Resource allocation method, device and equipment and computer readable storage medium

Abstract

The invention provides a resource allocation method and device, equipment and a computer readable storage medium, and relates to the technical field of data processing, and the technical scheme is as follows: obtaining first basic information of a resource requester and second basic information of an entity resource object of the resource requester; and obtaining a value evaluation result of the entity resource object. Starting a resource allocation process in response to a resource request submitted by a resource requester, determining a resource request limit based on the information, and setting the state of the resource allocation process as waiting for permission transfer and suspending the resource allocation process; and in response to processing permission transfer information sent by the management server of the entity resource object, updating the state of the resource allocation process from a waiting permission transfer state to an allocatable state, and allocating resources corresponding to the resource request quota to the resource requester. Through asynchronous control of the state machine, collaborative automation of entity resource object authority control in the resource allocation process is realized, and the resource allocation efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a resource allocation method, apparatus, device, and computer-readable storage medium. Background Technology

[0002] During the resource allocation process, applicants typically provide another form of resource as a trusted credential to increase the success rate of resource allocation requests.

[0003] Currently, in resource allocation processes based on physical resources as trusted credentials, the handover and verification of these resources require offline intervention, which can easily lead to operational errors and process interruptions. Therefore, this affects the smooth operation of the resource allocation process, thereby reducing its efficiency. Summary of the Invention

[0004] This application provides a resource allocation method, apparatus, device, and computer-readable storage medium to achieve collaborative automation of entity resource object permission control during resource allocation, reduce operational errors and process interruptions caused by offline intervention, and improve resource allocation efficiency.

[0005] To achieve the above objectives, the embodiments of this application adopt the following technical solutions: Firstly, a resource allocation method is provided, the method comprising: First, the system obtains the resource requester's primary basic information and the secondary basic information of the resource object. The resource object is physically located and immovable. The primary basic information includes the resource requester's identification information, social network information, resource output information, and trust information. The secondary basic information includes the type of resource object, its address, ownership, historical cost information, and utility parameters. Second, the system obtains the valuation results for the resource object from the valuation agency's management server. Then, in response to the resource request, the system initiates a resource allocation process. Based on the primary and secondary basic information and the valuation results, the system determines the resource request amount and sets the resource allocation process to a pending permission transfer state or pauses the allocation process. Finally, in response to the disposal authority transfer information sent by the management server of the entity resource object, the status of the resource allocation process is updated from the waiting for authority transfer status to the allocable status, and resources corresponding to the resource request amount are allocated to the resource requester; the disposal authority transfer information indicates that some or all disposal authority of the entity resource object is transferred to the resource provider.

[0006] This application provides a resource allocation method. First, it utilizes multi-dimensional data such as the spatial attributes, ownership relationships, and utility characteristics of entity resource objects, combined with the basic information of the resource requester, to determine the resource request quota through preset quota calculation rules. This provides multi-dimensional decision-making basis for subsequent resource allocation processes. Furthermore, by setting a "waiting for permission transfer" state, the transfer process of entity resource disposal permissions is transformed into a controllable process node. When disposal permission transfer information indicating completion is received, the process state is automatically updated from the waiting for permission transfer state to the allocable state, and resource allocation operations are executed. Compared to methods that rely on manual offline completion of permission transfer operations followed by manual status updates in the system, this application directly maps changes in the external state of entity resources to state transitions in the resource allocation process. This ensures that the resource allocation process maintains automation while achieving coordination with external operations, reducing operational errors and process interruptions caused by manual intervention. It ensures that business processes centered on entity resource objects are executed continuously and automatically, thereby improving resource allocation efficiency.

[0007] In one possible implementation of the first aspect, determining the resource request limit for the resource requester based on the first basic information, the second basic information, and the value assessment result includes: verifying the first basic information and the second basic information based on preset resource request conditions to obtain a resource request verification result. The preset resource request conditions include at least one of the following: Trust information indicating that the resource requester's overdue number of pre-paid resource returns is less than an overdue number threshold; resource output information indicating that the resource requester's resource output frequency is greater than a preset frequency threshold; ownership information of the entity resource object indicating that the resource requester has the authority to dispose of the entity resource object; and social network information indicating that the resource requester's overdue number of pre-paid resource returns for its resource request affiliates is less than an overdue number threshold. If the resource request verification result is successful, the resource requester's resource request limit is determined based on preset resource limit rules, the first basic information, and the value assessment result.

[0008] It should be understood that this implementation method automatically verifies the first and second basic information through preset multi-dimensional resource request conditions, establishes an access screening mechanism with the ownership of the entity resource object as the core, effectively excludes requests with unclear ownership or insufficient performance capabilities, reduces the risk of subsequent resource allocation process being interrupted due to subject qualification issues from the source, and ensures the operational foundation of the resource allocation process based on entity resources.

[0009] In another possible implementation of the first aspect, the resource request limit of the resource requester is determined based on preset resource quota rules, first basic information, and value assessment results. This includes: determining the basic resource request limit of the resource requester based on the value assessment results; performing time series regression analysis on the resource output information in the first basic information to obtain an output growth rate score; the output growth rate score is used to measure the growth rate of the resource output of the resource requester; performing performance analysis on the credible information in the first basic information to obtain a performance stability score; the performance stability score is used to quantify the overdue return of historical pre-paid resources of the resource requester; processing the output growth rate score and performance stability score through a quota adjustment coefficient model to determine a quota adjustment coefficient used to adjust the basic resource request limit; and determining the resource request limit of the resource requester based on the quota adjustment coefficient and the basic resource request limit.

[0010] It should be understood that this implementation method combines the value assessment results with behavioral characteristics such as the dynamic growth rate of resource output and the stability of historical performance to calculate the quota. This makes the resource request quota not only reflect the static value of the physical resources, but also related to their potential for continuous output and the credit behavior of the requester. Thus, based on the anchoring of physical resources, the quota allocation is made more precise and the risk is more controllable, thereby improving the overall rationality of resource allocation.

[0011] In another possible implementation of the first aspect, the quota adjustment coefficient model is obtained by: acquiring a training dataset. The training dataset includes samples corresponding to multiple historical resource requesters. Each sample includes: the first basic information of the corresponding historical resource requester and a quota adjustment coefficient label. The quota adjustment coefficient label is determined by the ratio of the historical resource requester's final resource request quota to its basic resource request quota. An initial model of the quota adjustment coefficient model is trained based on the training dataset to obtain the quota adjustment coefficient model.

[0012] It should be understood that this implementation method trains the quota adjustment coefficient model based on historical data, enabling the quota calculation to learn autonomously and adapt to the output patterns and performance characteristics of different resource requesters. This allows for dynamic optimization of the quota adjustment strategy based on the assessment of the value of physical resources, thereby enhancing the adaptability and accuracy of the resource allocation model in practical applications.

[0013] In another possible implementation of the first aspect, obtaining the second basic information of the entity resource object of the resource requester includes: obtaining it by sending an information query request to the management server of the entity resource object, and / or obtaining it through manual input.

[0014] It should be understood that this implementation method, by supporting both automatic querying from the management server and manual entry to obtain the second basic information, not only ensures the authority and real-time nature of the core data source of entity resources, but also provides flexibility for manual supplementation and verification in special circumstances. This ensures the integrity and reliability of the entity resource information on which the resource allocation process depends, and lays a solid data foundation for the automated execution of subsequent processes.

[0015] In another possible implementation of the first aspect, after obtaining the first basic information of the resource requester and the second basic information of the resource requester's entity resource object, the method further includes: obtaining a manually input verification result of the entity resource object regarding the second basic information. If the entity resource object verification result is unsuccessful, a prompt message is generated regarding the second basic information. The prompt message is used to instruct the resource requester to modify the second basic information.

[0016] It should be understood that this implementation method effectively identifies and corrects data deviations caused by information entry errors or ownership disputes by introducing a manual verification process and generating prompts for entity resource information that fails verification. This prevents flawed entity resources from entering the subsequent automated allocation process and reduces the operational risk of resource allocation being forced to be interrupted or resulting in errors due to inaccurate basic data.

[0017] In another possible implementation of the first aspect, the method further includes: updating the state of the resource allocation process from an allocable state to a pending return state, and periodically checking the state of the resource allocation process. In response to the resource allocation process state being updated to a returned state, a permission restoration request for the entity resource object is sent to the entity resource object's management server. In response to the permission restoration confirmation information sent by the entity resource object's management server, the resource allocation process is stopped.

[0018] It should be understood that this implementation method achieves full lifecycle management centered on the complete transfer and return of entity resource permissions by automatically triggering permission restoration requests and synchronously stopping the process after resource return. This ensures the clarity and compliance of entity resource ownership status at the end of the resource allocation process, forms a closed-loop control around changes in entity resource status, and improves the integrity and security of the entire resource allocation system.

[0019] Secondly, a resource allocation device is provided, the device comprising: The acquisition module is used to acquire the first basic information of the resource requester and the second basic information of the resource object. The resource object is physically located and immovable. The first basic information includes: the resource requester's identification information, social network information, resource output information, and trust information. The second basic information includes: the type of resource object, address information, ownership information, historical input cost information, and utility parameters. It also acquires the valuation results for the resource object sent by the valuation agency's management server.

[0020] The processing module, in response to a resource request submitted by a resource requester, initiates a resource allocation process. Based on the first basic information, the second basic information, and the value assessment result, it determines the resource request limit for the resource requester and sets the resource allocation process status to either "awaiting permission transfer" or "suspending the resource allocation process." In response to a permission transfer message for the entity resource object sent by the entity resource object's management server, it updates the resource allocation process status from "awaiting permission transfer" to "allocateable" and allocates resources corresponding to the requested resource limit to the resource requester. The permission transfer message indicates that some or all of the entity resource object's disposal permissions are transferred to the resource provider.

[0021] Thirdly, a resource allocation device is provided, comprising: a memory and at least one processor. The memory is communicatively connected to the processor. The memory is used to store computer program code, which includes computer instructions. When the processor executes the computer instructions, it causes the resource allocation device to perform the method as described in the first aspect and any possible implementation thereof.

[0022] Fourthly, a computer-readable storage medium is provided that stores computer instructions. When executed by a processor, the computer instructions are used to implement the method as described in the first aspect and any possible implementation thereof.

[0023] Fifthly, a computer program product is provided that, when run on a computer or executed by a computer's processor, implements the method described in the first aspect and any possible design thereof. The computer may be the resource allocation device described in the third aspect and any possible implementation thereof.

[0024] It is understood that the beneficial effects achieved by the resource allocation device described in the second aspect, the resource allocation equipment described in the third aspect, the computer-readable storage medium described in the fourth aspect, and the computer program product described in the fifth aspect can be referred to as the beneficial effects in the first aspect and any possible implementation thereof, and will not be repeated here. Attached Figure Description

[0025] Figure 1 This application provides an embodiment of an interactive flow diagram of a computing device. Figure 2 A flowchart illustrating a resource allocation method provided in an embodiment of this application; Figure 3 A flowchart illustrating another resource allocation method provided in an embodiment of this application; Figure 4 A flowchart illustrating a method for determining resource request quotas provided in an embodiment of this application; Figure 5 A flowchart illustrating a method for obtaining a credit limit adjustment coefficient model provided in an embodiment of this application; Figure 6 A flowchart illustrating yet another resource allocation method provided in an embodiment of this application; Figure 7 A flowchart illustrating yet another resource allocation method provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of a resource allocation device provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of a resource allocation device provided in an embodiment of this application. Detailed Implementation

[0026] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this embodiment, unless otherwise stated, "a plurality of" means two or more.

[0027] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0028] The technical solutions provided in this application, including the collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data, comply with relevant laws and regulations and do not violate public order and good morals.

[0029] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0030] In resource allocation processes based on trusted credentials for physical resources, the resource allocation process and the physical resource permission transfer process are disconnected, preventing the system from forming an automated closed loop. Specifically, when the resource allocation process reaches the point where it requires external assistance to complete the physical resource permission transfer, the system is forced to halt because it cannot automatically obtain and verify the permission transfer status. It must wait for manual offline operation to complete and manually report the result before continuing with subsequent allocation operations. This "system-human-system" collaboration model not only results in low process efficiency but also introduces risks such as operational delays, data inconsistencies, and human error.

[0031] Therefore, how to improve the efficiency of resource allocation when using physical resources as trusted credentials is a technical problem that we urgently need to solve.

[0032] This application provides a resource allocation method that transforms the entity resource permission transfer process into a controllable process node by establishing a "waiting for permission transfer" state. First, the resource request amount is determined based on multi-dimensional data of the entity resource and the requester's information. Then, the process is set to a "waiting for permission transfer" state. When permission transfer information is received, the state is automatically updated to an allocable state, and resource allocation is completed. This method directly maps external state changes to process state transitions, ensuring continuous and automated execution of the resource allocation process while eliminating manual intervention, thereby improving the efficiency of resource allocation.

[0033] The resource allocation method provided in this application can be applied to computing devices. Specifically, a computing device can be a single server, a server cluster consisting of multiple servers, a computer, or a processor or processing chip in a server or computer. This application does not limit the specific form of the computing device.

[0034] like Figure 1 The diagram shows the interaction flow of a computing device, which includes the following interactive entities: resource requester, computing device, evaluation agency management server, and entity resource object management server.

[0035] 1. Submit basic information.

[0036] The computing device obtains the first basic information and the second basic information of the entity resource object by actively submitting the request from the resource requester.

[0037] In some embodiments, the computing device may also obtain first basic information and second basic information of the entity resource object by manually entering and querying the management server of the entity resource object.

[0038] 2. Send a request for valuation of the entity resource object.

[0039] After submitting basic information to the computing device, the resource requester can send a value assessment request for the entity resource object to the assessment agency's management server.

[0040] 3. Send the value assessment results.

[0041] After assessing the value of the physical resource object, the assessment agency's management server sends the assessment results to the computing device based on the network interface of the computing device pre-stored in the resource requester's assessment request.

[0042] 4. Submit a resource request.

[0043] After obtaining basic information and value assessment results from the computing device, the resource requester submits the resource request.

[0044] 5. Initiate the resource allocation process.

[0045] 6. Perform access verification.

[0046] The computing device verifies the first basic information and the second basic information based on preset resource request conditions, and generates a resource request verification result.

[0047] If the verification result is unsuccessful, proceed to step 6.

[0048] If the verification result is successful, proceed to step 7.

[0049] 7. Send a notification message.

[0050] Send a notification message to the resource requester, instructing them to update or supplement their information.

[0051] 8. Determine the resource request limit.

[0052] Based on the received first basic information, second basic information, and value assessment results, the computing device determines the resource request limit of the resource requester according to a preset limit calculation rule. At this time, the computing device sets the resource allocation process status to a waiting permission transfer state and suspends the resource allocation process.

[0053] 9. Notify the resource request quota.

[0054] After determining the resource request limit, the computing device sends the resource request limit result to the resource requester.

[0055] 10. Initiate a request to transfer disposal authority.

[0056] The resource requester determines whether the resource request amount meets expectations. If it does not meet expectations, the requester can choose to stop the resource request, continue to supplement basic information, or change the entity resource object.

[0057] If the expected outcome is achieved, proceed to the following steps: the resource requester may send a disposal permission transfer request to the management server of the entity resource object. The disposal permission transfer request may include: the resource requester's identity information, the entity resource object identifier, and the type of permission to be transferred.

[0058] 11. Send information on the transfer of processing authority.

[0059] After the management server of the entity resource object performs the permission transfer operation, it executes the permission transfer operation of the entity resource object according to the content information of the permission transfer request.

[0060] 12. Resource allocation.

[0061] After receiving the information on the transfer of processing authority, the computing device updates the resource allocation process status from the waiting for authority transfer status to the allocable status, and allocates resources corresponding to the requested resource amount to the resource requester.

[0062] Specifically, resource allocation can be performed directly by the computing device or by a resource management server controlled by the computing device.

[0063] 13. Continuously monitor the status of resource return.

[0064] The computing device continuously monitors the resource return progress of the resource requester.

[0065] 14. Resource return.

[0066] After the agreed resource usage period ends, the resource requester shall return the resources in accordance with the agreed return method.

[0067] 15. Initiate a permission restoration request.

[0068] After the resources are fully returned, the computing device updates the resource allocation process status to the returned status and sends a permission restoration request to the management server of the entity resource object.

[0069] 16. Confirm the completion of permission restoration.

[0070] After the management server of the entity resource object performs the permission restoration operation, it sends permission restoration confirmation information to the computing device, and the computing device then terminates the resource allocation process.

[0071] 17. Notification permissions restored.

[0072] The computing device notifies the resource requester that permissions have been restored.

[0073] like Figure 2 As shown, the resource allocation method provided in this application embodiment, when applied to the above-mentioned computing device, specifically includes steps S101-S104: S101. Obtain the first basic information of the resource requester and the second basic information of the entity resource object of the resource requester.

[0074] Among them, physical resource objects are fixed in location in physical space and cannot be moved.

[0075] A resource requester is an entity that submits a request for resources to a computing device, typically an individual or organization that needs resources to support its operation or development.

[0076] A physical resource object refers to a physical asset owned or controlled by the resource requester, which is fixed in location in physical space and cannot be moved. It participates in the resource allocation process as a trusted credential.

[0077] The first set of basic information is a data set describing the state and characteristics of the resource requester. The second set of basic information is a data set describing the attributes and state of the entity resource object that serves as a trusted credential.

[0078] In some embodiments, physical resource objects can be classified according to their value source and utilization method, including those that depend on natural conditions or those that depend on man-made facilities.

[0079] Specifically, the value of physical resource objects that depend on natural conditions is closely tied to their geographical location and natural resources, such as farmland used for crop growth, standardized ponds used for aquaculture, and guesthouse courtyards located in specific scenic areas.

[0080] The value of physical resource objects that rely on man-made facilities is mainly reflected in the structures themselves, such as signal base station towers for communication transmission, school buildings for educational activities, warehouses for logistics storage, and farmhouses for living and business purposes. This application does not limit the specific types of physical resource objects.

[0081] In other embodiments, the physical resource object can be a farmer's house, agricultural production facilities, rural operating assets, etc. Among them, the status control of a farmer's house as a physical resource object is difficult, because its ownership management and physical status management are usually handled by different institutions, and the confirmation of status changes (such as the transfer of disposal authority) depends on feedback from external systems. In related technologies, it is often difficult to perceive its latest ownership status in real time and automatically, thus hindering the formation of an automated closed loop in the resource allocation process that relies on such objects as trusted credentials.

[0082] In other embodiments, a physical resource object can refer to a resource object that is physically fixed to a specific plot of land, and whose legal existence and disposal rights are associated with the land use rights of that plot. The realization of the value and transfer of ownership of such objects are constrained by the status of the land use rights. For example, agricultural facilities such as greenhouses and livestock pens built on agricultural land typically cannot be transferred independently of the land use rights they are attached to. In related technologies, this complex ownership dependency further increases the difficulty for the resource allocation system to automatically verify the integrity of its disposal rights, making the process more prone to interruption at the authorization verification stage, requiring additional manual intervention to coordinate information consistency between different ownership management systems.

[0083] Specifically, the first basic information includes: the resource requester's identification information, the resource requester's social network information, the resource requester's resource output information, and the resource requester's trust information.

[0084] Resource requester identification information refers to a set of data that can uniquely identify and distinguish different requesting entities, ensuring that requests processed by computing devices are accurately associated with specific requesters. This information provides the basic identity verification basis for computing devices to perform subsequent verification, query, and association operations.

[0085] Social network information of a resource requester refers to a set of data reflecting the relationships and interaction characteristics between the requester and other entities, used by computing devices to analyze the requester's attributes within a specific network structure. Based on this information, computing devices can assist in assessing the overall situation of the requester.

[0086] Resource output information of a resource requester refers to quantifiable results data that records the requester's specific activities over historical and current periods. This data is used by computing devices to analyze the regularity and sustainability of their output activities. This information is one of the key indicators upon which computing devices rely for dynamic evaluation.

[0087] Trustworthy information about a resource requester refers to data formed based on the requester's past behavioral records during interactions, used to quantify its performance and to allow computing devices to predict the likelihood of it adhering to established rules in subsequent processes. This information is one of the reference bases for computing devices to make automated decisions.

[0088] Specifically, the second basic information includes: the type of entity resource object, the address information of the entity resource object, the ownership information of the entity resource object, the historical input cost information of the entity resource object, and the utility parameters of the entity resource object.

[0089] The type of entity resource object refers to the identification information that classifies them according to their inherent physical attributes and core functions, so that computing devices can call the corresponding processing logic and evaluation parameters according to the type.

[0090] The address information of an entity resource object refers to the precise location data that describes the object's immovable location in physical space. It is used for spatial positioning by computing devices and provides necessary input for location-based verification services that may be triggered.

[0091] Ownership information of an entity resource object refers to credential data that proves a legally recognized control relationship between the resource requester and the object. This data is used by the computing device to verify whether the requester has the legal right to participate in the current process with respect to the object. This information is a fundamental prerequisite for the computing device to execute the authorization transfer process.

[0092] Historical input cost information for an entity resource object refers to a record of the total economic resources invested in acquiring or maintaining its state over a historical period related to that object. This information is used as a reference factor for computing devices when comprehensively evaluating the state of the object.

[0093] The utility parameters of an entity resource object refer to the indicator data that can quantitatively reflect the functions or output capabilities that the object can achieve under specific conditions.

[0094] For example, if the entity is agricultural land, its utility parameters could include the average annual grain yield per unit area calculated based on factors such as soil type, climate conditions, and irrigation facilities; if it is commercial property, its utility parameters could cover estimated foot traffic per unit time, a score of the completeness of surrounding infrastructure, or average space utilization based on historical data; if it is a photovoltaic power station, its utility parameters could be expressed as the average annual power generation under rated illumination conditions, equipment degradation coefficient, and grid connection stability rating. These quantified parameters provide the computing equipment with an objective basis for assessing the potential contribution of the entity.

[0095] In some embodiments, a computing device may receive electronic data directly input or uploaded by a resource requester through its user interface to obtain first basic information and second basic information.

[0096] In other embodiments, the computing device can obtain first basic information and second basic information by querying resource requester profiles and entity resource object registration data pre-stored in its internally stored database or a connected dedicated data storage server.

[0097] In some embodiments, regarding the acquisition of the second basic information, due to the dispersed state information and heterogeneous data sources of entity resource objects, diversified data acquisition strategies are required to ensure the integrity and reliability of the acquired information. Specifically, this may include: acquiring the information by sending an information query request to the management server of the entity resource object; and / or, acquiring the information through manual input.

[0098] For example, manual data entry can involve operators conducting on-site surveys and measurements of physical resource objects to obtain relevant data, and then inputting the survey results into the computing device through the data entry interface provided by the computing device. For instance, operators can measure the building area of ​​a farmer's house on-site, assess the current state of the building structure, and input this information, along with photos taken on-site, into the computing device.

[0099] It should be understood that this implementation method, by supporting both automatic querying from the management server and manual entry to obtain the second basic information, not only ensures the authority and real-time nature of the core data source of entity resources, but also provides flexibility for manual supplementation and verification in special circumstances. This ensures the integrity and reliability of the entity resource information on which the resource allocation process depends, and lays a solid data foundation for the automated execution of subsequent processes.

[0100] In some embodiments, the computing device can perform format compliance checks and data integrity verification on the received first and second basic information to ensure that the data structure conforms to its predefined specifications and that all required fields are complete. For abnormal or missing data items identified during the verification process, the computing device can automatically generate a report containing a specific error description and pause subsequent steps of the process.

[0101] S102. Obtain the valuation results for the entity resource object sent by the valuation agency's management server.

[0102] The assessment agency management server refers to a computing system deployed and managed by a third-party assessment agency, capable of performing quantitative value analysis on physical resource objects. The value assessment result refers to the quantitative value data of the physical resource object under specific benchmark conditions output by the server after executing a specific assessment model and algorithm. This result serves as a key basis for subsequent resource allocation decisions by the computing equipment.

[0103] Because physical resources are non-standardized and their value is difficult to quantify directly, resource allocation processes that use physical resources as credible evidence require reliance on objective value assessment data provided by professional evaluation agencies to establish a reliable value quantification benchmark. This enables computing devices to accurately match resources based on the true value level of physical resources.

[0104] In some embodiments, the valuation result may include: a quantified value, an identifier of the valuation method used, a valuation reference time point, and necessary explanatory information.

[0105] Specifically, the quantified value refers to the value quantity expressed in comparable units, obtained by the assessment agency's management server through the execution of a specific assessment algorithm. This value provides a benchmark anchor for the computing device to calculate resource allocation. The assessment method identifier refers to the internal system code or name used to specify the specific assessment methodology used to generate this value, such as a specific variant of the market comparison method, the discounted cash flow method, or the cost replacement method. This identifier helps the computing device understand the logic behind the assessment result and establish the corresponding confidence level in subsequent processes. The assessment benchmark time point refers to the specific timestamp corresponding to the value assessment result, clarifying the effective time reference of the assessed value and ensuring that the computing device can accurately consider the time decay or fluctuation characteristics of value when processing time-sensitive business logic. Necessary explanatory information may include descriptions of significant assumptions made during the assessment process, notes on special states of the assessed object, or limiting statements regarding the scope of application of the assessment results. This information provides crucial contextual basis for the computing device to identify potential special cases or trigger manual review in automated decision-making.

[0106] One possible implementation involves the computing device establishing an encrypted communication channel based on Hypertext Transfer Protocol Secure (HTTP) with the evaluation agency's management server via its integrated Application Programming Interface (API) gateway. Upon receiving an authorized evaluation request from a resource requester, the computing device constructs a request message conforming to the open API. This message contains a unique identifier for the entity resource object and the required evaluation parameters, and undergoes mutual authentication via a Secure Sockets Layer (SSL) certificate. After processing, the evaluation agency's management server pushes response data to the callback Uniform Resource Identifier (URI) specified by the computing device in JavaScript object notation. Upon receiving the data packet, the computing device first verifies the validity of the digital signature, then parses the evaluation result fields in the data packet, converting them into an internal data structure for subsequent processes.

[0107] S103. In response to the resource request submitted by the resource requester, initiate the resource allocation process, determine the resource request amount of the resource requester based on the first basic information, the second basic information and the value assessment result, and set the status of the resource allocation process to the waiting permission transfer status and the suspended resource allocation process.

[0108] The resource allocation process refers to a state-trackable, persistent, and business logic-driving instance of a business process created by a computing device to handle specific resource requests. At the software architecture level of the computing device, this process instance is uniformly modeled and driven by the workflow engine; at the physical runtime level, it is specifically manifested as a business object instance in memory and a state record in the database.

[0109] The resource request quota refers to the quantified resource value that a computing device calculates based on multi-dimensional input data and preset rules, and is intended to allocate to the resource requester. The "awaiting permission transfer" status is a specific status indicator in the resource allocation process. This status indicates that the progress of the process currently depends on the completion of an external event where some or all of the entity's resource object's disposal permissions are transferred to the resource provider.

[0110] One possible implementation is that the computing device can predefine a resource allocation process class through application code (such as a class definition in Java), and initiate the resource allocation process by instantiating a resource allocation process class object. Furthermore, by calling methods of this object, the values ​​of its internal member variables representing the process state can be directly modified to achieve state transitions in the resource allocation process. Specifically, this object has a member variable (or attribute / field) named `currentState`. This object provides methods such as `setStateToWaitingForPermission()`. When a state transition is needed, the computing device calls this method; the core operation performed internally by this method is an assignment statement, for example: `this.currentState = "WAITING_PERMISSION_TRANSFER";`.

[0111] In this scenario, the computing device can pause the resource allocation process by: calling a process control method (e.g., suspendProcess()) defined in the resource allocation process class to modify the value of a member variable representing the process execution state (e.g., isActive) to a boolean value indicating pause (e.g., false), or directly setting the process state variable (currentState) to a specific "paused" state (e.g., "SUSPENDED"). Subsequently, the main loop or event loop detection logic responsible for driving the process execution will stop executing subsequent business operations upon detecting this state value, thus pausing the process.

[0112] Another possible implementation is that the computing device can predefine resource allocation processes using its integrated workflow orchestration tools (such as Apache Airflow or Kubernetes Jobs). The resource allocation process is initiated by submitting a workflow execution task to the tool's server. Furthermore, the state transition of the resource allocation process is achieved by updating the corresponding task's status record in the task status database. Specifically, the workflow orchestration tool maintains a task status database containing a `status` field that identifies the workflow execution status. The computing device modifies this status by calling the status update interface provided by the tool. When a status transition is needed, the computing device calls this interface. The core operation performed internally by this interface is sending an SQL update statement to the database, for example: `UPDATE workflow_tasks SET status = 'WAITING_PERMISSION_TRANSFER' WHERE task_id = 'RA-20240521-001'`.

[0113] In this scenario, the computing device can pause resource allocation as follows: by calling the task management interface (e.g., the Pause Task API) provided by the workflow orchestration tool, a command to pause a specific workflow instance is sent to the server. This command causes the workflow orchestration tool to perform two core operations: first, it updates the status record of the task in its task status database (e.g., setting the status field to "PAUSED"); second, the scheduler stops allocating computing resources to the task and suspends the scheduling and execution of its subsequent tasks until a resumption command is received.

[0114] In some embodiments, the status values ​​can be in the form of string constants, such as using "PENDING_TRANSFER" to represent the waiting permission transfer status, "ALLOCATABLE" to represent the allocatable status, "TO_BE_RETURNED" to represent the pending return status, and "RETURNED" to represent the returned status. Alternatively, they can be in the form of integer enumeration values, such as using the number 1 to correspond to the waiting permission transfer status, the number 2 to the allocatable status, the number 3 to the pending return status, and the number 4 to the returned status.

[0115] In some embodiments, before determining the resource request amount, the computing device may also perform access verification on the first basic information and the second basic information of the resource requester to identify and eliminate the risk of process execution interruption caused by inconsistent subject qualifications or abnormal ownership status in the early stage of the process, so as to ensure the continuity of the subsequent automated allocation process.

[0116] For details, please refer to the following text. Figure 3 The corresponding descriptions are not detailed here.

[0117] In some embodiments, during the process of determining the resource request limit, the computing device can directly determine the limit value or limit calculation parameters by matching input conditions with rule outputs based on a pre-configured decision table method.

[0118] One possible implementation involves the computing device accessing a structured decision table. This table takes first basic information, second basic information, and specific fields from the value assessment results as input. Its output can be a direct resource request limit value or an adjustment coefficient used to adjust the base limit. The computing device matches the currently requested data against rules in the decision table and executes the output action specified by the first successfully matched rule. When the output is a direct limit value, this value is adopted as the final resource request limit; when the output is an adjustment coefficient, the computing device multiplies this coefficient by a pre-calculated base resource request limit based on the value assessment results to determine the final resource request limit.

[0119] Specifically, the decision table may include the following: the preset range in which the value assessment result of the entity resource object falls, whether the resource requester's resource output growth rate reaches a preset standard, and the performance rating of the resource requester based on its performance. The decision table predefines the output results corresponding to different combinations of these conditions.

[0120] For example, the decision table contains the following rule: when the value assessment result is in a high range, the resource output growth rate reaches a preset standard, and the historical performance rating is rated as excellent, an upward adjustment coefficient is output. The computing device triggers the corresponding output by matching the actual request data with the conditions of such rules in the decision table, thereby determining or adjusting the quota.

[0121] The resource output growth rate of the resource requester is obtained by performing a least-squares linear fit on the periodic output data points (e.g., monthly output over 24 consecutive months) contained in the resource output information in the first basic information, and calculating the slope of the fitted line. This slope is the quantified output growth rate, and its value can be directly compared with a preset growth rate threshold.

[0122] The performance rating determined by the resource requester based on performance is obtained through statistical processing of historical advance resource return records recorded in the trusted information of the first basic information. Specifically, the computing device will count the proportion of overdue transactions (i.e., overdue rate) among all completed advance resource returns within a preset time window (e.g., the past 36 months). Subsequently, by comparing this overdue rate with several preset threshold ranges (e.g., overdue rate ≤ 2% for Grade A, 2% < overdue rate ≤ 5% for Grade B, and overdue rate > 5% for Grade C), a specific performance rating identifier (such as A, B, or C) is mapped and output.

[0123] S104. In response to the transfer information of disposal authority for the entity resource object sent by the management server of the entity resource object, update the status of the resource allocation process from the waiting authority transfer status to the allocable status, and allocate resources corresponding to the resource request amount to the resource requester.

[0124] Among them, the information on the transfer of disposal authority indicates that some or all of the disposal authority of the entity resource object is transferred to the resource provider.

[0125] Disposal authority transfer information refers to electronic credential data generated by the management server of an entity resource object, used to confirm that some or all disposal authority of that entity resource object has been transferred to the resource provider. For example... Figure 1It can be seen that the permission transfer request of an entity resource object can be submitted by the resource requester to the entity resource object's management server, and then processed by the entity resource object's management server before sending the permission transfer information to the computing device.

[0126] The allocatable state is the state after the relay waiting for permission transfer state in the resource allocation process state machine. This state indicates that the preconditions for the process to continue have been met, and the computing device can perform resource allocation operations.

[0127] For example, the disposal permission transfer information can be a timestamped data packet containing a digital signature, which records the operation time of the permission transfer, the scope of the transferred permissions, the identifiers of the entity resource objects involved, and related party information.

[0128] In some embodiments, the computing device may use a direct resource transfer method to allocate resources corresponding to the requested resource amount to the resource requester.

[0129] One possible implementation involves the computing device initiating a resource transfer request to the resource provider's resource management terminal by calling its integrated resource scheduling interface. This request includes key parameters such as the resource requester's account identifier, the resource provider's account identifier, and the requested resource amount. After verifying the request's validity, the resource provider's terminal performs an atomic resource accounting operation: atomically deducting the specified amount of resources from the resource provider's available resource pool, simultaneously atomically adding an equivalent amount of resources to the resource requester's designated account, and generating a resource transfer voucher with a unique number. The entire resource allocation process ensures data consistency through database transactions, and the resource transfer result is returned to the computing device via the terminal interface.

[0130] In other embodiments, the computing device may also use a resource quota granting method to allocate resources corresponding to the resource request quota to the resource requester.

[0131] One possible implementation involves the computing device creating a new authorization record in the resource quota mapping table through its maintained resource authorization management module. This record includes core fields such as the resource requester's identity, the credit limit value, and the quota validity period, and uses a status field to indicate that the quota is in an "available" state. This authorization operation grants the resource requester the qualification to acquire resources in batches within the authorized scope and within the quota validity period. When the resource requester subsequently initiates a resource usage request, the computing device will check the remaining available quota in real time and perform quota deduction to ensure that the actual total resource allocation does not exceed the granted resource request quota.

[0132] It should be understood that when the management server of an entity resource object completes the permission transfer and generates disposal permission transfer information, this information is captured by the computing device as an external event signal, directly triggering the state transition of the resource allocation process. This design transforms independent external permission management activities into controllable nodes in the resource allocation process, enabling the computing device to drive the coherent execution of the process by sensing the actual state changes of the entity resource object without relying on manual intervention. By establishing a mapping relationship between the permission state of the entity resource object and the process state, the synchronization and coordination of internal and external system states are achieved, ensuring that business processes using entity resource objects as trusted credentials can form an automated closed loop.

[0133] This application provides a resource allocation method. First, it utilizes multi-dimensional data such as the spatial attributes, ownership relationships, and utility characteristics of entity resource objects, combined with the basic information of the resource requester, to determine the resource request quota through preset quota calculation rules. This provides multi-dimensional decision-making basis for subsequent resource allocation processes. Furthermore, by setting a "waiting for permission transfer" state, the transfer process of entity resource disposal permissions is transformed into a controllable process node. When disposal permission transfer information indicating completion is received, the process state is automatically updated from the waiting for permission transfer state to the allocable state, and resource allocation operations are executed. Compared to methods that rely on manual offline completion of permission transfer operations followed by manual status updates in the system, this application directly maps changes in the external state of entity resources to state transitions in the resource allocation process. This ensures that the resource allocation process maintains automation while achieving coordination with external operations, reducing efficiency losses and operational risks caused by manual intervention. It ensures that business processes centered on entity resource objects are executed coherently and automatically, thereby improving resource allocation efficiency.

[0134] In some embodiments, when determining the quota operation in the resource allocation process, the computing device may employ a phased processing strategy to improve the systematic nature and controllability of the decision. This strategy first verifies the eligibility of the resource requester and the entity resource object it provides, ensuring that the participating entities and the target resource meet preset conditions; then, based on successful verification, the quota calculation logic is initiated. In this case, the process is as follows: Figure 3 As shown, S103 specifically includes the following steps S201-202: S201. Based on the preset resource request conditions, verify the first basic information and the second basic information to obtain the resource request verification result.

[0135] The preset resource request conditions refer to the set of logical rules pre-configured in the computing device for admission judgment of resource requesters and their entity resource objects. The resource request verification result refers to the conclusive data generated by the computing device after performing all condition verifications, characterizing whether the current request meets the admission requirements.

[0136] Specifically, the preset resource request conditions include at least one of the following: trusted information indicating that the number of overdue resource returns for the resource requester is less than the overdue number threshold; resource output information indicating that the resource requester's resource output frequency is greater than the preset frequency threshold; ownership information of the entity resource object indicating that the resource requester has the authority to dispose of the entity resource object; and social network information indicating that the number of overdue resource returns for the resource requester's resource request affiliates is less than the overdue number threshold.

[0137] The trusted information indicates that the number of overdue resource repayments by the resource requester is less than a threshold for the number of overdue repayments. This condition is used to assess the reliability of the resource requester's behavior during historical resource usage. Specifically, the computing device queries the historical overdue resource records contained in the trusted information within the first basic information, performs statistical analysis on the overdue repayments within a preset statistical time range, and compares the statistical results with the preset threshold for the number of overdue repayments.

[0138] One possible implementation is that the computing device extracts the return time information of all historical prepaid resource records from trusted information, compares it with the agreed due time, counts the number of times the agreed time is exceeded, and compares the statistical value with the overdue number threshold stored in the computing device configuration.

[0139] For example, the threshold for the number of overdue payments can be 0, 1, or 2 times, etc. This application does not limit the specific value of the threshold for the number of overdue payments.

[0140] Resource output information indicates that the resource requester's resource output frequency is greater than a preset frequency threshold. This condition is used to assess the resource requester's ability to maintain continuous and stable operation. Specifically, the computing device parses the output time series data contained in the resource output information in the first basic information, calculates the number of outputs per unit time within a preset statistical period, and compares the calculation result with the preset frequency threshold.

[0141] One possible implementation involves the computing device performing frequency analysis on the timestamp data in the resource output information, calculating the average number of outputs over the most recent consecutive statistical periods, and comparing this average with a preset frequency threshold.

[0142] For example, the frequency threshold can be once a month, twice a week, or once a day. This application does not limit the specific value of the frequency threshold.

[0143] The ownership information of an entity resource object indicates that the resource requester has the authority to dispose of the entity resource object. This condition is used to confirm that the resource requester has the legal qualification to use the entity resource object as a trusted credential. Specifically, the computing device verifies the permission description field included in the ownership information in the second basic information and performs a consistency comparison with other authoritative data sources.

[0144] One possible implementation involves the computing device parsing the permission list field in the ownership information to confirm that it contains a disposal permission identifier, and simultaneously sending a query request to the management server of the entity resource object, comparing the returned current ownership status with the information provided by the requester.

[0145] For example, the disposal authority may specifically include mortgage authority, transfer authority, or rental authority. This application embodiment does not limit the specific type of disposal authority.

[0146] Social network information indicates that the number of overdue resource repayments from the resource requester's related parties is less than a threshold. This condition is used to assess the overall stability of the business ecosystem in which the resource requester operates. Specifically, the computing device first identifies the set of related parties based on association rules, then queries the trust information of each related party and performs overdue count statistics, ultimately ensuring that all related parties meet the preset overdue count requirements.

[0147] One possible implementation involves the computing device extracting a list of entities that meet a preset relevance threshold from social network information, batch querying the advance resource return records of these entities, counting the number of overdue payments for each entity, and comparing it with the overdue payment threshold specific to the related party.

[0148] For example, the threshold for the number of times related parties default can be 0 or 1. This application does not limit the specific value of the threshold for the number of times related parties default.

[0149] In the specific verification process, the computing device may select one or more of the above-preset resource request conditions. The embodiments of this application do not limit the specific combination of verification conditions used.

[0150] Accordingly, the verification results obtained based on the preset resource request conditions can include three states: pass, partially pass, and fail. Specifically, a pass state indicates that all selected verification conditions are met, and the resource allocation process can proceed to the next stage. A partially pass state indicates that only some of the selected verification conditions are met; in this case, the computing device can decide, according to a preset strategy, whether to conditionally allow entry into the subsequent process or trigger a supplementary verification mechanism. A fail state indicates that the core verification conditions are not met, and the computing device will terminate the current resource allocation process.

[0151] Furthermore, the numerical comparison relationships involved in the above conditional judgments are not limited to less than or greater than relationships, but may also include boundary condition cases such as less than or equal to, and greater than or equal to. This application does not limit the specific selection of comparison operators in its embodiments.

[0152] For example, when verifying resource output frequency, the condition can be set to either "resource output frequency is greater than a preset frequency threshold" or, depending on actual business needs, "resource output frequency is greater than or equal to a preset frequency threshold." Similarly, when verifying the number of overdue payments, the condition can be set to either "the number of overdue payments is less than an overdue payment threshold" or "the number of overdue payments is less than or equal to an overdue payment threshold." By configuring different comparison operators, computing devices can flexibly adapt to verification requirements in various business scenarios.

[0153] It should be understood that by verifying the resource requester and the entity resource object in the pre-allocation stage, it can be ensured that all requests entering the quota calculation and subsequent processes meet the basic requirements for the continuous operation of the automated process. This prevents the process from being interrupted due to fundamental issues such as inconsistent subject qualifications or abnormal ownership status, and ensures the continuity of the resource allocation process and the realization of the automated closed loop.

[0154] S202. If the resource request verification result is successful, the resource request quota of the resource requester is determined based on the preset resource quota rules, the first basic information and the value assessment result.

[0155] The preset resource quota rules refer to the predefined calculation logic and methods in the computing device used to convert the value of physical resources and related parameters into specific resource quantities. The resource request quota refers to the quantified resource value to be allocated to the resource requester, calculated by executing the above rules.

[0156] As stated in S201, the verification result includes three states: pass, partially pass, and fail. A resource request verification result of pass can include the following situations: all selected verification conditions are met; or, according to the preset strategy, the core verification conditions are met while some non-core verification conditions, although not fully met, are considered acceptable.

[0157] In some embodiments, the status of the resource allocation process may also include: resource request verification status, resource request verification failure status, and quota calculation status.

[0158] Specifically, when the computing device initiates the resource allocation process in response to a resource request submitted by the resource requester, it first sets the process status to the resource request verification status. At this time, the computing device starts to execute the preset resource request condition verification based on the first and second basic information it has obtained.

[0159] When the verification result indicates that the verification has failed, the computing device updates the status to "Resource Request Verification Failed". In this state, the computing device can execute different processing logic according to a preset strategy: for correctable verification failures, the process can be paused and the resource requester can be notified to supplement or modify information; for irreversible verification failures, the process execution is terminated. When the verification result indicates that the verification has passed, the computing device updates the status to "Rate Calculation Status". At this time, the computing device begins to execute the resource request rate calculation logic based on preset resource rate rules, second basic information, and value assessment results.

[0160] It should be understood that this state update mechanism, through explicit node identification and transition conditions, enables computing devices to execute differentiated processing strategies based on different verification results. This ensures the rigor of the process, provides a processing channel for repairable verification problems, and improves the flexibility and fault tolerance of the resource allocation process.

[0161] In some embodiments, the preset resource quota rule can also be a rule calculated based on a base quota and an adjustment factor. This rule first determines a base quota based on the value assessment results, then generates an adjustment factor by identifying key parameters in the second basic information, and finally combines the base quota and the adjustment factor to derive the resource request quota.

[0162] Specifically, the computing device performs the following operations: Based on the value assessment results, it determines the benchmark amount by querying a preset value-benchmark amount comparison table or applying a simple linear proportional relationship. Subsequently, the computing device matches a preset type adjustment coefficient according to the type of entity resource object in the second basic information; simultaneously, it analyzes historical input cost information, and if the ratio of this ratio to the value assessment result exceeds a specific threshold, it activates an additional cost compensation factor. The computing device multiplies the type adjustment coefficient by the cost compensation factor to obtain a comprehensive adjustment factor. Finally, the resource request amount is calculated by multiplying the benchmark amount by the comprehensive adjustment factor.

[0163] For example, for a physical resource object with an assessed value of 1 million units and classified as a "standardized grain warehouse," the calculation equipment determines the base quota to be 700,000 units. Since the type adjustment coefficient corresponding to "standardized grain warehouse" is 1.1, and its historical input cost triggers a cost compensation factor of 1.05, the comprehensive adjustment factor is 1.1 × 1.05 = 1.155, and the final resource request quota is 700,000 × 1.155 = 808,500 units.

[0164] In other embodiments, the preset resource quota rule can also be a classification calculation rule based on the evaluation method identifier. This rule selects the corresponding calculation logic and parameter set to determine the resource request quota based on the evaluation method identifier carried in the value evaluation result.

[0165] Specifically, the computing device performs the following operations: First, it parses the data fields in the valuation result to obtain the valuation method identifier. When the valuation method identifier is identified as "market comparison method," the computing device uses a calculation method based on the market volatility coefficient to multiply the valuation result by a market adjustment factor to obtain the resource request amount. The market adjustment factor is determined based on the volatility of recent transaction prices of similar physical resource objects. When the valuation method identifier is identified as "cost replacement method," the computing device uses a calculation method based on the depreciation rate to multiply the valuation result by the depreciation rate coefficient of the physical resource object to obtain the resource request amount. When the valuation method identifier is identified as "income present value method," the computing device uses a calculation method based on income stability to multiply the valuation result by an income stability coefficient to obtain the resource request amount. The income stability coefficient is determined based on the variance of the historical income data of the physical resource object.

[0166] In some embodiments, when performing resource request quota determination operations, the computing device may adopt a hierarchical computing architecture to improve the accuracy and risk controllability of quota allocation. This architecture first establishes a basic quota benchmark based on the objective value of the physical resource object, and then introduces quantitative indicators that reflect the dynamic operational capabilities and performance characteristics of the resource requester for fine-tuning, thereby achieving personalized configuration of resource allocation while ensuring a security boundary anchored to the value of the physical resource.

[0167] In this case, the process is as follows Figure 4 As shown, S202 specifically includes the following steps S301-S305: S301. Based on the value assessment results, determine the basic resource request amount for the resource requester.

[0168] Among them, the basic resource request quota refers to the initial numerical basis for the computing device to directly determine the quantified value contained in the value assessment results as the basis for resource allocation decisions.

[0169] S302. Perform time series regression analysis on the resource output information in the first basic information to obtain the output growth rate score.

[0170] The output growth rate score is used to measure the rate at which the resource requester's output resources grow.

[0171] Specifically, time series regression analysis refers to the process by which computing devices use statistical modeling methods to fit a chronologically ordered series of output data in order to quantify the strength of its trend over time. This process extracts the deterministic trend components from the series by establishing a mathematical relationship model between time variables and output variables.

[0172] In some embodiments, the computing device may also perform data preprocessing operations on the resource output information before performing time series regression analysis. Specifically, the computing device may detect and process missing or outlier values ​​in the raw data, for example, by filling in missing monthly output data using linear interpolation, or by using box plots to identify and smooth outliers caused by special events, to ensure the quality and consistency of the data input to the regression model.

[0173] In some embodiments, resource output information can be time-series resource output indicators. Specifically, these indicators include three core data types: output per unit time, cumulative output, and output month-on-month growth rate. Output per unit time records the average output rate within each statistical period; cumulative output records the total output from the start time to the current statistical period; and output month-on-month growth rate records the percentage change in output between adjacent statistical periods.

[0174] In some embodiments, the computing device can perform time-series regression analysis on the unit-time output sequence in resource output information. The computing device selects unit-time output data from multiple consecutive statistical periods as the analysis sample and fits a trend line using the least squares method. Specifically, the computing device converts the time period into a numerical independent variable, uses the unit-time output corresponding to each period as the dependent variable, constructs a univariate linear regression model, and finally determines the regression coefficient, i.e., the slope of the trend line, as the output growth rate score. This score quantifies the average change in output per unit time; positive values ​​indicate an upward trend, and negative values ​​indicate a downward trend.

[0175] For example, the computing device processes a simplified series containing six months of output data: outputs at time points 1 to 6 are 100, 115, 124, 139, 148, and 160 units, respectively. The computing device first calculates the mean of the time variable as 3.5 and the mean of output as 131. It then calculates the covariance of the time series as 52.5 and the variance of the time variable as 17.5. Using the regression coefficient calculation formula, the slope is obtained as 52.5 / 17.5 = 3.0. This result indicates that monthly output is growing at a steady rate of 3.0 units per month, and the computing device determines this value as the output growth rate score.

[0176] In some embodiments, the output growth rate score can be directly represented by the regression coefficients calculated above. These regression coefficients characterize the absolute change in output per unit time, and their numerical form intuitively reflects the actual growth rate of output resources.

[0177] In other embodiments, the computing device can also standardize the regression coefficients to generate a dimensionless output growth rate score. Specifically, the computing device ratios the regression coefficients to a selected benchmark value to generate a relative growth rate score expressed as a percentage. This standardization process eliminates the influence of different resource sizes on the scoring results, making resource requesters of different sizes comparable.

[0178] For example, when the calculated regression coefficient is 3.0 units per month and the initial monthly output is 100 units, the computing device obtains an output growth rate score of 3.0 / 100=3% after standardization. This score indicates that the monthly output increases by 3% relative to the initial level, which can more fairly assess the growth potential of resource requesters of different sizes.

[0179] S303. Analyze the performance of credible information in the first basic information to obtain a performance stability score.

[0180] Among them, the performance stability score is used to quantify the overdue return of historical pre-paid resources by resource requesters.

[0181] Specifically, performance analysis refers to the process by which computing devices use one or more data analysis methods to systematically examine and measure the past records of resource advance payments made by resource requesters, with the aim of extracting stability characteristics of their willingness and ability to perform from their performance patterns.

[0182] In some embodiments, trusted information includes historical records of pre-paid resources, resource return timestamps, due timestamps, overdue status indicators, and overdue durations. These data types collectively constitute the basic data set for assessing performance stability.

[0183] In some embodiments, the computing device may employ a time-decay weighted average method to process overdue records in the trusted information. The core of this method lies in assigning different impact weights to overdue behaviors at different times, allowing recent behavior to have a greater impact on the final score, thereby more sensitively reflecting the latest changes in the creditworthiness of the resource requester.

[0184] One possible implementation involves setting a statistical time window (e.g., 36 months) for the computing device. For each overdue record within the window, a weight is calculated using a decreasing function (e.g., linear or exponential decay) based on its proximity to the current time. Subsequently, the overdue days of all overdue records are multiplied by their corresponding weights, summed, and then divided by the total weight to obtain a weighted average overdue days. Finally, this value is converted into a standardized performance stability score using a pre-defined mapping function.

[0185] For example, the computing device discovers that the resource requester has three overdue records within the last 36 months: occurring 35 months ago, 20 months ago, and 3 months ago, with overdue days of 5 days, 10 days, and 7 days respectively. The computing device uses a linear decay model to calculate the weights (e.g., the weight for the nth month from the current date is (36-n) / 35), so the weights for the three records are 0.03, 0.44, and 0.91 respectively. The weighted average overdue days = (5*0.03 + 10*0.44 + 7*0.91) / (0.03+0.44+0.91) ≈ 7.2 days. Finally, the computing device converts 7.2 days into a 72-point percentage score according to a mapping table.

[0186] In other embodiments, the computing device may also employ a rolling cycle default rate calculation method for analysis. This method divides the entire statistical time window into multiple consecutive rolling sub-cycles, calculates the default rate for each sub-cycle, and then analyzes the trend of these default rates over time. This helps to identify whether performance behavior is improving, deteriorating, or remaining stable.

[0187] One possible implementation involves the computing device dividing the total 36-month time window into 12 consecutive rolling quarterly sub-cycles and calculating the default rate for each quarter sequentially. By comparing the difference in default rates between adjacent sub-cycles, the computing device determines the changing trend of performance behavior, quantifies this trend as a trend coefficient, and finally combines it with the average default rate of each sub-cycle to generate a performance stability score.

[0188] For example, the computing device analyzes the performance data of resource requesters over the past 12 quarters. Calculations show that the default rates for the most recent four quarters were 8%, 5%, 3%, and 2%, respectively. The computing device calculates the difference series of default rates between adjacent quarters as [-3%, -2%, -1%], with an average of -2%, indicating a stable downward trend in the default rate. Based on this, the computing device determines a trend coefficient of 1.1 (improving trend) and multiplies the average default rate of 4.5% over the four quarters by the trend coefficient to obtain an adjusted performance stability score of 4.95%. This score not only reflects the historical average default level but also includes positive signals of behavioral improvement.

[0189] In some embodiments, after obtaining the performance stability score, the computing device can also fuse multiple intermediate scores obtained based on different analysis methods to generate a more robust comprehensive performance stability score. Specifically, the computing device first unifies all intermediate scores to the same dimension and score range through preset standardization rules, and then performs fusion calculation using methods such as weighted average or worst-case principle.

[0190] One possible implementation involves the computing device standardizing the scores output by different methods into a percentage score ranging from 0 to 100. The computing device pre-sets a weighting coefficient for each analysis method, with the sum of these weighting coefficients being 1. Subsequently, the computing device multiplies each standardized score by its corresponding weighting coefficient and sums all the products to obtain the final comprehensive performance stability score.

[0191] For example, the calculation device uses two analysis methods: Method A (time decay weighted average method) outputs a weighted average overdue days of 7.2 days, and Method B (rolling cycle default rate calculation method) outputs an adjusted default rate of 4.95%. The calculation device has built-in standardization rules: it linearly maps overdue days of 0-15 days to 100-0 points, and linearly maps default rates of 0%-10% to 100-0 points. Accordingly, 7.2 days is mapped to 52 points, and 4.95% is mapped to 50.5 points. Assuming both methods have a weight of 0.5, the overall performance stability score = 52 * 0.5 + 50.5 * 0.5 = 51.25 points.

[0192] In other embodiments, the computing device may also employ a hierarchical fusion strategy. Specifically, the computing device first independently converts each intermediate score into a level (e.g., A, B, C, D) according to a preset threshold range, and then determines the final comprehensive level according to a set of decision rules (e.g., if any method assigns a D rating, the final result is D; or the worst level among multiple levels is selected). This comprehensive level is the qualitative comprehensive performance stability assessment result.

[0193] For example, a score of 52 from method A belongs to grade C (thresholds: A≥80, B≥60, C≥40, D<40), and a score of 50.5 from method B also belongs to grade C. Based on the rule of "taking the same grade," the overall evaluation result is determined to be grade C.

[0194] S304. Process the output growth rate score and performance stability score through the quota adjustment coefficient model to determine the quota adjustment coefficient used to adjust the basic resource request quota.

[0195] The quota adjustment coefficient refers to a multiplier factor generated by the computing equipment based on the dynamic operational capabilities and performance characteristics of the resource requester, used to fine-tune the basic resource request quota. This coefficient quantifies both output growth potential and performance risk level into an adjustment parameter that can be directly applied to quota calculation.

[0196] Specifically, the quota adjustment coefficient model refers to a decision logic pre-set in the computing device that uses output growth rate scores and performance stability scores as input features, and outputs an adjustment coefficient through specific mathematical rules or algorithms. This model establishes a quantitative relationship between dynamic behavioral characteristics and resource allocation strategies.

[0197] In some embodiments, the credit limit adjustment coefficient model can be a rule-based model based on a predefined decision table. This model discretizes continuous scoring inputs into preset intervals and directly outputs the corresponding adjustment coefficients by looking up a table.

[0198] One possible implementation involves the computing device maintaining a two-dimensional decision table. The row index of the table represents the performance stability level (e.g., A, B, C, D), and the column index represents the output growth rate level (e.g., high, medium, low). The computing device first converts the percentage-based performance stability score into an A / D level and the output growth rate score into a high-low level. Then, it queries the decision table to obtain the corresponding adjustment coefficients.

[0199] For example, computing devices are categorized as follows: a performance stability score of 80 or above is Grade A, and 60-80 is Grade B; an output growth rate score of 5% or above is high growth, and 2%-5% is medium growth. When a resource requester's performance stability score is 85 (Grade A) and its output growth rate score is 6% (high growth), the cell in the decision table where Grade A and high growth intersect is queried, yielding an adjustment factor of 1.2.

[0200] In other embodiments, the credit limit adjustment coefficient model can also be a machine learning model. This model learns the mapping relationship from features such as output growth rate score and performance stability score to the final credit limit adjustment coefficient by training on historical data, thereby automatically capturing complex patterns between multi-dimensional features and achieving more accurate credit limit adjustments.

[0201] For example, the machine learning model may specifically be a gradient boosting decision tree, a random forest, a support vector regression model, etc. This application does not limit the specific type of machine learning model.

[0202] Specifically, the training process for the credit limit adjustment coefficient model in the form of a machine learning model can be found below. Figure 5 The corresponding descriptions are not elaborated here.

[0203] One possible implementation, specifically a gradient boosting decision tree model, involves determining the adjustment coefficient for adjusting the basic resource request limit based on output growth rate scores and performance stability scores processed through a quota adjustment coefficient model. This process can be represented as follows: The computing device constructs two-dimensional feature vectors from the output growth rate score and the performance stability score, which are then input into a pre-trained gradient boosting decision tree model. During the model's inference process, each base decision tree assesses the input features: first, resource requesters are divided into different groups based on whether their output growth rate score exceeds a certain splitting threshold; then, within each group, they are further subdivided based on their performance stability score. Through this progressive conditional assessment, the model can capture the interaction effect between the two score categories—for example, identifying the risk characteristic of "high growth but poor stability." Finally, the model weights and sums the output values ​​of the leaf nodes of all decision trees to generate a precise credit limit adjustment coefficient. This coefficient comprehensively reflects the balance between the resource requester's growth potential and credit risk, resulting in a coefficient greater than 1 for high-growth and stable requesters, and a coefficient less than 1 for requesters with weak growth or high risk.

[0204] Another possible implementation, specifically a neural network model, involves determining the adjustment coefficient for adjusting the basic resource request limit based on output growth rate scores and performance stability scores processed through a quota adjustment coefficient model. This process can be represented as follows: The computing device standardizes the output growth rate score and the performance stability score to form an input vector, which is then fed into a fully connected neural network with two or more hidden layers. During the forward propagation of the network, the neurons in the first layer perform linear combination and nonlinear activation on the two types of scores, extracting preliminary composite features; subsequent hidden layers continue to perform deep transformations on these features, automatically learning complex patterns such as the "compensation effect of growth trend on performance risk". Finally, the output layer uses the sigmoid activation function to map the results to a reasonable range of [0.5, 1.5], generating the credit limit adjustment coefficient. This end-to-end deep learning approach can model the highly nonlinear relationship between the two types of scores. Especially when the output growth rate exhibits volatile characteristics, the neural network can more accurately assess its dynamic correlation with performance stability, thereby formulating a more realistic credit limit adjustment strategy.

[0205] S305. Based on the quota adjustment coefficient and the basic resource request quota, determine the resource request quota of the resource requester.

[0206] Specifically, the process of determining the resource request limit refers to the calculation process by which the computing device combines the basic resource request limit with the limit adjustment coefficient through arithmetic operations to generate the final allocated limit. This process establishes a direct quantitative relationship from the basic value to the final credit line.

[0207] In some embodiments, the computing device can determine the resource request limit through scalar multiplication. The computing device multiplies the basic resource request limit by a limit adjustment factor, and the product is the final resource request limit.

[0208] One possible implementation is that the computing device reads two numerical variables, the basic resource request quota and the quota adjustment coefficient, from memory, calls a multiplication instruction to perform an arithmetic multiplication operation, and stores the calculation result as the resource request quota.

[0209] For example, when the basic resource request limit is 1 million units and the limit adjustment coefficient is 1.2, the computing device performs a multiplication operation: 1 million × 1.2 = 1.2 million units, which is the final determined resource request limit.

[0210] In other embodiments, the computing device may also introduce a rounding operation after the multiplication operation. Specifically, the computing device processes the multiplication result according to a preset rounding rule, such as rounding down to the nearest ten-thousandth place, to meet the actual operational requirements of the resource allocation computing device.

[0211] For example, after the computing device calculates the initial limit as 1,234,500 units, it determines the final resource request limit as 1,230,000 units according to the rounding down rule.

[0212] It should be understood that this implementation method combines the value assessment results with behavioral characteristics such as the dynamic growth rate of resource output and the stability of historical performance to calculate the quota. This makes the resource request quota not only reflect the static value of the physical resources, but also related to their potential for continuous output and the credit behavior of the requester. Thus, based on the anchoring of physical resources, the quota allocation is made more precise and the risk is more controllable, thereby improving the overall rationality of resource allocation.

[0213] The following explains how to obtain the credit limit adjustment coefficient model.

[0214] In some embodiments, the computing device can obtain a trained quota adjustment coefficient model from an external device.

[0215] For example, by downloading pre-trained model files from a model service center or receiving validated model parameters from a dedicated model deployment server, and then loading them into the memory of a computing device to complete the deployment and initialization of the model, it can be directly used to process new resource allocation requests.

[0216] In other embodiments, the computing device can also train a preset initial model from locally stored historical business data to obtain a quota adjustment coefficient model adapted to the current business environment. This process is as follows: Figure 5 As shown, the specific steps include S401-S402: S401. Obtain the training data set.

[0217] The training dataset includes samples corresponding to multiple historical resource requesters. Each sample includes: the first basic information of the corresponding historical resource requester and a quota adjustment coefficient label. The quota adjustment coefficient label is determined by the ratio of the final resource request quota of the historical resource requester to the basic resource request quota.

[0218] Specifically, the training dataset refers to a structured dataset extracted and constructed by computing devices from historical business data for model training. Each sample in this dataset corresponds to a historical case that has completed the resource allocation process and contains the input features and training labels required for model training.

[0219] In some embodiments, the computing device may obtain a training data set by querying a resource allocation process database.

[0220] Specifically, the resource allocation process database refers to a database system (which can be a database system such as MySQL or Oracle) built in a computing device to store resource allocation business data. It includes data tables such as process instance tables, resource requester information tables, entity resource object information tables, and resource allocation record tables.

[0221] One possible implementation involves the computing device first constructing query conditions based on a preset time range and the resource allocation process status being "returned," to filter out completed historical process instances that are available for learning. Subsequently, the computing device performs multi-table join queries to obtain raw data. For each selected historical process instance, the computing device constructs it as an independent training sample: From the associated first basic information storage table, it extracts the resource output information and reliability information of the historical requester, and uses the same analysis methods as described in S302 and S303 to calculate the historical output growth rate score and historical performance stability score, using these two scores as input features for the sample. Simultaneously, it retrieves the final resource request quota and the basic resource request quota corresponding to the process instance from the resource quota record table, calculating their ratio as the quota adjustment coefficient label for the sample.

[0222] For example, the computing device selects a historical process instance created 24 months ago with a "returned" status. Based on the resource requester's identifier, the computing device queries the first basic information storage table for the requester's historical output data for the 24 months prior to the application process initiation, and calculates its historical output growth rate score as 4.5% through time series regression analysis. Simultaneously, it queries the requester's performance records for the 36 months prior to the process initiation, and calculates its historical performance stability score as 82 points through performance performance analysis. These two scores constitute the features of the sample. At the same time, the computing device retrieves the basic resource request quota for this process instance from the resource quota record table, finding it to be 1 million units, with a final resource request quota of 1.1 million units, and calculates the quota adjustment coefficient label as 1.1. This complete data pair consisting of the features [4.5%, 82 points] and the label 1.1 becomes a valid training sample.

[0223] In some embodiments, after calculating the credit limit adjustment coefficient label, the computing device can also perform a reasonableness screening on the label value. Specifically, the computing device can preset reasonable upper and lower bounds for the label value (e.g., limiting the label value to between 0.5 and 2.0). When the calculated label value exceeds this range, the computing device can treat the sample as an abnormal sample and remove it, or truncate it to the boundary value. This operation helps to eliminate extreme values ​​caused by data recording errors or special business scenarios, thereby improving the quality of the training dataset.

[0224] In some embodiments, after constructing all training samples, the computing device can also perform feature standardization on the training dataset. Specifically, the computing device can calculate the mean and standard deviation of the two features, historical output growth rate score and historical performance stability score, on the training set, and perform Z-score standardization on each feature value by subtracting the mean and dividing by the standard deviation. This process ensures that features with different dimensions and numerical ranges are on the same scale, which helps improve the stability and convergence speed of subsequent model training.

[0225] S402. Train the initial model of the quota adjustment coefficient model based on the training data set to obtain the quota adjustment coefficient model.

[0226] The initial model refers to a machine learning model structure that has not been trained or only has initial parameters. Specific types include gradient boosting decision trees, random forests, and neural networks. The training process refers to the process by which computing devices use samples from the training dataset to iteratively adjust the model's internal parameters through optimization algorithms, gradually bringing the model's output closer to the true label.

[0227] Specifically, the model training process includes forward propagation computation, loss function evaluation, and backpropagation optimization. The computing device feeds the input features of the training samples into the initial model to obtain the predicted credit limit adjustment coefficient, then compares it with the true credit limit adjustment coefficient label, calculates the prediction error based on the loss function, and finally updates the model parameters through optimization algorithms to reduce the error.

[0228] In some embodiments, during model training, the computing device may: first, randomly divide the training dataset into a training subset and a validation subset. Then, perform multiple rounds of iterative training using the training subset, evaluating the model performance using the validation subset after each round of training. When the performance on the validation subset no longer improves or reaches a preset number of training rounds, training is stopped and the parameters of the best-performing model are saved.

[0229] One possible implementation involves the computing device employing early stopping to prevent overfitting. After each training round, the computing device calculates the mean squared error of the model on the validation subset. When this error no longer decreases over multiple consecutive training rounds, training automatically terminates and rolls back to the model parameter state with the best performance on the validation set. The model in this state is then determined as the final quota adjustment coefficient model.

[0230] For example, the computing device uses the TensorFlow framework to build a three-layer fully connected neural network as the initial model. During training, the batch size is set to 64, the initial learning rate is 0.01, and the Adam optimizer is used for parameter updates. After 150 training rounds, the mean squared error of the model on the validation subset decreases from the initial 0.25 to 0.08, and shows no further improvement for 10 consecutive rounds, at which point training terminates. The model saved at this point is the completed quota adjustment coefficient model.

[0231] In some embodiments, after model training is completed, the computing device can also perform performance evaluation and deployment preparation on the trained quota adjustment coefficient model. Specifically, the computing device uses a reserved test dataset to evaluate the model's generalization ability, including calculating metrics such as mean absolute error and coefficient of determination. When the model performance meets the preset deployment criteria, it is serialized into a model file and registered in the model service for use in the resource allocation process.

[0232] It should be understood that this implementation method trains the quota adjustment coefficient model based on historical data, enabling the quota calculation to learn autonomously and adapt to the output patterns and performance characteristics of different resource requesters. This allows for dynamic optimization of the quota adjustment strategy based on the assessment of the value of physical resources, thereby enhancing the adaptability and accuracy of the resource allocation model in practical applications.

[0233] In some embodiments, the computing device can also pre-verify the accuracy of entity resource object information to ensure that the underlying data upon which subsequent resource allocation processes rely is authentic and reliable. In this case, such as... Figure 6 As shown, steps S501-S502 are included after S101: S501. Obtain the verification result of the entity resource object for the second basic information that is manually input.

[0234] The entity resource object verification result refers to the conclusive judgment entered into the computing device by qualified operators after manually verifying the authenticity, accuracy and validity of the various attributes of the entity resource object recorded in the second basic information through on-site inspection, ownership document verification and other methods.

[0235] Specifically, the verification result for an entity resource object can be either "passed" or "failed". The computing device receives the verification conclusion and related remarks submitted by the operator through its provided data input interface.

[0236] In some embodiments, the computing device can receive the verification result of the entity resource object input by the operator through its graphical user interface. After completing S101, the computing device automatically creates a to-do task, prompts the operator to verify the obtained second basic information, and submits the verification result through a preset drop-down selection box or radio button component.

[0237] One possible implementation is that the computing device creates a verification task record in the state management database and sets its status to "pending verification". When an operator accesses the task through a visual interface, the computing device displays the details of the second basic information obtained from step S101 and provides operation buttons such as "pass" and "fail". After the operator makes a judgment and clicks the corresponding button, the computing device updates the judgment result to the verification task record as the verification result of the entity resource object.

[0238] For example, after acquiring the second basic information of a piece of farmland (a physical resource object), the computing device generates a verification task. During on-site verification, the operator finds a significant difference between the farmland area recorded in the computing device and the actual measurement result. The operator then selects "Verification Failed" in the visualization interface and writes "Actual area is 15% less than registered area" in the remarks column. The computing device records this operation as the physical resource object verification result.

[0239] S502. If the entity resource object verification result fails, generate a prompt message for the second basic information. The prompt message is used to instruct the resource requester to modify the second basic information.

[0240] The notification message refers to the notification content automatically generated by the computing device to correct data problems found during the verification process. Its purpose is to clearly inform the resource requester of the specific data items that need to be revised and the revision guidelines.

[0241] Specifically, the prompt message may include a detailed description of the data anomaly, suggested correction directions, a list of required supporting documentation, and a deadline for submitting the revised version. The computing device selects or dynamically generates the corresponding prompt content from a pre-configured template library based on the specific reason for the verification failure.

[0242] In some embodiments, the computing device can construct prompt messages through a message generation engine. The computing device parses the failure reason code or text description in the entity resource object verification result, matches it with predefined business rules, and automatically fills it into the corresponding message template to generate a complete and clearly expressed prompt message.

[0243] One possible implementation involves the computing device accessing a prompt template configuration library. This library stores corresponding prompt message templates, using the verification failure reason as the key. When the verification result is unsuccessful, the computing device extracts the failure reason code, queries the configuration library to obtain the template, substitutes specific entity resource object identifiers, abnormal data item names, and other information into the template variables, and finally renders and generates a personalized prompt message.

[0244] For example, continuing from the previous example, the computing device, based on the failure reason code "area mismatch," matches a template from the template library: "The [data item name] of the [resource identifier] you registered does not match the actual verification. Please verify and modify it. Note: [Operator's Note]." After inputting the data, a prompt message is generated: "The cultivated land area of ​​the agricultural land (ID: NL-2023-001) you registered does not match the actual verification. Please verify and modify it. Note: The actual area is 15% less than the registered area." This prompt message will be sent to the resource requester via system in-app message or SMS.

[0245] In addition, if the entity resource object verification result is passed, the computing device can continue to execute steps S102-S104 and their implementation methods.

[0246] It should be understood that this implementation method effectively identifies and corrects data deviations caused by information entry errors or ownership disputes by introducing a manual verification process and generating prompts for entity resource information that fails verification. This prevents flawed entity resources from entering the subsequent automated allocation process and reduces the operational risk of resource allocation being forced to be interrupted or resulting in errors due to inaccurate basic data.

[0247] In some embodiments, the computing device can also automatically manage the resource return and permission revocation process after resource allocation is completed, to form a complete business process closed loop. In this case, such as Figure 7 As shown, after S104, the method further includes the following steps S601-S603: S601. Update the status of the resource allocation process from the allocable status to the pending return status, and periodically check the status of the resource allocation process.

[0248] In this context, the "pending return" state is a state marker in the resource allocation process state machine that indicates the resource has been allocated and is currently waiting for the requester to return the resource as agreed. Periodic checks refer to the computing device automatically checking at fixed time intervals whether process instances in specific states meet the conditions for state transition.

[0249] Specifically, the status update operation involves modifying the values ​​of member variables representing the process status in the process instance object, or modifying the status field value of the corresponding process instance record in the database to a status value representing the pending return status. Periodic checks can be implemented through the task scheduler built into the computing device, and its scanning cycle can be configured according to business needs.

[0250] In some embodiments, the computing device can update the process state to a pending return state by calling the state update method of the resource allocation process object. After successfully executing the resource allocation operation in S104, the computing device immediately calls the setStateToToBeReturned() method of the process instance object, which internally performs the operation of modifying the value of the currentState member variable to "TO_BE_RETURNED".

[0251] For example, the computing device completes resource allocation to the resource requester on January 15th, and then updates its status from "ALLOCATABLE" to "TO_BE_RETURNED" by calling a method of its resource allocation process object. At the same time, the computing device starts a scheduled task that runs every 24 hours to scan all process instances in the "TO_BE_RETURNED" state that have reached the agreed return start date and check their resource return progress.

[0252] In other embodiments, the computing device can also achieve state transitions by updating the corresponding state record for the task in the task state database. The computing device modifies this state by calling a state update interface provided by the process orchestration tool. When a state transition is required, the computing device calls this interface; the core operation performed internally by this interface is sending an SQL update statement to the database. For example, UPDATE workflow_tasks SET status = 'TO_BE_RETURNED' WHERE task_id = 'RA-20240521-001'.

[0253] In this case, the computing device can periodically check the status of the resource allocation process by querying the list of process instances with a status of "TO_BE_RETURNED" and that meet specific conditions (such as the agreed resource return start date has passed) through the query interface provided by the process orchestration tool, so as to carry out subsequent processing.

[0254] S602. In response to the status update of the resource allocation process to the returned status, a permission restoration request for the entity resource object is sent to the management server of the entity resource object.

[0255] The "returned" status is a specific status identifier in the resource allocation process state machine, indicating that the resource requester has fulfilled its obligation to return all resources. The "permission restoration request" is an instruction generated by the computing device to transfer the disposal rights of an entity resource object from the resource provider back to the original resource requester.

[0256] Specifically, a permission restoration request should include data items such as the unique identifier of the entity resource object involved, the identifier of the permission recipient (i.e., the original resource requester), the identifier of the associated resource allocation process, and the request timestamp. This request serves as a signal for inter-system collaboration, aiming to trigger the external management server to perform the actual permission status change operation.

[0257] In some embodiments, the computing device can send a permission restoration request to the management server through its integrated application programming interface (API) client. After detecting an event that the resource allocation process status has changed to "returned status", the computing device automatically constructs a request message conforming to the management server interface specification and transmits it through a secure communication protocol.

[0258] One possible implementation is as follows: The computing device maintains an event listener that continuously monitors the status fields of the resource allocation process. When the value of the status field changes to "RETURNED", the listener triggers a processing callback. This callback function first queries the associated entity resource object identifier and the original resource requester identifier based on the process instance identifier, then constructs a structured request object, and finally sends an HTTPS POST request to the predefined permission recovery API endpoint of the management server through an HTTP client configured with timeout and retry mechanisms.

[0259] For example, the computing device detects that the process instance identified as "WF-2024-001" has had its status updated to "RETURNED". The computing device then queries the database and obtains the corresponding entity resource object identifier as "RES-2023-005A" and the original resource requester identifier as "USER-10086". Then, the computing device sends a request to the management server's API address https: / / api.management-server.com / v1 / permission / restore, with the request body in JSON format: {"resourceId": "RES-2023-005A", "restoreToUser": "USER-10086", "bizProcessId":"WF-2024-001", "timestamp": "2024-05-21T10:00:00Z"}. The request header also contains an API Key used for authentication.

[0260] S603. In response to the permission restoration confirmation message sent by the management server of the entity resource object, stop the resource allocation process.

[0261] Permission restoration confirmation information refers to the electronic credential returned by the management server of the entity resource object to the computing device after successfully completing the permission restoration operation, indicating that the permission has been successfully transferred to the original resource requester. Stopping the resource allocation process refers to the computing device performing a series of finalization operations, marking the process instance as completely terminated, and releasing the relevant system resources.

[0262] Specifically, permission restoration confirmation information typically includes the processing result status (such as "success"), a unique identifier for the corresponding business process, a timestamp indicating the completion of the permission restoration operation, and possibly a transaction hash or digital signature. Operations to stop the resource allocation process include updating the process status to the final terminated state, recording the process completion time, and triggering a data archiving process.

[0263] In some embodiments, the computing device can receive permission restoration confirmation information sent by the management server through its integrated API endpoint. The computing device exposes a callback URL for the management server to invoke. When the endpoint receives an HTTP status code indicating success and a response body conforming to a predetermined structure, it is determined to be a valid permission restoration confirmation.

[0264] For example, when a computing device sends a permission restoration request via S602, it includes a callback URL in the request parameters, such as https: / / my-server.com / api / callback / permission-restore. After processing, the management server sends a POST request to this URL, with the request body containing {"status": "SUCCESS", "bizProcessId": "WF-2024-001", "completedAt": "2024-05-21T10:05:00Z"}. Upon receiving this information, the computing device's callback interface processor verifies its validity and then triggers the stop logic for the resource allocation process.

[0265] It should be understood that this implementation method achieves full lifecycle management centered on the complete transfer and return of entity resource permissions by automatically triggering permission restoration requests and synchronously stopping the process after resource return. This ensures the clarity and compliance of entity resource ownership status at the end of the resource allocation process, forms a closed-loop control around changes in entity resource status, and improves the integrity and security of the entire resource allocation system.

[0266] like Figure 8 This is a schematic diagram of a resource allocation device provided in an embodiment of this application. Figure 8 As shown, the resource allocation device includes: an acquisition module 801 and a processing module 802.

[0267] The acquisition module 801 is used to acquire the first basic information of the resource requester and the second basic information of the resource requester's entity resource object. The entity resource object has a fixed and immovable location in physical space. The first basic information includes: the resource requester's identification information, the resource requester's social network information, the resource requester's resource output information, and the resource requester's trust information. The second basic information includes: the type of entity resource object, the entity resource object's address information, the entity resource object's ownership information, the entity resource object's historical input cost information, and the entity resource object's utility parameters. It also acquires the value assessment results for the entity resource object sent by the assessment agency's management server.

[0268] Processing module 802 is used to respond to a resource request submitted by a resource requester, initiate a resource allocation process, determine the resource request limit based on the first basic information, the second basic information, and the value assessment result, and set the resource allocation process status to either "awaiting permission transfer" or "suspending the resource allocation process." In response to the entity resource object's management server sending disposal permission transfer information for the entity resource object, the processing module updates the resource allocation process status from "awaiting permission transfer" to "allocateable," and allocates resources corresponding to the resource request limit to the resource requester. The disposal permission transfer information indicates that some or all disposal permissions for the entity resource object are transferred to the resource provider.

[0269] In other embodiments, the processing module 802 is specifically used to: verify the first basic information and the second basic information based on preset resource request conditions to obtain a resource request verification result. The preset resource request conditions include at least one of the following: Trust information indicating that the resource requester's overdue number of pre-paid resource returns is less than an overdue number threshold; resource output information indicating that the resource requester's resource output frequency is greater than a preset frequency threshold; ownership information of the entity resource object indicating that the resource requester has the authority to dispose of the entity resource object; and social network information indicating that the resource requester's overdue number of pre-paid resource returns for its resource request affiliates is less than an overdue number threshold. If the resource request verification result is successful, the resource requester's resource request limit is determined based on preset resource limit rules, the first basic information, and the value assessment result.

[0270] In other embodiments, processing module 802 is specifically configured to: determine the basic resource request limit of the resource requester based on the value assessment results; perform time series regression analysis on the resource output information in the first basic information to obtain an output growth rate score. The output growth rate score is used to measure the growth rate of the resource output of the resource requester; perform performance analysis on the credible information in the first basic information to obtain a performance stability score. The performance stability score is used to quantify the overdue return of historical pre-paid resources by the resource requester; process the output growth rate score and the performance stability score through a limit adjustment coefficient model to determine a limit adjustment coefficient for adjusting the basic resource request limit; and determine the resource request limit of the resource requester based on the limit adjustment coefficient and the basic resource request limit.

[0271] In other embodiments, processing module 802 is specifically used to: acquire a training data set. The training data set includes samples corresponding to multiple historical resource requesters. Each sample includes: first basic information of the corresponding historical resource requester and a quota adjustment coefficient label. The quota adjustment coefficient label is determined by the ratio of the historical resource requester's final resource request quota to its basic resource request quota. An initial model of the quota adjustment coefficient model is trained based on the training data set to obtain the quota adjustment coefficient model.

[0272] In other embodiments, the acquisition module 801 is specifically used to: acquire information by sending an information query request to the management server of the entity resource object, and / or by manual input.

[0273] In other embodiments, the acquisition module 801 is specifically used to: acquire the verification result of the entity resource object for the second basic information input manually. If the entity resource object verification result fails, a prompt message is generated for the second basic information. The prompt message is used to instruct the resource requester to modify the second basic information.

[0274] In other embodiments, the processing module 802 is further configured to: update the status of the resource allocation process from an allocable state to a pending return state, and periodically check the status of the resource allocation process. In response to the resource allocation process status being updated to a returned state, a permission restoration request for the entity resource object is sent to the entity resource object's management server. In response to the permission restoration confirmation information sent by the entity resource object's management server, the resource allocation process is stopped.

[0275] The resource allocation device provided in this application embodiment can execute the method shown in the above method embodiment. Its implementation principle and beneficial effects can be referred to the relevant description in the method embodiment, and will not be repeated here.

[0276] Figure 9 This is a schematic diagram of the structure of a resource allocation device provided in an embodiment of this application. Figure 9 As shown, the resource allocation device includes: a memory 901, a transceiver 902, and at least one processor 903.

[0277] The transceiver 902 is used to interact with other devices to send and receive data.

[0278] For example, in this embodiment of the application, transceiver 902 may be used to obtain the first basic information of the resource requester and the second basic information of the entity resource object of the resource requester, or to allocate resources to the resource requester corresponding to the resource request amount.

[0279] The memory 901 is used to store computer program code, which includes computer instructions. These computer instructions run in the resource allocation device described above to implement the method shown in the above method embodiments. For example, the memory may include high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, portable hard drive, read-only memory, disk, or optical disc, etc.

[0280] Processor 903 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. Processor 903 can also be other general-purpose processors. The general-purpose processor can be a microprocessor or any conventional processor.

[0281] The memory 901, transceiver 902, and processor 903 are communicatively connected. For example, the memory 901 and transceiver 902 can be connected to the processor 903 via a system bus to communicate with each other. The system bus can be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, an industry standard architecture (ISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the figure, but this does not mean that there is only one bus or one type of bus.

[0282] Optionally, the memory 901 can be either standalone or integrated with the processor 903. When the memory 901 is set up independently, it is connected to the processor 903 via the system bus.

[0283] This application also provides a chip for executing instructions, which is used to execute the resource allocation method described in the above embodiments.

[0284] This application also provides a computer-readable storage medium storing computer instructions. When these computer instructions are executed by a processor, they are used to implement the technical solution of the resource allocation method described in the above embodiments. Specifically, when the computer instructions are executed by a processor, the resource allocation device can execute the technical solution of the resource allocation method described in the above embodiments.

[0285] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium, and when the at least one processor executes the computer program, it can implement the technical solution of the resource allocation method in the above embodiments.

[0286] The aforementioned computer-readable storage media can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as static random-access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer-readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0287] An exemplary computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in application-specific integrated circuits (ASICs). Alternatively, the processor and the computer-readable storage medium can exist as discrete components in an electronic control unit or main control device; this application does not limit this.

[0288] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0289] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0290] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0291] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0292] It should be understood that the steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0293] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0294] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A resource allocation method, characterized in that, The method includes: The system acquires first basic information about the resource requester and second basic information about the entity resource object of the resource requester; the entity resource object has a fixed and immovable location in physical space; the first basic information includes: the resource requester's identification information, the resource requester's social network information, the resource requester's resource output information, and the resource requester's trust information; the second basic information includes: the type of the entity resource object, the address information of the entity resource object, the ownership information of the entity resource object, the historical input cost information of the entity resource object, and the utility parameters of the entity resource object; Obtain the valuation results for the entity resource object sent by the valuation agency's management server; In response to the resource request submitted by the resource requester, a resource allocation process is initiated. Based on the first basic information, the second basic information, and the value assessment result, the resource request limit of the resource requester is determined, and the status of the resource allocation process is set to waiting for permission transfer and the resource allocation process is suspended. In response to the transfer information of disposal authority for the entity resource object sent by the management server of the entity resource object, the status of the resource allocation process is updated from the waiting for permission transfer status to the allocable status, and resources corresponding to the resource request amount are allocated to the resource requester; the disposal authority transfer information indicates that some or all disposal authority of the entity resource object is transferred to the resource provider.

2. The method according to claim 1, characterized in that, The step of determining the resource request limit of the resource requester based on the first basic information, the second basic information, and the value assessment result includes: Based on preset resource request conditions, the first basic information and the second basic information are verified to obtain a resource request verification result; the preset resource request conditions include at least one of the following: the trusted information indicates that the number of overdue advance resource returns of the resource requester is less than the overdue number threshold; the resource output information indicates that the resource output frequency of the resource requester is greater than a preset frequency threshold; the ownership information of the entity resource object indicates that the resource requester has the disposal authority of the entity resource object; the social network information indicates that the number of overdue advance resource returns of the resource requester's resource request affiliates is less than the overdue number threshold; If the resource request verification result is successful, the resource request quota of the resource requester is determined based on the preset resource quota rules, the first basic information and the value assessment result.

3. The method according to claim 2, characterized in that, The process of determining the resource request limit of the resource requester based on preset resource quota rules, the first basic information, and the value assessment result includes: Based on the value assessment results, the basic resource request limit of the resource requester is determined; A time series regression analysis is performed on the resource output information in the first basic information to obtain an output growth rate score; the output growth rate score is used to measure the growth rate of the resource output of the resource requester. The reliable information in the first basic information is analyzed to obtain a performance stability score; the performance stability score is used to quantify the overdue return of the resource requester's historical pre-paid resources. The output growth rate score and the performance stability score are processed by the quota adjustment coefficient model to determine the quota adjustment coefficient for adjusting the basic resource request quota. Based on the quota adjustment coefficient and the basic resource request quota, the resource request quota of the resource requester is determined.

4. The method according to claim 3, characterized in that, The credit limit adjustment coefficient model is obtained through the following method: Obtain a training dataset; the training dataset includes samples corresponding to multiple historical resource requesters; each sample includes: the first basic information of the corresponding historical resource requester and a quota adjustment coefficient label; the quota adjustment coefficient label is determined by the ratio of the final resource request quota of the historical resource requester to the basic resource request quota; The initial model of the quota adjustment coefficient model is trained based on the training dataset to obtain the quota adjustment coefficient model.

5. The method according to claim 1, characterized in that, Obtaining the second basic information of the entity resource object of the resource requester includes: The information is obtained by sending an information query request to the management server of the entity resource object; and / or; Obtained through manual data entry.

6. The method according to claim 1, characterized in that, After obtaining the first basic information of the resource requester and the second basic information of the entity resource object of the resource requester, the method further includes: Obtain the verification result of the entity resource object based on the second basic information, which is manually input; If the entity resource object verification result fails, a prompt message is generated for the second basic information; the prompt message is used to instruct the resource requester to modify the second basic information.

7. The method according to claim 1, characterized in that, The method further includes: The status of the resource allocation process is updated from the allocable status to the pending return status, and the status of the resource allocation process is periodically checked. In response to the status update of the resource allocation process to the returned status, a permission restoration request for the entity resource object is sent to the management server of the entity resource object. In response to the permission restoration confirmation information sent by the management server of the entity resource object, the resource allocation process is stopped.

8. A resource allocation device, characterized in that, include: The acquisition module is used to acquire the first basic information of the resource requester and the second basic information of the entity resource object of the resource requester. The physical resource object has a fixed position in physical space and cannot be moved; The first basic information includes: the resource requester's identification information, the resource requester's social network information, the resource requester's resource output information, and the resource requester's trust information; the second basic information includes: the type of the entity resource object, the entity resource object's address information, the entity resource object's ownership information, the entity resource object's historical input cost information, and the entity resource object's utility parameters; and the value assessment results for the entity resource object sent by the assessment agency's management server are obtained; The processing module is configured to, in response to a resource request submitted by the resource requester, initiate a resource allocation process, determine the resource request limit of the resource requester based on the first basic information, the second basic information, and the value assessment result, and set the status of the resource allocation process to a waiting permission transfer state and suspend the resource allocation process; in response to a disposal permission transfer information for the entity resource object sent by the entity resource object's management server, update the status of the resource allocation process from the waiting permission transfer state to the allocable state, and allocate resources corresponding to the resource request limit to the resource requester; the disposal permission transfer information indicates that some or all disposal permissions of the entity resource object are transferred to the resource provider.

9. A resource allocation device, characterized in that, include: A memory and at least one processor; the memory is communicatively connected to the processor; the memory is used to store computer program code, the computer program code including computer instructions; when the processor executes the computer instructions, it causes the resource allocation device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

11. A computer program product, characterized in that, When the computer program product is run on a computer / executed by the computer's processor, it implements the method as described in any one of claims 1-7.

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