Mining stage identification method and device, electronic equipment, storage medium and computer program product

Abstract

The invention provides a mining stage recognition method and device, electronic equipment, a storage medium and a computer program product, and belongs to the technical field of computers. According to the scheme, a pilot pressure signal and a pump end pressure signal of a target excavator at the current time step length, the operation time of the target excavator at the last time step length and the excavation stage of the last time step length are obtained; performing feature extraction on an input vector constructed based on the pilot pressure signal and the pump end pressure signal to obtain a pressure signal feature sequence; and according to the pressure signal feature sequence, the operation time of the previous time step and the excavation stage of the previous time step, determining an excavation stage identification result of the target excavator at the current time step. In combination with a pressure signal feature sequence, an excavation cycle operation stage sequence and operation time features, the whole vehicle state is more comprehensively described from three dimensions of a driver operation intention, a whole vehicle load and a working condition state, a final excavation stage identification result is determined, and the accuracy of the identification result is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a mining phase identification method, device, electronic device, computer-readable storage medium and computer program product. Background Art

[0002] When the existing excavator's working mode and gear are determined, the required engine power is a constant. However, the actual power of the excavator varies with different excavation cycle stages, and oversupply or undersupply of engine power often occurs. The excavator's working condition power control can meet the heavy-load power supply while reducing the energy waste of light loads. This requires the ability to accurately identify the excavator's operating conditions in real time.

[0003] However, when identifying the working conditions of an excavator, the following technical problems often occur:

[0004] 1. The existing technology only uses the pump-end pressure signal or the pilot pressure signal when identifying the excavation cycle stage. The former has hysteresis in the pump pressure change itself, resulting in the recognition result lagging behind the actual situation; while the pilot pressure only reflects the driver's operating intention, and does not reflect the vehicle load information, which can easily lead to misidentification (for example, from the excavation preparation stage to the excavation stage, the vehicle load can reflect whether the excavator encounters materials and performs excavation).

[0005] 2. The existing technology does not merge and compress the data. Since the neural network of the existing time series recognition (mining cycle recognition mostly uses time series as input vectors) itself has a large amount of calculation, the uncompressed data will increase the amount of calculation, which will lead to a higher controller load rate.

[0006] 3. The existing technology uses neural networks to identify mining cycle stages and identify the information of the time series involved, but does not consider the dependencies between stages and the working time characteristics of each stage, which is prone to misidentification.

[0007] 4. In order to avoid the gradient explosion phenomenon and reduce the computational complexity of the neural network model, the input vector of the neural network used in the prior art does not contain information of too long a time length. This also causes the recognition model to be unable to grasp the timing information of the entire mining cycle. This is also one of the important reasons for the misrecognition of the recognition model in the mining cycle stage.

[0008] It can be seen that there is an urgent need to develop a new mining stage identification method to address the current defects and shortcomings. Summary of the invention

[0009] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0010] Some embodiments of the present disclosure propose a mining phase identification method, apparatus, electronic device, computer-readable storage medium, and computer program product to at least partially solve the technical problems mentioned in the above background technology section.

[0011] In a first aspect, some embodiments of the present disclosure provide a method for identifying an excavation stage, the method comprising: obtaining a leading pressure signal and a pump-end pressure signal of a target excavator at a current time step, the operating time of the target excavator at a previous time step, and the excavation stage of the previous time step; constructing an input vector based on the leading pressure signal and the pump-end pressure signal; performing feature extraction on the input vector to obtain a pressure signal feature sequence; determining an identification result of the excavation stage of the target excavator at the current time step based on the pressure signal feature sequence, the operating time of the previous time step, and the excavation stage of the previous time step.

[0012] In a second aspect, some embodiments of the present disclosure provide an excavation stage identification device, the device comprising: an acquisition unit, configured to acquire a leading pressure signal and a pump-end pressure signal of a target excavator at a current time step, an operation time of the target excavator at a previous time step, and an excavation stage at a previous time step; a construction unit, configured to construct an input vector based on the leading pressure signal and the pump-end pressure signal; an extraction unit, configured to perform feature extraction on the input vector to obtain a pressure signal feature sequence; a determination unit, configured to determine an excavation stage identification result of the target excavator at the current time step based on the pressure signal feature sequence, the operation time of the previous time step, and the excavation stage of the previous time step.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by one or more processors, the one or more processors implement a method as described in any implementation method in the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any implementation manner in the first aspect.

[0015] In a fifth aspect, some embodiments of the present disclosure provide a computer program product, including a computer program, which implements the method described in any implementation manner in the above-mentioned first aspect when executed by a processor.

[0016] One of the various embodiments of the present disclosure has the following beneficial effects: combining the pressure signal feature sequence, the order of the excavation cycle operation phases and the operation time characteristics, starting from the three dimensions of the driver's operating intention, the vehicle load and the working condition, a more comprehensive description of the vehicle status is provided, thereby determining the final excavation stage recognition result and improving the accuracy of the recognition result. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0018] Figure 1 is a flow chart of some embodiments of the mining phase identification method according to the present disclosure;

[0019] Figure 2 is a schematic diagram of the structure of some embodiments of the excavation stage identification device according to the present disclosure;

[0020] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure.

[0021] The above drawings include the following reference numerals:

[0022] 301. Processing device; 302. Read-only memory; 303. Random access memory; 304. Bus; 305. Input / output interface; 306. Input device; 307. Output device; 308. Storage device; 309. Communication device. DETAILED DESCRIPTION

[0023] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0024] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0025] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0026] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0027] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0028] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0029] refer to Figure 1 , showing the process of some embodiments of the mining phase identification method according to the present disclosure. The mining phase identification method comprises the following steps:

[0030] Step S101, obtaining a pilot pressure signal and a pump end pressure signal of a target excavator at a current time step, the operation time of the target excavator at a previous time step, and the excavation stage at the previous time step.

[0031] In some embodiments, the executor of the excavation phase identification method may obtain the pilot pressure signal and the pump end pressure signal of the target excavator at the current time step, the operation time of the target excavator at the previous time step, and the excavation phase at the previous time step.

[0032] Here, the target excavator generally refers to the excavator whose excavation stage needs to be identified. The current time step generally refers to the time step corresponding to the acquisition of the pilot pressure signal and the pump end pressure signal. The excavation stage of the previous time step generally refers to the excavation stage that the target excavator was in at the previous time step. The operation time of the previous time step generally refers to the operation time that the target excavator was in a certain excavation stage at the previous time step.

[0033] Specifically, the pump end pressure usually refers to the pressure at the oil supply outlet of the excavator pump. For example, if the target excavator is supplied with oil by two pumps, the pump end pressure is the general term for the pressure of pump 1 and pump 2. In the hydraulic system of the excavator, pump 1 and pump 2 are the oil supply pumps of the actuators of each action of the excavator. Therefore, the pump end pressure can feedback the load condition of the excavator.

[0034] The above excavation stages can generally be divided into four stages: excavation preparation stage, excavation stage, return material transfer stage, and unloading stage.

[0035] It should be noted that the electronic device that executes the identification method in the mining phase can be hardware or software. When the electronic device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the electronic device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0036] Step S102, constructing an input vector according to the pilot pressure signal and the pump end pressure signal.

[0037] In some embodiments, the execution entity may construct an input vector according to the pilot pressure signal and the pump-end pressure signal.

[0038] As an example, the execution entity may construct an input vector by combining or vectorizing the pilot pressure signal and the pump-end pressure signal.

[0039] In some optional implementations of some embodiments, the execution subject may combine and compress the pilot pressure signal and the pump end pressure signal to obtain a combined compressed signal;

[0040] The input vector is constructed based on the above combined compressed signal and the following formula:

[0041]

[0042] Among them, n represents the time length of the selected input vector, represents the boom pilot pressure set at n sampling points, represents the set of arm pilot pressures at n sampling points, represents the bucket pilot pressure set of n sampling points, represents the set of rotary pilot pressures at n sampling points, represents the set of pump 1 pressures at n sampling points, Represents the set of pump 2 pressures at n sampling points.

[0043] Specifically, the above execution subject can perform the combined compression process in the following manner to obtain the combined compressed signal:

[0044] The pilot pressure signal and the pump end pressure signal are collected and filtered to remove noise. In addition, in order to reduce the number of input signals and reduce the amount of calculation, the signals are merged and compressed. The bucket retraction and outward turn signals will not occur at the same time, so the bucket retraction and outward turn signals are merged into the bucket signal according to the following formula: , where s is the bucket signal, s1 is the bucket retraction signal, and s2 is the bucket flip signal. Similarly, the boom up and down signals are combined into the boom signal, and the arm retraction and flip signals are combined into the arm signal.

[0045] Afterwards, the above execution body can be constructed as an input vector based on the combined signal, and the expression is as follows:

[0046] ,

[0047] Where n is the time length of the selected input vector, is the boom pilot pressure set of n sampling points, is the set of the arm pilot pressures at n sampling points, is the bucket pilot pressure set of n sampling points, is the set of rotary pilot pressures at n sampling points, is the set of pump 1 pressures at n sampling points, is the set of pump 2 pressures at n sampling points.

[0048] Here, pump end pressure refers to the pressure at the pump oil supply outlet. For example, if the excavator using this solution is supplied by two pumps, the pump end pressure is the general term for the pressure of pump 1 and pump 2. In the hydraulic system of the excavator, pump 1 and pump 2 are the oil supply pumps for the actuators of each action of the excavator. Therefore, the pump end pressure can feedback the load condition of the excavator.

[0049] The pilot pressure signal is combined and compressed to reduce the calculation amount of the model without affecting the model recognition accuracy, thereby reducing the controller load rate, reducing the requirements for the controller, and improving the stability of program operation.

[0050] Step S103, extracting features from the input vector to obtain a pressure signal feature sequence.

[0051] In some embodiments, the execution entity may perform feature extraction on the input vector to obtain a pressure signal feature sequence.

[0052] Here, the pressure signal feature sequence is usually used to characterize the probability that the input vector corresponds to different excavation stages. The pressure signal feature sequence usually includes the probability that the input vector corresponds to the excavation preparation stage, excavation stage, return material transfer stage, and unloading stage.

[0053] As an example, the execution entity may pre-establish a correspondence table between input vectors and pressure signal feature sequences based on a large amount of data, and determine the pressure signal feature sequence corresponding to the input vector according to the correspondence table.

[0054] As another example, the execution entity may also perform feature extraction on the input vector through a pre-trained neural network (eg, a bidirectional temporal neural network and / or a unidirectional temporal network) to obtain a pressure signal feature sequence.

[0055] In some optional implementations of some embodiments, the execution entity may utilize a pre-trained feature extraction model to perform feature extraction on the input vector to obtain a pressure signal feature sequence, wherein the feature extraction model is obtained by training the first initial model using a time series neural network as the first initial model, wherein the first initial model includes an input layer, a time series network processing layer, a hidden layer, a classification layer, and an output layer, and when the training of the first initial model is completed, the classification layer and the output layer in the first initial model are discarded to obtain a feature extraction model.

[0056] As an example, the above-mentioned time series neural network generally refers to a neural network structure with a time series classification function. The above-mentioned time series neural network can be a unidirectional time series neural network or a bidirectional time series neural network, such as BiRNN, LSTM, BiGRU, Transformer, etc. As an example, the above-mentioned time series network processing layer can be a unidirectional time series network. As another example, the above-mentioned time series network processing layer can also be composed of a bidirectional circulation layer and a splicing layer.

[0057] As an example, the above execution entity can construct a feature extraction model in the following manner:

[0058] The above execution entity can build a mining phase recognition model based on BiLSTM. The model is mainly divided into the following layers, where the combination of FlipLayer, two LSTMLayers and ConcatenationLayer achieves the purpose of two LSTMs processing data from the forward and reverse directions of the input vector respectively, thereby achieving the effect of BiLSTM:

[0059] InputLayer: Decentralizes and normalizes the input vector. The formula is as follows:

[0060] ,

[0061] in, is the input vector, is the output of InputLayer, is the mean of the training set, is the variance of the training set, is the minimum value of the decentralized training set, is the maximum value of the training set after decentralization.

[0062] FlipLayer: Flips the vector output by InputLayer. When .

[0063] LSTMLayer: Based on the data contained in the processed input vector, a hidden state containing the recognition result is formed.

[0064] ConcatenationLayer: Concatenates the hidden states of the forward and reverse LSTMLayer outputs.

[0065] FullConnectedLayer: The features output by LSTMLayer are compressed into four features with reduced dimensions. These features can be mapped to the probability that the working condition corresponding to the input belongs to the four mining cycle stages.

[0066] SoftmaxLayer: Softmax classifier, based on the previously extracted features, forms the probability of belonging to different stages at the current moment. This network layer has a standard structure, and its internal specific calculation formula is not repeated here.

[0067] OutputLayer: According to the output results of SoftmaxLayer, the stage with the highest probability among the four mining stages is identified as the current working stage, and the network loss is calculated for back propagation.

[0068] Among them, the results of SoftmaxLayer and OutputLayer are only used to evaluate the network loss of LSTMLayer as a support for model training, and are not used as recognition in the mining cycle stage of the final recognition.

[0069] During the training process, the time series features extracted by BiLSTM are limited to 4 dimensions, and the Softmax layer classification is added. The classification results are used as the model evaluation criteria. Therefore, the extracted features can be mapped to: the probability that the working condition corresponding to the input belongs to the 4 mining cycle stages.

[0070] When the pressure signal is a long time series, there is a need to further extract effective features. The pre-trained BiLSTM model is used to extract features of the pilot pressure and pump end pressure signals, and data processing is performed from the forward and reverse directions of the pressure signal to extract the time series features of the pressure signal.

[0071] The advantage of the BiLSTM model in capturing long-term dependencies in time series is fully utilized. The pressure signal is compressed from a two-dimensional feature vector to a one-dimensional feature vector, while the effective time series features in the pressure information are extracted, thereby improving the accuracy of network recognition.

[0072] During the pre-training process of the BiLSTM model, the extracted time series features are set to 4 dimensions and classified using the softmax layer. Finally, the classification is used as the evaluation criterion for the pre-training model, which achieves the effect of giving practical meaning to the extracted features - the extracted features can be mapped to: the probability that the working conditions corresponding to the input belong to the four mining cycle stages. This improves the interpretability of the mining cycle phased recognition model and improves the reliability of the recognition model.

[0073] Step S104, determining the identification result of the excavation stage of the target excavator in the current time step according to the pressure signal feature sequence, the operation time of the previous time step, and the excavation stage of the previous time step.

[0074] In some embodiments, the execution entity may determine the mining stage identification result of the target excavator in the current time step according to the pressure signal feature sequence, the operation time in the previous time step, and the mining stage in the previous time step.

[0075] In some optional implementation methods of some embodiments, the above-mentioned execution entity can input the above-mentioned pressure signal feature sequence, the operation time of the previous time step, and the mining stage of the previous time step into a pre-trained recognition model to obtain the recognition result of the mining stage, wherein the above-mentioned recognition model is obtained by training the above-mentioned second initial model with the DAGSVM model as the second initial model and the radial basis function as the kernel function of the second initial model.

[0076] Specifically, the input of the above DAGSVM is 4 time series feature sequences extracted by the feature extraction model, 2 features of the mining cycle stage to which the previous time step belongs and the time kept in the same mining stage in the previous time step, a total of 6 features.

[0077] These six features are constructed as the input vector of the DAGSVM classifier. The expression of the input vector of the DAGSVM classifier is: ,in, The four time series features extracted by the pre-trained feature extraction model can be mapped as: the probability that the working condition corresponding to the input belongs to the four mining cycle stages, is the mining cycle phase to which the previous time step belongs, is the time spent in the same mining phase during the previous time step.

[0078] The BiLSTM and SVM structures are combined. BiLSTM is used to obtain the time series characteristics of the pressure signal, and the sequence of the cycle operation phases and the operation time characteristics are added to jointly construct the input vector of the SVM model. The vehicle status is more comprehensively described from the three dimensions of driver's operation intention, vehicle load, and working condition, thereby improving recognition accuracy.

[0079] Among them, the pilot pressure reflects the driver's operating intention, the pump end pressure can reflect the excavator load information, and the sequence of excavation cycle operation stages and operation time characteristics can feedback the working condition of the excavator.

[0080] In some optional implementations of some embodiments, the DAGSVM model includes at least six binary classification nodes, and the at least six binary classification nodes are arranged in a directed acyclic graph in the DAGSVM model according to the following steps:

[0081] Determine the binary classification node with the highest classification accuracy from the at least six binary classification nodes as the top node in the directed acyclic graph;

[0082] For the remaining binary classification nodes, determine the related nodes with the top node from the remaining binary classification nodes, and combine the related nodes in pairs to obtain at least one binary classification node combination, and use the binary classification node combination with the highest average classification accuracy in the at least one binary classification node combination as the second layer node in the directed acyclic graph;

[0083] The nodes in the remaining binary classification nodes except the second-layer nodes are used as the third-layer nodes in the directed acyclic graph.

[0084] Here, the above binary classification node generally refers to a binary classifier. Specifically, the higher the classification accuracy of the binary classification, the easier it is to distinguish the two mining cycle stages. Therefore, the classification accuracy corresponding to each binary classifier is used as a basis to set the nodes of the directed acyclic graph.

[0085] As an example, the node selection rule is: select the group with the highest classification accuracy as the top node, such as 1VS4, and select the two nodes of the second layer, 2VS4 and 1VS3, and the group with the highest average classification accuracy among the two groups of 3VS4 and 1VS2, as the left and right nodes of the second layer, such as 1VS3 and 2VS4. Then, the nodes of the third layer are 1VS2, 2VS3, and 3VS4.

[0086] Due to the advantage that DAGSVM is more suitable for nonlinear classification than the Softmax layer, the present invention adopts DAGSVM as the final classification model, and customizes the DAGSVM classification node through the binary pre-classification result, thereby improving the applicability of the DAGSVM classifier to the phased identification of mining cycles and further improving the accuracy of the mining cycle identification model.

[0087] In some optional implementations of some embodiments, in response to determining that the mining phase recognition result satisfies the following rules, the execution subject may modify the mining phase recognition result according to the corresponding rules, and use the modified result as the mining phase recognition result:

[0088] The excavation stage of the previous time step is the excavation preparation stage. When the above excavation stage identification result is the excavation stage, if the arm outward-flipping pilot pressure is greater than the bucket inward-flipping pilot pressure, the above excavation stage identification result is corrected to the excavation preparation stage.

[0089] When the mining stage of the previous time step is the mining preparation stage and the above mining stage recognition result is not the mining stage, the above mining stage recognition result is corrected to the mining preparation stage;

[0090] When the excavation stage of the previous time step is the excavation stage, and the above excavation stage recognition result is the return material transfer stage, if the time t_2 in the excavation stage is less than the time threshold t_21, the above excavation stage recognition result is corrected to the excavation stage; t_2 is greater than or equal to t_21, less than the time threshold t_22, and the maximum values ​​of the boom rise pilot pressure and the rotation pilot pressure max1 are less than the maximum values ​​of the bucket retraction and the arm retraction pilot pressure max2, the above excavation stage recognition result is corrected to the excavation stage; if t_2 is greater than or equal to t_22, the above excavation stage recognition result is corrected to the return material transfer stage;

[0091] When the excavation stage of the previous time step is the excavation stage, and the above excavation stage recognition result is not the return material transfer stage, the above excavation stage recognition result is corrected to the excavation stage;

[0092] When the excavation stage of the previous time step is the return material transfer stage and the identification result of the above excavation stage is the unloading stage, the identification result of the above excavation stage is corrected to the unloading stage;

[0093] The excavation stage of the previous time step is the return material transfer stage. When the above excavation stage recognition result is the excavation preparation stage, if the maximum value of the boom and bucket overturning pilot pressure max3 is greater than the pilot threshold p1, the above excavation stage recognition result is corrected to the unloading stage; if max3 is less than or equal to p1, the correction result is the return material transfer stage;

[0094] When the excavation stage of the previous time step is the return material transfer stage and the above excavation stage recognition result is the excavation stage, the above excavation stage recognition result is corrected to the return material transfer stage;

[0095] When the excavation stage of the previous time step is the unloading stage and the above excavation stage recognition result is the excavation preparation stage, if the time t_4 in the unloading stage is less than the time threshold t_41, and max3 is greater than p1, the above excavation stage recognition result is corrected to the unloading stage. Otherwise, the above excavation stage recognition result is corrected to the excavation preparation stage;

[0096] When the excavation stage in the previous time step is the unloading stage and the above excavation stage recognition result is not the excavation preparation stage, the above excavation stage recognition result is corrected to the unloading stage.

[0097] By using correction rules to correct the recognition results, the influence of the model's insufficient grasp of the information in the entire mining cycle can be compensated, and the accuracy and reliability of the recognition results can be improved.

[0098] One of the above-mentioned embodiments of the present disclosure has the following beneficial effects: combining the characteristic sequence of the pressure signal, the sequence of the excavation cycle operation phases and the operation time characteristics, starting from the three dimensions of the driver's operation intention, the vehicle load and the working condition, a more comprehensive description of the vehicle status is made, thereby determining the final excavation stage recognition result and improving the accuracy of the recognition result.

[0099] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a mining phase identification device, and these device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0100] like Figure 2 As shown, the mining stage identification device of some embodiments includes: an acquisition unit 201, a construction unit 202, an extraction unit 203 and a determination unit 204. Among them, the acquisition unit 201 is used to obtain the leading pressure signal and the pump-end pressure signal of the target excavator at the current time step, the operation time of the above-mentioned target excavator at the previous time step, and the mining stage of the previous time step; the construction unit 202 is used to construct an input vector according to the above-mentioned leading pressure signal and the above-mentioned pump-end pressure signal; the extraction unit 203 is used to extract features from the above-mentioned input vector to obtain a pressure signal feature sequence; the determination unit 204 is used to determine the mining stage identification result of the above-mentioned target excavator at the current time step according to the above-mentioned pressure signal feature sequence, the operation time of the previous time step, and the mining stage of the previous time step.

[0101] It is understood that the units described in the above excavation stage identification device are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the above-mentioned excavation stage identification device and the units contained therein, and will not be described in detail here.

[0102] One of the above-mentioned embodiments of the present disclosure has the following beneficial effects: combining the characteristic sequence of the pressure signal, the sequence of the excavation cycle operation phases and the operation time characteristics, starting from the three dimensions of the driver's operation intention, the vehicle load and the working condition, a more comprehensive description of the vehicle status is made, thereby determining the final excavation stage recognition result and improving the accuracy of the recognition result.

[0103] Reference below Figure 3 , which shows a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0104] like Figure 3 As shown, the electronic device may include a processing device 301 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 302 (ROM) or a program loaded from a storage device 308 to a random access memory 303 (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device 301, the read-only memory 302 (ROM) and the random access memory 303 (RAM) are connected to each other via a bus 304. The input / output interface 305 (I / O interface) is also connected to the bus.

[0105] Typically, the following devices may be connected to the input / output interface 305 (I / O interface): input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0106] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0107] It should be noted that the computer-readable medium described above in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0108] In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0109] Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0110] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0111] The computer-readable medium may be included in the electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device implements: obtaining the pilot pressure signal and pump-end pressure signal of the target excavator at the current time step, the operation time of the target excavator at the previous time step, and the mining stage of the previous time step; constructing an input vector according to the pilot pressure signal and the pump-end pressure signal; performing feature extraction on the input vector to obtain a pressure signal feature sequence; determining the mining stage recognition result of the target excavator at the current time step according to the pressure signal feature sequence, the operation time of the previous time step, and the mining stage of the previous time step.

[0112] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0113] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0114] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be set in a processor, for example, may be described as: a processor includes an acquisition unit, a construction unit, an extraction unit and a determination unit. Among them, the names of these units do not constitute a limitation on the unit itself under certain circumstances. For example, the acquisition unit may also be described as "a unit for acquiring the pilot pressure signal and the pump end pressure signal of the target excavator at the current time step, the operation time of the above-mentioned target excavator at the previous time step and the excavation stage of the previous time step".

[0115] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0116] Some embodiments of the present disclosure further provide a computer program product, including a computer program, which implements any of the above-mentioned mining stage identification methods when executed by a processor.

[0117] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A mining phase identification method, characterized in that: include: Obtaining a pilot pressure signal and a pump end pressure signal of a target excavator at a current time step, an operation time of the target excavator at a previous time step, and an excavation stage at a previous time step; constructing an input vector according to the pilot pressure signal and the pump end pressure signal; Extracting features from the input vector to obtain a pressure signal feature sequence; According to the pressure signal feature sequence, the operation time of the previous time step, and the mining stage of the previous time step, the mining stage identification result of the target excavator in the current time step is determined.

2. The mining phase identification method according to claim 1, characterized in that: Constructing an input vector according to the pilot pressure signal and the pump end pressure signal, comprising: Combining and compressing the pilot pressure signal and the pump end pressure signal to obtain a combined compressed signal; The input vector is constructed based on the combined compressed signal and the following formula: ， Among them, n represents the time length of the selected input vector, represents the boom pilot pressure set at n sampling points, represents the set of arm pilot pressures at n sampling points, represents the bucket pilot pressure set of n sampling points, represents the set of rotary pilot pressures at n sampling points, represents the set of pump 1 pressures at n sampling points, Represents the set of pump 2 pressures at n sampling points.

3. The mining phase identification method according to claim 1, characterized in that: Performing feature extraction on the input vector to obtain a pressure signal feature sequence, including: The input vector is subjected to feature extraction using a pre-trained feature extraction model to obtain a pressure signal feature sequence, wherein the feature extraction model is obtained by training the first initial model using a time series neural network as the first initial model, wherein the first initial model includes an input layer, a time series network processing layer, a hidden layer, a classification layer, and an output layer. When the first initial model is trained, the classification layer and the output layer in the first initial model are discarded to obtain a feature extraction model.

4. The mining phase identification method according to claim 1, characterized in that: According to the pressure signal feature sequence, the operation time of the previous time step, and the mining stage of the previous time step, determining the current time step mining stage identification result of the target excavator, including: The pressure signal feature sequence, the operation time of the previous time step, and the mining stage of the previous time step are input into a pre-trained recognition model to obtain a mining stage recognition result, wherein the recognition model is obtained by training the second initial model with the DAGSVM model as the second initial model and the radial basis function as the kernel function of the second initial model.

5. The mining phase identification method according to claim 4, characterized in that: The DAGSVM model includes at least six binary classification nodes, and the at least six binary classification nodes are arranged in a directed acyclic graph in the DAGSVM model according to the following steps, including: Determine a binary classification node with the highest classification accuracy from the at least six binary classification nodes as a top node in the directed acyclic graph; For the remaining binary classification nodes, determine the related nodes with the top node from the remaining binary classification nodes, and combine the related nodes in pairs to obtain at least one binary classification node combination, and use the binary classification node combination with the highest average classification accuracy in the at least one binary classification node combination as the second layer node in the directed acyclic graph; The nodes in the remaining binary classification nodes except the second layer nodes are used as the third layer nodes in the directed acyclic graph.

6. The mining phase identification method according to claim 1, characterized in that: in, The excavation stage identification result includes an excavation preparation stage, an excavation stage, a return material transfer stage, and an unloading stage. The excavation stage identification method further includes: In response to determining that the mining phase recognition result satisfies the following rules, the mining phase recognition result is modified according to the corresponding rules, and the modified result is used as the mining phase recognition result: The excavation stage of the previous time step is the excavation preparation stage. When the excavation stage recognition result is the excavation stage, if the arm outward-flipping pilot pressure is greater than the bucket inward-flipping pilot pressure, the excavation stage recognition result is corrected to the excavation preparation stage. When the mining stage of the previous time step is the mining preparation stage and the mining stage recognition result is not the mining stage, the mining stage recognition result is corrected to the mining preparation stage; When the excavation stage of the previous time step is the excavation stage, and the excavation stage identification result is the return material transfer stage, if the time t_2 in the excavation stage is less than the time threshold t_21, the excavation stage identification result is corrected to the excavation stage; t_2 is greater than or equal to t_21, less than the time threshold t_22, and the maximum value max1 of the boom rise pilot pressure and the rotation pilot pressure is less than the maximum value max2 of the bucket retraction and the arm retraction pilot pressure, the excavation stage identification result is corrected to the excavation stage; if t_2 is greater than or equal to t_22, the excavation stage identification result is corrected to the return material transfer stage; When the excavation stage of the previous time step is the excavation stage, and the excavation stage recognition result is not the return material transfer stage, the excavation stage recognition result is corrected to the excavation stage; When the excavation stage of the previous time step is the return transfer stage and the identification result of the excavation stage is the unloading stage, the identification result of the excavation stage is corrected to the unloading stage; The excavation stage of the previous time step is the return material transfer stage. When the excavation stage recognition result is the excavation preparation stage, if the maximum value of the boom and bucket overturning pilot pressure max3 is greater than the pilot threshold p1, the excavation stage recognition result is corrected to the unloading stage; if max3 is less than or equal to p1, the correction result is the return material transfer stage; When the excavation stage of the previous time step is the return material transfer stage and the excavation stage recognition result is the excavation stage, the excavation stage recognition result is corrected to the return material transfer stage; When the excavation stage of the previous time step is the unloading stage and the excavation stage recognition result is the excavation preparation stage, if the time t_4 in the unloading stage is less than the time threshold t_41, and max3 is greater than p1, the excavation stage recognition result is corrected to the unloading stage; otherwise, the excavation stage recognition result is corrected to the excavation preparation stage; When the excavation stage in the previous time step is the unloading stage and the recognition result of the excavation stage is not the excavation preparation stage, the recognition result of the excavation stage is corrected to the unloading stage.

7. A digging stage identification device, characterized in that: include: An acquisition unit, used to acquire a pilot pressure signal and a pump end pressure signal of a target excavator at a current time step, an operation time of the target excavator at a previous time step, and an excavation stage at a previous time step; A construction unit, configured to construct an input vector according to the pilot pressure signal and the pump end pressure signal; An extraction unit, used for performing feature extraction on the input vector to obtain a pressure signal feature sequence; The determination unit is used to determine the mining stage identification result of the target excavator in the current time step according to the pressure signal feature sequence, the operation time of the previous time step, and the mining stage of the previous time step.

8. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the mining stage identification method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, wherein when the computer program is executed by a processor, the mining stage identification method according to any one of claims 1 to 6 can be implemented.

10. A computer program product, characterized in that The invention comprises a computer program, which can implement the mining stage identification method according to any one of claims 1 to 6 when being executed by a processor.

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