Loading adjustment method, device and equipment for railway intelligent loading system

Abstract

The present invention relates to a loading adjustment method, device and equipment for a railway intelligent loading system, and relates to the field of loading technology. The method, device and equipment combine a time series prediction model and PID control for the first time, update historical parameter data in real time, thereby updating the PID controller in real time, adjusting the rotation speed of the winch to adjust the carriage traction speed. When it is predicted that the coal in the chute is reduced, the winch is controlled to reduce the rotation speed and reduce the carriage traction speed, thereby waiting for the coal in the chute to be replenished; when it is predicted that the coal in the chute is increased, it means that the coal in the chute has been replenished, and the winch is controlled to increase the rotation speed and increase the carriage traction speed; through real-time control, when the coal is reduced, the carriage traction speed is reduced in advance, avoiding the frequent occurrence of coal shortage, eliminating the need for secondary replenishment, improving loading continuity, and ensuring loading efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle loading, and in particular to a vehicle loading adjustment method, device and equipment for a railway intelligent vehicle loading system. Background Art

[0002] Traditional intelligent loading systems at railway loading stations often utilize telescopic chute technology for bulk material loading. The operating principle is this: the chute opening is precisely lowered to the preset loading height of the carriage via hydraulic or mechanical devices. Once the gate is opened, the coal naturally accumulates under the action of gravity. Due to the physical properties of coal as a solid bulk material, its accumulation process is constrained by its angle of repose. When the coal accumulates within the carriage to a critical angle of repose, the material stops spreading laterally. At this point, the carriage must be moved to allow the coal in the chute to pour backward, forming a continuous loading cycle.

[0003] However, in actual operation, this system encountered a technical problem: when the coal supply in the chute was insufficient, the loading process was forced to be interrupted to wait for restocking. This intermittent downtime not only resulted in the carriages not reaching the expected loading volume (frequent coal shortages), but also required reversing to restock the car, severely disrupting the loading process and resulting in low loading efficiency.

[0004] Therefore, how to improve loading efficiency has become a technical problem that needs to be solved urgently in the existing technology. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a loading adjustment method, device and equipment for a railway intelligent loading system to overcome the current problem that intermittent shutdown not only causes the carriage loading volume to fall short of expectations (frequent material shortage), but also requires secondary material replenishment through reversing operation, which seriously damages the continuity of loading and causes low loading efficiency.

[0006] To achieve the above objectives, the present invention adopts the following technical solutions:

[0007] In one aspect, a method for adjusting loading of a railway intelligent loading system includes:

[0008] Acquire historical parameter data within a preset time period in a chute of a railway intelligent loading system, wherein the historical parameter data includes coal flow, coal moisture, and coal particle size;

[0009] Inputting the historical parameter data into a time series prediction model to predict future parameter data, the future parameter data including: changes in coal flow rate, changes in coal moisture, and changes in coal particle size;

[0010] The proportional coefficient of the PID controller is adjusted according to the change of the coal flow rate, the integral coefficient of the PID controller is adjusted according to the change of the coal moisture, and the differential coefficient of the PID controller is adjusted according to the change of the coal particle size; wherein the input of the PID controller is the deviation value between the actual vehicle speed and the set target vehicle speed, and the output of the PID controller is the vehicle speed control variable;

[0011] The winch speed is adjusted according to the vehicle speed control value output by the updated PID controller to adjust the carriage traction speed and ensure the loading uniformity of the railway intelligent loading system.

[0012] Optionally, adjusting the proportional coefficient of the PID controller according to the change of the coal flow, adjusting the integral coefficient of the PID controller according to the change of the coal moisture, and adjusting the differential coefficient of the PID controller according to the change of the coal particle size include:

[0013] When the coal flow rate changes to an increase in coal flow, the proportional coefficient in the PID controller is reduced; when the coal humidity changes to an increase in coal humidity, the integral coefficient in the PID controller is reduced; when the coal particle size changes to an increase in coal particle size, the differential coefficient in the PID controller is reduced;

[0014] When the coal flow rate changes to a decrease, increase the proportional coefficient in the PID controller; when the coal humidity changes to a decrease, increase the integral coefficient in the PID controller; when the coal particle size changes to a decrease, increase the differential coefficient in the PID controller.

[0015] Optionally, the control mode of the PID controller is:

[0016]

[0017] Among them, e(t) is the deviation between the actual vehicle speed and the set target speed, e(t)=V set -V actual , V set To set the target vehicle speed, V actual is the actual vehicle speed; Kp is the proportional coefficient, K i is the integral coefficient, K d is the differential coefficient.

[0018] Optionally, the time series prediction model is an ARIMA (p, d, q) model, and the expression is:

[0019]

[0020] Where Φ(B) is the p-order autoregressive coefficient polynomial, is the q-order moving average coefficient polynomial, B is the backshift operator, is a white noise sequence, d is the difference order, and Q(t) is the parameter data.

[0021] Optionally, inputting the historical parameter data into a time series prediction model to predict future parameter data includes:

[0022] The historical parameter data of coal flow are organized into a coal flow time series according to chronological order, and the coal flow time series is input into a time series prediction model to obtain the predicted data of coal flow in a preset time period in the future. The predicted data is compared with the current coal flow to obtain the change of coal flow corresponding to the historical parameter data of coal flow;

[0023] The historical parameter data of coal humidity are organized into a coal humidity time series according to the chronological order, and the coal humidity time series is input into the time series prediction model to obtain the predicted data of coal humidity in the future preset time period. The predicted data is compared with the current coal humidity to obtain the coal humidity change corresponding to the historical parameter data of coal humidity;

[0024] The historical parameter data of coal particle size are organized into a coal particle size time series according to chronological order, and the coal particle size time series is input into a time series prediction model to obtain the predicted data of coal particle size in a preset time period in the future. The predicted data is compared with the current coal particle size to obtain the change of coal particle size corresponding to the historical parameter data of coal particle size.

[0025] Optionally, also include:

[0026] Determine whether the current carriage traction speed reaches the preset carriage traction speed maximum value. If the current carriage traction speed is equal to the preset carriage traction speed maximum value, stop increasing the winch speed.

[0027] In another aspect, a loading adjustment device for a railway intelligent loading system includes:

[0028] An acquisition module is used to acquire historical parameter data within a preset time period in the chute of the railway intelligent loading system, wherein the historical parameter data includes coal flow, coal moisture, and coal particle size;

[0029] A prediction module is used to input the historical parameter data into a time series prediction model to predict future parameter data, wherein the future parameter data includes: changes in coal flow rate, changes in coal moisture, and changes in coal particle size;

[0030] an adjustment module, configured to adjust a proportional coefficient of the PID controller according to changes in the coal flow rate, an integral coefficient of the PID controller according to changes in the coal moisture content, and a differential coefficient of the PID controller according to changes in the coal particle size; wherein the input of the PID controller is a deviation between the actual vehicle speed and the set target vehicle speed, and the output of the PID controller is a vehicle speed control variable;

[0031] The control module is used to adjust the winch speed according to the vehicle speed control value output by the updated PID controller to adjust the carriage traction speed and ensure the loading uniformity of the railway intelligent loading system.

[0032] In another aspect, a loading adjustment device of a railway intelligent loading system includes a processor and a memory, wherein the processor is connected to the memory:

[0033] The processor is configured to call and execute the program stored in the memory;

[0034] The memory is used to store the program, and the program is at least used to execute the loading adjustment method of the railway intelligent loading system described in any one of the above items.

[0035] The technical solution provided by the present invention includes at least the following beneficial effects:

[0036] The technical solution provided by the embodiment of the present invention combines the time series prediction model and PID control for the first time, updates historical parameter data in real time, and thus updates the PID controller in real time, adjusts the speed of the winch to adjust the traction speed of the carriage. When it is predicted that the coal in the chute will decrease, the winch is controlled to reduce the speed and reduce the traction speed of the carriage, thereby waiting for the coal in the chute to be replenished; when it is predicted that the coal in the chute will increase, it means that the coal in the chute has been replenished, and the winch is controlled to increase the speed and increase the traction speed of the carriage; through real-time control, when the coal decreases, the traction speed of the carriage is reduced in advance to avoid frequent material shortages, without the need for secondary replenishment, thereby improving loading continuity and ensuring loading efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 Schematic diagrams of the front (left) and side (right) views of the loading process in the prior art;

[0039] Figure 2 A schematic flow chart of a loading adjustment method for a railway intelligent loading system provided by an embodiment of the present invention;

[0040] Figure 3 A schematic structural diagram of a loading adjustment device for a railway intelligent loading system provided by an embodiment of the present invention;

[0041] Figure 4 A schematic structural diagram of a loading adjustment device of a railway intelligent loading system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without inventive effort are within the scope of protection of the present invention.

[0043] As mentioned in the background technology, the intelligent loading system of traditional railway loading stations often uses telescopic chute technology for bulk material loading. Figure 1 ( Figure 1 (Figures 2 and 3 are schematic diagrams of the loading process in the prior art.) The operating principle is as follows: The chute is precisely lowered to the preset loading height of the carriage via hydraulic or mechanical means. After the gate is opened, the coal naturally accumulates under the action of gravity. Due to the physical properties of coal as a solid bulk material, its accumulation process is constrained by the angle of repose, a. When the coal accumulates within the carriage to a critical angle of repose, the material stops spreading laterally. At this point, the carriage must be moved to allow the coal in the chute to pour backward, forming a continuous loading cycle.

[0044] However, in actual operation, this system encountered a technical problem: when the coal supply in the chute was insufficient, the loading process was forced to be interrupted to wait for restocking. This intermittent downtime not only resulted in the carriages not reaching the expected loading volume (frequent coal shortages), but also required reversing to restock the car, severely disrupting the loading process and resulting in low loading efficiency.

[0045] Therefore, how to improve loading efficiency has become a technical problem that needs to be solved urgently in the existing technology.

[0046] Based on this, the embodiments of the present invention provide a loading adjustment method, device and equipment for a railway intelligent loading system to overcome the current intermittent shutdown, which not only causes the carriage loading volume to fall short of expectations (frequent material shortage), but also requires secondary material replenishment through reversing operation, seriously damaging the loading continuity and resulting in low loading efficiency.

[0047] Figure 2For a flow chart of a method for adjusting the loading of a railway intelligent loading system provided by an embodiment of the present invention, please refer to Figure 2 , this embodiment may include the following steps:

[0048] Step S101: Acquire historical parameter data within a preset time period in a chute of a railway intelligent loading system, wherein the historical parameter data includes coal flow rate, coal moisture, and coal particle size.

[0049] Among them, the preset time period can be 2 minutes, 5 minutes, 1 minute, 30 seconds, etc., which can be set according to needs.

[0050] The coal flow rate is the flow rate of coal in the chute. A level sensor can be installed at the top of the chute to detect the coal level in the chute, and the coal level in the chute can be used to indicate the coal flow rate. It is worth noting that an image recognition device can also be used to identify a photo of the coal in the chute to determine the coal flow rate. This is not specifically limited in this application, and other methods can also be used to determine the coal flow rate.

[0051] The coal humidity is the humidity of the coal in the chute, which can be the water content of the coal. The coal humidity can be detected by arranging a humidity sensor in the chute.

[0052] The coal particle size refers to the coal particle size in the chute. The particle size analyzer can be set to perform particle size analysis on the coal in the chute. Specifically, the coal particle size distribution can be obtained in real time based on image recognition.

[0053] Step S102: inputting the historical parameter data into a time series prediction model to predict future parameter data, wherein the future parameter data includes: changes in coal flow rate, changes in coal moisture, and changes in coal particle size;

[0054] The time series prediction model predicts future parameter data based on historical parameter data to obtain changes in coal flow, coal moisture, and coal particle size.

[0055] Step S103: adjusting the proportional coefficient of the PID controller according to the change in the coal flow rate, adjusting the integral coefficient of the PID controller according to the change in the coal moisture, and adjusting the differential coefficient of the PID controller according to the change in the coal particle size; wherein the input of the PID controller is the deviation between the actual vehicle speed and the set target vehicle speed, and the output of the PID controller is the vehicle speed control variable;

[0056] In some embodiments, adjusting the proportional coefficient of the PID controller according to the change in the coal flow rate, adjusting the integral coefficient of the PID controller according to the change in the coal moisture, and adjusting the differential coefficient of the PID controller according to the change in the coal particle size include:

[0057] When the coal flow rate changes to an increase in coal flow, the proportional coefficient in the PID controller is reduced; when the coal humidity changes to an increase in coal humidity, the integral coefficient in the PID controller is reduced; when the coal particle size changes to an increase in coal particle size, the differential coefficient in the PID controller is reduced;

[0058] When the coal flow rate changes to a decrease, increase the proportional coefficient in the PID controller; when the coal humidity changes to a decrease, increase the integral coefficient in the PID controller; when the coal particle size changes to a decrease, increase the differential coefficient in the PID controller.

[0059] Step S104: Adjust the winch speed according to the vehicle speed control value output by the updated PID controller to adjust the carriage traction speed and ensure the loading uniformity of the railway intelligent loading system.

[0060] The PID controller outputs the vehicle speed control variable to adjust the winch speed. The winch pulls the carriage to move, and the carriage pulling speed is adjusted by adjusting the winch speed to ensure uniform loading.

[0061] The technical solution provided by the embodiment of the present invention combines the time series prediction model and PID control for the first time, updates historical parameter data in real time, and thus updates the PID controller in real time, adjusts the speed of the winch to adjust the traction speed of the carriage. When it is predicted that the coal in the chute will decrease, the winch is controlled to reduce the speed and reduce the traction speed of the carriage, thereby waiting for the coal in the chute to be replenished; when it is predicted that the coal in the chute will increase, it means that the coal in the chute has been replenished, and the winch is controlled to increase the speed and increase the traction speed of the carriage; through real-time control, when the coal decreases, the traction speed of the carriage is reduced in advance to avoid frequent material shortages, without the need for secondary replenishment, thereby improving loading continuity and ensuring loading efficiency.

[0062] In some embodiments, the control mode of the PID controller is:

[0063]

[0064] Among them, e(t) is the deviation between the actual vehicle speed and the set target speed, e(t)=V set -V actual , V set To set the target vehicle speed, V actual is the actual vehicle speed; Kp is the proportional coefficient, K i is the integral coefficient, K d is the differential coefficient. Among them, the actual vehicle speed is the actual vehicle speed of the carriage traction.

[0065] It can be understood that u(t) is the vehicle speed control quantity output by the PID controller. The winch speed is adjusted in real time through u(t), thereby adjusting the speed of the car traction and improving the control accuracy.

[0066] In some embodiments, the time series prediction model is an ARIMA (p, d, q) model, which is expressed as:

[0067]

[0068] Where Φ(B) is the p-order autoregressive coefficient polynomial, is the q-order moving average coefficient polynomial, B is the backshift operator, is a white noise sequence, d is the difference order, and Q(t) is the parameter data.

[0069] It is worth noting that the values ​​of p, q, and d can be set according to needs.

[0070] In some embodiments, inputting the historical parameter data into a time series prediction model to predict future parameter data includes:

[0071] The historical parameter data of coal flow are organized into a coal flow time series according to chronological order, and the coal flow time series is input into a time series prediction model to obtain the predicted data of coal flow in a preset time period in the future. The predicted data is compared with the current coal flow to obtain the change of coal flow corresponding to the historical parameter data of coal flow;

[0072] The historical parameter data of coal humidity are organized into a coal humidity time series according to the chronological order, and the coal humidity time series is input into the time series prediction model to obtain the predicted data of coal humidity in the future preset time period. The predicted data is compared with the current coal humidity to obtain the coal humidity change corresponding to the historical parameter data of coal humidity;

[0073] The historical parameter data of coal particle size are organized into a coal particle size time series according to chronological order, and the coal particle size time series is input into a time series prediction model to obtain the predicted data of coal particle size in a preset time period in the future. The predicted data is compared with the current coal particle size to obtain the change of coal particle size corresponding to the historical parameter data of coal particle size.

[0074] For example, a coal flow time series {y1, y2, y3, y4, y5, y6, y7...y n}, where y n The coal flow rate value at time n is preprocessed on the coal flow time series. The preprocessing includes stationarity processing (trend and seasonality can be eliminated by d-order difference); missing value processing can also be performed: missing points need to be interpolated or deleted to ensure data continuity.

[0075] After the time series prediction model is applied, the coal flow rate at the future moments {K1, K2, K3} is predicted, where K1, K2, and K3 are the coal flow rates at the three future moments, respectively. It is worth noting that the number of K values ​​can be set, allowing for prediction of a single value or multiple values ​​at multiple moments.

[0076] After obtaining the predicted value, it is compared with the previous moment to obtain the change in coal flow.

[0077] It is worth noting that the method for obtaining the change in coal moisture and coal particle size is the same as the method for obtaining the change in coal flow rate, which will not be described in detail in this embodiment. Please refer to the method for obtaining the change in coal flow rate.

[0078] It is understandable that the technical solution provided in the embodiment can be used to accurately control the winch rotation speed in real time.

[0079] In some embodiments, further comprising:

[0080] Determine whether the current carriage traction speed reaches the preset carriage traction speed maximum value. If the current carriage traction speed is equal to the preset carriage traction speed maximum value, stop increasing the winch speed.

[0081] It can be understood that by adopting the technical solution provided in this embodiment, the maximum speed of the carriage traction can be limited, thereby avoiding the problem of excessive speed.

[0082] Based on a general inventive concept, an embodiment of the present invention further provides a loading adjustment device of a railway intelligent loading system, which is used to implement the above method embodiment.

[0083] Figure 3 A schematic diagram of a loading adjustment device for a railway intelligent loading system according to an embodiment of the present invention is shown in FIG. Figure 3 As shown, the device provided by the embodiment of the present invention may include the following structure:

[0084] An acquisition module 31 is used to acquire historical parameter data within a preset time period in a chute of a railway intelligent loading system, wherein the historical parameter data includes coal flow rate, coal moisture, and coal particle size;

[0085] The prediction module 32 is used to input the historical parameter data into the time series prediction model to predict future parameter data, wherein the future parameter data includes: changes in coal flow rate, changes in coal moisture, and changes in coal particle size;

[0086] an adjustment module 33 for adjusting a proportional coefficient of the PID controller according to changes in the coal flow rate, adjusting an integral coefficient of the PID controller according to changes in the coal moisture content, and adjusting a differential coefficient of the PID controller according to changes in the coal particle size; wherein the input of the PID controller is a deviation between the actual vehicle speed and the set target vehicle speed, and the output of the PID controller is a vehicle speed control variable;

[0087] The control module 34 is used to adjust the winch speed according to the vehicle speed control value output by the updated PID controller to adjust the carriage traction speed and ensure the loading uniformity of the railway intelligent loading system.

[0088] Optionally, the regulating module is specifically configured to reduce the proportional coefficient in the PID controller when the coal flow rate changes to an increase in the coal flow rate; reduce the integral coefficient in the PID controller when the coal humidity changes to an increase in the coal humidity; and reduce the differential coefficient in the PID controller when the coal particle size changes to an increase in the coal particle size.

[0089] When the coal flow rate changes to a decrease, increase the proportional coefficient in the PID controller; when the coal humidity changes to a decrease, increase the integral coefficient in the PID controller; when the coal particle size changes to a decrease, increase the differential coefficient in the PID controller.

[0090] Optionally, the control mode of the PID controller is:

[0091]

[0092] Among them, e(t) is the deviation between the actual vehicle speed and the set target speed, e(t)=V set -V actual , V set To set the target vehicle speed, V actual is the actual vehicle speed; Kp is the proportional coefficient, K i is the integral coefficient, K d is the differential coefficient.

[0093] Optionally, the time series prediction model is an ARIMA (p, d, q) model, and the expression is:

[0094]

[0095] Where Φ(B) is the p-order autoregressive coefficient polynomial, is the q-order moving average coefficient polynomial, B is the backshift operator, is a white noise sequence, d is the difference order, and Q(t) is the parameter data.

[0096] Optionally, a prediction module is specifically configured to organize the historical parameter data of coal flow into a coal flow time series according to chronological order, input the coal flow time series into a time series prediction model, obtain predicted data of coal flow in a preset time period in the future, compare the predicted data with the current coal flow, and obtain the coal flow change corresponding to the historical parameter data of coal flow;

[0097] The historical parameter data of coal humidity are organized into a coal humidity time series according to the chronological order, and the coal humidity time series is input into the time series prediction model to obtain the predicted data of coal humidity in the future preset time period. The predicted data is compared with the current coal humidity to obtain the coal humidity change corresponding to the historical parameter data of coal humidity;

[0098] The historical parameter data of coal particle size are organized into a coal particle size time series according to chronological order, and the coal particle size time series is input into a time series prediction model to obtain the predicted data of coal particle size in a preset time period in the future. The predicted data is compared with the current coal particle size to obtain the change of coal particle size corresponding to the historical parameter data of coal particle size.

[0099] Optionally, the control module is further used to determine whether the current carriage traction speed reaches the preset maximum value of the carriage traction speed. If the current carriage traction speed is equal to the preset maximum value of the carriage traction speed, the increase of the winch speed is stopped.

[0100] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0101] The technical solution provided by the embodiment of the present invention combines the time series prediction model and PID control for the first time, updates historical parameter data in real time, and thus updates the PID controller in real time, adjusts the speed of the winch to adjust the traction speed of the carriage. When it is predicted that the coal in the chute will decrease, the winch is controlled to reduce the speed and reduce the traction speed of the carriage, thereby waiting for the coal in the chute to be replenished; when it is predicted that the coal in the chute will increase, it means that the coal in the chute has been replenished, and the winch is controlled to increase the speed and increase the traction speed of the carriage; through real-time control, when the coal decreases, the traction speed of the carriage is reduced in advance to avoid frequent material shortages, without the need for secondary replenishment, thereby improving loading continuity and ensuring loading efficiency.

[0102] The present invention also provides a loading adjustment device for a railway intelligent loading system, which is used to implement the above method embodiment. Figure 4 A schematic diagram of the structure of a loading adjustment device of a railway intelligent loading system provided by an embodiment of the present invention is shown as follows: Figure 4As shown, the loading adjustment device of the railway intelligent loading system of this embodiment includes a processor 41 and a memory 42, and the processor 41 is connected to the memory 42. The processor 41 is used to call and execute the program stored in the memory 42; the memory 42 is used to store the program, and the program is used to execute at least the loading adjustment method of the railway intelligent loading system in the above embodiment.

[0103] The specific implementation scheme provided in the embodiments of this application can refer to the implementation scheme of the loading adjustment method of the railway intelligent loading system in any of the above embodiments, and will not be repeated here.

[0104] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0105] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0106] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0107] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0108] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0109] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0110] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0111] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0112] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for adjusting loading of a railway intelligent loading system, characterized in that: include: Acquire historical parameter data within a preset time period in a chute of a railway intelligent loading system, wherein the historical parameter data includes coal flow, coal moisture, and coal particle size; Inputting the historical parameter data into a time series prediction model to predict future parameter data, the future parameter data including: changes in coal flow rate, changes in coal moisture, and changes in coal particle size; The proportional coefficient of the PID controller is adjusted according to the change of the coal flow rate, the integral coefficient of the PID controller is adjusted according to the change of the coal moisture, and the differential coefficient of the PID controller is adjusted according to the change of the coal particle size; wherein the input of the PID controller is the deviation between the actual vehicle speed and the set target vehicle speed, and the output of the PID controller is the vehicle speed control amount, including: when the coal flow rate change is that the coal flow rate increases, the proportional coefficient in the PID controller is reduced; when the coal moisture change is that the coal moisture increases, the integral coefficient in the PID controller is reduced; when the coal particle size change is that the coal particle size increases, the differential coefficient in the PID controller is reduced; when the coal flow rate change is that the coal flow rate decreases, the proportional coefficient in the PID controller is increased; when the coal moisture change is that the coal moisture decreases, the integral coefficient in the PID controller is increased; when the coal particle size change is that the coal particle size decreases, the differential coefficient in the PID controller is increased; The winch speed is adjusted according to the vehicle speed control value output by the updated PID controller to adjust the carriage traction speed and ensure the loading uniformity of the railway intelligent loading system.

2. The method according to claim 1, characterized in that The control mode of the PID controller is: Among them, e(t) is the deviation between the actual vehicle speed and the set target speed, e(t)=V set -V actual , V set To set the target vehicle speed, V actual is the actual vehicle speed; Kp is the proportional coefficient, K i is the integral coefficient, K d is the differential coefficient.

3. The method according to claim 1, characterized in that The time series prediction model is the ARIMA (p, d, q) model, which is expressed as: Where Φ(B) is the p-order autoregressive coefficient polynomial, is the q-order moving average coefficient polynomial, B is the backshift operator, is a white noise sequence, d is the difference order, and Q(t) is the parameter data.

4. The method according to claim 1, wherein Inputting the historical parameter data into a time series prediction model to predict future parameter data includes: The historical parameter data of coal flow are organized into a coal flow time series according to chronological order, and the coal flow time series is input into a time series prediction model to obtain the predicted data of coal flow in a preset time period in the future. The predicted data is compared with the current coal flow to obtain the change of coal flow corresponding to the historical parameter data of coal flow; The historical parameter data of coal humidity are organized into a coal humidity time series according to the chronological order, and the coal humidity time series is input into the time series prediction model to obtain the predicted data of coal humidity in the future preset time period. The predicted data is compared with the current coal humidity to obtain the coal humidity change corresponding to the historical parameter data of coal humidity; The historical parameter data of coal particle size are organized into a coal particle size time series according to chronological order, and the coal particle size time series is input into a time series prediction model to obtain the predicted data of coal particle size in a preset time period in the future. The predicted data is compared with the current coal particle size to obtain the change of coal particle size corresponding to the historical parameter data of coal particle size.

5. The method according to claim 1, wherein Also includes: Determine whether the current carriage traction speed reaches the preset carriage traction speed maximum value. If the current carriage traction speed is equal to the preset carriage traction speed maximum value, stop increasing the winch speed.

6. A loading adjustment device for a railway intelligent loading system, characterized in that: include: An acquisition module is used to acquire historical parameter data within a preset time period in the chute of the railway intelligent loading system, wherein the historical parameter data includes coal flow, coal moisture, and coal particle size; A prediction module is used to input the historical parameter data into a time series prediction model to predict future parameter data, wherein the future parameter data includes: changes in coal flow rate, changes in coal moisture, and changes in coal particle size; The adjustment module is used to adjust the proportional coefficient of the PID controller according to the change of the coal flow rate, adjust the integral coefficient of the PID controller according to the change of the coal moisture, and adjust the differential coefficient of the PID controller according to the change of the coal particle size; wherein the input of the PID controller is the deviation between the actual vehicle speed and the set target vehicle speed, and the output of the PID controller is the vehicle speed control amount; specifically, when the change of the coal flow rate is an increase in the coal flow rate, reduce the proportional coefficient in the PID controller; when the change of the coal moisture is an increase in the coal moisture, reduce the integral coefficient in the PID controller; when the change of the coal particle size is an increase in the coal particle size, reduce the differential coefficient in the PID controller; when the change of the coal flow rate is a decrease in the coal flow rate, increase the proportional coefficient in the PID controller; when the change of the coal moisture is a decrease in the coal moisture, increase the integral coefficient in the PID controller; when the change of the coal particle size is a decrease in the coal particle size, increase the differential coefficient in the PID controller; The control module is used to adjust the winch speed according to the vehicle speed control value output by the updated PID controller to adjust the carriage traction speed and ensure the loading uniformity of the railway intelligent loading system.

7. A loading adjustment device for a railway intelligent loading system, characterized in that: The device comprises a processor and a memory, wherein the processor is connected to the memory: The processor is configured to call and execute the program stored in the memory; The memory is used to store the program, and the program is at least used to execute the loading adjustment method of the railway intelligent loading system according to any one of claims 1 to 5.

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