Loading adjusting method, device and equipment of railway intelligent loading system

Abstract

The invention relates to a loading adjustment method, device and equipment of a railway intelligent loading system, and relates to the technical field of loading, the method, device and equipment combine a time sequence prediction model and PID control for the first time, update historical parameter data in real time, update a PID controller in real time, adjust the rotating speed of a winch so as to adjust the traction speed of a carriage, and improve the traction speed of the carriage. When it is predicted that the coal in the chute is reduced, the winch is controlled to decrease the rotating speed, the traction speed of the carriage is decreased, and thus the coal in the chute waits to be supplemented; when it is predicted that the coal in the chute is increased, it shows that the coal in the coal chute is supplemented, the winch is controlled to increase the rotating speed, and the carriage traction speed is increased; through real-time control, when the coal material is reduced, the traction speed of the carriage is reduced in advance, the phenomenon of frequent material shortage is avoided, secondary material supplementing is not needed, the loading continuity is improved, and the loading efficiency is guaranteed.

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] The intelligent loading system of traditional railway loading stations often uses telescopic chute technology for bulk material loading. Its working principle is: the chute mouth is accurately lowered to the preset loading height of the carriage through hydraulic or mechanical devices, and the coal is naturally piled up under the action of gravity after the gate is opened. Due to the physical properties of coal as a solid bulk material, its accumulation process is restricted by the angle of repose - when the coal in the carriage accumulates to the critical state of the angle of repose, the material stops spreading horizontally. At this time, the carriage needs to be moved to make the coal in the chute pour backwards, forming a continuous loading operation cycle.

[0003] However, in actual operation, the system has the following technical problems: when the coal supply in the chute is insufficient, the loading process is forced to be interrupted to wait for refueling. This intermittent shutdown not only causes the carriage loading volume to fall short of expectations (frequent shortage of coal), but also requires secondary refueling through reverse operation, which seriously disrupts the continuity of loading and causes 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 problem that the current 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: On the one hand, a loading adjustment method of a railway intelligent loading system includes: Acquire historical parameter data in a chute of a railway intelligent loading system within a preset time period, wherein the historical parameter data includes coal flow, coal humidity, and coal particle size; 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 humidity, 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 humidity, 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 amount; 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.

[0007] 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 humidity, and adjusting the differential coefficient of the PID controller according to the change of the coal particle size include: When the coal flow rate changes to increase the coal flow rate, reduce the proportional coefficient in the PID controller; when the coal humidity changes to increase the coal humidity, reduce the integral coefficient in the PID controller; when the coal particle size changes to increase the coal particle size, reduce the differential coefficient in the PID controller; 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.

[0008] Optionally, the control mode of the PID controller is:

[0009] Where e(t) is the deviation between the actual vehicle speed and the set target vehicle 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 integration coefficient, K d is the differential coefficient.

[0010] Optionally, the time series prediction model is an ARIMA (p, d, q) model, expressed as:

[0011] 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.

[0012] Optionally, inputting the historical parameter data into a time series prediction model to predict future parameter data includes: The coal flow historical parameter data are organized into a coal flow time series according to the chronological order, and the coal flow time series is input into the time series prediction model to obtain the prediction data of the coal flow in the future preset time period, and the prediction data is compared with the current coal flow to obtain the coal flow change corresponding to the coal flow historical parameter data; 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 prediction data of coal humidity in the future preset time period, and the prediction data is compared with the current coal humidity to obtain the change of coal humidity 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 the coal particle size in a preset time period in the future, and the predicted data is compared with the current coal particle size to obtain the change of the coal particle size corresponding to the historical parameter data of the coal particle size.

[0013] Optionally, also include: It is determined 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.

[0014] In another aspect, a loading adjustment device of a railway intelligent loading system comprises: An acquisition module is used to acquire historical parameter data in a preset time period in a chute of a railway intelligent loading system, wherein the historical parameter data includes coal flow, coal humidity, 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, changes in coal humidity, and changes in coal particle size; A regulating module, used to adjust the proportional coefficient of the PID controller according to the change of the coal flow, adjust the integral coefficient of the PID controller according to the change of the coal humidity, 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 value between the actual vehicle speed and the set target vehicle speed, and the output of the PID controller is the vehicle speed control amount; The control module is used to adjust the winch speed according to the vehicle speed control quantity output by the updated PID controller to adjust the carriage traction speed and ensure the loading uniformity of the railway intelligent loading system.

[0015] 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: Wherein, the processor is used 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 as described in any one of the above items.

[0016] The technical solution provided by the present invention includes at least the following beneficial effects: 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 the 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, and 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

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 Schematic diagram of the front (left) and side (right) views of the loading process in the prior art; Figure 2 A schematic flow chart of a loading adjustment method of a railway intelligent loading system provided by an embodiment of the present invention; Figure 3 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; 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

[0019] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0020] As described in the background technology, the intelligent loading system of traditional railway loading stations often uses telescopic chute technology for bulk material loading, see Figure 1 ( Figure 1 The front and side views of the loading process in the prior art are shown in the figure. The working principle is as follows: the chute mouth is precisely lowered to the preset loading height of the carriage by hydraulic or mechanical devices, and the coal is naturally piled up under the action of gravity after the gate is opened. Due to the physical properties of coal as a solid bulk material, its accumulation process is restricted by the angle of repose a. When the coal in the carriage accumulates to the critical state of the angle of repose, the material stops spreading horizontally. At this time, the carriage needs to be moved to make the coal in the chute pour backwards, forming a continuous loading operation cycle.

[0021] However, in actual operation, the system has the following technical problems: when the coal supply in the chute is insufficient, the loading process is forced to be interrupted to wait for refueling. This intermittent shutdown not only causes the carriage loading volume to fall short of expectations (frequent shortage of coal), but also requires secondary refueling through reverse operation, which seriously disrupts the continuity of loading and causes low loading efficiency.

[0022] Therefore, how to improve loading efficiency has become a technical problem that needs to be solved urgently in the existing technology.

[0023] 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 that not only causes the carriage loading volume to fall short of expectations (frequent material shortages), but also requires secondary material replenishment through reversing operations, which seriously damages the continuity of loading and causes low loading efficiency.

[0024] Figure 2 For a flow chart of a method for adjusting a loading vehicle of a railway intelligent loading vehicle system provided by an embodiment of the present invention, please refer to Figure 2 , this embodiment may include the following steps: Step S101, obtaining 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 humidity, and coal particle size.

[0025] Among them, the preset time period can be the current 2 minutes, 5 minutes, 1 minute, 30 seconds, etc., which can be set according to needs.

[0026] The coal flow rate is the flow rate of coal in the chute. The height of the coal in the chute can be detected by installing a level sensor at the top of the chute, and the coal flow rate can be marked by the height of the coal in the chute. It is worth noting that an image recognition device can also be used to identify photos of coal in the chute to determine the coal flow rate. This is not specifically limited in this application, and the coal flow rate can also be determined by other methods.

[0027] The humidity of the coal is the humidity of the coal in the chute, which may be the water content of the coal. The humidity of the coal may be detected by arranging a humidity sensor in the chute.

[0028] The coal particle size refers to the coal particle size in the chute. The particle size analysis of the coal in the chute can be performed by setting up a particle size analyzer. Specifically, the coal particle size distribution can be obtained in real time based on image recognition.

[0029] Step S102: 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, changes in coal humidity, and changes in coal particle size; The time series prediction model predicts future parameter data based on historical parameter data to obtain changes in coal flow, coal humidity, and coal particle size.

[0030] Step S103, 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 humidity, and adjusting 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 value between the actual vehicle speed and the set target vehicle speed, and the output of the PID controller is the vehicle speed control amount; In some embodiments, 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 humidity, and adjusting the differential coefficient of the PID controller according to the change of the coal particle size include: When the coal flow rate changes to increase the coal flow rate, reduce the proportional coefficient in the PID controller; when the coal humidity changes to increase the coal humidity, reduce the integral coefficient in the PID controller; when the coal particle size changes to increase the coal particle size, reduce the differential coefficient in the PID controller; 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.

[0031] Step S104, adjusting 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] The PID controller outputs the vehicle speed control value to adjust the winch speed. The winch pulls the carriage to move, and the carriage traction speed is adjusted by adjusting the winch speed to ensure the uniformity of loading.

[0033] 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 the 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, and 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.

[0034] In some embodiments, the control mode of the PID controller is:

[0035] Where e(t) is the deviation between the actual vehicle speed and the set target vehicle 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 integration coefficient, K d is the differential coefficient. Among them, the actual vehicle speed is the actual vehicle speed pulled by the carriage.

[0036] 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.

[0037] In some embodiments, the time series prediction model is an ARIMA (p, d, q) model, expressed as:

[0038] 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.

[0039] It is worth noting that the values ​​of p, q, and d can be set according to requirements.

[0040] In some embodiments, the inputting the historical parameter data into a time series prediction model to predict future parameter data includes: The coal flow historical parameter data are organized into a coal flow time series according to the chronological order, and the coal flow time series is input into the time series prediction model to obtain the prediction data of the coal flow in the future preset time period, and the prediction data is compared with the current coal flow to obtain the coal flow change corresponding to the coal flow historical parameter data; 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 prediction data of coal humidity in the future preset time period, and the prediction data is compared with the current coal humidity to obtain the change of coal humidity 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 the coal particle size in a preset time period in the future, and the predicted data is compared with the current coal particle size to obtain the change of the coal particle size corresponding to the historical parameter data of the coal particle size.

[0041] For example, we can construct a coal flow time series {y1, y2, y3, y4, y5, y6, y7...y n}, where y n is the coal flow value at time n, and the coal flow time series is preprocessed. The preprocessing includes stationarity processing (trend and seasonality can be eliminated through d-order difference); missing value processing can also be performed: missing points need to be interpolated or deleted to ensure data continuity.

[0042] After the time series prediction model, the coal flow at the future time {K1, K2, K3} is predicted, where K1, K2, K3 are the coal flow at the next three time points. It is worth noting that the number of K can be set, and one value can be predicted, or the values ​​at multiple time points can be predicted.

[0043] After obtaining the predicted value, it is compared with the previous moment to obtain the change in coal flow.

[0044] 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.

[0045] It can be understood that, by adopting the technical solution provided in the embodiment, the winch rotation speed can be accurately controlled in real time.

[0046] In some embodiments, it also includes: It is determined 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.

[0047] 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.

[0048] 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.

[0049] Figure 3 A schematic diagram of a loading adjustment device for a railway intelligent loading system provided by 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: An acquisition module 31 is used to acquire historical parameter data in a preset time period in a chute of a railway intelligent loading system, wherein the historical parameter data includes coal flow, coal humidity, and coal particle size; The prediction module 32 is used to input the historical parameter data into the time series prediction model to predict future parameter data, and the future parameter data includes: changes in coal flow, changes in coal humidity, and changes in coal particle size; The adjustment module 33 is used to adjust the proportional coefficient of the PID controller according to the change of the coal flow, adjust the integral coefficient of the PID controller according to the change of the coal humidity, 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 value between the actual vehicle speed and the set target vehicle speed, and the output of the PID controller is the vehicle speed control amount; 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.

[0050] Optionally, the regulating module is specifically used to reduce the proportional coefficient in the PID controller when the coal flow rate changes to increase the coal flow rate; reduce the integral coefficient in the PID controller when the coal humidity changes to increase the coal humidity; and reduce the differential coefficient in the PID controller when the coal particle size changes to increase the coal particle size; 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.

[0051] Optionally, the control mode of the PID controller is:

[0052] Where e(t) is the deviation between the actual vehicle speed and the set target vehicle 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 integration coefficient, K d is the differential coefficient.

[0053] Optionally, the time series prediction model is an ARIMA (p, d, q) model, expressed as:

[0054] 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.

[0055] Optionally, a prediction module is specifically used to organize the coal flow historical parameter data into a coal flow time series according to the chronological order, input the coal flow time series into the time series prediction model, obtain the prediction data of the coal flow in the future preset time period, compare the prediction data with the current coal flow, and obtain the coal flow change corresponding to the coal flow historical parameter data; 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 prediction data of coal humidity in the future preset time period, and the prediction data is compared with the current coal humidity to obtain the change of coal humidity 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 the coal particle size in a preset time period in the future, and the predicted data is compared with the current coal particle size to obtain the change of the coal particle size corresponding to the historical parameter data of the coal particle size.

[0056] Optionally, the control module is also used to determine whether the current carriage traction speed reaches a 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.

[0057] Regarding the device 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.

[0058] 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 the 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, and 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.

[0059] 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 a loading adjustment device for a railway intelligent loading system provided by an embodiment of the present invention is shown in FIG. Figure 4 As 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 at least used to execute the loading adjustment method of the railway intelligent loading system in the above embodiment.

[0060] The specific implementation scheme provided in the embodiments of the present application can refer to the implementation scheme of the loading adjustment method of the railway intelligent loading system in any of the above embodiments, which will not be repeated here.

[0061] 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.

[0062] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0063] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes 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 not be performed in 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 belong.

[0064] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0065] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0066] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0067] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0068] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0069] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A loading adjustment method for a railway intelligent loading system, characterized in that: include: Acquire historical parameter data in a chute of a railway intelligent loading system within a preset time period, wherein the historical parameter data includes coal flow, coal humidity, and coal particle size; 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 humidity, 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 humidity, 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 amount; 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 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 humidity, and adjusting the differential coefficient of the PID controller according to the change of the coal particle size include: When the coal flow rate changes to increase the coal flow rate, reduce the proportional coefficient in the PID controller; when the coal humidity changes to increase the coal humidity, reduce the integral coefficient in the PID controller; when the coal particle size changes to increase the coal particle size, reduce the differential coefficient in the PID controller; 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.

3. The method according to claim 1, characterized in that The control mode of the PID controller is: Where e(t) is the deviation between the actual vehicle speed and the set target vehicle 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 integration coefficient, K d is the differential coefficient.

4. The method according to claim 1, characterized in that: The time series prediction model is the ARIMA (p, d, q) model, and the expression is: 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.

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

6. The method according to claim 1, characterized in that Also includes: It is determined 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.

7. A loading adjustment device for a railway intelligent loading system, characterized in that: include: An acquisition module is used to acquire historical parameter data in a preset time period in a chute of a railway intelligent loading system, wherein the historical parameter data includes coal flow, coal humidity, 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, changes in coal humidity, and changes in coal particle size; A regulating module, used to adjust the proportional coefficient of the PID controller according to the change of the coal flow, adjust the integral coefficient of the PID controller according to the change of the coal humidity, 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 value between the actual vehicle speed and the set target vehicle speed, and the output of the PID controller is the vehicle speed control amount; The control module is used to adjust the winch speed according to the vehicle speed control quantity output by the updated PID controller to adjust the carriage traction speed and ensure the loading uniformity of the railway intelligent loading system.

8. A loading adjustment device for a railway intelligent loading system, characterized in that: The invention comprises a processor and a memory, wherein the processor is connected to the memory: Wherein, the processor is used 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 1-6.

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