Method and device for determining the fastest pumping displacement, pumping device and engineering vehicle

Abstract

The application provides a method and device for determining the fastest pumping displacement, a pumping device and an engineering vehicle. The method comprises the following steps: obtaining multiple pumping data based on current working condition data of the pumping device and multiple preset pumping displacements; inputting the multiple pumping data into a pre-trained critical power prediction model to obtain a prediction result output by the critical power prediction model, wherein the prediction result comprises whether the critical power value is reached or not reached when the pumping device operates according to the pumping data; and determining the fastest pumping displacement under the current working condition data based on the multiple pumping data and the corresponding prediction result. According to the technical scheme, the fastest pumping displacement can be determined according to the prediction result of the critical power prediction model, and when the pumping device operates according to the fastest pumping displacement, the running power of the pumping device can be prevented from exceeding the critical power value while the pumping displacement is improved, the pumping efficiency is effectively improved, and the fuel consumption is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pumping, in particular to a method and device for determining the fastest pumping displacement, a pumping device and an engineering vehicle. BACKGROUND

[0002] During the operation of the pumping device, as the pumping displacement increases, the operating power also becomes larger and larger. In order to ensure the safety of the operation, the pumping device will automatically reduce the pumping speed when the operating power exceeds the critical power value, so as to maintain a constant operating power, but at the same time, it will cause the pumping operation efficiency to decrease, increase the fuel consumption, and affect the progress of the construction operation.

[0003] Therefore, how to improve the pumping displacement while avoiding the operating power of the pumping device from exceeding the critical power value, improve the pumping efficiency, and reduce the fuel consumption is a technical problem that needs to be solved by those skilled in the art. SUMMARY

[0004] Therefore, the present application provides a method and device for determining the fastest pumping displacement, a pumping device and an engineering vehicle, which can improve the pumping displacement while avoiding the operating power of the pumping device from exceeding the critical power value, improve the pumping efficiency, and reduce the fuel consumption.

[0005] The technical solution provided by the present application is as follows:

[0006] In a first aspect, the present application provides a method for determining the fastest pumping displacement, comprising:

[0007] Based on the current working condition data of the pumping device and a plurality of preset pumping displacements, a plurality of pumping data are obtained, wherein the working condition data includes at least one of material condition, arm posture and environmental temperature;

[0008] The plurality of pumping data are input into a pre-trained critical power prediction model to obtain a prediction result output by the critical power prediction model; the prediction result includes that the pumping device reaches the critical power value when operating according to the pumping data, or the pumping device does not reach the critical power value when operating according to the pumping data, wherein the critical power value represents the maximum allowable power value of the engine of the pumping device;

[0009] Based on the plurality of pumping data and the corresponding prediction results, the fastest pumping displacement under the current working condition data is determined.

[0010] In a second aspect, the present application provides a device for determining the fastest pumping displacement, comprising:

[0011] The first determining module is configured to obtain multiple pumping data based on current working condition data of the pumping device and multiple preset pumping displacements, wherein the working condition data comprises at least one of material condition, arm posture and environmental temperature;

[0012] The prediction module is configured to input the multiple pumping data into a pre-trained critical power prediction model to obtain a prediction result output by the critical power prediction model, wherein the prediction result comprises that the pumping device reaches a critical power value when operating according to the pumping data, or the pumping device does not reach the critical power value when operating according to the pumping data, wherein the critical power value represents an allowable maximum power value of an engine of the pumping device;

[0013] The second determining module is configured to determine the fastest pumping displacement under the current working condition data based on the multiple pumping data and the corresponding prediction results.

[0014] In a third aspect, the present application provides a pumping device, comprising the fastest pumping displacement determination apparatus described above.

[0015] In a fourth aspect, the present application provides an engineering vehicle, comprising the pumping device described above.

[0016] The fastest pumping displacement determination method provided by the present application can obtain multiple pumping data based on current working condition data of a pumping device and multiple preset pumping displacements, and then input the multiple pumping data into a pre-trained critical power prediction model to obtain a prediction result output by the critical power prediction model, wherein the prediction result comprises that the pumping device reaches a critical power value when operating according to the pumping data or does not reach the critical power value. The fastest pumping displacement under the current working condition data is determined based on the multiple pumping data and the corresponding prediction results. The technical solution provided by the present application can determine the fastest pumping displacement according to the prediction result of the critical power prediction model, and when operating according to the fastest pumping displacement, the pumping efficiency can be effectively improved and the fuel consumption can be reduced while avoiding the operating power of the pumping device exceeding the critical power value. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the present application, and those skilled in the art can obtain other drawings according to the provided drawings without creative labor.

[0018] Figure 1 is a flowchart of a fastest pumping displacement determination method provided by an embodiment of the present application;

[0019] Figure 2 is a schematic view of an arm support structure of a pumping device provided by an embodiment of the present application;

[0020] Figure 3 is a schematic view of a critical power prediction model provided by an embodiment of the present application;

[0021] Figure 4 is a schematic view of a determination device of a fastest pumping displacement provided by an embodiment of the present application;

[0022] Figure 5 is a schematic view of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] A pumping device is a kind of engineering machinery for continuously conveying concrete along a pipeline by using pressure, which is widely applied in various fields such as road engineering, bridge engineering, underground engineering, industrial and civil construction, etc.

[0024] During the pumping operation, under the conditions of material conditions, arm support postures, etc., as the pumping displacement increases, the operating power also becomes larger and larger. In order to prevent the engine output power of the pumping device from being too large, a critical power value is set during the pumping operation, and when the engine power of the pumping device reaches the critical power value, the pumping speed is reduced (for example, the hydraulic oil flow of the hydraulic cylinder is reduced) to reduce the pumping load.

[0025] Due to the existence of the critical power value, during the actual pumping operation, the higher the pumping displacement is, the faster the pumping speed is not. In the actual operation process, the pumping device operator often adopts the full displacement pumping mode in order to speed up the pumping speed, which not only makes the pumping speed not the fastest, but also relatively high oil consumption.

[0026] Based on this, the present application provides a determination method, device, pumping device and engineering vehicle of the fastest pumping displacement, which can improve the pumping displacement while avoiding the operating power of the pumping device exceeding the critical power value, thereby improving the pumping efficiency.

[0027] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0028] The embodiment of the present application provides a method for determining the fastest pumping displacement, which can be executed by an electronic device, which can be any device with data and instruction processing functions, such as a computer, a smart terminal, a server, etc. Referring to FIG. 1, Figure 1 The method comprises the following steps.

[0029] In S101, multiple pumping data are obtained based on current working condition data of the pumping device and multiple preset pumping displacements.

[0030] The current working condition data of the pumping device refers to various working data of the pumping device in the current working state, including at least one of material condition, boom posture and environmental temperature in the current working state. The material condition refers to the pumping difficulty of the material to be pumped in the pumping device, and the stirring pressure can be used to quantify the material condition, and the greater the stirring pressure, the more difficult the material to be pumped is. The boom posture refers to the posture of the boom, and the height of the end of each boom section can be used to quantify the boom posture. The environmental temperature refers to the temperature of the environment in which the pumping device is located in the current working state. It should be noted that the material to be pumped generally refers to concrete.

[0031] In one specific embodiment, the working condition data of the pumping device includes the material condition, the boom posture and the environmental temperature in the current working state. A pressure sensor can be arranged in the mixing bin of the pumping device to obtain the stirring pressure in the current working state based on the pressure sensor, and then the material condition is determined. A temperature sensor can be arranged on the pumping device to obtain the environmental temperature in the current working state based on the temperature sensor. An inclination sensor can be arranged on each boom section to detect the included angle between the boom sections based on the inclination sensor, so as to determine the height of the end of each boom section according to the length of the boom and the included angle between the boom sections, and then determine the boom posture.

[0032] Specifically, when the height of the end of each boom section is determined, the boom section that feeds the material of the pumping device can be defined as the first boom section, the boom section connected with the first boom section can be defined as the second boom section, and the other boom section connected with the second boom section can be defined as the third boom section, and so on. If the pumping device includes N (N is a positive integer) boom sections, the boom section that discharges the material is defined as the Nth boom section.

[0033] The first included angle is defined as the included angle between the first boom section and the horizontal direction, the second included angle is defined as the included angle between the first boom section and the second boom section, the third included angle is defined as the included angle between the second boom section and the third boom section, and so on. If the pumping device includes N boom sections, N included angles can be determined, and the Nth included angle is defined as the included angle between the (N-1)th boom section and the Nth boom section.

[0034] In some embodiments, the pumping device includes seven sections of the arm frame, the first section of the arm frame to the seventh section of the arm frame, and the first included angle to the seventh included angle, and the specific positions can be as shown in the following table. Figure 2

[0035] The height of the end of each section of the arm frame can be calculated according to the included angle of the arm frame and the length of the arm frame of each section of the arm frame.

[0036] If the pumping device includes N sections of the arm frame, the collected included angle of the arm frame is a = [a1, a2, a3, …, aN], wherein a1 is the first included angle, a2 is the second included angle, a3 is the third included angle, …, and aN is the Nth included angle; and the length of the arm frame of each section of the arm frame is l = [l1, l2, l3, …, lN], wherein l1 is the length of the arm frame of the first section of the arm frame, l2 is the length of the arm frame of the second section of the arm frame, l3 is the length of the arm frame of the third section of the arm frame, …, and lN is the length of the arm frame of the Nth section of the arm frame. N N N N

[0037] It should be noted that when the included angle between the arm frames is collected, the collection should be performed after the arm frame is adjusted to the working state, so as to obtain the included angle of the arm frame under the current working state. Based on the length of the arm frame and the included angle of the arm frame under the current working state, the height of the end of each section of the arm frame under the current working state is determined.

[0038] The angle cumulative value is calculated.

[0039]

[0040] The height of the end of each section of the arm frame is calculated.

[0041]

[0042] In some embodiments, in addition to calculating the height of the end of each section of the arm frame according to the included angle of the arm frame and the length of the arm frame of each section of the arm frame as described above, a laser ranging sensor can also be installed at the top end of each section of the arm frame, so as to calculate the height of each section of the arm frame by using the laser ranging sensor when the arm frame is located at the working position. The present embodiment is not limited.

[0043] For example, if the pumping device includes seven sections of the arm frame, the height of the end of each section of the arm frame can be represented as [10, 15, 8, 20, 15, 11, 5] meters.

[0044] After obtaining the current working condition data of the pumping device, the current working condition data of the pumping device can be fixed, and the current working condition data of the pumping device is combined with a plurality of preset pumping displacement combinations respectively, to obtain a plurality of pumping data.

[0045] ​​​​​The method for generating the plurality of preset pumping displacements includes, starting from a set pumping displacement, generating the plurality of preset pumping displacements at a set pumping displacement interval. Specifically, the plurality of preset pumping displacements can be generated starting from a set pumping displacement and increasing at a set pumping displacement interval, or the plurality of preset pumping displacements can be generated starting from a set pumping displacement and decreasing at a set pumping displacement interval, which is not limited in the embodiment.

[0046] It should be noted that the set pumping displacement and the set pumping displacement interval can be set according to the actual situation of the pumping device, which is not limited in the embodiment. In some embodiments, if the plurality of preset pumping displacements is generated starting from a set pumping displacement and increasing at a set pumping displacement interval, and 1% is set as the pumping displacement and 1% is set as the pumping displacement interval, then the plurality of preset pumping displacements includes 1%, 2%, 3%, …, and 100%. In other embodiments, if the plurality of preset pumping displacements is generated starting from a set pumping displacement and increasing at a set pumping displacement interval, and it is determined that the pumping device will not reach the critical power value before 30% of the displacement, then 30% can be set as the set pumping displacement and 1% can be set as the set pumping displacement interval, and the plurality of preset pumping displacements includes 30%, 31%, 32%, …, and 100%.

[0047] In S102, the plurality of pumping data is input into the pre-trained critical power prediction model to obtain a prediction result output by the critical power prediction model.

[0048] The critical power prediction model is used to predict whether the pumping device will reach the critical power value when operating according to the pumping data. Specifically, the critical power prediction model adopts a machine learning model, and the historical working condition data and the corresponding pumping displacement of the historical working condition data are used as model inputs, and the model is trained with the model output of reaching or not reaching the critical power value.

[0049] Specifically, the historical working condition data and the corresponding pumping displacement of the pumping device under multiple working conditions can be collected. The historical working condition data includes at least one of the material condition, the arm posture and the environmental temperature during the operation of the pumping device. In one specific embodiment, the historical working condition data includes the material condition, the arm posture and the environmental temperature. The acquisition method of the material condition, the arm posture and the environmental temperature can refer to the description of the above embodiments, which will not be repeated here. The pumping displacement corresponding to the historical working condition data is a control signal, which can be directly obtained from the controller of the pumping device.

[0050] The historical working condition data collected at the same time and the corresponding pumping displacement of the historical working condition data can be taken as a set of training samples, and whether the pumping device reaches the critical power value when performing pumping operation according to each set of training samples is determined as the corresponding training label. When determining whether the pumping device reaches the critical power value when performing pumping operation according to each set of training samples, the motor speed, system load pressure and other data at the time of collecting the set of training samples can be detected, and whether the critical power value is reached is judged based on the motor speed, system load pressure and other data at the time.

[0051] In some embodiments, the training label of the pumping device reaching the critical power value can be marked as 1, and the training label of the pumping device not reaching the critical power value can be marked as 0.

[0052] During training, the training samples can be input into the critical power prediction model to obtain the prediction result output by the critical power prediction model. By comparing the prediction result output by the critical power prediction model and the training label, the loss value of the critical power prediction model is determined, and the parameters of the critical power prediction model are adjusted to reduce the loss value of the critical power prediction model as the target. Then the above process is repeated until the loss value of the critical power prediction model is less than a set value. The set value can be set according to the actual situation, and the present embodiment is not limited.

[0053] The critical power prediction model can use a classification model as a base model, such as random forest, extreme gradient boosting tree (XGBoost), artificial neural network (ANN), etc., and the present embodiment is not limited.

[0054] After the critical power prediction model is trained, multiple pumping data can be input into the critical power prediction model to obtain the prediction result output by the critical power prediction model. The prediction result includes that the pumping device reaches the critical power value when performing operation according to the pumping data, or the pumping device does not reach the critical power value when performing operation according to the pumping data. It should be noted that the critical power value represents the maximum allowable power value of the engine of the pumping device. Specifically, when the pumping device increases the pumping displacement and the running power may exceed the critical power value, the pumping device will automatically reduce the pumping speed to maintain a constant running power.

[0055] Figure 3 As shown in the structure diagram of the critical power prediction model in an embodiment. As shown in the structure diagram of the critical power prediction model in an embodiment. Figure 3 The critical power prediction model includes an input layer, a hidden layer and an output layer. After the pumping data is input into the input layer and processed through the input layer, the hidden layer and the output layer, the prediction result output by the critical power prediction model is obtained.

[0056] S103, determine the fastest pumping displacement at the current working condition data based on the plurality of pumping data and the corresponding prediction results.

[0057] The fastest pumping displacement at the current working condition data refers to the maximum pumping displacement allowed by the pumping equipment when operating according to the current working condition data, that is, when the pumping equipment operates according to the current working condition data, if the pumping displacement exceeds the fastest pumping displacement at the current working condition data, the pumping equipment will automatically reduce the pumping speed to maintain constant power.

[0058] In the embodiments of the present application, the fastest pumping displacement that does not exceed the critical power value can be selected according to the plurality of pumping data and the corresponding prediction results, and the pumping equipment can operate according to the fastest pumping displacement to improve the pumping displacement while avoiding the operating power of the pumping equipment exceeding the critical power value, thereby achieving the purpose of improving the pumping efficiency.

[0059] In the above embodiments, the plurality of pumping data can be obtained based on the current working condition data of the pumping equipment and the plurality of preset pumping displacements, and then the plurality of pumping data can be input into the pre-trained critical power prediction model to obtain the prediction results output by the critical power prediction model, wherein the prediction results include that the pumping equipment operating according to the pumping data reaches the critical power value or does not reach the critical power value. The fastest pumping displacement at the current working condition data is determined based on the plurality of pumping data and the corresponding prediction results. The technical scheme provided in the embodiments of the present application can determine the fastest pumping displacement according to the prediction results of the critical power prediction model, and the pumping equipment can operate according to the fastest pumping displacement to improve the pumping displacement while avoiding the operating power of the pumping equipment exceeding the critical power value, thereby effectively improving the pumping efficiency and reducing fuel consumption.

[0060] In some embodiments, the fastest pumping displacement at the current working condition data can be determined by the following steps:

[0061] When the first prediction result is that the critical power value is reached, and the first pumping displacement is the smallest among the pumping displacements corresponding to all prediction results that the critical power value is reached, the first pumping displacement is determined as the fastest pumping displacement at the current working condition data, wherein the first pumping displacement is the pumping displacement in the pumping data corresponding to the first prediction result.

[0062] The first prediction result refers to the prediction result that the critical power value of the pumping equipment is reached and the corresponding pumping displacement is the first pumping displacement. The first pumping displacement is the smallest pumping displacement among the pumping displacements corresponding to all prediction results that the critical power value is reached. In the embodiments of the present application, the first pumping displacement is determined as the fastest pumping displacement at the current working condition data.

[0063] That is, the plurality of pumping data can be input into the critical power prediction model to obtain the prediction result of the output of the critical power prediction model. According to the prediction result output by the critical power prediction model, the pumping displacement that reaches the critical power value of the pumping device and is the smallest among the pumping displacements of the plurality of pumping data is selected as the fastest pumping displacement at the current working condition data.

[0064] For example, if the plurality of preset pumping displacements include 1%, 2%, 3%, …, 97%, 98%, 99%, and 100%, the pumping data obtained according to the pumping displacements are sequentially input into the critical power prediction model to obtain the prediction result of the output of the critical power prediction model. According to the prediction result output by the critical power prediction model, if it is determined that only 97%, 98%, 99%, and 100% among the pumping displacements of the plurality of pumping data reach the critical power value of the pumping device, it can be determined that 97% is the fastest pumping displacement at the current working condition data.

[0065] In the above embodiment, the fastest pumping displacement at the current working condition data is selected according to the prediction result of the critical power prediction model, which can improve the pumping displacement while avoiding the operating power of the pumping device exceeding the critical power value, effectively improving the pumping efficiency and reducing fuel consumption.

[0066] In some embodiments, the fastest pumping displacement at the current working condition data can also be determined by the following steps:

[0067] When the second prediction result is not reaching the critical power value, and the second pumping displacement is the largest among the pumping displacements corresponding to all the prediction results not reaching the critical power value, the second pumping displacement is determined as the fastest pumping displacement at the current working condition data, wherein the second pumping displacement is the pumping displacement in the pumping data corresponding to the second prediction result.

[0068] The second prediction result refers to a prediction result that is not reaching the critical power value of the pumping device and the corresponding pumping displacement is the second pumping displacement. The second pumping displacement is the largest pumping displacement among the pumping displacements corresponding to all the prediction results not reaching the critical power value. In the embodiments of the present application, the second pumping displacement is determined as the fastest pumping displacement at the current working condition data.

[0069] That is, the plurality of pumping data can be input into the critical power prediction model to obtain the prediction result of the output of the critical power prediction model. According to the prediction result output by the critical power prediction model, the pumping displacement that reaches the critical power value of the pumping device and is the smallest among the pumping displacements of the plurality of pumping data is selected as the fastest pumping displacement at the current working condition data.

[0070] For example, if the preset plurality of pumping displacements include 1%, 2%, 3%, …, 97%, 98%, 99%, 100%, the pumping data obtained according to the above pumping displacements is sequentially input into the critical power prediction model to obtain a prediction result of the output of the critical power prediction model. According to the prediction result output by the critical power prediction model, if it is determined that only 1% to 96% of the plurality of pumping displacements of the pumping data does not reach the critical power value of the pumping equipment, it can be determined that 96% is the fastest pumping displacement at the current operating condition data.

[0071] For example, if the preset plurality of pumping displacements include 1%, 2%, 3%, …, 97%, 98%, 99%, 100%, the pumping data obtained according to the above pumping displacements is sequentially input into the critical power prediction model to obtain a prediction result of the output of the critical power prediction model. According to the prediction result output by the critical power prediction model, if it is determined that only 1% to 96% of the plurality of pumping displacements of the pumping data does not reach the critical power value of the pumping equipment, it can be determined that 96% is the fastest pumping displacement at the current operating condition data.

[0072] In the above embodiments, the fastest pumping displacement at the current operating condition data is selected according to the prediction result of the critical power prediction model, which can improve the pumping displacement while avoiding the operating power of the pumping equipment exceeding the critical power value, effectively improving the pumping efficiency and reducing fuel consumption.

[0073] In some embodiments, when the plurality of pumping data is input into the pre-trained critical power prediction model to obtain the prediction result output by the critical power prediction model, the plurality of pumping data can be sorted in ascending order of pumping displacement, and the sorted pumping data is input into the pre-trained critical power prediction model one by one to obtain the prediction result output by the critical power prediction model.

[0074] Further, the fastest pumping displacement at the current operating condition data can be determined by the following steps:

[0075] When the third prediction result is the first time to reach the critical power value, the third pumping displacement is determined as the fastest pumping displacement at the current operating condition data, wherein the third pumping displacement is the pumping displacement in the pumping data corresponding to the third prediction result.

[0076] The above third prediction result refers to a prediction result that is the first time to reach the critical power value of the pumping equipment and the corresponding pumping displacement is the third pumping displacement. In the embodiments of the present application, the third pumping displacement is determined as the fastest pumping displacement at the current operating condition data.

[0077] In the embodiment, the plurality of pumping data can be sorted in ascending order of pumping displacement, for example, if the plurality of preset pumping displacements include 100%, 99%, 98%, 97%, …, 3%, 2%, 1%, the pumping data obtained after sorting the plurality of pumping data in ascending order of pumping displacement is 1%, 2%, 3%, …, 97%, 98%, 99%, 100%.

[0078] The sorted pumping data is input into the critical power prediction model one by one to obtain a prediction result of the output of the critical power prediction model, so as to determine whether the pumping device reaches the critical power value according to the prediction result of the output of the critical power prediction model, and determine the pumping displacement in the first pumping data that reaches the critical power value as the fastest pumping displacement at the current working condition data.

[0079] For example, the first pumping displacement, for example, 1%, can be obtained by starting from the set pumping displacement and increasing by the set pumping displacement interval. The pumping displacement is combined with the current working condition data of the pumping device to obtain a first set of pumping data. The set of pumping data is input into the pre-trained critical power prediction model to enable the critical power prediction model to predict whether the pumping device will reach the critical power value when operating according to the set of pumping data.

[0080] If the prediction result is that the pumping device will not reach the critical power value when operating according to the set of pumping data, the second pumping displacement, for example, 2%, can be obtained by starting from the set pumping displacement and increasing by the set pumping displacement interval. Then the pumping displacement is combined with the current working condition data of the pumping device to obtain a second set of pumping data. The set of pumping data is input into the pre-trained critical power prediction model to enable the critical power prediction model to predict whether the pumping device will reach the critical power value when operating according to the set of pumping data.

[0081] If the prediction result is that the pumping device will reach the critical power value when operating according to the set of pumping data, 2% can be selected as the fastest pumping displacement at the current working condition data; if the prediction result is that the pumping device will not reach the critical power value when operating according to the set of pumping data, the above process is repeated until a third prediction result is obtained, and the third pumping displacement is determined as the fastest pumping displacement at the current working condition data.

[0082] In the above embodiment, the fastest pumping displacement at the current working condition data is selected according to the prediction result of the critical power prediction model, which can improve the pumping displacement while avoiding the operating power of the pumping device exceeding the critical power value, effectively improving the pumping efficiency and reducing fuel consumption.

[0083] Corresponding to the above method for determining the fastest pumping displacement, the embodiment of the present application also discloses a device for determining the fastest pumping displacement, which is described with reference toFigure 4 The device comprises:

[0084] The first determination module 100 is configured to obtain a plurality of pumping data based on current working condition data of the pumping device and a plurality of preset pumping displacements, wherein the working condition data comprises at least one of material condition, arm posture and ambient temperature.

[0085] The prediction module 110 is configured to input the plurality of pumping data into a pre-trained critical power prediction model to obtain a prediction result output by the critical power prediction model, wherein the prediction result comprises that the pumping device reaches a critical power value when operating according to the pumping data, or the pumping device does not reach the critical power value when operating according to the pumping data, wherein the critical power value represents an allowable maximum power value of an engine of the pumping device.

[0086] The second determination module 120 is configured to determine the fastest pumping displacement at the current working condition data based on the plurality of pumping data and the corresponding prediction results.

[0087] As an optional implementation, in another embodiment of the present application, it is disclosed that the critical power prediction model adopts a machine learning model, and historical working condition data and corresponding pumping displacements of the historical working condition data are used as model inputs, and reaching or not reaching the critical power value is used as model output for model training.

[0088] As an optional implementation, in another embodiment of the present application, the second determination module 120 of the above embodiment is specifically configured to:

[0089] when the first prediction result is reaching the critical power value, and the first pumping displacement is the minimum among the pumping displacements corresponding to all the prediction results reaching the critical power value, determining that the first pumping displacement is the fastest pumping displacement at the current working condition data, wherein the first pumping displacement is the pumping displacement in the pumping data corresponding to the first prediction result.

[0090] As an optional implementation, in another embodiment of the present application, the second determination module 120 of the above embodiment is specifically configured to:

[0091] when the second prediction result is not reaching the critical power value, and the second pumping displacement is the maximum among the pumping displacements corresponding to all the prediction results not reaching the critical power value, determining that the second pumping displacement is the fastest pumping displacement at the current working condition data, wherein the second pumping displacement is the pumping displacement in the pumping data corresponding to the second prediction result.

[0092] As an optional implementation, in another embodiment of the present application, the prediction module 110 of the above embodiment inputs the plurality of pumping data into the pre-trained critical power prediction model to obtain the prediction result output by the critical power prediction model, and is specifically configured to:

[0093] sort the plurality of pumping data in ascending order of pumping displacement, and input the sorted pumping data into the pre-trained critical power prediction model one by one to obtain the prediction result output by the critical power prediction model;

[0094] The second determination module 120 of the above embodiment, when determining the fastest pumping displacement at the current working condition data based on the plurality of pumping data and the corresponding prediction results, is specifically configured to:

[0095] when the third prediction result is the first time to reach the critical power value, determining that the third pumping displacement is the fastest pumping displacement at the current working condition data, wherein the third pumping displacement is the pumping displacement in the pumping data corresponding to the third prediction result.

[0096] As an optional implementation, in another embodiment of the present application, the device of the above embodiment further comprises:

[0097] The generating module is configured to generate a plurality of preset pumping displacements starting from a set pumping displacement at a set pumping displacement interval.

[0098] As an optional implementation, in another embodiment of the present application, the device of the above embodiment further comprises:

[0099] The calculating module is configured to calculate the arm rack end height based on the arm rack included angle and the arm rack length.

[0100] Specifically, the specific working content of each unit of the fastest pumping displacement determination device described above can refer to the content of the above method embodiment, which will not be repeated here.

[0101] Corresponding to the above fastest pumping displacement determination method, the present application also discloses an electronic device, which can be seen from Figure 5 As shown in the figure, the electronic device comprises:

[0102] a memory 200 and a processor 210;

[0103] The memory 200 is connected with the processor 210, and is configured to store programs;

[0104] The processor 210 is configured to realize the fastest pumping displacement determination method disclosed in any of the above embodiments by running the programs stored in the memory 200.

[0105] In particular, the electronic device described above can further include a bus, a communication interface 220, an input device 230, and an output device 240.

[0106] The processor 210, the memory 200, the communication interface 220, the input device 230, and the output device 240 are connected to each other through the bus. Among them:

[0107] The bus can include a path for transmitting information between the various components of the computer system.

[0108] The processor 210 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of programs of the present application. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a ready-to-use programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component.

[0109] The processor 210 can include a main processor, and can also include a baseband chip, a modem, etc.

[0110] The memory 200 stores programs for executing the technical solutions of the present application, and can also store operating systems and other key services. In particular, the program can include program code, and the program code includes computer operation instructions. More specifically, the memory 200 can include read-only memory (ROM), other types of static storage devices that can store static information and instructions, random access memory (RAM), other types of dynamic storage devices that can store information and instructions, disk storage, flash, etc.

[0111] The input device 230 can include a device that receives data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor, etc.

[0112] The output device 240 can include a device that allows information to be output to a user, such as a display screen, a printer, a speaker, etc.

[0113] The communication interface 220 can include a device using any transceiver to communicate with other devices or communication networks, such as Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0114] The processor 210 executes programs stored in the memory 200 and invokes other devices, which can be used to implement the steps of the method for determining the fastest pumping displacement provided by the embodiments of the present application.

[0115] Another embodiment of the present application further provides a pumping device, which comprises the electronic device or the device for determining the fastest pumping displacement of the above embodiments.

[0116] The pumping device provided by the present embodiment belongs to the same application concept as the method for determining the fastest pumping displacement provided by the embodiments of the present application, can execute the method for determining the fastest pumping displacement provided by any of the embodiments of the present application, has the corresponding function modules and beneficial effects of executing the method for determining the fastest pumping displacement, and the technical details which are not described in detail in the present embodiment can be referred to the specific processing content of the method for determining the fastest pumping displacement provided by the embodiments of the present application, which will not be described here.

[0117] Another embodiment of the present application further provides an engineering vehicle, which comprises the pumping device of the above embodiments.

[0118] In addition to the above method and device, the embodiments of the present application can also be a computer program product, which comprises computer program instructions, and the computer program instructions make the processor execute the steps of the method for determining the fastest pumping displacement provided by the embodiments of the present application when the processor runs.

[0119] The computer program product can be written in any combination of one or more programming languages to perform the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0120] In addition, the embodiments of the present application can also be a computer readable storage medium, which stores computer program instructions, and the computer program instructions make the processor execute the steps of the method for determining the fastest pumping displacement provided by the embodiments of the present application when the processor runs.

[0121] The computer readable storage medium can be any combination of one or more computer readable medium. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0122] For each method embodiment described above, in order to simply describe, it is expressed as a series of action combination, but those skilled in the art should know that the application is not limited by the order of the described actions, because according to the application, some steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the application.

[0123] It should be noted that each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same and similar parts between each embodiment can be referred to each other. For device embodiments, since they are basically similar to method embodiments, they are described more simply, and the relevant parts are referred to the part of the method embodiment.

[0124] The steps in the method of each embodiment of the application can be adjusted, combined and reduced in sequence according to actual needs. The technical features recorded in each embodiment can be replaced or combined.

[0125] The modules and sub-modules in the device and terminal in each embodiment of the application can be combined, divided and reduced according to actual needs.

[0126] In several embodiments provided by the application, it should be understood that the disclosed terminal, device and method can be implemented by other ways. For example, the terminal embodiments described above are only schematic, and the division of modules or sub-modules is only a logical function division, and actual implementation can have another division way, for example, a plurality of sub-modules or modules can be combined or integrated into another module, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interface, device or module, which can be electrical, mechanical or other forms.

[0127] The modules or sub-modules described as separate components may or may not be physically separate, and the components of the modules or sub-modules may or may not be physical modules or sub-modules, i.e., may be located in one place or distributed over multiple network modules or sub-modules. Some or all of the modules or sub-modules can be selected as needed to achieve the purposes of the embodiments.

[0128] In addition, each functional module or sub-module in the various embodiments of the present application can be integrated into one processing module, or each module or sub-module can exist physically alone, or two or more modules or sub-modules can be integrated into one module. The integrated module or sub-module can be realized in the form of hardware or in the form of a software functional module or sub-module.

[0129] The skilled person can further realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0130] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be directly implemented by hardware, software units executed by a processor, or a combination of both. The software units can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0131] Finally, it should be noted that, in this document, relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0132] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Modifications of these embodiments will occur to persons of skill in the art, and, while certain embodiments according to the principles set forth herein are shown and described, it is to be understood that the same are not limiting of the scope of the application as it is set forth in the appended claims, and that various modifications are made within the scope of the present application. Therefore, it is manifestly intended that this application be limited only by the following claims and equivalents thereof.

Claims

1. A method for determining the fastest pumping displacement, characterized in that, include: Based on the current operating data of the pumping equipment and multiple preset pumping displacements, multiple pumping data are obtained, including at least one of material condition, boom posture and ambient temperature. Multiple pumping data are input into a pre-trained critical power prediction model to obtain the prediction result output by the critical power prediction model; the prediction result includes whether the pumping equipment reaches the critical power value when operating according to the pumping data, or whether the pumping equipment does not reach the critical power value when operating according to the pumping data, wherein the critical power value represents the maximum allowable power value of the engine of the pumping equipment. Based on multiple pumping data and the corresponding prediction results, the fastest pumping displacement is determined for the current operating condition data. The step of determining the fastest pumping displacement under the current operating conditions based on multiple pumping data and corresponding prediction results includes: When the first prediction result is that the critical power value has been reached, and the first pumping displacement is the smallest among all the pumping displacements corresponding to the critical power value being reached, the first pumping displacement is determined to be the fastest pumping displacement under the current operating conditions, wherein the first pumping displacement is the pumping displacement in the pumping data corresponding to the first prediction result; or When the second prediction result is that the critical power value has not been reached, and the second pumping displacement is the largest among all the pumping displacements corresponding to the prediction result that the critical power value has not been reached, the second pumping displacement is determined to be the fastest pumping displacement in the current operating condition data, wherein the second pumping displacement is the pumping displacement in the pumping data corresponding to the second prediction result.

2. The method for determining the fastest pumping displacement according to claim 1, characterized in that, The critical power prediction model employs a machine learning model, using historical operating condition data and the corresponding pumping displacement as model inputs, and outputting the model training based on whether the critical power value has been reached or not.

3. The method for determining the fastest pumping displacement according to claim 1, characterized in that, The step of inputting multiple pumping data into a pre-trained critical power prediction model to obtain the prediction result output by the critical power prediction model includes: Sort the multiple pumping data in ascending order of pumping displacement; The sorted pumping data are input one by one into the pre-trained critical power prediction model to obtain the prediction results output by the critical power prediction model. The step of determining the fastest pumping displacement under the current operating conditions based on multiple pumping data and corresponding prediction results includes: When the third prediction result is that the critical power value is reached for the first time, the third pumping displacement is determined to be the fastest pumping displacement under the current operating conditions, wherein the third pumping displacement is the pumping displacement in the pumping data corresponding to the third prediction result.

4. The method for determining the fastest pumping displacement according to claim 1, characterized in that, The method for generating the preset multiple pumping displacements includes generating the preset multiple pumping displacements according to the preset pumping displacement interval, starting from a set pumping displacement.

5. The method for determining the fastest pumping displacement according to claim 1, characterized in that, The boom attitude characterizes the boom end height, and the method for calculating the boom end height includes: The height of the boom end is calculated based on the boom angle and boom length.

6. A device for determining the fastest pumping displacement, characterized in that, include: The first determining module is used to obtain multiple pumping data based on the current operating condition data of the pumping equipment and multiple preset pumping displacements, wherein the operating condition data includes at least one of material condition, boom posture and ambient temperature. A prediction module is used to input multiple pumping data into a pre-trained critical power prediction model to obtain the prediction result output by the critical power prediction model; the prediction result includes whether the pumping equipment reaches the critical power value when operating according to the pumping data, or whether the pumping equipment does not reach the critical power value when operating according to the pumping data, wherein the critical power value represents the maximum allowable power value of the engine of the pumping equipment. The second determining module is used to determine the fastest pumping displacement under the current operating conditions based on multiple pumping data and the corresponding prediction results. The step of determining the fastest pumping displacement under the current operating conditions based on multiple pumping data and corresponding prediction results includes: When the first prediction result is that the critical power value has been reached, and the first pumping displacement is the smallest among all the pumping displacements corresponding to the critical power value being reached, the first pumping displacement is determined to be the fastest pumping displacement under the current operating conditions, wherein the first pumping displacement is the pumping displacement in the pumping data corresponding to the first prediction result; or When the second prediction result is that the critical power value has not been reached, and the second pumping displacement is the largest among all the pumping displacements corresponding to the prediction result that the critical power value has not been reached, the second pumping displacement is determined to be the fastest pumping displacement in the current operating condition data, wherein the second pumping displacement is the pumping displacement in the pumping data corresponding to the second prediction result.

7. A pumping device, characterized in that, Includes the device for determining the fastest pumping displacement as described in claim 6.

8. An engineering vehicle, characterized in that, Includes the pumping equipment as described in claim 7.

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