Belt conveyor control device based on big data decision system, belt conveyor and control method

Abstract

The application provides a belt conveyor control device based on a big data decision system, a belt conveyor and a control method, which are applied to a big data decision system, the big data decision system comprising a belt conveyor, the belt conveyor comprising a conveying belt, and the method comprising: acquiring a target load parameter of the conveying belt corresponding to a target object when the target object is conveyed by the conveying belt; detecting whether the target load parameter meets a preset condition; acquiring a target operating environment parameter of the belt conveyor when the target load parameter does not meet the preset condition; determining, by the big data decision system, a target control parameter of the belt conveyor corresponding to the target load parameter and the target operating environment parameter; and controlling the belt conveyor to work with the target control parameter, so that the conveying belt conveys the target object. The embodiment of the application can improve the intelligence of the belt conveyor.

Description

[0001] This application is a divisional application of Chinese Patent Application No. 202410662360.1, filed with the Chinese Patent Office on May 27, 2024, entitled "A Control Algorithm for a Belt Conveyor Based on a Big Data Decision System", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the fields of computer technology and big data technology, specifically to a belt conveyor control device, belt conveyor, and control method based on a big data decision system. Background Technology

[0003] Currently, taking intelligent mining systems as an example, coal is often transported via belt conveyors. However, the control parameters often need to be manually configured, making it difficult for the system's efficiency to match its actual load, thus reducing the intelligence of the belt conveyor. Therefore, the problem of how to improve the intelligence of belt conveyors urgently needs to be solved. Summary of the Invention

[0004] This application provides a belt conveyor control device, belt conveyor, and control method based on a big data decision-making system, which can improve the intelligence of the belt conveyor.

[0005] In a first aspect, embodiments of this application provide a belt conveyor control method based on a big data decision-making system, applied to a big data decision-making system including a belt conveyor, the belt conveyor including a conveyor belt, and the method comprising: When transporting a target object via the conveyor belt, the target load parameters of the conveyor belt corresponding to the target object are obtained; Detect whether the target load parameters meet the preset conditions; When the target load parameters do not meet the preset conditions, the target operating environment parameters of the belt conveyor are obtained; The big data decision-making system determines the target control parameters of the belt conveyor, which correspond to the target load parameters and the target operating environment parameters. The belt conveyor is controlled to operate with target control parameters so that the conveyor belt transports the target object.

[0006] Secondly, embodiments of this application provide a belt conveyor, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the steps in the first aspect of embodiments of this application.

[0007] Thirdly, embodiments of this application provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of embodiments of this application.

[0008] Fourthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program being operable to cause a computer to perform some or all of the steps described in the first aspect of embodiments of this application. The computer program product may be a software installation package.

[0009] Implementing the embodiments of this application has the following beneficial effects: As can be seen, the belt conveyor control device, belt conveyor, and control method based on a big data decision-making system described in this application embodiment are applied to a big data decision-making system. The big data decision-making system includes a belt conveyor, which includes a conveyor belt. When a target object is conveyed by the conveyor belt, the target load parameters corresponding to the target object are obtained, and it is detected whether the target load parameters meet preset conditions. If the target load parameters do not meet the preset conditions, the target operating environment parameters of the belt conveyor are obtained. The target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters are determined by the big data decision-making system. The belt conveyor is controlled to work with the target control parameters so that the conveyor belt conveys the target object. Thus, based on its actual situation, the corresponding control parameters are reasonably configured, which not only ensures the normal operation of the belt conveyor but also reduces the power consumption of the belt conveyor. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a belt conveyor control method based on a big data decision system provided in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a belt conveyor provided in an embodiment of this application; Figure 3 This is a functional unit block diagram of a belt conveyor control device based on a big data decision system provided in an embodiment of this application. Detailed Implementation

[0012] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not limited to the listed steps or units, but in one possible example includes steps or units not listed, or in one possible example includes other steps or units inherent to these processes, methods, products, or apparatuses.

[0013] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0015] Please see Figure 1 , Figure 1 This is a flowchart illustrating a belt conveyor control method based on a big data decision-making system, as provided in an embodiment of this application. The method is applied to a big data decision-making system, which includes a belt conveyor and a conveyor belt. The belt conveyor control method based on the big data decision-making system includes: 101. When transporting a target object via the conveyor belt, obtain the target load parameters of the conveyor belt corresponding to the target object.

[0016] In this embodiment of the application, the target object can be understood as the object to be transported on the conveyor belt of the belt conveyor. The object may include at least one of the following: coal, copper ore, iron ore, gold, rare earth, etc., without limitation.

[0017] The target load parameters may include at least one of the following: the load per unit area of ​​the conveyor belt, the overall load-bearing capacity of the conveyor belt, etc., which are not limited here.

[0018] In practice, when a target object is transported via a conveyor belt, the target load parameters of the target object corresponding to the conveyor belt can be obtained through a pressure sensor, camera, or weight detection device.

[0019] 102. Check whether the target load parameters meet the preset conditions.

[0020] The preset conditions can be set in advance or left as system defaults. These preset conditions can detect whether the belt conveyor is under light, moderate, or full load, allowing for the appropriate configuration of control parameters based on the actual situation. This not only ensures the normal operation of the belt conveyor but also reduces its power consumption.

[0021] Optionally, step 102 above, detecting whether the target load parameters meet preset conditions, may include the following steps: 21. Obtain the load parameter threshold of the target object; 22. When the target load parameter is less than or equal to the load parameter threshold, determine that the target load parameter meets the preset condition; 23. When the target load parameter is greater than the load parameter threshold, it is determined that the target load parameter does not meet the preset condition.

[0022] In a specific implementation, a pre-stored mapping relationship between preset objects and load parameter thresholds can be used. Then, the load parameter threshold of the target object can be obtained based on the mapping relationship. When the target load parameter is less than or equal to the load parameter threshold, it is determined that the target load parameter meets the preset condition, indicating that the belt conveyor is in a light load state. When the target load parameter is greater than the load parameter threshold, it is determined that the target load parameter does not meet the preset condition, indicating that the belt conveyor is not in a light load state.

[0023] 103. When the target load parameters do not meet the preset conditions, obtain the target operating environment parameters of the belt conveyor.

[0024] The target operating environment parameters may include software operating environment parameters, hardware operating environment parameters, and object environment parameters, etc., and are not limited here. Software operating environment parameters may include at least one of the following: system version, system resources, network bandwidth, etc., and are not limited here. Hardware operating environment parameters may include at least one of the following: hardware type, hardware configuration parameters, maintenance status, etc., and are not limited here. Object environment parameters may include at least one of the following: ambient temperature, ambient humidity, atmospheric pressure, magnetic field interference intensity, etc., and are not limited here.

[0025] If the target load parameters do not meet the preset conditions, it indicates that the belt conveyor is not in a light load state. In this case, the target operating environment parameters of the belt conveyor can be obtained to configure reasonable control parameters for the belt conveyor.

[0026] In practice, when the target load parameters meet the preset conditions, it means that the belt conveyor is not in a light load state, and the belt conveyor can be controlled to work with the default control parameters. The default control parameters can be preset or set by the system default.

[0027] The default control parameters may include at least one of the following: working mode, working current, working voltage, working power, conveyor belt speed, etc., which are not limited here.

[0028] 104. The target control parameters of the belt conveyor are determined by the big data decision system, which correspond to the target load parameters and the target operating environment parameters.

[0029] The target control parameters may include at least one of the following: working mode, working current, working voltage, working power, conveyor belt speed, etc., which are not limited here.

[0030] In this embodiment, a big data decision-making system can determine the target control parameters of the belt conveyor corresponding to the target load parameters and target operating environment parameters. Therefore, based on the actual situation, the corresponding control parameters can be rationally configured, ensuring not only the normal operation of the belt conveyor but also reducing its power consumption.

[0031] 105. Control the belt conveyor to operate with target control parameters so that the conveyor belt transports the target object.

[0032] In this embodiment, the belt conveyor can be controlled to operate with target control parameters so that the conveyor belt transports the target object. By rationally configuring the corresponding control parameters based on actual conditions, not only is the normal operation of the belt conveyor guaranteed, but its power consumption can also be reduced.

[0033] Optionally, the big data decision-making system includes a local big data decision-making system. Step 104 above, which determines the target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters through the big data decision-making system, may include the following steps: 41. Obtain historical transport data through the local big data decision-making system, and obtain multiple transport records of the target object from the historical transport data. Each transport record contains historical load parameters and corresponding historical control parameters related to a transport process. 42. Generate multiple coordinate points based on the historical load parameters and historical control parameters of each of the multiple transport records, with the horizontal axis of each coordinate point representing the load parameters and the vertical axis representing the control parameters; 43. Fit the data based on the multiple coordinate points to obtain the fitting function; 44. Determine the first control parameter corresponding to the target load parameter based on the fitting function; 45. Determine the target adjustment parameters corresponding to the target operating environment parameters; 46. ​​Adjust the first control parameter according to the target adjustment parameter to obtain the target control parameter.

[0034] The historical load parameters may include at least one of the following: the load per unit area of ​​the conveyor belt, the overall load-bearing capacity of the conveyor belt, etc., which are not limited here.

[0035] The historical control parameters may include at least one of the following: working mode, working current, working voltage, working power, conveyor belt speed, etc., which are not limited here.

[0036] The big data decision-making system may include a local big data decision-making system, which is used to collect and analyze the working records of the belt conveyor in the local area.

[0037] In this embodiment of the application, historical delivery data can be obtained through a local big data decision-making system. Multiple delivery records of the target object can be obtained from the historical delivery data. Each delivery record contains historical load parameters and corresponding historical control parameters related to a delivery process. Multiple coordinate points are generated based on the historical load parameters and historical control parameters of each delivery record. The horizontal axis of each coordinate point is the load parameter, and the vertical axis is the control parameter. Then, the multiple coordinate points can be fitted to obtain a fitting function. The first control parameter corresponding to the target load parameter is then determined based on the fitting function.

[0038] Next, a pre-stored mapping relationship between preset operating environment parameters and adjustment parameters can be established. The adjustment parameter can range from -0.12 to 0.12. Based on this mapping relationship, the target adjustment parameter corresponding to the target operating environment parameter can be determined. Then, the first control parameter is adjusted according to the target adjustment parameter to obtain the target control parameter, i.e., target control parameter = (1 + target adjustment parameter) * first control parameter. In this way, the control parameter corresponding to the current load condition can be determined by the relationship between load parameters and control parameters based on the local big data decision system. The control parameter can also be dynamically optimized based on the actual operating environment parameters, so that the optimized control parameter is more suitable for the actual situation. Thus, the corresponding control parameters can be reasonably configured, which not only ensures the normal operation of the belt conveyor but also reduces the power consumption of the belt conveyor.

[0039] Optionally, the big data decision-making system further includes a cloud-based big data decision-making system. Step 46 above, adjusting the first control parameter according to the target adjustment parameter to obtain the target control parameter, may include the following steps: 461. Adjust the first control parameter according to the target adjustment parameter to obtain the second control parameter; 462. Obtain the target attribute information of the belt conveyor; 463. The cloud-based big data decision-making system determines the control parameters corresponding to the target attribute information, the target load parameters, and the target operating environment parameters, resulting in multiple control parameters, each of which corresponds to a device operating status evaluation value. 464. Obtain the equipment operation status evaluation values ​​corresponding to the multiple control parameters to obtain multiple operation status evaluation values; 465. Select the maximum value among the multiple operating status evaluation values, and obtain the reference control parameter corresponding to the maximum value; 466. Determine the target root mean square deviation corresponding to the plurality of operational status evaluation values; 467. Determine the target weight pair corresponding to the target mean square error, the target weight pair including a target first weight and a target second weight, the target first weight being the weight corresponding to the second control parameter, and the target second weight being the weight corresponding to the reference control parameter; 468. The target control parameters are obtained by performing a weighted operation based on the second control parameters, the reference control parameters, the target first weight, and the target second weight.

[0040] In this embodiment, the big data decision-making system may include not only a local big data decision-making system, but also a cloud-based big data decision-making system. That is, the cloud computing advantages of the cloud-based big data decision-making system can be used to deeply optimize the control parameters, making them more adaptable to the actual situation and deeply realize the reasonable configuration of the corresponding control parameters. This not only ensures the normal operation of the belt conveyor, but also reduces the power consumption of the belt conveyor.

[0041] The target attribute information may include at least one of the following: the model of the belt conveyor, the service life of the belt conveyor, the frequency of use of the belt conveyor, the configuration of the belt conveyor, etc., which are not limited here.

[0042] In this embodiment, the first control parameter can be adjusted according to the target adjustment parameter to obtain the second control parameter, i.e., the second control parameter = (1 + target adjustment parameter) * first control parameter. Furthermore, the target attribute information of the belt conveyor can be obtained. Then, the control parameters corresponding to the target attribute information, target load parameters, and target operating environment parameters can be determined through a cloud-based big data decision-making system, resulting in multiple control parameters. Each control parameter corresponds to a device operating status evaluation value. Since the cloud platform deals with the operating conditions of all other belt conveyors corresponding to the target attribute information, it can deeply select control parameters of other belt conveyors whose load and operating environment are deeply matched with the current belt conveyor as a reference. The operating status evaluation value is used to evaluate the quality of the operating status; the larger the operating status evaluation value, the better the operating status; the smaller the operating status evaluation value, the worse the operating status.

[0043] Furthermore, multiple control parameters can be used to obtain equipment operation status evaluation values. By selecting the maximum value among these multiple evaluation values ​​and obtaining the reference control parameter corresponding to the maximum value, the best operating condition can be selected. Additionally, the target mean square deviation corresponding to multiple operation status evaluation values ​​can be determined. This allows us to ascertain the attribute information of external and local belt conveyors, as well as the operating stability of belt conveyors with similar loads and operating environments. This operating stability reflects, to some extent, the reliability of the control parameters of other belt conveyors, and this reliability is related to the weights. This means that a pre-defined mapping relationship between the mean squared error and the weight pair can be set. Then, based on this mapping relationship, the target weight pair corresponding to the target mean squared error can be determined. This target weight pair includes a target first weight and a target second weight. The target first weight is the weight corresponding to the second control parameter, and the target second weight is the weight corresponding to the reference control parameter. The target first weight + target second weight = 1. Next, a weighted operation can be performed based on the second control parameter, the reference control parameter, the target first weight, and the target second weight to obtain the target control parameter, i.e., target control parameter = second control parameter * target first weight + reference control parameter * target second weight. In this way, not only can the local big data decision system determine the control parameters corresponding to the current load condition based on the relationship between load parameters and control parameters, and dynamically optimize the control parameters based on actual operating environment parameters, making the optimized control parameters more suitable for the actual situation, but it can also leverage the cloud computing advantages of the cloud-based big data decision system to deeply optimize the control parameters, achieving a more reasonable configuration of the corresponding control parameters. This not only ensures the normal operation of the belt conveyor but also reduces the power consumption of the belt conveyor.

[0044] Optionally, multiple tension sensors can be installed on the surface of the conveyor belt. After controlling the belt conveyor to operate with the target control parameters in step 105 above, the following steps may also be included: S1. The tension of the conveyor belt is obtained through the multiple tension sensors to obtain multiple tension datasets. Each tension dataset includes multiple tension data, and each tension data corresponds to a sampling time. S2. By fitting each of the multiple tension datasets, multiple fitting curves are obtained; S3. Obtain the absolute value of the extreme point of each of the multiple fitted curves to obtain a set of multiple absolute extreme points; S4. Determine the mean of each absolute extreme point set in the plurality of absolute extreme point sets to obtain multiple mean values; S5. Obtain the target maintenance record of the conveyor belt; S6. Determine the target aging parameters corresponding to the target maintenance record; S7. Determine the amplitude threshold corresponding to the target aging parameter; S8. Determine the difference between each of the plurality of mean values ​​and the amplitude threshold to obtain a plurality of differences; S9 determines the number of differences greater than 0 among the plurality of differences to obtain a first number, and determines the number of differences less than 0 among the plurality of differences to obtain a second number; S10. Determine the sum of the first quantity and the second quantity; S11. Determine the target ratio between the first quantity and the sum; S12. Determine the target feedback optimization parameters corresponding to the target ratio; S13. Optimize the target control parameters according to the target feedback optimization parameters to obtain the optimized target control parameters; S14. Control the belt conveyor to operate with the optimized target control parameters.

[0045] In practice, multiple tension sensors can be installed at different locations on the surface of the conveyor belt.

[0046] The target maintenance record may include at least one of the following: conveyor belt usage time, conveyor belt service life, conveyor belt usage frequency, remaining service time of the conveyor belt, aging assessment parameters of the conveyor belt, etc., without limitation.

[0047] In this embodiment of the application, the tension of the conveyor belt can be obtained by multiple tension sensors to obtain multiple tension datasets. Each tension dataset includes multiple tension data, and each tension data corresponds to a sampling time. Then, each tension dataset in the multiple tension datasets is fitted to obtain multiple fitting curves. That is, each tension data in each tension dataset can be regarded as a point. The horizontal axis of the fitting curve is time, and the vertical axis is the specific tension value.

[0048] Furthermore, the absolute values ​​of the extreme points of each of the multiple fitted curves can be obtained, resulting in multiple sets of absolute extreme points. Each set of absolute extreme points can include the absolute values ​​of multiple extreme points. The mean of each set of absolute extreme points is then determined, resulting in multiple mean values. Furthermore, the target maintenance record of the conveyor belt can be obtained, and a pre-stored mapping relationship between preset maintenance records and aging parameters can be used. Based on this mapping relationship, the target aging parameter corresponding to the target maintenance record can be determined. Since different degrees of aging result in different load conditions for the conveyor belt, these load conditions can be represented by an amplitude threshold. That is, a pre-stored mapping relationship between preset aging parameters and amplitude thresholds can be used, and based on this mapping relationship, the amplitude threshold corresponding to the target aging parameter can be determined.

[0049] Next, the difference between each mean and the amplitude threshold can be determined, resulting in multiple differences. Then, the number of differences greater than 0 is determined, resulting in a first number. The number of differences less than 0 is determined, resulting in a second number. The sum of the first and second numbers is determined, and a target ratio between the first number and the sum is determined. This ratio reflects the tension of the conveyor belt to a certain extent; the larger the ratio, the greater the tension, and vice versa. A pre-stored mapping relationship between preset ratios and feedback optimization parameters can be established. The values ​​of the feedback optimization parameters can range from -0.08 to 0.08. Based on this mapping relationship, the target feedback optimization parameters corresponding to the target ratio can be determined. The target control parameters are then optimized based on the target feedback parameters to obtain the optimized target control parameters, i.e., optimized target control parameters = (1 + target feedback optimization parameters) * target control parameters. Finally, the belt conveyor can be controlled to operate with the optimized target control parameters. In this way, the tension sensor can be used to dynamically detect the tension of the conveyor belt, and the control parameters can be optimized based on the tension. This achieves reasonable configuration of the corresponding control parameters, which not only ensures the normal operation of the belt conveyor, but also reduces the power consumption of the belt conveyor, and takes into account the safety performance of the conveyor belt. The feedback optimization of the control parameters of the belt conveyor makes the operation of the belt conveyor safer.

[0050] As can be seen, the belt conveyor control method based on a big data decision-making system described in this application embodiment is applied to a big data decision-making system. The big data decision-making system includes a belt conveyor, which includes a conveyor belt. When a target object is transported by the conveyor belt, the target load parameters corresponding to the target object are obtained, and it is detected whether the target load parameters meet preset conditions. If the target load parameters do not meet the preset conditions, the target operating environment parameters of the belt conveyor are obtained. The target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters are determined by the big data decision-making system. The belt conveyor is controlled to work with the target control parameters so that the conveyor belt transports the target object. Thus, based on its actual situation, the corresponding control parameters are reasonably configured, which not only ensures the normal operation of the belt conveyor, but also reduces the power consumption of the belt conveyor.

[0051] Please see Figure 2 , Figure 2 This is a schematic diagram of a belt conveyor provided in an embodiment of this application. As shown in the figure, it includes a processor, a memory, a communication interface, and one or more programs. The one or more programs are stored in the memory and configured to be executed by the processor. In this embodiment, it is applied to a big data decision-making system, which includes the belt conveyor. The belt conveyor includes a conveyor belt, and the program includes instructions for performing the following steps: When transporting a target object via the conveyor belt, the target load parameters of the conveyor belt corresponding to the target object are obtained; Detect whether the target load parameters meet the preset conditions; When the target load parameters do not meet the preset conditions, the target operating environment parameters of the belt conveyor are obtained; The big data decision-making system determines the target control parameters of the belt conveyor, which correspond to the target load parameters and the target operating environment parameters. The belt conveyor is controlled to operate with target control parameters so that the conveyor belt transports the target object.

[0052] Optionally, the big data decision-making system includes a local big data decision-making system. In determining the target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters through the big data decision-making system, the above-mentioned program includes instructions for performing the following steps: Historical delivery data is obtained through the local big data decision-making system, and multiple delivery records of the target object are obtained from the historical delivery data. Each delivery record contains historical load parameters and corresponding historical control parameters related to a delivery process. Multiple coordinate points are generated based on the historical load parameters and historical control parameters of each of the multiple transport records. The horizontal axis of each coordinate point represents the load parameters, and the vertical axis represents the control parameters. A fitting function is obtained by fitting the multiple coordinate points. The first control parameter corresponding to the target load parameter is determined based on the fitting function; Determine the target adjustment parameters corresponding to the target operating environment parameters; The first control parameter is adjusted according to the target adjustment parameter to obtain the target control parameter.

[0053] Optionally, the big data decision-making system further includes a cloud-based big data decision-making system. In adjusting the first control parameter according to the target adjustment parameter to obtain the target control parameter, the above procedure includes instructions for performing the following steps: The first control parameter is adjusted according to the target adjustment parameter to obtain the second control parameter; Obtain the target attribute information of the belt conveyor; The cloud-based big data decision-making system determines control parameters corresponding to the target attribute information, the target load parameters, and the target operating environment parameters, resulting in multiple control parameters, each of which corresponds to a device operating status evaluation value. Obtain the equipment operation status evaluation values ​​corresponding to the multiple control parameters to obtain multiple operation status evaluation values; Select the maximum value among the multiple operational status evaluation values, and obtain the reference control parameter corresponding to the maximum value; Determine the target mean square error corresponding to the plurality of operational status evaluation values; Determine the target weight pair corresponding to the target mean square error, the target weight pair including a target first weight and a target second weight, the target first weight being the weight corresponding to the second control parameter, and the target second weight being the weight corresponding to the reference control parameter; The target control parameters are obtained by performing a weighted calculation based on the second control parameter, the reference control parameter, the target first weight, and the target second weight.

[0054] Optionally, multiple tension sensors may be provided on the surface of the conveyor belt. Following the step of controlling the belt conveyor to operate with target control parameters, the above procedure further includes instructions for performing the following steps: The tension of the conveyor belt is obtained by the multiple tension sensors, resulting in multiple tension datasets. Each tension dataset includes multiple tension data points, and each tension data point corresponds to a sampling time. By fitting each of the multiple tension datasets, multiple fitting curves are obtained; Obtain the absolute value of the extreme point of each of the multiple fitted curves to obtain a set of multiple absolute extreme points; Determine the mean of each absolute extreme point set in the plurality of absolute extreme point sets to obtain multiple means; Obtain the target maintenance record of the conveyor belt; Determine the target aging parameters corresponding to the target maintenance record; Determine the amplitude threshold corresponding to the target aging parameter; Determine the difference between each of the plurality of means and the amplitude threshold to obtain a plurality of differences; Determine the number of differences greater than 0 among the plurality of differences to obtain a first number, and determine the number of differences less than 0 among the plurality of differences to obtain a second number; Determine the sum of the first quantity and the second quantity; Determine the target ratio between the first quantity and the sum; Determine the target feedback optimization parameters corresponding to the target ratio; The target control parameters are optimized based on the target feedback optimization parameters to obtain the optimized target control parameters; The belt conveyor is controlled to operate with the optimized target control parameters.

[0055] Optionally, in detecting whether the target load parameters meet preset conditions, the above procedure includes instructions for performing the following steps: Obtain the load parameter threshold of the target object; When the target load parameter is less than or equal to the load parameter threshold, it is determined that the target load parameter meets the preset condition; When the target load parameter is greater than the load parameter threshold, it is determined that the target load parameter does not meet the preset condition.

[0056] As can be seen, the belt conveyor described in this application embodiment is applied to a big data decision-making system. The big data decision-making system includes the belt conveyor, which includes a conveyor belt. When a target object is transported by the conveyor belt, the target load parameters of the target object corresponding to the conveyor belt are obtained. It is then checked whether the target load parameters meet preset conditions. If the target load parameters do not meet the preset conditions, the target operating environment parameters of the belt conveyor are obtained. The big data decision-making system determines the target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters. The belt conveyor is then controlled to operate with the target control parameters so that the conveyor belt transports the target object. Thus, based on the actual situation, the corresponding control parameters are reasonably configured, which not only ensures the normal operation of the belt conveyor but also reduces the power consumption of the belt conveyor.

[0057] Figure 3 This is a functional unit block diagram of a belt conveyor control device 300 based on a big data decision-making system, as described in this application embodiment. The belt conveyor control device 300 is applied to a big data decision-making system, which includes a belt conveyor and a conveyor belt. The belt conveyor control device 300 includes: an acquisition unit 301, a detection unit 302, a determination unit 303, and a control unit 304. The acquisition unit 301 is used to acquire the target load parameters of the conveyor belt corresponding to the target object when the target object is conveyed by the conveyor belt. The detection unit 302 is used to detect whether the target load parameters meet preset conditions; The acquisition unit 301 is also used to acquire the target operating environment parameters of the belt conveyor when the target load parameters do not meet the preset conditions; The determining unit 303 is used to determine the target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters through the big data decision system; The control unit 304 is used to control the belt conveyor to operate with target control parameters so that the conveyor belt transports the target object.

[0058] Optionally, the big data decision-making system includes a local big data decision-making system. In determining the target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters through the big data decision-making system, the determining unit 303 is specifically used for: Historical delivery data is obtained through the local big data decision-making system, and multiple delivery records of the target object are obtained from the historical delivery data. Each delivery record contains historical load parameters and corresponding historical control parameters related to a delivery process. Multiple coordinate points are generated based on the historical load parameters and historical control parameters of each of the multiple transport records. The horizontal axis of each coordinate point represents the load parameters, and the vertical axis represents the control parameters. A fitting function is obtained by fitting the multiple coordinate points. The first control parameter corresponding to the target load parameter is determined based on the fitting function; Determine the target adjustment parameters corresponding to the target operating environment parameters; The first control parameter is adjusted according to the target adjustment parameter to obtain the target control parameter.

[0059] Optionally, the big data decision-making system further includes a cloud-based big data decision-making system. In the process of adjusting the first control parameter according to the target adjustment parameter to obtain the target control parameter, the determining unit 303 is specifically used for: The first control parameter is adjusted according to the target adjustment parameter to obtain the second control parameter; Obtain the target attribute information of the belt conveyor; The cloud-based big data decision-making system determines control parameters corresponding to the target attribute information, the target load parameters, and the target operating environment parameters, resulting in multiple control parameters, each of which corresponds to a device operating status evaluation value. Obtain the equipment operation status evaluation values ​​corresponding to the multiple control parameters to obtain multiple operation status evaluation values; Select the maximum value among the multiple operational status evaluation values, and obtain the reference control parameter corresponding to the maximum value; Determine the target mean square error corresponding to the plurality of operational status evaluation values; Determine the target weight pair corresponding to the target mean square error, the target weight pair including a target first weight and a target second weight, the target first weight being the weight corresponding to the second control parameter, and the target second weight being the weight corresponding to the reference control parameter; The target control parameters are obtained by performing a weighted calculation based on the second control parameter, the reference control parameter, the target first weight, and the target second weight.

[0060] Optionally, multiple tension sensors can be installed on the surface of the conveyor belt. Following the aspect of controlling the belt conveyor to operate with target control parameters, the belt conveyor control device 300 based on the big data decision-making system is further specifically used for: The tension of the conveyor belt is obtained by the multiple tension sensors, resulting in multiple tension datasets. Each tension dataset includes multiple tension data points, and each tension data point corresponds to a sampling time. By fitting each of the multiple tension datasets, multiple fitting curves are obtained; Obtain the absolute value of the extreme point of each of the multiple fitted curves to obtain a set of multiple absolute extreme points; Determine the mean of each absolute extreme point set in the plurality of absolute extreme point sets to obtain multiple means; Obtain the target maintenance record of the conveyor belt; Determine the target aging parameters corresponding to the target maintenance record; Determine the amplitude threshold corresponding to the target aging parameter; Determine the difference between each of the plurality of means and the amplitude threshold to obtain a plurality of differences; Determine the number of differences greater than 0 among the plurality of differences to obtain a first number, and determine the number of differences less than 0 among the plurality of differences to obtain a second number; Determine the sum of the first quantity and the second quantity; Determine the target ratio between the first quantity and the sum; Determine the target feedback optimization parameters corresponding to the target ratio; The target control parameters are optimized based on the target feedback optimization parameters to obtain the optimized target control parameters; The belt conveyor is controlled to operate with the optimized target control parameters.

[0061] Optionally, in detecting whether the target load parameters meet preset conditions, the detection unit 302 is specifically used for: Obtain the load parameter threshold of the target object; When the target load parameter is less than or equal to the load parameter threshold, it is determined that the target load parameter meets the preset condition; When the target load parameter is greater than the load parameter threshold, it is determined that the target load parameter does not meet the preset condition.

[0062] As can be seen, the belt conveyor described in this application embodiment is applied to a big data decision-making system. The big data decision-making system includes the belt conveyor, which includes a conveyor belt. When a target object is transported by the conveyor belt, the target load parameters of the target object corresponding to the conveyor belt are obtained. It is then checked whether the target load parameters meet preset conditions. If the target load parameters do not meet the preset conditions, the target operating environment parameters of the belt conveyor are obtained. The big data decision-making system determines the target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters. The belt conveyor is then controlled to operate with the target control parameters so that the conveyor belt transports the target object. Thus, based on the actual situation, the corresponding control parameters are reasonably configured, which not only ensures the normal operation of the belt conveyor but also reduces the power consumption of the belt conveyor.

[0063] It is understood that the functions of each program module of the belt conveyor control device based on the big data decision system in this embodiment can be specifically implemented according to the methods in the above method embodiments. The specific implementation process can be referred to the relevant descriptions in the above method embodiments, and will not be repeated here.

[0064] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes a belt conveyor.

[0065] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer includes a belt conveyor.

[0066] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0067] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0069] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0071] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0072] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0073] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A belt conveyor control device based on a big data decision-making system, characterized in that, This device is applied to a big data decision-making system, which includes a belt conveyor and a conveyor belt. The device comprises an acquisition unit, a detection unit, a determination unit, and a control unit. The acquisition unit is used to acquire the target load parameters of the conveyor belt corresponding to the target object when the target object is conveyed by the conveyor belt; The detection unit is used to detect whether the target load parameters meet preset conditions; The acquisition unit is also used to acquire the target operating environment parameters of the belt conveyor when the target load parameters do not meet the preset conditions; The determining unit is used to determine, through the big data decision system, the target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters; The control unit is used to control the belt conveyor to operate with target control parameters so that the conveyor belt transports the target object.

2. The apparatus according to claim 1, characterized in that, The big data decision-making system includes a local big data decision-making system. In determining the target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters through the big data decision-making system, the determining unit is specifically used for: Historical delivery data is obtained through the local big data decision-making system, and multiple delivery records of the target object are obtained from the historical delivery data. Each delivery record contains historical load parameters and corresponding historical control parameters related to a delivery process. Multiple coordinate points are generated based on the historical load parameters and historical control parameters of each of the multiple transport records. The horizontal axis of each coordinate point represents the load parameters, and the vertical axis represents the control parameters. A fitting function is obtained by fitting the multiple coordinate points. The first control parameter corresponding to the target load parameter is determined based on the fitting function; Determine the target adjustment parameters corresponding to the target operating environment parameters; The first control parameter is adjusted according to the target adjustment parameter to obtain the target control parameter.

3. The apparatus according to claim 2, characterized in that, The big data decision-making system further includes a cloud-based big data decision-making system. In the process of adjusting the first control parameter according to the target adjustment parameter to obtain the target control parameter, the determining unit is specifically used for: The first control parameter is adjusted according to the target adjustment parameter to obtain the second control parameter; Obtain the target attribute information of the belt conveyor; The cloud-based big data decision-making system determines control parameters corresponding to the target attribute information, the target load parameters, and the target operating environment parameters, resulting in multiple control parameters, each of which corresponds to a device operating status evaluation value. Obtain the equipment operation status evaluation values ​​corresponding to the multiple control parameters to obtain multiple operation status evaluation values; Select the maximum value among the multiple operational status evaluation values, and obtain the reference control parameter corresponding to the maximum value; Determine the target mean square error corresponding to the plurality of operational status evaluation values; Determine the target weight pair corresponding to the target mean square error, the target weight pair including a target first weight and a target second weight, the target first weight being the weight corresponding to the second control parameter, and the target second weight being the weight corresponding to the reference control parameter; The target control parameters are obtained by performing a weighted calculation based on the second control parameter, the reference control parameter, the target first weight, and the target second weight.

4. The apparatus according to any one of claims 1-3, characterized in that, The surface of the conveyor belt is equipped with multiple tension sensors. Following the control of the belt conveyor to operate with target control parameters, the device is further specifically used for: The tension of the conveyor belt is obtained by the multiple tension sensors, resulting in multiple tension datasets. Each tension dataset includes multiple tension data points, and each tension data point corresponds to a sampling time. By fitting each of the multiple tension datasets, multiple fitting curves are obtained; Obtain the absolute value of the extreme point of each of the multiple fitted curves to obtain a set of multiple absolute extreme points; Determine the mean of each absolute extreme point set in the plurality of absolute extreme point sets to obtain multiple means; Obtain the target maintenance record of the conveyor belt; Determine the target aging parameters corresponding to the target maintenance record; Determine the amplitude threshold corresponding to the target aging parameter; Determine the difference between each of the plurality of means and the amplitude threshold to obtain a plurality of differences; Determine the number of differences greater than 0 among the plurality of differences to obtain a first number, and determine the number of differences less than 0 among the plurality of differences to obtain a second number; Determine the sum of the first quantity and the second quantity; Determine the target ratio between the first quantity and the sum; Determine the target feedback optimization parameters corresponding to the target ratio; The target control parameters are optimized based on the target feedback optimization parameters to obtain the optimized target control parameters; The belt conveyor is controlled to operate with the optimized target control parameters.

5. The apparatus according to any one of claims 1-3, characterized in that, Regarding the detection of whether the target load parameters meet preset conditions, the detection unit is specifically used for: Obtain the load parameter threshold of the target object; When the target load parameter is less than or equal to the load parameter threshold, it is determined that the target load parameter meets the preset condition; When the target load parameter is greater than the load parameter threshold, it is determined that the target load parameter does not meet the preset condition.

6. A belt conveyor control method based on a big data decision-making system, characterized in that, Applied to a big data decision-making system, the big data decision-making system including a belt conveyor, the belt conveyor including a conveyor belt, the method includes: When transporting a target object via the conveyor belt, the target load parameters of the conveyor belt corresponding to the target object are obtained; Detect whether the target load parameters meet the preset conditions; When the target load parameters do not meet the preset conditions, the target operating environment parameters of the belt conveyor are obtained; The big data decision-making system determines the target control parameters of the belt conveyor, which correspond to the target load parameters and the target operating environment parameters. The belt conveyor is controlled to operate with target control parameters so that the conveyor belt transports the target object.

7. The method according to claim 6, characterized in that, The big data decision-making system includes a local big data decision-making system. The step of determining the target control parameters of the belt conveyor corresponding to the target load parameters and the target operating environment parameters through the big data decision-making system includes: Historical delivery data is obtained through the local big data decision-making system, and multiple delivery records of the target object are obtained from the historical delivery data. Each delivery record contains historical load parameters and corresponding historical control parameters related to a delivery process. Multiple coordinate points are generated based on the historical load parameters and historical control parameters of each of the multiple transport records. The horizontal axis of each coordinate point represents the load parameters, and the vertical axis represents the control parameters. A fitting function is obtained by fitting the multiple coordinate points. The first control parameter corresponding to the target load parameter is determined based on the fitting function; Determine the target adjustment parameters corresponding to the target operating environment parameters; The first control parameter is adjusted according to the target adjustment parameter to obtain the target control parameter.

8. The method according to claim 7, characterized in that, The big data decision-making system further includes a cloud-based big data decision-making system. The step of adjusting the first control parameter according to the target adjustment parameter to obtain the target control parameter includes: The first control parameter is adjusted according to the target adjustment parameter to obtain the second control parameter; Obtain the target attribute information of the belt conveyor; The cloud-based big data decision-making system determines control parameters corresponding to the target attribute information, the target load parameters, and the target operating environment parameters, resulting in multiple control parameters, each of which corresponds to a device operating status evaluation value. Obtain the equipment operation status evaluation values ​​corresponding to the multiple control parameters to obtain multiple operation status evaluation values; Select the maximum value among the multiple operational status evaluation values, and obtain the reference control parameter corresponding to the maximum value; Determine the target mean square error corresponding to the plurality of operational status evaluation values; Determine the target weight pair corresponding to the target mean square error, the target weight pair including a target first weight and a target second weight, the target first weight being the weight corresponding to the second control parameter, and the target second weight being the weight corresponding to the reference control parameter; The target control parameters are obtained by performing a weighted calculation based on the second control parameter, the reference control parameter, the target first weight, and the target second weight.

9. The method according to any one of claims 6-8, characterized in that, The surface of the conveyor belt is provided with multiple tension sensors. Following the step of controlling the belt conveyor to operate with target control parameters, the method further includes: The tension of the conveyor belt is obtained by the multiple tension sensors, resulting in multiple tension datasets. Each tension dataset includes multiple tension data points, and each tension data point corresponds to a sampling time. By fitting each of the multiple tension datasets, multiple fitting curves are obtained; Obtain the absolute value of the extreme point of each of the multiple fitted curves to obtain a set of multiple absolute extreme points; Determine the mean of each absolute extreme point set in the plurality of absolute extreme point sets to obtain multiple means; Obtain the target maintenance record of the conveyor belt; Determine the target aging parameters corresponding to the target maintenance record; Determine the amplitude threshold corresponding to the target aging parameter; Determine the difference between each of the plurality of means and the amplitude threshold to obtain a plurality of differences; Determine the number of differences greater than 0 among the plurality of differences to obtain a first number, and determine the number of differences less than 0 among the plurality of differences to obtain a second number; Determine the sum of the first quantity and the second quantity; Determine the target ratio between the first quantity and the sum; Determine the target feedback optimization parameters corresponding to the target ratio; The target control parameters are optimized based on the target feedback optimization parameters to obtain the optimized target control parameters; The belt conveyor is controlled to operate with the optimized target control parameters.

10. A belt conveyor, characterized in that, The belt conveyor includes a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor. The programs include instructions for performing the steps of the method as described in any one of claims 6-9.

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