Automatic fault early warning method and device for handcart type switch cabinet

Abstract

The invention discloses an automatic fault early warning method and device for a handcart type switch cabinet. The method comprises the following steps: under the condition that a handcart moving instruction is received, controlling a stepping motor to rotate according to the handcart moving instruction; in the process of controlling the stepping motor to rotate according to the handcart moving instruction, obtaining the current rotating time length and the rotating torque corresponding to the rotating time length; inputting the rotation duration into a torque prediction model, and processing the rotation duration by using the torque prediction model to obtain a target rotation torque corresponding to the rotation duration; calculating the difference value between the rotating torque corresponding to the rotating duration and the target rotating torque to obtain a torque difference value; and when the torque difference value is greater than the torque difference threshold value, a fault early warning signal is sent out. According to the invention, the technical problems that the operation progress of the screwing-in and screwing-out operation of the switch handcart in the handcart type switch cabinet by using the traditional method in the related technology cannot be well controlled and the potential safety hazard in the operation process is difficult to effectively monitor are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control of electric power equipment, and in particular to an automatic fault early warning method and device for a trolley-type switch cabinet. Background Art

[0002] When using the trolley-type switch cabinet, the switch trolley needs to be manually rotated in and out, which consumes human resources and is the bottleneck of remote control operation. There are also trials to use electric trolleys for rotation in and out, but due to the generally complex internal structure of the trolley and multiple locking conditions, excessive electric operating torque can easily cause component damage, and too little torque can cause the trolley to be unable to move at several normal resistance points.

[0003] Although the prior art provides some solutions to the above-mentioned problems, the solutions provided still have some defects and cannot effectively solve the potential safety hazards. For example, an existing solution mentions that the manipulator can be installed according to the length and width of the power switch cabinet body to reduce the time from receiving instructions to completing the operation, but it does not explain in detail how to achieve full adaptation to power switch cabinets of different models and specifications. If the adjustment range of the manipulator is limited, it may still not cover all types of power switch cabinets on the market, and it does not explain how to ensure stability and safety under various operating conditions, especially how to ensure the correct operation of the system in emergency situations. Moreover, the traditional methods in the prior art usually only allow staff to avoid the risks caused by close-range operation, and do not solve the core safety issues of handling the electric operation of the high-voltage switch cabinet trolley, such as forced operation may cause switch failure or even short circuit, thereby causing safety problems.

[0004] With regard to the problem that the in and out operations of the switch trolley in the trolley-type switch cabinet using the traditional method in the above-mentioned related technologies usually cannot well control the operation progress and it is difficult to effectively monitor the potential safety hazards during the operation, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiment of the present invention provides an automated fault warning method and device for a trolley-type switch cabinet, so as to at least solve the technical problem in the related art that the in and out operations of the switch trolley in the trolley-type switch cabinet using the traditional method usually cannot well control the operation progress and it is difficult to effectively monitor the potential safety hazards during the operation.

[0006] According to one aspect of an embodiment of the present invention, an automated fault warning method for a trolley-type switch cabinet is provided, comprising: upon receiving a trolley movement instruction, controlling a stepper motor to rotate according to the trolley movement instruction, wherein the stepper motor is connected to a main shaft via a transmission gear set, and during the rotation of the stepper motor, the main shaft is driven to rotate via the transmission gear set to push the trolley chassis to move the trolley; in the process of controlling the stepper motor to rotate according to the trolley movement instruction, obtaining a current rotation time and a rotation torque corresponding to the rotation time, wherein the rotation time refers to a cumulative value of a time after the stepper motor starts to rotate, and the rotation torque refers to a torque acting on the transmission gear set when the stepper motor rotates. torque on the gear set; inputting the rotation duration into a torque prediction model, so as to process the rotation duration using the torque prediction model to obtain a target rotation torque corresponding to the rotation duration, wherein the torque prediction model is a model constructed using a plurality of groups of sample data using a mathematical modeling method, each of the plurality of groups of sample data comprising: a sample rotation duration, a sample target rotation torque corresponding to the sample rotation duration, the target rotation torque being the torque required to be achieved by the transmission gear set under the rotation duration; calculating the difference between the rotation torque corresponding to the rotation duration and the target rotation torque to obtain a torque difference; and issuing a fault warning signal when the torque difference is greater than a torque difference threshold.

[0007] Optionally, controlling the stepper motor to rotate according to the trolley movement instruction includes: when receiving the trolley movement instruction, obtaining the current state of the safety lock, wherein the safety lock is used to be enabled when the trolley-type switch cabinet is in a dangerous state to prohibit the movement operation of the trolley; when the current state is an unenabled state, controlling the stepper motor to rotate according to the trolley movement instruction.

[0008] Optionally, in the process of controlling the stepper motor to rotate according to the trolley movement instruction, the current rotation time and the rotation torque corresponding to the rotation time are obtained, including: after controlling the stepper motor to start rotating according to the trolley movement instruction, real-time statistics of the cumulative value of the rotation time of the stepper motor; determining the current cumulative value of the time as the current rotation time at every predetermined time interval; using a torque sensor to obtain the rotation torque of the stepper motor acting on the transmission gear group at each rotation time, wherein the torque sensor is installed on the head of the stepper motor on the side connected to the transmission gear group.

[0009] Optionally, before the rotation time is input into a torque prediction model to process the rotation time using the torque prediction model to obtain a target rotation torque corresponding to the rotation time, the automatic fault warning method of the trolley-type switch cabinet also includes: responding to a manual rotation operation acting on a manual control hole, and real-time counting of a cumulative manual time value of the manual rotation operation, wherein the manual control hole is an interface for the target object to manually rotate the spindle, and the manual rotation operation is an operation acting on the spindle to drive the spindle to rotate; determining the current The accumulated value of the manual rotation time is the current manual rotation time; the manual rotation torque of the main shaft acting on the transmission gear group under each manual rotation time is obtained by using a torque sensor; each manual rotation time and the manual rotation torque corresponding to each manual rotation time are integrated according to a time series to obtain multiple groups of manual rotation data, wherein the multiple groups of manual rotation data are data with time series; the model parameters in the autoregressive integral sliding average model are solved by a mathematical modeling method using the multiple groups of manual rotation data to obtain the torque prediction model.

[0010] Optionally, a mathematical modeling method is used to solve the model parameters in the autoregressive integral moving average model using multiple groups of the manual rotation data to obtain the torque prediction model, including: preprocessing the multiple groups of the manual rotation data with the time series to remove the nonlinear data in the multiple groups of the manual rotation data to obtain a linear manual rotation data group, wherein the nonlinear data refers to the manual rotation data group whose values ​​change over time do not follow a linear relationship, and the linear manual rotation data group is a combination of other data in the multiple groups of manual rotation data except the nonlinear data; performing a stationarity evaluation on the linear manual rotation data group to obtain a first evaluation result; when the first evaluation result indicates that the stationarity of the linear manual rotation data group is less than a stationarity threshold, performing differential processing on the linear manual rotation data group to obtain a target linear manual rotation data group. dynamic data group; based on the target linear manual rotation data group, using the automatic model selection tool to determine the model parameters in the autoregressive integral moving average model, wherein the model parameters include: autoregressive term order, differential order, and moving average term order, the autoregressive term order refers to the number of terms of the autoregressive polynomial in the autoregressive integral moving average model, the differential order represents the number of times the time series is differentiated, and the moving average term order refers to the number of terms of the moving average term in the autoregressive integral moving average model; determining an initial torque prediction model according to the autoregressive term order, the differential order and the moving average term order; using the Akaike information criterion to perform a performance evaluation on the initial torque prediction model to obtain a second evaluation result; when the second evaluation result indicates that the initial torque prediction model has passed the performance verification, determining the initial torque prediction model as the torque prediction model.

[0011] Optionally, the automated fault warning method for the trolley-type switch cabinet also includes: extracting features from a nonlinear manual rotation data group to obtain a torque feature, wherein the nonlinear manual rotation data group is a combination of the nonlinear data in multiple groups of manual rotation data; integrating the torque features to obtain a torque feature library, wherein the torque feature library is used to perform further fault judgment on the rotation torque when the torque difference is not greater than the torque difference threshold; when the torque difference is not greater than the torque difference threshold, extracting features from the rotation torque to obtain a target torque feature; matching the target torque feature with the torque feature in the torque feature library to obtain a matching result; and issuing the fault warning signal when the matching result indicates that the torque feature identical to the target torque feature does not exist in the torque feature library.

[0012] Optionally, the automated fault warning method for the trolley-type switch cabinet also includes: in the process of using the torque prediction model to process the rotation duration to obtain the target rotation torque corresponding to the rotation duration, the torque prediction model is adaptively optimized using the rotation duration and the target rotation torque corresponding to the rotation duration, and the torque prediction model is updated to the optimized torque prediction model; in the process of matching the target torque feature with the torque feature in the torque feature library, the target torque feature is added to the torque feature library to update the torque feature library to obtain the updated torque feature library.

[0013] According to another aspect of an embodiment of the present invention, an automatic fault warning device for a trolley-type switch cabinet is also provided, including: a control unit, for controlling a stepper motor to rotate according to the trolley movement instruction when a trolley movement instruction is received, wherein the stepper motor and the main shaft are connected via a transmission gear set, and the main shaft is driven to rotate via the transmission gear set during the rotation of the stepper motor, so as to push the trolley chassis to move the trolley; a first acquisition unit, for acquiring a current rotation duration and a rotation torque corresponding to the rotation duration during the process of controlling the stepper motor to rotate according to the trolley movement instruction, wherein the rotation duration refers to the cumulative value of the duration after the stepper motor starts to rotate, and the rotation torque refers to the torque acting on the transmission gear set when the stepper motor rotates. torque; a second acquisition unit, used to input the rotation duration into a torque prediction model, so as to use the torque prediction model to process the rotation duration to obtain a target rotation torque corresponding to the rotation duration, wherein the torque prediction model is a model constructed by a mathematical modeling method using multiple groups of sample data, and each of the multiple groups of sample data includes: a sample rotation duration, a sample target rotation torque corresponding to the sample rotation duration, and the target rotation torque is the torque that the transmission gear set needs to achieve under the rotation duration; a third acquisition unit, used to calculate the difference between the rotation torque corresponding to the rotation duration and the target rotation torque to obtain a torque difference; a first warning unit, used to issue a fault warning signal when the torque difference is greater than a torque difference threshold.

[0014] Optionally, the control unit comprises: a first acquisition module, used to acquire the current state of the safety lock when receiving the trolley movement instruction, wherein the safety lock is used to be enabled when the trolley-type switch cabinet is in a dangerous state to prohibit the movement operation of the trolley; a control module, used to control the stepper motor to rotate according to the trolley movement instruction when the current state is an unenabled state.

[0015] Optionally, the first acquisition unit includes: a statistical module, which is used to count the cumulative value of the rotation time of the stepper motor in real time after controlling the stepper motor to start rotating according to the trolley movement instruction; a first determination module, which is used to determine the current cumulative value of the time as the current rotation time at every predetermined time interval; and a second acquisition module, which is used to use a torque sensor to obtain the rotational torque applied by the stepper motor to the transmission gear group at each rotation time, wherein the torque sensor is installed on the head of the stepper motor on the side connected to the transmission gear group.

[0016] Optionally, the automatic fault warning device of the trolley-type switch cabinet also includes: a response unit, which is used to respond to the manual rotation operation acting on the manual control hole before inputting the rotation time into the torque prediction model to process the rotation time using the torque prediction model to obtain the target rotation torque corresponding to the rotation time, and to count the accumulated manual time of the manual rotation operation in real time, wherein the manual control hole is an interface for the target object to manually rotate the main shaft, and the manual rotation operation is an operation acting on the main shaft to drive the main shaft to rotate; a determination unit, which is used to determine the current accumulated manual time at every predetermined time interval. The value is the current manual rotation time; the fourth acquisition unit is used to use the torque sensor to obtain the manual rotation torque of the main shaft acting on the transmission gear group under each manual rotation time; the fifth acquisition unit is used to integrate each manual rotation time and the manual rotation torque corresponding to each manual rotation time according to a time series to obtain multiple groups of manual rotation data, wherein the multiple groups of manual rotation data are data with time series; the fifth acquisition unit is used to use the multiple groups of manual rotation data to adopt a mathematical modeling method to solve the model parameters in the autoregressive integral sliding average model to obtain the torque prediction model.

[0017] Optionally, the fifth acquisition unit comprises: a third acquisition module, used for preprocessing the multiple groups of manual rotation data with the time series, so as to remove the nonlinear data in the multiple groups of manual rotation data to obtain a linear manual rotation data group, wherein the nonlinear data refers to the manual rotation data group whose values ​​change with time and do not follow a linear relationship, and the linear manual rotation data group is a combination of other data in the multiple groups of manual rotation data except the nonlinear data; a fourth acquisition module, used for performing a stationarity evaluation on the linear manual rotation data group to obtain a first evaluation result; a fifth acquisition module, used for performing a differential processing on the linear manual rotation data group to obtain a target linear manual rotation data group when the first evaluation result indicates that the stationarity of the linear manual rotation data group is less than a stationarity threshold; a second determination module, used for determining the target linear manual rotation data group based on the target linear manual rotation data group. The data group uses an automatic model selection tool to determine the model parameters in the autoregressive integrated moving average model, wherein the model parameters include: autoregressive term order, differential order, and moving average term order, wherein the autoregressive term order refers to the number of terms of the autoregressive polynomial in the autoregressive integrated moving average model, the differential order indicates the number of times the time series is differentiated, and the moving average term order refers to the number of terms of the moving average term in the autoregressive integrated moving average model; a third determination module is used to determine the initial moment prediction model based on the autoregressive term order, the differential order, and the moving average term order; a sixth acquisition module is used to perform performance evaluation on the initial moment prediction model using the Akaike information criterion to obtain a second evaluation result; a fourth determination module determines that the initial moment prediction model is the moment prediction model when the second evaluation result indicates that the initial moment prediction model has passed the performance verification.

[0018] Optionally, the automatic fault warning device of the trolley-type switch cabinet also includes: a sixth acquisition unit, used to perform feature extraction on the nonlinear manual rotation data group to obtain a torque feature, wherein the nonlinear manual rotation data group is a combination of the nonlinear data in multiple groups of manual rotation data; a seventh acquisition unit, used to integrate the torque features to obtain a torque feature library, wherein the torque feature library is used to perform further fault judgment on the rotation torque when the torque difference is not greater than the torque difference threshold; an eighth acquisition unit, used to perform feature extraction on the rotation torque when the torque difference is not greater than the torque difference threshold to obtain a target torque feature; a ninth acquisition unit, used to match the target torque feature with the torque feature in the torque feature library to obtain a matching result; a second warning unit, used to issue the fault warning signal when the matching result indicates that the torque feature identical to the target torque feature does not exist in the torque feature library.

[0019] Optionally, the automatic fault warning device of the trolley-type switch cabinet also includes: an optimization unit, which is used to adaptively optimize the torque prediction model using the rotation time and the target rotation torque corresponding to the rotation time during the process of processing the rotation time using the torque prediction model to obtain the target rotation torque corresponding to the rotation time, and update the torque prediction model to the optimized torque prediction model; an updating unit, which is used to add the target torque feature to the torque feature library to update the torque feature library during the process of matching the target torque feature with the torque feature in the torque feature library to obtain the updated torque feature library.

[0020] According to another aspect of an embodiment of the present invention, an automatic fault warning system for a trolley-type switch cabinet is provided. The automatic fault warning system for a trolley-type switch cabinet uses any of the automatic fault warning methods for a trolley-type switch cabinet described above.

[0021] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned automated fault warning methods for a trolley-type switch cabinet.

[0022] According to another aspect of an embodiment of the present invention, a processor is further provided, the processor being used to run a program, wherein when the program is run, any one of the above-mentioned automatic fault warning methods for a trolley-type switch cabinet is executed.

[0023] According to another aspect of an embodiment of the present invention, a computer program product is further provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the above-mentioned automatic fault warning methods for a trolley-type switch cabinet is executed.

[0024] In an embodiment of the present invention, when a trolley movement instruction is received, the stepper motor is controlled to rotate according to the trolley movement instruction, wherein the stepper motor and the main shaft are connected by a transmission gear set, and during the rotation of the stepper motor, the main shaft is driven to rotate through the transmission gear set to push the trolley chassis to move the trolley; in the process of controlling the stepper motor to rotate according to the trolley movement instruction, the current rotation time and the rotation torque corresponding to the rotation time are obtained, wherein the rotation time refers to the cumulative value of the time after the stepper motor starts to rotate, and the rotation torque refers to the torque acting on the transmission gear set when the stepper motor rotates; the rotation time is converted into the rotation torque. The torque prediction model is inputted, and the rotation duration is processed by using the torque prediction model to obtain a target rotation torque corresponding to the rotation duration, wherein the torque prediction model is a model constructed by using a plurality of groups of sample data using a mathematical modeling method, and each of the plurality of groups of sample data includes: a sample rotation duration, a sample target rotation torque corresponding to the sample rotation duration, and the target rotation torque is the torque that the transmission gear set needs to achieve under the rotation duration; the difference between the rotation torque corresponding to the rotation duration and the target rotation torque is calculated to obtain a torque difference; when the torque difference is greater than a torque difference threshold, a fault warning signal is issued. Through the above technical scheme, the purpose of comparing the rotational torque collected by the sensor under the same rotation time with the target rotational torque predicted by the torque prediction model is achieved, so as to issue a fault warning signal when the deviation between the rotational torque and the target rotational torque exceeds a certain threshold. The technical effect of controlling the rotation in and out of the trolley by a stepper motor and using the torque prediction model to monitor the torque acting on the transmission gear set by the stepper motor in real time so as to issue a warning signal in time when a fault occurs is achieved. The operation progress of the trolley's rotation in and out is improved, and potential safety hazards during the operation are avoided. The technical problem in the related technology that the rotation in and out operation of the switch trolley in the trolley-type switch cabinet using the traditional method is usually not able to control the operation progress well and it is difficult to effectively monitor the potential safety hazards during the operation is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 It is a hardware structure block diagram of a mobile terminal of an automatic fault warning method for a trolley-type switch cabinet according to an embodiment of the present invention;

[0027] Figure 2 is a flow chart of an automated fault warning method for a trolley-type switch cabinet according to an embodiment of the present invention;

[0028] Figure 3is a schematic diagram of an automated fault warning system for a trolley-type switch cabinet according to an embodiment of the present invention;

[0029] Figure 4 is a flow chart of an optional automated fault warning method for a trolley-type switch cabinet according to an embodiment of the present invention;

[0030] Figure 5 Schematic diagram of an automatic fault warning device for a trolley-type switch cabinet according to an embodiment of the present invention.

[0031] The above drawings include the following reference numerals:

[0032] 102. Processor; 104. Memory; 106. Transmission device; 108. Input and output device; 1. Transmission gear set; 2. Torque sensor; 3. Stepper motor; 4. Processing unit; 5. Spindle; 6. Fixed cross bar; 7. Cart chassis; 8. Manual control hole. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] The following are explanations of the terms or nouns involved in the embodiments of the present invention:

[0036] Trolley-type switchgear: a cabinet with an upper door, a middle door and a lower door on the front, and a removable baffle inside the cabinet, which separates the circuit breaker room, busbar room and cable room of the cabinet; the baffle is provided with a hole for inserting the moving contact of the integrated current transformer; the busbar room of the cabinet is equipped with circuit breakers, transformers, transformers used, lightning arresters and other components, the trolley track is in the middle of the switchgear (that is, the trolley-type switchgear is also called the center-mounted switchgear), close to the static contact of the isolation plug, and the trolley part has good interchangeability; during maintenance, only the trolley needs to be replaced, which can shorten the maintenance power outage time, and is suitable for places with more important loads or smaller building areas.

[0037] As introduced in the background technology, the conventional method used in the related art for rotating the switch trolley in the trolley-type switch cabinet usually cannot well control the operation progress and is difficult to effectively monitor potential safety hazards during the operation. In view of the above defects, an automatic fault warning method and device for a trolley-type switch cabinet are provided in an embodiment of the present invention.

[0038] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0039] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of an automatic fault warning method for a handcart-type switch cabinet according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0040] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the automatic fault warning method of the handcart-type switch cabinet in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0041] According to an embodiment of the present invention, a method embodiment of an automated fault warning method for a trolley-type switch cabinet is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] Figure 2 is a flow chart of an automated fault warning method for a trolley-type switch cabinet according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0043] Step S202, when receiving the trolley movement instruction, control the stepper motor to rotate according to the trolley movement instruction, wherein the stepper motor and the main shaft are connected through a transmission gear set, and during the rotation of the stepper motor, the main shaft is driven to rotate through the transmission gear set to push the trolley chassis to move the trolley.

[0044] In this embodiment, when a trolley movement instruction is received, the stepper motor can be controlled to rotate according to the trolley movement instruction, so as to drive the main shaft to rotate through the transmission gear set connecting the stepper motor and the main shaft, and then the trolley chassis is pushed to move under the rotation of the main shaft to realize the movement of the manual.

[0045] Combine the following Figure 3 The above embodiments of the present invention are described in detail. Figure 3 Schematic diagram of an automated fault warning system for a trolley-type switch cabinet according to an embodiment of the present invention.

[0046] like Figure 3 As shown in the figure, the automatic fault warning system mainly includes the following core components: 1. Transmission gear set: used to connect the main shaft and the power control mechanism; 2. Torque sensor: installed on the head of the stepper motor, used to monitor the torque applied to the driving gear set in real time, and can also serve as an information source for torque input; 3. Stepper motor: used to accurately control the torque and angle of rotation. The selection of the stepper motor should be determined according to the required maximum torque and speed; 4. Processing unit: provides control and drive power, integrates and processes data from sensors, and adjusts the operation of the stepper motor according to the preset algorithm to achieve intelligent control; 5. Spindle: The movement of the trolley chassis 7 is achieved through the rotation of the spindle; 6. Fixed cross bar: It is fixed to the cabinet through a latch to push the movement of the trolley chassis 7; 7. Trolley chassis: The chassis has four metal wheels for connecting the switch trolley. The chassis moves back and forth through the rotation of the spindle, so that the switch trolley can move back and forth, realizing the function of connecting and separating the conductive contacts; 8. Manual control hole: It is used to perform manual operation, which can be used as a backup means when the electric operation fails, and as the input end for torque information.

[0047] In the embodiments provided by the present invention, mainly by Figure 3 The various components in the automated fault warning system shown work together to achieve automated monitoring and early warning of faults during the process of pushing the trolley to move.

[0048] It should be noted that the transmission gear set, torque sensor, stepper motor, processing unit, spindle, fixed cross bar, trolley chassis and manual control hole mentioned in the embodiment of the present invention are Figure 3 The components shown in FIG. 1 and FIG. 2 will not be repeated in detail in the specific description. Figure 3 The corresponding relationship of the figure marks shown in .

[0049] According to the above embodiment of the present invention, in the above step S202, the stepper motor is controlled to rotate according to the trolley movement instruction, including: when the trolley movement instruction is received, the current state of the safety lock is obtained, wherein the safety lock is used to enable when the trolley-type switch cabinet is in a dangerous state to prohibit the movement operation of the trolley; when the current state is not enabled, the stepper motor is controlled to rotate according to the trolley movement instruction.

[0050] Combine the following Figure 4 The above embodiments of the present invention are described in detail. Figure 4It is a flow chart of an optional automatic fault warning method of a trolley-type switch cabinet according to an embodiment of the present invention.

[0051] like Figure 4 As shown, when receiving the trolley movement instruction, that is, detecting an input signal, the system will first detect whether the input signal of each device is normal and reset the safety lock; then judge the current state of the safety lock. If the safety lock is in the enabled state, it means that the system has detected or received an unsafe signal. At this time, the operation should be prohibited or suspended to prevent potential dangers. In this case, it will not enter the start-up rotation / extraction operation stage, but will directly jump to the end stage and stop further actions; if the safety lock is in the enabled state, it means that the system is currently in a safe state and no conditions that may cause danger are detected. At this time, the system is allowed to rotate the trolley in or out and enter the "start-up rotation / extraction operation" stage, that is, the stepper motor can be controlled to rotate according to the trolley movement instruction. At this stage, the system will start to execute the operation and monitor the torque and position in real time to ensure safety and stability during the operation.

[0052] Step S204, in the process of controlling the stepper motor to rotate according to the trolley movement instruction, obtain the current rotation time and the rotation torque corresponding to the rotation time, wherein the rotation time refers to the cumulative value of the time after the stepper motor starts to rotate, and the rotation torque refers to the torque acting on the transmission gear set when the stepper motor rotates.

[0053] In this embodiment, during the rotation of the stepper motor, the processing unit can be used to obtain the rotational torque of the transmission gear set under the rotation of the stepper motor according to the time series; here, during the rotation of the stepper motor, the processing unit can also adjust the output torque of the stepper motor according to the time series and feedback from the torque sensor to ensure that the torque is moderate, which can overcome the resistance points without causing damage to the components.

[0054] It should be noted that the time series and the above-mentioned rotation duration mentioned here are specifically counted after the stepper motor starts to rotate, that is, it can be understood that the timing starts from the moment the stepper motor starts to rotate. When the rotation duration accumulates to 1s, it can be considered that it has reached the first time point of the time series. When the rotation duration accumulates to 2s, it can be considered that it has reached the second time point of the time series, and so on. The correspondence between the rotation duration and the time series is like this. When analyzing the rotation torque, the rotation torque at each time point is also analyzed according to the time series. The specific numerical value mentioned here is only an optional example and does not represent a restriction on the specific value of the predetermined time interval.

[0055] According to the above embodiment of the present invention, in the above step S204, in the process of controlling the stepper motor to rotate according to the trolley movement instruction, the current rotation time and the rotation torque corresponding to the rotation time are obtained, including: after controlling the stepper motor to start rotating according to the trolley movement instruction, real-time statistics of the cumulative value of the rotation time of the stepper motor; determining the current cumulative value of the time as the current rotation time at every predetermined time interval; and using a torque sensor to obtain the rotation torque of the stepper motor acting on the transmission gear group under each rotation time, wherein the torque sensor is installed on the head of the stepper motor on the side connected to the transmission gear group.

[0056] In this embodiment, during the rotation of the stepper motor (i.e., performing a rotation in / out operation), the cumulative value of the rotation time of the stepper motor can be counted in real time, and the current cumulative value of the rotation time is determined as the current rotation time at every certain interval (i.e., a predetermined time interval), which is equivalent to obtaining a time series; in addition, in this process, the rotational torque corresponding to the transmission gear group at each time point in the time series can also be obtained through a torque sensor installed on the head of the stepper motor on the side connected to the transmission gear group.

[0057] Step S206, input the rotation duration into the torque prediction model, and use the torque prediction model to process the rotation duration to obtain a target rotation torque corresponding to the rotation duration, wherein the torque prediction model is a model constructed using multiple groups of sample data using a mathematical modeling method, and each of the multiple groups of sample data includes: a sample rotation duration, a sample target rotation torque corresponding to the sample rotation duration, and the target rotation torque is the torque that the transmission gear set needs to achieve under the rotation duration.

[0058] In this embodiment, a pre-constructed torque prediction model can be used to predict the target rotational torque corresponding to the transmission gear set at each time point in the time series, so as to monitor the rotational torque corresponding to the transmission gear set at each time point in the time series collected by the torque sensor in real time to determine whether there is a fault; here, the target rotational torque predicted by the torque prediction model is the torque that the sensing gear set needs to achieve at each time point in the time series.

[0059] According to the above embodiment of the present invention, before the above step S206, that is, before the rotation time is input into the torque prediction model to process the rotation time using the torque prediction model to obtain the target rotation torque corresponding to the rotation time, the automatic fault warning method of the trolley-type switch cabinet also includes: responding to the manual rotation operation acting on the manual control hole, and real-time counting of the accumulated manual time of the manual rotation operation, wherein the manual control hole is an interface for manually rotating the spindle of the target object, and the manual rotation operation is an operation acting on the spindle to drive the spindle to rotate; each Determine the current manual time duration cumulative value as the current manual rotation time duration at predetermined time intervals; use the torque sensor to obtain the manual rotation torque of the main shaft acting on the transmission gear set under each manual rotation time duration; integrate each manual rotation time duration and the manual rotation torque corresponding to each manual rotation time duration according to a time series to obtain multiple groups of manual rotation data, wherein the multiple groups of manual rotation data are data with time series; use the multiple groups of manual rotation data to adopt a mathematical modeling method to solve the model parameters in the autoregressive integral sliding average model to obtain a torque prediction model.

[0060] In this embodiment, before using the torque prediction model to predict the target rotational torque corresponding to the transmission gear set at each time point in the time series, it is first necessary to respond to the manual rotation operation acting on the manual control hole, that is, the relevant staff manually rotates the main shaft at the manual control hole to realize the movement of the trolley. In this process, the transmission gear set will also rotate driven by the rotation of the main shaft. At this time, the torque sensor will also collect the rotational torque of the transmission gear set at each time point in the time series in real time according to the time series (here refers to the above-mentioned manual rotational torque), which is used as the data basis for constructing the torque prediction model. Then, based on the data obtained during manual operation, the model parameters in the autoregressive integral sliding average model can be solved by mathematical modeling methods to obtain the torque prediction model.

[0061] It should be noted that the time series used in constructing the torque prediction model by manually moving the trolley and the time series used in predicting using the torque prediction model while controlling the stepper motor to move the trolley are the same.

[0062] In addition, regardless of whether the transmission gear set rotates under the drive of the main shaft or under the drive of the stepper motor, the torque sensor can be used to collect the rotational torque of the transmission gear set. In these two cases, the rotational torque of the transmission gear set and the main shaft, or the rotational torque of the transmission gear set and the stepper motor, can be approximately considered to be the same, so the purpose of constructing and using the torque prediction model can be achieved by monitoring the rotational torque of the transmission gear set.

[0063] In the above embodiment of the present invention, a mathematical modeling method is used to solve the model parameters in the autoregressive integral moving average model using multiple groups of manual rotation data to obtain a torque prediction model, including: preprocessing the multiple groups of manual rotation data with time series to strip off the nonlinear data in the multiple groups of manual rotation data to obtain a linear manual rotation data group, wherein the nonlinear data refers to a manual rotation data group whose values ​​do not follow a linear relationship over time, and the linear manual rotation data group is a combination of other data except the non-linear data in the multiple groups of manual rotation data; performing a stationarity evaluation on the linear manual rotation data group to obtain a first evaluation result; when the first evaluation result indicates that the stationarity of the linear manual rotation data group is less than the stationarity threshold, performing differential processing on the linear manual rotation data group to obtain the target A target linear manual rotation data group is obtained; based on the target linear manual rotation data group, the model parameters in the autoregressive integrated moving average model are determined using the automatic model selection tool, wherein the model parameters include: autoregressive term order, difference order, and moving average term order, the autoregressive term order refers to the number of terms of the autoregressive polynomial in the autoregressive integrated moving average model, the difference order indicates the number of times the time series is differentiated, and the moving average term order refers to the number of terms of the moving average term in the autoregressive integrated moving average model; the initial moment prediction model is determined according to the autoregressive term order, the difference order, and the moving average term order; the performance of the initial moment prediction model is evaluated using the Akaike information criterion to obtain a second evaluation result; when the second evaluation result indicates that the initial moment prediction model passes the performance verification, the initial moment prediction model is determined to be the moment prediction model.

[0064] Specifically, after obtaining the manual rotation torque data collected during the movement of the trolley by manual operation, the data can be preprocessed. Since nonlinear torque values ​​generally appear in the initial stage of the trolley switch in and out, it is necessary to separate the previous and next time series and use the feature library for analysis to avoid these nonlinear data from interfering with the subsequent ARIMA model (i.e., autoregressive integrated moving average model) prediction.

[0065] The following is a detailed description of the process of constructing a torque prediction model using the remaining manual rotation torque data after stripping the nonlinear data: Assume that the manual rotation torque with a time series is Y t , the formula of the ARIMA (p, d, q) model can be expressed as: Where: B is the backshift operator, defined as B k Y t =Y t-k ②, is an autoregressive polynomial and can be defined as: θ(B) is a sliding average polynomial, which can be defined as: θ(B) = 1 + θ 1 B+θ 2 B2 +……+θ q B q ④, (1-B) d ⑤ means to make the time series stable by taking d-order differences, ∈ t is a white noise process, with a mean of 0 and a variance of σ 2 Normal distribution; after stripping the nonlinear data, we can check whether the data is stable. If not, we can perform first-order difference processing, that is, the parameter d in formula ① and ⑤ is 0 or 1. Based on the normal torque characteristics of the trolley, the data with unstable differences is not processed by high-order difference, but the stripping time length of the endpoint torque data is extended; then the automatic model selection tool in Python's statsmodels library is used to determine the best ARIMA model parameters; then the selected ARIMA model parameters can be used to fit multiple trolley torque data of the same type and different batches, and then evaluated by Akaike Information Criterion (AIC), and other possible model evaluation indicators, such as BIC, are considered to ensure the generalization ability and interpretability of the model; repeat the above steps to obtain a trolley torque data model (torque prediction model) with appropriate fitting degree.

[0066] The ARIMA model here is a commonly used time series forecasting model, the full name of which is the AutoRegressive Integrated Moving Average model. In ARIMA (p, d, q), p, d, and q represent different components: 1) p (AR part): the order of the autoregressive term, which indicates the number of lagged observations (1agobservations) included in the model, which specifies the number of past values ​​used in the forecast; for example, if p = 1, the model uses the value of the previous time step to predict the current value; 2) 2: d (I part): difference (Integral The order of ted): indicates the number of times the difference is made to make the time series stationary, which helps eliminate the trend and seasonality of the time series; for example, if d = 1, the first-order difference is used, and if d = 2, the second-order difference is used; 3): q (MA part): the order of the moving average term (MovingAverage), which indicates the lag order of the model error (residual); it specifies the number of past forecast errors used in the forecast; for example, if q = 1, the model uses the forecast error of the previous time step to adjust the current forecast; in general, p determines how many lag values ​​of its own are included in the model, d determines how many times the difference is made to the original time series, and q determines how many lag values ​​of the forecast error are included in the model.

[0067] Step S208, calculating the difference between the rotation torque corresponding to the rotation duration and the target rotation torque to obtain a torque difference.

[0068] In this embodiment, whether the current trolley push fails can be determined by calculating the difference between the rotational torque collected by the torque sensor at the same time point in the time series and the target rotational torque predicted by the torque prediction model.

[0069] Step s210, when the torque difference is greater than the torque difference threshold, a fault warning signal is issued.

[0070] As above Figure 4 As shown, it is assumed that the rotational torque with time series collected by the torque sensor is: Y′ t The target rotation torque with time series predicted by the torque prediction model is: Y″ t , assuming that the torque difference threshold is E, then in |Y′ t -Y″ t When |>E, it can be considered that the torque judgment has not been passed, and a fault warning signal needs to be issued. At the same time, the safety lock is enabled to suspend the current operation and prompt the staff to perform fault detection and maintenance.

[0071] In an optional embodiment of the present invention, the automated fault warning method of the trolley-type switch cabinet also includes: extracting features from a nonlinear manual rotation data group to obtain a torque feature, wherein the nonlinear manual rotation data group is a combination of nonlinear data in multiple groups of manual rotation data; integrating the torque features to obtain a torque feature library, wherein the torque feature library is used to perform further fault judgment on the rotation torque when the torque difference is not greater than the torque difference threshold; when the torque difference is not greater than the torque difference threshold, extracting features from the rotation torque to obtain a target torque feature; matching the target torque feature with the torque feature in the torque feature library to obtain a matching result; and issuing a fault warning signal when the matching result indicates that there is no torque feature identical to the target torque feature in the torque feature library.

[0072] Specifically, for the nonlinear data stripped from the manual rotation data, feature extraction can be performed to establish a torque feature library, thereby improving the judgment data for fault judgment of the rotation torque collected by the torque sensor during the rotation of the stepper motor (equivalent to supplementing the judgment database); as above Figure 4 As shown, in |Y′ t -Y″ t|≤E, that is, when the torque judgment is passed, the rotational torque can be further feature extracted, and then the extracted torque feature can be matched with the pre-built torque feature library. If a feature matching the torque feature can be found in the torque feature library, it can be considered that the rotational torque has passed the feature judgment. If a feature matching the torque feature is not found in the torque feature library, it is considered that the rotational torque has not passed the feature judgment, and a fault warning signal needs to be issued. At the same time, the safety lock is enabled to suspend the current operation and prompt the staff to perform fault detection and maintenance.

[0073] According to the above embodiment of the present invention, the automatic fault warning method of the trolley-type switch cabinet also includes: in the process of using the torque prediction model to process the rotation time to obtain the target rotation torque corresponding to the rotation time, the torque prediction model is adaptively optimized using the rotation time and the target rotation torque corresponding to the rotation time, and the torque prediction model is updated to the optimized torque prediction model; in the process of matching the target torque feature with the torque feature in the torque feature library, the target torque feature is added to the torque feature library to update the torque feature library to obtain the updated torque feature library.

[0074] Specifically, in the process of using the torque prediction model to predict the target rotational torque according to the time series and / or using the torque feature library to match the torque characteristics of the rotational torque to monitor the rotational torque collected by the torque sensor during the rotation of the stepper motor, the torque prediction model and the torque feature library can be continuously optimized and updated using the newly collected rotational torque and judgment results to improve the accuracy of monitoring.

[0075] It can be seen from the above that, through the technical solution provided by the above embodiment of the present invention, when a trolley movement instruction is received, the stepper motor can be controlled to rotate according to the trolley movement instruction, wherein the stepper motor and the main shaft are connected by a transmission gear set, and the main shaft is driven to rotate through the transmission gear set during the rotation of the stepper motor to push the trolley chassis to move the trolley; in the process of controlling the stepper motor to rotate according to the trolley movement instruction, the current rotation time and the rotation torque corresponding to the rotation time are obtained, wherein the rotation time refers to the cumulative value of the time after the stepper motor starts to rotate, and the rotation torque refers to the torque acting on the transmission gear set when the stepper motor rotates; the rotation time is input into the torque prediction model, so as to process the rotation time using the torque prediction model to obtain the target rotation torque corresponding to the rotation time, wherein the torque prediction model is a model constructed by using a mathematical modeling method using multiple sets of sample data, and the multiple sets of sample data Each group includes: a sample rotation time, a sample target rotation torque corresponding to the sample rotation time, the target rotation torque is the torque that the transmission gear set needs to reach under the rotation time; the difference between the rotation torque corresponding to the rotation time and the target rotation torque is calculated to obtain a torque difference; when the torque difference is greater than the torque difference threshold, a fault warning signal is issued, thereby achieving the purpose of comparing the rotation torque collected by the sensor under the same rotation time with the target rotation torque predicted by the torque prediction model, so as to issue a fault warning signal when the deviation between the rotation torque and the target rotation torque exceeds a certain threshold, realizing the technical effect of controlling the rotation in and out of the trolley by a stepper motor, and using the torque prediction model to monitor the torque acting on the transmission gear set by the stepper motor in real time to issue a warning signal in time when a fault occurs, thereby improving the operation progress of the trolley rotation in and out, and avoiding potential safety hazards during the operation.

[0076] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problem in the related art that the in and out operations of the switch trolley in the trolley-type switch cabinet using the traditional method usually cannot well control the operation progress and it is difficult to effectively monitor the potential safety hazards during the operation.

[0077] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0078] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0079] According to an embodiment of the present invention, there is also provided an automatic fault warning device for a trolley-type switch cabinet for implementing the automatic fault warning method for the trolley-type switch cabinet. Figure 5 Schematic diagram of an automatic fault warning device for a trolley-type switch cabinet according to an embodiment of the present invention. Figure 5 As shown, the device comprises: a control unit 51, a first acquisition unit 53, a second acquisition unit 55, a third acquisition unit 57 and a first early warning unit 59. The automatic fault early warning device of the trolley type switch cabinet is described in detail below.

[0080] The control unit 51 is used to control the stepper motor to rotate according to the trolley movement instruction when receiving the trolley movement instruction, wherein the stepper motor and the main shaft are connected through a transmission gear set. During the rotation of the stepper motor, the main shaft is driven to rotate through the transmission gear set to push the trolley chassis to move the trolley.

[0081] The first acquisition unit 53 is used to acquire the current rotation time and the rotation torque corresponding to the rotation time in the process of controlling the stepper motor to rotate according to the trolley movement instruction, wherein the rotation time refers to the cumulative value of the time after the stepper motor starts to rotate, and the rotation torque refers to the torque acting on the transmission gear set when the stepper motor rotates.

[0082] The second acquisition unit 55 is used to input the rotation duration into the torque prediction model, so as to process the rotation duration using the torque prediction model to obtain a target rotation torque corresponding to the rotation duration, wherein the torque prediction model is a model constructed using a plurality of groups of sample data using a mathematical modeling method, and each of the plurality of groups of sample data includes: a sample rotation duration, a sample target rotation torque corresponding to the sample rotation duration, and the target rotation torque is the torque that the transmission gear set needs to achieve under the rotation duration.

[0083] The third acquisition unit 57 is used to calculate the difference between the rotation torque corresponding to the rotation duration and the target rotation torque to obtain a torque difference.

[0084] The first warning unit 59 is used to send out a fault warning signal when the torque difference value is greater than the torque difference threshold value.

[0085] It should be noted here that the above-mentioned control unit 51, first acquisition unit 53, second acquisition unit 55, third acquisition unit 57 and first early warning unit 59 correspond to steps S202 to S210 in the above-mentioned embodiments, and the five units have the same instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0086] It can be seen from the above that in the scheme recorded in the above-mentioned embodiment of the present invention, the control unit can be used to control the stepper motor to rotate according to the trolley movement instruction when receiving the trolley movement instruction, wherein the stepper motor and the main shaft are connected by a transmission gear set, and the main shaft is driven to rotate through the transmission gear set during the rotation of the stepper motor to push the trolley chassis to move the trolley; then, the first acquisition unit is used to obtain the current rotation time and the rotation torque corresponding to the rotation time in the process of controlling the stepper motor to rotate according to the trolley movement instruction, wherein the rotation time refers to the cumulative value of the time after the stepper motor starts to rotate, and the rotation torque refers to the torque acting on the transmission gear set when the stepper motor rotates; then, the second acquisition unit is used to input the rotation time into the torque prediction model, so as to process the rotation time using the torque prediction model to obtain the target rotation torque corresponding to the rotation time, wherein the torque prediction model is a model constructed by using a mathematical modeling method using multiple groups of sample data, and the multiple groups of sample data Each group of the data includes: a sample rotation time, a sample target rotation torque corresponding to the sample rotation time, the target rotation torque being the torque that the transmission gear set needs to reach under the rotation time; then the third acquisition unit is used to calculate the difference between the rotation torque corresponding to the rotation time and the target rotation torque to obtain a torque difference; finally, the first warning unit is used to issue a fault warning signal when the torque difference is greater than the torque difference threshold, thereby achieving the purpose of comparing the rotation torque collected by the sensor under the same rotation time with the target rotation torque predicted by the torque prediction model, so as to issue a fault warning signal when the deviation between the rotation torque and the target rotation torque exceeds a certain threshold, realizing the technical effect of controlling the rotation in and out of the trolley by a stepper motor, and using the torque prediction model to monitor the torque acting on the transmission gear set by the stepper motor in real time to issue a warning signal in time when a fault occurs, thereby improving the operation progress of the rotation in and out of the trolley and avoiding potential safety hazards during the operation.

[0087] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problem in the related art that the in and out operations of the switch trolley in the trolley-type switch cabinet using the traditional method usually cannot well control the operation progress and it is difficult to effectively monitor the potential safety hazards during the operation.

[0088] Optionally, the control unit includes: a first acquisition module, used to obtain the current state of the safety lock when a trolley movement instruction is received, wherein the safety lock is used to be enabled when the trolley-type switch cabinet is in a dangerous state to prohibit the movement operation of the trolley; a control module, used to control the stepper motor to rotate according to the trolley movement instruction when the current state is an unenabled state.

[0089] Optionally, the first acquisition unit includes: a statistical module, which is used to count the cumulative value of the rotation time of the stepper motor in real time after the stepper motor starts to rotate according to the trolley movement instruction; a first determination module, which is used to determine the current cumulative value of the time as the current rotation time at every predetermined time interval; and a second acquisition module, which is used to use a torque sensor to obtain the rotational torque of the stepper motor acting on the transmission gear group under each rotation time, wherein the torque sensor is installed on the head of the stepper motor on the side connected to the transmission gear group.

[0090] Optionally, the automatic fault warning device of the trolley-type switch cabinet also includes: a response unit, which is used to respond to the manual rotation operation acting on the manual control hole before inputting the rotation time into the torque prediction model to process the rotation time using the torque prediction model to obtain the target rotation torque corresponding to the rotation time, and to count the accumulated manual time of the manual rotation operation in real time, wherein the manual control hole is an interface for manually rotating the main shaft of the target object, and the manual rotation operation is an operation acting on the main shaft to drive the main shaft to rotate; a determination unit, which is used to determine the current accumulated manual time at every predetermined time interval The value is the current manual rotation time; the fourth acquisition unit is used to use the torque sensor to obtain the manual rotation torque of the main shaft acting on the transmission gear group under each manual rotation time; the fifth acquisition unit is used to integrate each manual rotation time and the manual rotation torque corresponding to each manual rotation time according to a time series to obtain multiple groups of manual rotation data, wherein the multiple groups of manual rotation data are data with time series; the fifth acquisition unit is used to use the multiple groups of manual rotation data to adopt a mathematical modeling method to solve the model parameters in the autoregressive integral sliding average model to obtain a torque prediction model.

[0091] Optionally, the fifth acquisition unit includes: a third acquisition module, which is used to pre-process the multiple groups of manual rotation data with time series to remove nonlinear data in the multiple groups of manual rotation data to obtain a linear manual rotation data group, wherein the nonlinear data refers to a manual rotation data group whose values ​​change with time and do not follow a linear relationship, and the linear manual rotation data group is a combination of other data except the non-linear data in the multiple groups of manual rotation data; a fourth acquisition module, which is used to perform a stationarity evaluation on the linear manual rotation data group to obtain a first evaluation result; a fifth acquisition module, which is used to perform a differential processing on the linear manual rotation data group to obtain a target linear manual rotation data group when the first evaluation result indicates that the stationarity of the linear manual rotation data group is less than a stationarity threshold; a second determination module, which is used to determine the target linear manual rotation data group based on the target linear manual rotation data group. The dynamic data group uses an automatic model selection tool to determine the model parameters in the autoregressive integrated moving average model, wherein the model parameters include: autoregressive term order, differential order, and moving average term order. The autoregressive term order refers to the number of terms of the autoregressive polynomial in the autoregressive integrated moving average model, the differential order indicates the number of times the time series is differentiated, and the moving average term order refers to the number of terms of the moving average term in the autoregressive integrated moving average model; the third determination module is used to determine the initial moment prediction model according to the autoregressive term order, the differential order, and the moving average term order; the sixth acquisition module is used to perform performance evaluation on the initial moment prediction model using the Akaike information criterion to obtain a second evaluation result; the fourth determination module determines that the initial moment prediction model is a moment prediction model when the second evaluation result indicates that the initial moment prediction model has passed the performance verification.

[0092] Optionally, the automatic fault warning device of the trolley-type switch cabinet also includes: a sixth acquisition unit, used to extract features from the nonlinear manual rotation data group to obtain a torque feature, wherein the nonlinear manual rotation data group is a combination of nonlinear data in multiple groups of manual rotation data; a seventh acquisition unit, used to integrate the torque features to obtain a torque feature library, wherein the torque feature library is used to perform further fault judgment on the rotation torque when the torque difference is not greater than the torque difference threshold; an eighth acquisition unit, used to extract features from the rotation torque when the torque difference is not greater than the torque difference threshold to obtain a target torque feature; a ninth acquisition unit, used to match the target torque feature with the torque feature in the torque feature library to obtain a matching result; a second warning unit, used to issue a fault warning signal when the matching result indicates that there is no torque feature identical to the target torque feature in the torque feature library.

[0093] Optionally, the automatic fault warning device of the trolley-type switch cabinet also includes: an optimization unit, which is used to adaptively optimize the torque prediction model using the rotation time and the target rotation torque corresponding to the rotation time when the rotation time is processed by the torque prediction model to obtain the target rotation torque corresponding to the rotation time, and update the torque prediction model to an optimized torque prediction model; an updating unit, which is used to add the target torque feature to the torque feature library to update the torque feature library in the process of matching the target torque feature with the torque feature in the torque feature library to obtain an updated torque feature library.

[0094] According to another aspect of an embodiment of the present invention, an automatic fault warning system for a trolley-type switch cabinet is further provided. The automatic fault warning system for a trolley-type switch cabinet uses any of the above-mentioned automatic fault warning methods for a trolley-type switch cabinet.

[0095] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, the computer-readable storage medium comprising a stored program, wherein the program executes any one of the above-mentioned automated fault warning methods for a trolley-type switch cabinet.

[0096] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0097] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: upon receiving a trolley movement instruction, controlling the stepper motor to rotate according to the trolley movement instruction, wherein the stepper motor and the main shaft are connected via a transmission gear set, and during the rotation of the stepper motor, the main shaft is driven to rotate via the transmission gear set to push the trolley chassis to move the trolley; in the process of controlling the stepper motor to rotate according to the trolley movement instruction, obtaining the current rotation time and the rotation torque corresponding to the rotation time, wherein the rotation time refers to the cumulative value of the time after the stepper motor starts to rotate, and the rotation torque refers to the torque acting on the stepper motor when it rotates. The torque on the transmission gear set; the rotation time is input into the torque prediction model, and the rotation time is processed by the torque prediction model to obtain the target rotation torque corresponding to the rotation time, wherein the torque prediction model is a model constructed by a mathematical modeling method using multiple groups of sample data, and each group of the multiple groups of sample data includes: a sample rotation time, a sample target rotation torque corresponding to the sample rotation time, and the target rotation torque is the torque that the transmission gear set needs to achieve under the rotation time; the difference between the rotation torque corresponding to the rotation time and the target rotation torque is calculated to obtain a torque difference; when the torque difference is greater than the torque difference threshold, a fault warning signal is issued.

[0098] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: when a trolley movement instruction is received, obtaining the current state of the safety lock, wherein the safety lock is used to enable when the trolley-type switch cabinet is in a dangerous state to prohibit the movement operation of the trolley; when the current state is not enabled, controlling the stepper motor to rotate according to the trolley movement instruction.

[0099] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: after controlling the stepper motor to start rotating according to the trolley movement instruction, real-time statistics are obtained on the cumulative value of the rotation time of the stepper motor; at each predetermined time interval, the current cumulative value is determined as the current rotation time; and a torque sensor is used to obtain the rotational torque of the stepper motor acting on the transmission gear group under each rotation time, wherein the torque sensor is installed on the head of the stepper motor on the side connected to the transmission gear group.

[0100] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: responding to a manual rotation operation acting on a manual control hole, and real-time statistics of the cumulative manual duration of the manual rotation operation, wherein the manual control hole is an interface for manually rotating the spindle of the target object, and the manual rotation operation is an operation acting on the spindle to drive the spindle to rotate; determining the current cumulative manual duration as the current manual rotation duration at every predetermined time interval; using a torque sensor to obtain the manual rotation torque of the spindle acting on the transmission gear set under each manual rotation duration; integrating each manual rotation duration and the manual rotation torque corresponding to each manual rotation duration according to a time series to obtain multiple groups of manual rotation data, wherein the multiple groups of manual rotation data are data with time series; using multiple groups of manual rotation data to adopt a mathematical modeling method to solve the model parameters in the autoregressive integral sliding average model to obtain a torque prediction model.

[0101] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: preprocessing multiple groups of manual rotation data with time series to strip nonlinear data from the multiple groups of manual rotation data to obtain a linear manual rotation data group, wherein the nonlinear data refers to a manual rotation data group whose values ​​do not follow a linear relationship over time, and the linear manual rotation data group is a combination of other data except the nonlinear data in the multiple groups of manual rotation data; performing a stationarity evaluation on the linear manual rotation data group to obtain a first evaluation result; when the first evaluation result indicates that the stationarity of the linear manual rotation data group is less than a stationarity threshold, performing a differential processing on the linear manual rotation data group to obtain a target linear manual rotation data group; based on The target linear manual rotation data group uses an automatic model selection tool to determine the model parameters in the autoregressive integrated moving average model, where the model parameters include: autoregressive term order, difference order, and moving average term order. The autoregressive term order refers to the number of terms of the autoregressive polynomial in the autoregressive integrated moving average model, the difference order indicates the number of times the time series is differentiated, and the moving average term order refers to the number of terms of the moving average term in the autoregressive integrated moving average model; the initial moment prediction model is determined according to the autoregressive term order, the difference order, and the moving average term order; the initial moment prediction model is evaluated for performance using the Akaike information criterion to obtain a second evaluation result; when the second evaluation result indicates that the initial moment prediction model passes the performance verification, the initial moment prediction model is determined to be the moment prediction model.

[0102] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: performing feature extraction on a nonlinear manual rotation data group to obtain a torque feature, wherein the nonlinear manual rotation data group is a combination of nonlinear data in multiple groups of manual rotation data; integrating the torque features to obtain a torque feature library, wherein the torque feature library is used to perform further fault judgment on the rotation torque when the torque difference is not greater than the torque difference threshold; when the torque difference is not greater than the torque difference threshold, performing feature extraction on the rotation torque to obtain a target torque feature; matching the target torque feature with the torque feature in the torque feature library to obtain a matching result; and issuing a fault warning signal when the matching result indicates that there is no torque feature identical to the target torque feature in the torque feature library.

[0103] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: in the process of processing the rotation duration using the torque prediction model to obtain a target rotation torque corresponding to the rotation duration, the torque prediction model is adaptively optimized using the rotation duration and the target rotation torque corresponding to the rotation duration, and the torque prediction model is updated to an optimized torque prediction model; in the process of matching the target torque feature with the torque feature in the torque feature library, the target torque feature is added to the torque feature library to update the torque feature library to obtain an updated torque feature library.

[0104] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned automatic fault warning methods for a trolley-type switch cabinet is executed.

[0105] According to another aspect of an embodiment of the present invention, a computer program product is provided, including computer instructions, and when the computer instructions are executed by a processor, any one of the above-mentioned automatic fault warning methods for a trolley-type switch cabinet is executed.

[0106] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0107] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0110] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0111] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0112] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An automatic fault warning method for a trolley-type switch cabinet, characterized in that: include: When receiving a trolley movement instruction, the stepper motor is controlled to rotate according to the trolley movement instruction, wherein the stepper motor is connected to the main shaft through a transmission gear set, and the main shaft is driven to rotate through the transmission gear set during the rotation of the stepper motor, so as to push the trolley chassis to move the trolley; In the process of controlling the stepper motor to rotate according to the trolley movement instruction, the current rotation time and the rotation torque corresponding to the rotation time are obtained, wherein the rotation time refers to the cumulative value of the time after the stepper motor starts to rotate, and the rotation torque refers to the torque acting on the transmission gear set when the stepper motor rotates; The rotation duration is input into a torque prediction model, so as to process the rotation duration using the torque prediction model to obtain a target rotation torque corresponding to the rotation duration, wherein the torque prediction model is a model constructed by using a plurality of groups of sample data using a mathematical modeling method, and each of the plurality of groups of sample data includes: a sample rotation duration, a sample target rotation torque corresponding to the sample rotation duration, and the target rotation torque is the torque that the transmission gear set needs to achieve under the rotation duration; Calculating the difference between the rotation torque corresponding to the rotation duration and the target rotation torque to obtain a torque difference; When the torque difference value is greater than the torque difference threshold value, a fault warning signal is issued.

2. The automatic fault warning method for a trolley-type switch cabinet according to claim 1 is characterized in that: Control the stepper motor to rotate according to the trolley movement instructions, including: When receiving the trolley movement instruction, obtaining the current state of the safety lock, wherein the safety lock is used to be enabled when the trolley switch cabinet is in a dangerous state to prohibit the movement operation of the trolley; When the current state is the inactive state, the stepper motor is controlled to rotate according to the trolley movement instruction.

3. The automatic fault warning method for a trolley-type switch cabinet according to claim 1 is characterized in that: In the process of controlling the stepper motor to rotate according to the trolley movement instruction, obtaining the current rotation time and the rotation torque corresponding to the rotation time includes: After controlling the stepper motor to start rotating according to the trolley movement instruction, real-time statistics are collected on the cumulative value of the rotation time of the stepper motor; Determine the current duration cumulative value as the current rotation duration at every predetermined time interval; A torque sensor is used to obtain the rotational torque of the stepper motor acting on the transmission gear set at each rotation duration, wherein the torque sensor is installed on the head of the stepper motor on the side connected to the transmission gear set.

4. The automatic fault warning method for a trolley-type switch cabinet according to claim 1 is characterized in that: Before inputting the rotation duration into the torque prediction model to process the rotation duration using the torque prediction model to obtain a target rotation torque corresponding to the rotation duration, the method further includes: Responding to a manual rotation operation acting on a manual control hole, and counting in real time the accumulated manual time of the manual rotation operation, wherein the manual control hole is an interface for the target object to manually rotate the spindle, and the manual rotation operation is an operation acting on the spindle to drive the spindle to rotate; Determine the current manual rotation duration cumulative value as the current manual rotation duration at every predetermined time interval; Using a torque sensor to obtain the manual rotation torque of the main shaft acting on the transmission gear set during each manual rotation time; Integrate each manual rotation duration and the manual rotation torque corresponding to each manual rotation duration in a time series to obtain multiple sets of manual rotation data, wherein the multiple sets of manual rotation data are data with a time series; The model parameters in the autoregressive integral moving average model are solved by using a mathematical modeling method using multiple groups of the manual rotation data to obtain the torque prediction model.

5. The automatic fault warning method for a trolley-type switch cabinet according to claim 4 is characterized in that: The model parameters in the autoregressive integrated moving average model are solved by using a mathematical modeling method using multiple groups of the manual rotation data to obtain the torque prediction model, including: Preprocessing the multiple groups of manual rotation data with the time series to remove nonlinear data in the multiple groups of manual rotation data to obtain a linear manual rotation data group, wherein the nonlinear data refers to the manual rotation data group whose values ​​do not follow a linear relationship as the time changes, and the linear manual rotation data group is a combination of other data in the multiple groups of manual rotation data except the nonlinear data; Performing a stability evaluation on the linear manual rotation data set to obtain a first evaluation result; When the first evaluation result indicates that the stability of the linear manual rotation data set is less than a stability threshold, performing differential processing on the linear manual rotation data set to obtain a target linear manual rotation data set; Determining the model parameters in the autoregressive integrated moving average model using an automatic model selection tool based on the target linear manual rotation data set, wherein the model parameters include: autoregressive term order, difference order, and moving average term order, wherein the autoregressive term order refers to the number of terms of the autoregressive polynomial in the autoregressive integrated moving average model, the difference order indicates the number of times the time series is differentiated, and the moving average term order refers to the number of terms of the moving average term in the autoregressive integrated moving average model; Determining an initial moment prediction model according to the autoregressive term order, the difference order and the moving average term order; Using the Akaike information criterion to perform a performance evaluation on the initial torque prediction model to obtain a second evaluation result; When the second evaluation result indicates that the initial torque prediction model passes the performance verification, the initial torque prediction model is determined to be the torque prediction model.

6. The automatic fault warning method for a trolley-type switch cabinet according to claim 5 is characterized in that: Also includes: Extracting features from a nonlinear manual rotation data set to obtain torque features, wherein the nonlinear manual rotation data set is a combination of the nonlinear data in a plurality of sets of manual rotation data; Integrating the torque characteristics to obtain a torque characteristic library, wherein the torque characteristic library is used to further perform fault judgment on the rotation torque when the torque difference value is not greater than the torque difference threshold value; When the torque difference is not greater than the torque difference threshold, extracting a feature of the rotational torque to obtain a target torque feature; Matching the target torque feature with the torque feature in the torque feature library to obtain a matching result; When the matching result indicates that the torque feature identical to the target torque feature does not exist in the torque feature library, the fault warning signal is issued.

7. The automatic fault warning method for a trolley-type switch cabinet according to any one of claims 1 to 6, characterized in that: Also includes: In the process of processing the rotation duration by using the torque prediction model to obtain the target rotation torque corresponding to the rotation duration, the torque prediction model is adaptively optimized by using the rotation duration and the target rotation torque corresponding to the rotation duration, and the torque prediction model is updated to the optimized torque prediction model; In the process of matching the target torque feature with the torque feature in the torque feature library, the target torque feature is added to the torque feature library to update the torque feature library to obtain the updated torque feature library.

8. An automatic fault warning device for a trolley-type switch cabinet, characterized in that: include: A control unit is used to control the stepper motor to rotate according to the trolley movement instruction when receiving the trolley movement instruction, wherein the stepper motor is connected to the main shaft through a transmission gear set, and the main shaft is driven to rotate through the transmission gear set during the rotation of the stepper motor, so as to push the trolley chassis to move the trolley; A first acquisition unit is used to acquire the current rotation time and the rotation torque corresponding to the rotation time in the process of controlling the stepper motor to rotate according to the trolley movement instruction, wherein the rotation time refers to the cumulative value of the time after the stepper motor starts to rotate, and the rotation torque refers to the torque acting on the transmission gear set when the stepper motor rotates; A second acquisition unit is used to input the rotation duration into a torque prediction model, so as to process the rotation duration using the torque prediction model to obtain a target rotation torque corresponding to the rotation duration, wherein the torque prediction model is a model constructed by using a plurality of groups of sample data using a mathematical modeling method, and each of the plurality of groups of sample data includes: a sample rotation duration, a sample target rotation torque corresponding to the sample rotation duration, and the target rotation torque is the torque that the transmission gear set needs to achieve under the rotation duration; A third acquisition unit is used to calculate the difference between the rotation torque corresponding to the rotation duration and the target rotation torque to obtain a torque difference; The first warning unit is used to send a fault warning signal when the torque difference value is greater than the torque difference threshold.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the automated fault warning method for a trolley-type switch cabinet as claimed in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the automatic fault warning method for the trolley-type switch cabinet according to any one of claims 1 to 7 is executed.

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