Control Method, Device, Equipment and Storage Medium for Loading Weight of Granular Goods

Abstract

The present invention provides a method, device, equipment and storage medium for controlling the loading weight of granular goods, relating to the technical field of weighing control. The method includes: when the target difference between the real-time loading weight and the target loading weight is not less than the preset difference threshold, continuously monitoring the latest target difference. After monitoring that the target difference is less than the preset difference threshold, continuously obtaining the real-time granularity and real-time humidity of the granular goods being loaded, calculating the gate closing trigger threshold, and determining whether the current target difference is less than or equal to the current gate closing trigger threshold. If so, close the gate; otherwise, continue to calculate the new gate closing trigger threshold and the target difference, and make a comparison until it is determined that the current target difference is less than or equal to the current gate closing trigger threshold. In this way, the present invention enables the control accuracy of the loading weight of granular goods to be improved under existing limited conditions without the need for a quantitative feeder and a quantitative bin.

Description

Technical Field

[0001] The present invention relates to the technical field of weighing control, and particularly relates to a method, device, equipment and storage medium for controlling the loading weight of granular goods. Background Art

[0002] At present, when loading granular goods such as coal and ore, during the tail feeding stage, the gate is repeatedly opened and closed to attempt to accurately control the loading weight of the granular goods. However, this method often results in overweight. Although the accuracy of the loading weight can be improved by installing a quantitative feeder and a quantitative bin, in some traditional loading stations, due to limited relevant conditions, there is no quantitative bin and it is impossible to newly install a quantitative feeder.

[0003] Based on this, how to improve the control accuracy of the loading weight of granular goods under the existing limited conditions has become an urgent technical problem to be solved. Summary of the Invention

[0004] In view of this, in order to solve the above technical problems, the present invention provides a method, device, equipment and storage medium for controlling the loading weight of granular goods.

[0005] The present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for controlling the loading weight of granular goods, including:

[0007] Obtain the basic information of the vehicle, where the basic information includes the target loading weight;

[0008] After the entire carriage of the vehicle is on the rail scale, obtain the real-time loading weight of the vehicle;

[0009] Calculate the target difference between the real-time loading weight and the target loading weight;

[0010] Determine whether the target difference is less than a preset difference threshold;

[0011] If the target difference is not less than the preset difference threshold, then jump to the step of obtaining the real-time loading weight of the vehicle;

[0012] If the target difference is less than the preset difference threshold, then obtain the real-time granularity and real-time humidity of the granular goods being loaded;

[0013] Substitute the current real-time granularity and the real-time humidity into the gate closing trigger threshold calculation formula to obtain the current gate closing trigger threshold; the gate closing trigger threshold calculation formula is as follows:

[0014]

[0015] Among them, the basic gate trigger threshold is a preset fixed value, and are both weight parameters, is the humidity deviation coefficient calculated according to the current real-time humidity and the preset reference humidity, is the granularity deviation coefficient calculated according to the current real-time granularity and the preset reference granularity;

[0016] Re-obtain the real-time loading weight, calculate the target difference, and determine whether the current target difference is less than or equal to the current gate trigger threshold;

[0017] If the current target difference is greater than the current gate trigger threshold, jump to the step of obtaining the real-time granularity and real-time humidity of the granular goods being loaded;

[0018] If the current target difference is less than or equal to the current gate trigger threshold, close the gate.

[0019] Optionally, after closing the gate, the control method for the loading weight of granular goods of the present invention further includes:

[0020] Obtain the actual loading weight of the vehicle, the target real-time granularity, target real-time humidity, and target gate trigger threshold corresponding to when the gate is closed;

[0021] Based on a linear model and an online learning algorithm, according to the actual loading weight, the target real-time granularity, the target real-time humidity, and the target gate trigger threshold, update the and the .

[0022] Optionally, the linear model is defined as:

[0023]

[0024] Among them, , is the loading error;

[0025] Based on a linear model and an online learning algorithm, according to the actual loading weight, the target real-time granularity, the target real-time humidity, and the target gate trigger threshold, update the and the , specifically including:

[0026] According to the target real-time granularity, calculate ;

[0027] According to the target real-time humidity, calculate ;

[0028] Calculate based on the actual loading weight, the target loading weight, and the target gate trigger threshold ;

[0029] Based on the stochastic gradient descent algorithm, according to the , the and the update the and the .

[0030] Optionally, based on the stochastic gradient descent algorithm, according to the , the and the update the and the , specifically including:

[0031] Substitute the , the and the into the following formula to obtain the updated and the :

[0032]

[0033] where is the learning rate, equals the opposite of the loading error.

[0034] Optionally, based on the stochastic gradient descent algorithm, according to the , the and the update the and the , specifically including:

[0035] Substitute the , the and the into the following formula to obtain the updated and the :

[0036]

[0037] where is the regularization coefficient.

[0038] Optionally, calculate the according to the target real-time granularity, specifically including:

[0039] Substitute the target real-time granularity and the preset reference granularity into the following formula to obtain the :

[0040]

[0041] wherein, is the real-time granularity, is the preset reference granularity;

[0042] Calculate according to the target real-time humidity, specifically including:

[0043] Substitute the target real-time humidity and the preset reference humidity into the following formula to obtain the :

[0044]

[0045] wherein, is the real-time humidity, is the preset reference humidity;

[0046] Calculate according to the actual loading weight, the target loading weight and the target gate trigger threshold, specifically including:

[0047] Let the actual loading weight minus the target loading weight to obtain the target loading error ;

[0048] Substitute the target gate trigger threshold and the into the following formula to obtain the :

[0049] .

[0050] Optionally, before obtaining the basic information of the vehicle, the control method for the loading weight of granular goods of the present invention further includes:

[0051] Obtain historical loading data, where the historical loading data includes multiple sample data, and the sample data includes the sample gate trigger threshold, carriage position, gross weight of the carriage, tare weight of the carriage, vehicle identity identifier, sample target loading weight, sample loading error, sample granularity of the granular goods being loaded, and sample humidity at the time of closing the gate for one loading;

[0052] Perform data preprocessing on the historical loading data; the data preprocessing includes missing value processing, outlier processing, and data normalization processing;

[0053] Based on a preset correlation analysis method, according to the historical loading data after data preprocessing, calculate the correlation coefficients between each sample feature and the gate closing trigger threshold; the sample features include carriage position, gross weight of the carriage, tare weight of the carriage, vehicle identification, target loading weight, loading error, granularity, and humidity;

[0054] Select the first sample features with correlation coefficients greater than the preset coefficient threshold;

[0055] Based on the recursive feature elimination algorithm, screen the second sample features from the first sample features;

[0056] Define the second sample features as the sample features for the control method of the loading weight of granular goods in this application.

[0057] In a second aspect, the present invention also provides a control device for the loading weight of granular goods, which is applied to the control method of the loading weight of granular goods as described above. The control device for the loading weight of granular goods includes:

[0058] A first acquisition module for acquiring the basic information of the vehicle, where the basic information includes the target loading weight;

[0059] A second acquisition module for acquiring the real-time loading weight of the vehicle after the entire carriage of the vehicle is on the rail scale;

[0060] A calculation module for calculating the target difference between the real-time loading weight and the target loading weight;

[0061] A first judgment module for judging whether the target difference is less than the preset difference threshold;

[0062] A first jump module for jumping to the step of acquiring the real-time loading weight of the vehicle if the target difference is not less than the preset difference threshold;

[0063] A third acquisition module for acquiring the real-time granularity and real-time humidity of the granular goods being loaded if the target difference is less than the preset difference threshold;

[0064] A substitution module for substituting the current real-time granularity and the current real-time humidity into the gate closing trigger threshold calculation formula to obtain the current gate closing trigger threshold; the gate closing trigger threshold calculation formula is as follows:

[0065]

[0066] Among them, the basic gate closing trigger threshold is a preset fixed value, and are both weight parameters, is the humidity deviation coefficient calculated according to the current real-time humidity and the preset reference humidity, is the granularity deviation coefficient calculated based on the current real-time granularity and the preset reference granularity;

[0067] The second judgment module is configured to re-obtain the real-time loading weight, calculate the target difference, and judge whether the current target difference is less than or equal to the current gate trigger threshold;

[0068] The second jump module is configured to, if the current target difference is greater than the current gate trigger threshold, jump to the step of obtaining the real-time granularity and real-time humidity of the granular goods being loaded;

[0069] The closing module is configured to close the gate if the current target difference is less than or equal to the current gate trigger threshold.

[0070] In a third aspect, the present invention further provides a control device for the loading weight of granular goods, including:

[0071] At least one processor; and,

[0072] A memory communicatively connected to the at least one processor; wherein,

[0073] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement the control method for the loading weight of granular goods as described above.

[0074] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the control method for the loading weight of granular goods as described above is implemented.

[0075] The present invention adopts the above technical solutions. In the first stage, that is, the stage where the target difference between the real-time loading weight and the target loading weight is not less than the preset difference threshold, the target difference between the real-time loading weight and the target loading weight is continuously monitored. When it is monitored that the target difference is less than the preset difference threshold, it enters the second stage. In the second stage, the real-time granularity and real-time humidity of the granular goods being loaded are continuously obtained, the gate trigger threshold is calculated, and it is judged whether the current target difference is less than or equal to the current gate trigger threshold. If so, the gate is closed. Otherwise, a new gate trigger threshold and target difference are calculated and compared until it is judged that the current target difference is less than or equal to the current gate trigger threshold. In this way, by dynamically adjusting the gate trigger threshold according to the real-time granularity and real-time humidity of the granular goods being loaded, the present invention does not require a quantitative feeder and a quantitative bin, and realizes improving the control accuracy of the loading weight of granular goods under the existing limited conditions. Description of the Drawings

[0076] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0077] Figure 1 It is a schematic diagram of the application scenario of a method for controlling the loading weight of granular goods provided by an embodiment of the present invention;

[0078] Figure 2 It is a schematic diagram of the control principle of a method for controlling the loading weight of granular goods provided by an embodiment of the present invention;

[0079] Figure 3 It is a schematic flowchart of a method for controlling the loading weight of granular goods provided by an embodiment of the present invention;

[0080] Figure 4 It is a schematic structural diagram of a device for controlling the loading weight of granular goods provided by an embodiment of the present invention;

[0081] Figure 5 It is a schematic structural diagram of a device for controlling the loading weight of granular goods provided by an embodiment of the present invention. Detailed implementation manners

[0082] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will describe the technical solutions of the present invention in detail. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other implementation manners obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0083] Figure 1 It is a schematic diagram of the application scenario of a method for controlling the loading weight of granular goods provided by an embodiment of the present invention. Figure 2 It is a schematic diagram of the control principle of a method for controlling the loading weight of granular goods provided by an embodiment of the present invention. Refer to Figure 1 and Figure 2 , the overall control logic of the present invention includes third-party devices, a sensor layer, a data processing layer, and a control layer. The third-party devices include a carriage position detection device, a rail scale, and a vehicle identity identification device; the sensor layer includes a moisture sensor and an image sensor. The moisture sensor is installed on the inner wall of the buffer bin of the feeder, and the image sensor is installed directly above the carriage. The image sensor can be a visible light camera; the data processing layer includes a data processing and calculation device and an image processing and calculation device, and the control layer includes a gate.

[0084] During the actual loading process, when the gate of the feeder is opened, the granular goods in the feeder will flow into the carriage. At the beginning of loading, the opening of the gate can be adjusted to 100% to improve the loading efficiency. During the entire loading process, the vehicle travels along the driving direction, and the carriage position detection device continuously obtains the carriage position information and sends it to the data processing and calculation device. After the data processing and calculation device determines that the entire carriage is on the rail scale based on the carriage position information, it confirms to enter the first stage of the tail precise feeding stage. At this time, the data processing and calculation device sends an opening adjustment instruction to the gate opening control device (not shown in the figure), and the gate opening control device adjusts the gate opening according to this opening adjustment instruction. This opening adjustment instruction is used to adjust the opening of the gate from 100% to 30% or other openings less than 100% to slow down the falling speed of the material and improve the accuracy of controlling the falling amount of the material.

[0085] In the first stage, the data processing and calculation device obtains the vehicle identity identifier through the vehicle identity identifier recognition device. The vehicle identity identifier can be a vehicle code or a license plate number. The data processing and calculation device queries the preset database according to the vehicle identity identifier to obtain the basic information of the vehicle. The basic information can include the target loading weight, the tare weight of the carriage, etc. The data processing and calculation device also obtains the gross weight of the carriage through the rail scale, subtracts the tare weight of the carriage from the gross weight of the carriage to obtain the real-time loading weight. After the data processing and calculation device obtains the real-time loading weight each time, it calculates the target difference between the real-time loading weight and the target loading weight. If the target difference is not less than the preset difference threshold, it continues to obtain the real-time loading weight. If the target difference is less than the preset difference threshold, it enters the second stage of the tail precise feeding stage.

[0086] In the second stage, the data processing and calculation device obtains the humidity information of the granular goods being loaded (i.e., the granular goods in the buffer bin) through the moisture sensor and calculates the real-time humidity of the granular goods being loaded, with the unit being percentage (%). The image processing and calculation device obtains the image information of the granular goods being loaded through the image sensor, calculates the real-time granularity of the granular goods being loaded, with the unit being millimeter (mm), and sends this real-time granularity to the data processing and calculation device. This real-time granularity can be the average diameter of each granular good in the image.

[0087] The data processing and computing device continuously obtains the real-time granularity and real-time humidity of the granular goods being loaded, and calculates the corresponding gate trigger threshold. After each calculation of the gate trigger threshold, the data processing and computing device determines whether the current target difference is less than or equal to the current gate trigger threshold. If so, it sends a gate closing instruction to the gate opening control device. The gate opening control device closes the gate according to the gate closing instruction and feeds back the opening adjustment result data to the data processing and computing device. If the current target difference is greater than the current gate trigger threshold, it continues to calculate a new gate trigger threshold and target difference, and makes a comparison until it determines that the current target difference is less than or equal to the current gate trigger threshold.

[0088] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0089] Figure 3 It is a schematic flowchart of a method for controlling the loading weight of granular goods provided by an embodiment of the present invention. As Figure 3 shown, this process includes:

[0090] Step 301: Obtain the basic information of the vehicle, and the basic information includes the target loading weight.

[0091] Specifically, the basic information of each vehicle is stored in a preset database. In the actual application process, the vehicle identity identifier can be obtained, and the database can be queried according to the vehicle identity identifier to obtain the basic information of the vehicle. The basic information can also include the tare weight of the carriage.

[0092] Step 302: After the entire carriage of the vehicle is on the rail scale, obtain the real-time loading weight of the vehicle.

[0093] It should be noted that the method for obtaining the real-time loading weight can refer to the foregoing related content and will not be elaborated here.

[0094] Step 303: Calculate the target difference between the real-time loading weight and the target loading weight.

[0095] Step 304: Determine whether the target difference is less than the preset difference threshold; if the target difference is not less than the preset difference threshold, jump to the step of obtaining the real-time loading weight of the vehicle; if the target difference is less than the preset difference threshold, execute step 305.

[0096] Among them, the preset difference threshold can be equal to 2 tons.

[0097] Step 305: Obtain the real-time granularity and real-time humidity of the granular goods being loaded.

[0098] It should be noted that the method for obtaining the real-time granularity and real-time humidity can refer to the foregoing related content and will not be elaborated here.

[0099] Step 306: Substitute the current real-time granularity and real-time humidity into the gate-closing trigger threshold calculation formula to obtain the current gate-closing trigger threshold, with the unit of Kg. The gate-closing trigger threshold calculation formula is as follows:

[0100] ...... (1)

[0101] Among them, the basic gate-closing trigger threshold is a preset fixed value. For example, the basic gate-closing trigger threshold is equal to 650, and are both weight parameters, is the humidity deviation coefficient calculated based on the current real-time humidity and the preset reference humidity, is the granularity deviation coefficient calculated based on the current real-time granularity and the preset reference granularity.

[0102] Step 307: Re-obtain the real-time loading weight, calculate the target difference, and determine whether the current target difference is less than or equal to the current gate-closing trigger threshold; if the current target difference is greater than the current gate-closing trigger threshold, then jump to the step of obtaining the real-time granularity and real-time humidity of the granular goods being loaded; if the current target difference is less than or equal to the current gate-closing trigger threshold, then execute Step 308.

[0103] Specifically, here it is necessary to re-obtain the real-time loading weight of the vehicle, calculate the target difference between the real-time loading weight and the target loading weight to obtain the current target difference. Then, determine whether the current target difference is less than or equal to the current gate-closing trigger threshold. If the current target difference is greater than the current gate-closing trigger threshold, then jump to the step of obtaining the real-time granularity and real-time humidity of the granular goods being loaded; if the current target difference is less than or equal to the current gate-closing trigger threshold, then execute Step 308.

[0104] Step 308: Close the gate.

[0105] Specifically, the gate can be a hydraulic gate. For the specific implementation process of closing the gate, please refer to the foregoing relevant content and will not be elaborated here.

[0106] In the embodiments of the present invention, the above technical solutions are adopted. In the first stage, that is, the stage where the target difference between the real-time loading weight and the target loading weight is not less than the preset difference threshold, the target difference between the real-time loading weight and the target loading weight will be continuously monitored. When it is detected that the target difference is less than the preset difference threshold, the second stage is entered. In the second stage, the real-time granularity and real-time humidity of the granular goods being loaded will be continuously obtained, the gate closing trigger threshold will be calculated, and it will be determined whether the current target difference is less than or equal to the current gate closing trigger threshold. If so, the gate will be closed; otherwise, a new gate closing trigger threshold and target difference will be calculated and compared until it is determined that the current target difference is less than or equal to the current gate closing trigger threshold. In this way, by dynamically adjusting the gate closing trigger threshold according to the real-time granularity and real-time humidity of the granular goods being loaded, the present invention can achieve high control accuracy of the loading weight of granular goods under existing limited conditions without a quantitative feeder and a quantitative bin.

[0107] In the embodiments of the present invention, after the gate is closed, the control method for the loading weight of the granular goods of the present invention may further include:

[0108] (1) Obtain the actual loading weight of the vehicle, the target real-time granularity, the target real-time humidity, and the target gate closing trigger threshold corresponding to the closing of the gate.

[0109] (2) Based on the linear model and the online learning algorithm, update and according to the actual loading weight, the target real-time granularity, the target real-time humidity, and the target gate closing trigger threshold.

[0110] In this way, the accuracy of and can be improved, and further, the control accuracy of the loading weight of the granular goods in the next carriage can be improved.

[0111] In the embodiments of the present invention, the linear model is defined as:

[0112] ......(2)

[0113] where , is the loading error.

[0114] Based on the linear model and the online learning algorithm, updating and according to the actual loading weight, the target real-time granularity, the target real-time humidity, and the target gate closing trigger threshold may specifically include:

[0115] (1) Calculate according to the target real-time granularity.

[0116] In an embodiment of the present invention, calculate according to the target real-time granularity , which may specifically include:

[0117] Substitute the target real-time granularity and the preset reference granularity into the following formula to obtain :

[0118] ...... (3)

[0119] Wherein, is the real-time granularity, is the preset reference granularity.

[0120] (2) Calculate according to the target real-time humidity .

[0121] In an embodiment of the present invention, calculate according to the target real-time humidity , which may specifically include:

[0122] Substitute the target real-time humidity and the preset reference humidity into the following formula to obtain :

[0123] ...... (4)

[0124] Wherein, is the real-time humidity, is the preset reference humidity.

[0125] (3) Calculate according to the actual loading weight, the target loading weight and the target gate trigger threshold .

[0126] In an embodiment of the present invention, calculate according to the actual loading weight, the target loading weight and the target gate trigger threshold , which may specifically include:

[0127] (3.1) Subtract the target loading weight from the actual loading weight to obtain the target loading error .

[0128] (3.2) Substitute the target gate trigger threshold and into the following formula to obtain :

[0129] ...... (5)

[0130] (4) Based on the stochastic gradient descent algorithm, update , and to update and .

[0131] In an embodiment of the present invention, based on the stochastic gradient descent algorithm, according to 、 and update and , specifically, it may include:

[0132] Substitute 、 and into the following formula to obtain the updated and :

[0133] ......(6)

[0134] Wherein, is the learning rate, is equal to the opposite of the loading error.

[0135] In an embodiment of the present invention, based on the stochastic gradient descent algorithm, according to 、 and update and , specifically, it may further include:

[0136] Substitute 、 and into the following formula to obtain the updated and :

[0137] ......(7)

[0138] Wherein, is the regularization coefficient.

[0139] In this way, by introducing the L2 regularization term, it is possible to prevent the parameters from overfitting or fluctuating too much, which is beneficial to further improving the accuracy of the control result of the present invention.

[0140] In an embodiment of the present invention, before obtaining the basic information of the vehicle, the control method for the loading weight of granular goods of the present invention may further include:

[0141] (1) Obtain historical loading data, where the historical loading data includes multiple sample data, and one sample data includes the sample gate closing trigger threshold, carriage position, gross weight of the carriage, tare weight of the carriage, vehicle identification, sample target loading weight, sample loading error, sample granularity of the granular goods being loaded, and sample humidity at the time of closing the gate for one loading.

[0142] It should be noted that, in order to ensure the quality and diversity of data, long-term data collection can be carried out under different working conditions, such as different weather and different coal sources, etc.

[0143] (2) Perform data preprocessing on the historical loading data; the data preprocessing includes missing value processing, outlier processing, and data normalization processing.

[0144] Specifically, during the missing value processing, for humidity and granularity data, if there are missing values, linear interpolation can be used for filling; for loading error data, if the missing values are few, the corresponding sample data can be deleted, and if the missing values are many, the mean or median can be used for filling.

[0145] During the outlier processing, use a statistics-based method, such as the Z-score method, to detect outliers in the data. For humidity, granularity, and loading error data that exceed the normal range, they are corrected or deleted. For example, if a certain humidity exceeds 100%, then it is determined that this humidity is an outlier, and this humidity can be modified or the sample data where this humidity is located can be deleted.

[0146] During the data normalization processing, numerical data such as sample granularity and sample humidity in the historical loading data are normalized and scaled to the [0, 1] interval to eliminate the influence of the dimension between different features. Specifically, the Min-Max normalization method can be used in the present invention, and the corresponding formula is:

[0147] ...... (8)

[0148] Among them, is the normalized value; is the original value; is the minimum value of this feature; is the maximum value of this feature.

[0149] (3) Based on a preset correlation analysis method, according to the historical loading data after data preprocessing, calculate the correlation coefficients between each sample feature and the gate triggering threshold respectively; the sample features include carriage position, gross weight of the carriage, tare weight of the carriage, vehicle identification, target loading weight, loading error, granularity, and humidity.

[0150] Specifically, the preset correlation analysis method can be the Pearson correlation coefficient method of the prior art. In a specific example, assuming that the historical loading data includes 100 sample data, there will be 100 sample granularities and sample gate trigger thresholds corresponding to each sample granularity. Substitute these 100 sample granularities and the sample gate trigger thresholds corresponding to each sample granularity into the calculation formula of the Pearson correlation coefficient to obtain the correlation coefficient between the granularity and the gate trigger threshold. In the same way, the correlation coefficients between other sample features and the gate trigger threshold can be calculated respectively, which will not be elaborated here.

[0151] (4) Select the first sample features whose correlation coefficients are greater than the preset coefficient threshold.

[0152] (5) Further screen the second sample features from the first sample features based on the recursive feature elimination algorithm, which is beneficial to improving the control efficiency and accuracy of the present invention.

[0153] (6) Define the second sample features as the sample features used in this application.

[0154] It should be noted that in the present invention, the second sample features include granularity and humidity. Moreover, the granular goods can be coal, ore, etc.

[0155] Based on a general inventive concept, the present invention also provides a control device for the loading weight of granular goods. Figure 4 It is a schematic structural diagram of a control device for the loading weight of granular goods provided by an embodiment of the present invention. As Figure 4 shown, this device includes:

[0156] The first acquisition module 41 is used to acquire the basic information of the vehicle, and the basic information includes the target loading weight.

[0157] The second acquisition module 42 is used to acquire the real-time loading weight of the vehicle after the whole carriage of the vehicle is on the rail scale.

[0158] The calculation module 43 is used to calculate the target difference between the real-time loading weight and the target loading weight.

[0159] The first judgment module 44 is used to judge whether the target difference is less than the preset difference threshold.

[0160] The first jump module 45 is used to jump to the step of acquiring the real-time loading weight of the vehicle if the target difference is not less than the preset difference threshold.

[0161] The third acquisition module 46 is used to acquire the real-time granularity and real-time humidity of the granular goods being loaded if the target difference is less than the preset difference threshold.

[0162] The substitution module 47 is used to substitute the current real-time granularity and real-time humidity into the gate closing trigger threshold calculation formula to obtain the current gate closing trigger threshold. The gate closing trigger threshold calculation formula is as follows:

[0163]

[0164] Among them, the basic gate closing trigger threshold is a preset fixed value, and are both weight parameters, is the humidity deviation coefficient calculated according to the current real-time humidity and the preset reference humidity, is the granularity deviation coefficient calculated according to the current real-time granularity and the preset reference granularity.

[0165] The second judgment module 48 is used to re-obtain the real-time loading weight, calculate the target difference, and judge whether the current target difference is less than or equal to the current gate closing trigger threshold.

[0166] The second jump module 49 is used to jump to the step of obtaining the real-time granularity and real-time humidity of the granular goods being loaded if the current target difference is greater than the current gate closing trigger threshold.

[0167] The closing module 410 is used to close the gate if the current target difference is less than or equal to the current gate closing trigger threshold.

[0168] Optionally, the control device for the loading weight of granular goods of the present invention may further include:

[0169] The fourth acquisition module is used to acquire the actual loading weight of the vehicle, the target real-time granularity, the target real-time humidity, and the target gate closing trigger threshold corresponding to the closing of the gate.

[0170] The parameter update module is used to update and .

[0171] Optionally, the linear model is defined as:

[0172]

[0173] Among them, , is the loading error.

[0174] The parameter update module may specifically include:

[0175] The first calculation unit is used to calculate .

[0176] A second calculation unit for calculating according to the target real-time humidity .

[0177] A third calculation unit for calculating according to the actual loading weight, the target loading weight, and the target gate triggering threshold .

[0178] A parameter update unit for updating based on the stochastic gradient descent algorithm according to , and and .

[0179] Optionally, the parameter update unit can specifically be used for:

[0180] Substitute , and into the following formula to obtain the updated and :

[0181]

[0182] wherein, is the learning rate, is equal to the opposite of the loading error.

[0183] Optionally, the parameter update unit can specifically also be used for:

[0184] Substitute , and into the following formula to obtain the updated and :

[0185]

[0186] wherein, is the regularization coefficient.

[0187] Optionally, the first calculation unit can specifically be used for:

[0188] Substitute the target real-time granularity and the preset reference granularity into the following formula to obtain :

[0189]

[0190] wherein, is the real-time granularity, is the preset reference granularity.

[0191] ​The second calculation unit can specifically be used for:

[0192] Substitute the target real-time humidity and the preset reference humidity into the following formula to obtain :

[0193]

[0194] where is the real-time humidity, is the preset reference humidity.

[0195] The third calculation unit can specifically be used for:

[0196] Let the actual loading weight minus the target loading weight to obtain the target loading error ;

[0197] Substitute the target gate-closing trigger threshold and into the following formula to obtain :

[0198] .

[0199] Optionally, the control device for the loading weight of granular goods of the present invention may further include: a feature screening module, which is used for:

[0200] (1) Obtain historical loading data, where the historical loading data includes multiple sample data, and the sample data includes the sample gate-closing trigger threshold, carriage position, gross weight of the carriage, tare weight of the carriage, vehicle identity identifier, sample target loading weight, sample loading error, sample granularity of the granular goods being loaded, and sample humidity when the gate is closed for one loading.

[0201] (2) Perform data preprocessing on the historical loading data; the data preprocessing includes missing value processing, outlier processing, and data normalization processing.

[0202] (3) Based on a preset correlation analysis method, calculate the correlation coefficients between each sample feature and the gate-closing trigger threshold according to the historical loading data after data preprocessing; the sample features include carriage position, gross weight of the carriage, tare weight of the carriage, vehicle identity identifier, target loading weight, loading error, granularity, and humidity.

[0203] (4) Select the first sample features whose correlation coefficients are greater than the preset coefficient threshold.

[0204] (5) Based on the recursive feature elimination algorithm, screen the second sample features from the first sample features.

[0205] (6) Define the second sample features as the sample features used in this application.

[0206] Based on a general inventive concept, the present invention also provides a control device for the loading weight of granular goods. Figure 5 It is a schematic structural diagram of a control device for the loading weight of granular goods provided by an embodiment of the present invention. As Figure 5 shown, the device 500 includes:

[0207] At least one processor 510; and,

[0208] A memory 530 communicatively connected to the at least one processor 510; wherein,

[0209] The memory 530 stores instructions 520 executable by the at least one processor 510, and the instructions 520 are executed by the at least one processor 510 so that the at least one processor 510 can implement the control method for the loading weight of granular goods as described above.

[0210] Based on a general inventive concept, the present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the control method for the loading weight of granular goods as described above is implemented.

[0211] It can be understood that the same or similar parts in the above embodiments can be referred to each other, and the content not detailed in some embodiments can be seen in the same or similar content of other embodiments.

[0212] It should be noted that in the description of the present invention, terms such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "a plurality" refers to at least two.

[0213] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, and this should be understood by those skilled in the technical field to which the embodiments of the present invention belong.

[0214] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0215] Those of ordinary skill in the art can understand that all or part of the steps carried by the method of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0216] In addition, in each embodiment of the present invention, each functional unit can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0217] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, or the like.

[0218] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0219] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for controlling the loading weight of granular goods, characterized in that, Including: Obtain the basic information of the vehicle, where the basic information includes the target loading weight; After the entire carriage of the vehicle is on the rail scale, obtain the real-time loading weight of the vehicle; Calculate the target difference between the real-time loading weight and the target loading weight; Determine whether the target difference is less than a preset difference threshold; If the target difference is not less than the preset difference threshold, jump to the step of obtaining the real-time loading weight of the vehicle; If the target difference is less than the preset difference threshold, obtain the real-time granularity and real-time humidity of the granular goods being loaded; Substitute the current real-time granularity and real-time humidity into the gate closing trigger threshold calculation formula to obtain the current gate closing trigger threshold; the gate closing trigger threshold calculation formula is as follows: Among them, the basic gate trigger threshold is a preset fixed value, and are both weight parameters, is a humidity deviation coefficient calculated based on the current real-time humidity and a preset reference humidity, is a granularity deviation coefficient calculated based on the current real-time granularity and a preset reference granularity; Re-obtain the real-time loading weight, calculate the target difference, and determine whether the current target difference is less than or equal to the current gate closing trigger threshold; If the current target difference is greater than the current gate closing trigger threshold, jump to the step of obtaining the real-time granularity and real-time humidity of the granular goods being loaded; If the current target difference is less than or equal to the current gate closing trigger threshold, close the gate; After closing the gate, the control method for the loading weight of granular goods further includes: Obtain the actual loading weight of the vehicle, the target real-time granularity, target real-time humidity, and target gate closing trigger threshold corresponding to when the gate is closed; Based on a linear model and an online learning algorithm, update the and the .

2. The control method for the loading weight of granular goods according to claim 1, characterized in that, The linear model is defined as: Among them, , is the loading error; Based on a linear model and an online learning algorithm, update the and the according to the actual loading weight, the target real-time granularity, the target real-time humidity, and the target gate trigger threshold, specifically including: Calculate according to the target real-time granularity ; Calculate according to the target real-time humidity ; Calculate based on the actual loading weight, the target loading weight, and the target gate trigger threshold ; Based on the stochastic gradient descent algorithm, according to the , the and the update the and the .

3. The control method for the loading weight of granular goods according to claim 2, wherein Based on the stochastic gradient descent algorithm, according to the and the and the update the and the , specifically including: Substitute the , the , and the into the following formula to obtain the updated and the : Among them, is the learning rate, which is equal to the negative of the loading error.

4. The control method for the loading weight of granular goods according to claim 2, characterized in that, Based on the stochastic gradient descent algorithm, according to the and the and the update the and the , specifically including: Substitute the , the , and the into the following formula to obtain the updated and the : Among them, is the regularization coefficient.

5. The control method for the loading weight of granular goods according to claim 2, wherein, Calculate according to the target real-time granularity , specifically including: Substitute the target real-time granularity and the preset reference granularity into the following formula to obtain the : Among them, is the real-time granularity, is the preset reference granularity; Calculate according to the target real-time humidity , specifically including: Substitute the target real-time humidity and the preset reference humidity into the following formula to obtain the : wherein, is the real-time humidity, is the preset reference humidity; Calculate according to the actual loading weight, the target loading weight, and the target gate trigger threshold , specifically including: Let the actual loaded weight minus the target loaded weight to obtain the target loading error ; Substitute the target gate trigger threshold and the into the following formula to obtain the : 。 6. The control method for the loading weight of granular goods according to claim 1, characterized in that, Before obtaining the basic information of the vehicle, it further includes: Obtain historical loading data, where the historical loading data includes multiple sample data, and the sample data includes the sample gate closing trigger threshold, carriage position, gross weight of the carriage, tare weight of the carriage, vehicle identification, sample target loading weight, sample loading error, sample granularity, and sample humidity of the granular goods being loaded during one loading; Perform data preprocessing on the historical loading data; the data preprocessing includes missing value processing, outlier processing, and data normalization processing; Based on a preset correlation analysis method, calculate the correlation coefficients between each sample feature and the gate closing trigger threshold according to the historical loading data after data preprocessing; the sample features include carriage position, gross weight of the carriage, tare weight of the carriage, vehicle identification, target loading weight, loading error, granularity, and humidity; Select the first sample features with correlation coefficients greater than the preset coefficient threshold; Based on the recursive feature elimination algorithm, screen the second sample features from the first sample features; Define the second sample features as the sample features for the control method of the loading weight of granular goods.

7. A control device for the loading weight of granular goods, characterized in that, Applied to the control method for the loading weight of granular goods according to any one of claims 1 to 6, the control device for the loading weight of granular goods includes: A first acquisition module for obtaining the basic information of the vehicle, where the basic information includes the target loading weight; A second acquisition module for obtaining the real-time loading weight of the vehicle after the entire carriage of the vehicle is on the rail scale; A calculation module for calculating the target difference between the real-time loading weight and the target loading weight; A first judgment module, configured to judge whether the target difference is less than a preset difference threshold; A first jump module, configured to jump to the step of obtaining the real-time loading weight of the vehicle if the target difference is not less than the preset difference threshold; A third acquisition module, configured to obtain the real-time granularity and real-time humidity of the granular goods being loaded if the target difference is less than the preset difference threshold; A substitution module, configured to substitute the current real-time granularity and the real-time humidity into a gate closing trigger threshold calculation formula to obtain the current gate closing trigger threshold; the gate closing trigger threshold calculation formula is as follows: Among them, the basic gate trigger threshold is a preset fixed value, and are both weight parameters, is a humidity deviation coefficient calculated based on the current real-time humidity and the preset reference humidity, is a granularity deviation coefficient calculated based on the current real-time granularity and the preset reference granularity; A second judgment module, configured to re-obtain the real-time loading weight, calculate the target difference, and judge whether the current target difference is less than or equal to the current gate closing trigger threshold; A second jump module, configured to jump to the step of obtaining the real-time granularity and real-time humidity of the granular goods being loaded if the current target difference is greater than the current gate closing trigger threshold; A closing module, configured to close the gate if the current target difference is less than or equal to the current gate closing trigger threshold.

8. A control device for the loading weight of granular goods, characterized in that, including: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to implement the control method for the loading weight of granular goods according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the control method for the loading weight of granular goods according to any one of claims 1 to 6.

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