Elevator energy-saving control system with multiple safety guarantee measures

Abstract

The invention belongs to the technical field of elevator energy-saving control, and discloses an elevator energy-saving control system with multiple safety guarantee measures. The system comprises a mechanical fatigue prediction module, a safety maintenance decision module, an operation energy efficiency optimization module, a multi-objective optimization control module and a customized energy-saving strategy module, a fatigue prediction model is established based on historical feature vectors, the feature vectors are input to obtain fatigue prediction values, the fatigue prediction values are graded, and the graded fatigue prediction values are obtained. And matching the grading result with a decision rule base to obtain a safety maintenance report and an energy efficiency optimization report, outputting to obtain an optimization control report, and adjusting system parameters according to different use seasons to obtain a customized energy saving report. The method has the remarkable advantages of being high in safety guarantee capacity, good in active risk response effect and large in multi-target comprehensive optimization energy-saving effect.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator energy-saving control, and more particularly to an elevator energy-saving control system with multiple safety measures. Background Art

[0002] An elevator is a permanent transportation device that serves several specific floors in a building, with its car running on at least two rows of rigid rails perpendicular to the horizontal plane or with an inclination angle of less than 15° to the plumb line. At the end of the 20th century, elevators used permanent magnet synchronous traction machines as their power, which greatly reduced the area occupied by the machine room. It also has the advantages of low energy consumption, energy saving and high efficiency, and fast lifting speed, which greatly promoted the development of real estate towards super-high-rise buildings. Among them, the traction machine, as an important component of the elevator, not only determines the intensity of elevator use, but is also directly related to the safety of elevator use. Moreover, the operation of the elevator requires a large amount of electricity as energy support. Therefore, how to reduce elevator energy consumption while ensuring elevator safety has become a major problem that the current elevator industry needs to solve.

[0003] The patent application publication number CN114291669B discloses an elevator energy-saving control system. By fully considering the home usage scenarios and the gradual widespread use of smart home devices such as smart door locks and door entry detection switches, it uses information interaction with the home Internet of Things to control the standby state of the elevator. It maintains extremely low standby power consumption during a large amount of time when no one is using the elevator, and ensures that passengers can quickly take the elevator when they need to use it without having to wait too long.

[0004] However, although the above-mentioned elevator energy-saving control system achieves the goal of balancing the elevator usage experience and energy-saving needs to a certain extent by utilizing information interaction with the home Internet of Things to control the elevator's standby state, during the use of the elevator, the existing elevator energy-saving control system mostly relies on traditional single data monitoring to ensure the safety of elevator operation, which easily causes a passive response to the safety of elevator use. At the same time, it is difficult to effectively balance passenger experience, energy consumption and safety risks. Moreover, in terms of energy saving, the dynamic changes of environmental factors are not taken into account by most elevator energy-saving control systems.

[0005] In view of this, the present invention proposes an elevator energy-saving control system with multiple safety measures to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solutions, including:

[0007] Fatigue data acquisition module, used to collect fatigue data sets, including running time data, load weight data, running number data and equipment temperature data;

[0008] A supplementary data acquisition module is used to collect a supplementary data set, the supplementary data set including environmental humidity data and vibration intensity data;

[0009] Interactive data processing module, used to preprocess the fatigue dataset and supplementary dataset, and perform feature extraction to obtain feature vectors;

[0010] Furthermore, the steps of preprocessing the fatigue dataset and the supplementary dataset and performing feature extraction include:

[0011] Q1: Complete data cleaning of all sub-data items in the basic data set by removing outliers, and normalize all sub-data items in the basic data set to the range of [0, 1] according to the normalization formula;

[0012] Q2: By calculating the running time data S a Multiply by the running number data S c , get the intensity characteristic data A a ;

[0013] Q3: Load weight data S b Multiply by the safety factor to get the load characteristic data A b ;

[0014] Q4: Calculate device temperature data S d Subtract the absolute value of 25 to obtain the temperature characteristic data A c ;

[0015] Q5: The vibration intensity data S f Amplify twice to get the vibration characteristic data A d ;

[0016] Q6: Calculate the sum of the intensity characteristic data and the load characteristic data and divide it by 2 to obtain the comprehensive environmental characteristic data A e ;

[0017] Q7: Packing strength characteristic data, load characteristic data, temperature characteristic data, vibration characteristic data, environmental comprehensive characteristic data and environmental humidity data are used to obtain a characteristic vector;

[0018] Mechanical fatigue prediction module, used to establish a fatigue prediction model based on historical eigenvectors and input eigenvectors to obtain fatigue prediction values;

[0019] Furthermore, the specific steps of establishing a fatigue prediction model based on the historical eigenvector and inputting the eigenvector to obtain the fatigue prediction value include:

[0020] Step 1: Obtain a set of historical feature vectors stored in the database, compare them with the current time based on the timestamp, and group the comparison results into corresponding groups from small to large. The labeled results are L1, L2, L3, ..., Ln, and the labeled results are used as the sample set;

[0021] Step 2: Divide the sample set into a 70% training set, a 15% test set, and a 15% validation set, and establish the first fatigue prediction model based on the sample set;

[0022] Step 3: Based on the main model in the multiple heterogeneous sub-models and according to the historical eigenvectors, calculate the first prediction value. The specific formula for the calculation is:

[0023]

[0024] Get the first prediction value Among them, e is a natural constant, θ1 and θ2 are dynamic weight factors, Ψ1 is the dynamic stress intensity index, and Ψ2 is the time-varying cumulative damage index;

[0025] The expression group of dynamic stress intensity index and time-varying cumulative damage index is:

[0026]

[0027] Among them, tanh is the hyperbolic tangent function, S g is the standard life value of the traction machine;

[0028] Step 4: Based on the time decay correction model in the multiple heterogeneous sub-models and the historical fatigue prediction value, calculate the second prediction value. The specific calculation formula is:

[0029]

[0030] Get the first prediction value Among them, N is the size of the historical data window, is the historical fatigue prediction value of the nth historical data, λ is the attenuation coefficient, t is the current time, t n is the recording time point of the nth historical fatigue prediction value, S h is the standard fatigue cycle of the traction machine;

[0031] Step 5: Based on the anomaly detection model in the multiple heterogeneous sub-models and the fatigue dataset, calculate the third prediction value The specific calculation formula is:

[0032]

[0033] The comprehensive anomaly index δ is obtained, where μ is the mean and σ is the standard deviation;

[0034] When the comprehensive abnormality index is less than 1.5, the output value of the third prediction value is 0.2 times the comprehensive abnormality index; when the comprehensive abnormality index is greater than or equal to 1.5 and less than 3, the output value of the third prediction value is 0.3+0.1×(δ-1.5); when the comprehensive abnormality index is less than or equal to 3, the output value of the third prediction value is 0.5;

[0035] Step 6: Perform weighted summation on the first prediction value, the second prediction value, and the third prediction value, and add a constant term to obtain the fatigue prediction value;

[0036] Step 7: Repeat steps 3 to 6 until the preset number of iterations is reached to obtain a fatigue prediction model;

[0037] Step 8: Input the feature vector into the fatigue prediction model, output the fatigue prediction value, and output the fatigue prediction value to the safety maintenance decision module and the multi-objective optimization control module;

[0038] The safety maintenance decision module is used to classify fatigue prediction values ​​and match the classification results with the decision rule library to obtain a safety maintenance report;

[0039] Furthermore, the fatigue prediction values ​​are graded and the classification results are matched with the decision rule base in the following ways:

[0040] Based on fatigue threshold intervals (R1, R2) and decision rule base;

[0041] When the fatigue prediction value is less than R1, a normal report is generated; when the fatigue prediction value is greater than or equal to R1 and less than R2, a warning report and a load reduction operation instruction are generated; when the fatigue prediction value is greater than or equal to R2, a danger report and an emergency stop instruction are generated;

[0042] The normal report includes a description of the predicted fatigue status of the designated traction machine and asks the staff to monitor the operating status of the designated elevator according to the preset operation process;

[0043] The warning report includes a load reduction operation instruction and indicates that the fatigue state of the specified traction machine is predicted to accumulate. The staff is requested to pay attention to continuously monitor the operating status of the specified elevator;

[0044] The hazard report includes an emergency stop instruction and indicates that the fatigue risk of the designated traction machine is predicted to be high. The staff is requested to immediately send a work order to the maintenance team and arrange to go to the designated elevator area to implement risk prevention and intervention measures;

[0045] The load reduction operation instruction contains a group of characters representing reducing the maximum load of the specified elevator by 10%;

[0046] The emergency stop command contains a set of characters indicating that the elevator will run to the nearest floor, open the door and stop running;

[0047] Pack normal reports, warning reports and danger reports to obtain safety maintenance reports;

[0048] Run the energy efficiency optimization module to calculate the energy savings of enabling deep sleep mode during off-peak hours, and embed a safety self-check mechanism to generate an energy efficiency optimization report;

[0049] Furthermore, the energy savings from enabling deep sleep mode during off-peak hours can be calculated and a safety self-check mechanism can be embedded, including:

[0050] When the traction machine is in the off-peak period, the sum of the energy consumption of shutting down or reducing some functions is calculated to obtain an energy efficiency optimization report;

[0051] Deep sleep mode means, for example, reducing the cabin lighting by 50% or turning off non-critical sensors;

[0052] The safety self-check mechanism includes checking whether the car door is fully closed and whether the device temperature data is greater than or equal to 60 degrees Celsius. When the car door is fully closed and the device temperature data is less than 60 degrees Celsius, the safety self-check is passed. Otherwise, it is necessary to wait 10 seconds and then re-enter the safety self-check mechanism. If the safety self-check mechanism fails three times in a row, the staff alarm is triggered;

[0053] A multi-objective optimization control module is used to balance the energy consumption, safety risks, and passenger experience of elevator operation based on fatigue prediction values, and output an optimization control report;

[0054] Furthermore, the steps of balancing the energy consumption, safety risks, and passenger experience of elevator operation based on the fatigue prediction value include:

[0055] W1: Based on the energy consumption, safety risk and passenger experience of elevator operation, the operation energy consumption coefficient, safety risk coefficient and average waiting coefficient are obtained;

[0056] W2: When the safety risk coefficient is greater than or equal to the safety risk threshold, a safety priority instruction is generated. Otherwise, the process proceeds to step W3. The safety priority instruction includes a set of characters representing that the maximum power of the traction machine is limited to 80% of the rated power.

[0057] W3: When the average waiting coefficient is greater than or equal to the average waiting threshold, an experience priority instruction is generated. Otherwise, the process proceeds to step W4. The experience priority instruction includes a set of characters representing increasing the maximum power of the traction machine to 120% of the rated power.

[0058] W4: When the operating energy consumption coefficient is greater than or equal to the operating energy consumption threshold, an energy efficiency priority instruction is generated. Otherwise, the current operating parameters are maintained. The energy efficiency priority instruction contains a set of characters representing limiting the single no-load distance to less than or equal to 5 floors and reducing the lighting equipment power to 70% of the rated lighting power.

[0059] W5: Packages security priority instructions, experience priority instructions, and energy efficiency priority instructions to obtain an optimization control report;

[0060] Customized energy-saving strategy module, used to adjust system parameters according to different seasons of use and obtain customized energy-saving reports;

[0061] Furthermore, the methods for adjusting system parameters according to different seasons of use include:

[0062] Calculate the external ambient temperature value, subtract 22, and then divide it by the seasonal coefficient to obtain the temperature compensation parameter;

[0063] When the temperature compensation parameter is greater than or equal to 0, an air conditioning energy-saving instruction is generated;

[0064] When the temperature compensation parameter is less than 0, a heating reduction instruction is generated;

[0065] The air conditioner energy saving instruction contains a set of characters representing raising the air conditioner set temperature by 2 degrees Celsius;

[0066] The heating reduction instruction contains a set of characters representing a reduction of the elevator heating device's rated power by 10%;

[0067] Package air conditioning energy saving instructions and heating reduction instructions to get customized energy saving reports;

[0068] System data security module, which is used to store system data sets, display safety maintenance reports and energy efficiency optimization reports through a visual panel, analyze safety maintenance reports, optimization control reports and customized energy-saving reports, and process them based on the analysis results;

[0069] Furthermore, the safety maintenance report, optimization control report and customized energy saving report are analyzed, and the processing methods based on the analysis results include:

[0070] Identify whether there are instructions to be executed in the safety maintenance report, optimization control report, and customized energy-saving report. If there are instructions to be executed, receive the instructions through the programmable logic controller, convert them into control signals, and transmit them to the intelligent controller;

[0071] System data sets include fatigue data sets, supplementary data sets, feature vectors, fatigue prediction values, safety maintenance reports, energy efficiency optimization reports, optimization control reports, and customized energy saving reports;

[0072] Further, S1: collecting a fatigue data set, the fatigue data set includes operation time data, load weight data, operation number data and equipment temperature data;

[0073] S2: Collect supplementary data sets, including environmental humidity data and vibration intensity data;

[0074] S3: Preprocess the fatigue dataset and supplementary dataset, perform feature extraction, and obtain feature vectors;

[0075] S4: Establish a fatigue prediction model based on the historical eigenvector and input the eigenvector to obtain the fatigue prediction value;

[0076] S5: Classify the fatigue prediction values ​​and match the classification results with the decision rule library to obtain a safety maintenance report;

[0077] S6: Calculate the energy savings of enabling deep sleep mode during off-peak hours, embed a safety self-check mechanism, and generate an energy efficiency optimization report.

[0078] S7: Balances the energy consumption, safety risks, and passenger experience of elevator operation based on fatigue prediction values, and outputs an optimized control report;

[0079] S8: Adjust system parameters according to different seasons of use to obtain customized energy saving reports;

[0080] S9: Store system data sets, display security maintenance reports and energy efficiency optimization reports through a visual panel, analyze security maintenance reports, optimization control reports, and customized energy-saving reports, and perform processing based on the analysis results.

[0081] The technical effects and advantages of the elevator energy-saving control system with multiple safety measures of the present invention are as follows:

[0082] The present invention collects a fatigue data set, which includes operating time data, load weight data, operating times data and equipment temperature data; collects a supplementary data set, which includes environmental humidity data and vibration intensity data; pre-processes the fatigue data set and the supplementary data set, and performs feature extraction to obtain a feature vector; establishes a fatigue prediction model based on the historical feature vector, and inputs the feature vector to obtain a fatigue prediction value; grades the fatigue prediction value, and matches the graded result with the decision rule library to obtain a safety maintenance report; calculates the energy saving amount of enabling the deep sleep mode during the off-peak period; and embeds a safety self-check mechanism to obtain an energy efficiency optimization report; balances the energy consumption, safety risks and passenger experience of the elevator operation based on the fatigue prediction value, and outputs an optimization control report; adjusts the system parameters according to the different seasons of use to obtain a customized energy-saving report; stores the system data set, and displays the safety maintenance report and the energy efficiency optimization report through a visual panel; The report is analyzed and processed according to the analysis results, so that the system can accurately quantify and predict the fatigue state of the traction machine by integrating multi-source data, which greatly reduces the risk of passive response brought about by traditional single data monitoring. In addition, the present invention also deeply explores and uses fatigue prediction values, so that the system can provide timely corresponding decision-making support to staff while giving priority to pre-emptive prevention and control of risks, thereby greatly improving the safety of elevator use. At the same time, by embedding a safety self-check mechanism in low-peak usage periods, it can further achieve the optimization of energy consumption while ensuring the safety of elevator use. Moreover, by optimizing control reports, the system can achieve an optimal balance of multiple objectives, greatly improving the applicability of the system. Finally, by incorporating seasonal change factors, the system can achieve the ultimate energy saving of elevators, thereby effectively reducing the use cost for elevator users. Overall, the present invention has the significant advantages of strong safety assurance capabilities, good active risk response effects and large multi-objective comprehensive optimization energy-saving effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 Schematic diagram of an elevator energy-saving control system with multiple safety measures according to the present invention;

[0084] Figure 2 Schematic diagram of the elevator energy-saving control method with multiple safety measures of the present invention. DETAILED DESCRIPTION

[0085] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0086] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a," "an," "the," and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.

[0087] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0088] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0089] In practice, the server-side device deployed in an elevator energy-saving control system with multiple security measures may be composed of one or more devices. The aforementioned elevator energy-saving control system with multiple security measures can be implemented as a service instance, a virtual machine, or a hardware device. For example, the elevator energy-saving control system with multiple security measures can be implemented as a service instance deployed on one or more devices in a cloud node. Simply put, the elevator energy-saving control system with multiple security measures can be understood as software deployed on a cloud node, which provides the elevator energy-saving control system with multiple security measures to each user terminal. Alternatively, the elevator energy-saving control system with multiple security measures can be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing each user terminal. Alternatively, the elevator energy-saving control system with multiple security measures can be implemented as a server-side device composed of multiple hardware devices of the same or different types, with one or more hardware devices configured to provide the elevator energy-saving control system with multiple security measures to each user terminal.

[0090] In terms of implementation, the elevator energy-saving control system with multiple security measures and the user end are mutually compatible. That is, if the elevator energy-saving control system with multiple security measures is an application installed on a cloud service platform, the user end is the client that establishes a communication connection with the application; or if the elevator energy-saving control system with multiple security measures is implemented as a website, the user end is implemented as a webpage; or if the elevator energy-saving control system with multiple security measures is implemented as a cloud service platform, the user end is implemented as a mini-program in an instant messaging application.

[0091] like Figure 1 FIG. 1 is a system architecture diagram of an elevator energy-saving control system with multiple safety measures provided by an embodiment of the present invention.

[0092] The elevator energy-saving control system with multiple safety measures described in the present invention can be set in a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed on the cloud (such as a mobile service operator's server, server cluster, etc.), or it can be developed as a website. According to the functions implemented, the elevator energy-saving control system with multiple safety measures can include a fatigue data acquisition module, a supplementary data acquisition module, an interactive data processing module, a mechanical fatigue prediction module, a safety maintenance decision module, an operation energy efficiency optimization module, a multi-objective optimization control module, a customized energy-saving strategy module and a system data security module. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0093] In an embodiment of the present invention, in an elevator energy-saving control system with multiple safety measures, each of the above modules can be implemented independently and called with other modules. The call here can be understood as a module that can connect to multiple modules of another type and provide corresponding services to the multiple modules connected to it. For example, the sharing evaluation module can call the same information acquisition module to obtain the information collected by the information acquisition module. Based on the above characteristics, in the elevator energy-saving control system with multiple safety measures provided by an embodiment of the present invention, the application scope of the elevator energy-saving control system with multiple safety measures can be adjusted by adding modules and directly calling them without modifying the program code, thereby realizing cluster-type horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the elevator energy-saving control system with multiple safety measures. In actual applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0094] Example 1

[0095] See also Figure 1As shown, the elevator energy-saving control system with multiple safety measures described in this embodiment includes:

[0096] The fatigue data acquisition module is used to collect fatigue data sets, which include running time data, load weight data, running number data and equipment temperature data;

[0097] It should be explained that the motor operation timer collects the cumulative power-on working hours of the specified traction machine to obtain the operation time data; the car pressure sensor collects the total weight value of the specified car load to obtain the load weight data; the start-stop counter collects the number of complete cycles from start to stop of the specified traction machine to obtain the operation number data; the infrared temperature sensor collects the surface temperature value of the specified traction machine shell to obtain the equipment temperature data;

[0098] The supplementary data acquisition module is used to acquire a supplementary data set, the supplementary data set including environmental humidity data and vibration intensity data;

[0099] It should be explained that the humidity sensor in the machine room collects the air humidity value in the specified elevator machine room area to obtain the ambient humidity data; the three-axis accelerometer collects the vibration amplitude values ​​of the specified traction machine base in the front-to-back, up-down and left-to-right directions to obtain the vibration intensity data;

[0100] The interactive data processing module is used to preprocess the fatigue dataset and the supplementary dataset, and perform feature extraction to obtain feature vectors;

[0101] Furthermore, the steps of preprocessing the fatigue dataset and the supplementary dataset and performing feature extraction include:

[0102] Q1: Complete data cleaning of all sub-data items in the basic data set by removing outliers, and normalize all sub-data items in the basic data set to the range of [0, 1] according to the normalization formula;

[0103] It should be explained that removing outliers means, for example, that the ambient humidity data is negative; the basic data set includes the fatigue data set and the supplementary data set; the specific expression of the normalization formula is: where X new is the normalized value, X is any sub-data item of the basic data, X max is the historical maximum value of any sub-data item, X min is the historical minimum value of any sub-data item; normalization is used to eliminate the dimensions of all sub-data items in the basic data set;

[0104] Q2: By calculating the running time data S a Multiply by the running number data S c, get the intensity characteristic data A a ;

[0105] Q3: Load weight data S b Multiply by the safety factor to get the load characteristic data A b ;

[0106] Q4: Calculate device temperature data S d Subtract the absolute value of 25 to obtain the temperature characteristic data A c ;

[0107] Q5: The vibration intensity data S f Amplify twice to get the vibration characteristic data A d ;

[0108] Q6: Calculate the sum of the intensity characteristic data and the load characteristic data and divide it by 2 to obtain the comprehensive environmental characteristic data A e ;

[0109] Q7: Packing strength characteristic data, load characteristic data, temperature characteristic data, vibration characteristic data, environmental comprehensive characteristic data and environmental humidity data are used to obtain a characteristic vector;

[0110] It should be explained that the sub-data items used in the basic data involved in steps Q2 to Q6 and all subsequent modules are all data values ​​after data cleaning and data normalization;

[0111] The mechanical fatigue prediction module is used to establish a fatigue prediction model based on the historical eigenvector and input the eigenvector to obtain a fatigue prediction value;

[0112] Furthermore, the specific steps of establishing a fatigue prediction model based on the historical eigenvector and inputting the eigenvector to obtain the fatigue prediction value include:

[0113] Step 1: Obtain a set of historical feature vectors stored in the database, compare them with the current time based on the timestamp, and group the comparison results into corresponding groups from small to large. The labeled results are L1, L2, L3, ..., Ln, and the labeled results are used as the sample set;

[0114] Step 2: Divide the sample set into a 70% training set, a 15% test set, and a 15% validation set, and establish the first fatigue prediction model based on the sample set;

[0115] Step 3: Based on the main model in the multiple heterogeneous sub-models and according to the historical eigenvectors, calculate the first prediction value. The specific formula for the calculation is:

[0116]

[0117] Get the first prediction value Among them, e is a natural constant, θ1 and θ2 are dynamic weight factors, Ψ1 is the dynamic stress intensity index, and Ψ2 is the time-varying cumulative damage index;

[0118] The expression group of dynamic stress intensity index and time-varying cumulative damage index is:

[0119]

[0120] Among them, tanh is the hyperbolic tangent function, S g is the standard life value of the traction machine;

[0121] It should be explained that the hyperbolic tangent function is used to constrain the output values ​​immediately within the brackets to be within the range of [-1,1];

[0122] Step 4: Based on the time decay correction model in the multiple heterogeneous sub-models and the historical fatigue prediction value, calculate the second prediction value. The specific calculation formula is:

[0123]

[0124] Get the first prediction value Among them, N is the size of the historical data window, is the historical fatigue prediction value of the nth historical data, λ is the attenuation coefficient, t is the current time, t n is the recording time point of the nth historical fatigue prediction value, S h is the standard fatigue cycle of the traction machine;

[0125] Step 5: Based on the anomaly detection model in the multiple heterogeneous sub-models and the fatigue dataset, calculate the third prediction value The specific calculation formula is:

[0126]

[0127] The comprehensive anomaly index δ is obtained, where μ is the mean and σ is the standard deviation;

[0128] When the comprehensive abnormality index is less than 1.5, the output value of the third prediction value is 0.2 times the comprehensive abnormality index; when the comprehensive abnormality index is greater than or equal to 1.5 and less than 3, the output value of the third prediction value is 0.3+0.1×(δ-1.5); when the comprehensive abnormality index is less than or equal to 3, the output value of the third prediction value is 0.5;

[0129] Step 6: Perform weighted summation on the first prediction value, the second prediction value, and the third prediction value, and add a constant term to obtain the fatigue prediction value;

[0130] Step 7: Repeat steps 3 to 6 until the preset number of iterations is reached to obtain a fatigue prediction model;

[0131] Step 8: Input the feature vector into the fatigue prediction model, output the fatigue prediction value, and output the fatigue prediction value to the safety maintenance decision module and the multi-objective optimization control module;

[0132] The safety maintenance decision module is used to classify fatigue prediction values ​​and match the classification results with the decision rule library to obtain a safety maintenance report;

[0133] Furthermore, the fatigue prediction values ​​are graded and the classification results are matched with the decision rule base in the following ways:

[0134] Based on fatigue threshold intervals (R1, R2) and decision rule base;

[0135] It should be explained that the fatigue threshold range is manually set and input into the system; the decision rule base is a rule base composed of the corresponding response measures provided based on the classification results;

[0136] When the fatigue prediction value is less than R1, a normal report is generated; when the fatigue prediction value is greater than or equal to R1 and less than R2, a warning report and a load reduction operation instruction are generated; when the fatigue prediction value is greater than or equal to R2, a danger report and an emergency stop instruction are generated;

[0137] The normal report includes a description of the predicted fatigue status of the designated traction machine and asks the staff to monitor the operating status of the designated elevator according to the preset operation process;

[0138] The warning report includes a load reduction operation instruction and indicates that the fatigue state of the specified traction machine is predicted to accumulate. The staff is requested to pay attention to continuously monitor the operating status of the specified elevator;

[0139] The hazard report includes an emergency stop instruction and indicates that the fatigue risk of the designated traction machine is predicted to be high. The staff is requested to immediately send a work order to the maintenance team and arrange to go to the designated elevator area to implement risk prevention and intervention measures;

[0140] The load reduction operation instruction contains a group of characters representing reducing the maximum load of the specified elevator by 10%;

[0141] The emergency stop command contains a set of characters indicating that the elevator will run to the nearest floor, open the door and stop running;

[0142] Pack normal reports, warning reports and danger reports to obtain safety maintenance reports;

[0143] The operation energy efficiency optimization module is used to calculate the energy consumption savings of enabling deep sleep mode during off-peak hours, and embed a safety self-check mechanism to obtain an energy efficiency optimization report;

[0144] Furthermore, methods for calculating the energy savings of enabling deep sleep mode during off-peak hours and embedding a safety self-check mechanism include:

[0145] When the traction machine is in the off-peak period, the sum of the energy consumption of shutting down or reducing some functions is calculated to obtain an energy efficiency optimization report;

[0146] Deep sleep mode means, for example, reducing the cabin lighting by 50% or turning off non-critical sensors;

[0147] It should be explained that the off-peak period is manually set and entered into the system, for example, 0:00-5:00 is the off-peak period;

[0148] The safety self-check mechanism includes checking whether the car door is fully closed and whether the device temperature data is greater than or equal to 60 degrees Celsius. When the car door is fully closed and the device temperature data is less than 60 degrees Celsius, the safety self-check is passed. Otherwise, it is necessary to wait 10 seconds and then re-enter the safety self-check mechanism. If the safety self-check mechanism fails three times in a row, the staff alarm is triggered;

[0149] It should be explained that the device temperature data used to run the energy efficiency optimization module are raw values;

[0150] The multi-objective optimization control module is used to balance the energy consumption, safety risks and passenger experience of elevator operation based on the fatigue prediction value, and output an optimization control report;

[0151] Furthermore, the steps for balancing the energy consumption, safety risks, and passenger experience of elevator operation based on fatigue prediction values ​​include:

[0152] W1: Based on the energy consumption, safety risk and passenger experience of elevator operation, the operation energy consumption coefficient, safety risk coefficient and average waiting coefficient are obtained;

[0153] It should be explained that the operating energy consumption coefficient is obtained by multiplying the traction machine power by the operating time; the safety risk coefficient is half of the sum of the fatigue prediction value and the vibration intensity data; the average waiting coefficient is the average waiting time of passengers;

[0154] W2: When the safety risk coefficient is greater than or equal to the safety risk threshold, a safety priority instruction is generated. Otherwise, the process proceeds to step W3. The safety priority instruction includes a set of characters representing that the maximum power of the traction machine is limited to 80% of the rated power.

[0155] W3: When the average waiting coefficient is greater than or equal to the average waiting threshold, an experience priority instruction is generated. Otherwise, the process proceeds to step W4. The experience priority instruction includes a set of characters representing increasing the maximum power of the traction machine to 120% of the rated power.

[0156] W4: When the operating energy consumption coefficient is greater than or equal to the operating energy consumption threshold, an energy efficiency priority instruction is generated. Otherwise, the current operating parameters are maintained. The energy efficiency priority instruction contains a set of characters representing limiting the single no-load distance to less than or equal to 5 floors and reducing the lighting equipment power to 70% of the rated lighting power.

[0157] W5: Packages security priority instructions, experience priority instructions, and energy efficiency priority instructions to obtain an optimization control report;

[0158] It should be explained that the security risk threshold, average waiting threshold, and operating energy consumption threshold are all manually set and input into the system;

[0159] The customized energy-saving strategy module is used to adjust system parameters according to different usage seasons and obtain customized energy-saving reports;

[0160] Furthermore, the methods for adjusting system parameters according to different seasons of use include:

[0161] Calculate the external ambient temperature value, subtract 22, and then divide it by the seasonal coefficient to obtain the temperature compensation parameter;

[0162] It should be explained that the seasonal coefficient refers to the compensation parameter that the staff sets different values ​​according to different seasons, for example, 0.8 in summer and 1.2 in winter;

[0163] When the temperature compensation parameter is greater than or equal to 0, an air conditioning energy-saving instruction is generated;

[0164] When the temperature compensation parameter is less than 0, a heating reduction instruction is generated;

[0165] The air conditioner energy saving instruction contains a set of characters representing raising the air conditioner set temperature by 2 degrees Celsius;

[0166] The heating reduction instruction contains a set of characters representing a reduction of the elevator heating device's rated power by 10%;

[0167] Package air conditioning energy saving instructions and heating reduction instructions to get customized energy saving reports;

[0168] The system data security module is used to store system data sets, display security maintenance reports and energy efficiency optimization reports through a visual panel, analyze security maintenance reports, optimization control reports and customized energy saving reports, and process them according to the analysis results;

[0169] Furthermore, the safety maintenance report, optimization control report and customized energy saving report are analyzed, and the processing methods based on the analysis results include:

[0170] Identify whether there are instructions to be executed in the safety maintenance report, optimization control report, and customized energy-saving report. If there are instructions to be executed, receive the instructions through the programmable logic controller, convert them into control signals, and transmit them to the intelligent controller;

[0171] It should be explained that the instructions to be executed include load reduction operation instructions, emergency stop instructions, safety priority instructions, experience priority instructions, energy efficiency priority instructions, air conditioning energy saving instructions and heating reduction instructions;

[0172] System data sets include fatigue data sets, supplementary data sets, feature vectors, fatigue prediction values, safety maintenance reports, energy efficiency optimization reports, optimization control reports, and customized energy saving reports;

[0173] This embodiment has the beneficial effects of collecting fatigue data sets, which include operating time data, load weight data, operating times data and equipment temperature data, collecting supplementary data sets, which include environmental humidity data and vibration intensity data, preprocessing the fatigue data sets and supplementary data sets, and performing feature extraction to obtain feature vectors, establishing a fatigue prediction model based on historical feature vectors, and inputting feature vectors to obtain fatigue prediction values, grading the fatigue prediction values, and matching the grading results with the decision rule library to obtain a safety maintenance report, calculating the energy savings of enabling deep sleep mode during off-peak periods, and embedding a safety self-check mechanism to obtain an energy efficiency optimization report, balancing the energy consumption, safety risks and passenger experience of elevator operation based on the fatigue prediction value, and outputting an optimization control report, adjusting system parameters according to different seasons of use to obtain a customized energy-saving report, storing the system data sets, and displaying the safety maintenance report and energy efficiency optimization report through a visual panel, and performing maintenance on the safety maintenance report, optimization control report and customized The energy-saving report is analyzed and processed according to the analysis results, so that the system can accurately quantify and predict the fatigue state of the traction machine by integrating multi-source data, which greatly reduces the risk of passive response brought by traditional single data monitoring. In addition, the present invention also deeply explores and uses fatigue prediction values, so that the system can provide timely corresponding decision-making support to staff while giving priority to pre-emptive prevention and control of risks, thereby greatly improving the safety of elevator use. At the same time, through the safety self-check mechanism embedded in low-peak usage periods, it can further achieve the optimization of energy consumption while ensuring the safety of elevator use. Moreover, through the optimization of control reports, the system can achieve an optimal balance of multiple objectives, greatly improving the applicability of the system. Finally, by incorporating seasonal change factors, the system can achieve the ultimate energy saving of elevators, thereby effectively reducing the use cost for elevator users. Overall, the present invention has the significant advantages of strong safety assurance capabilities, good active risk response effects and large multi-objective comprehensive optimization energy-saving effects.

[0174] Example 2

[0175] See also Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description of embodiment 1. A method for controlling energy-saving of an elevator with multiple safety measures is provided, the method comprising: S1: collecting a fatigue data set, the fatigue data set comprising running time data, load weight data, running number data, and equipment temperature data;

[0176] S2: Collect supplementary data sets, including environmental humidity data and vibration intensity data;

[0177] S3: Preprocess the fatigue dataset and supplementary dataset, perform feature extraction, and obtain feature vectors;

[0178] S4: Establish a fatigue prediction model based on the historical eigenvector and input the eigenvector to obtain the fatigue prediction value;

[0179] S5: Classify the fatigue prediction values ​​and match the classification results with the decision rule library to obtain a safety maintenance report;

[0180] S6: Calculate the energy savings of enabling deep sleep mode during off-peak hours, embed a safety self-check mechanism, and generate an energy efficiency optimization report.

[0181] S7: Balances the energy consumption, safety risks, and passenger experience of elevator operation based on fatigue prediction values, and outputs an optimized control report;

[0182] S8: Adjust system parameters according to different seasons of use to obtain customized energy saving reports;

[0183] S9: Store system data sets, display security maintenance reports and energy efficiency optimization reports through a visual panel, analyze security maintenance reports, optimization control reports, and customized energy-saving reports, and perform processing based on the analysis results.

[0184] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. An elevator energy-saving control system with multiple safety measures, characterized in that: The system includes: a mechanical fatigue prediction module, a safety maintenance decision module, an operation energy efficiency optimization module, a multi-objective optimization control module and a customized energy-saving strategy module, wherein: The mechanical fatigue prediction module is used to establish a fatigue prediction model based on the historical eigenvector and input the eigenvector to obtain a fatigue prediction value; The safety maintenance decision module is used to classify fatigue prediction values ​​and match the classification results with the decision rule library to obtain a safety maintenance report; The operation energy efficiency optimization module is used to calculate the energy consumption savings of enabling deep sleep mode during off-peak hours, and embed a safety self-check mechanism to obtain an energy efficiency optimization report; The multi-objective optimization control module is used to balance the energy consumption, safety risks and passenger experience of elevator operation based on the fatigue prediction value, and output an optimization control report; The customized energy-saving strategy module is used to adjust system parameters according to different usage seasons to obtain a customized energy-saving report.

2. The elevator energy-saving control system with multiple safety measures according to claim 1 is characterized in that: The system further comprises: a fatigue data acquisition module, a supplementary data acquisition module, an interactive data processing module and a system data assurance module, wherein: The fatigue data acquisition module is used to collect fatigue data sets, which include running time data, load weight data, running number data and equipment temperature data; The supplementary data acquisition module is used to acquire a supplementary data set, the supplementary data set including environmental humidity data and vibration intensity data; The interactive data processing module is used to preprocess the fatigue dataset and the supplementary dataset, and perform feature extraction to obtain feature vectors; The system data security module is used to store system data sets, display safety maintenance reports and energy efficiency optimization reports through a visual panel, analyze safety maintenance reports, optimization control reports and customized energy saving reports, and process them according to the analysis results.

3. The elevator energy-saving control system with multiple safety measures according to claim 2 is characterized in that: The steps of preprocessing the fatigue dataset and supplementary dataset and performing feature extraction include: Q1: Complete data cleaning of all sub-data items in the basic data set by removing outliers, and normalize all sub-data items in the basic data set to the range of [0, 1] according to the normalization formula; Q2: By calculating the running time data S a Multiply by the running number data S c , get the intensity characteristic data A a ; Q3: Load weight data S b Multiply by the safety factor to get the load characteristic data A b ; Q4: Calculate device temperature data S d Subtract the absolute value of 25 to obtain the temperature characteristic data A c ; Q5: The vibration intensity data S f Amplify twice to get the vibration characteristic data A d ; Q6: Calculate the sum of the intensity characteristic data and the load characteristic data and divide it by 2 to obtain the comprehensive environmental characteristic data A e ; Q7: Packing strength characteristic data, load characteristic data, temperature characteristic data, vibration characteristic data, environmental comprehensive characteristic data and environmental humidity data are used to obtain a characteristic vector.

4. The elevator energy-saving control system with multiple safety measures according to claim 1 is characterized in that: The specific steps of establishing a fatigue prediction model based on historical eigenvectors and inputting eigenvectors to obtain fatigue prediction values ​​include: Step 1: Obtain a set of historical feature vectors stored in the database, compare them with the current time based on the timestamp, and group the comparison results into corresponding groups from small to large. The labeled results are L1, L2, L3, ..., Ln, and the labeled results are used as the sample set; Step 2: Divide the sample set into a 70% training set, a 15% test set, and a 15% validation set, and establish the first fatigue prediction model based on the sample set; Step 3: Based on the main model in the multiple heterogeneous sub-models and according to the historical eigenvectors, calculate the first prediction value. The specific formula for the calculation is: Get the first prediction value Among them, e is a natural constant, θ1 and θ2 are dynamic weight factors, Ψ1 is the dynamic stress intensity index, and Ψ2 is the time-varying cumulative damage index; The expression group of dynamic stress intensity index and time-varying cumulative damage index is: Among them, tanh is the hyperbolic tangent function, S g is the standard life value of the traction machine; Step 4: Based on the time decay correction model in the multiple heterogeneous sub-models and the historical fatigue prediction value, calculate the second prediction value. The specific calculation formula is: Get the first prediction value Among them, N is the size of the historical data window, is the historical fatigue prediction value of the nth historical data, λ is the attenuation coefficient, t is the current time, t n is the recording time point of the nth historical fatigue prediction value, S h is the standard fatigue cycle of the traction machine; Step 5: Based on the anomaly detection model in the multiple heterogeneous sub-models and the fatigue dataset, calculate the third prediction value The specific calculation formula is: The comprehensive anomaly index δ is obtained, where μ is the mean and σ is the standard deviation; When the comprehensive abnormality index is less than 1.5, the output value of the third prediction value is 0.2 times the comprehensive abnormality index; when the comprehensive abnormality index is greater than or equal to 1.5 and less than 3, the output value of the third prediction value is 0.3+0.1×(δ-1.5); when the comprehensive abnormality index is less than or equal to 3, the output value of the third prediction value is 0.5; Step 6: Perform weighted summation on the first prediction value, the second prediction value, and the third prediction value, and add a constant term to obtain the fatigue prediction value; Step 7: Repeat steps 3 to 6 until the preset number of iterations is reached to obtain a fatigue prediction model; Step 8: Input the feature vector into the fatigue prediction model, output the fatigue prediction value, and output the fatigue prediction value to the safety maintenance decision module and the multi-objective optimization control module.

5. The elevator energy-saving control system with multiple safety measures according to claim 1 is characterized in that: Methods for grading fatigue prediction values ​​and matching the grading results with the decision rule base include: Based on fatigue threshold intervals (R1, R2) and decision rule base; When the fatigue prediction value is less than R1, a normal report is generated; when the fatigue prediction value is greater than or equal to R1 and less than R2, a warning report and a load reduction operation instruction are generated; when the fatigue prediction value is greater than or equal to R2, a danger report and an emergency stop instruction are generated; The normal report includes a description of the predicted fatigue status of the designated traction machine and asks the staff to monitor the operating status of the designated elevator according to the preset operation process; The warning report includes a load reduction operation instruction and indicates that the fatigue state of the specified traction machine is predicted to accumulate. The staff is requested to pay attention to continuously monitor the operating status of the specified elevator; The hazard report includes an emergency stop instruction and indicates that the fatigue risk of the designated traction machine is predicted to be high. The staff is requested to immediately send a work order to the maintenance team and arrange to go to the designated elevator area to implement risk prevention and intervention measures; The load reduction operation instruction contains a group of characters representing reducing the maximum load of the specified elevator by 10%; The emergency stop command contains a set of characters indicating that the elevator will run to the nearest floor, open the door and stop running; Pack normal reports, warning reports and danger reports to get safety maintenance reports.

6. The elevator energy-saving control system with multiple safety measures according to claim 1 is characterized in that: Ways to calculate the energy savings from enabling deep sleep mode during off-peak hours and embed safety self-checking mechanisms include: When the traction machine is in the off-peak period, the sum of the energy consumption of shutting down or reducing some functions is calculated to obtain an energy efficiency optimization report; Deep sleep mode means, for example, reducing the cabin lighting by 50% or turning off non-critical sensors; The safety self-check mechanism includes detecting whether the car door is fully closed and whether the equipment temperature data is greater than or equal to 60 degrees Celsius. When the car door is fully closed and the equipment temperature data is less than 60 degrees Celsius, the safety self-check is passed. Otherwise, it is necessary to wait for 10 seconds to re-enter the safety self-check mechanism. If the safety self-check mechanism fails three times in a row, the staff alarm is triggered.

7. The elevator energy-saving control system with multiple safety measures according to claim 1 is characterized in that: The steps to balance energy consumption, safety risks, and passenger experience of elevator operation based on fatigue prediction include: W1: Based on the energy consumption, safety risk and passenger experience of elevator operation, the operation energy consumption coefficient, safety risk coefficient and average waiting coefficient are obtained; W2: When the safety risk coefficient is greater than or equal to the safety risk threshold, a safety priority instruction is generated. Otherwise, the process proceeds to step W3. The safety priority instruction includes a set of characters representing that the maximum power of the traction machine is limited to 80% of the rated power. W3: When the average waiting coefficient is greater than or equal to the average waiting threshold, an experience priority instruction is generated. Otherwise, the process proceeds to step W4. The experience priority instruction includes a set of characters representing increasing the maximum power of the traction machine to 120% of the rated power. W4: When the operating energy consumption coefficient is greater than or equal to the operating energy consumption threshold, an energy efficiency priority instruction is generated. Otherwise, the current operating parameters are maintained. The energy efficiency priority instruction contains a set of characters representing limiting the single no-load distance to less than or equal to 5 floors and reducing the lighting equipment power to 70% of the rated lighting power. W5: Package the security priority instructions, experience priority instructions, and energy efficiency priority instructions to obtain an optimization control report.

8. The elevator energy-saving control system with multiple safety measures according to claim 1 is characterized in that: Ways to adjust system parameters according to different seasons include: Calculate the external ambient temperature value, subtract 22, and then divide it by the seasonal coefficient to obtain the temperature compensation parameter; When the temperature compensation parameter is greater than or equal to 0, an air conditioning energy-saving instruction is generated; When the temperature compensation parameter is less than 0, a heating reduction instruction is generated; The air conditioner energy saving instruction contains a set of characters representing raising the air conditioner set temperature by 2 degrees Celsius; The heating reduction instruction contains a set of characters representing a reduction of the elevator heating device's rated power by 10%; Package AC energy savings instructions and heating reduction instructions to get a customized energy savings report.

9. The elevator energy-saving control system with multiple safety measures according to claim 2, characterized in that: Analyze safety maintenance reports, optimization control reports, and customized energy-saving reports, and process them based on the analysis results in the following ways: Identify whether there are instructions to be executed in the safety maintenance report, optimization control report, and customized energy-saving report. If there are instructions to be executed, receive the instructions through the programmable logic controller, convert them into control signals, and transmit them to the intelligent controller; System data sets include fatigue data sets, supplementary data sets, feature vectors, fatigue prediction values, safety maintenance reports, energy efficiency optimization reports, optimization control reports, and customized energy saving reports.

10. An elevator energy-saving control method with multiple safety measures, implemented by an elevator energy-saving control system with multiple safety measures according to any one of claims 1 to 9, characterized in that: The following steps are included: S1: Collect fatigue data sets, which include running time data, load weight data, running times data and equipment temperature data; S2: Collect supplementary data sets, including environmental humidity data and vibration intensity data; S3: Preprocess the fatigue dataset and supplementary dataset, perform feature extraction, and obtain feature vectors; S4: Establish a fatigue prediction model based on the historical eigenvector and input the eigenvector to obtain the fatigue prediction value; S5: Classify the fatigue prediction values ​​and match the classification results with the decision rule library to obtain a safety maintenance report; S6: Calculate the energy savings of enabling deep sleep mode during off-peak hours, embed a safety self-check mechanism, and generate an energy efficiency optimization report. S7: Balances the energy consumption, safety risks, and passenger experience of elevator operation based on fatigue prediction values, and outputs an optimized control report; S8: Adjust system parameters according to different seasons of use to obtain customized energy saving reports; S9: Store system data sets, display security maintenance reports and energy efficiency optimization reports through a visual panel, analyze security maintenance reports, optimization control reports, and customized energy-saving reports, and perform processing based on the analysis results.

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