Detailed Description
In order to more clearly illustrate the general inventive concept, a detailed description is given below by way of example with reference to the accompanying drawings.
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to the description of the background technology, the existing monitoring on the operation and maintenance operation of the park is usually performed by manual or road side cameras for visual monitoring, however, the real-time accurate monitoring is difficult to achieve whether the monitoring is manual or video monitoring.
The applicant has found that the construction of the smart park at present often requires a multi-source data acquisition end, various sensors, visual acquisition devices and the like, and a plurality of passive sensing devices such as vibration cables, vibration optical fibers, ground wave sensors and the like are arranged underground in underground pipelines or underground at the roadside of the park for monitoring underground facilities or for perimeter security. Many environmental monitoring sensors, such as noise detection sensors, etc., are disposed on the ground. The applicant has found that these sensors, when collecting data, tend to collect all the data within the detection range in which they are located, and therefore the operation and maintenance operations can be monitored using the data collected by the sensors arranged in combination with vision. Based on this, the present application proposes an operation and maintenance job management method, referring to fig. 1, the method may include the following steps:
S10, acquiring first dynamic information of the operation and maintenance working device and/or second dynamic information of operation and maintenance personnel. As an exemplary embodiment, the campus operation and maintenance operation may include sanitation operation, road maintenance, greening maintenance, public facility maintenance, etc., where the operation and maintenance operation is performed, an operation and maintenance operation device and an operation and maintenance operator are often required to participate simultaneously, and sometimes only the operation and maintenance operator may participate. In an embodiment, the first dynamic information and the second dynamic information may include first positioning information of the operation and maintenance operation device and second positioning information of an operation and maintenance operator, and the first dynamic information may further include operation state information of the operation and maintenance operation device, and may include, for example, operation state information of a vehicle, for example, a running state or a stopping state of the vehicle, and may also include operation state information of the vehicle, for example, whether the sweeper is in a cleaning state, whether the sprinkler is in a sprinkling state, and the like. The system can also comprise the operation state of maintenance equipment, the use information of the operation and maintenance facilities by staff, and the like.
S20, acquiring operation vibration signals acquired by vibration monitoring devices arranged on the road side based on the first dynamic information and/or the second dynamic information. As an exemplary embodiment, the first dynamic information includes first positioning information of the operation and maintenance working device and/or the second dynamic information includes second positioning information of the operation and maintenance person.
The first positioning information and/or the second positioning information can be firstly obtained as vibration signals of the vibration source position for the acquisition of the operation vibration signals; and carrying out local analysis and filtering on the vibration signal in the time domain and the frequency domain to obtain the operation vibration signal.
As an exemplary embodiment, the position of the target vibration source may be determined through the first positioning information and/or the second positioning information, the preliminary screening may be performed on all vibration signals through the relative positional relationship and distance between the target vibration source and the vibration monitoring device, and in addition, the type of the operation and maintenance device may be acquired, the fixed vibration characteristics may be determined, and the vibration signals may be further screened through the fixed vibration characteristics.
As an exemplary embodiment, the vibration signal may be subjected to local analysis filtering in the time domain and the frequency domain, the original signal may be subjected to wavelet transformation to obtain wavelet coefficients in a plurality of scales through wavelet analysis, and then the time domain and the frequency domain information may be simultaneously extracted through the change of the wavelet coefficients.
However, characteristics of signals generated by different objects tend to be different, and vibration signals generated by an operation and maintenance device and an operation and maintenance operator tend to be relatively fixed, so that before wavelet analysis, input signals of the wavelet analysis can be screened again based on machine learning. To train a vibration signal classification model. After training is completed, the initially screened signal set is input into a trained vibration signal classification model, and the signals in the signal set can be classified according to the states of the operation and maintenance device and the operation and maintenance worker. And carrying out wavelet analysis on the classification result, and carrying out local analysis and filtering on the vibration signal in the time domain and the frequency domain to obtain the operation vibration signal.
In order to avoid interference of the operation vibration signal on the operation vibration signal, in this embodiment, the real-time operation state of the operation and maintenance device may be collected by an operation state collection device installed on the vehicle or the device, for example, an ECU of the vehicle or the device may collect the operation state of the engine and the running state of the vehicle in real time as the real-time operation state, and determine the operation vibration characteristics of the operation vibration signal corresponding to the real-time operation state by the real-time operation state, and filter the operation vibration signal based on the operation vibration characteristics to obtain the actual operation vibration signal of the operation and maintenance device.
S30, inputting the operation vibration signals into a twin feature extraction network to obtain operation vibration features, wherein the twin feature extraction network is trained based on sample pairs constructed by the operation vibration signals and the non-operation vibration signals. As an exemplary embodiment, the operation vibration signal may be, for example, a vibration signal generated when an operator performs a cleaning operation on a street, the characteristics of the operation vibration signal may include a time-series market of cleaning, a force of cleaning, a speed of cleaning (i.e., an interval between cleaning operation vibration signals), etc., and the operation vibration signal for the operation and maintenance operation device may include, for example, a speed of cleaning by a cleaning vehicle, a force of cleaning, a range vibration characteristic of cleaning. The operation efficiency (interval duration, etc. of the corresponding operation vibration signal) of the road surface restoration device may also be included. The operation vibration characteristics can represent the current working state of the operation device and/or the operation personnel, the working duration, the working efficiency, the working mode and other working characteristics.
As an exemplary embodiment, the training for the twin feature extraction network may construct a feature sample pair by different types of vibration signals, for example, a working vibration signal and a non-working vibration signal may be constructed as a feature sample pair, wherein the non-working vibration signal may be a disturbance vibration signal generated during working, for example, a vibration signal generated when a vehicle is driven on a road surface, a vibration signal generated when a pedestrian passes, or the like.
The samples in the sample pair are different types of vibration signals, namely, the operation vibration signal and the non-operation vibration signal are constructed into a pair of sample pairs, one network branch extracts the operation vibration characteristics of the operation vibration signal, one network branch extracts the non-operation vibration characteristics of the non-operation vibration signal, and the parameters of the two network branches are shared, namely, the parameters of the two network branches are the same.
In this embodiment, the twin feature extraction network includes: two neural network branches of weight sharing, wherein each neural network branch includes a convolutional layer and an attention module.
Inputting the operation vibration signal into a twin feature extraction network, wherein obtaining the operation vibration feature comprises:
and extracting a plurality of dimensional vibration characteristic information through the convolution layer. In the present embodiment, the initial vibration characteristic information of the multiple dimensions, such as the frequency, amplitude, phase, and the like, of the vibration characteristics is extracted by the convolution layer. The multi-dimensional initial vibration characteristics have a coupling relation, and the different specific characteristics corresponding to different operation types are different, and the specific characteristics corresponding to the operation and maintenance operation device and the operation and maintenance operation personnel are also different.
Therefore, in this embodiment, the multi-dimensional vibration feature information is input to the attention module to perform adaptive weighted fusion on the multi-dimensional vibration feature information, so as to obtain the operation vibration feature and the non-operation vibration feature.
Specifically, the attention weight of the operation and maintenance operation device and the operation and maintenance operation personnel on the characteristics can be learned based on the attention module, so that the operation types, the operation and maintenance operation device and the operation and maintenance operation personnel can be more accurately identified and distinguished through the attention deviation on some specific characteristics. Therefore, the attention module is used for carrying out weighted fusion on the characteristics of multiple dimensions, and the fused vibration characteristics of the vibration characteristic information are obtained. After the fusion vibration characteristics of the operation vibration signals and the fusion vibration characteristics of the non-operation vibration signals are obtained respectively, the operation and maintenance operation identification model can be trained by taking the fusion vibration characteristics of the non-operation vibration signals as the fusion vibration characteristic negative sample of the operation vibration signals, so that the problem of inaccurate identification caused by introducing the non-operation vibration characteristics of the unfiltered non-operation vibration signals during identification is avoided.
In the present embodiment, the job vibration signal may also be learned by the countermeasure network, or the job vibration feature may be separated from the non-job vibration feature by the clustering network.
S40, inputting the operation vibration characteristics into a pre-trained operation and maintenance operation recognition model to obtain an operation and maintenance operation first state recognition result, wherein the operation and maintenance operation recognition model is obtained by training based on operation vibration characteristic samples and corresponding operation state labels, and the operation vibration characteristic samples comprise first operation vibration signals of an operation and maintenance operation device in different operation states and second operation vibration signals of an operation and maintenance worker in the operation states. As an exemplary embodiment, the operation and maintenance operation recognition model may be trained through marked operation vibration feature samples, and non-operation vibration feature samples may be introduced as negative samples to train the operation and maintenance operation recognition model together, so that the recognition model has a capability of distinguishing non-operation vibration features.
In this embodiment, vibration signals collected by the road side vibration monitoring device are screened through state information of the operation and maintenance operation device and operation and maintenance operation personnel to obtain operation vibration signals, operation vibration characteristics are extracted more specifically through the twin characteristic extraction network, and real-time operation states of operation and maintenance operations are identified through the operation and maintenance operation identification model to identify the vibration characteristics. Through multiplexing underground facilities in wisdom garden and monitoring, or for perimeter security protection, arranged many passive sensing equipment underground, for example, vibration cable, vibration optic fibre, signal such as ground wave sensor, utilize its collection scope wide, the signal dimension is low, the sensitivity is high characteristics, can monitor the operation process of operation and maintenance operation in the maximum scope, on the one hand can reduce the problem that the resource cost is big because of image data occupies memory and calculation power is too big, on the other hand, through multiplexing the real-time characteristic of the low dimension data of other sensors, can more timely accurate carry out real-time supervision to operation and maintenance operation state under the condition that need not too high calculation power and too much storage resource.
As an alternative embodiment, the distinction between the operational vibration feature and the non-operational vibration feature may be learned by clustering, and in this embodiment, the twin feature extraction network further includes: and setting a first feature clustering module after the attention module.
Extracting operational vibration features in a first neural network branch and extracting non-operational vibration features in a second neural network; and inputting the operation vibration characteristics and the non-operation vibration characteristics into the first characteristic clustering module, and clustering the operation vibration characteristics and the non-operation vibration characteristics respectively so that the operation and maintenance operation identification model classifies the operation vibration characteristics and the non-operation vibration characteristics.
As an exemplary embodiment, in the present embodiment, one or more cluster centers are determined based on semantic units of the plurality of vibration feature entities. The clusters (or clusters) generated by the clustering operation are a collection of data objects that are similar to each other and different from the objects in the other clusters. The cluster center is the most important one of the objects in the cluster, which is the most representative of the cluster and the most interpretable for the other objects in the cluster. In some embodiments, a cluster has only one cluster center. In some embodiments, the cluster center may be one or more vibration feature entities selected from a plurality of vibration feature entities, each cluster center serving as a reference object in calculating a similarity between the cluster center and other vibration feature entities of the plurality of vibration feature entities. After the cluster center is obtained, the vibration characteristic of the cluster center is taken as the specific vibration characteristic corresponding to the embodiment.
The first clustering module is used for respectively obtaining the operation vibration characteristics and the non-operation vibration characteristics so that the operation vibration characteristics are more similar, the operation vibration characteristics and the non-operation vibration characteristics are more dissimilar, and the identification model is easier to distinguish the operation vibration characteristics and the non-operation vibration characteristics.
In one embodiment, the clustering of the operational vibration features and the non-operational vibration features, respectively, includes: respectively calculating a first similarity between the operation vibration characteristics, a second similarity between the non-operation vibration characteristics and a third similarity between the operation vibration characteristics and the non-operation vibration characteristics; reducing a distance between operational vibration characteristics based on the first similarity and the third similarity to increase the first similarity while reducing the third similarity; reducing a distance between the non-operational vibration features based on the second similarity and the third similarity to increase the second similarity while reducing the third similarity; and increasing a distance between the operational vibration feature and the non-operational vibration feature based on the third similarity to reduce the third similarity. And by calculating the similarity and adjusting the similarity, the distance between the working vibration characteristics and the non-working vibration characteristics is increased, so that the distances between the characteristics of the same type are smaller, and the distances between the characteristics of different types are larger.
As an exemplary embodiment, when some image acquisition devices exist and the image acquisition devices can acquire a clearer image, the operation and maintenance operation process can be acquired in real time based on the image acquisition devices, the operation and maintenance operation process acquired in real time is compared with the operation and maintenance operation state identification result obtained through the operation and maintenance operation identification model, and parameters of the operation and maintenance operation identification model are corrected and updated by utilizing the comparison result, so that the operation and maintenance operation identification model is more accurate.
Specifically, extracting the operation image change characteristics of the operation and maintenance operation device and/or operation and maintenance operation personnel; performing time sequence and dimension alignment on the operation vibration characteristic and the operation image change characteristic; clustering and fusing the aligned operation vibration characteristics and the operation image change characteristics through a second characteristic clustering module to obtain fused operation characteristics; and updating parameters of the operation and maintenance operation identification model based on the fusion operation characteristics.
As an exemplary embodiment, job image information is input to an image feature extraction network to extract job image features, in this embodiment, job image features may be extracted through a convolution network and a time sequence feature extraction network that are cascaded, static features of a job image may be extracted through the convolution network, after the static features are obtained, a static feature sequence is constructed, and time sequence associated features in the static feature sequence, that is, dynamic features of the job image, may be extracted through the time sequence feature extraction network.
As an alternative embodiment, in the static feature extraction network, two-dimensional operations may be performed on the image information first. Firstly, inputting data into a convolution layer, and activating by using a LeakyReLU activation layer after batch standardization operation; then input into the convolution layer with the convolution kernel size of 1×1, and use LeakyReLU activation layer to make secondary activation, finally use the largest pooling layer with the pooling size of 2×2 to pool the feature map.
After the static features are obtained, time-series related features in the static feature sequence are extracted through a cyclic neural network (e.g., LSTM, GRU) to characterize the image change features, such as continuous dynamic features of the work action, and time-series cumulative features of the work action.
Since the operation vibration feature and the image change feature of the operation have a temporal relationship, the features can be aligned based on the temporal relationship, and the image change feature and the operation vibration feature are also different in feature dimension, in this embodiment, the two features are also required to be aligned in dimension, and in this embodiment, the operation vibration feature can be decomposed, for example, a wavelet packet decomposition tree is used to extract a plurality of frequency band signal components, the energy of the signals of each frequency band is obtained, the feature vector is constructed by using the energy of the signal of each frequency band as an element, and the vector is normalized to obtain the histogram of the feature vector. And similarly, binarizing the image change features, and obtaining a feature vector histogram of the image change features to further realize dimension alignment.
Inputting the aligned public facility signal characteristics and the image change characteristics into a second characteristic clustering module for clustering, and in the embodiment, fusing through an attention mechanism to obtain fused operation characteristics; splitting the aligned operation vibration characteristic and the image change characteristic into an operation vibration characteristic sequence and an image change characteristic sequence according to time sequence to obtain a characteristic pair sequence; calculating the feature similarity of each feature pair; determining an attention weight of each feature pair based on the feature similarity, the fusion weight being positively correlated with the feature similarity; and carrying out weighted fusion on the feature pairs based on the attention weight to obtain the fusion feature.
In this embodiment, the fusion weight may be based on the similarity of each feature pair in the aligned vibration feature sequence and image change feature sequence, and determine the attention weight of each feature pair based on the feature similarity, that is, the feature pair with high similarity has a larger corresponding feature weight.
As another alternative embodiment, the second feature clustering module may employ a depth cross attention module that may include a correlation layer, an attention layer, and a depth correlation layer. Forming a attention pattern according to the correlation of the operation vibration characteristic and the image change characteristic in time sequence, so as to emphasize the characteristic with strong correlation in the two data; wherein the correlation layer is dedicated to integrating the two features rather than simply making a series, summation. Obtaining a correlation matrix based on the time sequence correlation of the operation vibration characteristics and the image change characteristics in a correlation layer, and calculating a cross attention matrix corresponding to the correlation matrix in an attention layer; learning the cross attention matrix by using an attention layer to obtain a non-exclusive relation between the operation vibration characteristic and the image change characteristic; fusing the non-mutually exclusive relations by using an attention mechanism to obtain a relation characteristic diagram of the vibration characteristic of the operation and the image change characteristic; and in the depth correlation layer, performing feature fusion on the operation vibration feature and the image change feature by applying a mutual convolution operation on the relation feature map.
The fusion operation feature can more accurately represent the current operation state, so that parameters of the operation and maintenance operation identification model can be updated through the fusion operation feature, and the operation and maintenance operation identification model can deeply fuse the actual image feature and the actual vibration feature and then can identify the operation vibration feature according to the identification capability.
The embodiment of the application also provides an operation and maintenance operation management device, as shown in fig. 2, comprising:
a first obtaining module 21, configured to obtain first dynamic information of the operation and maintenance working device and/or second dynamic information of an operation and maintenance person;
a second acquisition module 22, configured to acquire an operation vibration signal acquired by a vibration monitoring device disposed on a road side based on the first dynamic information and/or the second dynamic information;
The feature extraction module 23 is configured to input the operation vibration signal into a twin feature extraction network to obtain an operation vibration feature, where the twin feature extraction network is obtained by training based on a sample pair constructed by the operation vibration signal and the non-operation vibration signal;
The identifying module 24 is configured to input the operation vibration characteristic into a pre-trained operation and maintenance operation identifying model to obtain a first state identifying result of the operation and maintenance operation, where the operation and maintenance operation identifying model is obtained by training based on an operation vibration characteristic sample and a corresponding operation state label, and the operation vibration characteristic sample includes first operation vibration signals in different operation states of the operation and maintenance operation device, and second operation vibration signals in an operation state of an operation and maintenance worker.
It should be noted that, the first obtaining module 21 in this embodiment may be used to perform the above-mentioned step S10, the second obtaining module 22 in this embodiment may be used to perform the above-mentioned step S20, the feature extraction module 23 in this embodiment may be used to perform the above-mentioned step S30, and the identification module 24 in this embodiment may be used to perform the above-mentioned step S40.
It should be noted that the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the above embodiments. It should be noted that the above modules may be implemented in software or in hardware as part of the apparatus shown in fig. 3, where the hardware environment includes a network environment.
Thus, according to still another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the above operation and maintenance job management method, which may be a server, a terminal, or a combination thereof.
Fig. 3 is a block diagram of an alternative electronic device, according to an embodiment of the application, as shown in fig. 3, comprising a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302 and the memory 303 perform communication with each other via the communication bus 304, wherein,
A memory 303 for storing a computer program;
the processor 301 is configured to execute the computer program stored in the memory 303, and implement the following steps:
acquiring first dynamic information of an operation and maintenance working device and/or second dynamic information of operation and maintenance personnel;
Acquiring an operation vibration signal acquired by a vibration monitoring device arranged on a road side based on the first dynamic information and/or the second dynamic information;
Inputting the operation vibration signal into a twin feature extraction network to obtain operation vibration features, wherein the twin feature extraction network is obtained by training a sample pair constructed based on the operation vibration signal and the non-operation vibration signal;
The operation vibration characteristics are input into a pre-trained operation and maintenance operation identification model to obtain an operation and maintenance operation state identification result, wherein the operation and maintenance operation identification model is obtained by training based on operation vibration characteristic samples and corresponding operation state labels, and the operation vibration characteristic samples comprise first operation vibration signals of an operation and maintenance operation device in different operation states and second operation vibration signals of an operation and maintenance worker in the operation states.
Alternatively, in the present embodiment, the above-described communication bus may be a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard Architecture ) bus, or the like. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one thick line is shown in fig. 3, but not only one bus or one type of bus.
The communication interface is used for communication between the electronic device and other devices.
The memory may include RAM or may include non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general purpose processor and may include, but is not limited to: CPU (Central Processing Unit ), NP (Network Processor, network processor), etc.; but also DSP (DIGITAL SIGNAL Processing), ASIC (Application SPECIFIC INTEGRATED Circuit), FPGA (Field-Programmable GateArray, field programmable gate array) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is only illustrative, and the device implementing the operation and maintenance operation management method may be a terminal device, and the terminal device may be a smart phone (such as an Android Mobile phone, an iOS Mobile phone, etc.), a tablet computer, a palm computer, a Mobile internet device (Mobile INTERNET DEVICES, MID), a PAD, etc. Fig. 3 is not limited to the structure of the electronic device. For example, the terminal device may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in fig. 3, or have a different configuration than shown in fig. 3.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute in association with hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, ROM, RAM, magnetic or optical disk, etc.
According to yet another aspect of an embodiment of the present application, there is also provided a storage medium. Alternatively, in the present embodiment, the above-described storage medium may be used for executing the program code of the operation and maintenance job management method.
Alternatively, in this embodiment, the storage medium may be located on at least one network device of the plurality of network devices in the network shown in the above embodiment.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of:
acquiring first dynamic information of an operation and maintenance working device and/or second dynamic information of operation and maintenance personnel;
Acquiring an operation vibration signal acquired by a vibration monitoring device arranged on a road side based on the first dynamic information and/or the second dynamic information;
Inputting the operation vibration signal into a twin feature extraction network to obtain operation vibration features, wherein the twin feature extraction network is obtained by training a sample pair constructed based on the operation vibration signal and the non-operation vibration signal;
The operation vibration characteristics are input into a pre-trained operation and maintenance operation identification model to obtain an operation and maintenance operation state identification result, wherein the operation and maintenance operation identification model is obtained by training based on operation vibration characteristic samples and corresponding operation state labels, and the operation vibration characteristic samples comprise first operation vibration signals of an operation and maintenance operation device in different operation states and second operation vibration signals of an operation and maintenance worker in the operation states.
Alternatively, specific examples in the present embodiment may refer to examples described in the above embodiments, which are not described in detail in the present embodiment.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a U disk, ROM, RAM, a mobile hard disk, a magnetic disk or an optical disk.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
The integrated units in the above embodiments may be stored in the above-described computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing one or more computer devices (which may be personal computers, servers or network devices, etc.) to perform all or part of the steps of the method described in the embodiments of the present application.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In several embodiments provided by the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution provided in the present embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The application can be realized by adopting or referring to the prior art at the places which are not described in the application.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.