CN112507141A - Investigation task generation method and device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the application belongs to the field of artificial intelligence, is applied to the field of intelligent security and protection, and relates to an investigation task generation method, an investigation task generation device, computer equipment and a storage medium, wherein the investigation task generation method comprises the steps of receiving historical investigation data and a task sequence, extracting case labels in the historical investigation data, and generating case vectors based on the case labels, wherein the last task of the task sequence is a simple task; generating a training sample based on the case vector and the task sequence, training a preset task configuration model based on the training sample, and obtaining a trained task configuration model; acquiring data to be identified, extracting case labels in the data to be identified, generating case vectors to be identified based on the case labels in the data to be identified, inputting the case vectors to be identified into a trained task configuration model, and acquiring execution tasks, wherein the execution tasks belong to simple tasks. The trained task configuration model can be stored in the blockchain. The efficiency of case investigation is effectively promoted in this application.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for generating a survey task, a computer device, and a storage medium.
Background
With the continuous development of computer technology, computers are gradually applied to various industries, and the high-speed development of various industries is effectively promoted. Currently, the claim settlement and inspection of cases is to directly send the relevant information of cases to an investigator through a computer, so that the investigator can perform investigation and inspection on the cases online.
However, currently, investigators need to analyze cases according to personal experience, manually split cases, study investigation paths by themselves, determine investigation tasks, and then complete case investigation. The process requires that investigators have rich professional domain knowledge and that investigation experience parties can determine investigation tasks and perform professional verification on investigation articles. Further, the admission threshold of the investigators is high, the groups of the investigators are difficult to expand, the case investigation cost is high, the investigation efficiency is low, time and labor are consumed, and the investigation timeliness is low.
Disclosure of Invention
The embodiment of the application aims to provide an investigation task generation method, an investigation task generation device, computer equipment and a storage medium, and effectively improve the efficiency of case investigation.
In order to solve the above technical problem, an embodiment of the present application provides an investigation task generating method, which adopts the following technical solutions:
a survey task generating method comprises the following steps:
receiving historical investigation data and task sequences, extracting case labels in the historical investigation data, and generating case vectors based on the case labels, wherein the task sequences and the historical investigation data have one-to-one mapping relations, and the tasks of the task sequences are simple tasks;
generating a training sample based on the case vector and the corresponding task sequence, and training a preset task configuration model based on the training sample to obtain a trained task configuration model; and
acquiring data to be identified, extracting case labels in the data to be identified, generating case vectors to be identified based on the case labels in the data to be identified, inputting the case vectors to be identified into a trained task configuration model, and acquiring execution tasks, wherein the execution tasks belong to the simple tasks.
Further, the step of receiving historical survey data and a task sequence, extracting case labels in the historical survey data, and generating case vectors based on the case labels includes:
receiving historical investigation data and identifying the type of the historical investigation data;
and when the historical investigation data is an investigation picture, extracting a key entity in the investigation picture as the case label through a pre-trained character recognition model.
And when the historical investigation data is investigation characters, matching the investigation characters through a preset label table to obtain label characters serving as case labels.
Further, the step of generating case vectors based on the case labels comprises:
sorting the case labels based on a preset sequence to obtain sequence labels;
determining a vector value corresponding to a case label according to a pre-configured mapping relation table of the case label and the vector value;
a case vector is generated based on the sequence label and a vector value.
Further, the step of inputting the case vector to be recognized into the trained task configuration model to obtain the executed task includes:
inputting a first vector value in the case vector to be identified and a preset empty task into a trained task configuration model to obtain a first identification task;
determining whether the task configuration model outputs a termination identifier;
if the task configuration model outputs a termination identifier, taking the first recognition task as the execution task;
and if the task configuration model does not output a termination identifier, inputting a second vector value in the case vector to be recognized and the first recognition task into the trained task configuration model until the task configuration model outputs the termination identifier, and obtaining the execution task.
Further, the step of obtaining the execution task by inputting the case vector to be recognized and a preset empty task into the trained task configuration model includes:
inputting a first vector value in the case vector to be recognized and a preset empty task into a trained task configuration model to obtain a second recognition task;
determining whether the second identified task belongs to a simple task;
if the second recognition task belongs to a simple task, taking the second recognition task as an execution task;
and if the second recognition task does not belong to a simple task, inputting a second vector value in the case vector to be recognized and the first recognition task into a trained task configuration model until the task configuration model outputs the simple task, and obtaining the execution task.
Further, after the step of inputting the case vector to be recognized into the trained task configuration model and obtaining the executed task, the method further includes:
identifying all execution tasks output by the task configuration model within a preset time period;
generating a set of execution tasks based on the same execution task;
and acquiring a survey staff list, and randomly distributing the execution task set to any survey staff in the survey staff list.
Further, the step of obtaining a list of investigators and randomly assigning the set of executed tasks to any one of the investigators in the list of investigators includes:
identifying a preset task level corresponding to the execution task, wherein the task level and the execution task have a one-to-one mapping relation;
generating a set level of the set of executing tasks based on the task level;
acquiring a survey staff list, wherein each survey staff in the survey staff list corresponds to a survey grade;
determining the investigators with investigation levels higher than the set level as the persons to be allocated;
and randomly distributing the execution task set to any person to be distributed.
In order to solve the above technical problem, an embodiment of the present application further provides an investigation task generating device, which adopts the following technical solutions:
an investigation task generating apparatus comprising:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving historical investigation data and task sequences, extracting case labels in the historical investigation data and generating case vectors based on the case labels, the task sequences and the historical investigation data have one-to-one mapping relation, and the tasks of the task sequences are simple tasks;
the training module is used for generating a training sample based on the case vector and the corresponding task sequence, training a preset task configuration model based on the training sample, and obtaining a trained task configuration model; and
and the input model is used for acquiring data to be recognized, extracting case labels in the data to be recognized, generating case vectors to be recognized based on the case labels in the data to be recognized, and inputting the case vectors to be recognized into the trained task configuration model to obtain execution tasks, wherein the execution tasks belong to the simple tasks.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
a computer device comprising a memory having computer readable instructions stored therein and a processor, the processor implementing the steps of the survey task generating method described above when executing the computer readable instructions.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
a computer readable storage medium having computer readable instructions stored thereon, which when executed by a processor, implement the steps of the survey task generating method described above.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
according to the case vector generation method and device, case labels and corresponding task sequences in historical investigation data are extracted, and case vectors are generated based on the case labels, so that a follow-up task configuration model can be conveniently trained according to the case vectors and the task sequences. The computer can automatically and intelligently split the data to be identified through the trained task configuration model, a simple execution task which can be smoothly completed by an investigator is generated based on the data to be identified, the investigator is not required to split the task and plan the path according to experience, the admission threshold of the investigator is effectively reduced, the time of the investigator is effectively saved, the investigation efficiency is improved, the investigation timeliness is improved, the execution task belonging to the simple task is generated through the intelligent splitting case of the computer, the investigation accuracy can be effectively improved, and the problems that the investigation accuracy is low and the investigation task cannot be smoothly completed due to high investigation task difficulty are solved.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a survey task generation method according to the present application;
FIG. 3 is a schematic block diagram of one embodiment of a survey task generating apparatus according to the present application;
FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.
Reference numerals: 200. a computer device; 201. a memory; 202. a processor; 203. a network interface; 300. a survey task generating device; 301. a receiving module; 302. a training module; 303. and an input module.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the survey task generating method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the survey task generating apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continuing reference to FIG. 2, a flow diagram of one embodiment of a survey task generation method according to the present application is shown. The survey task generation method comprises the following steps:
s1: receiving historical investigation data and task sequences, extracting case labels in the historical investigation data, and generating case vectors based on the case labels, wherein the task sequences and the historical investigation data have one-to-one mapping relations, and the tasks of the task sequences are simple tasks.
In this embodiment, the historical survey data is split to generate a large number of case labels, including the number of days of occurrence, disease type, disease treatment time, disease treatment location, disease diagnosis and treatment department, medicine for disease use, the number of days of disease treatment, and the like. Case vectors are generated based on the case labels, each case vector represents the embedding (embedding) of a case, and the embedding (embedding) means that a low-dimensional vector represents a case. For example: case a, corresponding to a vector of (1,360,26,78,79,2,28, …), where: and 1 represents a depth case. 360 represents the number of days to risk that insurance will take effect, 26 represents the case's product number, 78 represents the disease category, 79 represents the hospital category of risk, 2 represents the case is of the accident type, and 28 represents the area of risk. And the subsequent process comprises a task sequence of simple tasks, the simple task is the last task in the task sequence, and the task finally output by the model is the simple task.
In the present embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the survey task generating method operates may receive the historical survey data and the task sequence through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
Specifically, the step of receiving historical survey data and a task sequence, extracting case labels in the historical survey data, and generating case vectors based on the case labels includes:
receiving historical investigation data and identifying the type of the historical investigation data;
and when the historical investigation data is an investigation picture, extracting a key entity in the investigation picture as the case label through a pre-trained character recognition model.
And when the historical investigation data is investigation characters, matching the investigation characters through a preset label table to obtain label characters serving as case labels.
In the present embodiment, the Character Recognition model in the present application is an OCR (Optical Character Recognition) model. The historical survey data in the application can be detailed descriptions of cases uploaded by clients and information of related personnel, events and the like. The method can be used for taking pictures and uploading or uploading after inputting characters on a front-end page. When the customer takes a picture and uploads the picture, the historical investigation data is an investigation picture, and key entities in the investigation picture can be extracted through a pre-trained character recognition model to generate case labels. The pre-trained character recognition model can effectively and automatically recognize the required key entities and automatically generate labels. When the client uploads the words input by the front-end page, the historical investigation data is investigation words, and only the investigation words need to be matched through a preset tag table, and the tag words are determined to be used as case tags, so that the case tags are generated quickly.
Specifically, the step of generating case vectors based on the case labels includes:
sorting the case labels based on a preset sequence to obtain sequence labels;
determining a vector value corresponding to a case label according to a pre-configured mapping relation table of the case label and the vector value;
a case vector is generated based on the sequence label and a vector value.
In this embodiment, after the case labels are generated, the case labels need to be sorted according to a preset sequence, so that after the case labels are converted into case vectors, it is ensured that vector values in different case vectors represent the same dimension, that is, represent the case labels of the same kind. Thereby realizing the subsequent model training and the practical application of the model in production.
It should be noted that: in production, the vector value ordering in the case vector to be identified generated based on the received data to be identified needs to be consistent with the vector value ordering in the case vector in the training process. Therefore, the case can be successfully split by the task configuration model.
S2: generating a training sample based on the case vector and the corresponding task sequence, and training a preset task configuration model based on the training sample to obtain a trained task configuration model.
In this embodiment, the task configuration model of the present application is an RNN (Recurrent Neural Network) model. RNNs are used to process sequence data. In the traditional neural network model, from an input layer to a hidden layer to an output layer, all layers are connected, and nodes between each layer are connectionless. The RNN memorizes the previous information and applies it to the calculation of the current output, i.e. the nodes between the hidden layers are no longer connected but connected, and the input of the hidden layer includes not only the output of the input layer but also the output of the hidden layer at the previous moment. In the training process, each case corresponds to one task sequence, namely each case vector corresponds to one task sequence. For example: case vector (1,360,26,78,79,2,28, …), corresponding Task sequence (Task1, Task2, Task3, Task4, Task5, Task6, …). The case vector and Task0 (initial Task or empty Task) are sent to the network, and the output is Task 1; sending the case vector and Task1 into the network, and outputting Task 2; and so on. The mapping relation among the task sequence, the case and the task sequence is configured manually and in a user-defined mode according to an actual scene. Wherein, the task sequences corresponding to different case vectors are different. For example: the Task sequence corresponding to the case vector (1, 357,98) is (Task1, Task2, Task 3). The Task sequence corresponding to case vector (2,357,98) is (Task3), and the Task sequence corresponding to case vector (2,765) is (Task 7).
In the following, the practical application scenario is exemplified, and since the difficulty of different tasks is different, the task assigned to the surveyor is simple and can be realized. Different case vectors need to be configured in advance according to actual conditions to serve as training samples to train the model, and the task finally output by the model is a simple and realizable task. Example 1: the vector value of the first position in the case vector is 1, and the Task1 in the corresponding Task sequence is the date of visiting the hospital for investigation and treatment, and the Task belongs to a difficult Task, so that the next Task needs to be configured according to the vector value of the second position in the case vector. The second bit vector value is 357 and the corresponding Task is Task2, and since this Task belongs to a difficult Task, it is necessary to continue to configure the next Task according to the vector data of the third bit in the case vector. Until the correspondingly configured Task3 (to visit a hospital for surgical investigation whether the patient is actually ill) is a simple Task. And completing the configuration of the task sequence. Example 2: the vector value of the first position in the case vector is 2, and the Task7 in the corresponding Task sequence belongs to a simple Task for the date of visiting the hospital for investigation and visiting, and then the configuration of the Task sequence is completed.
S3: acquiring data to be identified, extracting case labels in the data to be identified, generating case vectors to be identified based on the case labels in the data to be identified, inputting the case vectors to be identified into a trained task configuration model, and acquiring execution tasks, wherein the execution tasks belong to the simple tasks.
In this embodiment, during prediction, the case vector and Task0 (start Task) are also sent to the trained Task configuration model, and the Task configuration model predicts the first Task (Task x 1); the case vector and TaskX1 are sent into the network again, and the second task of the task sequence is predicted (TaskX 2); and so on until the output terminates the task (TaskEnd). Because the training of the task configuration model is completed, in the actual application process, only the vector value in the case vector and the predicted task output by the task configuration model need to be input in sequence, and the currently output task is an execution task until the task configuration model outputs a termination notice or outputs a simple task. According to the method and the device, the survey path with high efficiency and low cost is automatically generated through optimizing the survey path. And then an investigator can take a specific simple task after splitting the case instead of a complex case, the case investigated by manual experience is split into a single definite task through the task configuration model, and after the treatment of a system algorithm, the problem of overhigh threshold of the investigator is solved, and cost reduction and efficiency improvement of the downstream of the system are realized.
It should be noted that: the survey path in this application refers to the execution task that the surveyor finally wants to perform. The method avoids tasks which are difficult to execute being distributed to investigators, and improves investigation efficiency and investigation quality.
Specifically, the step of inputting the case vector to be recognized into the trained task configuration model to obtain the executed task includes:
inputting a first vector value in the case vector to be identified and a preset empty task into a trained task configuration model to obtain a first identification task;
determining whether the task configuration model outputs a termination identifier;
if the task configuration model outputs a termination identifier, taking the first recognition task as the execution task;
and if the task configuration model does not output a termination identifier, inputting a second vector value in the case vector to be recognized and the first recognition task into the trained task configuration model until the task configuration model outputs the termination identifier, and obtaining the execution task.
In the present embodiment, for example: and (3) inputting a first vector value of 3 and a null task into the trained task configuration model to obtain a first recognition task, wherein the case vector to be recognized is (3,89,6, 76). And determining whether the task configuration model outputs a termination identifier, and if the task configuration model outputs the termination identifier, taking the first recognition task as the execution task. And inputting the second vector value 89 and the first recognition task into the task configuration model to obtain a second recognition task until the task configuration model outputs a termination identifier, and taking the recognition task output by the current task configuration model as an execution task. And determining whether the task configuration model completes the output by identifying whether the task configuration model outputs the termination identifier.
In addition, as another embodiment of the present application, the step of obtaining the executed task in the task configuration model after the case vector to be recognized and the preset empty task are input into the training includes:
inputting a first vector value in the case vector to be identified and a preset empty task into a trained task configuration model to obtain a first identification task;
determining whether the first identified task belongs to a simple task;
if the first identification task belongs to a simple task, taking the first identification task as an execution task;
and if the first recognition task does not belong to a simple task, inputting a second vector value in the case vector to be recognized and the first recognition task into a trained task configuration model until the simple task is output by the task configuration model, and obtaining the execution task.
In this embodiment, the identification task output each time by the task configuration model is determined, and if the identification task belongs to a simple task, an execution task is obtained. The case is split through the task recognition model, and the finally output task is a simple task which can be successfully completed by an investigator.
In some optional implementation manners of this embodiment, after step S3, that is, after generating a case vector to be recognized based on case labels in data to be recognized, and inputting the case vector to be recognized into a trained task configuration model, and obtaining an execution task, the electronic device may further perform the following steps:
identifying all execution tasks output by the task configuration model within a preset time period;
generating a set of execution tasks based on the same execution task;
and acquiring a survey staff list, and randomly distributing the execution task set to any survey staff in the survey staff list.
In this embodiment, the same task among the execution tasks output by the task configuration model within the preset time period is distributed to the same investigator. Therefore, one investigator can simultaneously execute the same task of different cases, and the investigation efficiency of the cases is effectively improved.
The step of obtaining a survey staff list and randomly distributing the execution task set to any survey staff in the survey staff list comprises the following steps:
identifying a preset task level corresponding to the execution task, wherein the task level and the execution task have a one-to-one mapping relation;
generating a set level of the set of executing tasks based on the task level;
acquiring a survey staff list, wherein each survey staff in the survey staff list corresponds to a survey grade;
determining the investigators with investigation levels higher than the set level as the persons to be allocated;
and randomly distributing the execution task set to any person to be distributed.
In the present embodiment, a task level for performing a task, and a survey level of a surveyor are set in advance. And generating a set grade for executing the task set according to the task grade. Since the execution tasks that generate the execution task set belong to the same execution task, the corresponding task levels are also the same, and the generated set level may be the same as the task level. Of course, the set level may be adjusted according to actual needs, for example, the generated set level is the task level plus a preset level value. The staff level of the investigator is lower than the aggregate level of the set of performed tasks. Through the setting of the grade, the investigators can complete the execution task better, and relatively difficult tasks are prevented from being distributed to the investigators with low grades. The rank of the investigator may be set manually or may be set by dividing according to the number of tasks executed by the investigator.
According to the case vector generation method and device, case labels and corresponding task sequences in historical investigation data are extracted, and case vectors are generated based on the case labels, so that a follow-up task configuration model can be conveniently trained according to the case vectors and the task sequences. The computer can automatically and intelligently split the data to be identified through the trained task configuration model, a simple execution task which can be smoothly completed by an investigator is generated based on the data to be identified, the investigator is not required to split the task and plan the path according to experience, the admission threshold of the investigator is effectively reduced, the time of the investigator is effectively saved, the investigation efficiency is improved, the investigation timeliness is improved, the execution task belonging to the simple task is generated through the intelligent splitting case of the computer, the investigation accuracy can be effectively improved, and the problems that the investigation accuracy is low and the investigation task cannot be smoothly completed due to high investigation task difficulty are solved.
It is emphasized that, in order to further ensure the privacy and security of the trained task configuration model, the trained task configuration model may also be stored in a node of a blockchain.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The application can be applied to the field of intelligent security and protection, and can be particularly applied to target tracking in the intelligent security and protection, so that the construction of a smart city is promoted.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, can include processes of the embodiments of the methods described above. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a survey task generating device, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device may be applied to various electronic devices.
As shown in fig. 3, the survey task generating device 300 according to the present embodiment includes: a receiving module 301, a training module 302, and an input module 303. Wherein: a receiving module 301, configured to receive historical survey data and a task sequence, extract case labels in the historical survey data, and generate case vectors based on the case labels, where the task sequence and the historical survey data have a one-to-one mapping relationship, and an end task of the task sequence is a simple task; a training module 302, configured to generate a training sample based on the case vector and the corresponding task sequence, train a preset task configuration model based on the training sample, and obtain a trained task configuration model; and the input model 303 is used for acquiring data to be recognized, extracting case labels in the data to be recognized, generating case vectors to be recognized based on the case labels in the data to be recognized, and inputting the case vectors to be recognized into a trained task configuration model to obtain an execution task, wherein the execution task belongs to the simple task.
In the embodiment, case labels and corresponding task sequences in the historical survey data are extracted, and case vectors are generated based on the case labels, so that a follow-up task configuration model can be conveniently trained according to the case vectors and the task sequences. The computer can automatically and intelligently split the data to be identified through the trained task configuration model, a simple execution task which can be smoothly completed by an investigator is generated based on the data to be identified, the investigator is not required to split the task and plan the path according to experience, the admission threshold of the investigator is effectively reduced, the time of the investigator is effectively saved, the investigation efficiency is improved, the investigation timeliness is improved, the execution task belonging to the simple task is generated through the intelligent splitting case of the computer, the investigation accuracy can be effectively improved, and the problems that the investigation accuracy is low and the investigation task cannot be smoothly completed due to high investigation task difficulty are solved.
The receiving module 301 includes a receiving sub-module, an extracting sub-module, and a matching sub-module. The receiving submodule is used for receiving historical survey data and identifying the type of the historical survey data; the extraction submodule is used for extracting a key entity in the investigation picture as the case label through a pre-trained character recognition model when the historical investigation data is the investigation picture; and the matching sub-module is used for matching the investigation characters through a preset tag table to obtain tag characters serving as case tags when the historical investigation data are the investigation characters.
The receiving module 301 further includes a sorting sub-module, a determining sub-module, and a generating sub-module. The sorting submodule is used for sorting the case labels based on a preset sequence to obtain sequence labels; the determining submodule is used for determining a vector value corresponding to the case label according to a mapping relation table of the case label and the vector value configured in advance; the generation submodule is used for generating case vectors based on the sequence labels and the vector values.
The input module 303 comprises a first input submodule, a first judgment submodule, a first obtaining submodule and a second obtaining submodule; the first input submodule is used for inputting a first vector value in the case vector to be recognized and a preset empty task into a trained task configuration model to obtain a first recognition task; the first judgment submodule is used for determining whether the task configuration model outputs a termination identifier; the first obtaining submodule is used for taking the first recognition task as the execution task when the task configuration model outputs a termination identifier; and the second obtaining submodule is used for inputting a second vector value in the case vector to be recognized and the first recognition task into the trained task configuration model when the task configuration model does not output the termination identifier, and obtaining the execution task until the task configuration model outputs the termination identifier.
The input module 303 further includes a second input submodule, a second determination submodule, a second obtaining submodule, and a second obtaining submodule; the second input submodule is used for inputting a first vector value in the case vector to be recognized and a preset empty task into the trained task configuration model to obtain a second recognition task; the second judgment submodule is used for determining whether the second recognition task belongs to a simple task or not; and the second obtaining submodule is used for taking the second identification task as an execution task when the second identification task belongs to a simple task. And the second obtaining submodule is used for inputting a second vector value in the case vector to be recognized and the second recognition task into a trained task configuration model when the second recognition task does not belong to a simple task until the task configuration model outputs the simple task to obtain the execution task.
In some optional implementations of this embodiment, the apparatus 300 further includes: the system comprises an identification module, a generation module and an allocation module, wherein the identification module is used for identifying all execution tasks output by the task configuration model within a preset time period; the generating module is used for generating an execution task set based on the same execution task; the distribution module is used for acquiring a survey staff list and randomly distributing the execution task set to any survey staff in the survey staff list.
The distribution module comprises an identification unit, a generation unit, an acquisition unit, a determination unit and a distribution unit. The identification unit is used for identifying a preset task level corresponding to the execution task, wherein the task level and the execution task have a one-to-one mapping relation; the generating unit is used for generating a set level of the executing task set based on the task level; the acquisition unit is used for acquiring a survey staff list, wherein each survey staff in the survey staff list corresponds to a survey grade; the determining unit is used for determining the investigators with investigation levels higher than the set level as the persons to be allocated; the distribution unit is used for distributing the execution task set to any person to be distributed at random.
According to the case vector generation method and device, case labels and corresponding task sequences in historical investigation data are extracted, and case vectors are generated based on the case labels, so that a follow-up task configuration model can be conveniently trained according to the case vectors and the task sequences. The computer can automatically and intelligently split the data to be identified through the trained task configuration model, a simple execution task which can be smoothly completed by an investigator is generated based on the data to be identified, the investigator is not required to split the task and plan the path according to experience, the admission threshold of the investigator is effectively reduced, the time of the investigator is effectively saved, the investigation efficiency is improved, the investigation timeliness is improved, the execution task belonging to the simple task is generated through the intelligent splitting case of the computer, the investigation accuracy can be effectively improved, and the problems that the investigation accuracy is low and the investigation task cannot be smoothly completed due to high investigation task difficulty are solved.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 200 comprises a memory 201, a processor 202, a network interface 203 communicatively connected to each other via a system bus. It is noted that only computer device 200 having components 201 and 203 is shown, but it is understood that not all of the illustrated components are required and that more or fewer components may alternatively be implemented. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 201 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 201 may be an internal storage unit of the computer device 200, such as a hard disk or a memory of the computer device 200. In other embodiments, the memory 201 may also be an external storage device of the computer device 200, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 200. Of course, the memory 201 may also include both internal and external storage devices of the computer device 200. In this embodiment, the memory 201 is generally used for storing an operating system installed in the computer device 200 and various types of application software, such as computer readable instructions of a survey task generating method. Further, the memory 201 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 202 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 202 is generally operative to control overall operation of the computer device 200. In this embodiment, the processor 202 is configured to execute computer readable instructions or process data stored in the memory 201, for example, execute computer readable instructions of the survey task generating method.
The network interface 203 may comprise a wireless network interface or a wired network interface, and the network interface 203 is generally used for establishing communication connection between the computer device 200 and other electronic devices.
In the embodiment, the case is intelligently split through the computer, the execution task belonging to the simple task is generated, the investigation accuracy can be effectively improved, and the condition that the investigation accuracy is low due to the fact that the investigation task is difficult is avoided.
The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the survey task generating method as described above.
In the embodiment, the case is intelligently split through the computer, the execution task belonging to the simple task is generated, the investigation accuracy can be effectively improved, and the condition that the investigation accuracy is low due to the fact that the investigation task is difficult is avoided.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A method for generating a survey task is characterized by comprising the following steps:
receiving historical investigation data and task sequences, extracting case labels in the historical investigation data, and generating case vectors based on the case labels, wherein the task sequences and the historical investigation data have one-to-one mapping relations, and the tasks of the task sequences are simple tasks;
generating a training sample based on the case vector and the corresponding task sequence, and training a preset task configuration model based on the training sample to obtain a trained task configuration model; and
acquiring data to be identified, extracting case labels in the data to be identified, generating case vectors to be identified based on the case labels in the data to be identified, inputting the case vectors to be identified into a trained task configuration model, and acquiring execution tasks, wherein the execution tasks belong to the simple tasks.
2. The method of claim 1, wherein the step of receiving historical survey data and task sequences, extracting case tags from the historical survey data, and generating case vectors based on the case tags comprises:
receiving historical investigation data and identifying the type of the historical investigation data;
and when the historical investigation data is an investigation picture, extracting a key entity in the investigation picture as the case label through a pre-trained character recognition model.
And when the historical investigation data is investigation characters, matching the investigation characters through a preset label table to obtain label characters serving as case labels.
3. The method of claim 1, wherein the step of generating case vectors based on the case labels comprises:
sorting the case labels based on a preset sequence to obtain sequence labels;
determining a vector value corresponding to a case label according to a pre-configured mapping relation table of the case label and the vector value;
a case vector is generated based on the sequence label and a vector value.
4. The method for generating survey tasks according to claim 1, wherein the step of inputting the case vector to be recognized into the trained task configuration model to obtain the executed task comprises:
inputting a first vector value in the case vector to be identified and a preset empty task into a trained task configuration model to obtain a first identification task;
determining whether the task configuration model outputs a termination identifier;
if the task configuration model outputs a termination identifier, taking the first recognition task as the execution task;
and if the task configuration model does not output a termination identifier, inputting a second vector value in the case vector to be recognized and the first recognition task into the trained task configuration model until the task configuration model outputs the termination identifier, and obtaining the execution task.
5. The method for generating the survey task according to claim 1, wherein the step of obtaining the execution task by inputting the case vector to be recognized and the preset empty task into the trained task configuration model comprises:
inputting a first vector value in the case vector to be recognized and a preset empty task into a trained task configuration model to obtain a second recognition task;
determining whether the second identified task belongs to a simple task;
if the second recognition task belongs to a simple task, taking the second recognition task as an execution task;
and if the second recognition task does not belong to the simple task, inputting a second vector value in the case vector to be recognized and the second recognition task into a trained task configuration model until the task configuration model outputs the simple task, and obtaining the execution task.
6. The method for generating survey tasks according to claim 1, wherein after the step of inputting the case vector to be recognized into the trained task configuration model and obtaining the executed task, the method further comprises:
identifying all execution tasks output by the task configuration model within a preset time period;
generating a set of execution tasks based on the same execution task;
and acquiring a survey staff list, and randomly distributing the execution task set to any survey staff in the survey staff list.
7. The survey task generating method of claim 6, wherein the step of obtaining a list of survey people and randomly assigning the set of executed tasks to any survey people in the list of survey people comprises:
identifying a preset task level corresponding to the execution task, wherein the task level and the execution task have a one-to-one mapping relation;
generating a set level of the set of executing tasks based on the task level;
acquiring a survey staff list, wherein each survey staff in the survey staff list corresponds to a survey grade;
determining the investigators with investigation levels higher than the set level as the persons to be allocated;
and randomly distributing the execution task set to any person to be distributed.
8. An investigation task generating apparatus, comprising:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving historical investigation data and task sequences, extracting case labels in the historical investigation data and generating case vectors based on the case labels, the task sequences and the historical investigation data have one-to-one mapping relation, and the tasks of the task sequences are simple tasks;
the training module is used for generating a training sample based on the case vector and the corresponding task sequence, training a preset task configuration model based on the training sample, and obtaining a trained task configuration model; and
and the input model is used for acquiring data to be recognized, extracting case labels in the data to be recognized, generating case vectors to be recognized based on the case labels in the data to be recognized, and inputting the case vectors to be recognized into the trained task configuration model to obtain execution tasks, wherein the execution tasks belong to the simple tasks.
9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of the survey task generating method of any one of claims 1 to 7.
10. A computer-readable storage medium, having computer-readable instructions stored thereon, which, when executed by a processor, implement the steps of the survey task generating method of any one of claims 1 to 7.
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