CN117892913A - Emergency treatment scheme recommending method, device, equipment and storage medium - Google Patents
Emergency treatment scheme recommending method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a recommendation method, device, equipment and storage medium of an emergency treatment scheme. The method comprises the following steps: acquiring event alarm information; inputting the event alarm information into a target setting emergency treatment scheme recommendation model, and outputting a set number of target emergency treatment schemes corresponding to the event alarm information; the target setting emergency treatment scheme recommendation model comprises a first fusion module and a second fusion module; the first fusion module and the second fusion module are both neural networks of a set layer. According to the method and the device for obtaining the target emergency treatment schemes, when the event alarm information is faced, corresponding emergency treatment schemes can be timely and accurately obtained through the mode that the target emergency treatment scheme recommendation model is used for obtaining the set number of target emergency treatment schemes.
Description
Technical Field
The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a recommendation method, device and equipment for an emergency treatment scheme and a storage medium.
Background
With the continuous deep digital transformation, the full-line fine management is gradually advanced, and higher requirements are also put on emergency treatment. The system not only needs to cover all-round emergency plans in various fields, but also needs to rapidly recommend a matched treatment scheme when a production event occurs, fully plays the role of the emergency treatment scheme, and provides guarantee for safe and stable operation of the system. The current emergency plans also have limited functions, and when a production event actually occurs, it is difficult for a disposal person to quickly retrieve a disposal plan related to the event from numerous plans of the emergency plan, and most of the disposal plans are disposed of empirically.
Disclosure of Invention
The embodiment of the invention provides a recommendation method, device, equipment and storage medium for an emergency treatment scheme, which can improve the accuracy and efficiency of emergency treatment scheme recommendation.
In a first aspect, an embodiment of the present invention provides a method for recommending an emergency treatment solution, including: acquiring event alarm information; inputting the event alarm information into a target setting emergency treatment scheme recommendation model, and outputting a set number of target emergency treatment schemes corresponding to the event alarm information; the target setting emergency treatment scheme recommendation model comprises a first fusion module and a second fusion module; the first fusion module and the second fusion module are both neural networks of a set layer.
In a second aspect, an embodiment of the present invention further provides a recommendation device for an emergency treatment solution, including: the event alarm information acquisition module is used for acquiring event alarm information; the recommending module is used for inputting the event alarming information into a target setting emergency treatment scheme recommending model and outputting a set number of target emergency treatment schemes corresponding to the event alarming information; the target setting emergency treatment scheme recommendation model comprises a first fusion module and a second fusion module; the first fusion module and the second fusion module are both neural networks of a set layer.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the recommended method of emergency treatment protocol of any one of the embodiments of the invention.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium storing computer instructions for causing a processor to execute the recommended method for implementing the emergency treatment plan according to any one of the embodiments of the present invention.
The technical scheme provided by the invention obtains the event alarm information; inputting the event alarm information into a target setting emergency treatment scheme recommendation model, and outputting a set number of target emergency treatment schemes corresponding to the event alarm information; the target setting emergency treatment scheme recommendation model comprises a first fusion module and a second fusion module; the first fusion module and the second fusion module are both neural networks of a set layer. According to the method and the device for obtaining the target emergency treatment schemes, when the event alarm information is faced, corresponding emergency treatment schemes can be timely and accurately obtained through the mode that the target emergency treatment scheme recommendation model is used for obtaining the set number of target emergency treatment schemes.
Drawings
FIG. 1 is a flowchart of a method for recommending an emergency treatment plan according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a recommendation model of a target setting emergency treatment scheme according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a recommendation device for an emergency treatment scheme according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing an embodiment of the present invention.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings. The technical scheme of the application obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws and regulations.
Fig. 1 is a flowchart of a method for recommending an emergency treatment plan, which is provided by an embodiment of the present invention, and the embodiment may be applicable to determining a situation of an emergency treatment plan when an alarm event occurs, where the method may be executed by a recommending apparatus of the emergency treatment plan, and specifically includes the following steps:
s110, acquiring event alarm information.
It should be noted that, in the context of a bank, when the transaction amount or the response time exceeds the set threshold, event alert information will be generated, where the event alert information may include multiple dimensional features (i.e., multidimensional features), which is not limited in this embodiment. Optionally, the event alarm information includes alarm time, alarm source, alarm content and alarm description information. The alarm sources can be application alarms (such as alarms related to response time length, response success rate and the like), infrastructure alarms (such as machine room outage) and the like. The alarm content may include some alarm information that triggers an alarm rule, and the embodiment does not limit specific alarm content, for example, an alarm may occur on a certain machine because the trigger response duration exceeds a set threshold value. The alert description information may be a detailed description of the alert content, such as the specifics of the alert, the scope of influence, the possible reasons, etc.
S120, inputting the event alarm information into a target setting emergency treatment scheme recommendation model, and outputting a set number of target emergency treatment schemes corresponding to the event alarm information.
The target setting emergency treatment scheme recommendation model comprises a first fusion module and a second fusion module; the first fusion module and the second fusion module are both neural networks of a set layer.
The specific number of settings in this embodiment is not limited, and may be 3, for example. It should be noted that, the first fusion module and the second fusion module are the same neural network, specifically may be any kind of neural network, for example, may be BP (back propagation) neural networks, and each module is a neural network with a set layer. The setting layer may be 3 layers, two hidden layers and one output layer, respectively.
The target-setting contingency treatment plan recommendation model outputs target contingency treatment plans whose prediction scores are the pre-ranking set number. The present embodiment is not limited to a specific target emergency treatment scheme, and may be dependent on practical situations, such as restarting a machine, limiting current, and the like.
As shown in fig. 2, fig. 2 is a schematic structural diagram of a target setting emergency treatment plan recommendation model according to an embodiment of the present invention. Optionally, inputting the event alarm information into a target setting emergency treatment scheme recommendation model, and outputting a set number of target emergency treatment schemes corresponding to the event alarm information, including: performing first fusion processing on the event alarm information to form a serial feature vector; performing second fusion processing on the event alarm information to form parallel feature vectors; inputting the serial feature vector into the first fusion module, and outputting a first prediction result; inputting the parallel feature vectors into the second fusion module, and outputting a second prediction result; determining a target prediction result based on the first prediction result and the second prediction result; wherein the target prediction result is a set number of target emergency treatment plans.
The formulas of the first fusion module and the second fusion module are as follows:
Where X is a serial or parallel feature vector, θ is a set of parameters including weights w and offsets b, f 3 is the prediction probability (i.e., first or second prediction result) of the output layer, i.e., different contingency treatment schemes. f 2 and f 1 represent hidden layers. Wherein f 1 can also be considered as an input layer. g 2 and g 1 each represent an activation function.
Optionally, performing a first fusion process on the event alert information to form a serial feature vector, including: and converting the alarm time, the alarm source, the alarm content and the alarm description information into serial feature vectors in a serial mode.
Specifically, the alarm time, the alarm source, the alarm content and the alarm description information may be respectively converted into one-hot (one-hot coding) feature vectors, and connected in a serial manner to form serial feature vectors. For example, if the dimensions of the two input features x and y are p and q, the dimension of the fused output feature z is p+q.
Optionally, performing a second fusion process on the event alert information to form parallel feature vectors, including: and converting the alarm time, the alarm source, the alarm content and the alarm description information into parallel feature vectors in a parallel mode.
Specifically, the alarm time, the alarm source, the alarm content and the alarm description information may be respectively converted into one-hot (single hot coding) feature vectors, and connected in parallel to form parallel feature vectors, for example, feature vectors corresponding to the first behavior alarm time, feature vectors corresponding to the second behavior alarm source, for example, feature vectors corresponding to the third behavior alarm content, and feature vectors corresponding to the fourth behavior alarm description information, that is, 4 feature vectors are combined to form a complex vector.
Optionally, determining a target prediction result based on the first prediction result and the second prediction result includes: acquiring a first weight corresponding to the first prediction result; acquiring a second weight corresponding to the second prediction result; carrying out weighted summation based on the first prediction result, the first weight, the second prediction result and the second weight to obtain a target prediction result; and the sum of the first weight and the second weight is a set value.
Wherein the set value may be 1.
Illustratively, the target prediction result formula is as follows:
G＝ax'+by'
Where a represents a first weight, b represents a second weight, a+b=1. x 'represents the first predicted result, y' represents the second predicted result, and G is the target predicted result.
Optionally, the training mode of the target setting emergency treatment scheme recommendation model is as follows: acquiring historical event alarm information and a corresponding real emergency treatment scheme; inputting training serial feature vectors corresponding to the historical event alarm information into a first fusion module, and outputting a first training result; inputting training parallel feature vectors corresponding to the historical event alarm information into a second fusion module, and outputting a second training result; determining a first loss value between the first training result and the real emergency treatment plan based on a first set loss function; determining a second loss value between the second training result and the real contingency treatment plan based on a second set loss function; and performing iterative training on the first fusion module based on the first loss value and performing iterative training on the second fusion module based on the second loss value to obtain a target setting emergency treatment scheme recommendation model.
The historical event alarm information can be time alarm information of five years, and is stored in a database, and also comprises alarm time, alarm sources, alarm content and alarm description information. The real emergency treatment scheme is an emergency treatment scheme hit by the historical event warning information and is stored in a database.
In this embodiment, the processing is performed on the historical event alert information, for example, a first fusion process is performed to form a serial feature vector, a second fusion process is performed to form a parallel feature vector, the serial feature vector is used as training data of the first fusion module, the parallel feature vector is used as training data of the second fusion module, and the real emergency treatment scheme is processed into a one-hot feature vector which is used as labeling data of the training data of the first fusion module and the second fusion module.
In this embodiment, the first fusion module and the second fusion module are trained, and the training process of the network is divided into forward propagation and backward propagation.
Specifically, training serial feature vectors corresponding to the historical event alarm information are input into a first fusion module, and a first training result is output; inputting training parallel feature vectors corresponding to the historical event alarm information into a second fusion module, and outputting a second training result; determining a first loss value between the first training result and the real emergency treatment plan based on a first set loss function; determining a second loss value between the second training result and the real contingency treatment plan based on a second set loss function; and carrying out iterative training on the first fusion module based on the first loss value until the corresponding training iteration stop condition is met, so as to obtain the trained first fusion module. And carrying out iterative training on the second fusion module based on the second loss value until the corresponding training iteration stop condition is met, and obtaining the trained second fusion module, so that a target setting emergency treatment scheme recommendation model can be obtained.
The training iteration stop condition of the first fusion module may be that the first loss value is continuously set for a number of times smaller than or equal to the first set loss threshold value, or greater than or equal to the first training iteration number. The training iteration stop condition of the second fusion module may be that the second loss value is continuously set for a number of times less than or equal to the second set loss threshold value, or greater than or equal to the second training iteration number.
In this embodiment, the first set loss function and the second set loss function may be the same loss function or different loss functions, and may be adjusted according to training results, for example, MSE (mean square error), RMSE (root mean square error), and MAE (mean absolute error).
After the target emergency treatment plan recommendation model is used to obtain the set number of target emergency treatment plans, it may be determined whether the set number of target emergency treatment plans satisfy the set recommendation condition (combined with expert experience), and if not, the target emergency treatment plan recommendation model may be retrained.
According to the embodiment of the invention, an emergency treatment scheme recommendation model is set for the target, and the multidimensional features are subjected to fusion training in a neural network training mode until an optimal network structure and parameter combination are obtained; the target setting emergency treatment scheme recommendation model automatically matches and recommends the emergency treatment scheme of TOP3 according to the event alarm information. According to the method and the device for recommending the emergency treatment scheme, the emergency treatment scheme can be recommended for emergency treatment personnel to select and execute at the first time of occurrence of event alarm information, so that the emergency treatment efficiency is greatly improved, and the automatic management level of the emergency plan is improved.
According to the embodiment of the invention, the target setting emergency treatment scheme recommendation model is constructed by extracting the multidimensional features such as the alarm time, the alarm source, the alarm content and the alarm description information, and the emergency treatment scheme is automatically recommended for the treatment personnel to select by constructing the target setting emergency treatment scheme recommendation model. According to the invention, the dependence on expert experience can be greatly reduced, and the emergency treatment scheme can be recommended for emergency treatment personnel to select and execute at the first time of generating event alarm information, so that the emergency treatment efficiency is greatly improved, and the automatic management level of the emergency plan is improved.
The technical scheme provided by the invention obtains the event alarm information; inputting the event alarm information into a target setting emergency treatment scheme recommendation model, and outputting a set number of target emergency treatment schemes corresponding to the event alarm information; the target setting emergency treatment scheme recommendation model comprises a first fusion module and a second fusion module; the first fusion module and the second fusion module are both neural networks of a set layer. According to the method and the device for obtaining the target emergency treatment schemes, when the event alarm information is faced, corresponding emergency treatment schemes can be timely and accurately obtained through the mode that the target emergency treatment scheme recommendation model is used for obtaining the set number of target emergency treatment schemes.
Fig. 3 is a schematic structural diagram of a recommendation device for an emergency treatment scheme according to an embodiment of the present invention. As shown in fig. 3, the apparatus includes: an event alert information acquisition module 310 and a recommendation module 320;
an event alert information acquisition module 310, configured to acquire event alert information;
a recommendation module 320, configured to input the event alert information into a target setting emergency treatment plan recommendation model, and output a set number of target emergency treatment plans corresponding to the event alert information; the target setting emergency treatment scheme recommendation model comprises a first fusion module and a second fusion module; the first fusion module and the second fusion module are both neural networks of a set layer.
According to the technical scheme provided by the invention, the event alarm information is acquired through the event alarm information acquisition module; inputting the event alarm information into a target setting emergency treatment scheme recommendation model through a recommendation module, and outputting a set number of target emergency treatment schemes corresponding to the event alarm information; the target setting emergency treatment scheme recommendation model comprises a first fusion module and a second fusion module; the first fusion module and the second fusion module are both neural networks of a set layer. According to the method and the device for obtaining the target emergency treatment schemes, when the event alarm information is faced, corresponding emergency treatment schemes can be timely and accurately obtained through the mode that the target emergency treatment scheme recommendation model is used for obtaining the set number of target emergency treatment schemes.
Optionally, the event alarm information includes alarm time, alarm source, alarm content and alarm description information.
Optionally, the recommendation module is specifically configured to: performing first fusion processing on the event alarm information to form a serial feature vector; performing second fusion processing on the event alarm information to form parallel feature vectors; inputting the serial feature vector into the first fusion module, and outputting a first prediction result; inputting the parallel feature vectors into the second fusion module, and outputting a second prediction result; determining a target prediction result based on the first prediction result and the second prediction result; wherein the target prediction result is a set number of target emergency treatment plans.
Optionally, the recommendation module is further configured to: and converting the alarm time, the alarm source, the alarm content and the alarm description information into serial feature vectors in a serial mode.
Optionally, the recommendation module is further configured to: and converting the alarm time, the alarm source, the alarm content and the alarm description information into parallel feature vectors in a parallel mode.
Optionally, the recommendation module is further configured to: acquiring a first weight corresponding to the first prediction result; acquiring a second weight corresponding to the second prediction result; carrying out weighted summation based on the first prediction result, the first weight, the second prediction result and the second weight to obtain a target prediction result; and the sum of the first weight and the second weight is a set value.
Optionally, the device further includes a training module, where the training module is configured to: acquiring historical event alarm information and a corresponding real emergency treatment scheme; inputting training serial feature vectors corresponding to the historical event alarm information into a first fusion module, and outputting a first training result; inputting training parallel feature vectors corresponding to the historical event alarm information into a second fusion module, and outputting a second training result; determining a first loss value between the first training result and the real emergency treatment plan based on a first set loss function; determining a second loss value between the second training result and the real contingency treatment plan based on a second set loss function; and performing iterative training on the first fusion module based on the first loss value and performing iterative training on the second fusion module based on the second loss value to obtain a target setting emergency treatment scheme recommendation model.
The device can execute the method provided by all the embodiments of the invention, and has the corresponding functional modules and beneficial effects of executing the method. Technical details not described in detail in this embodiment can be found in the methods provided in all the foregoing embodiments of the invention.
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as recommendation of a method contingency treatment protocol.
In some embodiments, the recommendation of the method contingency treatment protocol may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more of the steps of the method emergency treatment protocol recommendation described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the recommendation of the method contingency treatment protocol in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of recommending an emergency treatment plan, comprising:
acquiring event alarm information;
Inputting the event alarm information into a target setting emergency treatment scheme recommendation model, and outputting a set number of target emergency treatment schemes corresponding to the event alarm information; the target setting emergency treatment scheme recommendation model comprises a first fusion module and a second fusion module; the first fusion module and the second fusion module are both neural networks of a set layer.
2. The method of claim 1, wherein the event alert information comprises an alert time, an alert source, alert content, and alert description information.
3. The method of claim 2, wherein inputting the event alert information into a target set contingency treatment plan recommendation model, outputting a set number of target contingency treatment plans corresponding to the event alert information, comprises:
performing first fusion processing on the event alarm information to form a serial feature vector;
Performing second fusion processing on the event alarm information to form parallel feature vectors;
Inputting the serial feature vector into the first fusion module, and outputting a first prediction result;
inputting the parallel feature vectors into the second fusion module, and outputting a second prediction result;
Determining a target prediction result based on the first prediction result and the second prediction result; wherein the target prediction result is a set number of target emergency treatment plans.
4. The method of claim 3, wherein performing a first fusion process on the event alert information to form a serial feature vector comprises:
And converting the alarm time, the alarm source, the alarm content and the alarm description information into serial feature vectors in a serial mode.
5. A method according to claim 3, wherein performing a second fusion process on the event alert information to form parallel feature vectors comprises:
And converting the alarm time, the alarm source, the alarm content and the alarm description information into parallel feature vectors in a parallel mode.
6. The method of claim 3, determining a target prediction result based on the first prediction result and the second prediction result, comprising:
acquiring a first weight corresponding to the first prediction result;
Acquiring a second weight corresponding to the second prediction result;
Carrying out weighted summation based on the first prediction result, the first weight, the second prediction result and the second weight to obtain a target prediction result; and the sum of the first weight and the second weight is a set value.
7. The method of claim 1, wherein the training of the targeted emergency treatment recommendation model is:
acquiring historical event alarm information and a corresponding real emergency treatment scheme;
inputting training serial feature vectors corresponding to the historical event alarm information into a first fusion module, and outputting a first training result;
Inputting training parallel feature vectors corresponding to the historical event alarm information into a second fusion module, and outputting a second training result;
determining a first loss value between the first training result and the real emergency treatment plan based on a first set loss function;
Determining a second loss value between the second training result and the real contingency treatment plan based on a second set loss function;
And performing iterative training on the first fusion module based on the first loss value and performing iterative training on the second fusion module based on the second loss value to obtain a target setting emergency treatment scheme recommendation model.
8. A recommendation device for an emergency treatment regimen, comprising:
The event alarm information acquisition module is used for acquiring event alarm information;
the recommending module is used for inputting the event alarming information into a target setting emergency treatment scheme recommending model and outputting a set number of target emergency treatment schemes corresponding to the event alarming information; the target setting emergency treatment scheme recommendation model comprises a first fusion module and a second fusion module; the first fusion module and the second fusion module are both neural networks of a set layer.
9. An electronic device, the electronic device comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the recommended method of emergency treatment protocol of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to execute the recommended method of emergency treatment plan according to any one of claims 1-7.
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