Elevator safety big data comprehensive management system

Abstract

The application discloses an elevator safety big data comprehensive management system and relates to the technical field of elevator safety, which solves the technical problem that the prior art can only give a warning when an elevator is abnormal, and cannot accurately identify possible safety hazards of the elevator, thereby affecting passenger safety; the application can monitor real-time operation data of the elevator through a safety monitoring module, analyze and identify the change trend of the real-time operation state of the elevator, timely find that the elevator is in an unsafe state, process the monitored problems, find possible safety hazards of the elevator, and check and maintain the elevator; the application can find and process the safety hazards of the elevator by combining historical operation data and real-time operation data to predict and analyze the faults of the elevator; and the application can make the maintenance personnel form a benign competition and increase the working enthusiasm of the maintenance personnel by implementing a credit score system for the maintenance personnel.

Description

Technical Field

[0001] This invention belongs to the field of elevator safety and relates to elevator safety management technology, specifically an elevator safety big data integrated management system. Background Technology

[0002] With the construction and rapid development of cities, elevators have become an indispensable means of transportation in people's daily lives. In recent years, elevator accidents have occurred frequently, threatening the personal safety of passengers. Therefore, elevator safety is very important. In daily life, elevators are often damaged or unsafe due to inadequate management.

[0003] Existing technologies acquire relevant elevator operation data in real time during elevator operation and compare it with a set standard data range to determine whether the elevator status is normal, thereby achieving elevator safety monitoring. However, existing technologies can only issue warnings when elevator abnormalities occur, but cannot accurately identify potential safety hazards in the elevator, thus affecting passenger safety.

[0004] This invention provides an elevator safety big data integrated management system to solve the above-mentioned technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an elevator safety big data integrated management system to solve the technical problem that the prior art can only issue warnings when elevators malfunction, but cannot accurately identify potential safety hazards in elevators, thus affecting passenger safety.

[0006] To achieve the above objectives, a first aspect of the present invention provides an elevator safety big data integrated management system, including a central control module, and a safety monitoring module and a management and maintenance module connected thereto;

[0007] Safety monitoring module: Acquires real-time elevator operation data through data acquisition equipment; analyzes whether the elevator's real-time operation status is safe based on the real-time operation data; if yes, it sends the real-time operation data to the central control module; if no, it activates the elevator's self-protection device, generates and sends an early warning signal to the central control module; wherein, the data acquisition equipment includes sensors or smart terminals;

[0008] Central control module: After receiving real-time operating data, it combines the elevator's historical operating data to determine if there are any safety hazards; if so, it generates an early warning signal; otherwise, it continues monitoring; and...

[0009] The safety type is determined based on the early warning signal, and a dispatch signal is generated based on the safety type. Maintenance personnel are dispatched based on the dispatch signal. The safety type includes abnormal operating status or the existence of safety hazards.

[0010] Management and maintenance module: Dispatch maintenance personnel to perform elevator maintenance and monitor the maintenance process; compile maintenance records and upload them to the central control module.

[0011] Preferably, the step of analyzing the elevator's real-time operating status based on real-time operating data to determine its safety includes:

[0012] Real-time elevator operation data is acquired through sensors; this real-time operation data includes motor temperature or elevator speed.

[0013] Determine whether the real-time operating data is within the data threshold range; if yes, analyze and identify the trend of change in the real-time operating data; otherwise, the elevator is in an unsafe state.

[0014] This invention monitors the real-time operation data of elevators and determines their operating status based on this data. This facilitates real-time monitoring of elevator status, enabling timely detection of unsafe operating conditions and prompt analysis and handling of any problems. It should be noted that the real-time operation data of this invention can also be passively acquired from passengers, such as by passengers uploading real-time elevator operation data via smart terminals.

[0015] Preferably, the analysis and identification of the changing trends of real-time operational data includes:

[0016] Real-time running data is extracted based on a set time period; a real-time running curve is obtained by fitting the real-time running data, and the function of the real-time running curve is labeled as F(t);

[0017] Obtain the average value JZ of the real-time running data within this time period; calculate the trend evaluation coefficient QPX using the formula QPX={∫[F(t)-JZ]-∫[JZ-F(t)]}; where the integral range of ∫[F(t)-JZ] is the continuous time range when the value of the function F(t) is greater than or equal to the average value JZ, and the integral range of ∫[JZ-F(t)] is the continuous time range when the value of the function F(t) is less than or equal to the average value JZ;

[0018] When the trend assessment coefficient QPX is greater than the set trend assessment threshold, the elevator is determined to be in an unsafe state; otherwise, the elevator is determined to be in a safe state.

[0019] This invention analyzes the changing trends of real-time elevator operating data, calculates a trend evaluation coefficient using a formula, and determines the elevator's safety status based on this coefficient. It can monitor abnormal changes in real-time operating data, identify potential safety hazards, and enable timely inspection and maintenance to eliminate these hazards. It should be noted that the real-time operating curve in this invention is based on fitting a specific type of real-time operating data, such as motor temperature.

[0020] Preferably, the self-protection device for starting the elevator includes:

[0021] The system acquires real-time operational data of elevator malfunctions, identifies the fault type based on the real-time operational data, and activates the appropriate elevator self-protection device based on the fault type. The elevator self-protection device includes a buffer device, an emergency braking device, or an emergency communication device.

[0022] This invention, by equipping elevators with self-protection devices, can identify the type of malfunction and activate the appropriate self-protection device when a malfunction occurs during elevator operation. This facilitates emergency handling of the elevator in the event of a malfunction, preventing accidents caused by elevator loss of control.

[0023] Preferably, the historical operating data of the combined elevator is used to determine whether there are any safety hazards in the elevator, including:

[0024] Obtain real-time elevator operation data and retrieve corresponding historical operation data;

[0025] An operational data sequence is generated based on historical and real-time operational data. The operational data sequence is then identified and predicted using a hazard prediction model to obtain the corresponding hazard labels for the elevator. The hazard prediction model is built based on an artificial intelligence model.

[0026] Preferably, the hazard prediction model is built based on an artificial intelligence model, including:

[0027] Obtain standard training data; wherein, standard training data includes standard input data consistent with the content attributes of the running data sequence, and standard output data consistent with the hazard labels;

[0028] The artificial intelligence model is trained using standard training data to obtain a hazard prediction model; the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0029] This invention identifies and predicts the elevator's operating sequence by constructing a hazard prediction model, obtains corresponding fault tags, predicts the elevator's operating data, determines whether there are safety hazards in the elevator, and enables the elevator to be inspected and repaired when safety hazards are found, thereby eliminating the safety hazards.

[0030] Preferably, the dispatching of maintenance personnel to perform maintenance on the elevator includes:

[0031] The system acquires the location data and reputation score of each maintenance worker, calculates the comprehensive score of each worker based on the weights of the location data and reputation score, and dispatches maintenance workers according to the comprehensive score. The weights of the location data and reputation score are set based on experience.

[0032] Determine whether the maintenance personnel arrived within the specified time; if yes, add credit points to the maintenance personnel's credit score; otherwise, deduct credit points from the maintenance personnel's credit score.

[0033] This invention assigns maintenance personnel based on their overall scores, which fosters healthy competition among them. Furthermore, it deducts credit points from maintenance personnel who fail to arrive on time, thereby motivating them to work more diligently.

[0034] Preferably, the central control module is communicatively and / or electrically connected to the safety monitoring module and the management and maintenance module, respectively.

[0035] Compared with existing technologies, the beneficial effects of this invention are: By monitoring the real-time operation data of the elevator, this invention can ensure that the elevator operates in a safe state; by capturing the changing trend of the elevator's real-time operation data within a preset time period and analyzing this trend, a trend evaluation coefficient is calculated according to a formula, and the elevator's safety status is determined based on this coefficient. This allows for monitoring abnormal changes in real-time operation data and identifying potential safety hazards; by installing a self-protection device in the elevator, emergency handling can be performed when the elevator malfunctions; and by constructing a hazard prediction model for the elevator's operating sequence... By identifying and predicting faults and obtaining corresponding fault tags, the system can predict elevator operation data, determine whether elevator malfunctions will occur, and inspect and repair elevators when potential hazards exist, eliminating safety risks. Furthermore, based on the location data and reputation score of maintenance personnel, the system dispatches maintenance personnel. The reputation score of maintenance personnel is adjusted based on whether they arrive at the elevator within the specified time, creating healthy competition among them, increasing their work enthusiasm, and encouraging them to perform regular elevator maintenance, ensuring the elevator operates safely and protecting passenger safety. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of the overall framework of the present invention;

[0038] Figure 2 This is a schematic diagram illustrating the specific workflow of the present invention;

[0039] Figure 3 This is a schematic diagram illustrating the specific process of hazard prediction in this invention;

[0040] Figure 4 This is a schematic diagram of the functions F(t) and JZ of the present invention. Detailed Implementation

[0041] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figures 1-4 The first aspect of the present invention provides an elevator safety big data integrated management system, including: a central control module, and a safety monitoring module and a management and maintenance module connected thereto; including a central control module, and a safety monitoring module and a management and maintenance module connected thereto.

[0043] Safety monitoring module: Acquires real-time elevator operation data through data acquisition equipment; acquires real-time elevator operation data through sensors; determines whether the real-time operation data is within the data threshold range; if yes, it analyzes and identifies the trend of change in the real-time operation data; if no, the elevator is in an unsafe state.

[0044] Real-time running data is extracted based on a set time period; a real-time running curve is obtained by fitting the real-time running data, and the function of the real-time running curve is labeled as F(t);

[0045] Obtain the average value JZ of the real-time running data within this time period; calculate the trend evaluation coefficient QPX using the formula QPX={∫[F(t)-JZ]-∫[JZ-F(t)]};

[0046] When the trend assessment coefficient QPX is greater than the set trend assessment threshold, the elevator is determined to be in an unsafe state; otherwise, the elevator is determined to be in a safe state.

[0047] The system determines whether the elevator is in a safe state; if yes, it sends real-time operating data to the central control module; if no, it acquires real-time operating data of elevator malfunctions, identifies the fault type based on the real-time operating data, activates the appropriate elevator self-protection device based on the fault type, and generates and sends an early warning signal to the central control module; the data acquisition equipment includes sensors or smart terminals.

[0048] Central Control Module: After receiving real-time operating data, it retrieves the corresponding historical operating data for the elevator; based on the historical and real-time operating data, it generates an operating data sequence; it then uses a hazard prediction model to identify and predict the operating data sequence, obtaining the corresponding hazard label for the elevator; it determines whether there is a safety hazard in the elevator's operation; if yes, it generates an early warning signal; otherwise, it continues monitoring; and...

[0049] The safety type is determined based on the early warning signal, and a dispatch signal is generated based on the safety type. Maintenance personnel are dispatched based on the dispatch signal. The safety type includes abnormal operating status or the existence of safety hazards.

[0050] Management and Maintenance Module: Acquires location data and reputation score of each maintenance personnel, calculates a comprehensive score for each maintenance personnel based on the weight of the location data and reputation score, dispatches maintenance personnel according to the comprehensive score; determines whether the maintenance personnel arrive within the specified time; if yes, adds reputation score to the maintenance personnel; if no, deducts reputation score from the maintenance personnel; monitors the maintenance process, organizes maintenance records and uploads them to the central control module.

[0051] The technical solution of this embodiment will be illustrated by an example. This embodiment provides an elevator safety big data integrated management system, which includes the following steps:

[0052] 1. The safety monitoring module acquires real-time elevator operation data through data acquisition equipment; acquires real-time elevator operation data through sensors; determines whether the real-time operation data is within the data threshold range; if yes, it analyzes and identifies the trend of change in the real-time operation data; if no, the elevator is in an unsafe state.

[0053] For example: There is a certain model of elevator with the following standard operating data threshold range: operating speed is 0-2.5m / s; passenger weight is 0-1050kg; motor temperature is 10-30 degrees Celsius. Obtain four sets of real-time operating data of this model of elevator at different times.

[0054] The first set of real-time operating data: operating speed 1.5 m / s; passenger weight 900 kg; motor temperature 15 degrees Celsius. All real-time operating data are within the threshold range of the standard operating data. Therefore, the trend of the elevator's real-time operating data is analyzed and identified. The second set of real-time operating data: operating speed 2.8 m / s; passenger weight 1000 kg; motor temperature 17 degrees Celsius. The real-time operating speed is higher than the standard operating speed, so the elevator is judged to be in an unsafe operating state at this moment. The third set of real-time operating data: operating speed 1.8 m / s; passenger weight 1120 kg; motor temperature 22 degrees Celsius. The real-time passenger weight is higher than the standard passenger weight, so the elevator is judged to be in an unsafe operating state at this moment. The fourth set of real-time operating data: operating speed 1.6 m / s; passenger weight 980 kg; motor temperature 33 degrees Celsius. The real-time motor temperature is higher than the standard motor temperature, so the elevator is judged to be in an unsafe operating state at this moment.

[0055] 2. Based on the set time period, extract real-time operating data, divide the time period into n moments, and obtain real-time operating data x1, x2...xn for the n moments; calculate the average value y of the time period according to the formula; calculate the variance S of the time period according to the formula; determine whether the variance S is less than the preset threshold; if yes, determine that the elevator is in a safe state; if no, determine that the elevator is in an unsafe state.

[0056] For example: Given a certain type of elevator, we can capture one minute of its real-time operating speed from one hour, divide that minute into six segments, and record the real-time operating speed data for each 10-second interval.

[0057] The operating speeds of the first group are: x1 = 1.0, x2 = 1.2, x3 = 1.4, x4 = 1.2, x5 = 2.0, x6 = 1.0; the average value y1 is calculated to be 1.3 according to the formula; the variance S1 is calculated to be 0.117 according to the formula; 0.117 is greater than 0.05, so the elevator is in an unsafe state.

[0058] The second set of operating speeds is: x1 = 1.2, x2 = 1.3, x3 = 1.4, x4 = 1.2, x5 = 1.4, x6 = 1.3; the average value y2 is calculated to be 1.3 according to the formula; the variance S2 is calculated to be 0.0067 according to the formula; since 0.0067 is less than 0.05, the elevator is in a safe state.

[0059] In some other preferred embodiments, the existence of safety hazards can also be predicted by calculating the trend assessment coefficient QPX, as follows:

[0060] During the time interval t1-t2, QPX1 = ∫[F(t)-JZ]; during the time interval t3-t4, QPX2 = ∫[F(t)-JZ]; during the time interval t5-t6, QPX3 = ∫[F(t)-JZ]; during the time interval t2-t3, QPX4 = ∫[JZ-F(t)]; during the time interval t4-t5, QPX5 = ∫[JZ-F(t)]; during the time interval t6-t7, QPX6 = ∫[JZ-F(t)];

[0061] During the time period t1-t7, the trend assessment coefficient QPX = QPX1 + QPX2 + QPX3 - (QPX4 + QPX5 + QPX6).

[0062] 3. Obtain real-time operational data of elevator malfunctions, identify the fault type based on the real-time operational data, activate the corresponding elevator self-protection device according to the fault type, and generate and send an early warning signal to the central control module.

[0063] For example: Based on three different sets of real-time operating data, it can be determined that the elevator is in an unsafe operating state at three different times. The fault type can be identified based on the abnormal real-time operating data.

[0064] The first set of elevator operating statuses: The elevator's real-time operating speed is higher than the standard operating speed, indicating that the elevator is in an overspeed state. The fault type is brake failure, and the emergency braking device is activated. The second set of elevator operating statuses: The elevator's passenger load is higher than the standard passenger load, indicating that the elevator is in an overload state. An alarm is issued to remind passengers that the elevator is overloaded. The elevator will resume operation only after passengers have exited the car and the passenger load is normal. The third set of elevator operating statuses: The elevator's motor temperature is higher than the standard motor temperature, indicating that the elevator motor is overheating. The motor fan is activated to cool the motor.

[0065] 4. After receiving the real-time operation data, the central control module retrieves the corresponding historical operation data of the elevator; based on the historical operation data and the real-time operation data, it generates an operation data sequence, identifies and predicts the operation data sequence through the hazard prediction model, and obtains the hazard label corresponding to the elevator.

[0066] For example: Given an elevator of a certain model, its real-time operating speed is 2.4 m / s. This real-time operating data falls within the threshold range of standard operating data. The following three sets of historical operating speed data are retrieved:

[0067] The first group is 1.8 m / s; the second group is 2.0 m / s; the third group is 2.3 m / s; the generated running data sequence is [1.8, 2.0, 2.3, 2.4]. The running data sequence is input into the hazard prediction model. According to the prediction model, the output data is 1; the hazard label corresponding to the output data 1 is elevator overspeed.

[0068] 5. Obtain the location data and reputation score of each maintenance personnel, calculate the comprehensive score of each maintenance personnel based on the weight of the location data and reputation score, dispatch maintenance personnel according to the comprehensive score; determine whether the maintenance personnel arrive within the specified time; if yes, add points to the reputation score of the maintenance personnel; if no, deduct points from the reputation score of the maintenance personnel; monitor the maintenance process; organize maintenance records and upload them to the central control module.

[0069] For example: There is a certain model of elevator that requires regular maintenance and inspection. Two maintenance personnel, A and B, are located near the elevator. Personnel A has a reputation score of 97, and personnel B has a reputation score of 98. Based on location data, personnel A is 2km away from the elevator, and personnel B is 5km away. The score is set as follows: location data within 3km scores 99, and location data within 6km scores 95, with a weighting of 0.5. The reputation score also has a weighting of 0.5. Personnel A's overall score is calculated to be 98, and personnel B's overall score is calculated to be 96.5. Therefore, personnel A is dispatched to the elevator at 15:00. Monitoring shows that personnel A arrived at the elevator at 13:40 and performed inspection and repairs. Personnel A's reputation score is increased by 1 point.

[0070] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0071] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An elevator safety big data integrated management system, comprising: A central control module, and a safety monitoring module and a management and maintenance module connected thereto; characterized in that, Safety monitoring module: Acquires real-time elevator operation data through data acquisition equipment; analyzes whether the elevator's real-time operation status is safe based on the real-time operation data; if yes, it sends the real-time operation data to the central control module; if no, it activates the elevator's self-protection device, generates and sends an early warning signal to the central control module; wherein, the data acquisition equipment includes sensors or smart terminals; Central control module: After receiving real-time operating data, it combines the elevator's historical operating data to determine if there are any safety hazards; if so, it generates an early warning signal; otherwise, it continues monitoring; and... The safety type is determined based on the early warning signal, and a dispatch signal is generated based on the safety type. Maintenance personnel are dispatched based on the dispatch signal. The safety type includes abnormal operating status or the existence of safety hazards. Management and maintenance module: Dispatches maintenance personnel to perform elevator maintenance and monitors the maintenance process; compiles maintenance records and uploads them to the central control module; The analysis of whether the elevator's real-time operating status is safe based on real-time operating data includes: Real-time elevator operation data is acquired through sensors; this real-time operation data includes motor temperature or elevator speed. Determine whether the real-time operating data is within the data threshold range; if yes, analyze and identify the trend of change in the real-time operating data; otherwise, the elevator is in an unsafe state. The analysis and identification of the changing trends of real-time operational data includes: Real-time running data is extracted based on a set time period; a real-time running curve is obtained by fitting the real-time running data, and the function of the real-time running curve is labeled as F(t); Obtain the average value JZ of the real-time running data within this time period; calculate the trend evaluation coefficient QPX using the formula QPX={∫[F(t)-JZ]-∫[JZ-F(t)]}; where the integral range of ∫[F(t)-JZ] is the continuous time range when the value of the function F(t) is greater than or equal to the average value JZ, and the integral range of ∫[JZ-F(t)] is the continuous time range when the value of the function F(t) is less than or equal to the average value JZ; When the trend assessment coefficient QPX is greater than the set trend assessment threshold, the elevator is determined to be in an unsafe state; otherwise, the elevator is determined to be in a safe state.

2. The elevator safety big data integrated management system according to claim 1, characterized in that, The self-protection device for starting the elevator includes: The system acquires real-time operational data of elevator malfunctions, identifies the fault type based on the real-time operational data, and activates the appropriate elevator self-protection device based on the fault type. The elevator self-protection device includes a buffer device, an emergency braking device, or an emergency communication device.

3. The elevator safety big data integrated management system according to claim 1, characterized in that, The historical operating data of the combined elevator is used to determine whether there are any safety hazards in the elevator, including: Obtain real-time elevator operation data and retrieve corresponding historical operation data; An operational data sequence is generated based on historical and real-time operational data. The operational data sequence is then identified and predicted using a hazard prediction model to obtain the corresponding hazard labels for the elevator. The hazard prediction model is built based on an artificial intelligence model.

4. The elevator safety big data integrated management system according to claim 3, characterized in that, The hazard prediction model is built based on an artificial intelligence model and includes: Obtain standard training data; wherein, standard training data includes standard input data consistent with the content attributes of the running data sequence, and standard output data consistent with the hazard labels; The artificial intelligence model is trained using standard training data to obtain a hazard prediction model; the artificial intelligence model includes a BP neural network model or an RBF neural network model.

5. The elevator safety big data integrated management system according to claim 1, characterized in that, The dispatch of maintenance personnel to perform maintenance on the elevator includes: Obtain the location data and reputation score of each maintenance personnel, and dispatch maintenance personnel based on the location data and reputation score; Determine whether the maintenance personnel arrived within the specified time; if yes, add credit points to the maintenance personnel's credit score; otherwise, deduct credit points from the maintenance personnel's credit score.

6. The elevator safety big data integrated management system according to claim 1, characterized in that, The central control module is communicated with and / or electrically connected to the safety monitoring module and the management and maintenance module, respectively.

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