CN111709613A

CN111709613A - Task automatic allocation method and device based on data statistics and computer equipment

Info

Publication number: CN111709613A
Application number: CN202010456495.4A
Authority: CN
Inventors: 陈昌伟
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2020-05-26
Filing date: 2020-05-26
Publication date: 2020-09-25

Abstract

The invention belongs to the technical field of artificial intelligence, is applied to the task allocation scene of each business data in the construction of smart cities, and discloses a task automatic allocation method and a device based on data statistics, wherein the method obtains the historical data of completed tasks and carries out cluster analysis on the historical data; calculating the execution condition of each executor on different types of completed tasks through the historical data so as to determine the grades of the executors in different task types; determining the task type and the geographic position of a task to be distributed; searching candidate executors within a preset range according to the geographic position of the task to be distributed; comparing the ratings of the candidate performers in the task types of the tasks to be assigned to determine a recommended performer with the highest rating; and the tasks to be distributed are automatically distributed to the recommended executives, so that objective and operable quantitative evaluation on the executives is realized, and effective management on the whole task distribution is conveniently realized.

Description

Task automatic allocation method and device based on data statistics and computer equipment

Technical Field

The invention relates to the technical field of data analysis in artificial intelligence, is applied to a task allocation scene of each service data in smart city construction, and particularly relates to a task automatic allocation method and device based on data statistics, computer equipment and a storage medium.

Background

At present, with the continuous development of electronic information technology, data analysis and intelligent systems based on statistical results of data analysis, such as recommendation systems, are beginning to be widely applied in many different fields, and the processing efficiency is greatly improved.

And wherein the management and distribution of tasks is also an important application area for data statistics and analysis. Some digitization systems and platforms related to task management and distribution are available at present, certain convenience can be provided, and users can conveniently select and execute tasks matched with the digitization systems and platforms.

However, the degree of intelligence of these digitizing systems and platforms is low, and problems such as one task being picked up by many people and some tasks being picked up by no people often occur, and tasks and executives cannot be matched with each other in a targeted manner, and it is difficult to provide accurate and objective assignment and management especially when task data is complex and specificity is high.

Disclosure of Invention

The embodiment of the invention provides a task automatic allocation method and device based on data statistics, computer equipment and a storage medium, and aims to solve the problems that in the prior art, when task data are complex and the specificity degree is high, accurate and objective allocation and management are difficult to provide, and data cannot be effectively quantized in a digital system.

In a first aspect, an embodiment of the present invention provides a method for automatically allocating tasks based on data statistics, where the method includes:

acquiring historical data of completed tasks; clustering the completed tasks according to the historical data through a clustering algorithm, and determining the task type of each completed task; determining an executor corresponding to each completed task; calculating the execution condition of each executor on different types of completed tasks through the historical data, wherein the execution condition is represented by a plurality of evaluation indexes, and the evaluation indexes comprise: the number of completed, the success rate and the duration of completing the task; determining the ratings of the performers in different task types according to the performance; determining the task type and the geographic position of a task to be distributed; searching candidate executors within a preset range according to the geographic position of the task to be distributed; comparing the ratings of the candidate performers in the task types of the tasks to be assigned to determine a recommended performer with the highest rating; and automatically distributing the tasks to be distributed to the recommendation performers.

In a second aspect, an embodiment of the present invention provides an automatic task allocation device based on data statistics, including:

a history data acquisition unit for acquiring history data of a completed task; the task type marking unit is used for clustering the completed tasks according to the historical data through a clustering algorithm and determining the task type of each completed task; the executor searching unit is used for determining an executor corresponding to each completed task; an execution condition calculation unit, configured to calculate, through the historical data, an execution condition of each executor on different types of completed tasks, where the execution condition is represented by a plurality of evaluation indexes, and the evaluation indexes include: the number of completed, the success rate and the duration of completing the task; the executor rating unit is used for determining the ratings of the executor in different task types according to the execution condition; the task to be distributed acquiring unit is used for determining the task type and the geographic position of the task to be distributed; the candidate executor searching unit is used for searching candidate executors within a preset range according to the geographic position of the task to be distributed; a rating analysis unit for comparing the ratings of the candidate performers in the task types of the tasks to be assigned to determine a recommended performer with the highest rating; and the task allocation unit is used for automatically allocating the tasks to be allocated to the recommendation performer.

In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the computer program, implements the method for automatically allocating tasks based on data statistics as described in the first aspect.

In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the method for task automatic assignment based on data statistics according to the first aspect.

The automatic task allocation method provided by the embodiment of the invention can better mine the data rule in the historical data of the completed task by analyzing the task type, and realize objective and operable quantitative evaluation on the executive personnel, so that the automatic task allocation result is more reasonable, and the effective management on the whole task allocation is convenient to realize.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic flow chart illustrating a method for automatically allocating tasks based on data statistics according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart illustrating a task automatic allocation method based on data statistics according to another embodiment of the present invention;

FIG. 3 is a flowchart of step 102 provided by an embodiment of the present invention;

FIG. 4 is a flowchart illustrating a step 1021 according to an embodiment of the present invention;

FIG. 5 is a schematic block diagram of an automatic task allocation device based on data statistics according to an embodiment of the present invention;

FIG. 6 is a schematic block diagram of a computer device provided by an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

Referring to fig. 1, fig. 1 is a schematic flow chart of an automatic task allocation method according to an embodiment of the present invention, where the automatic task allocation method based on data statistics may be applied to an electronic computing platform such as a server to implement an automatic task allocation function. The method is performed by application software installed in an electronic computing platform.

As shown in fig. 1, the method includes steps S101 to S110.

S101, acquiring historical data of completed tasks.

In this embodiment, for a clearer understanding of the technical solution, the following describes the terminal related to the electronic computing platform in detail. The technical scheme is described in the perspective of a server.

The user side, that is, an intelligent terminal (such as a smart phone, a tablet computer, etc.) used by the user is used for acquiring the tasks and displaying relevant information of the tasks to the executive staff, and also can acquire relevant information of the user side, such as positioning information, and upload the information to the server to realize automatic task allocation.

Generally, a user end and a server are connected, and the user end first downloads an APP application program provided by the server and then inputs a user name and a password, so that the APP application program serves as a medium for data interaction between the user end and the server.

And the server is used for executing relevant logic processing steps such as data statistics and analysis and providing corresponding data information for the user side. Historical data is a task that has been previously processed. Generally, according to different actual situations, various task attributes are adopted for representation. The historical data of the completed task stored in the server comprises data such as a task name, a task processing time period, a task processor, a task live-action picture, a task audio and the like. For example, taking an outworker task platform of an enterprise as an example, the completed historical data corresponding to each outworker generally includes an outworker task name, an outworker task processing time period, an outworker task processor, an outworker task live-action picture, an outworker task audio and the like.

S102, clustering the completed tasks according to the historical data through a clustering algorithm, and determining the task type of each completed task.

In this embodiment, the completed task is divided into a plurality of different task types by a clustering algorithm. The number of specific task types can be set according to the needs of actual conditions. The clustering algorithm can be selected according to the needs of the actual situation, such as a commonly used K-means algorithm.

In an embodiment, as shown in fig. 3, step 102 may specifically include:

s1021, converting the historical data of the completed tasks into a plurality of task attributes and attribute values corresponding to the task attributes.

In the present embodiment, the task attribute is one of the aspects for describing the completed task. Each completed task may be described by a plurality of task attributes. The attribute value is a value under a specific task attribute, and represents a specific situation of the completed task under the task attribute. The attribute values may be in different data forms depending on the task attribute.

In general, the history data of the completed task is mixed with various data forms including numbers, characters, audio, images, and the like. In order to effectively implement the conversion of the historical data and facilitate the processing of the electronic computing platform such as the server, as shown in fig. 4, step 1021 may specifically include:

and S1021a, converting the history data into text data in a text form.

In the present embodiment, all the history data may be converted into text data which occupies a large amount in order to unify and facilitate subsequent processing. This reduces the amount of pre-processed data.

And S1021b, determining task attributes forming the text data through a preset expert dictionary.

In the present embodiment, the expert dictionary is a dictionary that is set in advance by a technician in accordance with expert knowledge. The task attribute corresponding to different text data is recorded, so that disordered historical data is arranged into structured data which can be used by a computer.

S1021c, using the keyword dictionary corresponding to the task attribute, extracting a key character string in each task attribute as the attribute value.

In this embodiment, the keyword dictionary is similar to the expert dictionary and is a set of preset keyword screening rules. The unimportant or redundant text data in the historical data can be further screened out through the keyword dictionary, so that subsequent similarity calculation can be carried out better.

The key character string is data information which can be identified by a computer and can representatively describe the condition of the completed task in a specific task attribute.

Through the conversion and data preprocessing processes, disordered historical data can be effectively arranged into structured data information of character strings which can be recognized by a computer, so that automatic task classification of completed tasks becomes possible.

And S1022, calculating the similarity between the attributes of each task among different completed tasks according to the attribute values.

In this embodiment, the similarity is a percentage obtained by comparing the attribute values of different completed tasks under a certain task attribute, and indicates the closeness of the two in some aspect. Specifically, the similarity between the attribute value and the attribute value can be calculated by adopting a suitable measure or representation mode according to the data form of the attribute value. For example, for a digital attribute value, the ratio of the difference between the two to the total value can be used for representation.

In another embodiment, cosine similarity may be used to calculate similarity between attribute values of different completed tasks in the same task attribute. Cosine similarity is a good way to measure character vector isoportatic attribute values.

And S1023, according to a preset task attribute weight coefficient, carrying out weighted summation on the similarity among all task attributes of the completed tasks, and obtaining the distance among the completed tasks.

In this embodiment, the task attribute weight coefficient is a coefficient for measuring importance of different task attributes. The importance degree of different task attributes in the classification process can be reflected by adjusting the weight coefficient. The technician can set the proper task attribute weight coefficient according to the requirement of the actual situation.

And S1024, clustering through a K-means clustering algorithm according to the distance between the completed tasks, and determining the task type of the completed tasks.

In this embodiment, the distance between different completed tasks is the result of the weighted summation of all task attribute similarities, and the similarity or closeness of the completed tasks as a whole is fed back or expressed. The accurate task classification result can be obtained by taking the data as basic data of clustering.

Through a proper similarity measurement mode, the relevance among the completed tasks can be mined, so that the relevance can be used as the basis of K-means clustering, and the automatic classification of the completed tasks is realized. After automatic classification, the execution condition of the completed task can be better and more comprehensively analyzed and counted.

S103, determining the executor corresponding to each completed task.

In this embodiment, the performer is the user who completes the completed task. Depending on the tasks in the different domains, there may be corresponding types of actors. It may mark or record each task-completed performer in any suitable manner, such as by device ID or account password.

And S104, calculating the execution condition of each executor on different types of completed tasks through the historical data. The execution condition is represented by a plurality of evaluation indexes, and the evaluation indexes comprise: number of completed, success rate, and length of time to complete the task.

In this embodiment, the evaluation index refers to a relevant standard for measuring or evaluating the task completion condition. According to different practical application situations, the corresponding evaluation indexes can be selected to be used for representation. The evaluation index can be one or more to better reflect the completion condition of the task, including the number of completed tasks, the success rate, the time length for completing the task, and the like.

In an embodiment, as shown in fig. 2, step 104 may further include:

and S105, displaying the execution condition of the executor in a chart form according to the received display instruction.

Wherein the chart comprises a line chart and a pie chart, and an evaluation index is displayed by at least one chart. Of course, an appropriate chart may be selected and displayed according to the specific data type of the evaluation index.

Through the form of the chart display, the execution condition of each executor can be effectively fed back, objective and comprehensive evaluation is provided, and managers or executors can know the self condition conveniently so as to further improve the subsequent task processing efficiency.

And S106, determining the ratings of the performers in different task types according to the performance conditions.

In the present embodiment, the rating means a specific evaluation given for the execution situation of each performer. The rating may be expressed in any form, for example, a corresponding number of ratings may be set by setting a threshold value of a plurality of evaluation indexes, and the better the data of the evaluation indexes is, the higher the corresponding rating is.

And S107, determining the task type and the geographic position of the task to be distributed.

In this embodiment, the tasks to be assigned are newly generated and need to be assigned by the system to the corresponding processing personnel or performers for processing. The geographic location of a task refers to the location that the performer needs to reach to complete the task. For complex tasks, the geographic location is the data information that needs to be considered heavily in the distribution process.

And S108, searching candidate executors in a preset range according to the geographic position of the task to be distributed.

In this embodiment, the location information corresponding to the executor may be provided by the device information of the user side. In practical applications, the preset range may be an empirical value, and the preset range is set by a technician according to the needs of practical situations. For example, the business personnel within a set radius range can be screened as candidate executors by taking the geographic position of the push repair task as the center.

S109, comparing the grades of the candidate executors in the task types of the tasks to be distributed so as to determine the recommended executors with the highest grades.

In the embodiment, after the potential candidate performers are screened out by taking the geographical location as a primary screening condition, the recommended performers are selected by ranking the performers in the task category. The number of performers at the same rating may be multiple, and thus the recommendation performer may be multiple.

And S110, automatically distributing the tasks to be distributed to the recommendation executors.

In this embodiment, automatic allocation refers to a manner of providing tasks to be allocated to an adaptive executor to complete without depending on the result of manual data analysis. It may specifically take any suitable form. For example, the tasks to be distributed and the corresponding recommended executors can be directly bound in an active distribution mode, or a form of 'order grabbing' can be adopted, and the recommended executors complete automatic distribution of the tasks to be distributed in a bidirectional selection mode according to own wishes.

In an embodiment, as shown in fig. 2, step S110 is followed by:

and S111, searching a route between the task to be distributed and the recommended executor through the map mobile application.

In the present embodiment, the map mobile application refers to any type of mobile application program that can implement a path search between two points or a related navigation function. In practical application, the form of related functional interfaces can be called to realize the function of path search.

And S112, displaying the route to the recommended performer who receives the task to be distributed.

In this embodiment, the route may be presented on the customer premise device of the recommendation performer. Under the condition supported by the user end equipment, functions such as navigation and the like can be provided, so that the recommendation executor can better execute tasks.

In order to fully state the automatic task allocation method provided by the embodiment of the present invention, the following description will be made in detail by taking a use scenario in which the method is applied to vehicle push repair as an example.

The term "vehicle push repair" refers to an action in which after a vehicle accident occurs and insurance is reported, a dedicated person of the insurance company recommends a vehicle maintenance unit to the owner of the accident vehicle and guides the owner of the accident vehicle to the recommended vehicle maintenance unit to perform maintenance, for the purpose of ensuring the effect of vehicle maintenance and maintaining the cooperation between the insurance company and the vehicle maintenance unit.

Because each vehicle push-repair task has certain uniqueness, the manual work is difficult to quantify a large number of vehicle push-repair tasks according to a uniform standard. Therefore, the automatic data processing mode provided by the embodiment of the invention can be applied to realize objective and operable quantitative evaluation on the vehicle repair task and business personnel, and is convenient for distributing and managing the vehicle repair task.

Generally, in the present embodiment, several aspects of data statistics, business personnel evaluation, automatic allocation of vehicle repair tasks, and the like can be included.

1) Regarding data statistics:

first, historical data of completed push repair tasks is obtained. For a complete push repair task, it can be represented by a plurality of different projects. Such as the cause of the accident, the make and model of the vehicle being serviced, service items, recommended service units, maintenance costs, and owner feedback ratings. These items are task attributes for describing the respective vehicle push repair tasks.

And then, clustering the completed push repair tasks, and labeling the type of each push repair task. It will be appreciated that the general item content is mostly represented in text form. Therefore, the text data can be arranged into character strings which can be recognized by a computer through extraction and conversion.

For example, the content "fender is broken and needs to be replaced as a whole" of the task attribute of the maintenance event may be matched with the corresponding dictionary to delete the words without meaning, and the keywords such as "fender" and "replacement" may be retained as the attribute values.

And finally, clustering all the push repair tasks by using a set clustering algorithm, and automatically generating a plurality of categories so as to facilitate quantitative processing. That is, each category generated by clustering is provided with corresponding label information, which represents a type.

In this embodiment, clustering and task type labeling are accomplished using a K-means-based clustering algorithm.

Specifically, in clustering, for different pruning tasks, the cosine similarity is firstly used for calculating the text similarity degree between each item. And then, weighting and superposing the cosine similarity of all the projects of the repair tasks according to a preset weight coefficient to obtain the distance between the two repair tasks. And finally, setting the clustering numbers K and N according to a K-means clustering algorithm, and finishing clustering of the push-repair task after iterative convergence.

2) Evaluation on service personnel:

firstly, the push repair task completed by the service personnel is obtained. And each completed push repair task has corresponding business personnel. The service person is the performer in the above embodiment. Specifically, all the push repair tasks processed by a specific service person can be searched and determined from the data platform or the corresponding database.

And then, respectively calculating the execution conditions of the business personnel on different types of push repair tasks. For a vehicle push repair task, the evaluation index for measuring the execution condition may include, but is not limited to: the communication duration, the number of receptions, and the successful store-to-store ratio for certain types of push repair tasks.

And finally, determining the grades of the service personnel in different task types according to the execution condition.

The ratings may be specifically partitioned and rated according to statistics used by the execution. For example, the business persons may be classified into four levels a to D in terms of push-to-repair acceptance rate, customer contact rate, average communication time, and the like, and the rating thereof may be set to null in the case where the number of processes is below a set threshold.

The specific rating mode or the setting of the number of levels can be adjusted according to actual conditions. After the rating step is carried out, each business person has a three-dimensional and objective evaluation standard, so that the field of excellence of the business person can be highlighted. For example, the type with higher rating is the area that business people are good at.

In addition, relevant data provided by an external data system can be combined to perform corresponding statistics and output a specific statistical result in a visual and appropriate mode, so that other user roles such as a manager can objectively evaluate business personnel.

For example, based on the historical data analysis and conversion results that have been completed, the following information may be presented in the form of a line graph and a pie graph, in different task types:

1) the ratio between the maintenance output and the insurance premium generated by the service personnel and the affiliated network points in a set period of time (such as monthly or yearly).

2) After the business personnel and the network points thereof complete the push repair task, the business personnel agree with the ratio of the business personnel to the store, temporarily do not determine the business personnel and refuse to the store.

3) The push repair supporting rate, the customer contact rate and the average communication time of the business personnel in the push repair task of the type, and the average value of the network points. And receiving a user instruction, and displaying the execution conditions of the service personnel and the affiliated network points in a set mode.

The network points are convenient to manage and are divided into a plurality of different sub-areas in a large-area administrative area. Each network point is respectively responsible for related services such as vehicle insurance, push repair and the like in the corresponding area. The service personnel belong to different network points, and each network point can comprise a plurality of service personnel.

3) Automatic allocation of vehicle push repair tasks:

first, the relevant information of the newly generated vehicle push repair task is acquired. After the new push repair task is generated, information of the new push repair task can be collected and acquired according to the recorded task attributes as related information (at least including the geographic position).

And then, determining the type of the newly generated push repair task according to the related information. Based on the classification result of the push repair task determined by clustering, the similarity degree between the newly generated vehicle push repair task and the vehicle push repair tasks of different types can be calculated in the same way as the similarity calculation in the steps, so that the type of the newly generated vehicle push repair task is determined.

And finally, automatically pushing the service to corresponding business personnel according to the geographic position and the type of the push repair task. After the type and the geographic position are determined, the new push-repair task can be automatically pushed to a service person with a short distance to follow up, so that the optimal configuration of resources is realized, and the push-repair effect is improved.

Specifically, firstly, the geographic position of the vehicle repair task is taken as the center of a circle, and service personnel within a set radius range are screened as candidates. Then, among the candidates, the business person who is rated a or B in the type to which the push business belongs is preferentially selected. When there are no business persons rated as a or B, then business persons rated as empty are assigned.

In addition, in the process of screening the service personnel in the set radius range as candidates by taking the geographic position of the push repair task as the center of a circle, it can be specifically determined whether the position of the service personnel is in the set radius range by the following method:

first, the position of the intelligent mobile terminal of the service personnel is obtained. And then, selecting the candidate as the candidate when only the position of the service personnel belongs to the set radius range and the push repair task belongs to the responsibility of the website to which the service personnel belongs. And finally, under the condition that no candidate exists, the push-repair task is issued to the responsible network points and is automatically distributed by the network points.

4) Implementation of other functions:

firstly, in order to facilitate the use of service personnel and quickly guide the owner to the vehicle maintenance unit, the pushed vehicle push-repair task can be displayed to the service personnel to the optimal route from the push-repair task to the vehicle maintenance unit after being accepted by the service personnel.

Specifically, the route between the geographic position of the push repair task and the vehicle maintenance unit can be searched by calling an interface of the map mobile application, and the route is correspondingly displayed on the intelligent mobile terminal of the service staff.

Secondly, the processing progress of a vehicle maintenance unit can be fed back in real time. Namely, the networked vehicle maintenance units are synchronized to timely acquire the subsequent case processing progress corresponding to the push repair task, such as whether the vehicle arrives at a store, the vehicle maintenance progress and the like.

In actual operation, the corresponding relation between the case and the subsequent case can be established by pushing the vehicle license plate number related to the repair task, so that the processing progress of the subsequent case can be displayed to corresponding business personnel in real time.

It should be noted that the historical data may be data stored in a node of a block chain, and the technical solution of the present application may also be applicable to automatic allocation of other data and tasks stored in the block chain, where the block chain referred to in the present invention is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. 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 embodiment of the invention also provides a data statistics-based automatic task allocation device, which is used for executing any embodiment of the data statistics-based automatic task allocation method. Fig. 5 is a schematic block diagram of an automatic task allocation device based on data statistics according to an embodiment of the present invention. The automatic task assigning apparatus 500 based on data statistics may be configured in a server.

Specifically, referring to fig. 5, the task automatic distribution device 500 may include: a history data acquisition unit 501, a task type labeling unit 502, an executor searching unit 503, an execution situation calculating unit 504, an executor rating unit 505, a task to be distributed acquisition unit 506, a candidate executor searching unit 507, a rating analysis unit 508, and a task distributing unit 509.

The historical data acquiring unit 501 is configured to acquire historical data of a completed task; the task type labeling unit 502 is configured to cluster the completed tasks according to the historical data through a clustering algorithm, and determine a task type to which each of the completed tasks belongs; the executor searching unit 503 is configured to determine an executor corresponding to each completed task; the execution condition calculation unit 504 is configured to calculate, from the historical data, an execution condition of each of the executors on different types of completed tasks, where the execution condition is represented by a plurality of evaluation indexes, and the evaluation indexes include: the number of completed, the success rate and the duration of completing the task; the performer rating unit 505 is configured to determine ratings of the performers in different task types according to the performance; the to-be-distributed task obtaining unit 506 is configured to determine a task type and a geographic location to which a task to be distributed belongs; the candidate executor searching unit 507 is configured to search for a candidate executor within a preset range according to the geographic location of the task to be distributed; the rating analysis unit 508 is used for comparing the ratings of the candidate performers in the task types of the tasks to be distributed to determine a recommended performer with the highest rating; the task allocation unit 509 is configured to automatically allocate the task to be allocated to the recommended performer.

The automatic task allocation device provided by the embodiment of the invention can better mine the data rule in the historical data of the completed task by analyzing the task type, and realize objective and operable quantitative evaluation on the executive personnel, so that the automatic task allocation result is more reasonable, and the effective management on the whole task allocation is convenient to realize.

In an embodiment, the task automatic distribution device 500 may further include: a route search unit 510 and a route presentation unit 511.

The route searching unit 510 is configured to search for a route between the task to be distributed and the recommended performer through the map mobile application. The route presentation unit 511 is configured to present the route to the recommendation executor who accepts the task to be distributed.

By means of route searching and displaying, the executor can complete the task better and faster, and the efficiency of task execution is improved.

The above-mentioned automatic task allocation means based on data statistics may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 6.

Referring to fig. 6, fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device 600 is a server, and the server may be an independent server or a server cluster composed of a plurality of servers.

Referring to fig. 6, the computer device 600 includes a processor 602, memory, and a network interface 605 connected by a system bus 601, where the memory may include a non-volatile storage medium 603 and an internal memory 604.

The non-volatile storage medium 603 may store an operating system 6031 and computer programs 6032. The computer program 6032, when executed, may cause the processor 602 to perform a method for automatic assignment of tasks based on data statistics.

The processor 602 is used to provide computing and control capabilities that support the operation of the overall computer device 600.

The internal memory 604 provides an environment for the execution of a computer program 6032 in the non-volatile storage medium 603, which computer program 6032, when executed by the processor 602, may cause the processor 602 to perform a method for automatic assignment of tasks based on data statistics.

The network interface 605 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 6 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 600 to which aspects of the present invention may be applied, and that a particular computing device 600 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

The processor 602 is configured to run a computer program 6032 stored in the memory to implement the method for vehicle type recognition based on image recognition disclosed in the embodiment of the present invention.

Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 6 does not constitute a limitation on the specific construction of the computer device, and that in other embodiments a computer device may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 6, and are not described herein again.

It should be understood that, in the embodiment of the present invention, the Processor 602 may be a Central Processing Unit (CPU), and the Processor 602 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a non-volatile computer readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the method for vehicle type recognition based on image recognition disclosed by the embodiments of the present invention.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.

While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A task automatic allocation method based on data statistics is characterized by comprising the following steps:

acquiring historical data of completed tasks;

clustering the completed tasks according to the historical data through a clustering algorithm, and determining the task type of each completed task;

determining an executor corresponding to each completed task;

calculating the execution condition of each executor on different types of completed tasks through the historical data, wherein the execution condition is represented by a plurality of evaluation indexes, and the evaluation indexes comprise: the number of completed, the success rate and the duration of completing the task;

determining the ratings of the performers in different task types according to the performance;

determining the task type and the geographic position of a task to be distributed;

searching candidate executors within a preset range according to the geographic position of the task to be distributed;

comparing the ratings of the candidate performers in the task types of the tasks to be assigned to determine a recommended performer with the highest rating;

and automatically distributing the tasks to be distributed to the recommendation performers.

2. The method for task automatic distribution based on data statistics as claimed in claim 1, wherein after the task to be distributed is automatically distributed to the recommended performer, the method further comprises:

searching a route between the task to be distributed and the recommended executor through a map mobile application;

and displaying the route to the recommendation performer who accepts the task to be distributed.

3. The method of claim 1, wherein after calculating the performance of each of the performers on different types of completed tasks based on the historical data, the method further comprises:

displaying the execution condition of the executor in a chart form according to the received display instruction; the chart includes a line chart and a pie chart, and an evaluation index is displayed by at least one of the charts.

4. The method according to claim 1, wherein the determining the task type of each of the completed tasks by clustering the completed tasks according to the historical data through a clustering algorithm comprises:

converting the historical data of the completed tasks into a plurality of task attributes and attribute values corresponding to the task attributes;

calculating the similarity between the attributes of each task among different completed tasks according to the attribute values;

according to a preset task attribute weight coefficient, carrying out weighted summation on the similarity between all task attributes of the completed tasks to obtain the distance between the completed tasks;

and clustering by a K-means clustering algorithm according to the distance between the completed tasks, and determining the task type of the completed tasks.

5. The method according to claim 4, wherein the converting the historical data of the completed task into a plurality of task attributes and attribute values corresponding to the task attributes comprises:

converting the historical data into text data in a text form;

determining task attributes forming the text data through a preset expert dictionary;

and extracting a key character string in each task attribute as the attribute value by using a keyword dictionary corresponding to the task attribute.

6. The method of claim 4, wherein calculating the similarity between each task attribute between different completed tasks according to the attribute values comprises:

and calculating the similarity between the attribute values of different completed tasks in the same task attribute by using the cosine similarity.

7. An automatic task allocation device based on data statistics is characterized by comprising:

a history data acquisition unit for acquiring history data of a completed task;

the task type marking unit is used for clustering the completed tasks according to the historical data through a clustering algorithm and determining the task type of each completed task;

the executor searching unit is used for determining an executor corresponding to each completed task;

an execution condition calculation unit, configured to calculate, through the historical data, an execution condition of each executor on different types of completed tasks, where the execution condition is represented by a plurality of evaluation indexes, and the evaluation indexes include: the number of completed, the success rate and the duration of completing the task;

the executor rating unit is used for determining the ratings of the executor in different task types according to the execution condition;

the task to be distributed acquiring unit is used for determining the task type and the geographic position of the task to be distributed;

the candidate executor searching unit is used for searching candidate executors within a preset range according to the geographic position of the task to be distributed;

a rating analysis unit for comparing the ratings of the candidate performers in the task types of the tasks to be assigned to determine a recommended performer with the highest rating;

and the task allocation unit is used for automatically allocating the tasks to be allocated to the recommendation performer.

8. The apparatus according to claim 7, further comprising:

the route searching unit is used for searching a route between the task to be distributed and the recommended executor through a map mobile application;

and the route display unit is used for displaying the route to the recommendation executor receiving the task to be distributed.

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method for automatic task allocation based on data statistics according to any of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, causes the processor to execute the method of automatic task allocation based on data statistics according to any of claims 1 to 6.