CN113127207B - Crowd-sourced task resource allocation method and device, electronic equipment and storage medium - Google Patents
Crowd-sourced task resource allocation method and device, electronic equipment and storage medium Download PDFInfo
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- 238000013468 resource allocation Methods 0.000 title claims abstract description 151
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- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
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Abstract
The invention discloses a crowdsourcing task resource allocation method, a crowdsourcing task resource allocation device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a first characteristic of a task to be allocated; inputting the first characteristic into a preset resource allocation model; finding out a target node matched with the first feature from the resource allocation model, and acquiring a node value of the target node; and performing resource allocation on the task to be allocated according to the resource allocation parameters represented by the node value of the target node. The invention carries out branch creation limiting and personalized resource allocation on each node through the resource allocation model, and avoids the occurrence of the phenomenon of error resource allocation of the node caused by factors such as data scarcity, unstable statistics and the like. Meanwhile, the newly built node task is subjected to cold start protection, a buffering stage is added, personalized resource allocation is carried out on the node after stable data are accumulated, and then crowdsourcing markets of areas corresponding to the node are matched, so that the resource allocation reasonably accords with the real situation of the areas.
Description
Technical Field
The present invention relates to the field of data analysis technologies, and in particular, to a crowdsourcing task resource allocation method and apparatus, an electronic device, and a storage medium.
Background
In the prior art, a clustering algorithm (such as K-means clustering) is generally adopted to allocate resources for crowdsourcing tasks in each region, so that the crowdsourcing tasks conform to the actual conditions of each region
However, due to the uneven distribution of tasks in the existing crowdsourcing market across regions, such as: the urban area has dense tasks and the rural area has sparse tasks. If the clustering algorithm is directly adopted to operate the areas with uneven distribution so as to perform resource allocation of the tasks, the situation that the areas with lower density cannot perform resource allocation on the tasks due to too low task quantity is easily caused, for example: the rural area is too small in task quantity, so that enough samples are not used for clustering operation, and task resource allocation fails. If the task resource allocation policy of the region with higher density is directly used to allocate task resources to the region with lower density, the task resource allocation result of the region with lower density cannot truly reflect the real situation of the region with lower density, for example: the use of relatively low task pricing in first-line cities as task pricing in remote mountainous areas tends to result in increased task retraction risk or cost in remote mountainous areas.
Therefore, how to accurately allocate resources in areas with uneven distribution so as to better meet the actual situation of the areas is a great difficulty to be solved at present.
Disclosure of Invention
Aiming at the problem of how to accurately allocate resources in areas with uneven distribution in the prior art, the invention aims to provide a crowdsourcing task resource allocation method, a crowdsourcing task resource allocation device, electronic equipment and a storage medium.
In order to achieve the above object, the present invention provides a crowdsourcing task resource allocation method, which includes:
acquiring a first characteristic of a task to be allocated, wherein the first characteristic is used for representing address information in the task to be allocated;
inputting the first characteristic into a preset resource allocation model; the resource allocation model adopts a tree model, and comprises nodes and node values, wherein the nodes are used for representing address information, and the node values are used for representing resource allocation parameters applicable to the address information;
finding out a target node matched with the first characteristic from the resource allocation model, and
acquiring a node value of the target node;
and performing resource allocation on the task to be allocated according to the resource allocation parameters represented by the node value of the target node.
Preferably, the resource allocation model is constructed by:
acquiring a second characteristic of the historical task, wherein the second characteristic is used for representing address information in the historical task;
constructing nodes of the resource allocation model according to the second features, wherein the hierarchical relationship of the nodes corresponds to the hierarchical relationship of the address information represented in the second features;
acquiring third characteristics of the historical task based on the address information represented by the node, wherein the third characteristics are used for representing task information in the historical task;
generating a first resource configuration parameter corresponding to the node according to the third characteristic;
and assigning the corresponding nodes in the resource allocation model by taking the first resource allocation parameters as node values.
Preferably, after the node that builds the resource configuration model according to the second feature, the method further includes:
and returning the node to be reserved in the corresponding upper node when the number of the historical tasks corresponding to the address information represented by the node is smaller than a preset creation threshold value.
Preferably, before the generating the first resource configuration parameter corresponding to the node according to the third feature, the method further includes:
and when the number of the historical tasks corresponding to the address information represented by the node is greater than or equal to a preset starting threshold, assigning a first resource configuration parameter generated according to the third characteristic as a node value to the node.
Preferably, the allocation method further comprises:
and updating the resource configuration model according to the change of the historical task, wherein the update comprises the update of the node and/or the node value.
Preferably, when the number of historical tasks corresponding to the address information represented by the node is greater than or equal to a preset starting threshold, the method further includes:
the nodes form an inclusion set, all lower nodes of the nodes are correspondingly stored in the inclusion set of the nodes, and the first resource configuration parameters generated according to the third characteristic are used as node values to assign values to the nodes; and/or the number of the groups of groups,
the nodes form a rejection set, lower nodes meeting preset conditions are correspondingly stored in the rejection set of the nodes, and resource allocation is not carried out; wherein, the preset conditions include:
the number of the historical tasks corresponding to the address information represented by the lower node is larger than or equal to a preset starting threshold value; and
the subordinate nodes are in a preset blacklist.
Preferably, before the resource allocation parameter characterizing according to the node value of the target node allocates the resource to the task to be allocated, the method further includes:
acquiring an exclusion set corresponding to a superior node corresponding to the target node;
when the target node is not in the rejection set, acquiring a node value of the target node, and performing resource allocation on the task to be allocated according to a resource allocation parameter represented by the node value of the target node;
when the target node is in the exclusion set, no resource allocation is performed.
In order to achieve the above object, the present invention further provides a crowdsourcing task resource allocation device, including:
the device comprises a feature acquisition module, a feature distribution module and a feature distribution module, wherein the feature acquisition module is used for acquiring a first feature of a task to be distributed, and the first feature is used for representing address information in the task to be distributed;
the parameter matching module is used for inputting the first characteristic into a preset resource allocation model; the resource allocation model adopts a tree model, and comprises nodes and node values, wherein the nodes are used for representing address information, and the node values are used for representing resource allocation parameters applicable to the address information;
finding out a target node matched with the first characteristic from the resource allocation model, and acquiring a node value of the target node;
and the resource allocation module is used for allocating resources to the tasks to be allocated according to the resource allocation parameters represented by the node values of the target nodes.
To achieve the above object, the present invention also provides an electronic device including:
a memory storing at least one instruction; and
And the processor executes the instructions stored in the memory to realize the crowdsourcing task resource allocation method according to any one of the above.
To achieve the above object, the present invention further provides a computer readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the crowdsourcing task resource allocation method according to any one of the above.
The beneficial effects of the technical scheme are that:
according to the invention, through the resource configuration model, branch creation and personalized resource configuration are carried out on each node according to the preset creation threshold and the preset starting threshold, the node which does not reach the preset creation threshold is not subjected to branch creation and is reserved in the upper node, the resource configuration parameters of the upper node are adopted to carry out resource configuration, and the problem that the node carries out error resource configuration due to factors such as data scarcity and statistic instability is avoided, and the cost or withdrawal risk is increased. And performing cold start protection on the tasks which reach the preset creation threshold but do not reach the preset starting threshold node, not performing resource allocation to increase a buffering stage, accumulating stable data so as to enable personalized resource allocation after the later stage reaches the preset starting threshold, and further matching crowd-sourced markets of the areas corresponding to the nodes, so that the resource allocation reasonably accords with the real conditions of the areas, and the withdrawal risk and the task cost are reduced. Meanwhile, by containing the set and the rejection set, all tasks in the range are rapidly configured, a cutting mode that the upper level forcibly covers the lower level is avoided, and finally, accurate resource configuration is realized for each address node.
Drawings
FIG. 1 is a schematic flow chart a of a crowdsourcing task resource allocation method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart b of a first embodiment of a crowdsourcing task resource allocation method of the present invention;
FIG. 3 is a functional block diagram of a crowdsourcing task resource allocation device according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the crowdsourcing task resource allocation method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that the descriptions of "first," "second," etc. in the embodiments of the present application are for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the protection scope of the present application.
In the description of the present application, it should be understood that the numerical references before the steps do not identify the order of performing the steps, but are only used for convenience in describing the present application and distinguishing each step, and thus should not be construed as limiting the present application.
Example 1
Crowd-sourced tasks refer to the practice of a company or organization to outsource work tasks performed by employees in the past to unspecified mass volunteers in a free voluntary fashion. Crowd-sourced tasks are typically undertaken by individuals, but may also occur in the form of individual productions relying on open sources if tasks requiring multi-person collaboration are involved.
Referring to fig. 1, which is a flow chart diagram a of a first embodiment of a crowdsourcing task resource allocation method according to the present invention, it can be seen from the figure that the method specifically includes the following steps:
s100: and acquiring a first characteristic of the task to be allocated, wherein the first characteristic is used for representing address information in the task to be allocated.
S200: inputting the first characteristic into a preset resource allocation model; the resource allocation model adopts a tree model, and comprises nodes and node values, wherein the nodes are used for representing address information, and the node values are used for representing resource allocation parameters applicable to the address information.
S300: and searching a target node matched with the first characteristic from the resource allocation model, and acquiring a node value of the target node.
S400: and performing resource allocation on the task to be allocated according to the resource allocation parameters represented by the node value of the target node.
Uploading or transmitting the task to be distributed to the terminal equipment, wherein the uploaded or transmitted task to be distributed can comprise introduction contents such as address information, price information, difficulty information and the like of the task to be distributed. When the terminal equipment receives the task to be distributed, a first characteristic of the task to be distributed can be obtained from the corresponding introduction, and the first characteristic is used for representing address information in the task to be distributed.
For example: corresponding keywords can be set for the first feature in advance, and the terminal device can acquire the first feature by performing keyword retrieval on the introduction content. For example: the to-be-allocated task list can be stored in the form of an Excel file and the like, the Excel file stored with the to-be-allocated task list is uploaded or sent to the terminal equipment, and the terminal equipment can extract the first characteristics of the country, province, city or country, street and the like corresponding to the to-be-allocated task list from the Excel file. In this embodiment, the terminal device includes, but is not limited to: computing devices such as desktop computers, notebooks, palmtops, cloud servers, and the like.
After the terminal equipment acquires the first feature of the task to be distributed, the first feature is input into a preset resource allocation model, the resource allocation model adopts a tree model, the resource allocation model comprises nodes and node values, the nodes are used for representing address information, and the node values are used for representing resource allocation parameters applicable to the address information. And the resource allocation model matches each first characteristic with each node in a preset resource allocation model to find out a target node matched with the first characteristic, obtains a node value of the target node, and allocates resources to the task to be allocated according to the resource allocation parameter represented by the node value of the target node.
Referring to fig. 2, a flow chart b of a first embodiment of a crowdsourcing task resource allocation method according to the present invention is shown, specifically, the steps of constructing the resource allocation model are as follows:
s201, obtaining a second characteristic of the historical task, wherein the second characteristic is used for representing address information in the historical task.
And selecting a historical task within a preset time range when the resource allocation model is constructed. Wherein the preset time range can be set to be all tasks occurring during a period of time from the current time. It should be noted in particular that the predetermined time range requires that the market be relatively stable during this period. For example: before the resource allocation is carried out on the task, the task price is unchanged in 4 months historically, and the preset time range can be selected to be 4 months before; or after the resource allocation is carried out on the task, the task is updated just before 7 days historically, and the preset time range can be selected to be 7 days before.
S202: and constructing nodes of the resource allocation model according to the second features, wherein the hierarchical relationship of the nodes corresponds to the hierarchical relationship of the address information represented in the second features.
And constructing a node of a preset resource allocation model according to each second characteristic. For example: the node construction is carried out by taking the Pudong new region of Shanghai city in China as a second characteristic, the depth of the tree is 3, the first-stage root node is China, the second-stage child node is Shanghai city, and the third-stage leaf node is Pudong new region. It is known that the hierarchical relationship of the nodes corresponds to the hierarchical relationship of the address information characterized in the second feature. It should be noted that the following description is specific. The node is constructed according to each second characteristic in the historical task within the preset time range, namely, a tree is dynamically generated according to the task address acquisition process of the terminal equipment, and the finally obtained resource configuration model only comprises addresses appearing in the historical task.
In an exemplary embodiment, a tree model is built in a lazy manner as illustrated, with the following specific operations:
the terminal equipment acquires all second characteristics d of the historical task and performs tree addressing operation;
traversing each level of address in all second features D from the root node D [0] to address;
a) Suppose that a tree node D [ i ] corresponding to the i-level address D [ i ] has been found;
b) If the lower-level tree node does not have a node corresponding to the i+1-level address di+1, creating a child node di+1 under the current node di as a corresponding node of di+1;
c) And entering a node D [ i+1] to perform next-stage address comparison.
After addressing is finished, each history task is correspondingly stored in the last addressed node.
The lazy mode is utilized to construct the tree, so that the calculation and storage work can be saved in a self-adaptive mode, and the occupation of memory and calculation resources by a large number of irrelevant addresses is effectively avoided. For example: if 100 tasks are known, involving 3 provinces, 5 cities, 10 regions, 12 towns, the lazy approach only needs to build 1+3+5+10+12=31 nodes, while the complete tree contains: 1+34+283+2861+44821=48000 nodes.
Further, in order to avoid risks of unstable task data statistics and the like of some nodes in the resource allocation model due to the fact that the task number is scarce, when the number of historical tasks corresponding to the address information represented by the nodes is smaller than a preset creation threshold value, the nodes are returned and reserved to the corresponding upper nodes.
For some nodes with scarce task numbers, for example, the node has only 1 or 2 task feedback, the phenomenon of data abnormality in the nodes may be caused by the influence of external uncertainty factors such as excessive propaganda or network, the lower the statistical confidence is, the more the historical task of the node is forcibly used for statistical analysis, the error assessment is likely to be caused, and the withdrawal risk is increased.
In an exemplary embodiment, the node is lifted to the corresponding parent node in a "leaf-cutting" manner due to insufficient statistics at the level of the node, and statistics are performed with the corresponding parent node data. If the current corresponding father node still does not meet the statistical requirement, the current corresponding father node can continue to rise to the upper level node until the root node, and finally, the statistical result is returned to the area with scarce tasks for use. It should be noted that, since the same subtree belongs to the same major class, the task resource configuration of each node in the subtree is similar, so when one level node does not accord with statistics, the subtree can rise to a parent node, if the parent node still does not accord with statistics enough, the subtree can rise until reaching a root node.
S203: and acquiring a third characteristic of the historical task based on the address information represented by the node, wherein the third characteristic is used for representing task information in the historical task.
The task information may include external influencing factors such as task cost, distance, traffic, personnel concentration, etc., or may include direct influencing factors such as task order time difference, task order balance value, etc. The terminal equipment sends the task to the crowdsourcing platform, and the task is fed back to the terminal equipment after the user equipment receives the order successfully: task id, release time, order receiving time, completion time, order receiving person, task cost, distance, traffic, personnel concentration and other task information, the background server records and files the task information, and the terminal equipment can acquire a third characteristic of the task to be distributed from the corresponding task information. The user equipment refers to a receiving service party, such as a driver in a vehicle calling service.
S204: and generating a first resource configuration parameter corresponding to the node according to the third characteristic.
In an exemplary embodiment, the task information may include external influencing factors such as task cost, distance, traffic, personnel concentration, etc., and the task data is statistically analyzed to evaluate the tasks in a range to obtain corresponding resource configuration parameters.
In an exemplary embodiment, the task data may further include direct influencing factors such as a task order-taking time difference and a task order-taking balance value, and the tasks in a range are evaluated by performing statistical analysis on the task data to obtain corresponding resource configuration parameters.
It should be noted that, in the prior art, the manner of implementing resource allocation for tasks may be suitable for the task, which is not described herein.
Further, in the process of gradually adding tasks in the area with a scarce task number and forming regional personalized resource allocation, setting a first resource allocation parameter generated according to the third characteristic as a node value to assign a value to the node when the historical task number corresponding to the address information represented by the node is greater than or equal to a preset starting threshold value for stabilizing data so as to perform later personalized allocation.
When the number of tasks of any node is greater than or equal to a preset creation threshold, the creation condition of the node is met, an independent node is created, a personalized strategy of the current node is initialized, at the moment, no resource allocation is performed on the new task, and therefore a buffer stage exists, and stable data are accumulated for initializing calculation.
When the number of tasks of any node is greater than or equal to a preset starting threshold value and reaches a certain number, the tasks are enough to be independently counted, a first resource allocation parameter with pertinence is obtained based on historical tasks in the node, so that personalized resource allocation is carried out on the node, personalized resource allocation of the node area is met, and market demands of the node area are met.
S205: and assigning the corresponding nodes in the resource allocation model by taking the first resource allocation parameters as node values.
It is noted that the resource configuration model in this embodiment includes a node for characterizing the second feature and a node value for characterizing the first resource configuration parameter applicable to the second feature. In an exemplary embodiment, the resource configuration model may be updated according to changes in historical tasks, including updating of nodes and/or the node values.
And after the task to be allocated is allocated, the task to be allocated becomes a new historical task, the new historical task is used as a data sample, and the new data sample is continuously supplemented for each node in the resource allocation model, so that the node and/or the node value update of the corresponding resource allocation parameters is realized.
In an exemplary embodiment, the following examples are presented:
the preset creating threshold is 3, the preset starting threshold is 20, the resource allocation model creates a Shanghai city node, the node value of the Shanghai city node is 3%, and namely the resource allocation parameter of Shanghai city is 3%.
(1) The "Shanghai city" all cities use 3% of the resource allocation parameters for resource allocation. Then in the next stage, all newly added tasks in the whole market of Shanghai city are allocated 3% of resources on the basis (3% of price reduction or price increase in original price);
(2) Along with the continuous expansion of the market, the new task of the Shanghai city Pudong new region begins, and the resource allocation is carried out on the first 3 parts of the Shanghai city Pudong new region according to the resource allocation parameter of 3 percent of the Shanghai city;
(3) When the number of tasks in the whole area of the Shanghai Pudong new area reaches 3, the Shanghai Pudong new area meets the node creation condition, the resource configuration model creates independent nodes of the Shanghai Pudong new area, and a new personalized resource configuration parameter is initialized aiming at the nodes of the Shanghai Pudong new area. At this time, all tasks newly added in the whole region of the Shanghai Pudong new region are not allocated with resources (original price is kept);
(4) After a period of accumulation, when the task number in the whole area of the Shanghai city Pudong new area reaches 20 single, the Shanghai city Pudong new area meets the independent statistical condition, and the Shanghai city Pudong new area obtains a new resource configuration parameter of 5% according to the historical 20 single task data. Then in the next stage, all the tasks newly added in the whole area of the new region of Shanghai city, pudong, are allocated according to 5% of the resource allocation parameters of the new region of Shanghai city, pudong, and all the tasks newly added in the whole area of Shanghai city except the new region of Shanghai city are allocated according to 3% of the resource allocation parameters of Shanghai city.
Further, since 34 national provincial addresses, 283 municipal addresses, 2861 regional addresses and 44821 rural addresses are used, if every resource allocation update needs to be operated on approximately 4 ten thousand 8 thousands of addresses one by one, the operation is obviously inefficient. However, if a unified operation is performed on approximately 4 ten thousand 8 thousands of addresses by using a cut-off method, it is difficult to ensure that each node meets the actual situation of the corresponding region.
In an exemplary embodiment, the present invention rapidly configures each node in the resource configuration model by a double-set method, and the specific scheme is as follows:
the first scheme is as follows: when the number of the historical tasks corresponding to the address information represented by the node is greater than or equal to a preset starting threshold, the node forms an inclusion set, all lower nodes of the node are correspondingly stored in the inclusion set of the node, and the first resource configuration parameter generated according to the third characteristic is used as a node value to assign a value to the node.
Here, the inclusion set is represented as a city to be allocated with resources, and has a downward compatibility effect in a preset resource allocation model, that is, the upper address includes all the lower addresses. If the inclusion set is "Shenzhen city", which is a city level address, and the corresponding node name is "Shenzhen city", the region corresponds to all the lower level addresses such as district level addresses, village and town addresses in the "Shenzhen city" city level address.
For example:
(1) The first stage: when the number of the tasks in Shenzhen city is larger than or equal to a preset starting threshold, generating personalized resource configuration parameters a according to task information of historical tasks in the whole city range in Shenzhen city, and carrying out resource allocation by adopting the resource configuration parameters a in the whole city range in Shenzhen city.
Meanwhile, the corresponding node of Shenzhen city automatically forms an inclusion set of Shenzhen city, wherein the inclusion set of Shenzhen city comprises all the lower-level addresses such as district-level addresses, village-town addresses and the like in Shenzhen city-level addresses.
The number of the tasks in the Shenzhen city is larger than or equal to the preset starting threshold, and it is known that the number of the tasks in the Guangdong province is also larger than or equal to the preset starting threshold, and the Shenzhen city is located in the inclusion set in the Guangdong province.
(2) And a second stage: because of the sudden epidemic situation of the Guangdong province, resource reduction adjustment is needed to be carried out on the Guangdong province, so as to reduce the task cost, a resource configuration parameter b is generated according to task information containing historical tasks within the range of the Guangdong province, and the resource configuration parameter b is adopted for the Guangdong province to carry out resource allocation.
The second scheme is as follows: on the basis of the first scheme, the method further comprises the following steps:
the nodes form a rejection set, lower nodes meeting preset conditions are correspondingly stored in the rejection set of the nodes, and resource allocation is not carried out; wherein, the preset conditions include:
the number of the historical tasks corresponding to the address information represented by the lower node is larger than or equal to a preset starting threshold value; and
the subordinate nodes are in a preset blacklist.
Here, the exclusion set is represented as not performing resource allocation cities, and has a downward compatibility effect in a preset resource allocation model, that is, the upper level address contains all the lower level addresses. If the inclusion set is "Shenzhen city" and the exclusion set is "nan mountain region", the region corresponds to all region-level addresses except for the region-level address of "nan mountain region" in the region-level address of "Shenzhen city" and other lower-level addresses such as village and town addresses.
The blacklist is represented as a region where the node corresponding region cannot be subjected to resource adjustment due to the influence of other external factors such as local policies.
For example:
(1) The first stage: when the number of tasks in Guangdong province is larger than or equal to a preset starting threshold, the Guangdong province can generate personalized resource configuration parameters a according to task information of historical tasks in the whole province scope, and the resource configuration parameters a are adopted in the whole Guangdong province scope to carry out resource allocation.
And the corresponding node of the Guangdong province automatically forms a containing set of the Guangdong province and a rejection set of the 0, wherein the containing set of the Guangdong province comprises all area level addresses, village and town addresses and other lower-level addresses in the Guangdong province level address.
(2) And a second stage: when the number of tasks of Shenzhen city is greater than or equal to a preset starting threshold, an inclusion set of Shenzhen city and an exclusion set of 0 are formed by the same. However, since "Shenzhen city" cannot be resource-adjusted due to the influence of other external factors such as local policy, the "Shenzhen city" is put into the exclusion set corresponding to the upper node "Guangdong province".
The corresponding set of "Guangdong province" is: the set containing { "Guangdong province" }, the exclusion set { "Shenzhen City" }, then "Shenzhen City" does not allocate resources.
Further, when the resource allocation is performed on the task to be allocated, the rejection set corresponding to the upper node corresponding to the target node corresponding to the task to be allocated is acquired. When the target node is not in the rejection set, acquiring a node value of the target node, and performing resource allocation on the task to be allocated according to a resource allocation parameter represented by the node value of the target node; when the target node is in the exclusion set, no resource allocation is performed.
Example two
Fig. 3 is a functional block diagram of a crowdsourcing task resource allocation device according to a second embodiment of the crowdsourcing task resource allocation method of the present invention.
The device comprises a feature acquisition module 31, a parameter matching module 32 and a resource allocation module 33. The module referred to in the present invention refers to a series of computer program segments capable of being executed by a processor and of performing a fixed function, which are stored in a memory. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.
The feature acquisition module 31 is configured to acquire a first feature of a task to be allocated, where the first feature is used to characterize address information in the task to be allocated.
In an exemplary embodiment, the task to be distributed is uploaded or sent to the terminal device, and the uploaded or sent task to be distributed may include the introduction contents of address information, price information, difficulty information and the like of the task to be distributed. When the terminal device receives the task to be distributed, the feature obtaining module 31 may obtain the first feature of the task to be distributed from the corresponding introduction.
The parameter matching module 32 is configured to input the first feature to a preset resource configuration model; the resource allocation model adopts a tree model, and comprises nodes and node values, wherein the nodes are used for representing address information, and the node values are used for representing resource allocation parameters applicable to the address information;
and searching a target node matched with the first characteristic from the resource allocation model, and acquiring a node value of the target node.
In an exemplary embodiment, after the terminal device obtains the first features of the task to be allocated, the first features may be input into a preset resource configuration model, where the resource configuration model includes nodes and node values, the nodes are used to characterize address information, the node values are used to characterize resource configuration parameters applicable to the address information, and the parameter matching module 32 may match each first feature with each node in the preset resource configuration model, so as to obtain the resource configuration parameters corresponding to the task to be allocated.
The resource allocation module 33 is configured to allocate resources to the task to be allocated according to a resource configuration parameter represented by a node value of the target node.
Example III
Fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the crowdsourcing task resource allocation method of the present invention.
In an exemplary embodiment, the electronic device 4 includes, but is not limited to, a memory 41, a processor 42, and a computer program stored in the memory 41 and executable on the processor, such as a crowdsourcing task resource allocation program. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of an electronic device and is not limiting of the electronic device, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may also include an input-output device, a network access device, a bus, etc.
The memory 41 includes at least one type of computer-readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage module of the electronic device, such as a hard disk or a memory of the electronic device. In other embodiments, the memory 41 may also be an external storage device of an electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like. Of course, the memory 41 may also include both an internal memory module of the electronic device and an external memory device thereof. In this embodiment, the memory 41 is typically used to store an operating system and various types of application software installed on the electronic device. In addition, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a central processing module (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor 42 is an operation core and a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, and executes an operating system of the electronic device and various installed applications, program codes, etc.
The processor 42 executes the operating system of the electronic device as well as various types of applications installed. The processor 42 executes the application program to implement the steps of the various crowd-sourced task resource allocation method embodiments described above, such as steps S100, S200 shown in fig. 1.
Example IV
The present embodiment also provides a computer-readable storage medium such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor, performs the corresponding functions. The computer readable storage medium of the present embodiment is configured to store a computer program implementing the resource allocation method, and when executed by the processor 42, implements the crowdsourcing task resource allocation method of the first or second or third embodiment.
Claims (8)
1. A crowdsourcing task resource allocation method, the method comprising:
acquiring a first characteristic of a task to be allocated, wherein the first characteristic is used for representing address information in the task to be allocated;
inputting the first characteristic into a preset resource allocation model; the resource allocation model adopts a tree model, and comprises nodes and node values, wherein the nodes are used for representing address information, and the node values are used for representing resource allocation parameters applicable to the address information;
finding out a target node matched with the first characteristic from the resource allocation model, and acquiring a node value of the target node;
performing resource allocation on the task to be allocated according to the resource allocation parameters represented by the node values of the target nodes;
the resource allocation model is constructed through the following steps:
acquiring a second characteristic of the historical task, wherein the second characteristic is used for representing address information in the historical task;
constructing nodes of the resource allocation model according to the second features, wherein the hierarchical relationship of the nodes corresponds to the hierarchical relationship of the address information represented in the second features;
acquiring third characteristics of the historical task based on the address information represented by the node, wherein the third characteristics are used for representing task information in the historical task;
generating a first resource configuration parameter corresponding to the node according to the third characteristic;
assigning a value to the corresponding node in the resource allocation model by taking the first resource allocation parameter as a node value;
wherein after the node constructing the resource allocation model according to the second feature, the method further comprises:
and returning the node to be reserved in the corresponding upper node when the number of the historical tasks corresponding to the address information represented by the node is smaller than a preset creation threshold value.
2. The crowdsourcing task resource allocation method of claim 1, further comprising, prior to the generating a first resource configuration parameter corresponding to the node according to the third characteristic:
and when the number of the historical tasks corresponding to the address information represented by the node is greater than or equal to a preset starting threshold, assigning a first resource configuration parameter generated according to the third characteristic as a node value to the node.
3. The crowdsourcing task resource allocation method of claim 2, wherein the allocation method further comprises:
and updating the resource configuration model according to the change of the historical task, wherein the update comprises the update of the node and/or the node value.
4. The crowdsourcing task resource allocation method according to claim 2, wherein when the number of historical tasks corresponding to the address information represented by the node is greater than or equal to a preset starting threshold, the method further comprises:
the nodes form an inclusion set, all lower nodes of the nodes are correspondingly stored in the inclusion set of the nodes, and the first resource configuration parameters generated according to the third characteristic are used as node values to assign values to the nodes; and/or the number of the groups of groups,
the nodes form a rejection set, lower nodes meeting preset conditions are correspondingly stored in the rejection set of the nodes, and resource allocation is not carried out; wherein, the preset conditions include:
the number of the historical tasks corresponding to the address information represented by the lower node is larger than or equal to a preset starting threshold value; and
the subordinate nodes are in a preset blacklist.
5. The method of claim 4, further comprising, prior to the allocating the resources to the task to be allocated according to the resource allocation parameter characterized by the node value of the target node:
acquiring an exclusion set corresponding to a superior node corresponding to the target node;
when the target node is not in the rejection set, acquiring a node value of the target node, and performing resource allocation on the task to be allocated according to a resource allocation parameter represented by the node value of the target node;
when the target node is in the exclusion set, no resource allocation is performed.
6. A crowdsourcing task resource allocation device, comprising:
the device comprises a feature acquisition module, a feature distribution module and a feature distribution module, wherein the feature acquisition module is used for acquiring a first feature of a task to be distributed, and the first feature is used for representing address information in the task to be distributed;
the parameter matching module is used for inputting the first characteristic into a preset resource allocation model; the resource allocation model adopts a tree model, and comprises nodes and node values, wherein the nodes are used for representing address information, and the node values are used for representing resource allocation parameters applicable to the address information;
finding out a target node matched with the first characteristic from the resource allocation model, and acquiring a node value of the target node;
the resource allocation module is used for allocating resources to the tasks to be allocated according to the resource allocation parameters represented by the node values of the target nodes;
the resource allocation model is constructed through the following operations:
acquiring a second characteristic of the historical task, wherein the second characteristic is used for representing address information in the historical task;
constructing nodes of the resource allocation model according to the second features, wherein the hierarchical relationship of the nodes corresponds to the hierarchical relationship of the address information represented in the second features;
acquiring third characteristics of the historical task based on the address information represented by the node, wherein the third characteristics are used for representing task information in the historical task;
generating a first resource configuration parameter corresponding to the node according to the third characteristic;
assigning a value to the corresponding node in the resource allocation model by taking the first resource allocation parameter as a node value;
wherein after the node constructing the resource allocation model according to the second feature, the method further comprises:
and returning the node to be reserved in the corresponding upper node when the number of the historical tasks corresponding to the address information represented by the node is smaller than a preset creation threshold value.
7. An electronic device, the electronic device comprising:
a memory storing at least one instruction; and
A processor executing instructions stored in the memory to implement a crowdsourcing task resource allocation method as claimed in any one of claims 1 to 5.
8. A computer-readable storage medium having stored therein at least one instruction for execution by a processor in an electronic device to implement a crowdsourcing task resource allocation method as claimed in any one of claims 1 to 5.
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| CN104731663A (en) * | 2015-03-31 | 2015-06-24 | 北京奇艺世纪科技有限公司 | Task processing method and system |
| CN110766269A (en) * | 2019-09-02 | 2020-02-07 | 平安科技（深圳）有限公司 | Task allocation method and device, readable storage medium and terminal equipment |
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| CN104731663A (en) * | 2015-03-31 | 2015-06-24 | 北京奇艺世纪科技有限公司 | Task processing method and system |
| CN110766269A (en) * | 2019-09-02 | 2020-02-07 | 平安科技（深圳）有限公司 | Task allocation method and device, readable storage medium and terminal equipment |
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