An intelligent load monitoring method and system for an elevator car

Abstract

The application discloses an elevator car intelligent load monitoring method and system, the method comprises the following steps: obtaining the mechanical property parameters and geometric dimensions of the car floor; collecting discrete point deformation data of the car floor based on a strain sensor; reconstructing and generating an overall strain distribution nephogram of the car floor according to the discrete point deformation data; converting the overall strain distribution nephogram into an overall stress distribution nephogram based on the material mechanics constitutive relation and the mechanical property parameters of the car floor material, and obtaining the real-time stress state of the car floor; under different load conditions, extracting key mechanical indicators through the overall stress distribution nephogram, and constructing a load-stress state mapping model based on the mapping relationship between the key mechanical indicators and the total load and the load center position; obtaining the real-time overall stress distribution nephogram, and outputting the current elevator load information based on the load-stress state mapping model. The application can effectively overcome the position sensitivity of point measurement, and significantly improve the anti-unbalanced load capacity and measurement accuracy.

Description

Technical Field

[0001] This invention relates to the field of elevator load measurement technology, specifically to an intelligent load monitoring method and system for elevator cars. Background Technology

[0002] Elevator load monitoring is crucial for ensuring operational safety. Current technologies mainly rely on pressure sensors at the bottom of the car or motor torque to calculate the load, which has the following drawbacks:

[0003] (1) Point sensors are sensitive to load distribution. When passengers are concentrated on one side, the error is large and it is easy to cause eccentric load distortion.

[0004] (2) Most elevators use pressure sensors or weighing devices, which are easily affected by factors such as installation location, temperature and humidity, resulting in insufficient accuracy.

[0005] (3) It has a single function, only outputs weight data, and cannot sense the stress state of the car structure;

[0006] (4) Traditional methods rely on static weighing, which cannot capture load changes in real time (such as dynamic loads when passengers enter and exit), resulting in delayed overload warnings;

[0007] For example, in an elevator car bottom frame with patent publication number CN214989555U, the infrared transmitter and infrared receiver are misaligned after the base plate is deformed, thereby indicating the deformation of the car bottom frame and preventing elevator overload. However, it is essentially still in the category of point or local measurement, and fails to solve the measurement distortion problem caused by uneven load distribution (off-center load), and does not involve the perception of the overall mechanical state of the car.

[0008] For example, the elevator fault detection and alarm system and method disclosed in patent publication number CN120039733A uses a detection model based on a genetic wavelet neural network algorithm to realize the mapping from sensor data to load. However, this method lacks explicit modeling of the physical and mechanical behavior of the elevator car floor, which may lead to: (1) limited model generalization ability and poor adaptability to different car structures or sensor layouts; (2) difficulty in obtaining the overall strain / stress distribution of the car floor, making it impossible to achieve synchronous monitoring of structural health status; (3) the interpretability and reliability of the model depend on a large amount of training data for specific scenarios.

[0009] Therefore, there is an urgent need for an intelligent load monitoring technology for elevator cars that can resist eccentric loads and reflect the overall stress on the car floor. Summary of the Invention

[0010] To overcome the shortcomings and deficiencies of existing technologies, this invention provides an intelligent load monitoring method and system for elevator cars. This invention constructs a cascaded mapping chain of "discrete deformation data → full-field strain distribution → full-field stress distribution → load measurement," deeply integrating the advantages of data-driven and physical model-driven approaches. Utilizing machine learning's ability to efficiently process high-dimensional and nonlinear relationships, it reconstructs the full-field strain distribution reflecting the overall stress on the car floor from sparsely arranged sensor data. Based on the constitutive relations of material mechanics and structural characteristics, the reconstructed full-field strain distribution is converted into a full-field stress distribution. The load is calculated based on the load-stress state mapping model. This step-by-step, physically constrained inversion process effectively overcomes the positional sensitivity of point measurements, significantly improves the resistance to off-center loads and measurement accuracy, and provides direct evidence for structural health assessment and fatigue damage early warning through the overall stress distribution cloud map of the car floor. It achieves coordinated monitoring of load safety and structural safety, solving problems such as large load monitoring errors (especially off-center load distortion), slow response, poor compatibility, limited functionality, and the lack of physical constraints in existing machine learning applications.

[0011] To achieve the above objectives, the present invention adopts the following technical solution:

[0012] This invention provides an intelligent load monitoring method for elevator cars, comprising the following steps:

[0013] Obtain the mechanical properties and geometric dimensions of the car floor material;

[0014] Discrete point deformation data of the car floor are collected based on strain sensors;

[0015] The overall strain distribution cloud map of the car floor is reconstructed based on the collected discrete point deformation data;

[0016] Based on the constitutive relations of materials mechanics and the mechanical property parameters of the car floor material, the reconstructed overall strain distribution cloud map is converted into an overall stress distribution cloud map of the car floor to obtain the real-time stress state of the car floor.

[0017] Under different load conditions, key mechanical indicators are extracted by the overall stress distribution cloud map, and a load-stress state mapping model is constructed based on the mapping relationship between key mechanical indicators and total load and load center position.

[0018] Obtain a real-time overall stress distribution cloud map and output the current elevator load information based on the load-stress state mapping model.

[0019] As a preferred technical solution, the mechanical performance parameters of the car floor material include weight, density, elastic modulus, Poisson's ratio, and yield strength.

[0020] As a preferred technical solution, the strain sensor is installed in the key stress area of ​​the car floor, and the key stress area is determined according to the structural characteristics of the car floor.

[0021] As a preferred technical solution, an overall strain distribution cloud map of the car floor is reconstructed based on the collected discrete point deformation data, specifically including:

[0022] Based on the finite element analysis software, various load conditions are simulated, the full-field strain distribution cloud map of the car floor plate under each condition is calculated, and the deformation data of the simulated discrete points corresponding to the strain sensor positions are recorded. A training dataset is constructed based on the simulated discrete point deformation data and the corresponding full-field strain cloud map.

[0023] A deep learning model is trained based on the training dataset to learn the mapping relationship between discrete point strain data and the full-field strain cloud map of the car floor.

[0024] The collected discrete deformation data is input into the trained deep learning model to reconstruct the overall strain distribution cloud map of the car floor.

[0025] As a preferred technical solution, based on the constitutive relationship of materials mechanics and the mechanical property parameters of the car floor material, the reconstructed overall strain distribution cloud map is converted into an overall stress distribution cloud map of the car floor, specifically including:

[0026] Based on the generalized Hooke's law and the mechanical properties of the car floor material, the stress tensor of the overall stress distribution cloud map is calculated for each calculation unit in the reconstructed overall strain distribution cloud map, and is expressed as:

[0027] σ=[C]ε

[0028] Where [C] represents the stiffness matrix constructed based on material properties and constitutive relations, ε represents the strain tensor in the overall strain distribution cloud map, and σ represents the stress tensor in the overall stress distribution cloud map.

[0029] As a preferred technical solution, key mechanical indicators include the sum of the reaction forces at the base plate support points and the resultant force obtained by integrating the stress field.

[0030] The present invention also provides an intelligent load monitoring system for elevator cars, comprising: a car floor parameter acquisition module, a strain sensor, an overall strain distribution cloud map generation module, an overall stress distribution cloud map generation module, a load-stress state mapping model construction module, and a load information output module;

[0031] The car floor parameter acquisition module is used to acquire the mechanical property parameters and geometric dimensions of the car floor material.

[0032] The strain sensor is used to collect discrete point deformation data of the car floor.

[0033] The overall strain distribution cloud map generation module is used to reconstruct and generate an overall strain distribution cloud map of the car floor based on the collected discrete point deformation data.

[0034] The overall stress distribution cloud map generation module is used to convert the reconstructed overall strain distribution cloud map into an overall stress distribution cloud map of the car floor based on the material mechanics constitutive relationship and the mechanical performance parameters of the car floor material, so as to obtain the real-time stress state of the car floor.

[0035] The load-stress state mapping model construction module is used to construct a load-stress state mapping model. Under different load conditions, key mechanical indicators are extracted through the overall stress distribution cloud map, and a load-stress state mapping model is constructed based on the mapping relationship between the key mechanical indicators and the total load and the load center position.

[0036] The load information output module is used to obtain a real-time overall stress distribution cloud map and output the current elevator load information based on the load-stress state mapping model.

[0037] As a preferred technical solution, the strain sensor is installed in the key stress area of ​​the car floor, and the key stress area is determined according to the structural characteristics of the car floor.

[0038] As a preferred technical solution, the overall strain distribution cloud map generation module is used to reconstruct and generate an overall strain distribution cloud map of the car floor based on the collected discrete point deformation data, specifically including:

[0039] Based on the finite element analysis software, various load conditions are simulated, the full-field strain distribution cloud map of the car floor plate under each condition is calculated, and the deformation data of the simulated discrete points corresponding to the strain sensor positions are recorded. A training dataset is constructed based on the simulated discrete point deformation data and the corresponding full-field strain cloud map.

[0040] A deep learning model is trained based on the training dataset to learn the mapping relationship between discrete point strain data and the full-field strain cloud map of the car floor.

[0041] The collected discrete deformation data is input into the trained deep learning model to reconstruct the overall strain distribution cloud map of the car floor.

[0042] As a preferred technical solution, the overall stress distribution cloud map generation module is used to convert the reconstructed overall strain distribution cloud map into an overall stress distribution cloud map of the car floor based on the material mechanics constitutive relationship and the mechanical property parameters of the car floor material, specifically including:

[0043] Based on the generalized Hooke's law and the mechanical properties of the car floor material, the stress tensor of the overall stress distribution cloud map is calculated for each calculation unit in the reconstructed overall strain distribution cloud map, and is expressed as:

[0044] σ=[C]ε

[0045] Where [C] represents the stiffness matrix constructed based on material properties and constitutive relations, ε represents the strain tensor in the overall strain distribution cloud map, and σ represents the stress tensor in the overall stress distribution cloud map.

[0046] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0047] (1) This invention collects discrete point deformation data of the car floor in real time by using strain sensors, and reconstructs the overall strain distribution cloud map of the car floor based on machine learning algorithms. This reduces the measurement error caused by the position sensitivity of traditional point measurement, and the measurement accuracy is high. Furthermore, the sensors are only placed in key stress areas, which can effectively reduce the number of sensors and reduce deployment costs.

[0048] (2) This invention constructs a cascaded mapping chain of "discrete deformation data → full-field strain distribution → full-field stress distribution → load measurement", reconstructs the full-field strain distribution reflecting the overall stress on the car floor from sparsely arranged sensor data, and converts the reconstructed full-field strain distribution into a full-field stress distribution based on the material mechanics constitutive relationship and structural characteristics. The load is calculated based on the load-stress state mapping model, realizing the coordinated monitoring of load safety and structural safety, and solving the problems of large load monitoring error (especially off-center load distortion), slow response, poor compatibility, single function and lack of physical constraints in existing machine learning applications in the prior art. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating the intelligent load monitoring method for elevator cars according to the present invention.

[0050] Figure 2 This is a schematic diagram of the overall stress distribution cloud map of the car floor plate of the present invention; Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0052] Example 1

[0053] like Figure 1 As shown, this embodiment provides an intelligent load monitoring method for elevator cars, including the following steps:

[0054] S1: Obtain the mechanical properties and geometric dimensions of the car floor material;

[0055] In this embodiment, the mechanical property parameters of the car floor material include weight, density, elastic modulus, Poisson's ratio, and yield strength;

[0056] In this embodiment, the geometric dimensions include length, width, thickness, and shape;

[0057] For example: The car floor material is ordinary carbon steel, and its mechanical properties are as follows: elastic modulus 2.1 × 10⁻⁶. 11 N / m 2 Poisson's ratio 0.28, yield strength 2.2 × 10⁻⁶ 8 N / m 2 Density 7800 kg / m³ 3 The geometric dimensions are as follows: length 5525mm, width 3090mm, thickness 128mm;

[0058] S2: Based on the structural characteristics of the car floor, multiple strain sensors are installed in its key stress areas. During elevator operation, the strain sensors collect discrete deformation data of the car floor in real time.

[0059] In this embodiment, the structural features of the car bottom include the distribution, quantity, and support method of beams or reinforcing ribs;

[0060] In this embodiment, the strain sensor can be a strain gauge, a fiber optic grating sensor, a capacitive displacement sensor, or a laser displacement sensor.

[0061] For example, based on the structural characteristics of the car floor, the distribution and number of bottom reinforcing ribs are obtained, and multiple strain sensors are deployed in its key stress areas. Multiple resistance strain gauges are selected and installed at the bottom of the car using a physical installation method. Of course, laser displacement sensors can also be used as strain sensors. The car floor deforms under load, and discrete point deformation data of the car floor are obtained by collecting the reflected laser light after the car floor deforms.

[0062] S3: Based on the collected discrete point deformation data, the overall strain distribution cloud map of the car floor is reconstructed using machine learning algorithms;

[0063] In this embodiment, the machine learning algorithm is preferably a neural network or convolutional neural network based on physical information, or a deep learning model that incorporates physical constraints (such as strain compatibility equations and boundary conditions).

[0064] In this embodiment, the overall strain distribution cloud map of the car floor is reconstructed based on a machine learning algorithm, specifically including:

[0065] S31: The deep learning model is trained in a supervised manner using a full-field strain distribution dataset of the car floor plate, which is obtained in advance through finite element simulation or experimental calibration and covers typical load conditions (including different total weights and different distributions such as off-center loads).

[0066] Preferably, for car floor plates with specific geometric dimensions and mechanical performance parameters, a model can be established using finite element analysis software (FEA) to simulate various load conditions (including no load, point loads at different locations, uniformly distributed loads, off-center loads, etc.), calculate the full-field strain distribution cloud map of the car floor plate under each load condition, and record the simulated discrete point deformation data corresponding to the strain sensor locations. A training dataset is then constructed based on the simulated discrete point deformation data and the corresponding full-field strain cloud map.

[0067] During training, the input to the deep learning model is the simulated discrete-point deformation data (strain values) corresponding to the current sensor layout, and the output (learning target) of the model is the corresponding full-field strain distribution data (full-field strain contour map).

[0068] Preferably, when training the deep learning model, the collected sensor data is preprocessed such as normalization. The input layer of the model is designed to receive deformation data from multiple discrete points, and the output layer of the deep learning model is designed to generate a two-dimensional data grid representing the strain field of the entire base plate. The training objective is to minimize the error, such as mean square error, between the predicted strain field output by the model and the actual strain field calculated by the finite element analysis software (FEA). Regularization is added during the training process to prevent overfitting.

[0069] S32: Through training, the deep learning model learns to infer the mapping relationship of the entire base plate strain field that conforms to physical laws from finite discrete point strain data;

[0070] S33: During the real-time monitoring phase, the currently collected discrete point deformation data is input into the trained deep learning model, and the deep learning model outputs the reconstructed overall strain distribution cloud map of the current car floor.

[0071] S4: Based on the constitutive relationship of materials mechanics and the mechanical performance parameters of the car floor material, the reconstructed overall strain distribution cloud map (strain field) is converted into the overall stress distribution cloud map (stress field) of the car floor, thereby obtaining its real-time stress state;

[0072] In this embodiment, the reconstructed overall strain distribution cloud map (strain field) is converted into an overall stress distribution cloud map (stress field) of the car floor. This conversion process can be solved directly based on analytical formulas or quickly calculated using a finite element model.

[0073] In this embodiment, the real-time stress state includes, but is not limited to: overall stress level, location of maximum stress, stress concentration area and its value, average stress, etc.

[0074] Specifically, based on the generalized Hooke's law and the mechanical properties of the car floor material (such as elastic modulus, Poisson's ratio, etc.), the following formula is used to calculate the strain distribution in the overall curve:

[0075] σ=[C]ε

[0076] Where [C] represents the stiffness matrix constructed based on material properties and constitutive relations, ε represents the strain tensor in the global strain distribution contour map, and σ represents the stress tensor in the global stress distribution contour map, such as... Figure 2 As shown, the final calculation generates an overall stress distribution cloud map of the car floor, obtaining its real-time stress state (such as the location of maximum stress and the average stress level).

[0077] S5: Using a pre-defined load-stress state mapping model established based on physical principles or experimental calibration, calculate the current elevator load information according to the real-time stress distribution, and feed the load information back to the elevator main control system.

[0078] In this embodiment, under known different load conditions (total weight and distribution), key mechanical indicators are extracted by the overall stress distribution cloud map. The key mechanical indicators include the sum of the reaction forces at the bottom plate support points and the resultant force obtained by stress field integration. The mapping relationship between the key mechanical indicators and the total load and the load center position is constructed to obtain the load-stress state mapping model. The real-time overall stress distribution cloud map is input, and the current elevator load information is output based on the load-stress state mapping model.

[0079] In this embodiment, the load information includes empty load, light load, half load, full load, overload, off-center load, and load spatiotemporal distribution. For example, the load-force state mapping model outputs that the current elevator load is 1700kg. Given that the elevator's rated load is 1600kg, it is determined that the current load is overloaded.

[0080] S6: The elevator main control system executes the control strategy based on the received load information.

[0081] In this embodiment, the control strategies include overload alarm and prohibition of operation, load warning, operation parameter optimization, and abnormal off-center load alarm.

[0082] Example 2

[0083] This embodiment provides an intelligent load monitoring system for elevator cars, which is used to implement the intelligent load monitoring method for elevator cars in Embodiment 1 above. The system includes: a car floor parameter acquisition module, a strain sensor, an overall strain distribution cloud map generation module, an overall stress distribution cloud map generation module, a load-stress state mapping model construction module, and a load information output module.

[0084] In this embodiment, the car floor parameter acquisition module is used to acquire the mechanical property parameters and geometric dimensions of the car floor material;

[0085] In this embodiment, the strain sensor is used to collect discrete point deformation data of the car floor. The strain sensor is set in the key stress area of ​​the car floor, and the key stress area is determined according to the structural characteristics of the car floor.

[0086] In this embodiment, the overall strain distribution cloud map generation module is used to reconstruct and generate an overall strain distribution cloud map of the car floor based on the collected discrete point deformation data, specifically including:

[0087] Based on the finite element analysis software, various load conditions are simulated, the full-field strain distribution cloud map of the car floor plate under each condition is calculated, and the deformation data of the simulated discrete points corresponding to the strain sensor positions are recorded. A training dataset is constructed based on the simulated discrete point deformation data and the corresponding full-field strain cloud map.

[0088] A deep learning model is trained based on the training dataset to learn the mapping relationship between discrete point strain data and the full-field strain cloud map of the car floor.

[0089] The collected discrete deformation data is input into the trained deep learning model to reconstruct and generate the overall strain distribution cloud map of the car floor.

[0090] In this embodiment, the overall stress distribution cloud map generation module is used to convert the reconstructed overall strain distribution cloud map into an overall stress distribution cloud map of the car floor based on the material mechanics constitutive relationship and the mechanical performance parameters of the car floor material, so as to obtain the real-time stress state of the car floor.

[0091] In this embodiment, the reconstructed overall strain distribution cloud map is converted into an overall stress distribution cloud map of the car floor, specifically including:

[0092] Based on the generalized Hooke's law and the mechanical properties of the car floor material, the stress tensor of the overall stress distribution cloud map is calculated for each calculation unit in the reconstructed overall strain distribution cloud map, and is expressed as:

[0093] σ=[C]ε

[0094] Wherein, [C] represents the stiffness matrix constructed based on material properties and constitutive relations, ε represents the strain tensor in the overall strain distribution cloud map, and σ represents the stress tensor in the overall stress distribution cloud map;

[0095] In this embodiment, the load-stress state mapping model construction module is used to construct a load-stress state mapping model. Under different load conditions, key mechanical indicators are extracted through the overall stress distribution cloud map, and the load-stress state mapping model is constructed based on the mapping relationship between the key mechanical indicators and the total load and the load center position.

[0096] In this embodiment, the load information output module is used to obtain a real-time overall stress distribution cloud map and output the current elevator load information based on the load-stress state mapping model.

[0097] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for intelligent load monitoring of an elevator car, characterized in that, Includes the following steps: Obtain the mechanical properties and geometric dimensions of the car floor material; Discrete point deformation data of the car floor are collected based on strain sensors; The overall strain distribution cloud map of the car floor is reconstructed based on the collected discrete point deformation data, specifically including: Based on the finite element analysis software, various load conditions are simulated, the full-field strain distribution cloud map of the car floor plate under each condition is calculated, and the deformation data of the simulated discrete points corresponding to the strain sensor positions are recorded. A training dataset is constructed based on the simulated discrete point deformation data and the corresponding full-field strain cloud map. A deep learning model is trained based on the training dataset to learn the mapping relationship between discrete point deformation data and the full-field strain cloud map of the car floor. The collected discrete deformation data is input into the trained deep learning model to reconstruct and generate the overall strain distribution cloud map of the car floor. Based on the constitutive relations of materials mechanics and the mechanical property parameters of the car floor material, the reconstructed overall strain distribution cloud map is converted into an overall stress distribution cloud map of the car floor, thereby obtaining the real-time stress state of the car floor, specifically including: Based on the generalized Hooke's law and the mechanical properties of the car floor material, the stress tensor of the overall stress distribution cloud map is calculated for each calculation unit in the reconstructed overall strain distribution cloud map, and is expressed as: ； in, This represents the stiffness matrix constructed based on material properties and constitutive relations. This represents the strain tensor in the overall strain distribution contour map. The stress tensor representing the overall stress distribution contour map; Under different load conditions, key mechanical indicators are extracted by the overall stress distribution cloud map, and a load-stress state mapping model is constructed based on the mapping relationship between key mechanical indicators and total load and load center position. Obtain a real-time overall stress distribution cloud map and output the current elevator load information based on the load-stress state mapping model.

2. The intelligent load monitoring method for elevator cars according to claim 1, characterized in that, The mechanical properties of the car floor material include density, elastic modulus, Poisson's ratio, and yield strength.

3. The intelligent load monitoring method for elevator cars according to claim 1, characterized in that, The strain sensor is installed in the key stress area of ​​the car floor, and the key stress area is determined according to the structural characteristics of the car floor.

4. The intelligent load monitoring method for elevator cars according to claim 1, characterized in that, Key mechanical indicators include the sum of the reaction forces at the base plate support points and the resultant force obtained by integrating the stress field.

5. An intelligent load monitoring system for elevator cars, characterized in that, include: The system includes a car floor parameter acquisition module, a strain sensor, an overall strain distribution cloud map generation module, an overall stress distribution cloud map generation module, a load-stress state mapping model construction module, and a load information output module. The car floor parameter acquisition module is used to acquire the mechanical property parameters and geometric dimensions of the car floor material. The strain sensor is used to collect discrete point deformation data of the car floor. The overall strain distribution cloud map generation module is used to reconstruct and generate an overall strain distribution cloud map of the car floor based on the collected discrete point deformation data, specifically including: Based on the finite element analysis software, various load conditions are simulated, the full-field strain distribution cloud map of the car floor plate under each condition is calculated, and the deformation data of the simulated discrete points corresponding to the strain sensor positions are recorded. A training dataset is constructed based on the simulated discrete point deformation data and the corresponding full-field strain cloud map. A deep learning model is trained based on the training dataset to learn the mapping relationship between discrete point strain data and the full-field strain cloud map of the car floor. The collected discrete deformation data is input into the trained deep learning model to reconstruct and generate the overall strain distribution cloud map of the car floor. The overall stress distribution cloud map generation module is used to convert the reconstructed overall strain distribution cloud map into an overall stress distribution cloud map of the car floor based on the material mechanics constitutive relationship and the mechanical property parameters of the car floor material, thereby obtaining the real-time stress state of the car floor. Specifically, it includes: Based on the generalized Hooke's law and the mechanical properties of the car floor material, the stress tensor of the overall stress distribution cloud map is calculated for each calculation unit in the reconstructed overall strain distribution cloud map, and is expressed as: ； in, This represents the stiffness matrix constructed based on material properties and constitutive relations. This represents the strain tensor in the overall strain distribution contour map. The stress tensor representing the overall stress distribution contour map; The load-stress state mapping model construction module is used to construct a load-stress state mapping model. Under different load conditions, key mechanical indicators are extracted through the overall stress distribution cloud map, and a load-stress state mapping model is constructed based on the mapping relationship between the key mechanical indicators and the total load and the load center position. The load information output module is used to obtain a real-time overall stress distribution cloud map and output the current elevator load information based on the load-stress state mapping model.

6. The intelligent load monitoring system for elevator cars according to claim 5, characterized in that, The strain sensor is installed in the key stress area of ​​the car floor, and the key stress area is determined according to the structural characteristics of the car floor.

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