discloses an elevator maintenance system with a camera maintenance record query function. The elevator maintenance system comprises a mobile phone mobile terminal, a user remote computer and a maintenance master controller. A maintenance master controller maintenance plan generation module, a record storage module and a maintenance record query module are arranged in the maintenance master controller; the record storage module comprises a storage unit and a maintenance record acquisition unit, the storage unit is used for storing maintenance record information, the maintenance record information comprises a maintenance picture, maintenance time, maintenance personnel information and fault information, and the fault information comprises a fault name, a fault rating, fault occurrence time and weather temperature when a fault occurs; and the maintenance record query module is used for the mobile phone mobile terminal and the user remote computer to query the maintenance record information. According to the elevator maintenance record query system based on the camera, the problem that in the prior art, maintenance record photos cannot be checked due to the fact that the record that maintenance personnel arrive at the site cannot be shot is solved.

Description

Technical Field

[0001] The present invention relates to the field of elevator maintenance, and particularly to an elevator maintenance system based on a camera with a function of querying camera maintenance records. Background Art

[0002] A Chinese patent discloses an elevator maintenance operation control method based on an electronic fence and position positioning with an application number of CN202410288842.5. This elevator maintenance operation control method based on an electronic fence and position positioning includes: after the elevator is installed, the geographical location information of the elevator is entered into the cloud server to determine the actual geographical location of the elevator; an electronic fence area is set within a certain range of the actual geographical location of the elevator; when the maintenance personnel enter the maintenance information through the maintenance APP of the mobile terminal, the maintenance APP collects the position information of the maintenance personnel through the mobile terminal for positioning and detects whether the wireless communication with the cloud server is normal; if the wireless communication is normal and the current position is within the electronic fence area, the maintenance information entered by the maintenance personnel is uploaded to the cloud server; if the wireless communication is abnormal and the current position is within the electronic fence area, the time information, positioning information, and maintenance information at the time of entry by the maintenance personnel are cached and uploaded to the cloud server after the communication is normal; if the current position is not within the electronic fence area, the maintenance information entered by the maintenance personnel is not entered.

[0003] Although this elevator maintenance operation control method based on an electronic fence and position positioning can achieve the entry of maintenance records, the disadvantages still existing in this elevator maintenance operation control method based on an electronic fence and position positioning are: it cannot take pictures of the records of the maintenance personnel arriving at the scene, cannot view the maintenance record photos, and cannot meet the requirements of some owners. Summary of the Invention

[0004] The present invention aims to provide an elevator maintenance record query system based on a camera to solve the problem in the prior art that the records of the maintenance personnel arriving at the scene cannot be taken, resulting in the inability to view the maintenance record photos.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention discloses an elevator maintenance system with a function of querying camera maintenance records, including: a mobile phone mobile terminal, a user remote computer, and a maintenance general controller; the mobile phone mobile terminal and the user remote computer are both communicatively connected to the maintenance general controller; a maintenance plan generation module, a record storage module, and a maintenance record query module are provided in the maintenance general controller; the record storage module includes: a storage unit and a maintenance record acquisition unit, the storage unit is used to store maintenance record information, and the maintenance record information includes: maintenance photos, maintenance time, maintenance personnel information, and fault information, the maintenance record acquisition unit is used to obtain maintenance record information from the mobile phone mobile terminal, the mobile phone mobile terminal obtains maintenance photos through a camera, the fault information includes: fault name, fault rating, fault occurrence time, and weather temperature at the time of fault occurrence, and the weather temperature at the time of fault occurrence obtains the daily weather temperature from the mobile phone mobile terminal; the maintenance plan generation module is used to generate a maintenance plan according to a fault prediction model and push the maintenance plan to the mobile phone mobile terminal and the user remote computer; the maintenance record query module is used to provide the mobile phone mobile terminal and the user remote computer to query the maintenance record information.

[0007] Preferably, the maintenance plan generation module is used to automatically generate a maintenance plan according to the maintenance record information, including the following steps:

[0008] S1. Classify the fault name as a component fault, and the component faults include: door machine fault, safety gear fault, wire rope fault, and traction machine fault;

[0009] S2. Perform data processing on each type of component fault to obtain the processed cycle data: S21. Let the time of each type of component fault be (t 11 , t 12 , ……, t 1m ), and m is the number of faults; S22. Calculate the fault time interval t 2i , t 2i = t 1i - t 1i-1 , where i≥2 in this formula; S23. Calculate the fault interval period t 3i , and m - 1 fault interval periods t 3i form the cycle data T0, T0 = {t 32 , t 33 , …, t 3i , …t 3m} Let the single cycle be t0, then means that: when is a decimal, when the first digit of the decimal is greater than 0.5, take the smallest integer greater than ; when the first digit of the decimal is less than 0.5, take the largest integer less than ;

[0010] S3. Process the periodic data T0 using the GM(1,1) model to obtain a fault prediction model.

[0011] Preferably, step S3 includes the following steps:

[0012] S31. Let t0 = 1;

[0013] S32. Substitute the periodic data T0 into the grey GM(1,1) model to obtain the first fitted fault time series Y. Let the first fitted fault time series Y = {y(1), y(2), …, y(i), y(m)}, where y(i) is the first fitted fault time at the i-th frequency.

[0014] S33. Calculate the first residual sequence R1 between the fitted fault time series Y and the periodic data T0. R1 = {r1(2), r1(3), …, r1(i), r1(m)}, where r1(i) represents the fitting residual data between the fault interval period t3i at the i-th frequency and the corresponding fitted sequence data. Calculate the average value of the fitting residual data r1(i) in the first residual sequence R1.

[0015] S33. Establish a corrected residual sequence R2. R2 = {r2(2), r2(3), …, r2(i), r2(m)}, where r1(i) represents the corrected residual data at the i-th frequency.

[0016] S34. Use the first residual sequence R1 to calculate the parameters a0, a1, a2, and a3 by the least squares method.

[0017] S35. Let A(0 + t0) = a0, A(1 + t0) = a1, A(2 + t0) = a2, A(3 + t0) = a3 to obtain the corrected residual data in the t0-th corrected residual sequence R2.

[0018] S36. Calculate the average value of the corrected residual data r2(i) in the t0-th corrected residual sequence R2.

[0019] S37. Calculate t0 = t0 + 1 and determine whether t 3i *t0 is less than the minimum t 2i , if yes, then perform step S34; if no, then perform step S38;

[0020] S38. Find the smallest corrected residual sequence R2 among all the corrected residual sequences R2, and let the smallest corrected residual sequence R2 be the second residual sequence R3. Let the corrected periodic data W0 be equal to ​ The fault interval period t corresponding to the m - 1th ith frequency number in the minimum single period t0 3i constitute the period data T0;

[0021] S39. Substitute the corrected period data W0 into the grey GM(1,1) model to obtain the second - fitting fault time series Q. Let the second - fitting fault time series Q={q(1),q(2),…,q(i),q(m)}, where q(i) is the second - fitting fault time at the ith frequency number. The second - fitting fault time series Q is the fault prediction model.

[0022] Preferably, step S34 includes the following steps:

[0023] S341. Let [a0,a1,a2,a3] T =(B T B) -1 B T R1 T (Formula 1), where

[0024]

[0025] S342. Substitute the fitting residual data of R1 into Formula 1 and use the least - squares method to obtain the parameters a0, a1, a2, and a3.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] In this application, maintenance photos are obtained through the mobile rod terminal of the mobile phone, which is convenient for the owner to view real and reliable maintenance photos. At the same time, the input of maintenance information is established, which is convenient for the maintenance company to manage maintenance.

[0028] Other advantages, objectives, and features of the present invention will be partially reflected by the following description and partially understood by those skilled in the art through the research and practice of the present invention. Specific Embodiments

[0029] In order to make the technical means, creative features, achieved objectives, and functions of the present invention clearer and easier to understand, the present invention will be further described below in conjunction with specific embodiments.

[0030] The present invention discloses an elevator maintenance system with a function of querying camera maintenance records, including: a mobile phone terminal, a user remote computer, and a maintenance general controller; the mobile phone terminal and the user remote computer are both communicatively connected to the maintenance general controller; a maintenance plan generation module, a record storage module, and a maintenance record query module are provided in the maintenance general controller; the record storage module includes: a storage unit and a maintenance record acquisition unit, the storage unit is used to store maintenance record information, and the maintenance record information includes: maintenance photos, maintenance time, maintenance personnel information, and fault information, the maintenance record acquisition unit is used to obtain maintenance record information from the mobile phone terminal, the mobile phone terminal obtains maintenance photos through a camera, and the fault information includes: fault name, fault rating, fault occurrence time, and weather temperature at the time of fault occurrence, and the weather temperature at the time of fault occurrence obtains the daily weather temperature from the mobile phone terminal; the maintenance plan generation module is used to generate a maintenance plan according to a fault prediction model and push the maintenance plan to the mobile phone terminal and the user remote computer; the maintenance record query module is used to provide the mobile phone terminal and the user remote computer to query the maintenance record information.

[0031] The maintenance plan generation module is used to automatically generate a maintenance plan according to the maintenance record information, including the following steps:

[0032] S1. Classify the fault name as a component fault, and the component faults include: door machine fault, safety gear fault, wire rope fault, and traction machine fault;

[0033] S2. Perform data processing on each type of component fault to obtain the processed cycle data: S21. Let the time of each type of component fault be (t 11 , t 12 , ……, t 1m ), and m is the number of faults; S22. Calculate the fault time interval t 2i , t 2i = t 1i - t 1i-1 , where i ≥ 2 in this formula; S23. Calculate the fault interval period t 3i , and m - 1 fault interval periods t 3i at the i-th frequency form the cycle data T0, T0 = {t 32 , t 33 , …, t 3i , …t 3m}. Let the single cycle be t0, then represents: when is a decimal, when the first digit of the decimal is greater than 0.5, take the smallest integer greater than ; when the first digit of the decimal is less than 0.5, take the largest integer less than ;

[0034] S3. Process the periodic data T0 using the GM(1,1) model to obtain a fault prediction model. In this step, each component is classified and processed because the functions, service lives, and faults of each component are different. Therefore, the respective fault prediction models are obtained through classified statistical calculations.

[0035] Step S3 includes the following steps:

[0036] S31. Let t0 = 1;

[0037] S32. Substitute the periodic data T0 into the grey GM(1,1) model to obtain the first fitted fault time series Y. Let the first fitted fault time series Y = {y(1), y(2), …, y(i), y(m)}, where y(i) is the first fitted fault time at the i-th frequency.

[0038] S33. Calculate the first residual sequence R1 of the fitted fault time series Y and the periodic data T0. R1 = {r1(2), r1(3), …, r1(i), r1(m)}, where r1(i) represents the fitting residual data between the fault interval period t3i at the i-th frequency and the corresponding fitted sequence data. Calculate the average value of the fitting residual data r1(i) in the first residual sequence R1.

[0039] S33. Establish a corrected residual sequence R2. R2 = {r2(2), r2(3), …, r2(i), r2(m)}, where r1(i) represents the corrected residual data at the i-th frequency.

[0040] S34. Use the first residual sequence R1 to calculate the parameters a0, a1, a2, and a3 by the least squares method.

[0041] S35. Let A(0 + t0) = a0, A(1 + t0) = a1, A(2 + t0) = a2, A(3 + t0) = a3 to obtain the corrected residual data in the t0-th corrected residual sequence R2.

[0042] S36. Calculate the average value of the corrected residual data r2(i) in the t0-th corrected residual sequence R2.

[0043] S37. Let t0 = t0 + 1 and judge whether t0 is less than the minimum t. 3i *Whether t0 is less than the minimum t 2i If so, go to step S34; if not, go to step S38.

[0044] S38. Find the corrected residual sequence R2 with the smallest value among all the corrected residual sequences R2, and let the said the smallest corrected residual sequence R2, and let the said The smallest corrected residual sequence R2 is the second residual sequence R3, and the corrected cycle data W0 is set equal to The fault interval cycle t corresponding to m-1 times of the i-th frequency at the smallest single cycle t0 3i constitutes the cycle data T0;

[0045] S39. Substitute the corrected cycle data W0 into the grey GM(1,1) model to obtain the second fitted fault time series Q. Let the second fitted fault time series Q = {q(1), q(2), …, q(i), q(m)}, where q(i) is the second fitted fault time at the i-th frequency. The second fitted fault time series Q is the fault prediction model. In this application, instead of directly giving the value of the fault interval cycle t 3i , the influence of different fault interval cycles t 3i on the corrected residual sequence R2 is calculated through loop calculation, so as to calculate m-1 corrected residual sequences R2 corresponding to different fault interval cycles t 3i , and then find the smallest and thus find the corresponding smallest fault interval cycle t 3i , and use the corresponding smallest fault interval cycle t 3i to form W0. Then use the grey GM(1,1) model for the periodic W0 again to obtain the periodic second fitted fault time series Q, so that the obtained periodic second fitted fault time series Q fits the detection data (t 11 , t 12 , ……, t 1m ) more closely, which is convenient for periodic fault prediction.

[0046] Step S34 includes the following steps:

[0047] S341. Let [a0, a1, a2, a3] T =(B T B) -1 B T R1 T (Formula 1), where

[0048]

[0049] S342. Substitute the fitting residual data of R1 into Formula 1, and use the least squares method to obtain the parameters a0, a1, a2, and a3.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An elevator maintenance system with a camera maintenance record query function, characterized in that: include: Mobile phone terminal, user remote computer and maintenance master controller; The mobile phone terminal and the user's remote computer are both connected to the maintenance master controller; The maintenance master controller is provided with a maintenance master controller maintenance planning generation module, a record storage module and a maintenance record query module; The record storage module includes: a storage unit and a maintenance record acquisition unit. The storage unit is used to store maintenance record information. The maintenance record information includes: maintenance photos, maintenance time, maintenance personnel information and fault information. The maintenance record acquisition unit is used to obtain maintenance record information from a mobile phone terminal. The mobile phone terminal obtains maintenance photos through a camera. The fault information includes: fault name, fault rating, fault occurrence time and weather temperature when the fault occurs. The weather temperature when the fault occurs is obtained from the mobile phone terminal. The weather temperature of the day; The maintenance plan generation module is used to push the maintenance plan to the mobile terminal and the user's remote computer based on the fault prediction model; The maintenance record query module is used for mobile phone terminals and users to query maintenance record information remotely on their computers.

2. The elevator maintenance system with camera maintenance record query function according to claim 1 is characterized in that: The maintenance plan generation module is used to automatically generate a maintenance plan based on the maintenance record information, and includes the following steps: S1. Classify the fault into component faults according to the fault name. Component faults include: door machine fault, safety clamp fault, wire rope fault and traction main machine fault; S2. Process the data of each component failure to obtain the processed periodic data: S21. Assume that the failure time of each component is (t 11 , t 12 ,……,t 1m ), m is the number of failures; S22, calculate the failure time interval t when the i-th frequency is 2i , t 2i =t 1i -t 1i-1 , where i≥2; S23, calculate the fault interval period t when the i-th frequency is 3i , m-1th i-th frequency fault interval period t 3i Composition period data T0, T0 = {t 32 ,t 33 ,…,t 3i ,…t 3m }Let the single cycle be t0, then Indicates: When When the first decimal place is greater than 0.5, take the value greater than The smallest integer; if the first decimal place is less than 0.5, take the value less than The largest integer of ; S3. Use the GM(1,1) model to process the periodic data T0 to obtain a fault prediction model.

3. The elevator maintenance system with camera maintenance record query function according to claim 2 is characterized in that: Step S3 includes the following steps: S31, command t0=1; S32, substitute the periodic data T0 into the grey GM(1,1) model to obtain the first fitting fault time series Y, assuming that the first fitting fault time series Y={y(1),y(2),…,y(i),y(m)}, y(i) is the first fitting fault time at the i-th frequency; S33, calculate the first residual sequence R1 of the fitted fault time series Y and the periodic data T0, R1 = {r1(2), r1(3), ..., r1(i), r1(m)}, r1(i) represents the fitted residual data at the fault interval period t3i and the corresponding fitted sequence data at the i-th frequency, calculate the average value of the fitted residual data r1(i) in the first residual sequence R1 S33, establish a modified residual sequence R2, R2 = {r2(2), r2(3), ..., r2(i), r2(m)}, r1(i) represents the modified residual data at the i-th frequency, S34, using the first residual sequence R1, calculating parameters a0, a1, a2 and a3 by least square method; S35, A(0+t0)=a0, A(1+t0)=a1, A(2+t0)=a2, A(3+t0)=a3, and obtain the corrected residual data in the t0th corrected residual sequence R2 S36, calculate the average value of the corrected residual data r2(i) in the t0th corrected residual sequence R2 S37, calculate t0=t0+1, determine t 3i * Is t0 less than the minimum t 2i If yes, proceed to step S34; if no, proceed to step S38; S38, find all the corrected residual sequences R2 The smallest modified residual sequence R2, and the name The smallest corrected residual sequence R2 is the second residual sequence R3, and the corrected period data W0 is equal to The minimum single cycle t0 corresponds to the m-1 ith frequency number of the fault interval period t 3i Composition period data T0; S39. Substitute the corrected periodic data W0 into the grey GM(1,1) model to obtain the second fitting fault time series Q. Suppose the second fitting fault time series Q = {q(1), q(2), ..., q(i), q(m)}, q(i) is the second fitting fault time at the i-th frequency, and the second fitting fault time series Q is the fault prediction model.

4. The elevator maintenance system with camera maintenance record query function according to claim 2 is characterized in that: Step S34 includes the following steps: S341, let [a0, a1, a2, a3] T =(B T B) -1 B T R1 T (Formula 1), where S342, substitute the fitting residual data of R1 into Formula 1, and use the least squares method to obtain the parameters a0, a1, a2 and a3.

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