Task processing method based on dynamic allocation and related equipment

Abstract

The invention provides a task processing method based on dynamic allocation and related equipment. The method comprises the following steps: acquiring a to-be-processed task from an initiator; determining candidate processing objects of the to-be-processed task based on a preset allocation rule and the task information of the to-be-processed task; determining a target object in the candidate processing objects based on the current processing capability of the candidate processing objects; the current processing capability is determined based on at least one of the current workload, professional capability data or historical work records of the candidate processing object; distributing the to-be-processed task to the target object, so as to process the to-be-processed task based on a corresponding processing rule; and receiving a processing result for the to-be-processed task, and returning the processing result to the initiator.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to a task processing method based on dynamic allocation and related equipment. Background Art

[0002] In related technologies, the processing of task documents mainly relies on manual allocation and review, which has problems such as slow processing speed, low accuracy, and high labor costs. Summary of the invention

[0003] The present disclosure proposes a task processing method based on dynamic allocation and related equipment to solve technical problems such as low efficiency and low accuracy of task processing to a certain extent.

[0004] In a first aspect, the present disclosure provides a task processing method based on dynamic allocation, comprising:

[0005] Get pending tasks from the initiator;

[0006] Determine a candidate processing object of the task to be processed based on a preset allocation rule and the task information of the task to be processed;

[0007] Determine a target object among the candidate processing objects based on the current processing capability of the candidate processing objects; the current processing capability is determined based on at least one of the current workload, professional capability data or historical work records of the candidate processing objects;

[0008] Allocating the pending tasks to the target objects, so as to process the pending tasks based on corresponding processing rules;

[0009] The processing result of the task to be processed is received, and the processing result is returned to the initiator.

[0010] In a second aspect of the present disclosure, a task processing device based on dynamic allocation is provided, comprising:

[0011] An acquisition module, used to acquire pending tasks from the initiator;

[0012] A candidate module, used for determining a candidate processing object of the task to be processed based on a preset allocation rule and the task information of the task to be processed;

[0013] A target module, configured to determine a target object among the candidate processing objects based on a current processing capability of the candidate processing objects; the current processing capability is determined based on at least one of a current workload, professional capability data, or a historical work record of the candidate processing objects;

[0014] An allocation module, used for allocating the to-be-processed tasks to the target objects, so as to process the to-be-processed tasks based on corresponding processing rules;

[0015] A receiving module, used for receiving a processing result of the target object for the task to be processed;

[0016] The returning module is used to return the processing result to the initiator.

[0017] In a third aspect of the present disclosure, an electronic device is provided, comprising one or more processors, a memory; and one or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, and the programs include instructions for executing the method described in the first aspect.

[0018] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium containing a computer program is provided. When the computer program is executed by one or more processors, the processors execute the method described in the first aspect.

[0019] In a fifth aspect of the present disclosure, a computer program product is provided, comprising computer program instructions, which, when executed on a computer, enable the computer to execute the method described in the first aspect.

[0020] From the above, it can be seen that the present disclosure provides a task processing method and related equipment based on dynamic allocation, which receives the pending tasks from the initiator, screens the candidate processing objects based on the preset allocation rules and task information, and further evaluates the current processing capacity of the candidate objects in combination with their current workload, professional ability data or historical work records, so as to determine the most suitable target processing object. Subsequently, the system assigns the task to the target object and attaches the corresponding processing rules for execution. After the task is completed, the system receives and returns the processing results to the initiator. This not only improves the efficiency of task allocation and processing, optimizes resources, but also reduces labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 It is a schematic diagram of a task processing architecture based on dynamic allocation according to an embodiment of the present disclosure.

[0023] Figure 2The figure is a schematic diagram of the hardware structure of an exemplary electronic device according to an embodiment of the present disclosure.

[0024] Figure 3 The figure is a flowchart of a task processing method based on dynamic allocation according to an embodiment of the present disclosure.

[0025] Figure 4 The figure is a schematic diagram of the principle of task processing based on dynamic allocation according to an embodiment of the present disclosure.

[0026] Figure 5 It is a schematic diagram of a task processing device based on dynamic allocation according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0028] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0031] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0032] Figure 1 FIG. 1 is a schematic diagram showing a task processing architecture based on dynamic allocation according to an embodiment of the present disclosure. Figure 1 , the task processing architecture 100 based on dynamic allocation may include a server 110, a terminal 120, and a network 130 providing a communication link. The server 110 and the terminal 120 may be connected via a wired or wireless network 130. The server 110 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, security services, and CDN.

[0033] The terminal 120 may be implemented in hardware or software. For example, when the terminal 120 is implemented in hardware, it may be various electronic devices having a display screen and supporting page display, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, and desktop computers, etc. When the terminal 120 device is implemented in software, it may be installed in the electronic devices listed above; it may be implemented as multiple software or software modules (such as software or software modules used to provide distributed services), or it may be implemented as a single software or software module, which is not specifically limited here.

[0034] It should be noted that the task processing method based on dynamic allocation provided in the embodiment of the present application can be executed by the terminal 120 or by the server 110. It should be understood that Figure 1 The number of terminals, networks and servers in the embodiment is only for illustration and is not intended to limit the number of terminals, networks and servers. Any number of terminals, networks and servers may be provided as required.

[0035] Figure 2 FIG. 2 shows a schematic diagram of the hardware structure of an exemplary electronic device 200 provided in an embodiment of the present disclosure. Figure 2 As shown, the electronic device 200 may include: a processor 202, a memory 204, a network module 206, a peripheral interface 208 and a bus 210. The processor 202, the memory 204, the network module 206 and the peripheral interface 208 are connected to each other in communication within the electronic device 200 through the bus 210.

[0036] Processor 202 may be a central processing unit (CPU), a neural network processor (NPU), a microcontroller (MCU), a programmable logic device, a digital signal processor (DSP), an application specific integrated circuit (ASIC), or one or more integrated circuits. Processor 202 may be used to perform functions related to the technology described in this disclosure. In some embodiments, processor 202 may also include multiple processors integrated into a single logical component. For example, Figure 2 As shown, processor 202 may include a plurality of processors 202a, 202b, and 202c.

[0037] The memory 204 may be configured to store data (eg, instructions, computer code, etc.). Figure 2 As shown, the data stored in the memory 204 may include program instructions (for example, program instructions for implementing the task processing method based on dynamic allocation of the embodiment of the present disclosure) and data to be processed (for example, the memory may store configuration files of other modules, etc.). The processor 202 may also access the program instructions and data stored in the memory 204, and execute the program instructions to operate on the data to be processed. The memory 204 may include a volatile storage device or a non-volatile storage device. In some embodiments, the memory 204 may include a random access memory (RAM), a read-only memory (ROM), an optical disk, a magnetic disk, a hard disk, a solid-state drive (SSD), a flash memory, a memory stick, etc.

[0038] The network module 206 can be configured to provide communication with other external devices to the electronic device 200 via a network. The network can be any wired or wireless network capable of transmitting and receiving data. For example, the network can be a wired network, a local wireless network (e.g., Bluetooth, WiFi, near field communication (NFC) etc.), a cellular network, the Internet or a combination thereof. It is understood that the type of network is not limited to the above specific examples. In some embodiments, the network module 206 can include any number of network interface controllers (NICs), radio frequency modules, transceivers, modems, routers, gateways, adapters, cellular network chips, etc., in any combination.

[0039] The peripheral interface 208 can be configured to connect the electronic device 200 to one or more peripheral devices to achieve information input and output. For example, the peripheral devices can include input devices such as a keyboard, a mouse, a touch pad, a touch screen, a microphone, and various sensors, and output devices such as a display, a speaker, a vibrator, and an indicator light.

[0040] The bus 210 can be configured to transmit information between various components of the electronic device 200 (e.g., the processor 202, the memory 204, the network module 206, and the peripheral interface 208), such as an internal bus (e.g., a processor-memory bus), an external bus (USB port, PCI-E bus), etc.

[0041] It should be noted that, although the architecture of the electronic device 200 only shows the processor 202, the memory 204, the network module 206, the peripheral interface 208 and the bus 210, in the specific implementation process, the architecture of the electronic device 200 may also include other components necessary for normal execution. In addition, it can be understood by those skilled in the art that the architecture of the electronic device 200 may also only include the components necessary for implementing the embodiments of the present disclosure, and does not necessarily include all the components shown in the figure.

[0042] With the expansion of enterprise scale and business scope, the importance of task management in enterprise management has become increasingly prominent. In related technologies, the processing of task documents mainly relies on manual allocation and review, which has problems such as slow processing speed, low accuracy and high labor cost. Among them, financial task processing is a key link in task processing. As a new financial management model, financial shared services achieve scale effect and cost savings by centralizing repetitive and standardized financial business processes to shared service centers. However, under the financial shared service model, a large number of financial application documents need to be processed quickly and accurately, which puts higher requirements on the optimization of document processing processes. At present, the existing financial management systems have realized the basic electronic approval process. However, the dispatching mechanism of these systems and platforms is often too simple, unable to intelligently identify the specific content and characteristics of the documents, and unable to reasonably allocate according to the actual workload of employees. They lack intelligence, adaptability and scalability, and are difficult to adapt to business changes and the processing requirements of new document types. The existing technology fails to make full use of the key information in the documents, and cannot dynamically adjust the task allocation according to the current workload of employees, resulting in uneven resource allocation, some work groups or personnel are overburdened, while others are relatively relaxed. Due to the lack of intelligence and adaptability in dispatching orders, existing technologies are unable to quickly respond to changes in business needs, resulting in delayed document processing and affecting enterprise operational efficiency. Due to unreasonable resource allocation and low work efficiency, existing technologies often cause users to wait too long, document processing is inefficient and prone to errors, and reduces user experience. Therefore, how to improve the efficiency of task processing, reasonably allocate resources, and improve user experience have become technical issues that need to be solved urgently.

[0043] In view of this, the embodiment of the present disclosure provides a task processing method and related equipment based on dynamic allocation, which receives the pending tasks from the initiator, screens the candidate processing objects based on the preset allocation rules and task information, and further evaluates the current processing capacity of the candidate objects in combination with their current workload, professional ability data or historical work records, so as to determine the most suitable target processing object. Subsequently, the system assigns the task to the target object and attaches the corresponding processing rules for execution. After the task is completed, the system receives and returns the processing results to the initiator. This not only improves the efficiency of task allocation and processing, optimizes resources, but also reduces labor costs.

[0044] See also Figure 3 , Figure 3 The schematic flow chart of the task processing method based on dynamic allocation according to the embodiment of the present disclosure is shown. The task processing method based on dynamic allocation according to the embodiment of the present disclosure can be deployed on a terminal or a server. Figure 3 In the embodiment, the task processing method 300 based on dynamic allocation may further include the following steps.

[0045] In step S310, a task to be processed is obtained from the initiator.

[0046] Among them, pending tasks can refer to work items or requests that have not been completed and are waiting to be processed or executed. For example, in the financial management process, suppose a department needs to purchase a batch of new office equipment. The load personnel of the department can act as the initiator and submit a pending task of "purchasing office equipment" to the finance department. This pending task usually contains key content such as the list of purchased equipment, budget, supplier information, etc.

[0047] In step S320, candidate processing objects of the task to be processed are determined based on a preset allocation rule and the task information of the task to be processed.

[0048] The preset allocation rules may refer to a set of principles or standards set in advance to guide task allocation. These rules may be based on factors such as the type of task, urgency, required skills, resource availability, etc. The task information of the pending task may refer to information describing the specific content, requirements, objectives, etc. of the task. This information is essential to determine the nature, difficulty, and required processing capabilities of the task. Candidate processing objects may refer to individuals, teams, or systems that have the capabilities or resources to handle specific tasks. In the process of task allocation, these objects are regarded as potential undertakers.

[0049] For example, there is a financial approval process. When a department needs to reimburse an expense, it will submit a reimbursement application as a pending task. Preset allocation rules: According to the size of the reimbursement amount, the task is assigned to different levels of approvers. For example, a small reimbursement may be approved by the department manager, while a large reimbursement requires approval by a higher-level leader or the finance department. If the reimbursement application involves a special project or requires professional evaluation, the task may be assigned to an approver or team with relevant expertise. Task information of pending tasks: Detailed information of the reimbursement application, including the reimbursement amount, reimbursement category (such as travel expenses, office supplies, etc.), reimbursement person information, reimbursement voucher, etc. Specific requirements of the task, such as approval time limit, approval process, etc. Candidate processing objects may include department managers, finance department personnel, senior leaders, etc. Based on the preset allocation rules and the task information of the pending task, the system will determine which candidate processing objects have the ability and authority to process the reimbursement application. For another example, if a small travel expense reimbursement application is submitted, the system may assign it to the department manager as a candidate processing object for approval according to the preset rules. If the department manager approves it, the reimbursement process continues; if it is rejected, the reimbursement application is returned to the applicant. Through this process, it can be ensured that the approval process of reimbursement applications is both efficient and avoids unnecessary waste of resources and potential risks.

[0050] In some embodiments, the task information includes at least one dimension parameter, and the preset allocation rule includes an allocation priority of the dimension parameter;

[0051] Then determining the candidate processing object of the task to be processed based on the preset allocation rule and the task information of the task to be processed includes:

[0052] Based on the order of the allocation priorities from high to low, sequentially matching a preset number of the dimension parameters with the objects to be allocated for processing;

[0053] The to-be-allocated processing object that matches the dimension parameter is determined as the candidate processing object.

[0054] Among them, the candidate processing objects of the tasks to be processed can be determined by multi-dimensional parameters and preset allocation priorities. Specifically, the task information contains at least one dimensional parameter, which describes the different characteristics and requirements of the task. The preset allocation rules define the allocation priority of these dimensional parameters, that is, which parameters should be given priority when allocating tasks. During the allocation process, the system will match a preset number of dimensional parameters with the processing objects to be allocated in order from high to low allocation priority. The matching process may involve checking whether the capabilities, resources, experience, permissions, etc. of the processing objects match the dimensional parameters in the task information. Once the processing objects to be allocated that match the dimensional parameters are found, these objects are determined as candidate processing objects.

[0055] For example, in a financial approval process, the task information includes the following dimension parameters: reimbursement amount, reimbursement category, reimbursement person's department, and reimbursement urgency (such as urgent, ordinary). The preset allocation rules define the allocation priority of these parameters, for example: reimbursement urgency > reimbursement amount > reimbursement category > reimbursement person's department. A reimbursement application is a pending task, and its task information includes: reimbursement amount: 5,000 yuan; reimbursement category: travel expenses; reimbursement person's department: marketing department; reimbursement urgency: urgent. According to the preset allocation rules, the system will first consider the dimension parameter of reimbursement urgency. Since the reimbursement application is marked as urgent, the system may give priority to those who can respond to the urgent approval request quickly, such as the emergency approval specialist in the finance department. If the emergency approval specialist cannot handle it (perhaps because other urgent tasks take up time), the system will look for a processing object that can handle reimbursement applications below 5,000 yuan based on the next priority - reimbursement amount. These objects may include department managers, general approval personnel in the finance department, etc. If the reimbursement amount also fails to meet the conditions of a specific processing object (for example, a certain approval officer only handles reimbursements below 1,000 yuan), the system will continue to consider parameters such as reimbursement category and reimbursement department in order of priority until it finds a processing object to be assigned that matches all (or as many) dimensional parameters as possible. Ultimately, the system may identify one or more candidate processing objects that will process the reimbursement application based on their capabilities, permissions, and degree of match with the task information. If multiple processing objects meet the conditions, the system may further filter or assign tasks based on other factors (such as workload, response time, etc.).

[0056] In step S330, a target object is determined among the candidate processing objects based on the current processing capability of the candidate processing objects; the current processing capability is determined based on at least one of the current workload, professional capability data or historical work records of the candidate processing objects.

[0057] Among them, in the process of task allocation, individuals, teams or systems that have the ability or resources to handle tasks after preliminary screening but have not yet been determined as the objects that will eventually undertake the tasks are candidate processing objects. Current processing capacity can refer to the ability or resource status of the candidate processing object to handle tasks at the current moment or time period. This is usually based on a comprehensive evaluation of multiple factors, such as workload, professional ability, historical work performance, etc. The current workload can refer to the number of tasks or workload that the candidate processing object is currently processing. The size of the workload directly affects its ability and efficiency to handle new tasks. Professional ability data can refer to data that describes the professional knowledge, skills, experience, etc. required by the candidate processing object in a specific field or task. These data may include training, certification, certificates, work experience, etc. Historical work records can refer to the records of the candidate processing object's past completion of tasks, including task type, completion time, quality evaluation, etc. Historical work records reflect its ability and efficiency in handling tasks, as well as potential risks and problems.

[0058] For example, in the financial approval process, several candidate processing objects (such as department managers, different approval personnel in the financial department) have been identified after preliminary screening to handle a reimbursement application. Next, the system needs to determine the final target object based on the current processing capabilities of these candidate processing objects. In some embodiments, the current workload of each candidate processing object can be checked. For example, if a certain approval personnel has currently processed multiple emergency reimbursement applications, his / her workload may have reached saturation, and therefore may not be suitable for processing new emergency applications. In some embodiments, the professional capabilities of the candidate processing objects can be considered. For example, if the reimbursement application involves complex travel expense calculations or financial issues, the system may select an approval personnel with relevant financial knowledge or financial experience to handle it. In some embodiments, the historical work records of the candidate processing objects can be considered. For example, if a certain approval personnel has performed well in handling similar reimbursement applications in the past without errors or delays, then the system may be more inclined to select him / her as the target object.

[0059] Based on the above evaluation, the system ultimately determines one or more target objects to handle the reimbursement application. For example, if an approver currently has a moderate workload, has the ability to handle complex financial issues, and has performed well in handling similar tasks in the past, the system may identify him or her as the final target object. Through this process, it can ensure that reimbursement applications are approved in a timely, accurate, and professional manner, while avoiding approval delays or errors caused by excessive workload, insufficient capabilities, or historical issues.

[0060] In some embodiments, method 300 further includes:

[0061] Obtaining historical work records of the candidate processing object;

[0062] Determine the historical work efficiency and historical performance of the candidate processing object based on the historical work records;

[0063] The processing capacity model is used to determine the current processing capacity of the candidate processing object based on at least one of the current workload, the professional capacity data, the historical work efficiency or the historical performance.

[0064] The historical work records of the candidate processing object are obtained, and then the historical work efficiency and historical performance are analyzed and determined based on these records. A processing capacity model is used to comprehensively evaluate the current processing capacity of the candidate processing object by combining at least one factor of the current workload, professional capacity data, historical work efficiency and historical performance.

[0065] For example, in a financial approval scenario, this solution can be applied to determine the current processing capacity of candidate approvers who process reimbursement applications. The system retrieves the candidate approver's past history of processing reimbursement applications from the database. These records may include information such as the number of applications processed, processing time, approval results, and whether errors or omissions occurred. Based on the historical work records obtained, the system will quantitatively evaluate the candidate approver's work efficiency, such as calculating the average processing time for each application. At the same time, the system will also evaluate their historical performance, such as approval accuracy, whether they respond to urgent applications in a timely manner, etc. A pre-built processing capacity model can be used to comprehensively evaluate the current processing capacity of candidate approvers. This model may consider the following factors: Current workload can reflect the number of reimbursement applications currently being processed by the candidate approver to evaluate whether they have enough free time to process new applications. Professional ability data can reflect the candidate approver's professional skill level, such as financial knowledge and financial experience, to determine whether they are capable of processing complex reimbursement applications. Historical work efficiency can evaluate the candidate approver's work speed based on the average time to process reimbursement applications in the past. Historical performance can evaluate the candidate approver's work quality and reliability based on past approval accuracy, timeliness, and other indicators. By inputting these factors into the processing capacity model, the system can derive a comprehensive score to measure the current processing capacity of candidate approvers. Based on the current processing capacity scores of candidate approvers, one or more approvers with the highest scores can be selected as the target objects to process new reimbursement applications. This ensures that applications are approved in a timely, accurate and professional manner, and improves the efficiency and quality of the financial approval process. This can more scientifically and objectively evaluate the current processing capacity of candidate approvers, thereby optimizing the financial approval process and improving approval efficiency and accuracy.

[0066] In some embodiments, the current processing capacity of the candidate processing object is determined based on at least one of the current workload, the professional attribute, the historical work efficiency, and the historical performance using a processing capacity model, including:

[0067] The processing capability model scores at least one of the current workload, the professional attributes, the historical work efficiency and the historical performance to obtain a corresponding intermediate score;

[0068] Obtaining a comprehensive score based on a preset weight corresponding to at least one of the current workload, the professional attribute, the historical work efficiency, and the historical performance and the intermediate score;

[0069] The current processing capability is determined based on the comprehensive score.

[0070] The processing capacity model may be an evaluation model that scores key factors such as current workload, professional attributes, historical work efficiency, and historical performance, and calculates a comprehensive score based on these scores and corresponding preset weights. Ultimately, this comprehensive score is used to determine the current processing capacity of the candidate processing object.

[0071] In some embodiments, determining a target object from among the candidate processing objects based on current processing capabilities of the candidate processing objects includes:

[0072] The candidate processing object with the highest comprehensive score is determined as the target object.

[0073] When processing task allocation, the processing capability model can be used to calculate the comprehensive scores of candidate processing objects based on multiple dimensions such as current workload, professional attributes, historical work efficiency and historical performance. One or more of the candidates with the highest comprehensive scores can be selected from these candidate objects and determined as the target object responsible for processing a specific task.

[0074] For example, in financial approval, this solution can be applied to determine which approvers are best suited to handle a specific reimbursement application. The current workload of each candidate approver can be evaluated and a median score can be given based on the workload. For example, people with less workload may receive a higher score because they have more time to process new reimbursement applications. The professional attributes of the candidate approvers (such as financial knowledge, experience, etc.) can be evaluated and scored. People with richer professional knowledge and skills may receive higher scores. The efficiency of the candidate approvers in handling reimbursement applications in the past can be scored based on factors such as average processing time. More efficient people usually score higher. The historical performance of the candidate approvers, such as approval accuracy, timely response, etc., can be evaluated and scored accordingly. After obtaining the above scores, these scores can be weighted and summed according to the preset weights to calculate the comprehensive score of each candidate approver. The weight setting can be adjusted according to the actual needs of the company to reflect the importance of each factor in the approval process. Based on the comprehensive score, the system can determine the current processing capacity of each candidate approver. People with higher scores are generally considered to have stronger processing capabilities and are therefore more suitable for handling specific reimbursement applications. For example, in the financial approval process, if a reimbursement application involves complex financial issues, the system may give priority to those with rich financial experience and high professional attribute scores. If the application needs to be processed quickly, the system may choose those with less current workload and higher historical work efficiency. This can more scientifically and objectively evaluate the current processing capabilities of candidate approvers, thereby ensuring that reimbursement applications are approved in a timely, accurate and professional manner. At the same time, it also helps to optimize the approval process and improve approval efficiency and quality. It solves the problems of single allocation rules, unreasonable resource allocation, inability to dynamically adjust task allocation, low processing efficiency and poor user experience in the processing of financial shared business in the existing technology, thereby optimizing the financial document processing process and improving document processing efficiency and accuracy.

[0075] In step S340, the to-be-processed task is assigned to the target object so as to process the to-be-processed task based on the corresponding processing rule.

[0076] The processing rules may include elements such as effective processing time, processing flow (including at least one processing node), and processing standards. In the task allocation step, the task to be processed is accurately allocated to the target object, which must follow the various nodes in the processing flow within the effective processing time and meet the requirements of the processing standards to complete the processing of the task.

[0077] In some embodiments, the processing rules include an effective processing time, a processing flow including at least one processing node, and a processing standard;

[0078] Allocating the to-be-processed task to the target object to process the to-be-processed task based on a corresponding processing rule includes:

[0079] The to-be-processed task is allocated to the target object, so that the task is processed according to the processing node based on the processing standard within the effective processing time.

[0080] Among them, the effective processing time in the processing rules can refer to setting a reasonable approval time limit for different types of reimbursement applications. For example, ordinary reimbursement applications must be approved within 3 working days, while emergency reimbursement applications must be completed within 1 working day. The processing flow can refer to a clearly designed approval process, which includes multiple processing nodes, such as preliminary review, review, and final review. Each node has clear approval responsibilities and authorities. The processing standard can refer to detailed approval standards, such as approval basis, approval points, and determination of approval results, to ensure the accuracy and consistency of approval. The reimbursement applications to be processed are assigned to the selected target objects, and the effective processing time, processing flow, and processing standards are clearly informed. After receiving the task, the target object must approve it within the effective processing time according to each node in the processing flow. For example, first conduct a preliminary review to confirm the authenticity and rationality of the reimbursement application; then conduct a review to review the preliminary review results; and finally conduct a final review to make an approval decision. During the approval process, the target object must strictly follow the requirements of the processing standards to ensure the accuracy and fairness of the approval. This can ensure that reimbursement applications are approved in a timely, accurate and standardized manner, improve approval efficiency and satisfaction. At the same time, it will also help improve the professional quality and work efficiency of approval personnel and promote the continuous optimization and improvement of financial approval processes.

[0081] In step S350, a processing result for the task to be processed is received, and the processing result is returned to the initiator.

[0082] The processing results of the tasks to be processed sent by the target object or other associated objects (for example, after some tasks are assigned to the target object, the initial starting point of their processing flow is the target object, and after the corresponding processing flow, the end node is another associated object, and the associated object sends the processing results) can be received, and the results can be efficiently returned to the initiator. This not only ensures the accurate transmission of information, but also greatly improves the transparency and efficiency of task processing.

[0083] For example, when the target object (such as the approval personnel in the finance department) completes the processing of the pending task (such as the approval of the reimbursement application), the processing results can be submitted in the system. The processing results may include approval, rejection, or the need for additional materials. When receiving the processing results submitted by the target object, preliminary verification and collation are carried out. The accuracy and completeness of the processing results are ensured, laying a solid foundation for subsequent feedback work. After verification and collation, the processing results can be fed back to the initiator (such as the employee who submitted the reimbursement application) in a timely manner. The feedback process can be carried out through various methods such as email, SMS, system notification, etc., to ensure that the initiator can obtain the approval results in time and perform subsequent operations based on the results. It not only improves the efficiency and transparency of financial approval, but also greatly enhances the satisfaction and trust of employees. Employees can clearly understand the approval progress and results of the reimbursement application, so as to arrange their work and life more efficiently. At the same time, the finance department can also better grasp the overall situation of the approval work through this process, providing a basis for subsequent process optimization and decision-making.

[0084] In some embodiments, method 300 further includes:

[0085] Randomly select processed tasks, and review the processed tasks to obtain review results;

[0086] Based on the initiator feedback data of the processed task and the review result, the preset allocation rule and / or the processing rule are updated.

[0087] Among them, the preset allocation rules and processing rules are continuously optimized by randomly selecting and reviewing the processed tasks and combining the feedback data from the initiator. This mechanism ensures the continuous improvement of the task processing process and improves the accuracy and efficiency of task processing. Specifically, a part of the processed tasks can be randomly selected and reviewed by a special review team or system to check the accuracy and compliance of task processing. In this way, potential problems can be discovered and corrected in a timely manner to ensure the quality of task processing. The feedback data of the initiator on the processed tasks is collected, including information on satisfaction, processing time, processing results, etc. These data provide an important reference for subsequent rule updates. Based on the review results and the feedback data from the initiator, the preset allocation rules and processing rules are adjusted and optimized as necessary. For example, if it is found that the processing efficiency of certain types of tasks is low or the satisfaction is not high, the allocation rules can be adjusted to assign such tasks to teams or individuals who are better at processing; at the same time, the processing rules can also be optimized to improve the accuracy and efficiency of task processing.

[0088] For example, a portion of the completed reimbursement application approval tasks can be randomly selected and reviewed by the finance department or internal audit team. The review content includes the rationality of the approval process, the accuracy of the approval results, and the rationality of the approval time. Employees’ satisfaction feedback on the reimbursement application approval process can be collected through questionnaires, online evaluations, etc. At the same time, key information such as the processing time and approval results of each reimbursement application is recorded as a basis for subsequent analysis. According to the review results, if there are non-compliance or inefficiency problems in the approval process, the approval process can be adjusted in a timely manner, such as adding approval nodes and optimizing approval permissions. If employees are less satisfied with the approval process or the processing time is too long, the allocation rules can be optimized to assign the reimbursement application to the approval personnel or team who are more proficient in handling it; at the same time, clearer approval standards and time requirements can be formulated to improve approval efficiency and accuracy. In this way, the financial approval process can be continuously improved, the approval efficiency and accuracy can be improved, and the satisfaction and trust of employees can be enhanced.

[0089] See also Figure 4 , Figure 4 A schematic diagram showing the principles of a task processing method based on dynamic allocation according to an embodiment of the present disclosure is shown. Figure 4 In the process, a microservice technology architecture can be adopted, using Java8 development language and MySQL8.0 database to build an intelligent financial sharing business processing platform. The core functions of this platform include but are not limited to:

[0090] Financial shared operation pool: used to receive various financial application documents submitted by various business systems and enter the pool at key approval process nodes.

[0091] Intelligent identification module: Based on various preset rules, such as document type, business type, document initiator, etc., the documents entering the financial shared pool are intelligently identified and assigned to the corresponding workgroup. For example, machine learning algorithms can be used to intelligently identify and analyze key information of documents, realize intelligent allocation of documents, and ensure that document processing is more balanced and efficient.

[0092] Intelligent allocation module: provides workgroup configuration and adopts dynamic allocation algorithm to dynamically allocate tasks to the most suitable individuals or workgroups based on factors such as employees' current workload and professional skills.

[0093] Audit processing flow: Provide a complete audit processing flow to ensure that each document can be processed promptly and effectively.

[0094] Quality inspection optimization mechanism: dynamically extract processed jobs, review job processing quality, collect user feedback and system operation data, continuously improve the processing mechanism, continuously optimize the dispatching algorithm, and improve job processing quality, dispatching accuracy and user satisfaction.

[0095] It can be seen that according to the method of the embodiment of the present disclosure, by intelligently identifying the key information in the document and intelligently allocating it according to the configuration information of the processing personnel, the optimal allocation of resources is achieved and the work efficiency is improved. Through the intelligent dispatching method and dynamic allocation algorithm, it is possible to quickly respond to changes in business needs, realize the rapid allocation and processing of documents, reasonably arrange work tasks, improve the efficiency and time of document processing, reduce manual intervention, and improve the operational efficiency of the enterprise. The workload of manual document allocation is reduced and the labor cost is reduced. By optimizing resource allocation and improving work efficiency, user waiting time is reduced; through intelligent identification and dynamic algorithm analysis, it can better cope with complex and changeable financial approval needs; through the quality inspection optimization mechanism, document process defects are continuously improved; the accuracy of document allocation and processing is ensured, flexibility and scalability are enhanced, and the user experience is improved.

[0096] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0097] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] Based on the same technical concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides a task processing device based on dynamic allocation, see Figure 5 , the task processing device based on dynamic allocation, the device comprising:

[0099] An acquisition module, used to acquire index data of preset index parameters for carbon emission prediction in a target area;

[0100] A prediction module, used to perform carbon emission prediction based on a carbon emission prediction model and the index data to obtain the initial carbon emission of the target area; wherein the carbon emission prediction model is constructed based on the preset index parameters;

[0101] The correction module is used to correct the initial carbon emissions based on the correction model and correction data to obtain the predicted carbon emissions.

[0102] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0103] The device of the above embodiment is used to implement the corresponding task processing method based on dynamic allocation in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0104] Based on the same technical concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the task processing method based on dynamic allocation as described in any of the above embodiments.

[0105] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0106] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the task processing method based on dynamic allocation as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0107] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0108] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0109] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0110] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A task processing method based on dynamic allocation, comprising: Get pending tasks from the initiator; Determine a candidate processing object of the task to be processed based on a preset allocation rule and the task information of the task to be processed; Determine a target object among the candidate processing objects based on the current processing capability of the candidate processing objects; the current processing capability is determined based on at least one of the current workload, professional capability data or historical work records of the candidate processing objects; Allocating the pending tasks to the target objects, so as to process the pending tasks based on corresponding processing rules; The processing result of the task to be processed is received, and the processing result is returned to the initiator.

2. The method according to claim 1, wherein: The task information includes at least one dimension parameter, and the preset allocation rule includes an allocation priority of the dimension parameter; Then determining the candidate processing object of the task to be processed based on the preset allocation rule and the task information of the task to be processed includes: Based on the order of the allocation priorities from high to low, sequentially matching a preset number of the dimension parameters with the objects to be allocated for processing; The to-be-allocated processing object that matches the dimension parameter is determined as the candidate processing object.

3. The method according to claim 1, wherein: include: Obtaining historical work records of the candidate processing object; Determine the historical work efficiency and historical performance of the candidate processing object based on the historical work records; The processing capacity model is used to determine the current processing capacity of the candidate processing object based on at least one of the current workload, the professional capacity data, the historical work efficiency or the historical performance.

4. The method according to claim 3, wherein: Determining the current processing capacity of the candidate processing object based on at least one of the current workload, the professional attribute, the historical work efficiency, and the historical performance using a processing capacity model includes: The processing capability model scores at least one of the current workload, the professional attributes, the historical work efficiency and the historical performance to obtain a corresponding intermediate score; Obtaining a comprehensive score based on a preset weight corresponding to at least one of the current workload, the professional attribute, the historical work efficiency, and the historical performance and the intermediate score; The current processing capability is determined based on the comprehensive score.

5. The method according to claim 4, wherein: Determining a target object among the candidate processing objects based on current processing capabilities of the candidate processing objects includes: The candidate processing object with the highest comprehensive score is determined as the target object.

6. The method according to claim 1, wherein: The processing rules include effective processing time, processing flow including at least one processing node, and processing standards; Allocating the to-be-processed task to the target object to process the to-be-processed task based on a corresponding processing rule includes: The to-be-processed task is allocated to the target object, so that the task is processed according to the processing node based on the processing standard within the effective processing time.

7. The method according to claim 1, further comprising: Randomly select processed tasks, and review the processed tasks to obtain review results; Based on the initiator feedback data of the processed task and the review result, the preset allocation rule and / or the processing rule are updated.

8. A task processing device based on dynamic allocation, comprising: An acquisition module, used to acquire pending tasks from the initiator; A candidate module, used for determining a candidate processing object of the task to be processed based on a preset allocation rule and the task information of the task to be processed; A target module, configured to determine a target object among the candidate processing objects based on a current processing capability of the candidate processing objects; the current processing capability is determined based on at least one of a current workload, professional capability data, or a historical work record of the candidate processing objects; An allocation module, used for allocating the to-be-processed tasks to the target objects, so as to process the to-be-processed tasks based on corresponding processing rules; A receiving module, used for receiving a processing result of the target object for the task to be processed; The returning module is used to return the processing result to the initiator.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program. 10 . A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to claim 1 .

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