Material weight control method and device, engineering machinery and storage medium

Abstract

The invention relates to the technical field of engineering machinery, in particular to a material weight control method and device, engineering machinery and a storage medium. Comprising the steps of obtaining material weight measured by a weighing device; determining material flow according to the material weight; inputting the material flow into a fall prediction model to obtain a fall prediction value; determining a metering stop value according to the fall prediction value and the target material weight; and under the condition that 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, so that the control precision of the screw conveyor is improved, the screw conveyor can operate timely and accurately, and the metering precision of the material is improved.

Description

Technical Field

[0001] The present application relates to the technical field of engineering machinery, and in particular to a material weight control method, a control device, engineering machinery, and a storage medium. Background Art

[0002] Currently, efficient and high-precision material metering is a core component of control systems for construction machinery, such as dry-mix mortar plants. Accurately and quickly controlling the weight of material conveyed from a screw conveyor to a weighing device, such as a scale, is a crucial component of material metering control. Ideally, material metering involves using a load cell to measure the weight of the weighing device in real time and compare it to a preset value. If the two values ​​match, the screw conveyor stops and the material on the weighing device is discharged.

[0003] However, under actual working conditions, when the detected weight is consistent with the preset weight value, the screw machine is controlled to stop working. Since the screw machine has a certain inertia, it will continue to run for a period of time. During this period, the screw machine will continue to transport materials to the weighing device. In addition, because the metering device is a cumulative metering structure, the screw machine is above the weighing device with a certain height difference. When the screw machine receives the stop command, there is still a part of the material in the air that has not settled. These two factors cause the weight of the material actually falling into the weighing device to be greater than the preset weight value. The difference between the weight value at the moment the screw machine stops and the weight value after it settles is called the metering drop. In order to achieve accurate metering, it is necessary to control the screw machine to stop in advance. The calculation of the stop time of the screw machine needs to take into account the metering drop of the metering process as the stop advance.

[0004] The existing metering gap usually adopts the method of averaging multiple historical metering gaps to determine the next metering gap, resulting in insufficient metering accuracy and large metering errors, which makes it impossible to accurately control the shutdown of the screw machine, low metering reliability, and failure to meet the metering accuracy requirements of the material. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide a material weight control method, control device, engineering machinery and storage medium to solve the problems in the prior art of large material metering errors, inability to accurately control the shutdown of the screw machine, and unmet material metering accuracy requirements.

[0006] In order to achieve the above-mentioned object, the first aspect of the present application provides a material weight control method, which is applied to engineering machinery, wherein the engineering machinery includes a screw conveyor and a weighing device, and the control method includes:

[0007] Obtaining the material weight measured by the weighing device;

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

[0009] Input the material flow into the drop prediction model to obtain the drop prediction value;

[0010] Determine the metering stop value based on the drop prediction value and the target material weight;

[0011] When the error between the material weight measured by the weighing device and the metering stop value is within a preset range, the screw machine is controlled to stop.

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

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

[0014] In an embodiment of the present application, determining the historical measurement drop value of the weighing device at each historical material weighing based on multiple historical material weights in each historical material weighing process includes: obtaining the historical settled material weight of each historical material weighing; determining the difference between the historical material weight at the moment when the screw machine of each historical material weighing stops and the historical settled material weight of each historical material weighing as the historical measurement drop value of the weighing device at each historical material weighing.

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

[0016] In an embodiment of the present application, the control method also includes: after controlling the screw machine to stop, performing correlation processing on the material flow and the drop prediction value to obtain a second correlation coefficient between the material flow and the drop prediction value; updating the drop prediction model according to the material flow, the drop prediction value and the second correlation coefficient.

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

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

[0019] a memory configured to store instructions;

[0020] The processor is configured to call instructions from the memory and implement the above-mentioned material weight control method when executing the instructions.

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

[0022] A screw conveyor is used to convey materials to the weighing device;

[0023] Weighing device, comprising:

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

[0025] Weighing sensor, used to detect the weight of the material on the material receiving equipment;

[0026] The control device mentioned above.

[0027] A fourth aspect of the present application provides a machine-readable storage medium having stored thereon instructions for enabling a machine to execute the above-mentioned material weight control method.

[0028] Through the above technical solution, the material flow rate can be determined according to the material weight measured by the weighing device, and the material flow rate can be input into the drop prediction model to obtain the drop prediction value, and the metering stop value can be determined according to the drop prediction value and the target material weight; after obtaining the metering stop value, it is judged whether the error between the material weight measured by the weighing device and the metering stop value is within the preset range, and when the error between the material weight measured by the weighing device and the metering stop value is within the preset range, the screw machine is controlled to stop, so as to improve the accuracy of the control of the screw machine, so that the screw machine can operate in a timely and accurate manner, and improve the metering accuracy of the material.

[0029] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present application but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0031] Figure 1 The following schematically shows a flow chart of a method for controlling material weight according to an embodiment of the present application;

[0032] Figure 2 A schematic diagram schematically illustrates a method for controlling material weight according to an embodiment of the present application;

[0033] Figure 3 A schematic diagram of a drop prediction model according to an embodiment of the present application is schematically shown;

[0034] Figure 4 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0035] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0036] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0037] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0038] Figure 1 The following schematically shows a flow chart of a material weight control method according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a material weight control method, which is applied to engineering machinery. The engineering machinery includes a screw machine and a weighing device. The control method may include the following steps.

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

[0040] Step 102: Determine the material flow rate according to the material weight.

[0041] Engineering machinery is an important part of the equipment industry. Generally speaking, the mechanical equipment required for earthwork construction, road construction and maintenance, mobile lifting and loading and unloading operations, and comprehensive mechanized construction projects required for various construction projects can be called engineering machinery. In the embodiments of the present application, the engineering machinery can be concrete pumping equipment, dry-mix mortar plants, and concrete mixing plants. Screw conveyors are widely used in various industries, such as building materials, chemicals, electricity, metallurgy, coal, and grain, and are suitable for horizontal or inclined conveying of powdered, pulverized, and small-lump materials such as coal, ash, slag, cement, and grain. The weighing device is mainly used to measure the weight of the material and can be a scale, a weighing sensor, etc. The material can be dry-mix mortar, etc. The processor can obtain the material weight measured by the weighing device in real time. The weighing device measures the weight of the material in real time during the material weighing process. It can be understood that a material weighing process refers to the process from the screw conveyor conveying the material to the weighing device to the material settling 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 an embodiment of the present application, determining the material flow rate according to the material weight includes: determining the instantaneous flow rate according to 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 between the material weights at two adjacent moments to the interval between the two adjacent moments. After obtaining the instantaneous flow rate, the processor can filter the instantaneous flow rate to obtain the material flow rate.

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

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

[0046] Step 104: Determine the metering stop value according to the drop prediction value and the target material weight.

[0047] Step 105: When the error between the material weight measured by the weighing device and the metering stop value is within a preset range, the screw machine is controlled to stop.

[0048] After obtaining the real-time material flow, the processor can input the real-time material flow into the drop prediction model to obtain a 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. The target material weight is the user's demand for the weight of the material to be weighed this time. 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 material weight measured in real time 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 machine to stop, that is, the moment corresponding to the error being within the preset range is the stop moment of the screw machine. In an optional embodiment, if the error is 0, that is, the physical weight measured by the weighing device is equal to the metering stop value, the processor controls the screw machine to stop to achieve the best control effect, so that the weight of the material settled on the weighing device is exactly the target material weight.

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

[0050] When the error between the material weight measured by the weighing device and the metering stop value is outside the preset range, the processor can control the screw machine 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, when the error between the material weight measured by the weighing device for the first time and the metering stop value is within the preset range, the processor can determine this moment as the stop moment of the screw machine and control the screw machine to stop. In a specific embodiment, the processor can send a stop command to the screw machine so that the screw machine executes the stop command and then stops working. Or the processor can send a deceleration command to the screw machine so that the screw machine decelerates and runs until it stops.

[0051] In an embodiment of the present application, the control method also includes: after controlling the screw machine to stop, performing correlation processing on the material flow and the drop prediction value to obtain a second correlation coefficient between the material flow and the drop prediction value; updating the drop prediction model according to the material flow, the drop prediction value and the second correlation coefficient.

[0052] After the screw conveyor is controlled to stop, the processor can perform correlation processing on the material flow rate and the drop prediction value to obtain a second correlation coefficient between the material flow rate and the drop prediction value. After obtaining the second correlation coefficient, the processor can update the drop prediction model based on the material flow rate, the drop prediction value, and the second correlation coefficient to continuously optimize the drop prediction model and improve the performance of the drop prediction model.

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

[0054] The processor can obtain multiple historical material weights from multiple historical material weighing processes performed by a weighing device. After obtaining the historical material weights from the multiple historical material weighing processes, the processor can determine the historical downtime flow rate of the screw conveyor at the end of each historical material weighing process based on the multiple historical material weights from each historical material weighing process. Furthermore, the processor can determine the historical metrological drop value of the weighing device at each historical material weighing process based on the multiple historical material weights from each historical material weighing process. The historical metrological drop value refers to the difference between the actual weight of the material landed on the weighing device and the material weight measured at the moment the screw conveyor stopped. After obtaining the multiple historical downtime flow rates and historical metrological drop values, the processor can perform correlation processing on all the historical downtime flow rates and historical metrological drop values ​​to obtain a first correlation coefficient between all the historical downtime flow rates and historical metrological drop values. A regression model can then be fitted based on all the historical downtime flow rates, all the historical metrological drops, and the first correlation coefficient to obtain a drop prediction model, which can then be used to predict the real-time drop prediction value during the material weighing process, thereby improving the accuracy of the drop prediction value.

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

[0056] The processor can determine the historical shutdown flow of the screw machine at the end of each historical material weighing based on multiple historical material weights in each historical material weighing process. Specifically, for each historical material weighing, the processor can determine the difference between the historical material weights of each two adjacent historical moments in the historical material weighing process, and can also determine the interval duration between each two adjacent historical moments. After determining the difference between the historical material weights of each two adjacent historical moments and the interval duration between each two adjacent historical moments in the historical material weighing process, the processor can determine the ratio between the difference between the historical material weights of each two adjacent historical moments in the historical material weighing process and the interval duration between each adjacent historical moments, and determine the ratio as the historical instantaneous flow in the historical material weighing process. After obtaining the historical instantaneous flow in the historical material weighing process, the processor can filter all instantaneous flows in the historical material weighing process to obtain the historical shutdown flow of the screw machine at the end of the historical material weighing.

[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 in each historical material weighing process within the flow calculation model, and then filter the instantaneous flow to obtain the historical shutdown flow of the screw machine at the end of each historical material weighing output by the flow calculation model.

[0058] In an embodiment of the present application, determining the historical measurement drop value of the weighing device at each historical material weighing based on multiple historical material weights in each historical material weighing process includes: obtaining the historical settled material weight of each historical material weighing; determining the difference between the historical material weight at the moment when the screw machine of each historical material weighing stops and the historical settled material weight of each historical material weighing as the historical measurement drop value of the weighing device at each historical material weighing.

[0059] The processor may determine, based on multiple historical material weights during each historical material weighing process, a historical metrological drop value of the weighing device at each historical material weighing. Specifically, the processor may obtain the historical settled material weight of each historical material weighing. The processor may measure the historical material weight of each historical material weighing using a weighing device, such as a weighing sensor, and determine the difference between the historical material weight at the moment when the screw machine for each historical material weighing stops and the historical settled material weight of each historical material weighing as the historical metrological drop value of the weighing device at each historical material weighing.

[0060] In the embodiments of this application, Figure 2 As shown, the weighing sensor can measure the weight of the material delivered to the material receiving equipment by the screw conveyor in real time. The metering controller can obtain 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 weights at high frequency and obtain the metering status bit from the PLC. The weight collected by big data is input into the flow calculation model, which processes the weight data to obtain the instantaneous flow rate of the material being weighed. The instantaneous flow rate is then processed using a filtering algorithm and output as the flow rate. The flow rate output by the flow calculation model is used to train the drop prediction model offline to obtain a trained drop prediction model. The PLC can perform drop adaptive control using the drop prediction model. The PCL obtains the ratio information and control parameters of the host production system and the real-time weight sent by the metering controller, and combines this with the drop prediction model to control the speed and start and stop of the metering screw conveyor. Specifically, the PLC can further control the metering screw conveyor by controlling the frequency converter, thereby improving metering accuracy and reliability and reducing metering errors.

[0061] The training of the drop prediction model specifically includes: extracting features from the weight data and flow data of the weighed materials to obtain the shutdown flow and the actual drop. The shutdown flow and the actual drop are input as input features to the regression model (W T X) to fit the regression model through polynomials. Figure 3 As shown, the weight data of the weighing material (such as gray cement) is obtained by sampling at a sampling frequency of 50Hz, and more than 200 groups of shutdown flow (flow) and actual drop (Lc) are obtained after processing by the flow calculation model. Based on the data distribution characteristics of more than 200 groups of shutdown flow and actual drop, a suitable regression model is selected. More than 200 groups of shutdown flow and actual drop are input into the regression model, and the fitting parameters of the regression model are set to perform model fitting. When the goodness of fit is greater than 85%, it is determined that the model fitting is successful, and the successfully fitted regression model is determined as the drop prediction model to output the predicted drop value through the drop prediction model.

[0062] Through the above technical solution, the control accuracy of the screw machine is improved, so that the screw machine can run in a timely and accurate manner, and the metering accuracy of the material is improved.

[0063] Figure 1 FIG. 1 is a flow chart of a method for controlling material weight in one embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed 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 part of the sub-steps or stages of other steps.

[0064] The present application also provides a control device, including:

[0065] a memory configured to store instructions;

[0066] The processor is configured to call instructions from the memory and implement the above-mentioned material weight control method when executing the instructions.

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

[0068] The data acquisition module is used to store the historical weighing data of the material. The historical weighing data may include weight data, flow data, and actual measurement drop data of multiple historical weighing processes;

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

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

[0071] The present application also provides an engineering machine, including:

[0072] A screw conveyor is used to convey materials to the weighing device;

[0073] Weighing device, comprising:

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

[0075] Weighing sensor, used to detect the weight of the material on the material receiving equipment;

[0076] The control device mentioned above.

[0077] In the embodiment of the present application, the engineering machinery may further include:

[0078] Frequency converter, used to control the start and stop control and / or speed control of the screw machine;

[0079] Motor, used to drive the reducer;

[0080] Reducer, used to connect the motor and screw machine.

[0081] The engineering machinery of this embodiment includes traditional engineering machinery vehicles, as well as new energy vehicles used in the field of engineering machinery, such as new energy mixer trucks, new energy pump trucks, new energy excavators, etc.; in addition, the engineering machinery vehicles of this embodiment also belong to intelligent connected vehicles. The engineering machinery vehicles include sensing systems, communication systems, etc., and collect vehicle operation data and vehicle surrounding environment information through the sensing system in the vehicle, and realize network connection with other vehicles and the cloud through the communication system, and share the collected vehicle operation data, vehicle surrounding environment information, etc. to the cloud and other authorized vehicles to realize data sharing, remote analysis, intelligent driving and other operations.

[0082] In an embodiment of the present application, a material weighing system is further provided, the system comprising:

[0083] Screw conveyor, used to convey materials to the scale body;

[0084] The scale body has an opening connected to the output port of the screw machine for receiving the material conveyed by the screw machine;

[0085] A weighing sensor is provided on the scale body and is used to measure the weight of the material in the scale body;

[0086] The controller can be connected to the weighing sensor and the frequency converter to control the screw machine according to the weight of the material measured by the weighing sensor.

[0087] An embodiment of the present application also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the above-mentioned material weight control method.

[0088] In one embodiment, a computer device is provided. The computer device 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 in the figure) and a database (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an 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 in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store data such as material weight, material flow, drop prediction value, metering stop value, etc. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a material weight control method is implemented.

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

[0090] An embodiment of the present application provides 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, the following steps are implemented: obtaining the material weight measured by a 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 machine 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 in the weighing process of multiple historical materials weighed by a weighing device; determining the historical shutdown flow of the screw machine at the end time of each historical material weighing based on the multiple historical material weights in each historical material weighing process; determining the historical measurement drop value of the weighing device at each historical material weighing based on the multiple historical material weights in each historical material weighing process; performing correlation processing on all historical shutdown flows and historical measurement drop values ​​to obtain a first correlation coefficient between all historical shutdown flows and historical measurement drop values; fitting a regression model based on all historical shutdown flows, all historical measurement drops and the first correlation coefficient to obtain a drop prediction model.

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

[0093] In one embodiment, determining the historical measurement drop value of the weighing device at each historical material weighing based on multiple historical material weights in each historical material weighing process includes: obtaining the historical settled material weight of each historical material weighing; and determining the difference between the historical material weight at the moment when the screw machine of each historical material weighing stops and the historical settled material weight of each historical material weighing as the historical measurement drop value of the weighing device at each historical material weighing.

[0094] In one embodiment, determining the material flow rate according to the material weight includes: determining the instantaneous flow rate according to 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 also includes: after controlling the screw machine to stop, performing correlation processing on the material flow and the drop prediction value to obtain a second correlation coefficient between the material flow and the drop prediction value; updating the drop prediction model according to the material flow, the drop prediction value and the second correlation coefficient.

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

[0097] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the program steps of the control method for initializing the weight of a material.

[0098] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

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

[0103] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0104] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0106] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for controlling material weight, characterized in that: Applied to engineering machinery, the engineering machinery includes a screw machine and a weighing device, and the control method includes: Obtaining the weight of the material measured by the weighing device; determining a material flow rate according to the weight of the material; Inputting the material flow into a drop prediction model to obtain a drop prediction value; Determine the metering stop value according to the drop prediction value and the target material weight; When the error between the material weight measured by the weighing device and the metering stop value is within a preset range, the screw machine is controlled to stop.

2. The material weight control method according to claim 1, characterized in that: The drop prediction model is trained by the following steps: Acquiring multiple historical material weights during a weighing process of the weighing device weighing multiple historical materials; Determine the historical shutdown flow of the screw machine at the end of each historical material weighing according to multiple historical material weights in each historical material weighing process; Determine the historical measurement difference value of the weighing device when each historical material is weighed according to multiple historical material weights in each historical material weighing process; Performing correlation processing on all historical downtime flows and historical metering drop values ​​to obtain a first correlation coefficient between all historical downtime flows and historical metering drop values; A regression model is fitted according to all the historical downtime flows, all the historical metering gaps and the first correlation coefficient to obtain the gap prediction model.

3. The material weight control method according to claim 2, characterized in that: The historical stop flow of the screw machine at the end time of each historical material weighing is determined according to the weight of multiple historical materials in each historical material weighing process, and includes: For each historical material weighing, determine the ratio between the difference between the historical material weights of each two adjacent historical moments in the historical material weighing process and the interval between each two adjacent historical moments, and determine the ratio as the historical instantaneous flow rate in the historical material weighing process; For each historical material weighing, all historical instantaneous flow rates during the historical material weighing process are filtered to obtain the historical shutdown flow rate of the screw machine at the end of the historical material weighing.

4. The material weight control method according to claim 2, characterized in that: Determining the historical measurement drop value of the weighing device when weighing each historical material according to the weight of multiple historical materials in each historical material weighing process includes: Get the historical settled material weight of each historical material weighing; The difference between the historical material weight at the moment when the screw machine stops for each historical material weighing and the historical settled material weight for each historical material weighing is determined as the historical metering drop value of the weighing device for each historical material weighing.

5. The material weight control method according to claim 1, characterized in that: Determining the material flow rate according to the material weight includes: Determine the instantaneous flow rate based on the weight of the material at two adjacent moments; The instantaneous flow is filtered to obtain the material flow.

6. The material weight control method according to claim 1, characterized in that: The control method further comprises: After the screw machine is controlled to stop, correlation processing is performed on the material flow rate and the drop prediction value to obtain a second correlation coefficient between the material flow rate and the drop prediction value; The drop prediction model is updated according to the material flow, the drop prediction value and the second correlation coefficient.

7. The material weight control method according to claim 1, characterized in that: The control method further comprises: When the error between the material weight measured by the weighing device and the metering stop value is outside the preset range, the screw machine is controlled to continue working and returns 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.

8. A control device, characterized in that: include: a memory configured to store instructions; A processor is configured to call the instruction from the memory and implement the material weight control method according to any one of claims 1 to 7 when executing the instruction.

9. An engineering machine, characterized in that: include: A screw conveyor for conveying material to the weighing device; The weighing device comprises: A material receiving device, used for receiving the material conveyed by the screw conveyor; A weighing sensor, used to detect the weight of the material on the material receiving device; A control device according to claim 8.

10. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for enabling the machine to execute the material weight control method according to any one of claims 1 to 7.

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