CN116313127A - Decision support system based on pre-hospital first-aid big data - Google Patents

Decision support system based on pre-hospital first-aid big data Download PDF

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CN116313127A
CN116313127A CN202310297299.0A CN202310297299A CN116313127A CN 116313127 A CN116313127 A CN 116313127A CN 202310297299 A CN202310297299 A CN 202310297299A CN 116313127 A CN116313127 A CN 116313127A
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张赪
涂建刚
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Zhuhai Anke Electronic Technology Co ltd
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Abstract

The invention relates to the technical field of big data, and discloses a decision support system based on pre-hospital first-aid big data, which comprises the following components: a terminal information acquisition module for determining the ID of the help seeking terminal of the help seeking person through the attribution of the help seeking terminal; the associated information acquisition module acquires associated information according to the ID of the help seeker help terminal; a correlation information processing module for generating correlation timing information based on the correlation information in a time order; a model generation module for generating a decision learning model; the decision classification module is used for inputting the associated time sequence information generated for the current help seeker into the decision learning model and outputting the first-aid class label; a rescue decision module that selects a corresponding rescue means based on the currently output first aid category label; the invention can carry out deep learning to construct a decision model based on the help seeker terminal help record such a data source comprehensive pre-hospital big data so as to judge the emergency degree of the first aid requirement of the help seeker.

Description

Decision support system based on pre-hospital first-aid big data
Technical Field
The invention relates to the technical field of big data, in particular to a decision support system based on pre-hospital first-aid big data.
Background
For emergency help seekers who can normally inquire the illness state through communication, it is feasible to judge the emergency degree of emergency through manual recording of the illness state, but for some patients who cannot communicate with an operator, the illness state of the patient cannot be obtained through communication, whether the help seeker misreports or needs emergency cannot be judged, and the emergency degree of emergency needs cannot be judged.
Disclosure of Invention
The invention provides a decision support system based on pre-hospital emergency big data, which solves the technical problem that emergency call telephones which cannot communicate normally in the related art cannot judge the emergency degree of emergency demands.
The invention provides a decision support system based on pre-hospital first-aid big data, which comprises:
a terminal information acquisition module for determining the ID of the help seeking terminal of the help seeking person through the attribution of the help seeking terminal;
the associated information acquisition module acquires associated information according to the ID of the help seeker help terminal;
the system comprises a related information processing module, a time sequence processing module and a time sequence processing module, wherein the related information processing module is used for generating related time sequence information based on the related information according to time sequence from the time when the help seeking terminal terminates the help seeking call, and one sequence unit of the related time sequence information comprises corresponding related information before one time point;
a model generation module for generating a decision learning model;
the first aid category labels output by the decision learning model correspond to false alarms, emergency first aid and general rescue;
a model training module for training the decision learning model through historical associated time sequence information and corresponding first aid class labels;
the decision classification module is used for inputting the associated time sequence information generated for the current help seeker into the decision learning model and outputting the first-aid class label;
and a rescue decision module for selecting a corresponding rescue means based on the currently output first aid class label.
Further, the associated information includes other terminals contacted by the help seeking terminal, call duration corresponding to the contacted terminal, time corresponding to the contacted terminal, and IP address corresponding to the contacted terminal.
Further, the correlation timing information is generated from the correlation information after normalization.
Further, the decision learning model comprises an LSTM neural network, wherein the LSTM neural network comprises a plurality of LSTM units, and each LSTM unit inputs a sequence unit associated with time sequence information;
output state h of each LSTM cell t The calculation formula of (2) is as follows:
h t ＝o t ⊙tanh(C t )
wherein the output door
Figure BDA0004143648080000021
W xo Representing input x t Transfer to o t The corresponding weight matrix is used to determine the weight matrix,
Figure BDA0004143648080000022
representing the output state h of the last LSTM cell t-1 Transfer to o t Corresponding weight matrix, b o Representing bias terms, σ representing a sigmoid function;
wherein the output state
Figure BDA0004143648080000023
f t ⊙C t-1 Door f for indicating forgetfulness t And output state C of last LSTM cell t-1 Multiplying point by point, f t ∈(0,1)；
Figure BDA0004143648080000024
Indicating input/output gate i t And intermediate state->
Figure BDA0004143648080000025
Performing point-by-point multiplication;
wherein, forget the door
Figure BDA0004143648080000026
W fx Representing input x t Transfer to f t The corresponding weight matrix is used to determine the weight matrix,
Figure BDA0004143648080000027
representing the last LSTM cell state h t-1 Transfer to f t Corresponding weight matrix, b f Representing bias terms, σ representing a sigmoid function;
wherein the input door
Figure BDA0004143648080000028
W xi Representing input x t Transfer to i t The corresponding weight matrix is used to determine the weight matrix,
Figure BDA0004143648080000029
representing the last LSTM cell state h t-1 Transfer to i t Corresponding weight matrix, b i Represents the bias term, σ represents the sigmoid function.
Wherein the intermediate state
Figure BDA0004143648080000031
W xC Representing input x t To transfer to
Figure BDA0004143648080000032
Corresponding weight matrix, < >>
Figure BDA0004143648080000033
Representing the last LSTM cell state h t-1 Transfer to->
Figure BDA0004143648080000034
Corresponding weight matrix, b C Representing the bias term, tanh represents the hyperbolic tangent function.
Further, the output of the last LSTM cell of the LSTM neural network is connected to the softmax classifier, and the output emergency class labels correspond to false alarms, emergency and general rescue.
Further, if the first aid class label is an emergency first aid, determining the position directly based on the help seeker terminal, and dispatching the ambulance;
if the first-aid class label is false report, recording and marking;
if the first aid category is general rescue, the community or property where the help seeker is located is contacted to check the situation by the door-to-door system.
Further, vector alignment is performed when the sequence unit inputs the decision learning model.
Further, the vector of the sequence unit with the largest dimension is used as an alignment vector, the vector to be aligned is compared with the vector of the sequence unit with the largest digit, the missing dimension is determined, and vector alignment is performed by interpolation of 0 or 0.1.
The invention provides a first-aid decision-making method, which is implemented by applying the decision-making support system based on pre-hospital first-aid big data and comprises the following steps:
step 101, determining the ID of a help seeking terminal of a help seeking person through the attribution of the help seeking terminal;
step 102, acquiring associated information according to the ID of the help seeker help-seeking terminal;
step 103, generating associated time sequence information based on the associated information according to a time sequence from the time when the help terminal terminates the help call;
step 104, generating a decision learning model;
step 105, training the decision learning model through the historical associated time sequence information and the corresponding first aid class labels;
step 106, inputting the associated time sequence information generated for the current help seeker into a decision learning model, and outputting an emergency class label;
step 107, selecting a corresponding rescue means based on the currently output first aid class label.
The invention has the beneficial effects that:
the invention can carry out deep learning based on the help-seeking terminal to record the data source of the data source before the comprehensive hospital to construct a decision model so as to judge the emergency degree of the emergency demand of the help-seeking person, and solves the problem that the emergency call which cannot normally communicate cannot judge the emergency degree of the emergency demand.
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Fig. 1 is a schematic block diagram of a decision support system based on pre-hospital emergency big data according to the present invention.
In the figure: the system comprises a terminal information acquisition module 101, a correlation information acquisition module 102, a correlation information processing module 103, a model generation module 104, a model training module 105, a decision classification module 106 and a rescue decision module 107.
Detailed Description
The subject matter described herein will now be discussed with reference to example embodiments. It is to be understood that these embodiments are merely discussed so that those skilled in the art may better understand and implement the subject matter described herein and that changes may be made in the function and arrangement of the elements discussed without departing from the scope of the disclosure herein. Various examples may omit, replace, or add various procedures or components as desired. In addition, features described with respect to some examples may be combined in other examples as well.
Example 1
As shown in fig. 1, a decision support system based on pre-hospital emergency big data includes:
a terminal information acquisition module 101 for determining an ID of a help-seeking terminal of a help-seeking person through attribution of the help-seeking terminal;
an associated information acquisition module 102 that acquires associated information from the ID of the help seeker help terminal;
generally, a help seeking terminal is a terminal with a fixed communication ID such as a mobile phone.
The associated information comprises other terminals which are contacted by the help seeking terminal, the call duration corresponding to the contacted terminal, the time corresponding to the contacted terminal and the IP address corresponding to the contacted terminal;
the related information processing module 103 is configured to generate related timing information based on the related information according to a time sequence from when the help terminal terminates the help call, where a sequence unit of the related timing information includes corresponding related information before a time point, that is, the sequence unit is generated by accumulating, so as to avoid a situation that a corresponding related information blank occurs at some time points, resulting in a sequence unit blank;
in one embodiment of the invention, the correlation timing information is generated from the correlation information after normalization.
A model generation module 104 for generating a decision learning model;
the decision learning model comprises an LSTM neural network, wherein the LSTM neural network comprises a plurality of LSTM units, and each LSTM unit inputs a sequence unit associated with time sequence information;
output state h of each LSTM cell t The calculation formula of (2) is as follows:
h t ＝o t ⊙tanh(C t )
wherein the output door
Figure BDA0004143648080000051
W xo Representing input x t Transfer to o t The corresponding weight matrix is used to determine the weight matrix,
Figure BDA0004143648080000052
representing the output state h of the last LSTM cell t-1 Transfer to o t Corresponding weight matrix, b o Representing bias terms, σ representing a sigmoid function;
wherein the output state
Figure BDA0004143648080000053
f t ⊙C t-1 Door f for indicating forgetfulness t And output state C of last LSTM cell t-1 Multiplying point by point, f t ∈(0,1)；
Figure BDA0004143648080000054
Indicating input/output gate i t And intermediate state->
Figure BDA0004143648080000055
Performing point-by-point multiplication;
wherein, forget the door
Figure BDA0004143648080000056
W fx Representing input x t Transfer to f t The corresponding weight matrix is used to determine the weight matrix,
Figure BDA0004143648080000057
representing the last LSTM cell state h t-1 Transfer to f t Corresponding weight matrix, b f Representing bias terms, σ representing a sigmoid function;
wherein the input door
Figure BDA0004143648080000058
W xi Representing input x t Transfer to i t The corresponding weight matrix is used to determine the weight matrix,
Figure BDA0004143648080000061
representing the last LSTM cell state h t-1 Transfer to i t Corresponding weight matrix, b i Represents the bias term, σ represents the sigmoid function.
Wherein the intermediate state
Figure BDA0004143648080000062
W xC Representing input x t To transfer to
Figure BDA0004143648080000063
Corresponding toWeight matrix (W/W)>
Figure BDA0004143648080000064
Representing the last LSTM cell state h t-1 Transfer to->
Figure BDA0004143648080000065
Corresponding weight matrix, b C Representing a bias term, tanh representing a hyperbolic tangent function;
the output of the last LSTM unit of the LSTM neural network is connected with a softmax classifier, and the output first aid category label corresponds to false alarm, emergency first aid and general rescue;
a model training module 105 that trains the decision learning model by historical correlated timing information and corresponding first aid category labels;
in one embodiment of the invention, vector alignment is performed when the sequence unit inputs the decision learning model;
as a vector alignment mode, taking a vector of a sequence unit with the largest dimension as an alignment vector, comparing the vector to be aligned with a vector of a sequence unit with the largest digit, determining the missing dimension, and carrying out vector alignment by interpolation 0 or 0.1;
a decision classification module 106 for inputting the associated time sequence information generated for the current help seeker into a decision learning model and outputting an emergency class label;
a rescue decision module 107 that selects a corresponding rescue means based on the currently output first aid class label;
for example, if the emergency class tag is an emergency, the location is determined directly based on the help seeker terminal, and the ambulance is dispatched.
If the first-aid class label is false report, recording and marking;
if the first aid category is general rescue, the community or property where the help seeker is can be contacted to check the situation by the gate-on system.
In one embodiment of the present invention, an emergency decision method using the decision support system based on pre-hospital emergency big data is provided, which comprises the following steps:
step 101, determining the ID of a help seeking terminal of a help seeking person through the attribution of the help seeking terminal;
step 102, acquiring associated information according to the ID of the help seeker help-seeking terminal;
step 103, generating associated time sequence information based on the associated information according to a time sequence from the time when the help terminal terminates the help call;
step 104, generating a decision learning model;
step 105, training the decision learning model through the historical associated time sequence information and the corresponding first aid class labels;
step 106, inputting the associated time sequence information generated for the current help seeker into a decision learning model, and outputting an emergency class label;
step 107, selecting a corresponding rescue means based on the currently output first aid class label.
The embodiment has been described above with reference to the embodiment, but the embodiment is not limited to the above-described specific implementation, which is only illustrative and not restrictive, and many forms can be made by those of ordinary skill in the art, given the benefit of this disclosure, are within the scope of this embodiment.

Claims (9)

1. A pre-hospital emergency big data based decision support system, comprising:
a terminal information acquisition module for determining the ID of the help seeking terminal of the help seeking person through the attribution of the help seeking terminal;
the associated information acquisition module acquires associated information according to the ID of the help seeker help terminal;
the system comprises a related information processing module, a time sequence processing module and a time sequence processing module, wherein the related information processing module is used for generating related time sequence information based on the related information according to time sequence from the time when the help seeking terminal terminates the help seeking call, and one sequence unit of the related time sequence information comprises corresponding related information before one time point;
a model generation module for generating a decision learning model;
the first aid category labels output by the decision learning model correspond to false alarms, emergency first aid and general rescue;
a model training module for training the decision learning model through historical associated time sequence information and corresponding first aid class labels;
the decision classification module is used for inputting the associated time sequence information generated for the current help seeker into the decision learning model and outputting the first-aid class label;
and a rescue decision module for selecting a corresponding rescue means based on the currently output first aid class label.
2. The decision support system based on pre-hospital emergency big data according to claim 1, wherein the associated information includes other terminals that call for help to connect with the terminal, and a call duration corresponding to the connected terminal, a time corresponding to the connected terminal, and an IP address corresponding to the connected terminal.
3. The pre-hospital emergency big data-based decision support system of claim 1, wherein the associated timing information is generated from the associated information after normalization.
4. The pre-hospital emergency big data-based decision support system according to claim 1, wherein the decision learning model comprises an LSTM neural network, the LSTM neural network comprising a plurality of LSTM units, each LSTM unit inputting a sequence unit associated with timing information;
output state h of each LSTM cell t The calculation formula of (2) is as follows:
h t ＝o t ⊙tanh(C t )
wherein the output door
Figure FDA0004143648070000011
W xo Representing input x t Transfer to o t Corresponding toIs used for the weight matrix of the (c),
Figure FDA0004143648070000021
representing the output state h of the last LSTM cell t-1 Transfer to o t Corresponding weight matrix, b o Representing bias terms, σ representing a sigmoid function;
wherein the output state
Figure FDA0004143648070000022
f t ⊙C t-1 Door f for indicating forgetfulness t And output state C of last LSTM cell t-1 Multiplying point by point, f t ∈(0,1)；
Figure FDA0004143648070000023
Indicating input/output gate i t And intermediate state->
Figure FDA0004143648070000024
Performing point-by-point multiplication;
wherein, forget the door
Figure FDA0004143648070000025
W fx Representing input x t Transfer to f t The corresponding weight matrix is used to determine the weight matrix,
Figure FDA0004143648070000026
representing the last LSTM cell state h t-1 Transfer to f t Corresponding weight matrix, b f Representing bias terms, σ representing a sigmoid function;
wherein the input door
Figure FDA0004143648070000027
W xi Representing input x t Transfer to i t The corresponding weight matrix is used to determine the weight matrix,
Figure FDA0004143648070000028
representing the last LSTM cell state h t-1 Transfer to i t Corresponding weight matrix, b i Representing bias terms, σ representing a sigmoid function;
wherein the intermediate state
Figure FDA0004143648070000029
W xC Representing input x t To transfer to
Figure FDA00041436480700000210
Corresponding weight matrix, < >>
Figure FDA00041436480700000212
Representing the last LSTM cell state h t-1 Transfer to->
Figure FDA00041436480700000211
Corresponding weight matrix, b C Representing the bias term, tanh represents the hyperbolic tangent function.
5. The pre-hospital emergency big data-based decision support system of claim 4, wherein an output of a last LSTM cell of the LSTM neural network is connected to a softmax classifier, and the outputted emergency class labels correspond to false alarms, emergency and general aid.
6. The pre-hospital emergency big data-based decision support system of claim 1, wherein if the emergency category label is emergency, determining the location directly based on the help seeker terminal, dispatching the ambulance;
if the first-aid class label is false report, recording and marking;
if the first aid category is general rescue, the community or property where the help seeker is located is contacted to check the situation by the door-to-door system.
7. The pre-hospital emergency big data-based decision support system of claim 1, wherein the sequence unit performs vector alignment when inputting the decision learning model.
8. The decision support system based on pre-hospital emergency big data according to claim 7, wherein the vector of the sequence unit with the largest dimension is used as an alignment vector, the vector to be aligned is compared with the vector of the sequence unit with the largest digit, the missing dimension is determined, and the vector alignment is performed by interpolating 0 or 0.1.
9. A method of emergency decision making, characterized in that the application of a pre-hospital emergency big data based decision support system according to any of claims 1-8 performs the following steps:
step 101, determining the ID of a help seeking terminal of a help seeking person through the attribution of the help seeking terminal;
step 102, acquiring associated information according to the ID of the help seeker help-seeking terminal;
step 103, generating associated time sequence information based on the associated information according to a time sequence from the time when the help terminal terminates the help call;
step 104, generating a decision learning model;
step 105, training the decision learning model through the historical associated time sequence information and the corresponding first aid class labels;
step 106, inputting the associated time sequence information generated for the current help seeker into a decision learning model, and outputting an emergency class label;
step 107, selecting a corresponding rescue means based on the currently output first aid class label.
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