CN113807622A - Event decision generation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses an event decision generation method and device, electronic equipment and a storage medium, which are used for automatically generating an event decision so as to assist a decision maker in making a decision. The method comprises the steps of obtaining attribute information of an event to be decided, which is input by a user; determining a recommendation parameter corresponding to each historical event according to the attribute information of the event to be decided and the attribute information of the plurality of historical events, and determining a target event from the plurality of historical events; and adjusting the historical decision information of the target event according to the attribute information of the event to be decided to obtain the decision information of the event to be decided. The embodiment of the invention utilizes the decision-making experience of abundant historical events to automatically generate the decision-making information suitable for the event to be decided on the basis of the historical decision-making information of the target event, thereby avoiding the problem of low emergency response efficiency caused by excessive dependence on the personal experience of decision-makers, improving the emergency decision-making generation efficiency and rapidly and efficiently responding to the emergency.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to an event decision generating method and apparatus, an electronic device, and a storage medium.
Background
The emergency event refers to a natural disaster, an accident disaster, a public health event, a social security event and the like which suddenly occur to cause or possibly cause serious social hazards, when the emergency event occurs, corresponding emergency measures need to be taken to deal with the emergency event, in order to reduce the loss and the hazards brought by the emergency event, a decision maker needs to make a quick, efficient and scientific emergency decision, and gold rescue time is obtained to ensure the safety of people's lives and properties.
At present, emergency decisions depend on personal experience and knowledge level of decision makers too much, and no method for automatically generating the emergency decisions is provided for the decision makers to refer to.
Disclosure of Invention
The invention provides an event decision generation method and device, electronic equipment and a storage medium, which are used for automatically generating an event decision, improving emergency decision generation efficiency and rapidly and efficiently dealing with an emergency.
In a first aspect, an embodiment of the present invention provides an event decision generating method, where the method includes:
acquiring attribute information of an event to be decided, which is input by a user;
determining a recommendation parameter corresponding to each historical event according to the attribute information of the event to be decided and the attribute information of the plurality of historical events, and determining a target event from the plurality of historical events according to the recommendation parameter corresponding to each historical event; wherein the recommendation parameter represents a degree of similarity between a historical event and the event to be decided;
and adjusting the historical decision information of the target event according to the attribute information of the event to be decided to obtain the decision information of the event to be decided.
In the event decision generating method provided by the embodiment of the invention, the target event with the highest similarity degree with the event to be decided is determined according to the attribute information of the event to be decided and the attribute information of the historical event by acquiring the attribute information of the event to be decided input by a user, and the historical decision information of the target event is adjusted according to the attribute information of the event to be decided to obtain the decision information of the event to be decided. In the embodiment of the invention, the decision information suitable for the event to be decided is automatically generated on the basis of the historical decision information of the target event by utilizing the decision experience of abundant historical events, so that the problem of low emergency response efficiency caused by excessive dependence on the personal experience of a decision maker is avoided, the emergency decision generation efficiency is improved, and the emergency can be responded quickly and efficiently.
In an optional implementation manner, the determining, according to the attribute information of the event to be decided and the attribute information of the plurality of historical events, a recommendation parameter corresponding to each historical event includes:
determining a probability value of each event type corresponding to the event to be decided according to the attribute information of the event to be decided, and determining a probability value of each event type corresponding to each historical event according to the attribute information of each historical event;
forming the probability value of each event type corresponding to the event to be decided into a feature vector of the event to be decided according to a preset event type sequence, and forming the probability value of each event type corresponding to each historical event into the feature vector of each historical event according to the preset event type sequence;
and for any historical event, determining a recommendation parameter corresponding to the historical event according to the cosine similarity between the feature vector of the historical event and the feature vector of the event to be decided.
In the event decision generation method provided by the embodiment of the invention, the recommendation parameters of the historical events are determined according to the attribute information of the event to be decided and the attribute information of the historical events, the target case with higher similarity to the event to be decided is determined from a plurality of historical events according to the recommendation parameters of the historical events, the historical decision information of the target case is further determined, and a decision maker is assisted in making decisions. In addition, the similarity degree between the events is determined according to the cosine similarity of the feature vectors, the similarity degree between the two events is quantized, comparison of the similarity degrees is facilitated, a historical event with higher similarity degree with the event to be decided is selected as a target event, and the historical decision information of the target case is higher in referential performance.
In an optional implementation manner, the determining, according to the attribute information of the event to be decided, a probability value of each event type to which the event to be decided belongs and determining, according to the attribute information of each historical event, a probability value of each event type to which the event to be decided belongs, where the probability values include:
and inputting the attribute information of the event to be decided and the attribute information of the plurality of historical cases into a trained event classification model, and acquiring the probability value of each event type corresponding to the event to be decided and the probability value of each event type corresponding to each historical event, which are output by the event classification model.
In the event decision generation method provided by the embodiment of the invention, the probability value of each event type corresponding to the event is determined through the event classification model, and the method is high in calculation speed and high in accuracy.
In an alternative embodiment, the event classification model is trained according to the following:
performing word segmentation processing on text information corresponding to a plurality of historical events to obtain attribute information corresponding to each historical case in the historical events;
taking the number of preset event types and the attribute information corresponding to each historical case as the input of an event classification model, and determining the event type corresponding to each attribute information in all the attribute information through a Gibbs sampling algorithm;
counting event types corresponding to each attribute information in attribute information corresponding to any historical event, and determining probability values of each event type corresponding to each historical event;
and taking the event type corresponding to each attribute information and the probability value of each event type corresponding to each historical event as the output of the event classification model, and training the event classification model.
In the event decision generation method provided by the embodiment of the invention, the event classification model is trained through the attribute information of a large number of historical events, the trained event classification model can obtain the probability value capability of each event type corresponding to the determined event, and the feature vector corresponding to the event is quickly and accurately determined by utilizing the advantages of the deep learning technology.
In an optional implementation manner, the adjusting the historical decision information of the target event according to the attribute information of the event to be decided to obtain the decision information of the event to be decided includes:
determining scheduling resource information of an event to be decided according to scheduling resource information in the historical decision of the target event; determining scheduling position information of resource information required to be scheduled by the event to be decided according to position information in the attribute information of the event to be decided;
generating scheduling route information from a first position corresponding to the scheduling position information of the event to be decided to a second position corresponding to the position information of the event to be decided;
and adjusting the scheduling route information in the historical decision of the target event into the scheduling route information of the event to be decided to obtain scheduling resource information of the event to be decided and decision information of the scheduling route information.
In the event decision generating method provided by the embodiment of the invention, the historical decision information of the target event is adjusted according to the attribute information of the event to be decided, and a decision suitable for the event to be decided is generated. Compared with the method for obtaining the historical decision information of the target event, the method provided by the embodiment of the invention can further adjust according to the specific attribute information of the event to be decided, so that the obtained decision of the event to be decided is more referential and better assists a decision maker in making a decision.
In a second aspect, an embodiment of the present invention provides an event decision generating apparatus, including: the acquisition module is used for acquiring attribute information of the event to be decided, which is input by a user;
the determining module is used for determining a recommendation parameter corresponding to each historical event according to the attribute information of the event to be decided and the attribute information of the plurality of historical events, and determining a target event from the plurality of historical events according to the recommendation parameter corresponding to each historical event; wherein the recommendation parameter represents a degree of similarity between a historical event and the event to be decided;
and the generating module is used for adjusting the historical decision information of the target event according to the attribute information of the event to be decided to obtain the decision information of the event to be decided.
In an optional implementation manner, the determining module is specifically configured to:
determining a probability value of each event type corresponding to the event to be decided according to the attribute information of the event to be decided, and determining a probability value of each event type corresponding to each historical event according to the attribute information of each historical event;
forming the probability value of each event type corresponding to the event to be decided into a feature vector of the event to be decided according to a preset event type sequence, and forming the probability value of each event type corresponding to each historical event into the feature vector of each historical event according to the preset event type sequence;
and for any historical event, determining a recommendation parameter corresponding to the historical event according to the cosine similarity between the feature vector of the historical event and the feature vector of the event to be decided.
In an optional implementation manner, the determining module is specifically configured to:
and inputting the attribute information of the event to be decided and the attribute information of the plurality of historical cases into a trained event classification model, and acquiring the probability value of each event type corresponding to the event to be decided and the probability value of each event type corresponding to each historical event, which are output by the event classification model.
In an optional embodiment, the event classification module is further configured to train the event classification model according to the following manner:
performing word segmentation processing on text information corresponding to a plurality of historical events to obtain attribute information corresponding to each historical case in the historical events;
taking the number of preset event types and the attribute information corresponding to each historical case as the input of an event classification model, and determining the event type corresponding to each attribute information in all the attribute information through a Gibbs sampling algorithm;
counting event types corresponding to each attribute information in attribute information corresponding to any historical event, and determining probability values of each event type corresponding to each historical event;
and taking the event type corresponding to each attribute information and the probability value of each event type corresponding to each historical event as the output of the event classification model, and training the event classification model.
In an optional implementation manner, the generating module is specifically configured to:
determining scheduling resource information of an event to be decided according to scheduling resource information in the historical decision of the target event; determining scheduling position information of resource information required to be scheduled by the event to be decided according to position information in the attribute information of the event to be decided;
generating scheduling route information from a first position corresponding to the scheduling position information of the event to be decided to a second position corresponding to the position information of the event to be decided;
and adjusting the scheduling route information in the historical decision of the target event into the scheduling route information of the event to be decided to obtain scheduling resource information of the event to be decided and decision information of the scheduling route information.
In a third aspect, another 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 instructions executable by the at least one processor to enable the at least one processor to perform any of the event decision making methods provided by the first aspect of the embodiments of the present invention.
In a fourth aspect, another embodiment of the present invention further provides a computer storage medium, where the computer storage medium stores computer-executable instructions, and the computer-executable instructions are configured to cause a computer to execute any one of the event decision generating methods provided in the first aspect of the embodiments of the present invention.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
Fig. 1 is a flowchart of an event decision generating method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an interface for acquiring attribute information of an event to be decided according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an interface for acquiring attribute information of an event to be decided according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for determining recommended parameters of historical events according to an embodiment of the present invention;
fig. 5 is a front-end interface diagram of an event decision generating system according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an event decision generating system according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an event decision generating apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of another event decision generating apparatus according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
Emergency decision is the main content in emergency management work, when an emergency occurs, a decision maker is usually required to make a quick, efficient and scientific emergency decision so as to reduce loss and harm caused by the emergency and strive for gold rescue time to ensure the life and property safety of people.
The embodiment of the invention provides a method for automatically generating an event decision, which can be applied to generating an emergency decision to assist a decision maker in commanding rescue work when an emergency occurs, can also be applied to generating an event decision in the process of simulating the emergency, performing disaster relief simulation drilling and the like, and the specific application scene of the embodiment of the invention is not limited.
As shown in fig. 1, a flowchart of an event decision generating method provided in an embodiment of the present invention includes:
in step S101, attribute information of an event to be decided, which is input by a user, is acquired;
in the embodiment of the invention, the user can be a decision maker; the event to be decided can be a real emergent event or a simulated emergent event. The attribute information of the event to be decided may include an event occurrence time, an event occurrence location, event details, and the like, which are only examples, and the attribute information may also include a severity of an emergency event or other information, which is not specifically limited in the embodiment of the present invention.
Fig. 2 shows that the event decision generating system front-end interface provided in the embodiment of the present invention can obtain attribute information of an event to be decided.
In implementation, the user inputs the attribute information of the event to be decided in the input box corresponding to the attribute information, wherein when the occurrence location of the event to be decided is input, the address of the event occurrence can be directly input in the input box corresponding to the position information, or as shown in fig. 3, the user can also mark a position in a map shown in a front-end interface, and the user clicks a confirmation button to automatically acquire the position information corresponding to the marked position.
In step S102, according to the attribute information of the event to be decided and the attribute information of the plurality of historical events, determining a recommendation parameter corresponding to each historical event, and according to the recommendation parameter corresponding to each historical event, determining a target event from the plurality of historical events;
wherein the recommendation parameter represents the similarity degree between the historical event and the event to be decided.
The event decision generating system provided by the embodiment of the invention can store a large amount of attribute information of historical events in a back-end server, the attribute information of the historical events comprises time, place, event details, event titles, historical decision information, event evaluation summary and the like when the historical events occur, a recommendation parameter corresponding to each historical event is determined according to the attribute information of the event to be decided and the attribute information of a plurality of historical events, the recommendation parameter corresponding to the historical event identifies the similarity between the historical events and the event to be decided, the historical event with the largest recommendation parameter can be selected as a target event, the similarity between the target event and the event to be decided is the largest, the reference value is the best, and the decision information of the event to be decided can be generated according to the historical decision information of the target event.
It should be noted that the attribute information of the historical event may be obtained according to the text information of the recorded event stored in the case library, and the text information may be a complete article. The obtaining of the attribute information of the historical events can be realized by performing Chinese word segmentation on the text recording the events, removing useless stop words, only keeping meaningful words in the text, and obtaining the attribute information corresponding to each historical event after word segmentation.
In the embodiment of the invention, word segmentation processing can be performed through a word segmentation application program ICTCCLAS and the like.
The obtained attribute information of each historical event can be shown in table 1:
| historical events | Attribute information |
| Event 1 | Fire, fire extinguisher, fire department, urban area |
| Event 2 | Traffic accident, evacuation, truck, high speed |
| Event 3 | High temp. epidemic situation thermometer and protective clothing |
TABLE 1
In the embodiment of the invention, the attribute information corresponding to the historical event after word segmentation processing can be stored, or word segmentation processing can be carried out again each time the recommendation parameter corresponding to the historical event is determined.
In step S103, the historical decision information of the target event is adjusted according to the attribute information of the event to be decided, so as to obtain the decision information of the event to be decided.
After the target event with the highest similarity degree with the event to be decided is determined, due to the fact that the occurrence time, the place, the surrounding environment and other attribute information of different events are different, the historical decision information of the target event cannot be directly used as the decision information of the event to be decided, the historical decision information of the target event needs to be subjected to applicability adjustment according to the attribute information of the event to be decided, and the event decision with more reference significance is obtained and is used for a decision maker to refer to.
In the event decision generating method provided by the embodiment of the invention, the target event with the highest similarity degree with the event to be decided is determined according to the attribute information of the event to be decided and the attribute information of the historical event by acquiring the attribute information of the event to be decided input by a user, and the historical decision information of the target event is adjusted according to the attribute information of the event to be decided to obtain the decision information of the event to be decided. In the embodiment of the invention, the decision information suitable for the event to be decided is automatically generated on the basis of the historical decision information of the target event by utilizing the decision experience of abundant historical events, so that the problem of low emergency response efficiency caused by excessive dependence on the personal experience of a decision maker is avoided, the emergency decision generation efficiency is improved, and the emergency can be responded quickly and efficiently.
When the recommendation parameter corresponding to the historical event is determined, the recommendation parameter can be determined according to the attribute information of the event to be decided and the attribute information of the historical event. As shown in fig. 4, a flowchart of a method for determining recommendation parameters of historical events according to an embodiment of the present invention includes:
in step S401, determining a probability value of each event type to which the event to be decided belongs according to the attribute information of the event to be decided, and determining a probability value of each event type to which the event to be decided belongs according to the attribute information of each historical event;
in the embodiment of the invention, the event to be decided and the historical event are mapped to the event type space according to the attribute information of the event to be decided and the historical event, the probability value of each event type corresponding to the event to be decided is determined according to the attribute information of the event to be decided, and the probability value of each event type corresponding to each historical event is determined according to the attribute information of each historical event.
The event type and the number of types may be preset, or may be determined according to attribute information of all events. Assuming that there are 5 event types, type 1, type 2, type 3, type 4, and type 5, the event types may represent types of emergency events, such as fire, flood, earthquake, traffic accident, etc., and may be divided into different grades, such as major traffic accident, general traffic accident, etc.
Supposing that the probability values of each event type corresponding to the event to be decided are determined according to the attribute information of the event to be decided as follows: the probability value belonging to type 1 is 0.1, the probability value belonging to type 2 is 0.4, the probability value belonging to type 3 is 0.2, the probability value belonging to type 4 is 0.9, and the probability value belonging to type 5 is 0.6. It should be noted that the sum of the probability values of each event type corresponding to each event may be 1, or may not be 1, and the embodiment of the present invention is not particularly limited; similarly, determining the probability value of each event type corresponding to each historical event according to the attribute information of each historical event; as shown in table 2, the probability value of each event type to which the event to be decided corresponds to the historical event is:
| type 1 | Type 2 | Type 3 | Type 4 | Type 5 | |
| Historical event 1 | 0.4 | 0.2 | 0.1 | 0.8 | 0 |
| Historical event 2 | 0.6 | 0.2 | 0.9 | 0.2 | 0.3 |
| Historical event 3 | 0.1 | 0.8 | 0.2 | 0.1 | 0.1 |
| Event to be decided | 0.1 | 0.4 | 0.2 | 0.9 | 0.6 |
TABLE 2
The number of the historical events, the number of the event types, and the probability value of each event type corresponding to each event are only examples.
The embodiment of the invention provides a method for determining probability values of each event type corresponding to events according to attribute information of the events.
An optional implementation manner is that the attribute information of the event to be decided and the attribute information of the plurality of historical cases are input into a trained event classification model, and a probability value of each event type corresponding to the event to be decided, which is output by the event classification model, and a probability value of each event type corresponding to each historical event are obtained.
In the embodiment of the invention, the probability value of each event type corresponding to the event can be determined through the trained event classification model, the event to be decided and the attribute information corresponding to the plurality of historical events are input into the trained event classification model, the event classification model classifies the event according to the attribute information of the event to be decided and the historical events, and the probability value of each event type corresponding to the event to be decided and the historical events is output.
In the embodiment of the invention, before the event classification model is called, the event classification model needs to be trained based on a large amount of sample data, attribute information of a plurality of historical events is used as input of the event classification model, the probability value of each event type corresponding to each historical event is used as output of the event classification model, the event classification model is trained for a plurality of times, and after the event classification model is converged, the completion of the training of the event classification model is determined.
Wherein, the event classification model can be LDA model.
In the implementation, the attribute information of an event to be decided, which is input by a user, and the attribute information of a pre-stored historical event are acquired, the attribute information of the event is input into a trained LDA model, the trained LDA model can output event type-attribute information distribution and event type-event type distribution, wherein the LDA model is clustered according to the attribute information of the event, and each type of attribute information can implicitly describe the event type; and determining the probability value of each event type corresponding to each event according to the event type corresponding to each attribute information in the attribute information corresponding to each event.
The event-event type distribution output by the LDA model is a distribution matrix, the row number of the distribution matrix is the number of events input into the LDA model, and the column number of the distribution matrix is the number of event types, wherein the number of event types can be preset during training of the LDA model. Method for determining the number of event types the embodiments of the present invention are not specifically limited, and it is assumed that the number of event types is the optimal number of event types.
In step S402, the probability value of each event type corresponding to the event to be decided forms a feature vector of the event to be decided according to a preset event type sequence, and the probability value of each event type corresponding to each historical event forms a feature vector of each historical event according to a preset event type sequence;
and determining the characteristic vectors corresponding to the event to be decided and the historical event according to the same preset event type sequence.
Assuming that the preset event type sequence is type 1, type 2, type 3, type 4, and type 5, the feature vector corresponding to the historical event 1 is [ 0.40.20.10.80 ]; the feature vector corresponding to the historical event 2 is [ 0.60.20.90.20.3 ]; the feature vector corresponding to the historical event 3 is [ 0.10.80.20.10.1 ]; the feature vector corresponding to the event to be decided is [ 0.10.40.20.90.6 ].
Assuming that the preset event type sequence is type 2, type 1, type 4, type 5, and type 3, the feature vector corresponding to the historical event 1 is [ 0.20.40.800.1 ]; the feature vector corresponding to the historical event 2 is [ 0.20.60.20.30.9 ]; the feature vector corresponding to the historical event 3 is [ 0.80.10.10.10.2 ]; the feature vector corresponding to the event to be decided is [ 0.40.10.90.60.2 ].
In step S403, for any historical event, determining a recommended parameter corresponding to the historical event according to the cosine similarity between the feature vector of the historical event and the feature vector of the event to be decided.
In the embodiment of the invention, the similarity between two events can be determined according to the cosine similarity of the feature vector.
In the embodiment of the invention, the cosine similarity between the feature vectors of two events is determined according to the following formula:
a, B represents feature vectors corresponding to two events; m represents the number of elements of the feature vector, and can also represent the total number of event types; i represents the ith element and can also represent any event type; v. ofAiThe value of the ith element in the feature vector corresponding to the event A is represented, and the probability value of the event type i corresponding to the event A can also be represented; v. ofBiThe value of the ith element in the feature vector corresponding to the event B may also be represented as a probability value of the event type i to which the event B belongs.
Determining cosine similarity between the feature vector corresponding to each historical event and the feature vector corresponding to the event to be decided according to the formula, wherein the cosine similarity is used as a recommended parameter corresponding to each historical event, and the recommended parameter corresponding to the historical event 1 is 0.79, the recommended parameter corresponding to the historical event 2 is 0.50, and the recommended parameter corresponding to the historical event 3 is 0.53.
The historical event 1 with the highest recommended parameter can be used as a target event, the historical decision information of the historical event 1 is determined, and the historical decision information of the historical event 1 is adjusted according to the attribute information of the event to be decided to obtain the decision information of the event to be decided.
In the embodiment of the invention, when the historical decision information of the target event is adjusted to obtain the decision information of the event to be decided, the decision information of the target event is adjusted according to the attribute information of the event to be decided, and the generated decision information of the event to be decided can comprise scheduling resource information and scheduling route information.
In the implementation, the scheduling resource information of the event to be decided is determined according to the scheduling resource information in the historical decision of the target event. The scheduling resource information in the embodiment of the invention can comprise personnel and materials.
Scheduling resource information in historical decision of the target event can be directly used as scheduling resource information of the event to be decided, for example, the target event is a fire disaster occurring at a certain place at a certain time, and the scheduled resources in the historical decision information include the number of fire extinguishers and fire extinguishers, the number of fire trucks called, a list of fire fighters called, a list of fire experts called, and the like. The scheduling resource information of the event to be decided can be directly adopted;
or the scheduling resource information of the event to be decided can be determined according to the similarity degree of the target event and the event to be decided, for example, the target event is that a large fire disaster occurs at a certain place at a certain time, the event to be decided is that a small fire disaster occurs at a certain place at a certain time, the number of fire extinguishers, the number of fire trucks, the number of called firefighters, the number of called fire experts and the like in the historical decision information can be reduced properly, and for example, half of the previous number is selected as the scheduling resource information of the event to be decided.
And determining scheduling position information of resource information required to be scheduled by the event to be decided according to the position information in the attribute information of the event to be decided. The scheduling position for scheduling the scheduling resource can be determined according to the place where the event to be decided occurs.
For example, the position of the event to be decided is the place C, the position of the scheduling resource in the preset range around the place C is determined, the scheduling position information can be determined according to the condition that the scheduling resource is owned by the places M and N around the place C, and one scheduling position is selected from the places M and N if the materials owned by the places M and N meet the required scheduling material. In implementation, the scheduling position can be selected according to real-time road conditions, distance and the like. The preset range may be input by the decision maker, for example, a preset range formed by a preset radius centered on C.
In implementation, the scheduling position can be selected according to real-time road conditions, distance and the like.
And generating a scheduling route from a first position corresponding to the scheduling position information to a second position where the event to be decided occurs, and adjusting the scheduling route information in the historical decision into the scheduling route information of the event to be decided.
In implementation, the scheduling route can be determined according to real-time road conditions, distances and the like.
In the embodiment of the invention, the historical decision information of the target event can be adjusted according to the GIS technology to obtain the decision information of the event to be decided. In the embodiment of the invention, the GIS system can integrate spatial attribute data acquisition, organization management, analysis processing and visual output, and has good capacities of organization management, comprehensive processing analysis and visual output on multi-source heterogeneous decision data. The decision data managed by the GIS system mainly comprises seven types of emergency resources, rivers, administrative divisions, real-time road conditions, video monitoring, weather information, reservoir information, individual soldier information and other various types of data. The accident site of the event to be decided can be directly positioned and video monitoring around the accident site can be called through a GIS technology, the scheduling position information and the scheduling route information are determined, various scheduling tools such as telephone scheduling, voice scheduling and short message scheduling are integrated, and a decision maker is assisted to quickly, accurately and comprehensively master the accident situation and make a decision problem and target clear.
As shown in fig. 5, for the front-end interface diagram of the event decision generating system provided in the embodiment of the present invention, a user may directly input attribute information of an event to be decided in an input box corresponding to attribute information of the front-end interface, and a position may be marked in the map for an occurrence location of the event to be decided in the attribute information; after the target event is determined, displaying the historical decision of the target event in an interface; scheduling resource information of the event to be decided, which is determined according to the scheduling resource information in the historical decision information, can also be displayed; determining scheduling position information with schedulable resources in a preset range according to the occurrence place of the event to be decided and the scheduling radius input by the user; generating a dispatching route from a dispatching position to the occurrence place of the event to be decided according to the monitoring around the occurrence place of the event to be decided and the road condition between the dispatching position and the occurrence place of the event to be decided and displaying the dispatching route on an interface; and the scheduling personnel can be contacted or the decision execution of the event to be decided can be directed through the scheduling tool displayed on the front-end interface.
It should be noted that the front-end interface diagram of the event decision generating system provided in the embodiment of the present invention is merely an example, and does not constitute a limitation to the scope of the present invention.
As shown in fig. 6, a schematic structural diagram of an event decision generating system provided in the embodiment of the present invention includes a client and a server;
the client responds to attribute information of the event to be decided input by a user and sends the attribute information of the event to be decided to a server;
the server inputs attribute information of the event to be decided and attribute information of the historical event into an LDA model, obtains probability values of the event to be decided output by the LDA model and each event type corresponding to the historical event, determines recommended parameters corresponding to each historical event, determines a target event according to the recommended parameters and sends the historical decision information of the target event to the client; based on the GIS system, the historical decision information of the target event is adjusted according to the attribute information of the event to be decided, so that the decision information of the event to be decided is obtained and sent to the client;
and the client displays the historical decision information of the target event and the obtained decision information of the event to be decided.
The historical decision information of the target event can be displayed in a text form.
The embodiment of the invention also provides a method for training the event classification model, which comprises the following steps:
performing word segmentation processing on text information corresponding to a plurality of historical events to obtain attribute information corresponding to each historical case in the historical events; the number of preset event types and attribute information corresponding to each historical case are used as input of an event classification model, and the event type corresponding to each attribute information in all the attribute information is determined through a Gibbs sampling algorithm; counting event types corresponding to each attribute information in attribute information corresponding to any historical event, and determining probability values of each event type corresponding to each historical event; and taking the event type corresponding to each attribute information and the probability value of each event type corresponding to each historical event as the output of the event classification model, and training the event classification model.
In the embodiment of the present invention, the event classification model may be an LDA model.
The historical events used to train the event classification model may or may not be the same as the historical events used in determining the target events. In implementation, the text recording the historical events can be subjected to Chinese word segmentation to remove useless stop words, only meaningful words in the text are reserved, and after word segmentation, the attribute information corresponding to each historical event is obtained. In the embodiment of the invention, word segmentation processing can be performed through a word segmentation application program ICTCCLAS and the like.
In the embodiment of the invention, the attribute information of the historical event obtained in the training process can be stored, and the attribute information of the historical event can be obtained without performing word segmentation processing in the using process.
In the embodiment of the present invention, the method for determining the number of the preset event types is not specifically limited in the embodiment of the present invention, and it is assumed that an appropriate number of event types has been obtained.
Inputting the number of preset event types and attribute information corresponding to a plurality of historical events into an LDA model, and determining event type-attribute information distribution and event-event type distribution by the LDA model according to a Gibbs sampling algorithm, wherein the event-event type distribution is determined according to the attribute information corresponding to each historical event and the event type-attribute information distribution, and the event type-attribute information distribution represents the event type corresponding to each attribute information; and the event-event type distribution represents the probability value of each event type corresponding to each historical event, the event type-attribute information distribution and the event-event type distribution are used as the output of the LDA model, the parameters of the LDA model are adjusted until the training is finished, and the trained LDA model has the capability of determining the probability value of each event type corresponding to the event according to the attribute information of the event.
Based on the same inventive concept, the embodiment of the present invention further provides an event decision generating device, and as the principle of the device for solving the problem is the same as the event decision generating method of the embodiment of the present invention, the implementation of the device may refer to the implementation of the method, and repeated details are not repeated.
As shown in fig. 7, an embodiment of the present invention provides an event decision generating apparatus, including:
an obtaining module 701, configured to obtain attribute information of an event to be decided, where the attribute information is input by a user;
a determining module 702, configured to determine, according to the attribute information of the event to be decided and the attribute information of the multiple historical events, a recommendation parameter corresponding to each historical event, and determine, according to the recommendation parameter corresponding to each historical event, a target event from the multiple historical events; wherein the recommendation parameter represents a degree of similarity between a historical event and the event to be decided;
the generating module 703 is configured to adjust historical decision information of a target event according to the attribute information of the event to be decided, so as to obtain decision information of the event to be decided.
In an optional implementation manner, the determining module 702 is specifically configured to:
determining a probability value of each event type corresponding to the event to be decided according to the attribute information of the event to be decided, and determining a probability value of each event type corresponding to each historical event according to the attribute information of each historical event;
forming the probability value of each event type corresponding to the event to be decided into a feature vector of the event to be decided according to a preset event type sequence, and forming the probability value of each event type corresponding to each historical event into the feature vector of each historical event according to the preset event type sequence;
and for any historical event, determining a recommendation parameter corresponding to the historical event according to the cosine similarity between the feature vector of the historical event and the feature vector of the event to be decided.
In an optional implementation manner, the determining module 702 is specifically configured to:
and inputting the attribute information of the event to be decided and the attribute information of the plurality of historical cases into a trained event classification model, and acquiring the probability value of each event type corresponding to the event to be decided and the probability value of each event type corresponding to each historical event, which are output by the event classification model.
As shown in fig. 8, an event decision generating apparatus according to an embodiment of the present invention further includes a training module 704, where the training module 704 is configured to train the event classification model according to the following manner:
performing word segmentation processing on text information corresponding to a plurality of historical events to obtain attribute information corresponding to each historical case in the historical events;
taking the number of preset event types and the attribute information corresponding to each historical case as the input of an event classification model, and determining the event type corresponding to each attribute information in all the attribute information through a Gibbs sampling algorithm;
counting event types corresponding to each attribute information in attribute information corresponding to any historical event, and determining probability values of each event type corresponding to each historical event;
and taking the event type corresponding to each attribute information and the probability value of each event type corresponding to each historical event as the output of the event classification model, and training the event classification model.
In an optional implementation manner, the generating module 703 is specifically configured to:
determining scheduling resource information of an event to be decided according to scheduling resource information in the historical decision of the target event; determining scheduling position information of resource information required to be scheduled by the event to be decided according to position information in the attribute information of the event to be decided;
generating scheduling route information from a first position corresponding to the scheduling position information of the event to be decided to a second position corresponding to the position information of the event to be decided;
and adjusting the scheduling route information in the historical decision of the target event into the scheduling route information of the event to be decided to obtain scheduling resource information of the event to be decided and decision information of the scheduling route information.
Based on the same inventive concept, the embodiment of the invention also provides an electronic device, and as the principle of solving the problem of the electronic device is the same as the license plate detection method of the embodiment of the invention, the implementation of the electronic device can refer to the implementation of the method, and repeated details are not repeated.
Referring to fig. 9, an electronic device 900 according to an embodiment of the present invention includes:
at least one processor 901, and
a memory 902 communicatively coupled to the at least one processor;
wherein the memory 902 stores instructions executable by the at least one processor 901, and the at least one processor 901 implements the event decision generation method by executing the instructions stored in the memory.
An embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the event decision generation method described above.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (12)
1. A method for event decision generation, the method comprising:
acquiring attribute information of an event to be decided, which is input by a user;
determining a recommendation parameter corresponding to each historical event according to the attribute information of the event to be decided and the attribute information of the plurality of historical events, and determining a target event from the plurality of historical events according to the recommendation parameter corresponding to each historical event; wherein the recommendation parameter represents a degree of similarity between a historical event and the event to be decided;
and adjusting the historical decision information of the target event according to the attribute information of the event to be decided to obtain the decision information of the event to be decided.
2. The method of claim 1, wherein the determining, according to the attribute information of the event to be decided and the attribute information of the plurality of historical events, a recommendation parameter corresponding to each historical event comprises:
determining a probability value of each event type corresponding to the event to be decided according to the attribute information of the event to be decided, and determining a probability value of each event type corresponding to each historical event according to the attribute information of each historical event;
forming the probability value of each event type corresponding to the event to be decided into a feature vector of the event to be decided according to a preset event type sequence, and forming the probability value of each event type corresponding to each historical event into the feature vector of each historical event according to the preset event type sequence;
and for any historical event, determining a recommendation parameter corresponding to the historical event according to the cosine similarity between the feature vector of the historical event and the feature vector of the event to be decided.
3. The method as claimed in claim 2, wherein the determining the probability value of each event type corresponding to the event to be decided according to the attribute information of the event to be decided, and determining the probability value of each event type corresponding to each historical event according to the attribute information of each historical event comprises:
and inputting the attribute information of the event to be decided and the attribute information of the plurality of historical cases into a trained event classification model, and acquiring the probability value of each event type corresponding to the event to be decided and the probability value of each event type corresponding to each historical event, which are output by the event classification model.
4. The method of claim 3, wherein the event classification model is trained according to the following:
performing word segmentation processing on text information corresponding to a plurality of historical events to obtain attribute information corresponding to each historical case in the historical events;
taking the number of preset event types and the attribute information corresponding to each historical case as the input of an event classification model, and determining the event type corresponding to each attribute information in all the attribute information through a Gibbs sampling algorithm;
counting event types corresponding to each attribute information in attribute information corresponding to any historical event, and determining probability values of each event type corresponding to each historical event;
and taking the event type corresponding to each attribute information and the probability value of each event type corresponding to each historical event as the output of the event classification model, and training the event classification model.
5. The method according to claim 3, wherein the adjusting the historical decision information of the target event according to the attribute information of the event to be decided to obtain the decision information of the event to be decided comprises:
determining scheduling resource information of an event to be decided according to scheduling resource information in the historical decision of the target event; determining scheduling position information of resource information required to be scheduled by the event to be decided according to position information in the attribute information of the event to be decided;
generating scheduling route information from a first position corresponding to the scheduling position information of the event to be decided to a second position corresponding to the position information of the event to be decided;
and adjusting the scheduling route information in the historical decision of the target event into the scheduling route information of the event to be decided to obtain scheduling resource information of the event to be decided and decision information of the scheduling route information.
6. An event decision generating apparatus, comprising:
the acquisition module is used for acquiring attribute information of the event to be decided, which is input by a user;
the determining module is used for determining a recommendation parameter corresponding to each historical event according to the attribute information of the event to be decided and the attribute information of the plurality of historical events, and determining a target event from the plurality of historical events according to the recommendation parameter corresponding to each historical event; wherein the recommendation parameter represents a degree of similarity between a historical event and the event to be decided;
and the generating module is used for adjusting the historical decision information of the target event according to the attribute information of the event to be decided to obtain the decision information of the event to be decided.
7. The apparatus of claim 6, wherein the determination module is specifically configured to:
determining a probability value of each event type corresponding to the event to be decided according to the attribute information of the event to be decided, and determining a probability value of each event type corresponding to each historical event according to the attribute information of each historical event;
forming the probability value of each event type corresponding to the event to be decided into a feature vector of the event to be decided according to a preset event type sequence, and forming the probability value of each event type corresponding to each historical event into the feature vector of each historical event according to the preset event type sequence;
and for any historical event, determining a recommendation parameter corresponding to the historical event according to the cosine similarity between the feature vector of the historical event and the feature vector of the event to be decided.
8. The apparatus of claim 7, wherein the determination module is specifically configured to:
and inputting the attribute information of the event to be decided and the attribute information of the plurality of historical cases into a trained event classification model, and acquiring the probability value of each event type corresponding to the event to be decided and the probability value of each event type corresponding to each historical event, which are output by the event classification model.
9. The apparatus of claim 8, further comprising a training module to train the event classification model according to:
performing word segmentation processing on text information corresponding to a plurality of historical events to obtain attribute information corresponding to each historical case in the historical events;
taking the number of preset event types and the attribute information corresponding to each historical case as the input of an event classification model, and determining the event type corresponding to each attribute information in all the attribute information through a Gibbs sampling algorithm;
counting event types corresponding to each attribute information in attribute information corresponding to any historical event, and determining probability values of each event type corresponding to each historical event;
and taking the event type corresponding to each attribute information and the probability value of each event type corresponding to each historical event as the output of the event classification model, and training the event classification model.
10. The apparatus of claim 8, wherein the generation module is specifically configured to:
determining scheduling resource information of an event to be decided according to scheduling resource information in the historical decision of the target event; determining scheduling position information of resource information required to be scheduled by the event to be decided according to position information in the attribute information of the event to be decided;
generating scheduling route information from a first position corresponding to the scheduling position information of the event to be decided to a second position corresponding to the position information of the event to be decided;
and adjusting the scheduling route information in the historical decision of the target event into the scheduling route information of the event to be decided to obtain scheduling resource information of the event to be decided and decision information of the scheduling route information.
11. An electronic device, comprising:
at least one processor, and
a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the at least one processor performing the event decision generation method of any of claims 1-5 by executing the instructions stored by the memory.
12. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a computer, is adapted to perform the event decision generation method of any of claims 1 to 5.
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