A forklift motor control system

Abstract

The present invention discloses a forklift motor control system, which belongs to the field of vehicle manufacturing and control technology, and comprises a lifting subsystem, a forward subsystem, an analysis module and a server; the lifting subsystem is used to control the output power of the lifting motor of the electric forklift to adjust the working mode of the electric forklift, and comprises a first acquisition module, a first detection module and a first control module; the forward subsystem is used to control the output power of the driving motor of the electric forklift to adjust the speed of the electric forklift, and comprises a second acquisition module, a second detection module and a second control module; through the mutual cooperation between the lifting subsystem, the forward subsystem and the analysis module, it is possible to detect whether the lifting motor and the driving motor fail in real time according to the collected data, and generate corresponding control signals in time when a failure occurs, and perform intelligent adjustment corresponding to the current forklift, thereby improving the safety of use.

Description

Technical Field

[0001] The invention belongs to the technical field of vehicle manufacturing and control, and in particular is a forklift motor control system. Background Art

[0002] Forklifts play a very important role in the logistics system of enterprises and are the main force in material handling equipment. They are widely used in various sectors of the economy, such as stations, ports, airports, factories, warehouses, etc. With the rapid development of the economy, the material handling of most enterprises has been separated from the original manual handling, and replaced by mechanized handling mainly based on forklifts. However, when the current forklift fails, if it is not properly handled, it is very likely to cause a safety accident. Therefore, in order to solve this problem, the present invention provides a forklift motor control system. Summary of the invention

[0003] In order to solve the problems existing in the above-mentioned solutions, the present invention provides a forklift motor control system.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A forklift motor control system includes a lifting subsystem, a forward subsystem, an analysis module and a server;

[0006] The lifting subsystem is used to control the output power of the lifting motor of the electric forklift to adjust the working mode of the electric forklift, and includes a first acquisition module, a first detection module, and a first control module;

[0007] The working methods of the lifting subsystem include:

[0008] Real-time data acquisition is performed through the first acquisition module to obtain lifting data, and the lifting data is detected through the first detection module to determine whether a fault occurs. When it is detected that no fault occurs, no operation is performed. When a fault is detected, a lifting fault signal is generated, and the generated lifting fault signal is sent to the analysis module, and the lifting motor control signal fed back by the analysis module is received, and the operation of the lifting motor is controlled through the first control module according to the received lifting motor control signal;

[0009] The forward subsystem is used to control the output power of the driving motor of the electric forklift to adjust the speed of the electric forklift, and includes a second acquisition module, a second detection module and a second control module;

[0010] The working methods of the forward subsystem include:

[0011] Performing real-time data acquisition through the second acquisition module to obtain forward data, detecting the forward data through the second detection module to determine whether a fault occurs, and when it is detected that no fault occurs, no operation is performed, and when a fault is detected, a forward fault signal is generated, the generated forward fault signal is sent to the analysis module, and a forward motor control signal fed back by the analysis module is received, and the operation of the drive motor is controlled through the second control module according to the received forward motor control signal;

[0012] When a fault occurs, the analysis module generates a control signal for the corresponding motor. When a lifting fault signal or a forward fault signal is received, the fault analysis data is obtained, the fault analysis data is converted into corresponding fault analysis coordinates, a fault control model is established, the fault analysis coordinates are input into the fault analysis model, and the forward motor control signal and the lifting motor control signal are output, and are sent to the forward subsystem and the lifting subsystem respectively.

[0013] Furthermore, the first acquisition module is used to collect the lifting data of the electric forklift, and the lifting data includes the mast height data, mast tilt data, mast forward tilt data, mast backward tilt data, mast lifting data, lifting motor overtemperature data, radiator overtemperature data, switch data and mast tilt angle data of the electric forklift;

[0014] The first detection module is used to detect whether the lifting subsystem of the electric forklift fails;

[0015] The first control module is used to control the lifting motor according to the received control signal.

[0016] Furthermore, the working method of the first detection module includes:

[0017] Real-time acquisition of lifting motor over-temperature data and radiator over-temperature data in lifting data, conversion of lifting motor over-temperature data and radiator over-temperature data into corresponding lifting motor over-temperature values ​​and radiator over-temperature values, and integration of lifting motor over-temperature values ​​and radiator over-temperature values ​​into lifting detection points;

[0018] Establish a lifting point detection map, input the lifting detection points into the lifting point detection map, and match the corresponding detection results. The detection results include whether a fault occurs or not.

[0019] Furthermore, the method for establishing a lifting point detection map includes:

[0020] Acquire the normal lifting motor overtemperature data and radiator overtemperature data, integrate them into normal lifting data, convert the normal lifting data into corresponding simulation coordinates, establish a coordinate system, input the simulation coordinates into the coordinate system, perform clustering based on the clustering algorithm, obtain the corresponding clusters, mark the corresponding initial area according to the obtained clustering, expand and confirm the boundaries based on the obtained initial area, obtain the normal detection area, and establish the lifting point detection map according to the obtained normal detection area.

[0021] Furthermore, the method of obtaining the initial region corresponding to the obtained cluster mark is:

[0022] Identify the coordinates of the cluster boundary and mark them as boundary points. Establish a connection model based on the minimum range principle. Analyze each boundary point through the established connection model, connect the boundary points and obtain the initial area.

[0023] Furthermore, the method for expanding and confirming the boundary based on the obtained initial area includes:

[0024] Identify the boundary contour of the initial area, generate an extended contour based on the identified area boundary contour, verify whether the extended points on the extended contour meet the normal operation requirements, make corresponding adjustments to the extended contour according to the verification results, and obtain a new area boundary contour. When the extended contour cannot be generated based on the area boundary contour, the area surrounded by the current area boundary contour is marked as a normal detection area.

[0025] Compared with the prior art, the beneficial effects of the present invention are: through the cooperation between the lifting subsystem, the forward subsystem and the analysis module, it is possible to detect in real time whether the lifting motor and the drive motor have faults according to the collected data, and to generate corresponding control signals in time when a fault occurs, and to make intelligent adjustments to the current forklift to improve the safety of use. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. 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 creative work are within the scope of protection of the present invention.

[0029] like Figure 1 As shown, a forklift motor control system includes a lifting subsystem, a forward subsystem, an analysis module and a server;

[0030] The lifting subsystem is used to control the output power of the lifting motor of the electric forklift to adjust the working mode of the electric forklift, and includes a first acquisition module, a first detection module, and a first control module;

[0031] The first acquisition module is used to collect the lifting data of the electric forklift, and the lifting data includes the mast height data, mast tilt data, mast forward tilt data, mast backward tilt data, mast lifting data, lifting motor overtemperature data, radiator overtemperature data, switch data and mast tilt angle data of the electric forklift;

[0032] The first detection module is used to detect whether the lifting subsystem of the electric forklift fails;

[0033] The first control module is used to control the lifting motor according to the received control signal.

[0034] The working methods of the lifting subsystem include:

[0035] Real-time data acquisition is performed through the first acquisition module to obtain lifting data, and the lifting data is detected through the first detection module to determine whether a fault occurs. When it is detected that no fault occurs, no operation is performed. When a fault is detected, a lifting fault signal is generated, and the generated lifting fault signal is sent to the analysis module, and the lifting motor control signal fed back by the analysis module is received. The operation of the lifting motor is controlled through the first control module according to the received lifting motor control signal.

[0036] For the collection process of the first collection module, corresponding data can be acquired through existing collection methods.

[0037] The working method of the first detection module includes:

[0038] Real-time acquisition of lifting motor over-temperature data and radiator over-temperature data in lifting data, conversion of lifting motor over-temperature data and radiator over-temperature data into corresponding lifting motor over-temperature values ​​and radiator over-temperature values, and integration of lifting motor over-temperature values ​​and radiator over-temperature values ​​into lifting detection points;

[0039] Establish a lifting point detection map, input the lifting detection points into the lifting point detection map, and match the corresponding detection results. The detection results include whether a fault occurs or not.

[0040] The method for converting the lifting motor overtemperature data and the radiator overtemperature data into the corresponding lifting motor overtemperature values ​​and the radiator overtemperature values ​​is: establishing a corresponding lifting conversion model based on the CNN network or the DNN network, manually setting the corresponding training set for training, and analyzing the lifting motor overtemperature data and the radiator overtemperature data through the successfully trained lifting conversion model to obtain the corresponding lifting motor overtemperature value and the radiator overtemperature value; the specific establishment and training process is common knowledge in this field.

[0041] The method of establishing a lifting point detection map includes:

[0042] Obtaining possible normal lifting motor overtemperature data and radiator overtemperature data is based on the existing big data analysis, obtaining the lifting motor overtemperature data and radiator overtemperature data when there is no fault, integrating them into normal lifting data, and converting the normal lifting data into corresponding simulation coordinates, that is, converting the normal lifting data into corresponding coordinates through the lifting conversion model, marking them as simulation coordinates; establishing a coordinate system, inputting the simulation coordinates into the coordinate system, clustering based on the clustering algorithm, obtaining corresponding clusters, marking the corresponding initial area according to the obtained clustering mark, expanding and confirming the boundaries based on the obtained initial area, obtaining the normal detection area, and establishing the lifting point detection map according to the obtained normal detection area.

[0043] When the lifting detection point falls into the normal detection area, no fault occurs, otherwise a fault occurs.

[0044] Clustering is performed based on a clustering algorithm. Clustering is performed based on an existing clustering algorithm. Corresponding coordinate clustering can be achieved by combining the corresponding coordinate distribution with various existing clustering algorithms.

[0045] The method of obtaining the initial region corresponding to the cluster mark is:

[0046] Identify the coordinates of the cluster boundary and mark them as boundary points. Establish a connection model based on the minimum range principle. Analyze each boundary point through the established connection model, connect the boundary points and obtain the initial area.

[0047] The minimum range principle is to ensure that the boundary connection line is the connection line with the smallest range. The connection line is marked as far inward as possible to reduce the initial area range. A connection model is established based on the CNN network or DNN network. The corresponding training set is set manually for training, which is used to connect the boundary points based on the minimum range principle.

[0048] Methods for expanding and confirming the boundary based on the obtained initial area include:

[0049] Identify the boundary contour of the initial area, generate an extended contour based on the identified area boundary contour, verify whether the extended points on the extended contour meet the normal operation requirements, make corresponding adjustments to the extended contour according to the verification results, and obtain a new area boundary contour. When the extended contour cannot be generated based on the area boundary contour, the area surrounded by the current area boundary contour is marked as a normal detection area.

[0050] The method for generating an expanded contour based on the identified region boundary contour is as follows: establishing an expansion model based on a CNN network or a DNN network, manually setting a corresponding training set for training, without verification data for the first time, expanding toward a smooth curve according to the corresponding contour shape, and generating several positioning points corresponding to the expanded boundary, that is, several representative points with the largest expansion, obtaining verification data after the expansion, identifying the region boundary contour adjusted according to the verification data, expanding again based on the last verification data, and manually establishing a training set for training based on the above method.

[0051] Verify whether the expansion points on the expansion contour meet the normal operation requirements. The expansion points are the positioning points generated by the expansion model. According to the data corresponding to the expansion points, it can be judged whether they meet the normal operation requirements.

[0052] According to the verification results, the corresponding expansion contour is adjusted, the expansion points that fail the verification are identified, the corresponding areas are marked, and the corresponding connecting lines are generated through the connecting line model according to the adjacent qualified points and the corresponding original qualified boundary points to obtain the new area boundary contour.

[0053] The forward subsystem is used to control the output power of the driving motor of the electric forklift to adjust the speed of the electric forklift, and includes a second acquisition module, a second detection module and a second control module; the second acquisition module is used to collect the forward data of the electric forklift, and the forward data includes the driving motor overtemperature data, the radiator overtemperature data, the mast limit switch data, the load weight data and the steering data.

[0054] The second detection module is used to detect whether the forward subsystem of the electric forklift fails;

[0055] The working methods of the forward subsystem include:

[0056] Real-time data acquisition is performed through the second acquisition module to obtain forward data, and the forward data is detected through the second detection module to determine whether a fault occurs. When it is detected that no fault occurs, no operation is performed. When a fault is detected, a forward fault signal is generated, and the generated forward fault signal is sent to the analysis module, and the forward motor control signal fed back by the analysis module is received. The operation of the drive motor is controlled through the second control module according to the received forward motor control signal.

[0057] The working method of the second detection module includes:

[0058] Acquire the drive motor over-temperature data and the radiator over-temperature data in the forward data in real time, convert the drive motor over-temperature data and the radiator over-temperature data into corresponding drive motor over-temperature values ​​and radiator over-temperature values, and integrate the drive motor over-temperature values ​​and the radiator over-temperature values ​​into a drive detection point;

[0059] A drive point detection map is established, and the drive detection points are input into the drive point detection map to match the corresponding detection results, which include whether a fault occurs or not.

[0060] The method of establishing a driving point detection map includes:

[0061] Possible normal drive motor over-temperature data and radiator over-temperature data are obtained, integrated into normal drive data, the normal drive data are converted into corresponding simulation coordinates, a coordinate system is established, the simulation coordinates are input into the coordinate system, clustering is performed based on a clustering algorithm, corresponding clusters are obtained, initial areas corresponding to the obtained cluster marks are marked, boundary expansion and confirmation are performed based on the obtained initial areas, a normal detection area is obtained, and a drive point detection map is established based on the obtained normal detection area.

[0062] The method of obtaining the initial region corresponding to the cluster mark is:

[0063] Identify the coordinates of the cluster boundary and mark them as boundary points. Establish a connection model based on the minimum range principle. Analyze each boundary point through the established connection model, connect the boundary points and obtain the initial area.

[0064] The specific process is similar to the working method of the first detection module. By understanding the working method of the first detection module, the corresponding working method of the second detection module can be understood.

[0065] When a fault occurs, the analysis module generates a control signal for the corresponding motor. The specific method includes:

[0066] When a lifting fault signal or a forward fault signal is received, fault analysis data is obtained, the fault analysis data including gantry height data, gantry tilt data, gantry forward tilt data, gantry backward tilt data, gantry lifting data, gantry tilt angle data, gantry limit switch data, load weight data and steering data; the fault analysis data is converted into corresponding fault analysis coordinates, a fault control model is established, the fault analysis coordinates are input into the fault analysis model, the forward motor control signal and the lifting motor control signal are output, and are sent to the forward subsystem and the lifting subsystem respectively.

[0067] The method for converting fault analysis data into corresponding fault analysis coordinates includes: establishing a corresponding assignment model based on a CNN network or a DNN network, manually setting a corresponding training set for training, converting the fault analysis data into corresponding assignments through the successfully trained assignment model, and combining the obtained assignments into corresponding fault analysis coordinates.

[0068] The fault control model is established based on a CNN network or a DNN network, and a corresponding training set is established manually for training. The specific training process is common knowledge in this field.

[0069] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A forklift motor control system, characterized in that: It includes lifting subsystem, forward subsystem, analysis module and server; The lifting subsystem is used to control the output power of the lifting motor of the electric forklift to adjust the working mode of the electric forklift, and includes a first acquisition module, a first detection module, and a first control module; The working methods of the lifting subsystem include: Real-time data acquisition is performed through the first acquisition module to obtain lifting data, and the lifting data is detected through the first detection module to determine whether a fault occurs. When it is detected that no fault occurs, no operation is performed. When a fault is detected, a lifting fault signal is generated, and the generated lifting fault signal is sent to the analysis module, and the lifting motor control signal fed back by the analysis module is received, and the operation of the lifting motor is controlled through the first control module according to the received lifting motor control signal; The forward subsystem is used to control the output power of the driving motor of the electric forklift to adjust the speed of the electric forklift, and includes a second acquisition module, a second detection module and a second control module; The working methods of the forward subsystem include: Performing real-time data acquisition through the second acquisition module to obtain forward data, detecting the forward data through the second detection module to determine whether a fault occurs, and when it is detected that no fault occurs, no operation is performed, and when a fault is detected, a forward fault signal is generated, the generated forward fault signal is sent to the analysis module, and a forward motor control signal fed back by the analysis module is received, and the operation of the drive motor is controlled through the second control module according to the received forward motor control signal; When a fault occurs, the analysis module generates a control signal for the corresponding motor. When a lifting fault signal or a forward fault signal is received, the analysis module obtains fault analysis data, converts the fault analysis data into corresponding fault analysis coordinates, establishes a fault control model, inputs the fault analysis coordinates into the fault analysis model, outputs a forward motor control signal and a lifting motor control signal, and sends them to the forward subsystem and the lifting subsystem respectively; The first acquisition module is used to collect the lifting data of the electric forklift, and the lifting data includes the mast height data, mast tilt data, mast forward tilt data, mast backward tilt data, mast lifting data, lifting motor overtemperature data, radiator overtemperature data, switch data and mast tilt angle data of the electric forklift; The first detection module is used to detect whether the lifting subsystem of the electric forklift fails; The first control module is used to control the lifting motor according to the received control signal; The working method of the first detection module includes: Real-time acquisition of lifting motor over-temperature data and radiator over-temperature data in lifting data, conversion of lifting motor over-temperature data and radiator over-temperature data into corresponding lifting motor over-temperature values ​​and radiator over-temperature values, and integration of lifting motor over-temperature values ​​and radiator over-temperature values ​​into lifting detection points; Establish a lifting point detection map, input the lifting detection points into the lifting point detection map, and match the corresponding detection results, which include whether a fault occurs or not; The method of establishing a lifting point detection map includes: The normal lifting motor overtemperature data and radiator overtemperature data are obtained, integrated into the normal lifting data, the normal lifting data are converted into the corresponding simulation coordinates, a coordinate system is established, the simulation coordinates are input into the coordinate system, clustering is performed based on the clustering algorithm, the corresponding clusters are obtained, the coordinates of the cluster boundaries are identified, and marked as boundary points. A connection model is established based on the minimum range principle, and each boundary point is analyzed through the established connection model. The boundary points are connected to obtain the initial area, the boundary contour of the initial area is identified, and an extended contour is generated based on the identified regional boundary contour. It is verified whether the extended points on the extended contour meet the normal operating requirements. According to the verification results, the corresponding extended contour is adjusted to obtain a new regional boundary contour. When the extended contour cannot be generated according to the regional boundary contour, the area surrounded by the current regional boundary contour is marked as a normal detection area; the normal detection area is obtained, and a lifting point detection map is established according to the obtained normal detection area.

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