CN111428864A - Automatic transaction generation method and system based on neural network - Google Patents
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Abstract
The invention discloses an automatic transaction generation method based on a neural network, which comprises the following steps: acquiring equipment data in real time; and inputting the equipment data acquired in real time into a pre-trained transaction generation model based on the neural network to obtain a processing scheme. According to the invention, a plurality of acquisition terminals are adopted to detect the current information of some old people in real time, and then the current information is displayed in real time through complex analysis, and relevant information and information are pushed at any time, so that a nursing staff can process the current information at the first time, and the real-time detection of the condition of each old person in an old people home can be more real-time and more efficient.
Description
Technical Field
The invention relates to the field of data processing, in particular to an automatic transaction generation method and system based on a neural network.
Background
With the increasing aging of society, the demand for endowment services in society is increasing, so the scale of an endowment hospital and the allocation of hands of people are very critical indexes, but the simple patrol does not always find problems in time because the personnel patrol has a time blank period, especially in the sleeping period of the old at night. And the nursing home is difficult to look after the old one by one, so that the emergency is difficult to find in time.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, it is an object of the present invention to provide an automatic transaction generation method and system based on neural network, which is used to solve the shortcomings of the prior art.
To achieve the above and other related objects, the present invention provides an automatic transaction generation method based on a neural network, including:
acquiring equipment data in real time;
and inputting the equipment data acquired in real time into a pre-trained transaction generation model based on the neural network to obtain a processing scheme.
Optionally, the training method of the neural network-based transaction generation model includes:
acquiring training data;
constructing a training sample set according to the training data, wherein the training data comprises one or more groups of equipment data, and each group of equipment data corresponds to one processing scheme;
and training the neural network based on the training sample set to obtain a transaction generation model.
Optionally, the neural network is retrained using the processing scheme generated by the transaction generation model and the corresponding device data as training data.
Optionally, the method further comprises: and normalizing the equipment data acquired in real time and the training data in the training sample set.
Optionally, the device data acquired in real time is judged, and when the preset condition is not met, an alarm prompt is sent out; and inputting the equipment data which does not meet the preset conditions into a pre-trained transaction generation model based on the neural network to obtain a corresponding equipment operation method.
To achieve the above and other related objects, the present invention provides an automatic transaction generation system based on neural network, comprising:
the data acquisition module is used for acquiring equipment data in real time;
and the scheme generation module is used for inputting the equipment data acquired in real time into a neural network-based transaction generation model trained in advance by the model training module to obtain a processing scheme.
Optionally, the model training module includes:
a data acquisition unit for acquiring training data;
the sample set construction unit is used for constructing a training sample set according to the training data, the training data comprises one or more groups of equipment data, and each group of equipment data corresponds to one processing scheme;
and the model generation unit is used for training the neural network based on the training sample set to obtain a transaction generation model.
Optionally, the neural network is retrained using the processing scheme generated by the transaction generation model and the corresponding device data as training data.
Optionally, the data processing module is configured to perform normalization processing on the device data acquired in real time and the training data in the training sample set.
Optionally, the device data acquired in real time is judged, and when the preset condition is not met, an alarm prompt is sent out; and inputting the equipment data which does not meet the preset conditions into a pre-trained transaction generation model based on the neural network to obtain a corresponding equipment operation method.
As described above, the automatic transaction generation method and system based on neural network of the present invention have the following advantages:
the invention can better realize automatic nursing home management, and has great advantages in the aspects of personnel workload and real-time response of work. And aiming at the special group of the old people, more detailed and accurate monitoring is realized. And intelligent control is performed aiming at a plurality of blind areas of manual nursing, so that the old-care activities are not complicated and the state of a leak is changed.
Drawings
FIG. 1 is a flow chart of a neural network based method for automatic transaction generation in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of a method for training a neural network-based transaction generation model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an automatic transaction generation system based on neural networks according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a model training module according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an automatic transaction generation system based on neural networks in an embodiment of the present invention;
fig. 6 is a schematic diagram of an automatic transaction generation system based on a neural network according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
As shown in fig. 1, an automatic transaction generation method based on neural network includes:
s11, acquiring equipment data in real time;
s12, inputting the equipment data acquired in real time to a pre-trained transaction generation model based on the neural network to obtain a processing scheme.
In one embodiment, the acquisition of equipment data is collected through the acquisition end, and the acquisition end includes door magnetism, temperature and humidity sensor, intelligent mattress, a key alarm, PM2.5 air quality detector, face identification camera etc.. Correspondingly, the equipment data comprises door magnetism data, temperature and humidity, intelligent mattress data, one-key alarm data, PM2.5 air quality data, human face data and the like. The device data may be received by wire or wirelessly, or may be retrieved from memory. In this embodiment, since the state of the elderly needs to be monitored in real time, a method of real-time transmission by a wired or wireless method is selected.
As shown in fig. 2, the training method of the neural network-based transaction generation model includes:
s21 obtaining training data;
s22, constructing a training sample set according to the training data, wherein the training data comprises one or more groups of equipment data, and each group of equipment data corresponds to one processing scheme;
s23, training the neural network based on the training sample set to obtain a transaction generation model.
In one embodiment, the neural network is retrained using the processing plan generated by the transaction generation model and the corresponding device data as training data.
The present invention collects the equipment data of each time as training data and stores the equipment data into a database. After the processing scheme is generated, the newly generated processing scheme and the device data corresponding to the processing scheme are used as training data to retrain the neural network. And updating the processing algorithm aiming at each time of processing the data so as to ensure that the most reasonable operation scheme is more accurately performed on the current acquired data.
For training of the model and generation of the scheme, normalization processing can be performed on the device data acquired in real time and the training data in the training sample set, and the data can be normalized.
For example, for fixed length 100 data x1,x2,...,x100And (3) carrying out transformation:
after conversion, then y1,y2,...,y100∈[0,1]。
The normalized data enters normal logic judgment output, the actual output is compared with expected output again, the expected output is an expected result which is continuously corrected by personnel in the early stage, and the algorithm automatically updates the latest algorithm after comparison, so that the judgment after comparison is closer to the expected output result.
In one embodiment, a neural network includes an input layer, an intermediate layer, and an output layer;
inputting training data via the input layer;
calculating the input training data to obtain an intermediate representation by each of one or more computing nodes in the intermediate layer;
outputting, via the output layer, a training result, the training result including a processing scheme.
When the loss function of the neural network meets a preset condition, obtaining the trained neural network model, namely a transaction generation model; and when the loss function of the neural network does not meet the preset condition, continuing to input training data to repeatedly execute the training process, wherein the loss function of the neural network is determined according to the training result.
The input layer of the neural network takes n-10 neurons in total, which respectively represent input ends x1 and x2 … x10, the middle layer is provided with m-5 neurons y1 and y2 … y5, and the output layer is provided with 1 neuron; the transfer function of the neural network may also be called a neuron characteristic function as a unipolar S-type function, and specifically, the neuron characteristic function is:
the connection weight value of the i-layer neuron and the j-layer neuron in the next layer in the neural network is WijInput is XjThe output is Xi'；
θiAs an initial parameter, a value of 0.5 can be taken; xj' denotes the output of the j-th layer.
Wherein:
Wij(n+1)＝Wij(n)+ΔWij(n+1)
in the formula: Δ Wij(n +1) represents the difference value between the connection weight of the n layer neuron and the connection weight of the n +1 layer neuron, omega is the learning rate and takes the value of 0.9, α is the momentum coefficient and takes the value of 0.8;joutputting an error for the neuron;
in one embodiment, the weight is adjusted by using a systematic error gradient method, that is:j＝-(Xi'-Xj)·Xj'·(1-Xj')。
in one embodiment, the equipment data acquired in real time is judged, and when the preset conditions are not met, an alarm prompt is sent out; and inputting the equipment data which does not meet the preset conditions into a pre-trained transaction generation model based on the neural network to obtain a corresponding equipment operation method.
According to the invention, a plurality of acquisition terminals are adopted to detect the current information of some old people in real time, and then the current information is displayed in real time through complex analysis, and relevant information and information are pushed at any time, so that a nursing staff can process the current information at the first time, and the real-time detection of the condition of each old person in an old people home can be more real-time and more efficient.
As shown in fig. 3, an automatic transaction generation system based on neural network includes:
a data acquisition module 31, configured to acquire device data in real time;
and the scheme generation module 32 is configured to input the device data acquired in real time into a neural network-based transaction generation model trained in advance by the model training module to obtain a processing scheme.
As shown in fig. 4, the model training module includes:
a data acquisition unit 41 for acquiring training data;
a sample set constructing unit 42, configured to construct a training sample set according to the training data, where the training data includes one or more sets of device data, and each set of device data corresponds to one processing scheme;
and the model generating unit 43 is configured to train the neural network based on the training sample set to obtain a transaction generation model.
In one embodiment, the neural network is retrained using the processing plan generated by the transaction generation model and the corresponding device data as training data.
In an embodiment, the data processing module is configured to perform normalization processing on the device data acquired in real time and the training data in the training sample set.
In one embodiment, the equipment data acquired in real time is judged, and when the preset conditions are not met, an alarm prompt is sent out; and inputting the equipment data which does not meet the preset conditions into a pre-trained transaction generation model based on the neural network to obtain a corresponding equipment operation method.
Since the embodiment of the apparatus portion and the embodiment of the method portion correspond to each other, please refer to the description of the embodiment of the method portion for the content of the embodiment of the apparatus portion, which is not repeated here.
As shown in fig. 5, an automatic transaction generation system based on neural network includes an acquisition end, a collection end, a processing end and a human-computer interaction end; the system comprises an acquisition end, a monitoring end and a control end, wherein the acquisition end is used for acquiring equipment data and comprises a door magnet, a temperature and humidity sensor, an intelligent mattress, a one-key alarm, a PM2.5 air quality detector, a face recognition camera and the like; the collecting end collects data collected by the collecting end and sends the data to the processing end, and the processing end generates a corresponding processing scheme by using a pre-trained transaction generation model based on a neural network; and the human-computer interaction terminal displays the corresponding processing scheme and pushes the corresponding processing scheme at the same time.
In one embodiment, the collecting end is a WIFI intelligent gateway, and the equipment data are transmitted to the collecting end in a ZigBee communication mode; the processing end is a PC control background and runs the method shown in the figure 1 to generate a processing scheme.
As shown in fig. 6, the self-learning algorithm adopted in the present invention is a real-time process control structure, that is, the system obtains the current state from the outside in real time, performs the preprocessing, and performs the timely control according to the processing result, that is, a feedback loop structure.
The system takes the output quantity Y as a control object, takes Y as a control target, takes U process control, takes a neural network system identification model which is offline in advance for weight initialization training and continuously performs online learning in the process as a control model, calculates the value of the control quantity U required at a certain moment by the judgment model, constructs a real-time control system based on the neural network, and realizes automatic tracking of Ysr.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed 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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may comprise any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, etc.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. An automatic transaction generation method based on a neural network, comprising:
acquiring equipment data in real time;
and inputting the equipment data acquired in real time into a pre-trained transaction generation model based on the neural network to obtain a processing scheme.
2. The neural network-based automatic transaction generation method of claim 1, wherein the training method of the neural network-based transaction generation model comprises:
acquiring training data;
constructing a training sample set according to the training data, wherein the training data comprises one or more groups of equipment data, and each group of equipment data corresponds to one processing scheme;
and training the neural network based on the training sample set to obtain a transaction generation model.
3. The automatic transaction generation method based on neural network as claimed in claim 2, wherein the neural network is retrained using the processing scheme generated by the transaction generation model and the corresponding device data as training data.
4. The neural network-based automatic transaction generation method of claim 2, further comprising:
and normalizing the equipment data acquired in real time and the training data in the training sample set.
5. The automatic transaction generation method based on neural network as claimed in claim 1, wherein the device data acquired in real time is judged, and when the preset condition is not satisfied, an alarm prompt is issued; and inputting the equipment data which does not meet the preset conditions into a pre-trained transaction generation model based on the neural network to obtain a corresponding equipment operation method.
6. An automatic transaction generation system based on neural networks, comprising:
the data acquisition module is used for acquiring equipment data in real time;
and the scheme generation module is used for inputting the equipment data acquired in real time into a neural network-based transaction generation model trained in advance by the model training module to obtain a processing scheme.
7. The automatic neural network-based transaction generating system of claim 6, wherein the model training module comprises:
a data acquisition unit for acquiring training data;
the sample set construction unit is used for constructing a training sample set according to the training data, the training data comprises one or more groups of equipment data, and each group of equipment data corresponds to one processing scheme;
and the model generation unit is used for training the neural network based on the training sample set to obtain a transaction generation model.
8. The automatic neural network-based transaction generating system of claim 7, wherein the neural network is retrained using the processing plan generated by the transaction generating model and the corresponding device data as training data.
9. The automatic neural network-based transaction generating system of claim 7, further comprising:
and the data processing module is used for carrying out normalization processing on the equipment data acquired in real time and the training data in the training sample set.
10. The automatic transaction generation system based on neural network as claimed in claim 7, wherein the device data acquired in real time is judged, and when the preset condition is not satisfied, an alarm prompt is issued; and inputting the equipment data which does not meet the preset conditions into a pre-trained transaction generation model based on the neural network to obtain a corresponding equipment operation method.
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