Customer demand response-oriented service resource automatic matching method and system

Abstract

The invention discloses a customer demand response-oriented service resource automatic matching method and system, and relates to the technical field of resource matching. The method comprises the following steps: performing feature analysis on customer demand response, and determining a demand response type; establishing response characteristics of the service resources, performing allocation variable parameter analysis on the response characteristics, and determining resource matching parameter characteristics; constructing a dynamic resource pool; matching in the dynamic resource pool by using the demand response type and the demand constraint parameter thereof as an engine to obtain a matching resource relationship; and evaluating and screening the matching resource relationship by taking the demand constraint parameter as an evaluation target to obtain a service resource configuration strategy, and carrying out service resource allocation. The technical problems that in the prior art, service resource allocation is not intelligent enough, and appropriate service resources are difficult to allocate according to customer demand response are solved, and the technical effect of automatic matching of the service resources is achieved by dynamically constructing the resource pool and matching according to the features and constraints of the customer demand response.

Description

Technical Field

[0001] The present invention relates to the technical field of resource matching, and in particular to a method and system for automatically matching service resources in response to customer needs. Background Art

[0002] Customer needs are becoming increasingly diverse, personalized, and dynamic. For example, in service industries such as e-commerce, finance, logistics, and healthcare, customer expectations for service are no longer limited to basic functionality, but now demand higher standards in terms of service quality, responsiveness, and customization. Faced with such complex and ever-changing customer needs, efficiently and accurately allocating service resources and achieving a rapid and accurate match between customer needs and service resources has become crucial for the service industry to enhance its competitiveness and achieve sustainable development. Traditional resource matching systems, lacking flexible dynamic adjustments and real-time data tracking, often result in inefficient resource allocation and an inability to respond promptly to changing customer needs. Summary of the Invention

[0003] This application provides a method and system for automatically matching service resources in response to customer needs, which solves the technical problem in the prior art that service resource allocation is not intelligent enough and it is difficult to allocate appropriate service resources according to customer demand response.

[0004] A first aspect of the present application provides a method for automatically matching service resources in response to customer needs, the method comprising:

[0005] Perform feature analysis on customer demand response to determine the demand response type; establish response characteristics of service resources, perform variable parameter analysis on the response characteristics, and determine resource matching parameter characteristics; construct a dynamic resource pool based on the resource matching parameter characteristics and the real-time status of service resources obtained by tracking; use the demand response type and its demand constraint parameters as an engine to perform matching in the dynamic resource pool to obtain matching resource relationships; evaluate and screen the matching resource relationships according to the demand constraint parameters of the customer demand response as evaluation targets to obtain a service resource configuration strategy, which is used to allocate service resources.

[0006] A second aspect of the present application provides a service resource automatic matching system for responding to customer needs, the system comprising:

[0007] A feature analysis module is used to perform feature analysis on customer demand responses and determine the demand response type; a parameter analysis module is used to establish response characteristics of service resources, perform variable parameter analysis on the response characteristics, and determine resource matching parameter characteristics; a resource pool construction module is used to construct a dynamic resource pool based on the resource matching parameter characteristics and the real-time status of service resources obtained by tracking; a matching module is used to use the demand response type and its demand constraint parameters as an engine to perform matching in the dynamic resource pool and obtain matching resource relationships; an evaluation and screening module is used to evaluate and screen the matching resource relationships according to the demand constraint parameters of the customer demand response as the evaluation target, and obtain a service resource configuration strategy, which is used to allocate service resources.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, the customer demand response characteristics are analyzed to determine the demand response type. Next, the response characteristics of the service resources are established, and the variable parameters of the response characteristics are analyzed to determine the resource matching parameter characteristics. Then, based on the resource matching parameter characteristics and the real-time status of the tracked service resources, a dynamic resource pool is constructed. Next, using the demand response type and its demand constraint parameters as an engine, matching is performed within the dynamic resource pool to obtain matching resource relationships. Finally, using the demand constraint parameters of the customer demand response as the evaluation target, matching resource relationships are evaluated and screened to obtain a service resource allocation strategy, which is used to allocate service resources. This solves the technical problem in existing technologies of insufficiently intelligent service resource allocation, making it difficult to allocate appropriate service resources based on customer demand responses. By dynamically constructing a resource pool and matching based on the characteristics and constraints of the customer demand response, the technical effect of automatic service resource matching is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flow chart of a method for automatically matching service resources to meet customer needs provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of the structure of a service resource automatic matching system for responding to customer needs provided in an embodiment of the present application.

[0013] Explanation of the accompanying drawings: feature analysis module 11, parameter analysis module 12, resource pool construction module 13, matching module 14, evaluation and screening module 15. DETAILED DESCRIPTION

[0014] This application solves the technical problem in the prior art that service resource allocation is not intelligent enough and it is difficult to allocate appropriate service resources according to customer demand response by providing a method and system for automatic matching of service resources in response to customer demand.

[0015] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0017] Example 1, as Figure 1 As shown, the present application provides a method for automatically matching service resources in response to customer needs, wherein the method includes:

[0018] Analyze the characteristics of customer demand response and determine the demand response type.

[0019] The system receives customer demand response information and analyzes its characteristics to extract customer demand response features, including service type, target resources, service target quantification, time requirements, requested service area, and user attributes. Based on these characteristics, the system then categorizes customer demand response into different demand response types.

[0020] Furthermore, the customer demand response is characterized and the demand response type is determined, including:

[0021] The service content requested by the customer is identified in terms of service type, target resources, service target quantification, time requirements, requested service area, and user attributes, and customer demand response characteristics are extracted; demand characteristics are clustered based on the customer demand response characteristics, and demand response type discrimination characteristics are established; the customer request content is analyzed and judged based on the demand response type discrimination characteristics, and the demand response type is determined, and the demand response type has a response characteristic label.

[0022] By analyzing the content of customer service requests, we identify characteristics across various dimensions, including service type, target resources, service goal quantification, time requirements, requested service area, and user attributes. Service type specifies the type of service requested, such as technical support or equipment repair; target resources define the specific resources required, such as personnel, equipment, and funding; service goal quantification defines the specific service objectives, such as processing volume and time limits; time requirements reflect the customer's time requirements for service execution; requested service area specifies the geographic location within which the customer wishes the service to occur; and user attributes include the customer's identity information and the urgency of the service.

[0023] The system clusters demand characteristics based on the extracted customer demand response characteristics, classifies demands with similar characteristics, and forms several demand type groups. Specifically, by clustering the various dimensional characteristics of customer demand responses (such as service type, target resources, time requirements, etc.), demands with similar characteristics are grouped together. For example, customer demand types may include routine services, emergency services, long-term projects, etc. These demand types have similar characteristics, and the system uses a clustering algorithm to group them into the same group. Through demand feature clustering, the similarities between different customer demands are identified, and then demand response type discriminant features are created for each group. These demand response type discriminant features include the key features of each demand type, which are used to clearly distinguish the specific attributes of each type of demand, such as the urgency of the service type, the scale of required resources, etc. The system further parses and determines the customer request content based on these demand response type discriminant features, determines the specific demand response type, and assigns a unique response feature label to each demand response type.

[0024] Establish the response characteristics of service resources, analyze the variable parameters of the response characteristics, and determine the resource matching parameter characteristics.

[0025] The response characteristics of service resources include the resource's performance, availability, response speed, and resource consumption in different demand response scenarios. For example, in a customer demand response scenario, a customer requests emergency equipment repair services, and the required resource is a maintenance personnel with rapid response capabilities. In this case, the response characteristics of the service resource include the resource's processing capacity (such as the maintenance personnel's skill level), response speed (such as the time required for the maintenance personnel to arrive at the site), availability (such as the maintenance personnel's current workload), and resource consumption (such as the cost of the maintenance personnel's working time).

[0026] By analyzing the variable parameters of the response characteristics, we can identify the core variables that influence resource matching, including the resource's processing capacity, load bearing capacity, response time, and validity period. For example, response speed and availability are crucial parameters for emergency task matching, while processing capacity and resource consumption are less important factors. By quantifying these factors, we can determine which parameters play a decisive role in resource matching. Based on the analysis results, we determine the resource matching parameter characteristics, which describe the resource's adaptability to specific demand response types. For example, emergency tasks prioritize response speed and resource availability, and the system will select the maintenance personnel who arrives on site the shortest time.

[0027] Furthermore, the response characteristics are analyzed for variable parameter allocation to determine resource matching parameter characteristics, including:

[0028] Analyze the resource description characteristics of the service resources and the response performance relationship characteristics of the service needs to obtain response characteristics, which are the performance, behavioral characteristics or state change characteristics of the service resources when responding to customer needs; extract the core variable parameters that affect the matching decision based on the response characteristics, and determine the resource matching parameter characteristics.

[0029] Resource description features refer to the static properties of the resource itself, such as resource type, service scope, capacity, and schedulability. Response performance relationship features, on the other hand, represent the dynamic behavior exhibited by the resource in responding to customer requests, including response time, execution efficiency, task completion quality, and load variations. By analyzing resource description features and response performance relationship features, the response characteristics of the resource in the actual service response process are obtained. These response characteristics comprehensively reflect the service resource's performance (e.g., processing speed), behavioral characteristics (e.g., task acceptance frequency and load status), and state variation characteristics (e.g., fluctuations in availability over time) when responding to customer requests. The response characteristics are then used to extract the core variable parameters that influence the matching decision, identifying the parameters that are critical to the successful and effective matching of resources to customer requests. For example, for a high-priority customer request, the resource's response latency, task execution success rate, and current task load status may be key considerations. The system constructs these key variable parameters into resource matching parameter features to guide subsequent resource screening and dynamic matching operations.

[0030] For example, in a remote technical support service scenario, a customer submits a request for "remote diagnosis of network equipment within 30 minutes." The system then needs to match available technical support resources. First, the system analyzes the service resources of each technical support technician, extracting resource description features (such as professional expertise, technical level, service hours, and language proficiency) as well as response performance characteristics related to the service requirements (such as average past response time, fault diagnosis success rate, current online status, and the time between the last three service calls). This generates resource response characteristics. The system then performs variable parameter analysis on these response characteristics, extracting core variable parameters such as response delay (in seconds), remote problem resolution rate (%), and current task saturation (%), and constructs a resource matching parameter feature set based on these parameters. Based on this, the system prioritizes resources according to the aforementioned parameters based on the time constraints and service goals in the customer's requirements, and selects the most suitable technician to complete the service scheduling.

[0031] A dynamic resource pool is constructed based on the resource matching parameter characteristics and the real-time status of the service resources obtained by tracking.

[0032] The system categorizes service resources based on their matching parameters (e.g., response speed, processing power, task load, etc.) and stores them in a resource pool. The system then monitors the status of service resources in real time, tracking, for example, each resource's load, current task status, and availability.

[0033] For example, if a technical support staff member is currently handling multiple urgent tasks, the load status of this resource may reach a high level, and the system will mark it as "high load" and temporarily exclude it from the dynamic resource pool or adjust its priority based on its current availability.

[0034] Dynamic resource pools are built based on this real-time data. The system continuously updates the status of service resources in the resource pool to ensure that the data in the resource pool reflects the most accurate current resource status. For example, the system assigns a status label to each resource in the resource pool, such as "Idle," "Highly Loaded," or "Allocated," and dynamically adjusts the availability of resources within the pool as their status changes.

[0035] Furthermore, based on the resource matching parameter characteristics and the real-time status of the service resources obtained by tracking, a dynamic resource pool is constructed, including:

[0036] Classify the resources according to the resource matching parameter characteristics and construct multiple types of resource containers according to the classification results; mark each type of resource container according to the classification results and construct a resource pool; track the matching execution status and resource utilization status of each type of service resource in real time, establish a dynamic update mechanism for the resource pool, dynamically update the resource pool in real time, and obtain the dynamic resource pool.

[0037] The system categorizes all resources based on their matching parameter characteristics (such as response time, availability, and task load). For example, suppose the system contains service resources such as technical support personnel, equipment maintenance resources, and transport vehicles. Each resource type has different key parameter characteristics when matching customer needs. The system classifies different types of resources into multiple resource categories based on these characteristics, such as the response speed of technical support personnel, the processing capacity of equipment maintenance resources, and the availability of transport vehicles. Based on these classification results, the system creates a separate resource container for each resource category and labels it to facilitate subsequent querying and management. For example, a service resource pool may include multiple resource containers, such as a container for technical support personnel, a container for equipment maintenance resources, and a container for transport vehicles. Each container not only contains basic resource information such as the resource ID, name, status, and current tasks, but can also add more useful identification information such as resource availability, response speed, and historical task completion status based on specific needs.

[0038] The system monitors the real-time usage of each resource by tracking the matching execution status (e.g., task execution progress, service quality, etc.) and resource utilization status (e.g., idle, allocated, busy, etc.) of each type of service resource in real time. For example, the system tracks each technical support staff member's current tasks, response time, and load status, the work progress and usage frequency of equipment maintenance resources, and the driving status and availability of transport vehicles. Based on this real-time data, the system establishes a dynamic update mechanism to ensure that the data in the resource pool reflects the latest resource status. Whenever the resource status changes, the resource pool is automatically updated to ensure that the resource pool only contains currently available service resources.

[0039] Furthermore, the demand response type and its demand constraint parameters are used as an engine to perform matching in the dynamic resource pool to obtain a matching resource relationship, which also includes:

[0040] According to the demand response type and the customer requested service content, the response type constraint parameters and the request task constraint parameters are parsed; the response type constraint parameters and the request task constraint parameters are cross-fused to determine the demand constraint parameters.

[0041] The system identifies the relevant constraints for this type of service based on the customer's demand response type (such as emergency service, regular service, or long-term service). For example, if a customer requests emergency service, the response type constraint parameters may include response time limits (such as the service must be completed within 30 minutes), resource capability requirements (such as technicians with specific qualifications are required), and service level requirements (such as certain processing quality standards must be met). Next, the system combines the customer's requested service content and extracts the requested task constraint parameters, including task time requirements (such as the service needs to be completed within a specific time period), task scope (such as the service is limited to a specific area), and resource requirements (such as the need for a specific type or quantity of service resources). By parsing the response type constraint parameters and the request task constraint parameters, a detailed and accurate basis is provided for subsequent resource matching.

[0042] After parsing the response type constraint parameters and the request task constraint parameters, the system cross-integrates the two to determine the demand constraint parameters. The cross-integration process is to combine different constraint parameters to form a comprehensive constraint condition, which will be used for subsequent resource matching. For example, if a customer requests an emergency repair service, the response type constraint parameter may include strict requirements on the service response time (such as "must be completed within 30 minutes"), while the request task constraint parameter may involve specific requirements of the service (such as "the device model must match a specific version"). In this case, the system generates a comprehensive demand constraint parameter by cross-integrating the two constraints, such as "the response time must be within 30 minutes, and the provided equipment model must fully match the task requirements." This is the demand constraint parameter.

[0043] The demand response type and its demand constraint parameters are used as an engine to perform matching in the dynamic resource pool to obtain matching resource relationships.

[0044] The system uses the customer's demand response type (such as emergency service, regular service, etc.) and demand constraint parameters (such as response time, service quality, etc.) as input conditions for the matching engine. These conditions will guide the system to filter out the resources that best meet the customer's needs from the dynamic resource pool. For example, when a customer requests emergency equipment repair service, the system will mark the demand response type as "emergency service" and combine it with demand constraint parameters (such as "repair time does not exceed 30 minutes"). The system will use these conditions as an engine to start looking for available repair resources in the dynamic resource pool. The service resources in the dynamic resource pool (such as technicians, maintenance tools, vehicles, etc.) will be evaluated based on their availability, response time, skill level, etc. to select the best matching resources.

[0045] By performing resource matching operations within the dynamic resource pool, the system automatically finds the most suitable resources based on the customer's demand response type and demand constraints. Ultimately, the system outputs a matching resource relationship: a list of resources that meet the customer's needs and their corresponding configuration information, providing an accurate basis for subsequent service scheduling.

[0046] Furthermore, using the demand response type and its demand constraint parameters as an engine, matching is performed in the dynamic resource pool to obtain a matching resource relationship, including:

[0047] The resource container type is matched in the dynamic resource pool using the response characteristics of the demand response type, deployed to the matching node of the corresponding type resource container, and the service resource matching operation is performed according to the demand constraint parameters to obtain the matching resource relationship.

[0048] The system uses the response characteristics of the demand response type (such as response time, resource requirements, and service objectives) to match resource container types. Resources in the dynamic resource pool are categorized into different types of resource containers, such as "technical support personnel container," "equipment maintenance resource container," and "transport vehicle container," each corresponding to a different type of resource. Based on the characteristics of the demand response type, the system directs resource requests to the corresponding resource container, ensuring that the selected service resource matches the type of customer demand. After determining the resource container type, the system takes the customer's demand constraint parameters (such as time requirements, task priority, and resource availability) as input and performs a service resource matching operation at the matching node in the corresponding resource container. Based on these constraint parameters, such as the task completion time limit and the specific attributes of the required resources (such as the technician's professional skills), the system selects the optimal resources that meet the customer's needs and then outputs matching resource relationships, which clearly identify which service resources match the customer's needs and the specific configuration of these resources.

[0049] Furthermore, obtaining the matching resource relationship further includes:

[0050] When customer service requests exceed the processing load threshold of the matching node, resource pool nodes are expanded based on historical request data and real-time load forecasts, where the number of nodes to be expanded is determined based on the load volume, node processing capacity, real-time load and load forecasts; customer service requests are load balanced using the matching nodes after resource pool node expansion; service resource matching operations are performed on customer service requests based on the allocation node operation relationship to obtain the matching resource relationship.

[0051] When the load of customer service requests exceeds the processing capacity of the currently matched nodes, the system expands the resource pool nodes based on historical request data and real-time load forecasts. Specifically, the system estimates the processing capacity that may be needed in the future based on historical request data (such as the service load and processing time of similar requests in the past) and real-time load forecasts (such as the current node's processing load and service request volume). The system then calculates the number of nodes that need to be expanded based on the current resource pool load and the processing capacity of each node, and dynamically expands the resource pool to add more available resource nodes.

[0052] After resource pool nodes are expanded, the system balances load across the expanded matching nodes. By analyzing the load on all nodes, the system distributes customer service requests to different nodes based on priority and availability. This load balancing mechanism ensures that each node's processing load remains within a reasonable range, preventing overloading of individual nodes and improving overall service efficiency and responsiveness.

[0053] The system performs service resource matching based on the allocation node computational relationship—the relationship between allocated service nodes and customer needs. This matching computation enables precise resource scheduling at each allocation node, ensuring that each customer service request is handled by the most appropriate resource. Ultimately, the system achieves a matching resource relationship, clearly defining which service resources are assigned to which customer request, ensuring optimal resource allocation and improved service quality.

[0054] Furthermore, obtaining the dynamic resource pool further includes:

[0055] Perform service response evaluation on service resources and set up multi-level service resources; perform multi-level configuration of resource pools based on the multi-level service resources to build a multi-level resource pool, which is used for resource matching and scheduling corresponding to multi-level customer demand responses.

[0056] The system evaluates each service resource's response, assessing its actual performance under different demand scenarios. By assessing factors such as response speed, processing quality, and task completion rate, the system can determine the actual performance of each service resource.

[0057] Based on the evaluation results of service resources, the system divides them into multiple tiers. For example, some resources may be high-priority (such as highly skilled technicians or advanced equipment), while others may be low-priority (such as general support staff or standard equipment). Through this tiering, the system can prioritize the dispatch of high-level resources based on the level of response to different customer needs, ensuring that critical tasks and urgent requests are handled first.

[0058] Based on multi-level service resources, the system configures resource pools at multiple levels. Specifically, the system divides service resources into multiple resource pools according to their level and responsiveness, with each resource pool corresponding to different levels of customer demand. For example, high-level customer demands (such as emergency services or high-priority tasks) will be dispatched from high-priority resource pools, while low-level customer demands (such as routine maintenance services) will be dispatched from low-priority resource pools. Through this configuration, the system can more precisely match resources and customer needs.

[0059] According to the demand constraint parameters of the customer demand response as the evaluation target, the matching resource relationship is evaluated and screened to obtain a service resource configuration strategy, which is used to allocate service resources.

[0060] The system determines evaluation criteria based on the customer's demand constraints, such as time requirements, resource requirements, and task priority. The system then uses these criteria to evaluate each candidate resource in the matching resource relationship, assessing whether it meets the customer's demand constraints. For example, if a customer's demand places strict requirements on response time, the system will prioritize resources that meet the required response time and eliminate those with longer response times. Through this screening process, the system selects the most suitable resources based on the customer's demand constraints. Based on the screening results, the system generates a service resource allocation policy, which specifies which resources should be allocated to which tasks and defines the specific allocation method for each resource. The service resource allocation policy serves as the basis for actual service resource allocation, ensuring that resources meet customer needs in the most effective manner, optimizing resource utilization, and improving overall service efficiency.

[0061] Furthermore, the matching resource relationships are evaluated and screened according to the demand constraint parameters of the customer demand response as the evaluation target to obtain a service resource configuration strategy, including:

[0062] According to the demand constraint parameters of the customer demand response, the evaluation weights of each constraint feature are configured; according to the customer type level and the urgency of the demand response type, the task priority is set; according to the evaluation weights and task priority of each constraint feature, the positive evaluation function of the evaluation target of this request is determined, and an evaluation penalty function is constructed according to the matching influence relationship between the customer demand response and other related tasks; the positive evaluation function is combined with the evaluation penalty function to construct a target evaluation function; the matching resource relationship is evaluated by means of the target evaluation function, and the matching resource relationship with the highest evaluation result is used as the service resource configuration strategy.

[0063] The system assigns appropriate evaluation weights to each constraint characteristic based on the demand constraint parameters of the customer demand response (such as time requirements, resource requirements, task priority, etc.). For example, some customer demands may have high requirements for response time, while other demands may focus more on service quality. By assigning weights to each constraint characteristic, the system ensures that the characteristics that customers care about most occupy a more prominent position in the evaluation. The system sets the priority of each task based on the customer type level (such as ordinary customers, VIP customers, etc.) and the urgency of the demand response type (such as urgent tasks, regular tasks, etc.). For example, the system will give higher priority to the urgent needs of VIP customers, while regular needs will be reasonably scheduled based on the urgency of the task.

[0064] Based on the evaluation weights of each constraint feature and the task priority, the system determines a positive evaluation function, that is, a positive score is assigned based on the priority of the task and the degree of satisfaction of the constraint conditions. Specifically, the system calculates a positive score based on the various constraints of the customer demand response (such as service time, resource type, service quality, etc.), combined with the evaluation weights of these constraints. For example, for a task that requires a quick response, the weight of response time may be relatively large. The system will calculate a positive score based on the difference between the actual response time and the target time for task completion; for the resource requirements of the task, the system will score based on the availability and matching degree of resources.

[0065] The system also constructs an evaluation penalty function based on the impact of the matching relationship between customer demand responses and other related tasks, penalizing matching relationships that fail to fully meet customer needs. Specifically, the penalty function penalizes matching relationships that fail to meet resource requirements based on resource load, task urgency, and the interrelationships between tasks.

[0066] Optionally, the system defines target factors for penalties, including resource load, response delay, task priority, and resource contention. Based on these factors, the system sets different penalty factors. For example, when the resource load exceeds a preset threshold, the system assigns a higher load penalty factor to the task matching that resource. If the task's response delay exceeds a specified time, the system increases the penalty based on the extent of the delay. When multiple tasks compete for the same resource, the system increases the penalty coefficient to ensure that higher-priority tasks are processed promptly. The system then combines these penalty factors into a penalty function using a weighted approach, where each factor is assigned a weight based on its importance.

[0067] The system combines a positive evaluation function with a penalty function to form a target evaluation function. This function integrates the positive and penalty scores to comprehensively evaluate the quality of matching resource relationships. Specifically, the positive evaluation function primarily measures positive factors in resource matching, such as resource availability, response time, and compatibility, while the penalty function penalizes substandard matches, such as those caused by excessive resource load, excessive latency, or resource contention. For example, the positive evaluation function might calculate a preliminary score for a resource match, indicating its strength in meeting customer needs, while the penalty function deducts points from the matching result based on resource load, latency, or other constraints. By combining the positive and penalty scores, the system generates a target evaluation function that integrates all factors influencing resource matching, ensuring that resource selection not only considers resource strengths but also avoids issues that could compromise service quality. The target evaluation function = w1 × positive evaluation function - w2 × penalty function, where w1 and w2 are the weighting coefficients for the positive evaluation function and the penalty function.

[0068] The system evaluates all possible matching resource relationships by calculating the target evaluation function, and finally selects the matching resource relationship with the highest evaluation result and uses it as the final service resource configuration strategy.

[0069] In summary, the embodiments of the present application have at least the following technical effects:

[0070] First, the customer demand response characteristics are analyzed to determine the demand response type. Next, the response characteristics of the service resources are established, and the variable parameters of the response characteristics are analyzed to determine the resource matching parameter characteristics. Then, based on the resource matching parameter characteristics and the real-time status of the tracked service resources, a dynamic resource pool is constructed. Next, using the demand response type and its demand constraint parameters as an engine, matching is performed within the dynamic resource pool to obtain matching resource relationships. Finally, using the demand constraint parameters of the customer demand response as the evaluation target, matching resource relationships are evaluated and screened to obtain a service resource allocation strategy, which is used to allocate service resources. This solves the technical problem in existing technologies of insufficiently intelligent service resource allocation, making it difficult to allocate appropriate service resources based on customer demand responses. By dynamically constructing a resource pool and matching based on the characteristics and constraints of the customer demand response, the technical effect of automatic service resource matching is achieved.

[0071] Embodiment 2 is based on the same inventive concept as the automatic matching method of service resources for customer demand response in the above embodiment. Figure 2 As shown, the present application provides a service resource automatic matching system for responding to customer needs, wherein the system includes:

[0072] The feature analysis module 11 is used to perform feature analysis on the customer demand response and determine the demand response type; the parameter analysis module 12 is used to establish the response characteristics of the service resources, perform variable parameter analysis on the response characteristics, and determine the resource matching parameter characteristics; the resource pool construction module 13 is used to construct a dynamic resource pool based on the resource matching parameter characteristics and the real-time status of the service resources obtained by tracking; the matching module 14 is used to use the demand response type and its demand constraint parameters as an engine to perform matching in the dynamic resource pool and obtain matching resource relationships; the evaluation and screening module 15 is used to evaluate and screen the matching resource relationships according to the demand constraint parameters of the customer demand response as the evaluation target, and obtain a service resource configuration strategy, which is used to allocate service resources.

[0073] Furthermore, the resource pool construction module 13 is used to execute the following method:

[0074] Classify the resources according to the resource matching parameter characteristics and construct multiple types of resource containers according to the classification results; mark each type of resource container according to the classification results and construct a resource pool; track the matching execution status and resource utilization status of each type of service resource in real time, establish a dynamic update mechanism for the resource pool, dynamically update the resource pool in real time, and obtain the dynamic resource pool.

[0075] Furthermore, the feature analysis module 11 is used to perform the following method:

[0076] The service content requested by the customer is identified in terms of service type, target resources, service target quantification, time requirements, requested service area, and user attributes, and customer demand response characteristics are extracted; demand characteristics are clustered based on the customer demand response characteristics, and demand response type discrimination characteristics are established; the customer request content is analyzed and judged based on the demand response type discrimination characteristics, and the demand response type is determined, and the demand response type has a response characteristic label.

[0077] Furthermore, the parameter analysis module 12 is used to perform the following method:

[0078] Analyze the resource description characteristics of the service resources and the response performance relationship characteristics of the service needs to obtain response characteristics, which are the performance, behavioral characteristics or state change characteristics of the service resources when responding to customer needs; extract the core variable parameters that affect the matching decision based on the response characteristics, and determine the resource matching parameter characteristics.

[0079] Furthermore, the matching module 14 is configured to execute the following method:

[0080] According to the demand response type and the customer requested service content, the response type constraint parameters and the request task constraint parameters are parsed; the response type constraint parameters and the request task constraint parameters are cross-fused to determine the demand constraint parameters.

[0081] Furthermore, the matching module 14 is configured to execute the following method:

[0082] The resource container type is matched in the dynamic resource pool using the response characteristics of the demand response type, deployed to the matching node of the corresponding type resource container, and the service resource matching operation is performed according to the demand constraint parameters to obtain the matching resource relationship.

[0083] Furthermore, the matching module 14 is configured to execute the following method:

[0084] When customer service requests exceed the processing load threshold of the matching node, resource pool nodes are expanded based on historical request data and real-time load forecasts, where the number of nodes to be expanded is determined based on the load volume, node processing capacity, real-time load and load forecasts; customer service requests are load balanced using the matching nodes after resource pool node expansion; service resource matching operations are performed on customer service requests based on the allocation node operation relationship to obtain the matching resource relationship.

[0085] Furthermore, the resource pool construction module 13 is used to execute the following method:

[0086] Perform service response evaluation on service resources and set up multi-level service resources; perform multi-level configuration of resource pools based on the multi-level service resources to build a multi-level resource pool, which is used for resource matching and scheduling corresponding to multi-level customer demand responses.

[0087] Furthermore, the evaluation and screening module 15 is used to perform the following method:

[0088] According to the demand constraint parameters of the customer demand response, the evaluation weights of each constraint feature are configured; according to the customer type level and the urgency of the demand response type, the task priority is set; according to the evaluation weights and task priority of each constraint feature, the positive evaluation function of the evaluation target of this request is determined, and an evaluation penalty function is constructed according to the matching influence relationship between the customer demand response and other related tasks; the positive evaluation function is combined with the evaluation penalty function to construct a target evaluation function; the matching resource relationship is evaluated by means of the target evaluation function, and the matching resource relationship with the highest evaluation result is used as the service resource configuration strategy.

[0089] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0091] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for automatically matching service resources in response to customer needs, characterized in that: The method comprises: Analyze the characteristics of customer demand response and determine the demand response type; Establish the response characteristics of service resources, analyze the variable parameters of the response characteristics, and determine the resource matching parameter characteristics; Building a dynamic resource pool based on the resource matching parameter characteristics and the real-time status of the service resources obtained by tracking; Using the demand response type and its demand constraint parameters as an engine, matching is performed in the dynamic resource pool to obtain a matching resource relationship; According to the demand constraint parameters of the customer demand response as the evaluation target, the matching resource relationship is evaluated and screened to obtain a service resource configuration strategy, which is used to allocate service resources.

2. The automatic matching method for service resources in response to customer demand according to claim 1 is characterized in that: Based on the resource matching parameter characteristics and the real-time status of the service resources obtained by tracking, a dynamic resource pool is constructed, including: Classify the resources according to the matching parameter characteristics, and construct multiple types of resource containers according to the classification results; Label each type of resource container according to the classification results and build a resource pool; The matching execution status and resource utilization status of each type of service resource are tracked in real time, and a dynamic update mechanism of the resource pool is established to dynamically update the resource pool in real time to obtain the dynamic resource pool.

3. The automatic matching method for service resources in response to customer needs according to claim 2, characterized in that: Analyze the characteristics of customer demand response and determine the demand response type, including: Identify the service type, target resources, service target quantification, time requirements, requested service area, and user attributes of the customer's requested service content, and extract the customer demand response characteristics; Clustering the customer demand characteristics according to the customer demand response characteristics to establish demand response type discrimination characteristics; The customer request content is parsed and determined according to the demand response type discrimination feature to determine the demand response type, and the demand response type has a response feature label.

4. The automatic matching method for service resources in response to customer demand according to claim 1 is characterized in that: Perform variable parameter analysis on the response characteristics to determine the resource matching parameter characteristics, including: Analyze the resource description characteristics of the service resource and the response performance relationship characteristics of the service demand to obtain the response characteristics, which are the performance, behavior characteristics or state change characteristics of the service resource when responding to the customer demand; The core variable parameters that affect the matching decision are extracted according to the response characteristics, and the resource matching parameter characteristics are determined.

5. The automatic matching method for service resources in response to customer needs according to claim 3 is characterized in that: Using the demand response type and its demand constraint parameters as an engine, matching is performed in the dynamic resource pool to obtain a matching resource relationship, which also includes: Analyze the response type constraint parameters and the request task constraint parameters according to the demand response type and the customer request service content; The response type constraint parameters are cross-fused with the request task constraint parameters to determine the demand constraint parameters.

6. The automatic matching method for service resources in response to customer needs according to claim 5 is characterized in that: Using the demand response type and its demand constraint parameters as an engine, matching is performed in the dynamic resource pool to obtain a matching resource relationship, including: The resource container type is matched in the dynamic resource pool using the response characteristics of the demand response type, deployed to the matching node of the corresponding type resource container, and the service resource matching operation is performed according to the demand constraint parameters to obtain the matching resource relationship.

7. The automatic matching method for service resources in response to customer needs according to claim 6, characterized in that: Obtaining the matching resource relationship further includes: When customer service requests exceed the processing load threshold of the matching node, resource pool nodes are expanded based on historical request data and real-time load forecasts. The number of nodes to be expanded is determined based on the load volume, node processing capacity, and real-time load and load forecasts. Use the matching nodes after resource pool nodes are expanded to load balance customer service requests; A service resource matching operation is performed on the customer service request based on the allocation node operation relationship to obtain the matching resource relationship.

8. The automatic matching method for service resources in response to customer needs according to claim 7 is characterized in that: Obtaining the dynamic resource pool further includes: Evaluate service response of service resources and set up multi-level service resources; Based on the multi-level service resources, the resource pool is configured in a multi-level manner to construct a multi-level resource pool, which is used for resource matching and scheduling corresponding to multi-level customer demand responses.

9. The automatic matching method for service resources in response to customer demand according to claim 1, characterized in that: Based on the demand constraint parameters of the customer demand response as the evaluation target, the matching resource relationships are evaluated and screened to obtain a service resource configuration strategy, including: According to the demand constraint parameters of the customer demand response, configure the evaluation weight of each constraint feature; Set task priorities based on customer type level and urgency of demand response type; Determine the positive evaluation function of the request evaluation target based on the evaluation weights and task priorities of the constraint features, and construct an evaluation penalty function based on the matching impact relationship between the customer demand response and other related tasks; Combining the forward evaluation function with the evaluation penalty function to construct a target evaluation function; The matching resource relationship is evaluated by the target evaluation function, and the matching resource relationship with the highest evaluation result is used as the service resource configuration strategy.

10. The automatic matching system of service resources for responding to customer needs is characterized by: A system for implementing the customer demand response-oriented service resource automatic matching method according to any one of claims 1 to 9, comprising: Feature analysis module, used to analyze the characteristics of customer demand response and determine the demand response type; The parameter analysis module is used to establish the response characteristics of service resources, perform variable parameter analysis on the response characteristics, and determine the resource matching parameter characteristics; A resource pool construction module is used to construct a dynamic resource pool based on the resource matching parameter characteristics and the real-time status of the service resources obtained by tracking; a matching module, configured to use the demand response type and its demand constraint parameters as an engine to perform matching in the dynamic resource pool to obtain a matching resource relationship; The evaluation and screening module is used to evaluate and screen the matching resource relationships according to the demand constraint parameters of the customer demand response as the evaluation target, and obtain a service resource configuration strategy, which is used to allocate service resources.

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