Method and device for controlling weight of material, engineering machine and storage medium

Abstract

The application relates to the technical field of engineering machinery, in particular to a material weight control method and device, engineering machinery and a storage medium. The method comprises the following steps: obtaining the material weight measured by a weighing device; determining the material flow according to the material weight; inputting the material flow into a drop prediction model to obtain a drop prediction value; determining a metering stop value according to the drop prediction value and a target material weight; and controlling the screw machine to stop under the condition that the error between the material weight measured by the weighing device and the metering stop value is within a preset range, so as to improve the precision of the screw machine control, make the screw machine operate in time and accurately, and improve the metering precision of the material.

Description

Technical Field

[0001] This application relates to the field of engineering machinery technology, specifically to a method, control device, engineering machinery, and storage medium for controlling the weight of materials. Background Technology

[0002] Currently, efficient and accurate material metering is a core component of the control system for engineering machinery such as dry-mix mortar plants. Furthermore, accurately and quickly controlling the weight of material conveyed from the screw conveyor to the weighing device, such as the scale, is a crucial aspect of material metering control. Ideally, material metering involves real-time detection of the weight on the weighing device using load cells, comparing it to a preset weight value, and if they match, stopping the screw conveyor and outputting the material from the weighing device.

[0003] However, in actual operation, when the detected weight matches the preset weight value, the screw conveyor is stopped. Due to the screw conveyor's inertia, it will continue to run for a period of time, continuously feeding material into the weighing device. Furthermore, because the weighing device is an additive weighing structure, and the screw conveyor is above it at a certain height, some material remains suspended in the air when the screw conveyor receives the stop command. These two factors result in the actual weight of the material falling into the weighing device being greater than the preset weight value. The difference between the weight at the moment the screw conveyor stops and the weight after it has settled is called the weighing drop. To achieve accurate weighing, the screw conveyor needs to be stopped in advance. The calculation of the screw conveyor's stopping time must consider the weighing drop during the weighing process as a stop lead time.

[0004] Existing metering methods typically use the average of multiple historical metering drops to determine the next metering drop, resulting in insufficient metering accuracy and large metering errors. This leads to inaccurate control of the screw conveyor shutdown, low metering reliability, and failure to meet the metering accuracy requirements of materials. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, machinery and storage medium for controlling material weight, in order to solve the problems of large material measurement error, inability to accurately control the stop of screw conveyor, and failure to meet the measurement accuracy requirements of materials in the prior art.

[0006] To achieve the above objectives, the first aspect of this application provides a method for controlling the weight of materials, applied to engineering machinery, which includes a screw conveyor and a weighing device. The control method includes:

[0007] To obtain the weight of the material measured by the weighing device;

[0008] Determine the material flow rate based on the material weight;

[0009] The material flow rate is input into the drop prediction model to obtain the drop prediction value;

[0010] The metering stop value is determined based on the predicted drop value and the target material weight.

[0011] If the error between the material weight measured by the weighing device and the metering stop value is within a preset range, the screw conveyor will be stopped.

[0012] In this embodiment, the drop prediction model is trained through the following steps: obtaining the weights of multiple historical materials during the weighing process of multiple historical materials by the weighing device; determining the historical shutdown flow rate of the screw conveyor at the end of the weighing of each historical material based on the weights of multiple historical materials during the weighing process of each historical material; determining the historical metering drop value of the weighing device at the time of weighing each historical material based on the weights of multiple historical materials during the weighing process of each historical material; performing correlation processing on all historical shutdown flow rates and historical metering drop values ​​to obtain a first correlation coefficient between all historical shutdown flow rates and historical metering drop values; and fitting a regression model based on all historical shutdown flow rates, all historical metering drops, and the first correlation coefficient to obtain the drop prediction model.

[0013] In this embodiment of the application, determining the historical shutdown flow rate of the screw conveyor at the end of each historical material weighing process based on the weights of multiple historical materials during each historical material weighing process includes: for each historical material weighing, determining the ratio between the difference between the weights of historical materials at each adjacent two historical moments during the weighing process and the interval between each adjacent two historical moments, and determining the ratio as the historical instantaneous flow rate during the weighing process of the historical material; for each historical material weighing, filtering all the historical instantaneous flow rates during the weighing process of the historical material to obtain the historical shutdown flow rate of the screw conveyor at the end of the weighing process of the historical material.

[0014] In this embodiment of the application, determining the historical measurement drop value of the weighing device at the time of weighing each historical material based on the weights of multiple historical materials during the weighing process includes: obtaining the historical final weight of each historical material weighing; and determining the difference between the historical material weight at the time the screw conveyor stops and the historical final weight of each historical material weighing as the historical measurement drop value of the weighing device at the time of weighing each historical material.

[0015] In this embodiment of the application, determining the material flow rate based on the material weight includes: determining the instantaneous flow rate based on the material weight at two adjacent moments; and filtering the instantaneous flow rate to obtain the material flow rate.

[0016] In this embodiment of the application, the control method further includes: after the screw conveyor is stopped, performing correlation processing on the predicted material flow rate and drop value to obtain a second correlation coefficient between the predicted material flow rate and drop value; and updating the drop prediction model based on the predicted material flow rate, drop value, and the second correlation coefficient.

[0017] In this embodiment of the application, the control method further includes: when the error between the material weight measured by the weighing device and the metering stop value is outside a preset range, controlling the screw conveyor to continue working and returning to the step of obtaining the material weight measured by the weighing device until the error between the material weight measured by the weighing device and the metering stop value is within a preset range.

[0018] A second aspect of this application provides a control device, comprising:

[0019] The memory is configured to store instructions;

[0020] The processor is configured to retrieve instructions from memory and, when executing the instructions, to implement the aforementioned method for controlling the weight of materials.

[0021] A third aspect of this application provides an engineering machine, comprising:

[0022] Screw conveyors are used to transport materials to weighing devices.

[0023] Weighing device, including:

[0024] Material receiving equipment, used to receive materials conveyed by the screw conveyor;

[0025] Weighing sensors are used to detect the weight of materials on material receiving equipment;

[0026] The aforementioned control device.

[0027] A fourth aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to perform the aforementioned material weight control method.

[0028] The above technical solution enables the determination of material flow rate based on the material weight measured by the weighing device. The material flow rate is then input into the drop prediction model to obtain the drop prediction value. Based on the drop prediction value and the target material weight, a metering stop value is determined. After obtaining the metering stop value, it is determined whether the error between the material weight measured by the weighing device and the metering stop value is within a preset range. If the error between the material weight measured by the weighing device and the metering stop value is within the preset range, the screw conveyor is controlled to stop, thereby improving the accuracy of screw conveyor control and enabling the screw conveyor to operate in a timely and accurate manner, thus improving the metering accuracy of the material.

[0029] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0030] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0031] Figure 1 The illustration shows a flowchart of a method for controlling the weight of materials according to an embodiment of this application;

[0032] Figure 2 The illustration shows a schematic diagram of a method for controlling the weight of materials according to an embodiment of this application;

[0033] Figure 3 The illustration shows a schematic diagram of a drop prediction model according to an embodiment of this application;

[0034] Figure 4 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0036] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0037] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0038] Figure 1 The illustration shows a schematic flowchart of a material weight control method according to an embodiment of this application. Figure 1 As shown in the figure, this application provides a method for controlling the weight of materials, which is applied to engineering machinery. The engineering machinery includes a screw conveyor and a weighing device. The control method may include the following steps.

[0039] Step 101: Obtain the weight of the material measured by the weighing device.

[0040] Step 102: Determine the material flow rate based on the material weight.

[0041] Construction machinery is an important component of the equipment manufacturing industry. Broadly speaking, construction machinery refers to the mechanical equipment necessary for comprehensive mechanized construction projects, including earthwork construction, road construction and maintenance, mobile lifting and unloading operations, and various building projects. In this embodiment, construction machinery can be concrete pumping equipment, dry mortar plants, or concrete mixing plants. Screw conveyors are widely used in various industries, such as building materials, chemicals, power, metallurgy, coal, and grain, and are suitable for horizontal or inclined conveying of powdery, granular, and small lump materials, such as coal, ash, slag, cement, and grain. Weighing devices are mainly used to measure the weight of materials and can be scales, weighing sensors, etc. The material can be dry mortar, etc. The processor can acquire the material weight measured by the weighing device in real time. The weighing device measures the material weight in real time during the weighing process; one material weighing process can be understood as the process from the screw conveyor conveying material to the weighing device until the material settles on the weighing device. After obtaining the real-time material weight measured by the weighing device, the processor can determine the material flow rate in real time based on the real-time material weight.

[0042] In this embodiment of the application, determining the material flow rate based on the material weight includes: determining the instantaneous flow rate based on the material weight at two adjacent moments; and filtering the instantaneous flow rate to obtain the material flow rate.

[0043] The processor can determine the material flow rate based on the material weight. Specifically, the processor can determine the instantaneous flow rate based on the material weight at two adjacent moments. For example, the instantaneous flow rate can be determined as the ratio of the difference in material weight at two adjacent moments to the time interval between the two adjacent moments. After obtaining the instantaneous flow rate, the processor can filter the instantaneous flow rate to obtain the final material flow rate.

[0044] In an optional embodiment, the processor can input the material weight into the flow calculation model to calculate the instantaneous flow rate and perform filtering, and then output the material flow rate.

[0045] Step 103: Input the material flow rate into the drop prediction model to obtain the drop prediction value.

[0046] Step 104: Determine the metering stop value based on the predicted drop value and the target material weight.

[0047] Step 105: If the error between the material weight measured by the weighing device and the metering stop value is within the preset range, control the screw conveyor to stop.

[0048] After obtaining the real-time material flow rate, the processor can input the real-time material flow rate into the drop prediction model to obtain the real-time drop prediction value. After obtaining the real-time drop prediction value, the processor can determine the real-time metering stop value based on the real-time drop prediction value and the target material weight, which is the user's required material weight for this weighing. In a specific embodiment, the metering stop value can be the difference between the target material weight and the drop prediction value. After obtaining the real-time metering stop value, the processor can determine the error between the real-time material weight measured by the weighing device and the real-time metering stop value, and determine whether the error is within a preset range. If the error is within the preset range, the processor can control the screw conveyor to stop; that is, the time corresponding to the error being within the preset range is the screw conveyor's stop time. In an optional embodiment, when the error is 0, that is, the physical weight measured by the weighing device equals the metering stop value, the processor controlling the screw conveyor to stop achieves the best control effect, ensuring that the weight of the material falling on the weighing device is exactly the target material weight.

[0049] In this embodiment of the application, the control method further includes: when the error between the material weight measured by the weighing device and the metering stop value is outside a preset range, controlling the screw conveyor to continue working and returning to the step of obtaining the material weight measured by the weighing device until the error between the material weight measured by the weighing device and the metering stop value is within a preset range.

[0050] If the error between the material weight measured by the weighing device and the metering stop value is outside a preset range, the processor can control the screw conveyor to continue working and return to the step of obtaining the material weight measured by the weighing device until the error between the material weight measured by the weighing device and the metering stop value is within the preset range. That is, once the error between the material weight measured by the weighing device and the metering stop value is within the preset range for the first time, the processor can determine this moment as the screw conveyor's stop time and control the screw conveyor to stop. In a specific embodiment, the processor can send a stop command to the screw conveyor to cause it to execute the stop command and thus stop working. Alternatively, the processor can send a deceleration command to the screw conveyor to cause it to decelerate until it stops.

[0051] In this embodiment of the application, the control method further includes: after the screw conveyor is stopped, performing correlation processing on the predicted material flow rate and drop value to obtain a second correlation coefficient between the predicted material flow rate and drop value; and updating the drop prediction model based on the predicted material flow rate, drop value, and the second correlation coefficient.

[0052] After the screw conveyor is shut down, the processor can perform correlation processing on the predicted material flow rate and drop height to obtain a second correlation coefficient between them. After obtaining the second correlation coefficient, the processor can update the drop height prediction model based on the predicted material flow rate, drop height, and the second correlation coefficient to continuously optimize the model and improve its performance.

[0053] In this embodiment, the drop prediction model is trained through the following steps: obtaining the weights of multiple historical materials during the weighing process of multiple historical materials by the weighing device; determining the historical shutdown flow rate of the screw conveyor at the end of the weighing of each historical material based on the weights of multiple historical materials during the weighing process of each historical material; determining the historical metering drop value of the weighing device at the time of weighing each historical material based on the weights of multiple historical materials during the weighing process of each historical material; performing correlation processing on all historical shutdown flow rates and historical metering drop values ​​to obtain a first correlation coefficient between all historical shutdown flow rates and historical metering drop values; and fitting a regression model based on all historical shutdown flow rates, all historical metering drops, and the first correlation coefficient to obtain the drop prediction model.

[0054] The processor can acquire multiple historical material weights during the weighing process of various historical materials. After obtaining these historical material weights, the processor can determine the historical shutdown flow rate of the screw conveyor at the end of each historical material weighing process. It also determines the historical metering drop value of the weighing device at the time of each historical material weighing process, based on these historical material weights. The historical metering drop refers to the difference between the actual weight of the material falling onto the weighing device and the weight measured at the time the screw conveyor stops. After obtaining multiple historical shutdown flow rates and historical metering drop values, the processor can perform correlation processing on all historical shutdown flow rates and historical metering drop values ​​to obtain a first correlation coefficient between them. Then, based on all historical shutdown flow rates, all historical metering drops, and the first correlation coefficient, a regression model is fitted to obtain a drop prediction model, which is then used to predict the real-time drop value during the material weighing process, improving the accuracy of the drop prediction.

[0055] In this embodiment of the application, determining the historical shutdown flow rate of the screw conveyor at the end of each historical material weighing process based on the weights of multiple historical materials during each historical material weighing process includes: for each historical material weighing, determining the ratio between the difference between the weights of historical materials at each adjacent two historical moments during the weighing process and the interval between each adjacent two historical moments, and determining the ratio as the historical instantaneous flow rate during the weighing process of the historical material; for each historical material weighing, filtering all the historical instantaneous flow rates during the weighing process of the historical material to obtain the historical shutdown flow rate of the screw conveyor at the end of the weighing process of the historical material.

[0056] The processor can determine the historical shutdown flow rate of the screw conveyor at the end of each historical material weighing process based on the weights of multiple historical materials during that process. Specifically, for each historical material weighing, the processor can determine the difference between the weights of the historical materials at each adjacent historical moment during the weighing process, and also determine the interval between each adjacent historical moment. After determining the difference between the weights of the historical materials at each adjacent historical moment and the interval between each adjacent historical moment during the weighing process, the processor can determine the ratio between the difference between the weights of the historical materials at each adjacent historical moment and the interval between each adjacent historical moment, and define this ratio as the historical instantaneous flow rate during the weighing process. After obtaining the historical instantaneous flow rate during the weighing process, the processor can filter all the instantaneous flow rates during the weighing process to obtain the historical shutdown flow rate of the screw conveyor at the end of the weighing process.

[0057] In an optional embodiment, the processor can input the weight data of each historical material weighing process into the flow calculation model to calculate the instantaneous flow rate of each historical material weighing process within the flow calculation model, and then filter the instantaneous flow rate to obtain the historical shutdown flow rate of the screw conveyor at the end of each historical material weighing process output by the flow calculation model.

[0058] In this embodiment of the application, determining the historical measurement drop value of the weighing device at the time of weighing each historical material based on the weights of multiple historical materials during the weighing process includes: obtaining the historical final weight of each historical material weighing; and determining the difference between the historical material weight at the time the screw conveyor stops and the historical final weight of each historical material weighing as the historical measurement drop value of the weighing device at the time of weighing each historical material.

[0059] The processor can determine the historical measurement drop value of the weighing device at the time of each historical material weighing based on the weights of multiple historical materials during each historical material weighing process. Specifically, the processor can obtain the historical final weight of each historical material weighing. The processor can measure the historical material weight of each historical material weighing using a weighing device such as a weighing sensor, and determine the historical measurement drop value of the weighing device at the time of each historical material weighing by the difference between the historical material weight at the time the screw conveyor stops and the historical final weight of each historical material weighing.

[0060] In the embodiments of this application, such as Figure 2 As shown, the weighing sensor can measure the weight of the material conveyed by the screw conveyor to the material receiving equipment in real time. The metering controller can acquire the weight data collected by the weighing sensor in real time and send the real-time weight to the PLC (controller). Big data acquisition can collect weight data at high frequency and obtain the metering status bit from the PLC. The weight data from the big data acquisition is input to the flow calculation model, which processes the weight data to obtain the instantaneous flow rate of the material being weighed. A filtering algorithm is then used to process the instantaneous flow rate, and finally, the flow rate is output. The flow rate output from the flow calculation model is used to train the drop prediction model offline, resulting in a trained drop prediction model. The PLC can perform drop adaptive control using the drop prediction model. The PLC acquires the proportioning information and control parameters from the host computer production system, as well as the real-time weight sent by the metering controller, and combines this with the drop prediction model to perform speed control and start / stop control of the metering screw conveyor. Specifically, the PLC can further control the metering screw conveyor by controlling the frequency converter to improve metering accuracy, metering reliability, and reduce metering errors.

[0061] The training of the drop prediction model specifically includes: extracting features from the weight and flow data of the symmetrically measured material to obtain the shutdown flow rate and actual drop. The shutdown flow rate and actual drop are then input as features into the regression model (W). T X), to fit a regression model using a multinomial. For example... Figure 3 As shown, weight data of the weighed material (such as cement ash) was obtained by sampling at a sampling frequency of 50Hz. After processing by the flow calculation model, more than 200 sets of shutdown flow rates and actual drop values ​​(Lc) were obtained. Based on the data distribution characteristics of the more than 200 sets of shutdown flow rates and actual drop values, a suitable regression model was selected. The more than 200 sets of shutdown flow rates and actual drop values ​​were input into the regression model, and the fitting parameters of the regression model were set to perform model fitting. When the goodness of fit is greater than 85%, the model is determined to be successfully fitted. The successfully fitted regression model is determined as the drop prediction model, and the predicted drop value is output through the drop prediction model.

[0062] By employing the above technical solutions, the precision of screw conveyor control is improved, enabling the screw conveyor to operate in a timely and accurate manner, thereby enhancing the metering accuracy of materials.

[0063] Figure 1 This is a flowchart illustrating a method for controlling material weight in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0064] This application embodiment also provides a control device, including:

[0065] The memory is configured to store instructions;

[0066] The processor is configured to retrieve instructions from memory and, when executing the instructions, to implement the aforementioned method for controlling the weight of materials.

[0067] In this embodiment of the application, the control device may further include:

[0068] The data acquisition module is used to store historical weighing data of materials. The historical weighing data can include weight data, flow data, and actual measurement drop data from multiple historical weighing processes.

[0069] The prediction module is used to calculate the material flow rate and perform filtering based on historical weighing data. It is also used to build a drop prediction model based on historical weighing data to predict the drop value in the current weighing process.

[0070] The control module is used to control the screw conveyor to stop conveying material when the actual weighing value measured by the weighing device reaches the metering stop value. The metering stop value is the difference between the target material weight and the predicted drop value.

[0071] This application also provides an engineering machinery, including:

[0072] Screw conveyors are used to transport materials to weighing devices.

[0073] Weighing device, including:

[0074] Material receiving equipment, used to receive materials conveyed by the screw conveyor;

[0075] Weighing sensors are used to detect the weight of materials on material receiving equipment;

[0076] The aforementioned control device.

[0077] In this embodiment of the application, the construction machinery may further include:

[0078] A frequency converter is used to control the start-stop and / or speed of a screw conveyor.

[0079] The electric motor is used to drive the speed reducer;

[0080] A speed reducer is used to connect a motor and a screw conveyor.

[0081] The construction machinery in this embodiment includes traditional construction machinery vehicles, as well as new energy vehicles used in the construction machinery field, such as new energy mixer trucks, new energy pump trucks, and new energy excavators. In addition, the construction machinery vehicles in this embodiment are also intelligent connected vehicles. Construction machinery vehicles include sensing systems, communication systems, etc. The in-vehicle sensing system collects vehicle operation data and information about the vehicle's surrounding environment, and the communication system enables network connection with other vehicles and the cloud. The collected vehicle operation data and information about the vehicle's surrounding environment are shared with the cloud and other authorized vehicles to achieve data sharing, remote analysis, intelligent driving and other operations.

[0082] In this application embodiment, a material weighing system is also provided, the system comprising:

[0083] Screw conveyors are used to transport materials to the weighing platform.

[0084] The scale body has an opening that connects to the output port of the screw conveyor, and is used to receive the material conveyed by the screw conveyor.

[0085] A weighing sensor, installed on the scale body, is used to measure the weight of the materials in the scale body;

[0086] The controller can be connected to a weighing sensor and a frequency converter to control the screw conveyor based on the weight of the material measured by the weighing sensor.

[0087] This application also provides a machine-readable storage medium storing instructions for causing a machine to perform the aforementioned material weight control method.

[0088] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The database stores data such as material weight, material flow rate, drop prediction, and metering stop value. The network interface A02 is used for communication with external terminals via a network connection. When the computer program B02 is executed by the processor A01, it implements a method for controlling material weight.

[0089] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0090] This application provides an embodiment of a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring the weight of the material measured by the weighing device; determining the material flow rate based on the material weight; inputting the material flow rate into a drop prediction model to obtain a drop prediction value; determining a metering stop value based on the drop prediction value and the target material weight; and controlling the screw conveyor to stop when the error between the material weight measured by the weighing device and the metering stop value is within a preset range.

[0091] In one embodiment, the drop prediction model is trained through the following steps: obtaining multiple historical material weights during the weighing process of multiple historical materials by the weighing device; determining the historical shutdown flow rate of the screw conveyor at the end of the weighing of each historical material based on the multiple historical material weights during the weighing process of each historical material; determining the historical metering drop value of the weighing device at the time of weighing each historical material based on the multiple historical material weights during the weighing process of each historical material; performing correlation processing on all historical shutdown flow rates and historical metering drop values ​​to obtain a first correlation coefficient between all historical shutdown flow rates and historical metering drop values; and fitting a regression model based on all historical shutdown flow rates, all historical metering drops, and the first correlation coefficient to obtain the drop prediction model.

[0092] In one embodiment, determining the historical shutdown flow rate of the screw conveyor at the end of each historical material weighing process based on multiple historical material weights during each historical material weighing process includes: for each historical material weighing, determining the ratio between the difference between the historical material weights of two adjacent historical moments during the weighing process and the interval between two adjacent historical moments, and determining the ratio as the historical instantaneous flow rate during the weighing process; for each historical material weighing, filtering all historical instantaneous flow rates during the weighing process to obtain the historical shutdown flow rate of the screw conveyor at the end of the weighing process.

[0093] In one embodiment, determining the historical measurement drop value of the weighing device at the time of weighing each historical material based on the weights of multiple historical materials during the weighing process includes: obtaining the historical final weight of each historical material weighing; and determining the difference between the historical material weight at the time the screw conveyor stops and the historical final weight of each historical material weighing as the historical measurement drop value of the weighing device at the time of weighing each historical material.

[0094] In one embodiment, determining the material flow rate based on the material weight includes: determining the instantaneous flow rate based on the material weight at two adjacent moments; and filtering the instantaneous flow rate to obtain the material flow rate.

[0095] In one embodiment, the control method further includes: after controlling the screw conveyor to stop, performing correlation processing on the predicted material flow rate and drop value to obtain a second correlation coefficient between the predicted material flow rate and drop value; and updating the drop prediction model based on the predicted material flow rate, drop value, and the second correlation coefficient.

[0096] In one embodiment, the control method further includes: if the error between the material weight measured by the weighing device and the metering stop value is outside a preset range, controlling the screw conveyor to continue working and returning to the step of obtaining the material weight measured by the weighing device until the error between the material weight measured by the weighing device and the metering stop value is within the preset range.

[0097] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing the steps of a method for controlling the initial weight of materials.

[0098] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0103] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0104] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0106] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for controlling the weight of materials, characterized in that, Applied to construction machinery, the construction machinery including a screw conveyor and a weighing device, the control method includes: Obtain the weight of the material measured by the weighing device; The material flow rate is determined based on the material weight. The material flow rate is input into the drop prediction model to obtain the drop prediction value; The metering stop value is determined based on the predicted drop value and the target material weight. If the error between the material weight measured by the weighing device and the metering stop value is within a preset range, the screw conveyor is controlled to stop. The elevation difference prediction model is trained through the following steps: The weights of multiple historical materials are obtained during the weighing process of the weighing device. The historical shutdown flow rate of the screw conveyor at the end of each historical material weighing process is determined based on the weights of multiple historical materials during each historical material weighing process. The historical measurement drop value of the weighing device at the time of each historical material weighing process is determined based on the weight of multiple historical materials during each historical material weighing process. Correlation processing is performed on all historical outage flow rates and historical metering drop values ​​to obtain the first correlation coefficient between all historical outage flow rates and historical metering drop values; The drop prediction model is obtained by fitting a regression model based on all historical outage flow rates, all historical metering drops, and the first correlation coefficient. The historical shutdown flow rate of the screw conveyor at the end of each historical material weighing process is determined based on the weights of multiple historical materials during each historical material weighing process. This includes: For each historical material weighing, the ratio between the difference in historical material weight between every two adjacent historical moments during the weighing process and the interval between every two adjacent historical moments is determined, and the ratio is determined as the historical instantaneous flow rate during the weighing process of the historical material. For each historical material weighing, all historical instantaneous flow rates during the weighing process are filtered to obtain the historical shutdown flow rate of the screw conveyor at the end of the weighing process.

2. The method for controlling material weight according to claim 1, characterized in that, The historical measurement discrepancy value of the weighing device at the time of each historical material weighing process is determined based on the weights of multiple historical materials during each historical material weighing process, including: Obtain the historical final weight of each historical material weighing; The difference between the historical material weight at the time the screw conveyor stopped and the historical settling weight of each historical material weighing is determined as the historical measurement drop value of the weighing device when each historical material is weighed.

3. The method for controlling material weight according to claim 1, characterized in that, Determining the material flow rate based on the material weight includes: The instantaneous flow rate is determined based on the material weight at two adjacent moments; The instantaneous flow rate is filtered to obtain the material flow rate.

4. The method for controlling material weight according to claim 1, characterized in that, The control method further includes: After the screw conveyor is stopped, the material flow rate and the predicted drop value are subjected to correlation processing to obtain a second correlation coefficient between the material flow rate and the predicted drop value. The drop prediction model is updated based on the material flow rate, the predicted drop value, and the second correlation coefficient.

5. The method for controlling material weight according to claim 1, characterized in that, The control method further includes: If the error between the material weight measured by the weighing device and the metering stop value is outside the preset range, the screw conveyor is controlled to continue working and return to the step of obtaining the material weight measured by the weighing device until the error between the material weight measured by the weighing device and the metering stop value is within the preset range.

6. A control device, characterized in that, include: The memory is configured to store instructions; The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for controlling the weight of the material according to any one of claims 1 to 5.

7. An engineering machinery, characterized in that, include: Screw conveyors are used to transport materials to weighing devices. The weighing device includes: Material receiving equipment, used to receive the material conveyed by the screw conveyor; A weighing sensor is used to detect the weight of the material on the material receiving device; The control device according to claim 6.

8. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform a method for controlling the weight of material according to any one of claims 1 to 5.

Images