Elevator abnormal data collecting and processing system

Abstract

The invention provides an elevator abnormal data collecting and processing system which comprises at least one monitoring node used for converting collected abnormal signals into analog signals to form abnormal data; the processing terminal is provided with an acquisition module which is used for receiving abnormal data from the monitoring node; the configuration module is used for setting a data parameter of the configuration module based on the conversion modulus of the abnormal data, and analyzing an abnormal signal and an abnormal signal offset contained in the abnormal data based on the data parameter; the cache module is used for respectively storing the abnormal signal and the abnormal signal offset obtained by analysis in the cache module; the processing module is provided with a preprocessor, an abnormal signal stripping unit and a response unit; according to the method, the identification features of the deviation levels are obtained, the deviation levels of different degrees are obtained through the identification features, and whether control intervention is carried out or not can be judged based on the deviation levels.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator abnormality processing, and in particular to an elevator abnormality data collection and processing system. Background Art

[0002] Abnormal operation of elevators is basically caused by failures in different parts. Traditional monitoring of elevator operation status basically uses various sensors to monitor basic operation data, and analyzes basic operation data to check whether the elevator operation is abnormal. However, it is impossible for elevators to not have some operation deviations. For example, an overweight state will cause an overweight detection abnormality, and jumping in the elevator without an overweight state will also cause an overweight detection abnormality. Some abnormalities need to be corrected in time, while others do not. Therefore, it is not necessary to perform abnormal control on the elevator when an abnormality occurs. Summary of the invention

[0003] In view of this, the main purpose of the present invention is to provide an elevator abnormal data collection and processing system.

[0004] The technical solution adopted by the present invention is as follows:

[0005] Elevator abnormal data collection and processing system, including:

[0006] at least one monitoring node, used to convert the collected abnormal signal into an analog signal to form abnormal data, and different monitoring nodes have different conversion moduli;

[0007] A processing terminal, the processing terminal having:

[0008] A collection module, used for receiving abnormal data from monitoring nodes;

[0009] A configuration module, configured to set data parameters of the configuration module based on the conversion modulus of the abnormal data, and to respectively analyze the abnormal signal and the abnormal signal offset contained in the abnormal data based on the data parameters;

[0010] A cache module, used for storing the abnormal signal and the abnormal signal offset obtained by analysis in the cache module respectively;

[0011] A processing module, comprising a preprocessor, an abnormal signal stripping unit and a response unit;

[0012] The preprocessor is configured to classify based on the conversion modulus of the abnormal signal, to load the abnormal signal from the cache module and generate a marked feature based on the modulus size of the conversion modulus contained in the abnormal signal, and to store the corresponding abnormal signal in the abnormal signal library with the marked feature;

[0013] The abnormal signal stripping unit is configured to perform stripping to different degrees based on the deviation degree of the abnormal signal offset, and is used to load the abnormal signal from the cache module, and read the deviation level from the abnormal signal offset based on the comparison between the deviation degree and a set comparison benchmark;

[0014] A response unit is loaded with the deviation level, and a corresponding warning signal and intervention signal are formed with the deviation level. The response unit automatically configures a corresponding response actuator according to the generated warning signal and intervention signal, and sends the warning signal to the alarm through the response actuator to alarm, and sends the intervention signal to the controller through the response actuator to intervene in the control unit corresponding to the elevator monitoring node, so that the control unit corresponding to the elevator monitoring node corrects the matching device parameters to achieve the purpose of correcting the abnormal signal.

[0015] Furthermore, the conversion modulus is set based on the equipment rated parameters of the elevator equipment node collected synchronously by the monitoring node, specifically:

[0016] Setting monitoring logic, and forming monitoring instructions executable by the controller based on the monitoring logic;

[0017] The monitoring instruction is stored in a storage unit of the monitoring node and connected to a controller;

[0018] Based on the monitoring logic set by the controller monitoring node to monitor the generation instructions, the monitoring instructions are read and executed from the storage unit to drive the monitoring node to synchronously collect the equipment rated parameters of the elevator equipment node to set the benchmark parameters of the set monitoring logic, and the corresponding modulus reference setting is performed based on the benchmark parameters to form the conversion modulus.

[0019] Furthermore, the monitoring node is set in the controller based on rated parameters of the operation and control of different elevator devices.

[0020] Furthermore, the configuration module has:

[0021] Configuration logic unit,

[0022] An analysis module, wherein the analysis module has a plurality of analyzers, and the analyzers are activated accordingly based on the configuration instructions of the configuration logic unit to obtain data information of operations of different components in the abnormal data by loading corresponding data parameters;

[0023] Each analyzer has a configurator, which is used to write reference parameters in the configuration program of data information based on the data parameters corresponding to the analyzer to form a reference for converting abnormal signals or abnormal signal offsets.

[0024] Furthermore, the preprocessor has:

[0025] a plurality of first pre-processing units,

[0026] A first configuration program, configured to configure a first logic operation parameter of each first pre-processing unit based on a reference parameter, and independently run a processing program based on the first logic operation parameter to load the abnormal signal, and obtain an abnormal feature of the abnormal signal from an instance of the abnormal signal;

[0027] The machine learning unit traverses and compares the abnormal feature in the abnormal signal library to check whether there is a historical annotation corresponding to the abnormal feature in the abnormal signal library. If there is no corresponding historical annotation, it is sent to the feature annotator. The feature annotator generates an annotation feature based on the modulus size of the conversion modulus contained in the abnormal signal; if there is a corresponding historical annotation, the historical annotation is loaded, and the historical annotation / annotation feature is output and merged with the deviation level obtained by the abnormal signal stripping unit, so that the historical annotation / annotation feature is used as an identification feature of the deviation level.

[0028] Furthermore, the abnormal signal stripping unit has:

[0029] a plurality of second pre-processing units;

[0030] A second configuration program is used to configure the second logic operation parameters of each second pre-processing unit based on the reference parameters, independently run the offset simulator based on the second logic operation parameters to load the abnormal signal offset, and after the loading is completed, use the peak detection unit to detect the abnormal peak in the simulation curve of the offset simulator to obtain the detection data of the abnormal signal offset of different components;

[0031] The processor loads a plurality of the detection data for stripping operation and stripping extraction, and strips the offsets of different degrees in the plurality of detection data one by one. After stripping, a plurality of deviation levels are formed by comparing the stripped data of different degrees with the reference parameters, and the deviation levels are sent to the machine learning unit via the serial port. The machine learning unit fuses the historical annotation / annotation features with the deviation levels, so that the historical annotation / annotation features are used as identification features of the deviation levels.

[0032] The present application obtains the identification features of the deviation level, obtains the deviation levels of different degrees through the identification features, and judges whether to perform control intervention based on the deviation level. The advantage of the present application is that different abnormal signals are divided into different deviation levels, and by judging the deviation level and the persistence of the deviation degree, it can be determined which deviation levels require intervention and which do not, thereby ensuring the accuracy of the control process.

[0033] The monitoring node converts the collected abnormal signal into an analog signal to form abnormal data; the processing terminal receives the abnormal data from the monitoring node; the data parameters of the configuration module are set based on the conversion modulus of the abnormal data, and the abnormal signal and the abnormal signal offset contained in the abnormal data are respectively parsed based on the data parameters; the parsed abnormal signal and the abnormal signal offset are respectively stored in the cache module; the preprocessor is configured to classify based on the conversion modulus of the abnormal signal, and is used to load the abnormal signal from the cache module and generate a labeling feature based on the modulus size of the conversion modulus contained in the abnormal signal, and store the corresponding abnormal signal in the abnormal signal library with the labeling feature; the abnormal signal stripping unit is configured to classify based on the abnormal signal The degree of deviation of the signal offset is stripped to different degrees, so as to load the abnormal signal from the cache module, and read the deviation level from the abnormal signal offset based on the comparison of the deviation degree with the set comparison benchmark; the deviation level is loaded, and the corresponding early warning signal and intervention signal are formed with the deviation level, and the response unit automatically configures the corresponding response actuator according to the generated early warning signal and intervention signal, sends the early warning signal to the alarm through the response actuator, and sends the intervention signal to the controller through the response actuator to intervene in the control unit corresponding to the elevator monitoring node, so that the control unit corresponding to the elevator monitoring node corrects the matching device parameters to achieve the purpose of correcting the abnormal signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The following drawings are only used to illustrate and explain the present invention, and are not used to limit the scope of the present invention, wherein:

[0035] Figure 1 It is the overall diagram of the framework principle of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution, design method and advantages of the present invention clearer, the present invention is further described in detail by specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0037] The present invention provides an elevator abnormal data collection and processing system, comprising:

[0038] at least one monitoring node, used to convert the collected abnormal signal into an analog signal to form abnormal data, and different monitoring nodes have different conversion moduli;

[0039] A processing terminal, the processing terminal having:

[0040] A collection module, used for receiving abnormal data from monitoring nodes;

[0041] A configuration module, configured to set data parameters of the configuration module based on the conversion modulus of the abnormal data, and to respectively analyze the abnormal signal and the abnormal signal offset contained in the abnormal data based on the data parameters;

[0042] A cache module, used for storing the abnormal signal and the abnormal signal offset obtained by analysis in the cache module respectively;

[0043] A processing module, comprising a preprocessor, an abnormal signal stripping unit and a response unit;

[0044] The preprocessor is configured to classify based on the conversion modulus of the abnormal signal, to load the abnormal signal from the cache module and generate a marked feature based on the modulus size of the conversion modulus contained in the abnormal signal, and to store the corresponding abnormal signal in the abnormal signal library with the marked feature;

[0045] The abnormal signal stripping unit is configured to perform stripping to different degrees based on the deviation degree of the abnormal signal offset, and is used to load the abnormal signal from the cache module, and read the deviation level from the abnormal signal offset based on the comparison between the deviation degree and a set comparison benchmark;

[0046] A response unit is loaded with the deviation level, and a corresponding warning signal and intervention signal are formed with the deviation level. The response unit automatically configures a corresponding response actuator according to the generated warning signal and intervention signal, and sends the warning signal to the alarm through the response actuator to alarm, and sends the intervention signal to the controller through the response actuator to intervene in the control unit corresponding to the elevator monitoring node, so that the control unit corresponding to the elevator monitoring node corrects the matching device parameters to achieve the purpose of correcting the abnormal signal.

[0047] Furthermore, the conversion modulus is set based on the equipment rated parameters of the elevator equipment node collected synchronously by the monitoring node, specifically:

[0048] Setting monitoring logic, and forming monitoring instructions executable by the controller based on the monitoring logic;

[0049] The monitoring instruction is stored in a storage unit of the monitoring node and connected to a controller;

[0050] Based on the monitoring logic set by the controller monitoring node to monitor the generation instructions, the monitoring instructions are read and executed from the storage unit to drive the monitoring node to synchronously collect the equipment rated parameters of the elevator equipment node to set the benchmark parameters of the set monitoring logic, and the corresponding modulus reference setting is performed based on the benchmark parameters to form the conversion modulus.

[0051] Furthermore, the monitoring node is set in the controller based on rated parameters of the operation and control of different elevator devices.

[0052] Furthermore, the configuration module has:

[0053] Configuration logic unit,

[0054] An analysis module, wherein the analysis module has a plurality of analyzers, and the analyzers are activated accordingly based on the configuration instructions of the configuration logic unit to obtain data information of operations of different components in the abnormal data by loading corresponding data parameters;

[0055] Each analyzer has a configurator, which is used to write reference parameters in the configuration program of data information based on the data parameters corresponding to the analyzer to form a reference for converting abnormal signals or abnormal signal offsets.

[0056] Furthermore, the preprocessor has:

[0057] a plurality of first pre-processing units,

[0058] A first configuration program, configured to configure a first logic operation parameter of each first pre-processing unit based on a reference parameter, and independently run a processing program based on the first logic operation parameter to load the abnormal signal, and obtain an abnormal feature of the abnormal signal from an instance of the abnormal signal;

[0059] The machine learning unit traverses and compares the abnormal feature in the abnormal signal library to check whether there is a historical annotation corresponding to the abnormal feature in the abnormal signal library. If there is no corresponding historical annotation, it is sent to the feature annotator. The feature annotator generates an annotation feature based on the modulus size of the conversion modulus contained in the abnormal signal; if there is a corresponding historical annotation, the historical annotation is loaded, and the historical annotation / annotation feature is output and merged with the deviation level obtained by the abnormal signal stripping unit, so that the historical annotation / annotation feature is used as an identification feature of the deviation level.

[0060] Furthermore, the abnormal signal stripping unit has:

[0061] a plurality of second pre-processing units;

[0062] A second configuration program is used to configure the second logic operation parameters of each second pre-processing unit based on the reference parameters, independently run the offset simulator based on the second logic operation parameters to load the abnormal signal offset, and after the loading is completed, use the peak detection unit to detect the abnormal peak in the simulation curve of the offset simulator to obtain the detection data of the abnormal signal offset of different components;

[0063] The processor loads a plurality of the detection data for stripping operation and stripping extraction, and strips the offsets of different degrees in the plurality of detection data one by one. After stripping, a plurality of deviation levels are formed by comparing the stripped data of different degrees with the reference parameters, and the deviation levels are sent to the machine learning unit via the serial port. The machine learning unit fuses the historical annotation / annotation features with the deviation levels, so that the historical annotation / annotation features are used as identification features of the deviation levels.

[0064] In some embodiments, the monitoring logic may be implemented by running a setting program on the monitoring node.

[0065] In the above, the monitoring node is set in the controller based on the rated parameters of the operation and control of different elevator equipment, that is, the monitoring node can be automatically set based on the control of the controller based on the generation of the acquisition task, and the controller calls the clock chip to provide a reference clock for each of the monitoring nodes.

[0066] The present application obtains the identification features of the deviation level, obtains the deviation levels of different degrees through the identification features, and judges whether to perform control intervention based on the deviation level. The advantage of the present application is that different abnormal signals are divided into different deviation levels, and by judging the deviation level and the persistence of the deviation degree, it can be determined which deviation levels require intervention and which do not, thereby ensuring the accuracy of the control process.

[0067] The principle of the present application is as follows: the monitoring node converts the collected abnormal signal into an analog signal to form abnormal data; the processing terminal receives the abnormal data from the monitoring node; the data parameters of the configuration module are set based on the conversion modulus of the abnormal data, and the abnormal signal and the abnormal signal offset contained in the abnormal data are respectively parsed based on the data parameters; the parsed abnormal signal and the abnormal signal offset are respectively stored in the cache module; the preprocessor is configured to classify based on the conversion modulus of the abnormal signal, and is used to load the abnormal signal from the cache module and generate a labeling feature based on the modulus size of the conversion modulus contained in the abnormal signal, and store the corresponding abnormal signal in the abnormal signal library with the labeling feature; the abnormal signal stripping unit is configured based on The degree of deviation of the abnormal signal offset is stripped to different degrees, so as to load the abnormal signal from the cache module, and read the deviation level from the abnormal signal offset based on the comparison of the deviation degree with the set comparison benchmark; load the deviation level, and form a corresponding early warning signal and intervention signal with the deviation level, and the response unit automatically configures the corresponding response actuator according to the generated early warning signal and intervention signal, sends the early warning signal to the alarm through the response actuator, and sends the intervention signal to the controller through the response actuator to intervene in the control unit corresponding to the elevator monitoring node, so that the control unit corresponding to the elevator monitoring node corrects the matching device parameters to achieve the purpose of correcting the abnormal signal.

[0068] The present invention also provides a method for collecting and processing abnormal data of an elevator, comprising the following steps:

[0069] The monitoring node generates abnormal data; specifically, a monitoring logic is set at the monitoring node, and a monitoring instruction executable by the controller is formed based on the monitoring logic, and the monitoring instruction is stored in a storage unit of the monitoring node and connected to the controller; based on the monitoring logic set by the controller monitoring node to monitor the generation instruction, the monitoring instruction is read from the storage unit and executed to drive the monitoring node to synchronously collect the equipment rated parameters of the elevator equipment node to set the benchmark parameters of the set monitoring logic, and the corresponding modulus reference is set based on the benchmark parameters to form a conversion modulus; the monitoring node converts the collected abnormal signal into an analog signal to form abnormal data.

[0070] The processing terminal receives abnormal data from the monitoring node; the analyzer in the processing terminal loads the corresponding data parameters to obtain data information of different components in the abnormal data; and based on the data parameters corresponding to the analyzer, the reference parameters are written into the configuration program of the data information to form a reference for converting the abnormal signal or the abnormal signal offset; the abnormal signal and the abnormal signal offset obtained by the analysis are respectively stored in the cache module;

[0071] The first logic operation parameters of each first preprocessing unit are configured based on the reference parameters, and the processing program is independently run based on the first logic operation parameters to load the abnormal signal, and the abnormal features of the abnormal signal are obtained from the instance of the abnormal signal; the abnormal features are traversed and compared in the abnormal signal library to check whether there is a historical annotation corresponding to the abnormal feature in the abnormal signal library, and if there is no corresponding historical annotation, it is sent to the feature annotator, and the feature annotator generates an annotation feature based on the modulus size of the conversion modulus contained in the abnormal signal; if there is a corresponding historical annotation, the historical annotation is loaded;

[0072] The second logic operation parameters of each second preprocessing unit are configured based on the reference parameters, and the offset simulator is independently run based on the second logic operation parameters to load the abnormal signal offset. After loading, the peak detection unit is used to detect the abnormal peak in the simulation curve of the offset simulator to obtain the detection data of the abnormal signal offset of different components; multiple detection data are loaded for stripping operation and stripping extraction, and the offsets of different degrees in the multiple detection data are stripped one by one. After stripping, multiple deviation levels are formed by comparing the stripping data of different degrees with the reference parameters, and the deviation level is sent to the machine learning unit via the serial port. The machine learning unit merges the historical annotation / annotation feature and the deviation level, so that the historical annotation / annotation feature is used as the identification feature of the deviation level.

[0073] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. Elevator abnormal data collection and processing system, characterized in that: include: at least one monitoring node, used to convert the collected abnormal signal into an analog signal to form abnormal data, and different monitoring nodes have different conversion moduli; A processing terminal, the processing terminal having: A collection module, used for receiving abnormal data from monitoring nodes; A configuration module, configured to set data parameters of the configuration module based on the conversion modulus of the abnormal data, and to respectively analyze the abnormal signal and the abnormal signal offset contained in the abnormal data based on the data parameters; A cache module, used for storing the abnormal signal and the abnormal signal offset obtained by analysis in the cache module respectively; A processing module, comprising a preprocessor, an abnormal signal stripping unit and a response unit; The preprocessor is configured to classify based on the conversion modulus of the abnormal signal, to load the abnormal signal from the cache module and generate a marked feature based on the modulus size of the conversion modulus contained in the abnormal signal, and to store the corresponding abnormal signal in the abnormal signal library with the marked feature; The abnormal signal stripping unit is configured to perform stripping to different degrees based on the deviation degree of the abnormal signal offset, and is used to load the abnormal signal from the cache module, and read the deviation level from the abnormal signal offset based on the comparison between the deviation degree and a set comparison benchmark; A response unit is loaded with the deviation level, and a corresponding warning signal and intervention signal are formed with the deviation level. The response unit automatically configures a corresponding response actuator according to the generated warning signal and intervention signal, and sends the warning signal to the alarm through the response actuator to alarm, and sends the intervention signal to the controller through the response actuator to intervene in the control unit corresponding to the elevator monitoring node, so that the control unit corresponding to the elevator monitoring node corrects the matching device parameters to achieve the purpose of correcting the abnormal signal.

2. The elevator abnormal data collection and processing system according to claim 1, characterized in that: The conversion modulus is set based on the equipment rated parameters of the elevator equipment node collected synchronously by the monitoring node, specifically: Setting monitoring logic, and forming monitoring instructions executable by the controller based on the monitoring logic; The monitoring instruction is stored in a storage unit of the monitoring node and connected to a controller; Based on the monitoring logic set by the controller monitoring node to monitor the generation instructions, the monitoring instructions are read and executed from the storage unit to drive the monitoring node to synchronously collect the equipment rated parameters of the elevator equipment node to set the benchmark parameters of the set monitoring logic, and the corresponding modulus reference setting is performed based on the benchmark parameters to form the conversion modulus.

3. The elevator abnormal data collection and processing system according to claim 2, characterized in that: The monitoring node is set in the controller based on the rated parameters of the operation and control of different elevator devices.

4. The elevator abnormal data collection and processing system according to claim 1, characterized in that: The configuration module has: Configuration logic unit, An analysis module, wherein the analysis module has a plurality of analyzers, and the analyzers are activated accordingly based on the configuration instructions of the configuration logic unit to obtain data information of operations of different components in the abnormal data by loading corresponding data parameters; Each analyzer has a configurator, which is used to write reference parameters in the configuration program of data information based on the data parameters corresponding to the analyzer to form a reference for converting abnormal signals or abnormal signal offsets.

5. The elevator abnormal data collection and processing system according to claim 1, characterized in that: The preprocessor has: a plurality of first pre-processing units, A first configuration program, configured to configure a first logic operation parameter of each first pre-processing unit based on a reference parameter, and independently run a processing program based on the first logic operation parameter to load the abnormal signal, and obtain an abnormal feature of the abnormal signal from an instance of the abnormal signal; The machine learning unit traverses and compares the abnormal feature in the abnormal signal library to check whether there is a historical annotation corresponding to the abnormal feature in the abnormal signal library. If there is no corresponding historical annotation, it is sent to the feature annotator. The feature annotator generates an annotation feature based on the modulus size of the conversion modulus contained in the abnormal signal; if there is a corresponding historical annotation, the historical annotation is loaded, and the historical annotation / annotation feature is output and merged with the deviation level obtained by the abnormal signal stripping unit, so that the historical annotation / annotation feature is used as an identification feature of the deviation level.

6. The elevator abnormal data collection and processing system according to claim 1, characterized in that: The abnormal signal stripping unit comprises: a plurality of second pre-processing units; A second configuration program is used to configure the second logic operation parameters of each second pre-processing unit based on the reference parameters, independently run the offset simulator based on the second logic operation parameters to load the abnormal signal offset, and after the loading is completed, use the peak detection unit to detect the abnormal peak in the simulation curve of the offset simulator to obtain the detection data of the abnormal signal offset of different components; The processor loads a plurality of the detection data for stripping operation and stripping extraction, and strips the offsets of different degrees in the plurality of detection data one by one. After stripping, a plurality of deviation levels are formed by comparing the stripped data of different degrees with the reference parameters, and the deviation levels are sent to the machine learning unit via the serial port. The machine learning unit fuses the historical annotation / annotation features with the deviation levels, so that the historical annotation / annotation features are used as identification features of the deviation levels.

Images