Human resource dispatching service management method and system based on cloud platform

Abstract

The invention provides a human resource dispatching service management method and system based on a cloud platform, and belongs to the technical field of human resource management and resource scheduling distribution. The method comprises the following steps: receiving a human resource dispatching service request; analyzing the service request to obtain a plurality of fields and corresponding field values and field types; based on the field values and the field types, a human resource dispatching task flow chart is constructed, and the task flow chart comprises a plurality of nodes; based on the human resource dispatching operation corresponding to each node, a cloud platform resource scheduling mode of each node in the task flow chart is determined, and the scheduling mode comprises scheduling of private cloud resources or scheduling of public cloud resources; and based on the execution efficiency of each node, reallocating the resource proportion of the mixed cloud resources in the cloud platform. The invention also discloses a human resource dispatching service management system and a computer program product for executing the method. According to the technical scheme of the invention, the cloud platform resources can be reasonably allocated based on the data security levels of different human resource dispatching tasks.

Description

Technical Field

[0001] The present invention belongs to the technical field of human resource management and resource scheduling and allocation, and in particular relates to a human resource dispatch service management method and system based on a cloud platform, a computer-readable storage medium / computer program product, and a terminal device for implementing the method. Background Art

[0002] Human resource dispatch services, commonly known as talent leasing or labor dispatch, are a form of human resource outsourcing. Enterprises entrust all or part of the non-core work of human resource management to professional talent service agencies, and the entrusted personnel still belong to the entrusting enterprise. In essence, it is a comprehensive and high-level personnel agency service. When enterprises choose human resource dispatch services, they will comprehensively consider compliance, service agency qualifications, risk management, cost-effectiveness and other dimensions to ensure legal employment and reduce management risks. Correspondingly, relevant service agencies (enterprises / companies / social organizations, etc.) that provide human resource dispatch services also face various management risks during operations. These risks may arise from multiple dimensions such as legal compliance, personnel management, cooperative relationships, financial security, etc., and they also need to improve their management level from the above dimensions.

[0003] In the real world, human resource dispatch services also include a broader definition, such as human resource exchanges between different departments within an enterprise, cross-departmental human resource exchanges, human resource secondments and flows between companies (branches), and even the common matching services between job seekers and job posting companies in the human resources market can be called human resource dispatch. For example, in the related art, Chinese invention patent application number CN202410738745.1 discloses a cloud computing-based human resource dispatch management method. By using a cloud synchronization matching algorithm and a dynamic resource adjustment algorithm, it improves the efficiency and adaptability of human resource allocation, reduces the error rate and resource waste of manual operations, and enhances the ability to quickly respond to changes in project requirements.

[0004] Human resource dispatch services typically involve allocating tasks across multiple departmental nodes. Tasks at these nodes correspond to different personnel (or data), and management permissions and upstream and downstream relationships vary between nodes. Traditional local resource models are unable to adapt to this multi-node task processing, making cloud resource scheduling the mainstream. However, due to the relative openness of cloud resources, data security issues arise when data related to human resource dispatch management is processed on cloud platform resources. Balancing the efficiency of human resource dispatch task processing with the security of human resource data has become a critical issue in the management of cloud-based human resource dispatch services. Summary of the Invention

[0005] In response to the above technical problems, the present invention proposes a human resource dispatch service management method and system based on a cloud platform, a computer-readable storage medium / computer program product, and a terminal device for implementing the method.

[0006] In a first aspect of the present invention, a human resource dispatch service management method based on a cloud platform is proposed. The cloud platform provides hybrid cloud resources, and the hybrid cloud resources include private cloud resources and public cloud resources. The method includes the following steps:

[0007] Receive human resources dispatch service requests;

[0008] Parsing the service request to obtain multiple fields and corresponding field values ​​and field types;

[0009] Based on the field value and field type, a human resource dispatch task flow chart is constructed, wherein the task flow chart includes a plurality of nodes; each node corresponds to a human resource dispatch operation;

[0010] Determine a cloud platform resource scheduling method for each node in the task flow diagram based on the human resource dispatch operation corresponding to each node, wherein the scheduling method includes scheduling private cloud resources or scheduling public cloud resources;

[0011] Based on the execution efficiency of each node, the resource ratio of the hybrid cloud resources in the cloud platform is reallocated.

[0012] The human resource dispatch operation includes: human resource source, human resource destination, human resource salary attribution, human resource management attribution, human resource start time, and human resource end time.

[0013] More specifically, in the above method, a human resource dispatch service request described in natural language is received; then, through natural language processing technology, the human resource dispatch service request described in natural language is pre-processed by word segmentation, and multiple fields therein and the field values ​​and field types corresponding to the multiple fields are identified.

[0014] Based on the human resource dispatch operation corresponding to each node, determine the cloud platform resource scheduling method for each node in the task flow diagram, specifically including:

[0015] Based on the task flow chart, determining data security levels of human resource dispatch operations corresponding to a plurality of nodes included in the task flow chart;

[0016] Based on the data security level, the cloud platform resource scheduling mode of each node is determined to be one of scheduling private cloud resources or scheduling public cloud resources.

[0017] The human resource dispatch operations corresponding to the multiple nodes of the task flow chart constitute a distributed atomic operation transaction;

[0018] The plurality of nodes are divided into a first category and a second category in the task flow chart;

[0019] The first type of nodes are executed using private cloud resources, and the second type of nodes are executed using public cloud resources.

[0020] The first type of nodes are executed first. When all the first type of nodes are successfully executed, the second type of nodes are executed. If not all the second type of nodes are successfully executed, the status of all nodes is rolled back.

[0021] In a second aspect of the present invention, in order to implement the method described in the first aspect, a human resource dispatch service management system based on a cloud platform is provided, the system comprising:

[0022] A service receiving unit, configured to receive a human resources dispatch service request described in a natural language;

[0023] A service parsing unit, configured to parse the service request to obtain a plurality of fields and corresponding field values ​​and field types;

[0024] A task flow chart construction unit is used to construct a human resource dispatch task flow chart based on the field value and field type, wherein the task flow chart includes a plurality of nodes; each node corresponds to a human resource dispatch operation; each human resource dispatch operation corresponds to a different data security level in different human resource dispatch service requests;

[0025] a resource scheduling mode determining unit, configured to determine, based on the human resource dispatch operation corresponding to each node, a cloud platform resource scheduling mode for each node in the task flow diagram, as one of scheduling private cloud resources or scheduling public cloud resources;

[0026] The resource adjustment unit is used to reallocate the resource ratio of the hybrid cloud resources in the cloud platform based on the execution efficiency of each node.

[0027] The service parsing unit includes a natural language processing module, which uses natural language processing technology to perform word segmentation preprocessing on the human resource dispatch service request described in natural language, and then identifies multiple fields and field values ​​and field types corresponding to the multiple fields.

[0028] The human resource dispatch operation includes: human resource source, human resource destination, human resource salary attribution, human resource management attribution, human resource start time, and human resource end time;

[0029] The resource scheduling mode determining unit determines, based on the data security level, a cloud platform resource scheduling mode for each node, as one of scheduling private cloud resources or scheduling public cloud resources.

[0030] In the third aspect of the present invention, a terminal device is also proposed, which includes a processor and a memory; the memory is used to store computer program code; when the program code is executed by the processor, the cloud platform-based human resource dispatch service management described in the first aspect is implemented.

[0031] The technical solution of the present invention is based on the human resource dispatch operation corresponding to each node, determines the cloud platform resource scheduling method for each node in the task flow chart, and then reasonably allocates cloud platform resources based on the data security level of different human resource dispatch tasks, taking into account the efficiency of human resource dispatch task processing and human resource data security. Its specific advantages and implementation principles will be further reflected in detail in the specific embodiment part in combination with the drawings of the specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 This is a schematic diagram of the main process of a human resource dispatch service management method based on a cloud platform according to an embodiment of the present invention;

[0034] Figure 2 yes Figure 1 A schematic diagram of the principle of constructing a human resources dispatch task flow chart in the method;

[0035] Figure 3 This is a schematic diagram of the main functional modules of a cloud platform-based human resource dispatch service management system according to an embodiment of the present invention;

[0036] Figure 4 yes Figure 3 Schematic diagram of part of the working principle of the system. DETAILED DESCRIPTION

[0037] In the specific implementation of this application, if the embodiments of the relevant technical solutions involve user-related data, when the embodiments of this application are applied to specific products or technologies, user permission or consent must be obtained, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0038] In the traditional local resource model, each department node (such as recruitment, training, compensation, and risk control) uses an independent local system, and data is stored in a decentralized manner. Cross-node collaboration requires manual information transmission, which is inefficient and prone to errors (such as delayed employee file updates and inconsistent salary calculation calibers). The local system's authority hierarchy is fixed, making it difficult to dynamically adapt to the needs of multi-node collaboration (for example, the headquarters needs to monitor the employment compliance of regional nodes, but under the traditional model, authority adjustments require technical personnel to intervene, and the response is slow). In addition, the local server's computing and storage resources are fixed, and the system is prone to jamming due to excessive load during the peak season, while resources are idle and wasted during the off-season, making it impossible to expand or shrink capacity quickly. Moreover, branches in multiple locations need to maintain their own local IT infrastructure, and data synchronization relies on VPN or file transfer, which has low security and significant latency. This is especially problematic when dealing with social security payments and policy adaptation for cross-regional dispatched employees.

[0039] As can be seen, human resource dispatch services often involve assigning tasks to multiple departmental nodes. Tasks at these nodes correspond to different personnel (data), and management permissions and upstream and downstream relationships vary between nodes. Traditional on-premises resource models are unable to adapt to this multi-node task processing. Cloud resource scheduling, with its flexibility, scalability, and collaboration, is a key solution to addressing the limitations of traditional on-premises models. For example, within a cloud platform's visual interface, the dispatch process can be broken down into nodes such as recruitment, background checks, training, onboarding, and monitoring. These nodes are automatically assigned to the corresponding departmental personnel, with real-time task progress tracking. The cloud service provider's load balancer evenly distributes tasks such as resume screening and salary calculation across multiple server instances to avoid overloading a single point of failure. Furthermore, a built-in knowledge base of regulations such as the "Interim Provisions on Labor Dispatch" and the "Labor Contract Law" can be used to automatically verify compliance with employment positions, dispatch ratios, contract terms, and other aspects. Cloud-based big data analysis can identify abnormal employment patterns (e.g., a company consistently returning more than 30% of its employees), triggering risk alerts and disseminating them to risk management departments.

[0040] However, while choosing cloud resources for HR dispatch services improves efficiency, it also presents a range of risks, requiring a comprehensive assessment from various perspectives, including data security, system stability, compliance, vendor dependency, and cost control. For example, employee personal information (such as ID cards, salary data, and attendance records) can be leaked during cloud storage and transmission due to vulnerabilities in the cloud service provider's systems, hacker attacks, or loss of internal user privileges.

[0041] To this end, the technical solution of this application is proposed.

[0042] See also Figure 1 , Figure 1 This is a schematic diagram of the main process of a human resource dispatch service management method based on a cloud platform according to an embodiment of the present invention.

[0043] Figure 1 The cloud platform on which the method is based includes a variety of cloud platform callable resources, which are specifically realized as hybrid cloud resources, and the hybrid cloud resources include private cloud resources and public cloud resources.

[0044] The method comprises the following steps:

[0045] Receive human resources dispatch service requests;

[0046] Parsing the service request to obtain multiple fields and corresponding field values ​​and field types;

[0047] Based on the field value and field type, a human resource dispatch task flow chart is constructed, wherein the task flow chart includes a plurality of nodes; each node corresponds to a human resource dispatch operation;

[0048] Determine a cloud platform resource scheduling method for each node in the task flow diagram based on the human resource dispatch operation corresponding to each node, wherein the scheduling method includes scheduling private cloud resources or scheduling public cloud resources;

[0049] Based on the execution efficiency of each node, the resource ratio of the hybrid cloud resources in the cloud platform is reallocated.

[0050] In a specific embodiment, the human resource dispatch operation includes: human resource source, human resource destination, human resource salary attribution, human resource management attribution, human resource start time, and human resource end time.

[0051] In a specific implementation, the method may receive a human resource dispatch service request described in natural language;

[0052] By using natural language processing technology, after word segmentation pre-processing is performed on the human resource dispatch service request described in natural language, multiple fields and field values ​​and field types corresponding to the multiple fields are identified.

[0053] The determining of the cloud platform resource scheduling mode for each node in the task flow diagram based on the human resource dispatch operation corresponding to each node specifically includes:

[0054] Based on the task flow chart, determining data security levels of human resource dispatch operations corresponding to a plurality of nodes included in the task flow chart;

[0055] Based on the data security level, the cloud platform resource scheduling mode of each node is determined to be one of scheduling private cloud resources or scheduling public cloud resources.

[0056] The human resource dispatch operations corresponding to the multiple nodes of the task flow chart constitute a distributed atomic operation transaction;

[0057] The plurality of nodes are divided into a first category and a second category in the task flow chart;

[0058] The first type of nodes are executed using private cloud resources, and the second type of nodes are executed using public cloud resources.

[0059] The first type of nodes are executed first. When all the first type of nodes are successfully executed, the second type of nodes are executed. If not all the second type of nodes are successfully executed, the status of all nodes is rolled back.

[0060] To describe the principle of the above method embodiment in more detail, Figure 2 Taking the principle diagram of constructing a human resource dispatch task flow chart as an example, the above steps are introduced in detail.

[0061] It should be pointed out that Figure 2 The examples described are merely illustrative and do not constitute a limitation on the technical solution or protection scope of the present invention.

[0062] exist Figure 2 In the example, a human resource dispatch service request described in natural language is first received;

[0063] In related technologies, when a human resources dispatch service request is generated, it may be necessary to submit a standardized form request through the system.

[0064] In the embodiment of the present application, a human resource dispatch service request described in natural language by a relevant service requester may be received.

[0065] Human resource dispatch service requests described in natural language can be sent in the form of written documents or in the form of voice commands by managers based on voice interaction devices (including smartphones, phones, etc.).

[0066] Figure 2 An example of a human resources dispatch service request described in natural language is shown as follows:

[0067] "We need to assign 100 people to work at the XX branch for 30 days starting March 1st, responsible for ZZ work. Employees will remain with their original departments..." (Example 1)

[0068] Figure 2 The example only provides a partial natural language description of the human resources dispatch service request (other content is omitted as ...). For example, the request may also include more specific requirements, such as employee length of service, job title, etc.

[0069] As another example, as a more general human resources dispatch service, such as the "matching service between job seekers and job posting companies commonly found in the human resources market" mentioned in the background technology, another example of a human resources dispatch service request described in natural language is:

[0070] "Our company needs to recruit 5 Java development engineers for the Shanghai branch and dispatch them to a certain technology company. The positions are auxiliary positions with a 2-year contract period. The salary range is 15-20K / month. They need to pay Shanghai social security and provident fund..." (hereinafter referred to as Example 2).

[0071] After receiving the above-mentioned human resource dispatch service request described in natural language, the human resource dispatch service request described in natural language is pre-processed by word segmentation using natural language processing technology to identify multiple fields and the field values ​​and field types corresponding to the multiple fields.

[0072] In the above example 1, after word segmentation pre-processing of the human resource dispatch service request described in natural language by natural language processing technology, multiple fields and their corresponding field values ​​and field types are identified. Figure 2 , for example it could be:

[0073] Human Resources Source: (Original Department) Selected by each department, with a maximum of 10 people per department (field type is text + integer value)

[0074] Human Resources Destination: XX Branch Location (Field Type: Text)

[0075] Human Resources Salary Attribution: (Original Department) (Field Type: Text)

[0076] Human Resources Management Affiliation: (Original Department) (Field type: Text)

[0077] Human Resources start time: March 1 (field type is integer value)

[0078] Human resources end time: March 31 (field type is integer value).

[0079] Fields, field types, and field values ​​have different definitions and requirements in different human resource dispatch service scenarios. For example, in Example 2 above, natural language processing technology is used to perform word segmentation preprocessing on the human resource dispatch service request described in natural language, and multiple fields and their corresponding field values ​​and field types are identified. See the table below, for example, it can be:

[0080] Field Type Field Name Field Value Employer information Name of employing unit A technology company Sending organization information Name of client Our company Dispatch job information Job Title Java Development Engineer Dispatch position type Nature of position Auxiliary positions Personnel demand information Number of recruits 5 people Work Location Working City Shanghai Contract and Salary Contract Term 2 years Salary range Salary level 15K-20K / month Social security payment place Social Security City Shanghai Benefit Type Social Security and Provident Fund Yes (payable) .

[0081] Continue with Figure 2 For example, based on the field value and field type, a human resource dispatch task flowchart is constructed, and the task flowchart includes multiple nodes; each node corresponds to a human resource dispatch operation.

[0082] In Example 1, the human resource dispatch operation includes: human resource source, human resource destination, human resource salary attribution, human resource management attribution, human resource start time, and human resource end time.

[0083] Similarly, in Example 2, the corresponding human resource dispatch operation can also be identified.

[0084] Figure 2 Six human resource dispatch operations are shown, corresponding to six task nodes, where the human resource source node and the human resource destination node are two upstream and downstream task nodes (referred to as node 1 and node 2, Figure 2 For the sake of simplicity, the node numbers are omitted (the same applies below); the human resources salary attribution node and the human resources management attribution node (referred to as nodes 3 and 4) are both set as associated nodes (child nodes) of the human resources destination node; the management nodes corresponding to the human resources start time node and the human resources end time node (referred to as nodes 5 and 6) are two related and independent task nodes.

[0085] The tasks performed on node 1 (human resource source node) include: distributing personnel selection tasks to various departments, which may include specific personnel selection requirements, and receiving the selection results from various departments;

[0086] The tasks performed at node 2 (human resource destination node) include: sending a destination notification to each selected person, including the destination unit, departure time, etc.;

[0087] The tasks performed at nodes 3 and 4 (human resource salary attribution node and human resource management attribution node) include human resource salary attribution management and human resource management attribution (i.e., attribution of management affairs other than salary payment);

[0088] Node 5 and node 6 trigger the execution start time and execution end time of the above task.

[0089] Next, based on the human resource dispatch operation corresponding to each node, the cloud platform resource scheduling method for each node in the task flow chart is determined, specifically including:

[0090] Based on the task flow chart, determining data security levels of human resource dispatch operations corresponding to a plurality of nodes included in the task flow chart;

[0091] Based on the data security level, the cloud platform resource scheduling mode of each node is determined to be one of scheduling private cloud resources or scheduling public cloud resources.

[0092] In the above example, Node 1 needs to receive the selection results of each department. The selection results include specific personnel names, length of service, current salary, personnel permissions, and other data involving personal privacy and data assets. Therefore, the data security level of the human resource dispatch operation corresponding to Node 1 is higher, and it is appropriate to adopt a data task processing method with a higher security level. Therefore, the cloud platform resource scheduling method of Node 1 is selected to schedule private cloud resources for related tasks and data processing;

[0093] In the above example, the tasks performed at node 2 (human resources destination node) include: sending a destination notification to each selected person, including the destination unit, departure time, etc.

[0094] Since the list and specific information of each selected personnel have been determined through private cloud resources at this time, only relevant public information needs to be sent individually, and basically no data asset security issues are involved. Therefore, the cloud platform resource scheduling method of node 2 is selected to schedule public cloud resources for related tasks and data processing.

[0095] The processing method for other nodes is similar. For example, for node 3, private cloud resources are used because it involves personal salary management matters; for node 4, private cloud resources can be preferably used; if the private cloud has insufficient available resources (it is necessary to prioritize ensuring that other working nodes that must use private cloud resources are available), then public cloud resources can be used to process general management matters in node 4 (ordinary matters with lower priority), and then use the private cloud to process special matters with higher priority involved in node 4 when the private cloud is idle.

[0096] That is to say, some nodes can use private cloud resources and public cloud resources alternately for transaction processing and task scheduling.

[0097] Of course, as a more preferred embodiment, the human resource dispatch operations corresponding to the multiple nodes of the task flow chart constitute a distributed atomic operation transaction;

[0098] The plurality of nodes are divided into a first category and a second category in the task flow chart;

[0099] The first type of nodes are executed using private cloud resources, and the second type of nodes are executed using public cloud resources.

[0100] The first type of nodes are executed first. When all the first type of nodes are successfully executed, the second type of nodes are executed. If not all the second type of nodes are successfully executed, the status of all nodes is rolled back.

[0101] In this preferred example, data security can be further ensured by introducing the concept of distributed atomic operation transactions.

[0102] Distributed atomic operations originally refer to ensuring that operations across multiple nodes (or services) in a distributed system are either all successfully submitted or all failed and rolled back, thus ensuring data consistency and integrity. Their core goal is to address the atomicity issue of cross-node operations in a distributed environment.

[0103] However, in this embodiment, the concept of distributed atomic operations is creatively introduced, combined with the characteristics of hybrid cloud resources, to ensure that the possibility of data leakage in human resource dispatch operations based on the cloud platform is minimized.

[0104] Specifically, based on the data security levels corresponding to the human resource dispatch operations corresponding to multiple nodes in the task flowchart, the multiple nodes are divided into the first category and the second category in the task flowchart. The first category nodes are executed using private cloud resources, and the second category nodes are executed using public cloud resources.

[0105] That is to say, task nodes with a high data security level are classified as first-class nodes, and task nodes with a low data security level are classified as second-class nodes.

[0106] At this time, the private cloud resources are first called to execute the first type of nodes. When all the first type of nodes are successfully executed, the second type of nodes will be executed.

[0107] Since private cloud resources have higher security, when all the first-type nodes are successfully executed, it means that tasks and data operations with higher data levels have been processed on private cloud resources, and corresponding data encryption operations have been performed, and can be sent and processed through public cloud channels.

[0108] However, when there are multiple first-class nodes, since the human resource dispatch operations corresponding to multiple nodes in the task flow chart constitute distributed atomic operation transactions, as long as one first-class node fails to complete the processing (for example, insufficient private cloud resources), all first-class node tasks need to be rolled back to the initial state to ensure data security (to avoid data leakage when a node fails to execute).

[0109] On this basis, when all first-type nodes are executed successfully, the second-type nodes are executed; if all second-type nodes are executed successfully, the task is completed.

[0110] However, if a second type of node fails to execute, the status of all nodes needs to be rolled back to ensure the consistency and integrity of the distributed atomic operation transactions of the human resource dispatch operations corresponding to multiple nodes of the task flow graph.

[0111] Preferably, if a certain second-type node fails to execute, the status of all second-type nodes is rolled back.

[0112] At this point, since all first-category nodes have successfully executed, to conserve private cloud resources, there's no need to rollback the first-category nodes; only the states of all second-category nodes need to be rolled back. This demonstrates that the improved embodiment leverages the strengths of existing distributed atomic operations while also incorporating targeted improvements and associations based on the inherent characteristics of human resource dispatch operations on cloud platforms. This eliminates the need to rollback all nodes; only the states of all second-category nodes need to be rolled back, thereby conserving private cloud resources while maintaining data security.

[0113] In practice, a private cloud is a cloud computing environment used independently by a single organization. Resources (servers, storage, and network) are exclusively owned by that organization and can be deployed in a local data center or hosted by a third party. Dedicated resources, not shared with other organizations, enhance data privacy and security (meeting industry compliance requirements such as finance and healthcare). The majority of private cloud costs are incurred through self-hosting or hosting, and in actual use, these costs are negligible. However, resource scale is limited by the physical capacity of the local data center, and scalability is less robust than that of a public cloud.

[0114] A public cloud is a pool of cloud computing resources centrally built and managed by a cloud service provider (such as AWS, Azure, and Alibaba Cloud). It provides services to multiple users over the internet, with resources shared and allocated on demand. Public cloud resources can be considered unlimited, but they require ongoing operating expenses (OPEX) and are billed dynamically based on usage.

[0115] To this end, in a further embodiment of the present invention, the method further reallocates the resource ratio of the hybrid cloud resources in the cloud platform based on the execution efficiency of each node.

[0116] For example, for the first type of nodes, if their task execution efficiency is low, the available proportion of private cloud resources in the cloud platform will be increased, specifically by exiting other tasks with lower priority but occupying private cloud resources; for the second type of nodes, if a node becomes idle after execution, the proportion of public cloud resources will be reduced, such as reclaiming part of the public cloud.

[0117] Figure 1-Figure 2 The embodiment of the method for realizing the technical solution of the present invention is shown. Figure 1-Figure 2 Correspondingly, 3- Figure 4 A system (product) embodiment of the technical solution of the present invention is given.

[0118] See first Figure 3 , Figure 3 This is a schematic diagram of the main functional modules of a cloud platform-based human resource dispatch service management system according to an embodiment of the present invention.

[0119] Figure 3 A cloud-based human resources dispatch service management system includes:

[0120] A service receiving unit, configured to receive a human resources dispatch service request described in a natural language;

[0121] A service parsing unit, configured to parse the service request to obtain a plurality of fields and corresponding field values ​​and field types;

[0122] A task flow chart construction unit is used to construct a human resource dispatch task flow chart based on the field value and field type, wherein the task flow chart includes a plurality of nodes; each node corresponds to a human resource dispatch operation; each human resource dispatch operation corresponds to a different data security level in different human resource dispatch service requests;

[0123] a resource scheduling mode determining unit, configured to determine, based on the human resource dispatch operation corresponding to each node, a cloud platform resource scheduling mode for each node in the task flow diagram, as one of scheduling private cloud resources or scheduling public cloud resources;

[0124] The resource adjustment unit is used to reallocate the resource ratio of the hybrid cloud resources in the cloud platform based on the execution efficiency of each node.

[0125] exist Figure 3 Based on this, see further Figure 4 .

[0126] The service parsing unit includes a natural language processing module, which uses natural language processing technology to perform word segmentation preprocessing on the human resource dispatch service request described in natural language, and then identifies multiple fields and field values ​​and field types corresponding to the multiple fields.

[0127] After word segmentation preprocessing, multiple fields and their corresponding field values ​​and field types are identified. Related processing methods also include: keyword matching, stop word removal, etc.

[0128] Natural language processing technology is well known to those skilled in the art, and the related technology belongs to the existing technology, which will not be elaborated in detail in the present invention.

[0129] The human resource dispatch operation includes: human resource source, human resource destination, human resource salary attribution, human resource management attribution, human resource start time, and human resource end time;

[0130] The resource scheduling mode determining unit determines, based on the data security level, a cloud platform resource scheduling mode for each node, as one of scheduling private cloud resources or scheduling public cloud resources.

[0131] Although not shown in the accompanying drawings, preferably, further product embodiments may also include an electronic device, particularly a terminal electronic device, comprising a memory and one or more processors. The memory stores one or more application programs, which are adapted to be executed by the one or more processors to implement the aforementioned cloud-based human resource dispatch service management method.

[0132] Although not shown in the accompanying drawings, more embodiments also include a computer medium that stores a computer program. When the computer program is executed, all or part of the steps of the aforementioned cloud platform-based human resource dispatch service management method are implemented.

[0133] It can be understood that the system, product, device, medium embodiments and method implementations correspond to each other and can reference each other. Their principles are similar or the same, so they will not be repeated.

[0134] For other technologies, principles, algorithms or models not elaborated in detail in this application, please refer to the existing technology.

[0135] The foregoing has shown and described the method embodiments and system of the present invention, but it is understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A human resource dispatch service management method based on a cloud platform, wherein the cloud platform provides hybrid cloud resources, and the hybrid cloud resources include private cloud resources and public cloud resources, characterized in that: The method comprises the following steps: Receive human resources dispatch service requests; Parsing the service request to obtain multiple fields and corresponding field values ​​and field types; Based on the field value and field type, a human resource dispatch task flow chart is constructed, wherein the task flow chart includes a plurality of nodes; each node corresponds to a human resource dispatch operation; Determine a cloud platform resource scheduling method for each node in the task flow diagram based on the human resource dispatch operation corresponding to each node, wherein the scheduling method includes scheduling private cloud resources or scheduling public cloud resources; Based on the execution efficiency of each node, the resource ratio of the hybrid cloud resources in the cloud platform is reallocated.

2. A human resource dispatch service management method based on a cloud platform as claimed in claim 1, characterized in that: The human resource dispatch operation includes: human resource source, human resource destination, human resource salary attribution, human resource management attribution, human resource start time, and human resource end time.

3. A cloud platform-based human resource dispatch service management method according to claim 1, characterized in that: Receive human resource dispatch service requests described in natural language; By using natural language processing technology, after word segmentation pre-processing is performed on the human resource dispatch service request described in natural language, multiple fields and field values ​​and field types corresponding to the multiple fields are identified.

4. A cloud platform-based human resource dispatch service management method according to claim 1, characterized in that: Based on the human resource dispatch operation corresponding to each node, determine the cloud platform resource scheduling method for each node in the task flow diagram, specifically including: Based on the task flow chart, determining data security levels of human resource dispatch operations corresponding to a plurality of nodes included in the task flow chart; Based on the data security level, the cloud platform resource scheduling mode of each node is determined to be one of scheduling private cloud resources or scheduling public cloud resources.

5. The human resource dispatch service management method based on a cloud platform according to claim 1, characterized in that: The human resource dispatch operations corresponding to the multiple nodes of the task flow chart constitute a distributed atomic operation transaction; The plurality of nodes are divided into a first category and a second category in the task flow chart; The first type of nodes are executed using private cloud resources, and the second type of nodes are executed using public cloud resources.

6. A cloud platform-based human resource dispatch service management method according to claim 5, characterized in that: The first type of nodes are executed first. When all the first type of nodes are successfully executed, the second type of nodes are executed. If not all the second type of nodes are successfully executed, the status of all nodes is rolled back.

7. A human resource dispatch service management system based on a cloud platform, characterized in that: The system comprises: A service receiving unit, configured to receive a human resources dispatch service request described in a natural language; A service parsing unit, configured to parse the service request to obtain a plurality of fields and corresponding field values ​​and field types; A task flow chart construction unit is used to construct a human resource dispatch task flow chart based on the field value and field type, wherein the task flow chart includes a plurality of nodes; each node corresponds to a human resource dispatch operation; each human resource dispatch operation corresponds to a different data security level in different human resource dispatch service requests; a resource scheduling mode determining unit, configured to determine, based on the human resource dispatch operation corresponding to each node, a cloud platform resource scheduling mode for each node in the task flow diagram, as one of scheduling private cloud resources or scheduling public cloud resources; The resource adjustment unit is used to reallocate the resource ratio of the hybrid cloud resources in the cloud platform based on the execution efficiency of each node.

8. The human resource dispatch service management system based on a cloud platform according to claim 7, characterized in that: The service parsing unit includes a natural language processing module, which uses natural language processing technology to perform word segmentation preprocessing on the human resource dispatch service request described in natural language, and then identifies multiple fields and field values ​​and field types corresponding to the multiple fields.

9. The human resource dispatch service management system based on a cloud platform according to claim 7, characterized in that: The human resource dispatch operation includes: human resource source, human resource destination, human resource salary attribution, human resource management attribution, human resource start time, and human resource end time; The resource scheduling mode determining unit determines, based on the data security level, a cloud platform resource scheduling mode for each node, as one of scheduling private cloud resources or scheduling public cloud resources.

10. A computer program product comprising instructions, characterized in that The computer program is stored in a computer-readable storage medium; when the computer-readable storage medium is connected to an electronic device comprising a processor, the electronic device executes the computer program through the processor, thereby implementing a cloud platform-based human resource dispatch service management method as described in any one of claims 1 to 6.

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