CN113283742A - Task allocation method and device - Google Patents
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Abstract
The invention discloses a task allocation method and a task allocation device, and relates to the technical field of automatic program design. One embodiment of the method comprises: acquiring a candidate object set and a pre-configured distribution rule set for executing task nodes according to task node information of a target task; determining that the task node information and the candidate object set meet the triggering condition of a target distribution rule according to the priority of the distribution rule in the distribution rule set; executing the target distribution rule, screening target candidate objects meeting the target distribution rule from the candidate object set, and distributing the target tasks to the target candidate objects. According to the embodiment, the allocation rule and the task allocation process are unbound, and the target allocation rule is acquired and executed according to the task node information during task allocation, so that the task allocation can be realized on the premise of not modifying codes, the development period is shortened, and the service development efficiency is improved.
Description
Technical Field
The invention relates to the technical field of automatic program design, in particular to a task allocation method and a task allocation device.
Background
Task allocation is an important ring in insurance business. In the prior art, an allocation rule of task allocation is usually written in a code for realizing a task allocation process, and the allocation rule is complex and frequently changed, so that the code needs to be frequently modified and redeployed for verification, the whole process period is long, and the service development efficiency is low.
Disclosure of Invention
In view of this, embodiments of the present invention provide a task allocation method and apparatus, where an allocation rule is unbound from a task allocation process, and when allocating a task, a target allocation rule is obtained according to task node information and executed, so that the task allocation can be implemented without modifying a code, a development cycle is shortened, and service development efficiency is improved.
To achieve the above object, according to an aspect of an embodiment of the present invention, a task allocation method is provided.
The task allocation method of the embodiment of the invention comprises the following steps: acquiring a candidate object set and a pre-configured distribution rule set for executing task nodes according to task node information of a target task; determining that the task node information and the candidate object set meet the triggering condition of a target distribution rule according to the priority of the distribution rule in the distribution rule set; executing the target distribution rule, screening target candidate objects meeting the target distribution rule from the candidate object set, and distributing the target tasks to the target candidate objects.
Optionally, the obtaining a candidate object set for executing a task node according to task node information of a target task includes: acquiring task execution information and task roles of task nodes according to task node information of a target task; inquiring a target object with the task role to generate target object information; the target object information comprises task execution information corresponding to the target object; adding the target object information to the set of candidate objects.
Optionally, determining that the task node information and the candidate object set satisfy the trigger condition of the target allocation rule according to the priority of the allocation rule in the allocation rule set includes: selecting: according to the priority of the distribution rule in the distribution rule set, selecting the current distribution rule from the distribution rule set according to the sequence of the priority from high to low; a judging step: judging whether the task node information and the candidate object set meet the triggering condition of the current distribution rule; a determination step: if the task node information and the candidate object set meet the trigger condition, taking the current distribution rule as a target distribution rule; and if the task node information and the candidate object set do not meet the trigger condition, repeatedly executing the selecting step, the judging step and the determining step until the distribution rule set is traversed and ended.
Optionally, before the step of determining that the task node information and the candidate object set satisfy the trigger condition of the target allocation rule, the method further includes: passing the task node information, the set of candidate objects, and the allocation rule set to an KIE engine to cause the KIE engine to load the allocation rules of the allocation rule set.
Optionally, the method further comprises: configuring the allocation rule; wherein, the distribution rule is a drools rule.
Optionally, the configuring the allocation rule includes: and abstracting the service rule corresponding to the task node into a drools rule, and describing a set keyword in the drools rule by using a JsonSchema object.
Optionally, the method further comprises: generating a drools rule object by the drools rule, and converting the JsonSchema object into a Java class; compiling the drools rule object and the Java class into a KJar package by using an KIE engine, and storing the KJar package.
Optionally, the saving the KJar package includes: storing the KJar packet into a memory and a cache; obtaining an allocation rule set for executing a task node, comprising: and sequentially trying to load corresponding KJar packages from the memory and the cache according to the task node information of the target task to obtain a distribution rule set.
Optionally, the method further comprises: inquiring the on-off state of a distribution rule configured for the task node according to the task node information; the selecting a current distribution rule from the distribution rule set according to the priority of the distribution rule in the distribution rule set and the sequence of the priority from high to low comprises the following steps: selecting the distribution rule with the switch state being on from the distribution rule set to obtain an optimized rule set; and selecting the current distribution rule from the optimization rule set according to the priority of the distribution rule in the optimization rule set and the sequence of the priority from high to low.
Optionally, the method further comprises: testing the distribution rule based on set test data to obtain a test result; and determining that the test result meets the expectation, and issuing the distribution rule to a production environment.
Optionally, the allocation rule is a minimum task amount allocation rule, and the test data is a task amount of a candidate object in the candidate object set; based on the set test data, testing the distribution rule to obtain a test result, including: and transmitting the test data into the distribution rule, and executing the distribution rule to obtain a test result.
Optionally, the method further comprises: setting a version identifier for the distribution rule, and carrying out version control on the distribution rule based on an optimistic lock mechanism; the executing the target allocation rule includes: acquiring the version identification of the target distribution rule from a cache; and determining that the obtained version identification is inconsistent with the current version identification of the target distribution rule in the database, and loading the target distribution rule in the database to a cache.
Optionally, the method further comprises: and counting the execution result of the target distribution rule, and analyzing the execution result.
To achieve the above object, according to another aspect of the embodiments of the present invention, there is provided a task assigning apparatus.
The task allocation device of the embodiment of the invention comprises: the acquisition module is used for acquiring a candidate object set for executing the task node and a pre-configured distribution rule set according to the task node information of the target task; the determining module is used for determining that the task node information and the candidate object set meet the triggering condition of a target distribution rule according to the priority of the distribution rule in the distribution rule set; and the distribution module is used for executing the target distribution rule, screening target candidate objects meeting the target distribution rule from the candidate object set, and distributing the target tasks to the target candidate objects.
Optionally, the obtaining module is further configured to: acquiring task execution information and task roles of task nodes according to task node information of a target task; inquiring a target object with the task role to generate target object information; the target object information comprises task execution information corresponding to the target object; and adding the target object information to the set of candidate objects.
Optionally, the determining module is further configured to: selecting: according to the priority of the distribution rule in the distribution rule set, selecting the current distribution rule from the distribution rule set according to the sequence of the priority from high to low; a judging step: judging whether the task node information and the candidate object set meet the triggering condition of the current distribution rule; a determination step: if the task node information and the candidate object set meet the trigger condition, taking the current distribution rule as a target distribution rule; and if the task node information and the candidate object set do not meet the trigger condition, repeatedly executing the selecting step, the judging step and the determining step until the distribution rule set is traversed and ended.
Optionally, the apparatus further comprises: a loading module to pass the task node information, the set of candidate objects, and the allocation rule set to KIE engine to cause the KIE engine to load the allocation rules of the allocation rule set.
Optionally, the apparatus further comprises: a configuration module for configuring the allocation rule; wherein, the distribution rule is a drools rule.
Optionally, the configuration module is further configured to abstract a service rule corresponding to the task node into a drools rule, and describe a set keyword in the drools rule by using a JsonSchema object.
Optionally, the apparatus further comprises: the compiling module is used for generating a drools rule object by the drools rule and converting the JsonSchema object into a Java class; compiling the drools rule object and the Java class into a KJar package by using an KIE engine, and storing the KJar package.
Optionally, the compiling module is further configured to store the KJar packet in a memory and a cache; the obtaining module is further configured to sequentially attempt to load corresponding KJar packages from the memory and the cache according to the task node information of the target task, so as to obtain a distribution rule set.
Optionally, the apparatus further comprises: the query module is used for querying the on-off state of the distribution rule configured for the task node according to the task node information; the determining module is further configured to select the distribution rule with the on-off state from the distribution rule set to obtain an optimized rule set; and selecting the current distribution rule from the optimization rule set according to the priority of the distribution rule in the optimization rule set and the sequence of the priority from high to low.
Optionally, the apparatus further comprises: the test module is used for testing the distribution rule based on set test data to obtain a test result; and determining that the test result meets the expectation, and issuing the distribution rule to a production environment.
Optionally, the allocation rule is a minimum task amount allocation rule, and the test data is a task amount of a candidate object in the candidate object set; the test module is further configured to transmit the test data to the allocation rule, and execute the allocation rule to obtain a test result.
Optionally, the apparatus further comprises: the version control module is used for setting a version identifier for the distribution rule and carrying out version control on the distribution rule based on an optimistic lock mechanism; the distribution module is further configured to obtain a version identifier of the target distribution rule from a cache; and determining that the obtained version identification is inconsistent with the current version identification of the target distribution rule in the database, and loading the target distribution rule in the database to a cache.
Optionally, the apparatus further comprises: and the statistical analysis module is used for counting the execution result of the target distribution rule and analyzing the execution result.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided an electronic apparatus.
An electronic device of an embodiment of the present invention includes: one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement a task allocation method according to an embodiment of the present invention.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided a computer-readable medium.
A computer-readable medium of an embodiment of the present invention has stored thereon a computer program that, when executed by a processor, implements a task allocation method of an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: by unbinding the allocation rule and the task allocation process, when the task is allocated, the candidate object set is obtained according to the task node information, and then the target allocation rule is determined and executed, so that the task allocation can be realized on the premise of not modifying the code, the development period is shortened, and the service development efficiency is improved. And acquiring the task role through the task node information, so that the candidate object in the candidate object set has the authority of processing the target task, and the target task is distributed in the candidate object set, thereby improving the distribution accuracy.
By setting priorities for the allocation rules, rule conflicts are avoided. The engine loads the allocation rules into the memory based on KIE, further improving the allocation efficiency. Based on the drools engine and the JsonSchema object format, the flexible and centralized configuration of the distribution rules is realized, the management is easy, and the configuration is standard. The allocation rule is stored in the memory and the cache, the allocation rule is directly obtained from the memory when needing to be used, and the allocation rule is obtained from the cache when the allocation rule does not exist in the memory, so that the efficiency is improved.
By setting the on-off state for the allocation rules, execution of each allocation rule in the allocation rule set is controlled conveniently. The test is carried out before the distribution rule is issued, so that the normal operation of the distribution rule is ensured, and the non-stop updating of the distribution rule is realized. By an optimistic locking mechanism, the version control of the distribution rule is realized, and the latest version of the executed target distribution rule is ensured. By carrying out statistical analysis on the execution result, the subsequent further optimization and adjustment of the distribution rule are facilitated.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main steps of a task assignment method according to an embodiment of the invention;
FIG. 2 is a schematic main flow chart diagram of a task allocation method according to another embodiment of the present invention;
FIG. 3 is a schematic main flow chart of a task allocation method according to still another embodiment of the present invention;
FIG. 4 is a schematic diagram of the main modules of a task assignment device according to an embodiment of the present invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
FIG. 6 is a schematic diagram of a computer apparatus suitable for use in an electronic device to implement an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of the main steps of a task allocation method according to an embodiment of the present invention. As shown in fig. 1, the task allocation method according to the embodiment of the present invention mainly includes the following steps:
step S101: and acquiring a candidate object set for executing the task node and a pre-configured distribution rule set according to the task node information of the target task. And on the basis of the service rules of the task nodes, abstracting a distribution rule set containing distribution rules in advance, and setting priorities for the distribution rules. After a target task of a workflow is received, a candidate object set for executing the task node and an allocation rule set configured for the task node are obtained according to task node information of the target task.
The workflow records task nodes contained in the work and the execution sequence of each task node. One task node corresponds to one type of target task. The task node information may include a node function description, an allocation rule identifier corresponding to a node, a node state, a task role corresponding to a node, and task execution information.
When the candidate object set is obtained, the target object with the task role can be inquired according to the task role corresponding to the node in the task node information, and the task execution information is added to the target object information corresponding to the target object, so that the candidate object set is obtained. When the distribution rule set is obtained, the corresponding distribution rules can be queried according to the distribution rule identifications corresponding to the nodes in the task node information, and the distribution rules form the distribution rule set.
Step S102: and determining that the task node information and the candidate object set meet the triggering condition of the target distribution rule according to the priority of the distribution rule in the distribution rule set. And selecting the current distribution rule from the distribution rule set according to the sequence of the priorities from high to low, and judging whether the task node information and the candidate object set meet the triggering conditions of the current distribution rule.
And if the task node information and the candidate object set meet the triggering condition of the current distribution rule, triggering the current distribution rule, namely taking the current distribution rule as a target distribution rule, and then not triggering the subsequent distribution rule any more. And if the task node information and the candidate object set do not meet the triggering condition of the current distribution rule, reselecting the current distribution rule, and judging that the triggering condition is met enough until the distribution rule set is traversed and ended.
The trigger condition refers to a condition that triggers execution of the allocation rule. For example, the trigger condition of the assignment rule of the concurrency piece may be that the task node information includes a concurrency piece identifier, and a candidate object of the candidate object set has a right to process the concurrency piece. For another example, the trigger condition of the problem part allocation rule may be that the task node information includes a problem part identifier, and a candidate object of the candidate object set has a right to process a problem part.
The concurrent documents refer to a plurality of documents (such as insurance policies) with specified characteristics, and need to be distributed to the same object for processing. For example, task a and task B both have specified characteristics and need to be assigned to the same object for processing, and task a and task B belong to the same piece of hardware. The problem piece means that the bill information is not qualified.
Step S103: executing the target distribution rule, screening target candidate objects meeting the target distribution rule from the candidate object set, and distributing the target tasks to the target candidate objects. And by executing the target allocation rule, screening target candidate objects meeting the target allocation rule from the candidate object set, and then allocating the target tasks to the target candidate objects, thereby realizing the automatic allocation of the target tasks.
For example, the target allocation rule is a peer allocation rule, and the peer allocation rule is used for searching a candidate object with the smallest task amount from candidate objects with the authority of processing the peer. In this step, when the peer-to-peer distribution rule is executed, the candidate object having the right to process the peer-to-peer is first searched from the candidate object set, and then the candidate object with the minimum task amount is further searched, and the searched candidate object is used as the target candidate object, and the target task is further distributed to the target candidate object.
Fig. 2 is a main flow diagram of a task allocation method according to another embodiment of the present invention. As shown in fig. 2, the task allocation method according to the embodiment of the present invention mainly includes the following steps:
step S201: and configuring an allocation rule for the task node. In an embodiment, the allocation rule is implemented based on a drools engine and is in a JsonSchema object format. Where drools is an open-source rule engine. The JsonSchema is a Json file, and the content of the file is the constraint on the structure and the content of Json data.
When the allocation rule is configured, a service rule corresponding to the task node may be abstracted to a drools rule, and a JsonSchema object is used to describe a set keyword in the drools rule. The keyword can be set by self, such as a candidate object set, a target candidate object, and the like.
Taking the distribution rule of the same-progress as an example, the code configured by the drools can be as follows:
in the codes, the code in line 1 is a rule name, namely a concurrent distribution rule; line 2 shows the allocation rules in the pick _ assign _ group, and as long as there is a hit, the other allocation rules will be deactivated; row 3 indicates the priority of the assignment rule of the same element.
And the code corresponding to the while key word shows that if the concurrence item identifier (togetherValue) is not null, the state (status) is selected from the candidate object set (groupMembers) to be 1, the task quantity does not reach the upper limit, the description information contains the candidate object of the concurrence item identifier, and the candidate object is added to the election result (candidatorList). And the code corresponding to the then keyword represents the 1 st candidate object taken from the election result, and the target task is distributed to the candidate object.
Taking the description of election results (candidatorList) as an example, the code configured by JsonSchema may be as follows:
{"$schema":"http://json-schema.org/draft-04/schema#",
"title":"DecisionResult",
"description":"Test log",
"type":"object",
"properties" { "result": result code "," type ": integer" }, "d ecisionType" { "description": decision type "," type ": string", "enum": [ task _ as sign "] }," extraInfo ": type": object "," additionalpproperties ": {" type ": string" },
"required":["result","decisionType","extraInfo"]}
in the code, the $ schema keyword represents the version specification used by the JsonSchema configuration; title key words represent the title of the JsonSchema configuration; the description keyword represents the description information of the JsonSchema configuration; the type key indicates the type of the element to be checked (i.e., election result); the properties key words represent specific information of elements to be checked; the required key represents the key name of the element to be checked.
In a preferred embodiment, the distribution rule applicable to the task node can be selected from the existing distribution rules, so as to avoid repeated configuration. After the allocation rules are selected or created (i.e., configured), the allocation rules may be stored in a database, such as a relational database.
In a preferred embodiment, the drools is configured to generate a drools rule object, the JsonSchema is configured to generate a Java class, the KjarBuilderUtil of an Execution Server (Execution Server) is called KIE, the drools rule object and the Java class are compiled into a KJar package, and the KJar package is updated to a memory and a cache (such as Redis).
Step S202: and receiving the target task, and acquiring a candidate object set for executing the task node and a corresponding distribution rule set according to the task node information of the target task. And when the target task is received, reading basic information of the target task, including task node information, a mechanism to which the task belongs, task service information and the like.
According to the task role corresponding to the node in the task node information, inquiring a target object with the task role, adding task execution information to target object information corresponding to the target object, namely adding executed task information and unexecuted task information in a set time period to each target object information, and packaging the target object information into a candidate object set. The task execution information comprises executed task information and non-executed task information of a set time period.
When the distribution rule set is obtained, the distribution rule corresponding to the task node can be tried to be inquired from the memory and the cache in sequence according to the distribution rule identification corresponding to the node in the task node information, and if the corresponding distribution rule is found, the distribution rule is read from the database and is synchronized into the cache. These allocation rules constitute an allocation rule set.
In this embodiment, the allocation rules are stored in the database through two layers of cache, where the first layer is stored in KIE (i.e., memory) and the second layer is stored in Redis (i.e., cache). If the allocation rule corresponding to the task node is not queried in KIE, querying from Redis; and if the distribution rule corresponding to the task node is not queried in the Redis, reading from the database and synchronizing to the Redis, so that the query speed is increased.
Step S203: the task node information, the set of candidate objects, and the set of allocation rules are passed to the KIE engine to cause the KIE engine to load the allocation rules of the set of allocation rules. The task node information is encapsulated into the allocation parameter object and passed to the KIE engine along with the candidate object set, the rule name of the allocation rule set. KIE the engine loads the general rule first and then loads the rule corresponding to the rule name according to the rule name, and constructs the distribution parameter object and the candidate object set object corresponding to the rule name for receiving the incoming data.
Step S204: and determining that the task node information and the candidate object set meet the triggering condition of the target distribution rule according to the priority of the distribution rule in the distribution rule set. KIE the engine filters in turn according to the priority of the allocation rule, if the incoming data meets the trigger condition of the current allocation rule, the current allocation rule is triggered, and the following allocation rule is not triggered. In an embodiment, the priority of the allocation rule from high to low may be: problem part distribution rules, concurrent part distribution rules and minimum task amount distribution rules.
Step S205: and executing a target distribution rule, screening target candidate objects meeting the target distribution rule from the candidate object set, and distributing the target tasks to the target candidate objects. The business parameters required for executing the target distribution rule are obtained, such as a policy number, a policy attribution mechanism, a task priority and task generation time, and also can be staff names, staff roles and the like, the business parameters of different businesses are different, and the target distribution rule is executed according to the obtained parameters. And when the target distribution rule is executed, acquiring data of a corresponding position according to the configured JsonSchema object format.
And after the target distribution rule is executed, if the target candidate object is screened out, distributing the target task to the target candidate object, and advancing the continuation of the workflow. If the target candidate object cannot be screened out, the operator can manually pick up the target task.
The following takes the task node as the security entry node as an example to further explain the embodiment.
In the security entry node, a special rule (such as a security authority acquisition rule) and a public rule (i.e., an allocation rule) configured for the node are loaded first, the security authority acquisition rule is executed first according to the priority of each rule, and a task role for processing the security authority is acquired in a security configuration table according to a security item corresponding to the task node. And then acquiring the staff with the task role through the staff information of the organization. And then executing an allocation rule (such as a minimum task amount allocation rule), inquiring the task amount currently processed by the employees, and allocating the tasks to the employees with the minimum task amount.
Fig. 3 is a main flow diagram of a task allocation method according to still another embodiment of the present invention. As shown in fig. 3, the task allocation method according to the embodiment of the present invention mainly includes the following steps:
step S301: and configuring an allocation rule for the task node. The specific implementation of this step is shown in step S201, which is not described herein again.
Step S302: and testing the distribution rule, and storing the distribution rule into a distribution rule set after the test is passed. Testing the configured distribution rule based on the set test data to obtain a test result; and determining that the test result meets the expectation, indicating that the allocation rule is configured without errors, and issuing the allocation rule to the production environment. And if the test result is determined not to meet the expectation, adjusting the distribution rule.
Taking the minimum task amount distribution rule as an example, the test data is the task amount of the candidate object in the candidate object set. The test process is as follows: and transmitting the test data into a minimum task amount distribution rule, executing the minimum task amount distribution rule, and printing the candidate object information of each step and the finally selected candidate object information.
The function of printing the test result is realized by configuring the printing rule. The printing rule is used for printing variable values of all links, wherein all the links are used for printing the changed variables when the variables in the task change every time in the process of executing all the rules by the current task, so that testing personnel can check the changed variables. The variable value of each link is specific business data or object, such as a current candidate object set, a candidate object with the least current task amount, and the like.
Step S303: and receiving the target task and configuring the switch state of the distribution rule. The execution of the allocation rules is controlled by configuring the switch states of the allocation rules. In an embodiment, the switch state is on, indicating that the allocation rule can be executed; the switch state is off, indicating that the allocation rule may not be executed.
Step S304: and acquiring a candidate object set for executing the task node and a corresponding distribution rule set according to the task node information of the target task. The specific implementation of this step is shown in step S202, which is not described herein again.
Step S305: the task node information, the set of candidate objects, and the set of allocation rules are passed to the KIE engine to cause the KIE engine to load the allocation rules of the set of allocation rules. The specific implementation of this step is shown in step S203, which is not described herein again.
Step S306: and determining that the task node information and the candidate object set meet the triggering condition of the target distribution rule according to the priority and the switch state of the distribution rule in the distribution rule set. The priority of the switch is higher than the priority of the set allocation rule.
Firstly, selecting an allocation rule with an on-off state from an allocation rule set to obtain an optimized rule set; and then selecting the current distribution rule from the optimization rule set according to the priority of the distribution rule in the optimization rule set and the sequence of the priority from high to low. Other implementations of this step are the same as step S204, and are not described here again.
Step S307: and executing a target distribution rule, screening target candidate objects meeting the target distribution rule from the candidate object set, and distributing the target tasks to the target candidate objects. The specific implementation of this step is shown in step S206, which is not described herein again.
After the target distribution rule is executed, the execution result of the target distribution rule may be counted, and the execution result may be analyzed for the execution success rate, the execution time, and the like. The embodiment completes the management of the distribution task through configuration and test, does not need to restart the service, and realizes the non-stop updating of the rule.
In an optional embodiment, for the configured allocation rule, an interface is provided externally, and the workflow may monitor the flow circulation, call the interface at the corresponding node to perform task allocation, and also support the user to directly call the task allocation at any time through the interface. In addition, when the distribution rules are configured, the distribution rules can be labeled, so that the distribution rules can be conveniently classified, for example, the distribution rules with the same-entering piece labels are classified into the same-entering piece rules; and classifying the distribution rules with the blacklist labels into the blacklist rules.
In a preferred embodiment, to avoid conflict, when configuring the allocation rule, a version identifier is set for the allocation rule, and the allocation rule is version-controlled based on an optimistic lock mechanism. When the target distribution rule is executed, firstly acquiring the version identification of the target distribution rule from a cache; judging whether the obtained version identification is consistent with the current version identification of the target distribution rule in the database, if not, loading the target distribution rule in the database into a cache to execute the target distribution rule of the new version; if consistent, the target assignment rule of the version may be executed directly. Through the processing, the distribution rule of the old version can be still executed after the distribution rule of the new version is effective.
Fig. 4 is a schematic diagram of main blocks of a task assigning apparatus according to an embodiment of the present invention. As shown in fig. 4, the task assigning apparatus 400 according to the embodiment of the present invention mainly includes:
the obtaining module 401 is configured to obtain a candidate object set and a pre-configured allocation rule set for executing a task node according to task node information of a target task. And on the basis of the service rules of the task nodes, abstracting a distribution rule set containing distribution rules in advance, and setting priorities for the distribution rules. After a target task of a workflow is received, a candidate object set for executing the task node and an allocation rule set configured for the task node are obtained according to task node information of the target task.
The workflow records task nodes contained in the work and the execution sequence of each task node. One task node corresponds to one type of target task. The task node information may include a node function description, an allocation rule identifier corresponding to a node, a node state, a task role corresponding to a node, and task execution information.
When the candidate object set is obtained, the target object with the task role can be inquired according to the task role corresponding to the node in the task node information, and the task execution information is added to the target object information corresponding to the target object, so that the candidate object set is obtained. When the distribution rule set is obtained, the corresponding distribution rules can be queried according to the distribution rule identifications corresponding to the nodes in the task node information, and the distribution rules form the distribution rule set.
A determining module 402, configured to determine, according to the priority of the distribution rule in the distribution rule set, that the task node information and the candidate object set satisfy a trigger condition of a target distribution rule. And selecting the current distribution rule from the distribution rule set according to the sequence of the priorities from high to low, and judging whether the task node information and the candidate object set meet the triggering conditions of the current distribution rule.
And if the task node information and the candidate object set meet the triggering condition of the current distribution rule, triggering the current distribution rule, namely taking the current distribution rule as a target distribution rule, and then not triggering the subsequent distribution rule any more. And if the task node information and the candidate object set do not meet the triggering condition of the current distribution rule, reselecting the current distribution rule, and judging that the triggering condition is met enough until the distribution rule set is traversed and ended.
The trigger condition refers to a condition that triggers execution of the allocation rule. For example, the trigger condition of the assignment rule of the concurrency piece may be that the task node information includes a concurrency piece identifier, and a candidate object of the candidate object set has a right to process the concurrency piece. For another example, the trigger condition of the problem part allocation rule may be that the task node information includes a problem part identifier, and a candidate object of the candidate object set has a right to process a problem part.
An allocating module 403, configured to execute the target allocation rule, screen out a target candidate object that satisfies the target allocation rule from the candidate object set, and allocate the target task to the target candidate object. And by executing the target allocation rule, screening target candidate objects meeting the target allocation rule from the candidate object set, and then allocating the target tasks to the target candidate objects, thereby realizing the automatic allocation of the target tasks.
For example, the target allocation rule is a peer allocation rule, and the peer allocation rule is used for searching a candidate object with the smallest task amount from candidate objects with the authority of processing the peer. In this step, when the peer-to-peer distribution rule is executed, the candidate object having the right to process the peer-to-peer is first searched from the candidate object set, and then the candidate object with the minimum task amount is further searched, and the searched candidate object is used as the target candidate object, and the target task is further distributed to the target candidate object.
In addition, the task assigning apparatus 400 according to the embodiment of the present invention may further include: a loading module, a configuration module, a compilation module, a query module, a test module, a version control module, and a statistical analysis module (not shown in FIG. 4). Wherein the loading module is configured to transfer the task node information, the set of candidate objects, and the allocation rule set to KIE engine, so that the KIE engine loads the allocation rules of the allocation rule set.
A configuration module for configuring the allocation rule; wherein, the distribution rule is a drools rule. The compiling module is used for generating a drools rule object by the drools rule and converting the JsonSchema object into a Java class; compiling the drools rule object and the Java class into a KJar package by using an KIE engine, and storing the KJar package.
And the query module is used for querying the on-off state of the distribution rule configured for the task node according to the task node information. The test module is used for testing the distribution rule based on set test data to obtain a test result; and determining that the test result meets the expectation, and issuing the distribution rule to a production environment.
And the version control module is used for setting a version identifier for the distribution rule and carrying out version control on the distribution rule based on an optimistic lock mechanism. And the statistical analysis module is used for counting the execution result of the target distribution rule and analyzing the execution result.
From the above description, it can be seen that, by unbinding the allocation rule from the task allocation process, during task allocation, the candidate object set is obtained according to the task node information, and then the target allocation rule is determined and executed, so that task allocation can be realized on the premise of not modifying the code, the development cycle is shortened, and the service development efficiency is improved.
Fig. 5 illustrates an exemplary system architecture 500 to which the task assigning method or the task assigning apparatus according to the embodiments of the present invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 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 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server that provides various services, such as a background management server that processes target tasks transmitted by an administrator using the terminal apparatuses 501, 502, 503. The background management server may obtain the allocation rule set, determine the target allocation rule, execute the target allocation rule, perform task allocation, and feed back a processing result (e.g., a task allocation result) to the terminal device.
It should be noted that the task allocation method provided in the embodiment of the present application is generally executed by the server 505, and accordingly, the task allocation device is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The invention also provides an electronic device and a computer readable medium according to the embodiment of the invention.
The electronic device of the present invention includes: one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement a task allocation method according to an embodiment of the present invention.
The computer-readable medium of the present invention has stored thereon a computer program which, when executed by a processor, implements a task allocation method of an embodiment of the present invention.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use with the electronic device implementing an embodiment of the present invention. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the computer system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, the processes described above with respect to the main step diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program containing program code for performing the method illustrated in the main step diagram. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module, a determination module, and an assignment module. The names of these modules do not form a limitation to the module itself in some cases, for example, the obtaining module may also be described as a module for obtaining a candidate object set for executing a task node and a pre-configured allocation rule set according to task node information of a target task.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring a candidate object set and a pre-configured distribution rule set for executing task nodes according to task node information of a target task; determining that the task node information and the candidate object set meet the triggering condition of a target distribution rule according to the priority of the distribution rule in the distribution rule set; executing the target distribution rule, screening target candidate objects meeting the target distribution rule from the candidate object set, and distributing the target tasks to the target candidate objects.
According to the technical scheme of the embodiment of the invention, the distribution rule and the task distribution process are unbound, and during task distribution, the candidate object set is obtained according to the task node information, and then the target distribution rule is determined and executed, so that the task distribution can be realized on the premise of not modifying the code, the development period is shortened, and the service development efficiency is improved.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (17)
1. A task allocation method, comprising:
acquiring a candidate object set and a pre-configured distribution rule set for executing task nodes according to task node information of a target task;
determining that the task node information and the candidate object set meet the triggering condition of a target distribution rule according to the priority of the distribution rule in the distribution rule set;
executing the target distribution rule, screening target candidate objects meeting the target distribution rule from the candidate object set, and distributing the target tasks to the target candidate objects.
2. The method of claim 1, wherein the obtaining a candidate object set for executing a task node according to task node information of a target task comprises:
acquiring task execution information and task roles of task nodes according to task node information of a target task;
inquiring a target object with the task role to generate target object information; the target object information comprises task execution information corresponding to the target object;
adding the target object information to the set of candidate objects.
3. The method of claim 1, wherein determining that the task node information and the set of candidate objects satisfy the triggering condition of the target allocation rule according to the priority of the allocation rule in the allocation rule set comprises:
selecting: according to the priority of the distribution rule in the distribution rule set, selecting the current distribution rule from the distribution rule set according to the sequence of the priority from high to low;
a judging step: judging whether the task node information and the candidate object set meet the triggering condition of the current distribution rule;
a determination step: if the task node information and the candidate object set meet the trigger condition, taking the current distribution rule as a target distribution rule; and
and if the task node information and the candidate object set do not meet the triggering condition, repeatedly executing the selecting step, the judging step and the determining step until the distribution rule set is traversed and ended.
4. The method of claim 3, wherein prior to the step of determining that the task node information and the set of candidate objects satisfy a trigger condition of a target allocation rule, the method further comprises:
passing the task node information, the set of candidate objects, and the allocation rule set to an KIE engine to cause the KIE engine to load the allocation rules of the allocation rule set.
5. The method of claim 1, further comprising:
configuring the allocation rule; wherein, the distribution rule is a drools rule.
6. The method of claim 5, wherein the configuring the allocation rule comprises:
and abstracting the service rule corresponding to the task node into a drools rule, and describing a set keyword in the drools rule by using a JsonSchema object.
7. The method of claim 6, further comprising:
generating a drools rule object by the drools rule, and converting the JsonSchema object into a Java class;
compiling the drools rule object and the Java class into a KJar package by using an KIE engine, and storing the KJar package.
8. The method of claim 7, wherein saving the KJar package comprises: storing the KJar packet into a memory and a cache;
obtaining an allocation rule set for executing a task node, comprising:
and sequentially trying to load corresponding KJar packages from the memory and the cache according to the task node information of the target task to obtain a distribution rule set.
9. The method of claim 3, further comprising:
inquiring the on-off state of a distribution rule configured for the task node according to the task node information;
the selecting a current distribution rule from the distribution rule set according to the priority of the distribution rule in the distribution rule set and the sequence of the priority from high to low comprises the following steps:
selecting the distribution rule with the switch state being on from the distribution rule set to obtain an optimized rule set;
and selecting the current distribution rule from the optimization rule set according to the priority of the distribution rule in the optimization rule set and the sequence of the priority from high to low.
10. The method of claim 5, further comprising:
testing the distribution rule based on set test data to obtain a test result;
and determining that the test result meets the expectation, and issuing the distribution rule to a production environment.
11. The method of claim 10, wherein the allocation rule is a minimum task size allocation rule, and the test data is a task size of a candidate object in the set of candidate objects;
based on the set test data, testing the distribution rule to obtain a test result, including:
and transmitting the test data into the distribution rule, and executing the distribution rule to obtain a test result.
12. The method of claim 5, further comprising:
setting a version identifier for the distribution rule, and carrying out version control on the distribution rule based on an optimistic lock mechanism;
the executing the target allocation rule includes:
acquiring the version identification of the target distribution rule from a cache;
and determining that the obtained version identification is inconsistent with the current version identification of the target distribution rule in the database, and loading the target distribution rule in the database to a cache.
13. The method according to any one of claims 1 to 12, further comprising:
and counting the execution result of the target distribution rule, and analyzing the execution result.
14. A task assigning apparatus, comprising:
the acquisition module is used for acquiring a candidate object set for executing the task node and a pre-configured distribution rule set according to the task node information of the target task;
the determining module is used for determining that the task node information and the candidate object set meet the triggering condition of a target distribution rule according to the priority of the distribution rule in the distribution rule set;
and the distribution module is used for executing the target distribution rule, screening target candidate objects meeting the target distribution rule from the candidate object set, and distributing the target tasks to the target candidate objects.
15. The apparatus of claim 14, wherein the obtaining module is further configured to:
acquiring task execution information and task roles of task nodes according to task node information of a target task;
inquiring a target object with the task role to generate target object information; the target object information comprises task execution information corresponding to the target object; and
adding the target object information to the set of candidate objects.
16. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-13.
17. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-13.
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