Material task allocation method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a material task allocation method and device, equipment and a storage medium, and the method comprises the steps: obtaining a material task, and matching a plurality of candidate execution objects from a set execution object set according to the creation type and content information of the material task; querying at least one distributed task currently processed by each candidate execution object, and calculating a load state coefficient based on the current completion progress and the execution level corresponding to the at least one distributed task; calculating a task quality coefficient according to the modification frequency and the quality score of each historical task completed by each candidate execution object; and performing fusion calculation based on the execution level, the load state coefficient and the task quality coefficient of the material task to obtain a matching score of each candidate execution object, and allocating the material task to the candidate execution object with the highest matching score. According to the scheme, the material task can be accurately matched with the execution object, and the task completion quality is guaranteed while the task processing efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, device, and storage medium for allocating material tasks. Background Technology

[0002] With the continuous development of internet businesses, project promotion needs are becoming increasingly important. These needs often require various types of materials, such as posters, videos, and copywriting. High-quality materials can better attract audience attention, convey core information, enhance the dissemination and influence of related businesses, and ensure the quality of publicity. Due to the increasing demand for material production and the limited number of people to whom materials can be produced, it is necessary to allocate material production tasks rationally to ensure the smooth operation of promotional activities.

[0003] In related technologies, material task allocation often adopts a simple sequential or random allocation method, which easily leads to poor compatibility between material tasks and execution objects, resulting in low task processing efficiency, unstable task completion quality, and inability to meet the increasingly diverse needs of massive task processing. Summary of the Invention

[0004] This application provides a material task allocation method, apparatus, device, and storage medium, which solves the problem in related technologies that the adaptability between material tasks and execution objects is poor, resulting in low task processing efficiency, unstable task completion quality, and inability to meet the increasingly diverse needs of massive task processing. It can make material task allocation decisions by comprehensively considering the load status of the execution object and the task completion quality, accurately matching material tasks with execution objects, improving task processing efficiency while ensuring task completion quality, and meeting the increasingly diverse needs of massive task processing.

[0005] In a first aspect, embodiments of this application provide a material task allocation method, the method comprising: Obtain material tasks, and match multiple candidate execution objects from the set execution object set according to the creation type and content information of the material tasks; Query at least one assigned task currently being processed by each of the candidate execution objects, and calculate the load state coefficient based on the current completion progress and execution level corresponding to each of the at least one assigned task; The task quality coefficient is calculated based on the number of modifications and quality scores of each historical task completed by each candidate execution object. The matching score for each candidate execution object is calculated by fusing the execution level of the material task, the load state coefficient, and the task quality coefficient, and the material task is assigned to the candidate execution object with the highest matching score.

[0006] Secondly, embodiments of this application also provide a material task allocation device, including: The candidate object determination module is configured to acquire material tasks and match multiple candidate execution objects from a set of execution objects based on the creation type and content information of the material tasks. The coefficient determination module is configured to query at least one assigned task currently being processed by each of the candidate execution objects, calculate the load status coefficient based on the current completion progress and execution level corresponding to the at least one assigned task, and calculate the task quality coefficient based on the number of modifications and quality scores of each historical task completed by each of the candidate execution objects. The object determination module is configured to perform a fusion calculation based on the execution level of the material task, the load state coefficient, and the task quality coefficient to obtain a matching score for each candidate execution object, and to assign the material task to the candidate execution object with the highest matching score.

[0007] Thirdly, embodiments of this application also provide a material task allocation device, the device comprising: One or more processors; Storage device, configured to store one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the material task allocation method described in the embodiments of this application.

[0008] Fourthly, embodiments of this application also provide a non-volatile storage medium for storing computer-executable instructions, which, when executed by a computer processor, are configured to perform the material task allocation method described in embodiments of this application.

[0009] In this embodiment, multiple candidate execution objects are matched from a set of execution objects based on the creation type and content information of the material task. Execution objects lacking processing capabilities can be filtered out based on the specific attributes of the material task, retaining those capable of executing the material task. A load status coefficient is calculated based on the current completion progress and execution level of at least one assigned task, which can quantitatively assess the current real-time workload of the candidate execution objects. A task quality coefficient is calculated based on the number of modifications and quality scores of each candidate execution object's completed historical tasks, which can quantitatively assess the past work quality of the candidate execution objects. A matching score for each candidate execution object is obtained by fusing the execution level, load status coefficient, and task quality coefficient of the material task, which can accurately quantify the comprehensive matching degree of the candidate execution object with the current material task, facilitating the decision of the most suitable execution object. This solution comprehensively considers the load status and task completion quality of the execution objects to make material task allocation decisions, accurately matching material tasks with execution objects, improving task processing efficiency while ensuring task completion quality, and meeting the increasingly diverse needs of handling massive tasks. Attached Figure Description

[0010] Figure 1 A flowchart illustrating a material task allocation method provided in an embodiment of this application; Figure 2 A schematic diagram illustrating the object selection process of a material task allocation method provided in this application embodiment; Figure 3 A flowchart illustrating a material task allocation method that includes a process for calculating load state coefficients, provided as an embodiment of this application; Figure 4 A flowchart illustrating a material task allocation method that includes a process for calculating task quality coefficients, provided for embodiments of this application; Figure 5 A flowchart of a material task allocation method, including a process of calculating a matching score, is provided for an embodiment of this application; Figure 6 A flowchart of a material task allocation method, including a process for determining multiple candidate execution objects, is provided for an embodiment of this application. Figure 7 A flowchart of a material task allocation method, including a process of reallocating material tasks, is provided for embodiments of this application; Figure 8 A structural block diagram of a material task allocation device provided in an embodiment of this application; Figure 9 This is a schematic diagram of a material task allocation device provided in an embodiment of this application. Detailed Implementation

[0011] The embodiments of this application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of this application and are not intended to limit the scope of the embodiments. Furthermore, it should be noted that, for ease of description, only the parts relevant to the embodiments of this application are shown in the accompanying drawings, not the entire structure.

[0012] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0013] The material task allocation method provided in this application embodiment can be executed by a computer device. The computer device refers to any electronic device with data computing, processing and storage capabilities, such as a server. This application embodiment does not limit this.

[0014] Figure 1 A flowchart of a material task allocation method provided in an embodiment of this application is shown below. Figure 1 As shown, the material task allocation method specifically includes the following steps: Step S101: Obtain material tasks and match multiple candidate execution objects from the set execution object set according to the creation type and content information of the material tasks.

[0015] The material task can be a pre-created specific creative task to be completed by an execution object. Taking game promotion as an example, the material task could be designing product promotional posters, editing event videos, etc. The creation type can be a specific creative form or field of the task, such as text creation, graphic design, video production, or audio processing. The content information can be a text description containing specific requirements, themes, styles, etc., corresponding to the material task. The execution object set can be a collection of execution objects currently capable of creating material. These execution objects can be designers, design teams, etc., which are not limited in this application. Since each execution object has its own preferred creation type and skill preferences, preliminary screening can be performed based on the creation type and content information of the material task to match candidate execution objects capable of handling the material task. In one embodiment, each execution object can be set with supported creation type and skill preference information. First, execution objects with matching creation types are selected from the execution object set as a first object set. Then, keywords are extracted from the content information, and execution objects whose skill preference information contains the keywords are selected from the first object set as candidate execution objects. In one embodiment, each execution object can be set with supported creation types and skill preference tags. First, execution objects with matching creation types are selected from the set of execution objects as the first object set. Then, content information is identified and content tags are generated. Finally, multiple candidate execution objects with matching values ​​between skill preference tags and content tags that are higher than a threshold are selected from the first object set.

[0016] Optionally, the following processes may also be included before acquiring the material task: Obtain the projects to be executed, parse the projects to obtain the material description information and material distribution channels; query the task flow templates corresponding to the material distribution channels, and integrate the material description information and task flow templates to obtain the material tasks.

[0017] The projects to be executed can be business projects configured and submitted by business personnel in the system. The material description information can describe the specific content and requirements of the task. The material distribution channel can be the target platform or channel through which the project deliverables will ultimately be published, displayed, or delivered, such as a WeChat official account or web platform. The task workflow template can be a predefined standardized task execution framework; for example, it can include necessary steps, step requirements, delivery specifications, and review rules. Different material distribution channels can be pre-bound to corresponding task workflow templates. Filling in the material description information into the task workflow template generates structured material tasks, facilitating subsequent management of material tasks.

[0018] Step S102: Query at least one assigned task currently being processed by each candidate execution object, and calculate the load state coefficient based on the current completion progress and execution level corresponding to each of the at least one assigned task.

[0019] The current completion progress can be the percentage of the assigned tasks that have been completed. The execution level can be a quantitative indicator of the priority of the assigned tasks; the higher the execution level, the more important and prioritized the task. In one embodiment, a preset coefficient corresponding to the execution level of each assigned task can be queried, where the higher the execution level, the larger the preset coefficient. The preset coefficient corresponding to each assigned task is divided by its corresponding current completion progress to obtain the task load coefficient, and the task load coefficients of all assigned tasks are added together to obtain the load status coefficient. In another embodiment, the estimated remaining time can be calculated based on the current completion progress and elapsed time of each assigned task, and the urgency weight corresponding to its execution level can be queried. The estimated remaining time of each assigned task is then weighted according to the corresponding urgency weight to obtain the load status coefficient. This load status coefficient can quantify the current workload pressure of the candidate execution objects.

[0020] Step S103: Calculate the task quality coefficient based on the number of modifications and quality scores of each historical task completed by each candidate execution object.

[0021] In this context, "historical tasks" can refer to previously completed material tasks of the candidate execution object. "Number of modifications" can be the cumulative number of times adjustments were made based on feedback during the execution of the material task. In one embodiment, the number of modifications and quality scores of each completed historical task for each candidate execution object can be normalized to obtain normalized modification counts and normalized quality scores. The normalized quality score for each completed historical task of each candidate execution object is divided by the normalized number of modifications to obtain a task quality value. The task quality values ​​for each completed historical task of each candidate execution object are then summed to obtain a task quality coefficient. In another embodiment, the number of modifications for each completed historical task of each candidate execution object can be statistically analyzed to obtain an average number of modifications, and the quality scores for each completed historical task can be statistically analyzed to obtain an average quality score. The average number of modifications and the average quality score for each candidate execution object are then weighted to obtain a task quality coefficient. This task quality coefficient is used to quantify the past work quality of the candidate execution object.

[0022] Step S104: Based on the execution level, load status coefficient and task quality coefficient of the material task, perform a fusion calculation to obtain the matching score of each candidate execution object, and assign the material task to the candidate execution object with the highest matching score.

[0023] The matching score represents the degree of matching between the candidate execution object and the material task; the higher the matching score, the higher the degree of matching. In one embodiment, a preset ratio coefficient corresponding to the execution level of the material task can be queried, where a higher execution level corresponds to a smaller preset ratio coefficient. This preset coefficient is multiplied by the task quality coefficient to obtain the target task quality coefficient, and then divided by the load state coefficient to obtain the matching score. Alternatively, in another embodiment, a preset ratio coefficient corresponding to the execution level of the material task can be queried, where a higher execution level corresponds to a smaller preset ratio coefficient. This preset coefficient is multiplied by the task quality coefficient to obtain the target task quality coefficient, and the load state coefficient is substituted into a preset attenuation function to obtain the target load state coefficient. Finally, a weighted average of the target task quality coefficient and the target load state coefficient is calculated to obtain the matching score.

[0024] Figure 2 This is a schematic diagram illustrating the object selection process of a material task allocation method provided in an embodiment of this application, as shown below. Figure 2 As shown, the execution object set 201 includes multiple selectable execution objects. Based on the creation type and content information of the material task, multiple candidate execution objects 202 can be matched from the execution object set 201. Then, based on the load state coefficient and task quality coefficient calculated for each candidate execution object 202, the matching score of each candidate execution object 202 can be determined. For example, the matching scores of the three candidate execution objects shown in the figure are A1, A2, and A3, where A2 > A1 > A3. Finally, the material task 203 is assigned to the candidate execution object with the highest matching score, i.e., the candidate execution object with a matching score of A2.

[0025] The above-described method matches multiple candidate execution objects from a set of execution objects based on the creation type and content information of the material task. It filters out execution objects lacking processing capabilities based on the specific attributes of the material task, retaining those capable of executing the task. A load status coefficient is calculated based on the current completion progress and execution level of at least one assigned task, quantifying the current real-time workload of the candidate execution objects. A task quality coefficient is calculated based on the number of modifications and quality scores of each candidate execution object's completed historical tasks, quantifying the past work quality of the candidate execution objects. Finally, a matching score is calculated by fusing the execution level, load status coefficient, and task quality coefficient of the material task, accurately quantifying the comprehensive matching degree of the candidate execution object with the current material task, thus facilitating the decision of the most suitable execution object. This solution comprehensively considers the load status and task completion quality of the execution objects to make material task allocation decisions, accurately matching material tasks with execution objects, improving task processing efficiency while ensuring task completion quality, and meeting the increasingly diverse needs of handling massive tasks.

[0026] Figure 3 A flowchart of a material task allocation method, including a process for calculating load state coefficients, is provided for embodiments of this application. Figure 3 As shown, the material task allocation method specifically includes the following steps: Step S301: Obtain material tasks and match multiple candidate execution objects from the set execution object set according to the creation type and content information of the material tasks.

[0027] Step S302: Query the multiple assigned tasks currently being processed by each candidate execution object, and calculate the estimated remaining time based on the current completion progress and time consumed for each assigned task; query the urgency weight corresponding to the execution level of each assigned task, and calculate the task load coefficient based on the urgency weight and the estimated remaining time; integrate the task load coefficients corresponding to multiple assigned tasks to obtain the load status coefficient.

[0028] The elapsed time can be the actual time spent on an assigned task from the start of execution to the present. The specific formula for calculating the estimated remaining time is as follows: (1 Current progress)

[0029] The preset remaining time reflects how much longer the assigned task will need to occupy the candidate execution object. The execution level can be a quantitative indicator of the assigned task's priority, such as high, medium, or low, reflecting the urgency of the task's resource requirements. The urgency weight corresponding to the execution level can be obtained by querying a database or stored mapping relationship; the higher the execution level, the greater the urgency weight. The task load coefficient is obtained by multiplying the urgency weight by the estimated remaining time. It should be noted that even with the same estimated remaining time, a higher urgency weight indicates a higher task load. In one embodiment, the load status coefficient is obtained by summing the task load coefficients corresponding to all assigned tasks, reflecting the overall busyness or pressure on the candidate execution object. In another embodiment, the weight of each task load coefficient can be assigned based on the creation time of the assigned task; the earlier the creation time, the greater the weight. The load status coefficient is then calculated by weighting the load coefficients of each task.

[0030] Step S303: Calculate the task quality coefficient based on the number of modifications and quality scores of each historical task completed by each candidate execution object.

[0031] Step S304: Based on the execution level, load status coefficient and task quality coefficient of the material task, perform a fusion calculation to obtain the matching score of each candidate execution object, and assign the material task to the candidate execution object with the highest matching score.

[0032] The above-mentioned calculation of the estimated remaining time of each assigned task and weighting its urgency, followed by integration to obtain the load status coefficient of the candidate execution object, can provide an accurate load assessment index, quantify the task carrying capacity of the candidate execution object, and facilitate subsequent decision-making on the appropriate execution object.

[0033] Figure 4 A flowchart of a material task allocation method, including a process for calculating task quality coefficients, is provided for embodiments of this application. Figure 4 As shown, the material task allocation method specifically includes the following steps: Step S401: Obtain material tasks and match multiple candidate execution objects from the set execution object set according to the creation type and content information of the material tasks.

[0034] Step S402: Query at least one assigned task currently being processed by each candidate execution object, and calculate the load state coefficient based on the current completion progress and execution level corresponding to each of the at least one assigned task.

[0035] Step S403: Calculate the average number of modifications for each historical task completed by each candidate execution object; calculate the average quality score for each historical task completed by each candidate execution object; and integrate the average number of modifications and the average quality score to obtain the task quality coefficient.

[0036] The number of modifications refers to the number of times the output needs to be corrected, iterated, or adjusted during the completion of a historical task. More modifications indicate more problems during execution. The average number of modifications represents the average rework level of the candidate task, specifically obtained by summing the number of modifications for each historical task and dividing by the total number of tasks. The quality score is a quantitative score given after evaluating the final output or execution process of a completed historical task. A higher quality score indicates better task completion quality. The average quality score represents the average quality level of the candidate task, specifically obtained by summing the quality scores for each historical task and dividing by the total number of tasks. In one embodiment, the average number of modifications and the average quality score can be normalized to obtain normalized modification counts and normalized quality scores. The specific formula for calculating the task quality coefficient is as follows:

[0037] Where k is a preset constant.

[0038] In one embodiment, the specific formula for calculating the task quality coefficient is as follows:

[0039] in, and is the preset weighting coefficient, and k is a preset constant.

[0040] Step S404: Based on the execution level, load status coefficient and task quality coefficient of the material task, perform a fusion calculation to obtain the matching score of each candidate execution object, and assign the material task to the candidate execution object with the highest matching score.

[0041] The above-mentioned method, by statistically analyzing the average number of modifications and average quality scores of historical tasks and integrating them to calculate the task quality coefficient, can accurately quantify the task execution quality level of candidate execution objects, which is beneficial for subsequent decision-making on suitable execution objects.

[0042] Figure 5 A flowchart of a material task allocation method, including a process for calculating a matching score, is provided for an embodiment of this application, as shown below. Figure 5 As shown, the material task allocation method specifically includes the following steps: Step S501: Obtain material tasks and match multiple candidate execution objects from the set execution object set according to the creation type and content information of the material tasks.

[0043] Step S502: Query at least one assigned task currently being processed by each candidate execution object, and calculate the load state coefficient based on the current completion progress and execution level corresponding to each of the at least one assigned task.

[0044] Step S503: Calculate the task quality coefficient based on the number of modifications and quality scores of each historical task completed by each candidate execution object.

[0045] Step S504: Calculate the third calculation result based on the load state coefficient and the set attenuation function; convert the execution level of the material task into an adjustment factor according to the preset mapping relationship, and calculate the fourth calculation result based on the task quality coefficient and the adjustment factor; integrate the third calculation result and the fourth calculation result to obtain the matching score.

[0046] The attenuation function can be a linear attenuation function, an exponential attenuation function, etc. For example, the specific formula for calculating the third result is as follows:

[0047] For example, the specific formula for calculating the third result is as follows:

[0048] The adjustment factor can be a weighted coefficient obtained by converting the execution level of the material task through a preset mapping relationship. It can adjust the influence of the task quality coefficient on the matching score. The larger the adjustment factor, the higher the historical quality requirements of the task for the object being executed. A fourth calculation result can be obtained by multiplying the task quality coefficient and the adjustment factor. In one embodiment, the third calculation result and the fourth calculation result can be added together to obtain the matching score. In another embodiment, the third calculation result and the fourth calculation result can be weighted to obtain the matching score.

[0049] Step S505: Assign the material task to the candidate execution object with the highest matching score.

[0050] As described above, by integrating the load status coefficient and the task quality coefficient to obtain the matching score, the task load capacity and historical quality reliability of the candidate execution object can be balanced, and the degree of matching between the candidate execution object and the material task can be accurately quantified.

[0051] Figure 6 A flowchart of a material task allocation method, including a process for determining multiple candidate execution objects, is provided for an embodiment of this application. Figure 6 As shown, the material task allocation method specifically includes the following steps: Step S601: Obtain material tasks, select multiple first execution objects from the set execution object set that match the creation type of the material tasks; perform keyword recognition on the content information and generate text tags, calculate the similarity between the skill preference tags and text tags of each first execution object to obtain the matching degree value, and select multiple candidate execution objects from multiple first execution objects whose matching degree value is higher than the preset threshold.

[0052] The first execution object can be an execution object initially selected from the set of execution objects that is capable of handling the material task, such as text creation, video production, or audio processing. Keyword recognition can be performed using natural language processing or preset template matching to extract keywords representing the task theme, requirements, or style. The text label can be obtained by combining the identified keywords. The skill preference label can be a pre-set label representing the skills, expertise, or interests of the first execution object. For example, for the first execution object of "image generation type," its skill preference label could be ["character creation," "anime style," etc.]. The degree of matching between the skill preference label and the text label can be evaluated by performing similarity calculations. Specifically, the skill preference label and the text label can be converted into vector representations respectively, and the cosine similarity or Euclidean distance between the vectors can be calculated to obtain a matching degree value. If the matching degree value is higher than a preset threshold, the execution object can be considered to have the ability to handle the material task.

[0053] Step S602: Query at least one assigned task currently being processed by each candidate execution object, and calculate the load state coefficient based on the current completion progress and execution level corresponding to each of the at least one assigned task.

[0054] Step S603: Calculate the task quality coefficient based on the number of modifications and quality scores of each historical task completed by each candidate execution object.

[0055] Step S604: Based on the execution level, load status coefficient and task quality coefficient of the material task, perform a fusion calculation to obtain the matching score of each candidate execution object, and assign the material task to the candidate execution object with the highest matching score.

[0056] As described above, the first execution target with basic task processing ability can be screened based on the creation type. Then, by matching similarity based on content tags and skill preference tags, candidate execution targets with relevant abilities can be further screened, narrowing the candidate range and improving the efficiency of determining the optimal candidate execution target in the subsequent process.

[0057] Figure 7 A flowchart of a material task allocation method, including a process of reallocating material tasks, is provided for an embodiment of this application, as shown below. Figure 7 As shown, the material task allocation method specifically includes the following steps: Step S701: Obtain material tasks and match multiple candidate execution objects from the set execution object set according to the creation type and content information of the material tasks.

[0058] Step S702: Query at least one assigned task currently being processed by each candidate execution object, and calculate the load state coefficient based on the current completion progress and execution level corresponding to each of the at least one assigned task.

[0059] Step S703: Calculate the task quality coefficient based on the number of modifications and quality scores of each historical task completed by each candidate execution object.

[0060] Step S704: Based on the execution level, load status coefficient and task quality coefficient of the material task, perform a fusion calculation to obtain the matching score of each candidate execution object, and assign the material task to the candidate execution object with the highest matching score.

[0061] Step S705: If the candidate execution object with the highest matching score has not confirmed receiving the material task, the waiting time is obtained by subtracting the current system time from the material task allocation time; if the waiting time exceeds the preset time threshold, the material task is reassigned to another candidate execution object with the highest matching score.

[0062] Since the candidate execution object with the highest matching score may fail to confirm the receipt of the task in a timely manner due to system reasons or human factors, this embodiment can actively monitor the allocation status. The waiting time represents the elapsed period during which the candidate execution object with the highest matching score has not yet confirmed the receipt of the task. If the waiting time exceeds a preset time threshold, it can be considered that the material task cannot be normally confirmed and needs to be reassigned to another candidate execution object with the highest matching score.

[0063] The above-mentioned method can effectively solve the problem of unresponsive execution objects after task allocation by monitoring whether the assigned material tasks are confirmed to be received within a preset time threshold, ensuring that material tasks can be started and executed within a reasonable time, and guaranteeing the timeliness and reliability of task processing.

[0064] Figure 8 This is a structural block diagram of a material task allocation device provided in an embodiment of this application. The device is configured to execute the material task allocation method provided in the above embodiment, and has corresponding functional modules and beneficial effects for executing the method. For example... Figure 8 As shown, the device specifically includes: The candidate object determination module 801 is configured to obtain material tasks and match multiple candidate execution objects from the set execution object set according to the creation type and content information of the material tasks. The coefficient determination module 802 is configured to query at least one assigned task currently being processed by each candidate execution object, calculate the load status coefficient based on the current completion progress and execution level corresponding to the at least one assigned task, and calculate the task quality coefficient based on the number of modifications and quality scores of each historical task completed by each candidate execution object. The object determination module 803 is configured to perform a fusion calculation based on the execution level, load status coefficient and task quality coefficient of the material task to obtain the matching score of each candidate execution object, and assign the material task to the candidate execution object with the highest matching score.

[0065] In one possible embodiment, the coefficient determination module 802 is further configured to: Calculate the estimated remaining time based on the current progress and time already consumed for each assigned task; Query the urgency weight corresponding to the execution level of each assigned task, and calculate the task load coefficient based on the urgency weight and the estimated remaining time. The load status coefficient is obtained by integrating the task load coefficients corresponding to multiple assigned tasks.

[0066] In one possible embodiment, the coefficient determination module 802 is further configured to: The average number of modifications is obtained by statistically analyzing the number of modifications made to each historical task completed by each candidate execution object. The average quality score is obtained by statistically analyzing the quality scores of each historical task completed by each candidate execution object. The task quality coefficient is obtained by integrating the average number of revisions with the average quality score.

[0067] In one possible embodiment, the object determination module 803 is further configured to: The third calculation result is obtained based on the load state coefficient and the set attenuation function; The execution level of the material task is converted into an adjustment factor according to a preset mapping relationship, and the fourth calculation result is obtained based on the task quality coefficient and the adjustment factor. The matching score is obtained by integrating the third and fourth calculation results.

[0068] In one possible embodiment, the candidate object determination module 801 is configured as follows: Select multiple first execution objects from the set of execution objects that match the creation type of the material task; Keyword recognition is performed on the content information and text tags are generated. The similarity calculation of the skill preference tags of each first execution object with the text tags is used to obtain the matching degree value. Then, multiple candidate execution objects with matching degree values ​​higher than a preset threshold are selected from multiple first execution objects.

[0069] In one possible embodiment, a material task determination module is also included, configured as follows: Obtain the projects to be executed, and parse the projects to obtain material description information and material distribution channels; Search for the task flow template corresponding to the material distribution channel, and integrate the material description information and task flow template to obtain the material task.

[0070] In one possible embodiment, a task reassignment module is also included, configured as follows: If the candidate execution object with the highest matching score has not confirmed receiving the material task, the waiting time is obtained by subtracting the current system time from the material task allocation time. If the waiting time exceeds the preset time threshold, the material task will be reassigned to the other candidate execution object with the highest matching score.

[0071] Figure 9 This is a schematic diagram of the structure of a material task allocation device provided in an embodiment of this application, such as... Figure 9 As shown, the device includes a processor 901, a memory 902, an input device 903, and an output device 904; the number of processors 901 in the device can be one or more. Figure 9 Taking a processor 901 as an example; the processor 901, memory 902, input device 903, and output device 904 in the device can be connected via a bus or other means. Figure 9 Taking a bus connection as an example, the memory 902, as a computer-readable storage medium, can be configured to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the material task allocation method in this embodiment. The processor 901 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 902, thereby implementing the aforementioned material task allocation method. The input device 903 can be configured to receive input digital or character information and generate key signal inputs related to user settings and function control of the device. The output device 904 may include a display screen or other display device.

[0072] The material task allocation device provided above can be used to execute the material task allocation method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0073] This application also provides a non-volatile storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are configured to perform a material task allocation method described in the above embodiments. The method includes: acquiring material tasks; matching multiple candidate execution objects from a set of execution objects based on the creation type and content information of the material tasks; querying at least one assigned task currently being processed by each candidate execution object, and calculating a load status coefficient based on the current completion progress and execution level corresponding to each of the at least one assigned task; calculating a task quality coefficient based on the number of modifications and quality scores of each historical task completed by each candidate execution object; performing a fusion calculation based on the execution level, load status coefficient, and task quality coefficient of the material tasks to obtain a matching score for each candidate execution object, and allocating the material tasks to the candidate execution object with the highest matching score.

[0074] Storage medium – any type of memory device or storage device. The term “storage medium” is intended to include: mounting media, such as CD-ROMs, floppy disks, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media, optical storage; registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. Furthermore, storage media may reside in a first computer system in which the program is executed, or may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term “storage medium” can include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). Storage media may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0075] Of course, the storage medium containing computer-executable instructions provided in the embodiments of this application is not limited to the material task allocation method described above, but can also execute related operations in the material task allocation method provided in any embodiment of this application.

[0076] It should be noted that the numbering of each step in this solution is only used to describe the overall design framework of this solution and does not indicate a necessary sequential relationship between the steps. As long as the overall implementation process conforms to the overall design framework of this solution, it falls within the protection scope of this solution. The literal order in the description is not an exclusive limitation on the specific implementation process of this solution. Those skilled in the art should understand that the embodiments of this application can be provided as methods, systems, or computer program products. In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory. Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0077] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0078] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for allocating material tasks, characterized in that, include: Obtain material tasks, and match multiple candidate execution objects from the set execution object set according to the creation type and content information of the material tasks; Query at least one assigned task currently being processed by each of the candidate execution objects, and calculate the load state coefficient based on the current completion progress and execution level corresponding to each of the at least one assigned task; The task quality coefficient is calculated based on the number of modifications and quality scores of each historical task completed by each candidate execution object. The matching score for each candidate execution object is calculated by fusing the execution level of the material task, the load state coefficient, and the task quality coefficient, and the material task is assigned to the candidate execution object with the highest matching score.

2. The material task allocation method according to claim 1, characterized in that, When there are multiple assigned tasks, the calculation of the load state coefficient based on the current completion progress and execution level corresponding to each of the at least one assigned task includes: Calculate the estimated remaining time based on the current completion progress and time already consumed for each of the assigned tasks; Query the urgency weight corresponding to the execution level of each assigned task, and calculate the task load coefficient based on the urgency weight and the estimated remaining time. The load status coefficient is obtained by integrating the task load coefficients corresponding to multiple assigned tasks.

3. The material task allocation method according to claim 1, characterized in that, The step of calculating the task quality coefficient based on the number of modifications and quality scores of each historical task completed by each candidate execution object includes: The average number of modifications is obtained by statistically analyzing the number of modifications made to each historical task completed by each candidate execution object. The average quality score is obtained by statistically analyzing the quality scores of each historical task completed by each candidate execution object. The task quality coefficient is obtained by integrating the average number of modifications with the average quality score.

4. The material task allocation method according to claim 1, characterized in that, The matching score for each candidate execution object is calculated by fusing the execution level of the material task, the load status coefficient, and the task quality coefficient, including: The third calculation result is obtained based on the load state coefficient and the set attenuation function; The execution level of the material task is converted into an adjustment factor according to a preset mapping relationship, and a fourth calculation result is obtained based on the task quality coefficient and the adjustment factor; The third calculation result is combined with the fourth calculation result to obtain the matching score.

5. The material task allocation method according to claim 1, characterized in that, The step of matching multiple candidate execution objects from a set of execution objects based on the creation type and content information of the material task includes: Select multiple first execution objects from the set of execution objects that match the creation type of the material task; The content information is subjected to keyword recognition and text tags are generated. The similarity calculation of the skill preference tags of each first execution object with the text tags is performed to obtain the matching degree value. Then, multiple candidate execution objects with matching degree values ​​higher than a preset threshold are selected from the multiple first execution objects.

6. The material task allocation method according to claim 1, characterized in that, Prior to the material acquisition task, the following is also included: Obtain the project to be executed, and parse the project to obtain the material description information and material distribution channels; Query the task flow template corresponding to the material distribution channel, and integrate the material description information and the task flow template to obtain the material task.

7. The material task allocation method according to claim 1, characterized in that, After assigning the material task to the candidate execution object with the highest matching score, the process further includes: If the candidate execution object with the highest matching score does not confirm receiving the material task, the waiting time is obtained by subtracting the current system time from the allocation time of the material task. If the waiting time exceeds a preset time threshold, the material task will be reassigned to the other candidate execution object with the highest matching score.

8. A material task allocation device, characterized in that, include: The candidate object determination module is configured to acquire material tasks and match multiple candidate execution objects from a set of execution objects based on the creation type and content information of the material tasks. The coefficient determination module is configured to query at least one assigned task currently being processed by each of the candidate execution objects, calculate the load status coefficient based on the current completion progress and execution level corresponding to the at least one assigned task, and calculate the task quality coefficient based on the number of modifications and quality scores of each historical task completed by each of the candidate execution objects. The object determination module is configured to perform a fusion calculation based on the execution level of the material task, the load state coefficient, and the task quality coefficient to obtain a matching score for each candidate execution object, and to assign the material task to the candidate execution object with the highest matching score.

9. A material task allocation device, characterized in that, The device includes: one or more processors; and a storage device configured to store one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the material task allocation method according to any one of claims 1-7.

10. A non-volatile storage medium for storing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are configured to perform the material task allocation method as described in any one of claims 1-7.

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