CN113127207A - Crowdsourcing task resource allocation method and device, electronic equipment and storage medium - Google Patents
Crowdsourcing 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 76
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- 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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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
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- G06F9/48—Program initiating; Program switching, e.g. by interrupt
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- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
Abstract
The invention discloses a method and a device for allocating crowdsourcing task resources, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a first characteristic of a task to be distributed; inputting the first characteristic into a preset resource configuration model; finding out a target node matched with the first characteristic from the resource configuration 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 values of the target nodes. According to the invention, the resource allocation model is used for performing branch creation limitation and personalized resource allocation on each node, so that the phenomenon that the node performs wrong resource allocation due to factors such as data scarcity and unstable statistics is avoided. Meanwhile, cold start protection is carried out on the newly-built node task, a buffering stage is added, personalized resource allocation is carried out on the node after stable data are accumulated, and then the crowdsourcing market of the area corresponding to the node is matched, so that the resource allocation is reasonably consistent with the real situation of the area.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a crowdsourcing task resource allocation method and device, electronic equipment and a storage medium.
Background
In the prior art, a clustering algorithm (such as K-means clustering) is usually adopted to allocate resources to crowdsourcing tasks of various regions, so that the resources are in accordance with the real situation of the various regions
However, the existing crowdsourcing market has uneven distribution of tasks in various regions, such as: the tasks in urban areas are dense, and the tasks in rural areas are sparse. If the clustering algorithm is directly adopted to calculate the areas with uneven distribution so as to allocate resources to the tasks, the situation that the areas with lower density cannot allocate resources to the tasks due to too low task amount is easily caused, such as: in rural areas, due to the fact that the task amount is too small, insufficient samples are used for clustering operation, and therefore task resource allocation fails. If the resource allocation strategy of the area with higher density is directly used for allocating the task resources to the area with lower density, the result of the task resource allocation of the area with lower density cannot truly reflect the actual situation of the area with lower density, such as: the relatively low task pricing of the first-line city is used as the task pricing for the remote mountainous area, which tends to result in an increased risk or cost of task withdrawal for the remote mountainous area.
Therefore, how to accurately allocate resources to an area with uneven distribution to better meet the real situation of the area is a big problem to be solved at present.
Disclosure of Invention
The invention aims to provide a method and a device for allocating crowdsourcing task resources, electronic equipment and a storage medium, aiming at the problem of how to accurately allocate resources to areas with uneven distribution in the prior art.
In order to achieve the above object, the present invention provides a method for allocating resources of a crowdsourcing task, comprising:
acquiring first characteristics of a task to be distributed, wherein the first characteristics are used for representing address information in the task to be distributed;
inputting the first characteristic into a preset resource configuration model; the resource configuration model adopts a tree model, the resource configuration model comprises nodes and node values, the nodes are used for representing address information, and the node values are used for representing resource configuration parameters applicable to the address information;
finding out the target node matched with the first characteristic from the resource configuration 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 values of the target nodes.
Preferably, the resource allocation model is constructed by the following steps:
acquiring a second characteristic of a historical task, wherein the second characteristic is used for representing address information in the historical task;
constructing nodes of the resource configuration model according to the second characteristics, wherein the hierarchical relationship of the nodes corresponds to the hierarchical relationship of the address information characterized in the second characteristics;
acquiring a third feature of the historical task based on the address information represented by the node, wherein the third feature is 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 node in the resource configuration model by using the first resource configuration parameter as a node value.
Preferably, after the building the node of the resource configuration model according to the second feature, the method further includes:
and when the historical task quantity corresponding to the address information represented by the node is smaller than a preset creation threshold value, returning and reserving the node to a corresponding superior node.
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 historical task quantity corresponding to the address information represented by the node is greater than or equal to a preset starting threshold value, assigning a value to the node by using a first resource configuration parameter generated according to the third characteristic as a node value.
Preferably, the allocation method further comprises:
and updating the resource configuration model according to the change of the historical task, wherein the updating comprises the updating of the node and/or the node value.
Preferably, 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 method further includes:
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; and/or the presence of a gas in the gas,
the nodes form a exclusion set, and subordinate nodes meeting preset conditions are correspondingly stored in the exclusion set of the nodes without resource allocation; wherein the preset conditions include:
the historical task number corresponding to the address information represented by the subordinate node is greater than or equal to a preset starting threshold; and
and the lower node is in a preset blacklist list.
Preferably, before the resource allocation is performed on the task to be allocated according to the resource configuration parameter characterized by the node value of the target node, the method further includes:
acquiring an exclusion set corresponding to a superior node corresponding to the target node;
when the target node is not located in the exclusive set, acquiring a node value of the target node, and performing resource allocation on the task to be allocated according to a resource configuration parameter represented by the node value of the target node;
when the target node is in the exclusive set, no resource allocation is performed.
In order to achieve the above object, the present invention further provides a device for allocating crowdsourcing task resources, including:
the system comprises a characteristic acquisition module, a task processing module and a task processing module, wherein the characteristic acquisition module is used for acquiring first characteristics of a task to be allocated, and the first characteristics are used for representing address information in the task to be allocated;
the parameter matching module is used for inputting the first characteristics to a preset resource configuration model; the resource configuration model adopts a tree model, the resource configuration model comprises nodes and node values, the nodes are used for representing address information, and the node values are used for representing resource configuration parameters applicable to the address information;
searching a target node matched with the first characteristic from the resource configuration model, and acquiring a node value of the target node;
and the resource allocation module is used for allocating resources to the task to be allocated according to the resource configuration parameters represented by the node values of the target nodes.
In order to achieve the above object, the present invention also provides an electronic device, including:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement any one of the above methods for resource allocation for crowdsourced tasks.
To achieve the above object, the present invention further provides a computer-readable storage medium, where at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement any one of the above methods for allocating resources for crowdsourced tasks.
The beneficial effects of the above 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, branch creation is not carried out on nodes which do not reach the preset creation threshold and the nodes are reserved in the superior node, and resource configuration parameters of the superior node are adopted for resource configuration, so that the node is prevented from carrying out wrong resource configuration due to factors such as data scarcity and unstable statistics, and the cost or withdrawal risk is increased. And performing cold start protection on the tasks which reach the preset establishment threshold but do not reach the preset start threshold node, not performing resource allocation to increase a buffering stage, accumulating stable data so as to facilitate individualized resource allocation after the preset start threshold is reached at a later stage, and further matching the crowdsourcing market of the area corresponding to the node, so that the resource allocation is reasonably consistent with the real condition of the area, and the withdrawal risk and the task cost are reduced. Meanwhile, all tasks in the range are rapidly configured through the inclusion set and the exclusion set, the phenomenon that the superior forcibly covers the next cutting mode is avoided, and accurate resource configuration is finally carried out on each address node.
Drawings
Fig. 1 is a schematic flow chart a of a crowdsourced task resource allocation method according to a first embodiment of the present invention;
FIG. 2 is a schematic flowchart b illustrating a first embodiment of a method for allocating resources for crowdsourced tasks according to the present invention;
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 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 invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the descriptions relating to "first", "second", etc. in the embodiments of the present application are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is 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 merely serve to facilitate the description of the present application and to distinguish each step, and therefore should not be construed as limiting the present application.
Example one
Crowdsourcing refers to the practice of a company or organization to outsource work tasks performed by employees to unspecified public volunteers in a free-voluntary manner. Crowdsourcing tasks are usually undertaken by individuals, but may also occur in the form of individual production relying on an open source if it involves tasks that require multiple persons to collaborate.
Please refer to fig. 1, which is a schematic flowchart a of a method for allocating resources of a crowdsourced task according to a first embodiment of the present invention, and as can be seen from the diagram, the method specifically includes the following steps:
s100: the method comprises the steps of obtaining first characteristics of tasks to be distributed, wherein the first characteristics are used for representing address information in the tasks to be distributed.
S200: inputting the first characteristic into a preset resource configuration model; the resource configuration model adopts a tree model, the resource configuration model comprises nodes and node values, the nodes are used for representing address information, and the node values are used for representing resource configuration parameters applicable to the address information.
S300: and finding out a target node matched with the first characteristic from the resource configuration 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 values of the target nodes.
The tasks to be distributed are uploaded or sent to the terminal equipment, and the uploaded or sent tasks to be distributed can include introduction contents such as address information, price information, difficulty information and the like of the tasks to be distributed. When receiving the task to be distributed, the terminal device can obtain a first characteristic of the task to be distributed from the corresponding introduction content, wherein the first characteristic is used for representing address information in the task to be distributed.
For example: the corresponding keyword can be set for the first feature in advance, and the terminal device can obtain the first feature by performing keyword retrieval on the introduction content. For example: the method comprises the steps of storing a to-be-distributed task list in an Excel file or other forms, uploading or sending the Excel file in which the to-be-distributed task list is stored to the terminal equipment, and extracting first characteristics of the country, province, city or county, street and the like corresponding to the to-be-distributed task list from the Excel file by the terminal equipment. In this embodiment, the terminal device includes but is not limited to: desktop computers, notebooks, palm computers, cloud servers, and other computing devices.
After the terminal device obtains the first feature of the task to be allocated, inputting the first feature into a preset resource configuration model, wherein the resource configuration model adopts a tree model and comprises nodes and node values, the nodes are used for representing address information, and the node values are used for representing resource configuration parameters applicable to the address information. And the resource configuration model matches each first characteristic with each node in a preset resource configuration model to find out a target node matched with the first characteristic, obtains a node value of the target node, and performs resource allocation on the task to be allocated according to a resource configuration parameter represented by the node value of the target node.
With reference to fig. 2, it is a schematic flow chart b of a first embodiment of the crowdsourced task resource allocation method of the present invention, specifically, the steps of constructing the resource configuration model are as follows:
s201, acquiring a second characteristic of the historical task, wherein the second characteristic is used for representing address information in the historical task.
And selecting historical tasks within a preset time range when the resource configuration model is constructed. The preset time range can be set to be all tasks which occur in a period of time before the current time. It should be noted that the preset time range requires that the market be relatively stable during this period. For example: before resource allocation is carried out on the tasks, the task price is unchanged within 4 months historically, and the preset time range can be selected from the current time range before 4 months; or after the task is allocated with the resources, the task is updated before 7 days in history, and the preset time range can be selected to be from 7 days to the present.
S202: and constructing nodes of the resource configuration model according to the second characteristics, wherein the hierarchical relationship of the nodes corresponds to the hierarchical relationship of the address information characterized in the second characteristics.
And constructing nodes of the preset resource configuration model according to the second characteristics. For example: the node construction is carried out by taking the Pudong new area of Shanghai City in China as a second characteristic, the depth of the tree is 3, the first-level root node is China, the second-level child node is Shanghai city, and the third-level leaf node is the Pudong new area. It is noted that the hierarchical relationship of the nodes corresponds to the hierarchical relationship of the address information characterized in the second feature. This is specifically noted. The nodes are constructed according to each second characteristic in the historical tasks within a preset time range, namely, according to the process of acquiring the task address by the terminal equipment, the dynamic spanning tree and the finally obtained resource configuration model, only the addresses appearing in the historical tasks are included.
In an exemplary embodiment, the tree model is built in a lazy manner as an example, and the specific operations are as follows:
the terminal equipment acquires all second characteristics d of the historical tasks and carries out tree addressing operation;
traversing all the addresses of each level in the second characteristics D from the root node D [0] for addressing;
a) suppose that the tree node D [ i ] corresponding to the i-level address D [ i ] has been found;
b) if no node corresponding to the i +1 level address D [ i +1] exists in the lower level tree node, creating a child node D [ i +1] as a corresponding node of D [ i +1] under the current node D [ i ];
c) and entering a node D [ i +1] to carry out next-stage address comparison.
And after addressing is finished, correspondingly storing each historical task in the node obtained by final addressing.
The tree is built in a lazy mode, calculation and storage work can be saved in a self-adaptive mode, and 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, and 12 towns, the lazy approach only needs to establish 31 nodes, and the complete tree contains: 1+34+283+2861+44821 is 48000 nodes.
Further, in order to avoid the risk that task data statistics is unstable and the like due to the fact that the number of tasks is scarce in some nodes in the resource configuration model, 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 corresponding superior nodes.
For some nodes with a scarce task quantity, if the node only has 1 or 2 task feedbacks, the phenomenon that data are abnormal may occur due to the influence of external uncertain factors such as excessive publicity or network, and the lower the statistical confidence is, the more wrong evaluation is likely to be caused by forcibly using the historical tasks of the node to perform statistical analysis, and the withdrawal risk is increased.
In an exemplary embodiment, because the statistics are not enough at the node level, the node is raised to the corresponding parent node in a 'leaf-cutting' manner, and the statistics are performed by means of the corresponding parent node data. And if the current corresponding father node still does not meet the statistical requirement, continuing to ascend to the previous level node until reaching the root node, and finally returning the statistical result to the area with scarce tasks for use. It should be noted that, because the same subtree belongs to the same category, the task resource configuration of each node in the subtree is similar, so when one of the nodes does not conform to the statistics, the node can be raised to the father node, and if the father node still has insufficient statistics, the node can be raised to the root node.
S203: and acquiring a third feature of the historical task based on the address information represented by the node, wherein the third feature is used for representing task information in the historical task.
The task information may include external influence factors such as task cost, distance, traffic, personnel intensity and the like, or may include direct influence factors such as task order-grabbing time difference and task order-grabbing balance value. The terminal equipment sends the task to the crowdsourcing platform, and the task is fed back to the terminal equipment after the user equipment succeeds in order receiving: task information such as task id, release time, order receiving time, completion time, order receivers, task cost, distance, traffic and personnel intensity is recorded and archived by the background server, and the terminal equipment can acquire the third characteristics of the tasks 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 influence factors such as task cost, distance, traffic, personnel concentration, and the like, and the task data is statistically analyzed to evaluate the tasks within the range to obtain the corresponding resource configuration parameters.
In an exemplary embodiment, the task data may further include direct influence factors such as a task order taking time difference and a task order taking balance value, and the tasks within the 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, all the manners of resource allocation for tasks may be suitable, and are not described herein.
Further, in the process of gradually adding tasks in the area with the scarce task quantity and forming area personalized resource configuration, setting a first resource configuration parameter generated according to the third characteristic as a node value to assign values to the nodes when the historical task quantity corresponding to the address information represented by the nodes is greater than or equal to a preset starting threshold value for stabilizing data so as to facilitate later personalized configuration.
When the task number of any node is larger than or equal to a preset creation threshold value, 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, the resource allocation is not carried out on a new task, and therefore a buffering stage is provided, and stable data are accumulated for initialization calculation.
When the number of tasks of any node is larger than or equal to a preset starting threshold value and reaches a certain number, the node is sufficient for independent statistics, and based on historical tasks in the node, a first resource configuration parameter with pertinence is obtained so as to perform personalized resource allocation on the node and meet personalized resource configuration of the node area and market requirements of the node area.
S205: and assigning the corresponding node in the resource configuration model by using the first resource configuration parameter as a node value.
It can be known that the resource configuration model in this embodiment includes a node and a node value, where the node is used to characterize the second feature, and the node value is used to characterize the first resource configuration parameter applicable to the second feature. In an exemplary embodiment, the resource configuration model may be updated according to a change of a historical task, and the update includes an update of a node and/or a value of the node.
And after the task to be allocated is allocated, the task 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 node value corresponding to the resource allocation parameter is updated.
In an exemplary embodiment, the following example is illustrated:
the preset creating threshold is 3, the preset starting threshold is 20, the resource configuration model is created with a Shanghai city node, the node value of the Shanghai city node is 3%, namely the resource configuration parameter of the Shanghai city is 3%.
(1) The whole Shanghai city uses 3% of resource allocation parameters for resource allocation. Then, in the next stage, all newly added tasks in the whole market range of the Shanghai city are allocated with 3 percent of resources (the price is reduced or increased by 3 percent on the original price) on the basis;
(2) with the continuous expansion of the market, a new task starts to be provided in a Pudong new area of Shanghai city, and the original 3 parts of the Pudong new area of Shanghai city are subjected to resource allocation according to 3 percent of resource allocation parameters of the Shanghai city;
(3) when the number of tasks in the whole range of the Pudong new area of Shanghai city reaches 3, the Pudong new area of Shanghai city meets the node creation condition, the resource configuration model creates an independent node of the Pudong new area of Shanghai city, and a new personalized resource configuration parameter is initialized aiming at the node of the Pudong new area of Shanghai city. At the moment, all newly added tasks in the whole region of the Pudong New region of Shanghai city are not subjected to resource allocation (original price is kept);
(4) after a period of time accumulation, when the number of tasks in the whole area range of the Pudong new area in Shanghai city reaches 20, the Pudong new area in Shanghai city meets the independent statistical condition, and the Pudong new area in Shanghai city obtains a new resource configuration parameter of 5% according to the historical 20 single task data. In the next stage, all newly added tasks in the whole range of the Pudong new area in Shanghai City are allocated according to the resource allocation parameter of 5 percent, and all newly added tasks in the whole range of the Pudong new area in Shanghai City except the Pudong new area in Shanghai City are allocated according to the resource allocation parameter of 3 percent.
Further, since there are 34 national provincial addresses, 283 urban addresses, 2861 district addresses and 44821 township addresses, if each resource allocation update needs to be performed on nearly 4 ten thousand and 8 thousand addresses one by one, the operation is obviously inefficient. However, if a cutting method is adopted to operate approximately 4 ten thousand and 8 thousand addresses in a unified manner, it is difficult to ensure that each node conforms to the real situation of the corresponding area.
In an exemplary embodiment, the present invention performs fast configuration on 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: and when the historical task quantity corresponding to the address information represented by the node is greater than or equal to a preset starting threshold value, the node forms an inclusion set, all lower-level 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 the node.
Here, the inclusion set is represented as a city to be resource allocated, and has a downward compatibility effect in a preset resource configuration model, that is, an upper level address includes all lower level addresses. If the containing set is a 'Shenzhen city', is a market level address, and the name of the corresponding node is 'Shenzhen city', the region is correspondingly subordinate addresses such as all region level addresses and county level addresses in the 'Shenzhen city', etc.
For example:
(1) the first stage is as follows: when the number of tasks of the Shenzhen market is greater than or equal to a preset starting threshold value, the Shenzhen market can generate a personalized resource configuration parameter a according to the task information of the historical tasks in the whole market range, and the resource configuration parameter a is adopted in the whole market range of the Shenzhen market for resource allocation.
Meanwhile, the corresponding node of the Shenzhen city automatically forms a containing set of { "Shenzhen city" }, and the containing set { "Shenzhen city" } contains all subordinate addresses such as the region level address and the county level address in the "Shenzhen city".
Because the number of tasks in Shenzhen City is greater than or equal to the preset launch threshold, it can be known that the number of tasks in Guangdong province is also greater than or equal to the preset launch threshold, and the Shenzhen City is located in the inclusion set in Guangdong province.
(2) And a second stage: because the Guangdong province has sudden epidemic situation, the resource reduction and adjustment need to be carried out on the Guangdong province, so as to reduce the task cost, the resource configuration parameter b is generated according to the task information of the historical tasks in the range including the set { 'Guangdong province' } in the whole province, and the resource configuration parameter b is adopted for carrying out resource allocation on the Guangdong province.
The second scheme is as follows: on the basis of the first scheme, the method further comprises the following steps:
the nodes form a exclusion set, and subordinate nodes meeting preset conditions are correspondingly stored in the exclusion set of the nodes without resource allocation; wherein the preset conditions include:
the historical task number corresponding to the address information represented by the subordinate node is greater than or equal to a preset starting threshold; and
and the lower node is in a preset blacklist list.
Here, the exclusive set is expressed as a city where no resource allocation is performed, and the exclusive set has a downward compatibility effect in a preset resource configuration model, that is, an upper level address includes all lower level addresses. If the containing set is 'Shenzhen city', and the excluding set is 'Nanshan region', the region corresponds to all subordinate addresses such as the regional address, the county address and the like except the regional address of the 'Nanshan region' in the 'Shenzhen city' local level address.
The blacklist is expressed as a region where the node corresponding region cannot adjust resources due to the influence of other external factors such as local policy.
For example:
(1) the first stage is as follows: when the number of the tasks of the Guangdong province is larger than or equal to a preset starting threshold value, the Guangdong province can generate personalized resource configuration parameters a according to the task information of the historical tasks in the whole province scope, and resource allocation is carried out by adopting the resource configuration parameters a in the whole province scope of the Guangdong province.
And the corresponding node of Guangdong province automatically forms an inclusion set of { "Guangdong province" } and an exclusion set of {0}, wherein the inclusion set of { "Guangdong province" } includes all subordinate addresses such as regional addresses and village and town addresses in the province address of Guangdong province.
(2) And a second stage: when the number of tasks of the 'Shenzhen market' is larger than or equal to the preset starting threshold value, a containing set of { 'Shenzhen market' } and an exclusive set of {0} are formed in the same way. However, because the 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 superior node, Guangdong province.
The corresponding set of "Guangdong province" is: the method comprises a set { "Guangdong province" } and an exclusive set { "Shenzhen City" }, and resource allocation is not carried out in the Shenzhen City.
Further, when the resource allocation is performed on the task to be allocated, the exclusive set corresponding to the upper node corresponding to the target node corresponding to the task to be allocated is obtained first. When the target node is not located in the exclusive set, acquiring a node value of the target node, and performing resource allocation on the task to be allocated according to a resource configuration parameter represented by the node value of the target node; when the target node is in the exclusive set, no resource allocation is performed.
Example two
Fig. 3 is a functional block diagram of a crowdsourced task resource allocation device according to a second embodiment of the crowdsourced task resource allocation method of the invention.
The device comprises a feature acquisition module 31, a parameter matching module 32 and a resource allocation module 33. The module referred to herein is a series of computer program segments stored in a memory that can be executed by a processor and that can perform a fixed function. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.
The feature obtaining module 31 is configured to obtain a first feature of a task to be allocated, where the first feature is used to represent 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 introduction contents such as 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 content.
The parameter matching module 32 is configured to input the first feature into a preset resource configuration model; the resource configuration model adopts a tree model, the resource configuration model comprises nodes and node values, the nodes are used for representing address information, and the node values are used for representing resource configuration parameters applicable to the address information;
and finding out a target node matched with the first characteristic from the resource configuration model, and acquiring a node value of the target node.
In an exemplary embodiment, after acquiring the first feature of the task to be allocated, the terminal device may input the first feature into a preset resource configuration model, where the resource configuration model includes nodes and node values, the nodes are used to represent address information, the node values are used to represent 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 perform resource allocation on the task to be allocated according to the resource configuration parameter represented by the 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 the exemplary embodiment, electronic device 4 includes, but is not limited to, a memory 41, a processor 42, and a computer program, such as a crowd-sourced task resource allocation program, stored in memory 41 and executable on the processor. It will be appreciated by those skilled in the art that the schematic diagrams are merely examples of an electronic device and do not constitute a limitation of an electronic device, and may include more or fewer components than those shown, or some components in combination, or different components, for example, the electronic device may also include input output devices, network access devices, buses, etc.
The memory 41 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the 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 the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device. Of course, the memory 41 may also include both internal and external memory modules of the electronic device. In this embodiment, the memory 41 is generally used for storing an operating system and various types of application software installed in the electronic device. Further, 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 Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The processor 42 is an operation core and a control center of the electronic device, and is connected to each part of the whole electronic device by various interfaces and lines, and executes an operating system of the electronic device and various installed application programs, program codes, and the like.
The processor 42 executes the operating system of the electronic device as well as various applications installed. The processor 42 executes the application program to implement the steps in the above-mentioned embodiments of the crowdsourced task resource allocation method, such as the steps S100 and S200 shown in fig. 1.
Example four
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 mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer readable storage medium of the embodiment is used for storing a computer program for implementing the resource allocation method, and when executed by the processor 42, implements the resource allocation method of the crowdsourcing task of the embodiment one or two or three.
Claims (10)
1. A method for allocating resources of a crowdsourcing task, the method comprising:
acquiring first characteristics of a task to be distributed, wherein the first characteristics are used for representing address information in the task to be distributed;
inputting the first characteristic into a preset resource configuration model; the resource configuration model adopts a tree model, the resource configuration model comprises nodes and node values, the nodes are used for representing address information, and the node values are used for representing resource configuration parameters applicable to the address information;
searching a target node matched with the first characteristic from the resource configuration 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 values of the target nodes.
2. The method of claim 1, wherein the resource allocation model is constructed by:
acquiring a second characteristic of a historical task, wherein the second characteristic is used for representing address information in the historical task;
constructing nodes of the resource configuration model according to the second characteristics, wherein the hierarchical relationship of the nodes corresponds to the hierarchical relationship of the address information characterized in the second characteristics;
acquiring a third feature of the historical task based on the address information represented by the node, wherein the third feature is 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 node in the resource configuration model by using the first resource configuration parameter as a node value.
3. The method of claim 2, wherein after the building the node of the resource configuration model according to the second feature, the method further comprises:
and when the historical task quantity corresponding to the address information represented by the node is smaller than a preset creation threshold value, returning and reserving the node to a corresponding superior node.
4. The method of claim 3, wherein prior to the generating the first resource configuration parameter corresponding to the node according to the third feature, further comprising:
and when the historical task quantity corresponding to the address information represented by the node is greater than or equal to a preset starting threshold value, assigning a value to the node by using a first resource configuration parameter generated according to the third characteristic as a node value.
5. The method of claim 4, wherein the method further comprises:
and updating the resource configuration model according to the change of the historical task, wherein the updating comprises the updating of the node and/or the node value.
6. The method for allocating crowdsourcing task resources according to claim 3, wherein when the historical task number corresponding to the address information represented by the node is greater than or equal to a preset starting threshold, the method further comprises:
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; and/or the presence of a gas in the gas,
the nodes form a exclusion set, and subordinate nodes meeting preset conditions are correspondingly stored in the exclusion set of the nodes without resource allocation; wherein the preset conditions include:
the historical task number corresponding to the address information represented by the subordinate node is greater than or equal to a preset starting threshold; and
and the lower node is in a preset blacklist list.
7. The method of claim 6, wherein before the resource allocation of the task to be allocated according to the resource configuration parameter characterized by the node value of the target node, the method further comprises:
acquiring an exclusion set corresponding to a superior node corresponding to the target node;
when the target node is not located in the exclusive set, acquiring a node value of the target node, and performing resource allocation on the task to be allocated according to a resource configuration parameter represented by the node value of the target node;
when the target node is in the exclusive set, no resource allocation is performed.
8. A crowdsourced task resource allocation apparatus, comprising:
the system comprises a characteristic acquisition module, a task processing module and a task processing module, wherein the characteristic acquisition module is used for acquiring first characteristics of a task to be allocated, and the first characteristics are used for representing address information in the task to be allocated;
the parameter matching module is used for inputting the first characteristics to a preset resource configuration model; the resource configuration model adopts a tree model, the resource configuration model comprises nodes and node values, the nodes are used for representing address information, and the node values are used for representing resource configuration parameters applicable to the address information;
searching a target node matched with the first characteristic from the resource configuration model, and acquiring a node value of the target node;
and the resource allocation module is used for allocating resources to the task to be allocated according to the resource configuration parameters represented by the node values of the target nodes.
9. An electronic device, characterized in that the electronic device comprises:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the method of crowdsourced task resource allocation of any one of claims 1 to 7.
10. A computer-readable storage medium having at least one instruction stored therein, the at least one instruction being executable by a processor in an electronic device to implement the method of crowd-sourced task resource allocation as recited in any one of claims 1 to 7.
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| CN113342509A (en) * | 2021-08-03 | 2021-09-03 | 北京达佳互联信息技术有限公司 | Data processing method and device, electronic equipment and storage medium |
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