Home decoration order processing method, system and device and storage medium

Abstract

The invention relates to the technical field of intelligent cooling, in particular to a home decoration order processing method, system and device and a storage medium, and the method comprises the steps: extracting key features to generate order feature vectors when a home decoration order of a client is received; based on the order feature vector and a preset merchant service radius library, screening out a first merchant set capable of serving a target area; based on the order feature vector, a preset merchant capability model and a collaborative filtering algorithm, performing construction capability and historical service evaluation matching analysis on merchants in the first merchant set, and generating a matched merchant list; sending an order receiving task notification to a merchant end in the matched merchant list; when the merchant end confirms order receiving, the order state is updated, and a construction plan is generated; and sending an order receiving confirmation book to the client and scheduling construction. The method has the effects of high-configuration and high-use and low-configuration and low-demand home decoration orders.

Description

Technical Field

[0001] The present application relates to the field of intelligent cooling technology, and in particular to a method, system, device and storage medium for processing home improvement orders. Background Art

[0002] Traditional home improvement order processing technology has core flaws: the matching mechanism relies too much on manual experience and static rules, and only performs extensive screening through geographic location or simple tags. It can neither analyze the structured requirements such as house type and budget range implied in the order, nor establish a merchant capability stratification model, resulting in a high mismatch rate between "high-spec low-use" and "low-spec high-demand".

[0003] Therefore, improvements are needed. Summary of the Invention

[0004] In order to solve the above problems, the present application provides a home improvement order processing method, system, device and storage medium.

[0005] The first object of the invention of this application is achieved through the following technical solutions:

[0006] A method for processing a home improvement order, comprising:

[0007] When receiving a home improvement order from a client, key features are extracted to generate an order feature vector;

[0008] Based on the order feature vector and a preset merchant service radius library, screening out a first set of merchants that can serve the target area;

[0009] Based on the order feature vector, the preset merchant capability model and the collaborative filtering algorithm, performing a matching analysis on the construction capabilities and historical service evaluations of merchants in the first merchant set to generate a matching merchant list;

[0010] Sending an order acceptance task notification to the merchant terminals in the matching merchant list;

[0011] When the merchant confirms the order, the order status is updated and a construction plan is generated;

[0012] Send order confirmation to the client and schedule construction.

[0013] In a preferred embodiment, when receiving a home improvement order from a client, the step of extracting keywords to generate an order feature vector includes:

[0014] Extracting geographic location keywords from the home improvement order and outputting order geographic location features;

[0015] Extract demand category keywords from the home improvement order and output order demand category features;

[0016] Extract house area and house type keywords from the home improvement order and output basic house features;

[0017] extracting budget keywords from the home improvement order and outputting budget features;

[0018] The order location features, order demand category features, house basic features, and budget features are integrated into the order feature vector and output.

[0019] In a preferred embodiment, the step of screening out a first set of merchants that can serve the target area based on the geographic location features in the order feature vector and a preset merchant service radius library includes:

[0020] Based on the geographic location characteristics of the order, output the latitude and longitude coordinates of the order;

[0021] Query a preset merchant service radius library and output the merchant's latitude and longitude coordinates and maximum service distance. The service radius library stores a mapping relationship between merchant ID and maximum service distance.

[0022] Based on the order's longitude and latitude coordinates and the merchant's longitude and latitude coordinates, calculate the distance between the order's geographic location and each merchant's location;

[0023] Filter merchants whose distance is ≤ the maximum service distance to generate the first merchant set.

[0024] In a preferred embodiment, the step of performing a matching analysis of the construction capabilities and historical service evaluations of merchants in the first merchant set based on the order feature vector, a preset merchant capability model, and a collaborative filtering algorithm to generate a matching merchant list includes:

[0025] The preset merchant capability model includes hard capability indicators and soft evaluation indicators;

[0026] The hard ability indicators include qualification level and project experience type;

[0027] The soft evaluation indicators include historical praise rate and construction period achievement rate;

[0028] Based on the order demand category characteristics, screening merchants that meet the hard capability indicators from the first merchant set to generate a candidate merchant set;

[0029] Based on the soft evaluation index and the order feature vector, a collaborative filtering algorithm is used to calculate the matching score between each merchant in the candidate merchant set and the order;

[0030] Generate a list of matching merchants sorted by matching scores.

[0031] In a preferred embodiment, the step of screening merchants that meet the hard capability indicators from the first merchant set based on the order demand category characteristics to generate a candidate merchant set includes:

[0032] The order demand category characteristics include "custom design" orders and "basic decoration" orders;

[0033] When the order requirement category is "custom design" orders, merchants with qualification level ≥ A and experience in "customization" projects are screened;

[0034] When the order requirement category feature is a "basic decoration" order, merchants with qualification level ≥ B will be screened.

[0035] In a preferred embodiment, the step of calculating the matching score between each merchant in the candidate merchant set and the order using a collaborative filtering algorithm based on the soft evaluation index and the order feature vector includes:

[0036] The calculation formula of the matching score is S=N×F1+M×F2;

[0037] Among them: F1 is the historical praise rate, F2 is the completion rate of the project period;

[0038] N and M are dynamic weights, and N+M=1, which are adjusted according to the characteristics of the order demand category:

[0039] When the order requirement category feature is a “custom design” order, N=N1, M=M1, N1, and M1 are preset parameters;

[0040] When the order demand category feature is a "basic decoration" order, N=N2, M=M2, N2, and M2 are preset parameters.

[0041] The second object of the invention of this application is achieved through the following technical solutions:

[0042] A home improvement order processing system, comprising:

[0043] The first module: when receiving a home improvement order from a client, extract key features to generate an order feature vector;

[0044] The second module: based on the order feature vector and the preset merchant service radius library, screening out a first set of merchants that can serve the target area;

[0045] The third module: based on the order feature vector, the preset merchant capability model and the collaborative filtering algorithm, the first merchant;

[0046] Module 4: Perform matching analysis on the construction capabilities and historical service evaluation of merchants in the collection and generate a list of matching merchants;

[0047] The fifth module: sending an order task notification to the merchant end in the matching merchant list;

[0048] Module 6: When the merchant confirms the order, the order status is updated and a construction plan is generated;

[0049] Module 7: Send order confirmation to the client and schedule construction.

[0050] The second objective of this application is achieved through the following technical solutions:

[0051] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned home improvement order processing method are implemented.

[0052] The third objective of this application is achieved through the following technical solutions:

[0053] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned home improvement order processing method.

[0054] In summary, this application includes at least one of the following beneficial technical effects:

[0055] A comprehensive dispatching system for home improvement orders is built through multi-dimensional feature engineering and intelligent matching algorithms, optimizing the entire process from order analysis to merchant selection and service scheduling. The system first utilizes natural language processing technology to deeply analyze home improvement orders, extracting key information such as location, demand type, property attributes, and budget, and encoding it into structured vectors to eliminate natural language ambiguity. Subsequently, combining a geographic information system with a merchant service radius database, the system dynamically selects merchants with physical reachability through spatial distance calculations, mitigating fulfillment risks associated with cross-region dispatch. Furthermore, a two-stage matching mechanism is employed. In the first stage, differentiated qualification thresholds are set based on order type to ensure a good match between merchant capabilities and project complexity. In the second stage, a collaborative filtering algorithm integrates historical evaluation data, dynamically adjusting the weightings of positive review rate and deadline achievement rate to generate a matching list that precisely balances service quality and efficiency. Finally, after merchant confirmation of order acceptance, status synchronization and automatic construction plan generation enable digital control of the entire order fulfillment process. This solution has been proven to improve order matching rates, reduce customer complaints, and significantly optimize logistics costs and service response speed, establishing a new paradigm for intelligent dispatching in the home improvement industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flowchart of an implementation of a method for processing a home improvement order according to an embodiment of the present application;

[0057] Figure 2 This is a flowchart for implementing step S10 in an embodiment of a method for processing a home improvement order in the present application;

[0058] Figure 3 This is a flowchart for implementing step S20 in an embodiment of a method for processing a home improvement order in the present application;

[0059] Figure 4 This is a principle block diagram of a computer device of the present application. DETAILED DESCRIPTION

[0060] The following is combined with Figure 1-4 This application is described in further detail.

[0061] In one embodiment, if Figure 1 As shown, this application discloses a method for processing home improvement orders, which specifically includes the following steps:

[0062] S10: When receiving a home improvement order from a client, extract key features to generate an order feature vector;

[0063] S20: Based on the order feature vector and a preset merchant service radius library, screening out a first set of merchants that can serve the target area;

[0064] S30: Based on the order feature vector, the preset merchant capability model and the collaborative filtering algorithm, performing a matching analysis on the construction capabilities and historical service evaluations of merchants in the first merchant set to generate a matching merchant list;

[0065] S40: Sending an order acceptance task notification to the merchant terminals in the matching merchant list;

[0066] S50: When the merchant confirms the order, the order status is updated and a construction plan is generated;

[0067] S60: Send order confirmation to the client and schedule construction.

[0068] In this embodiment, an intelligent home improvement order dispatching system is constructed using natural language processing and geographic information system technologies, optimizing the entire process from order parsing to construction scheduling. The system first uses semantic analysis to extract key information from orders, such as location, requirement type, property attributes, and budget, and encodes this information into structured feature vectors, effectively eliminating the impact of natural language ambiguity on matching accuracy. Subsequently, the system dynamically selects merchants with physical service capabilities through spatial distance calculations, integrating with a merchant service radius database to avoid fulfillment delays and increased costs caused by cross-regional scheduling. Furthermore, a two-stage matching mechanism is employed. In the first stage, differentiated qualification thresholds are set based on, for example, order type to ensure a precise match between merchant expertise and project complexity. In the second stage, a collaborative filtering algorithm integrates historical service evaluation data, dynamically adjusting the weightings of positive review rate and deadline achievement rate to generate a recommendation list that balances service quality and efficiency. Finally, an automated notification mechanism and a construction plan generation module enable digital management and control of the entire order fulfillment chain. This solution significantly improves order matching rates and customer satisfaction, while optimizing logistics costs and response speed, providing an innovative solution for the intelligent transformation of the home improvement industry.

[0069] like Figure 2 As shown, step S10 includes:

[0070] S101: Extracting geographic location keywords from the home improvement order and outputting order geographic location features;

[0071] S102: Extracting demand category keywords from the home improvement order and outputting order demand category features;

[0072] S103: Extracting keywords such as house area and house type from the home improvement order and outputting basic house features;

[0073] S104: extracting budget keywords from the home improvement order and outputting budget features;

[0074] S105: Integrate the order geographic location features, order demand category features, house basic features, and budget features into an order feature vector and output it.

[0075] In this embodiment, a digital representation system for home improvement orders is constructed through multi-dimensional feature parsing and vectorized modeling techniques, achieving efficient conversion of unstructured text information into structured data. This system uses natural language processing to deeply parse order text, accurately identifying location keywords and converting them into spatial coordinates, forming a quantitative basis for service coverage. It also extracts demand category keywords to construct service type labels, distinguishing between scenarios such as customized design and standardized construction, providing a basis for differentiated service matching. Furthermore, pattern recognition techniques are used to extract physical attributes such as floor area and floor plan. Combined with budget range divisions, a composite feature model is constructed that incorporates spatial attributes, functional requirements, physical characteristics, and cost constraints. Finally, feature concatenation and vectorized encoding techniques are used to integrate discrete features into continuous numerical vectors, forming an order representation in a high-dimensional feature space. This system effectively eliminates natural language ambiguity, improves data computability, and provides high-quality input for subsequent merchant screening and service matching. Field tests have shown that it can shorten order processing cycles and improve feature parsing completeness, laying the foundation for the digital upgrade of the home improvement service chain.

[0076] like Figure 3 As shown, step S20 includes:

[0077] S201: Output the order's latitude and longitude coordinates based on the order's geographic location features;

[0078] S202: Query a preset merchant service radius library and output the merchant's latitude and longitude coordinates and maximum service distance. The service radius library stores a mapping relationship between merchant ID and maximum service distance.

[0079] S203: Calculate the distance between the order's geographic location and each merchant's location based on the order's latitude and longitude coordinates and the merchant's latitude and longitude coordinates;

[0080] S204: Filter merchants whose distance is less than or equal to the maximum service distance to generate a first merchant set.

[0081] In this embodiment, a merchant service range screening mechanism is constructed through geographic information system and database query technology to achieve accurate matching of home improvement orders and the physical accessibility of merchants. The mechanism first converts the geographical location characteristics of the order into longitude and latitude coordinates to provide standardized input for spatial calculations; at the same time, it queries the preset merchant service radius library to obtain the merchant's longitude and latitude coordinates and maximum service distance parameters to construct service range constraints. The spatial distance algorithm is further used to calculate the actual distance between the order and the merchant, and by comparing it with the maximum service distance, a set of merchants with service capabilities is dynamically screened. This mechanism effectively ensures that orders are only dispatched to physically accessible merchants, avoiding the performance risk caused by excessive distance. It can reduce cross-regional scheduling costs and improve service response speed, laying the foundation for efficiency optimization of the home improvement service link.

[0082] Step S30 includes:

[0083] S301: Preset the merchant capability model including hard capability indicators and soft evaluation indicators;

[0084] S302: The hard capability indicators include qualification level and project experience type;

[0085] S303: The soft evaluation indicators include historical praise rate and construction period achievement rate;

[0086] S304: Based on the order demand category characteristics, screen merchants that meet the hard capability indicators from the first merchant set to generate a candidate merchant set;

[0087] S305: Based on the soft evaluation index and the order feature vector, a collaborative filtering algorithm is used to calculate the matching score between each merchant in the candidate merchant set and the order;

[0088] S306: Generate a matching merchant list by sorting by matching scores.

[0089] In this embodiment, a merchant service matching system is constructed through hierarchical capability assessment and intelligent recommendation algorithms to precisely match order requirements with merchant resources. This system first establishes a capability model that includes hard indicators such as qualification level and project experience. Differentiated entry barriers are set based on order type. For example, custom design orders require merchants to possess Class A qualifications and experience in similar projects, ensuring a good match between professional capabilities and project complexity. Softer evaluation indicators, such as historical positive review rate and deadline completion rate, are also introduced to quantify merchant service quality through a collaborative filtering algorithm. This system further incorporates information such as location and budget from the order feature vector to dynamically adjust the weights of evaluation indicators. For example, custom orders prioritize historical reputation, while basic renovation orders prioritize fulfillment efficiency. This generates a matching score that balances service quality and cost constraints. Finally, a ranking mechanism is used to generate a recommended list, enabling optimal resource allocation. This system significantly improves order matching rates and customer satisfaction, reduces project risks caused by capability mismatches, and reduces manual intervention through algorithmic automation, providing a core solution for intelligent order dispatching in the home improvement industry.

[0090] Step S304 includes:

[0091] SB1: The order demand category characteristics include "custom design" orders and "basic decoration" orders;

[0092] SB2: When the order requirement category is "custom design" orders, select merchants with qualification level ≥ A and experience in "customization" projects;

[0093] SB3: When the order requirement category feature is a "basic decoration" order, merchants with qualification level ≥ B will be screened.

[0094] In this embodiment, a specialized resource allocation model for home improvement services is constructed through a hierarchical capability verification mechanism driven by order type. This mechanism, deeply integrated with industry characteristics, establishes a differentiated capability access system for two types of orders: custom design and basic renovation. For custom design orders, an A-level qualification threshold is used to ensure that merchants possess complex solution design capabilities, experience in high-end material application, and full-process management capabilities. Experience in custom projects is also required to verify their ability to respond to personalized needs. For basic renovation orders, a B-level qualification threshold is used as the baseline, focusing on the merchant's mastery of standardized construction techniques and basic delivery reliability. This dual-track screening strategy creates a three-fold optimization effect: First, it reduces order fulfillment risk through pre-qualification of capabilities, avoiding rework or disputes caused by insufficient merchant capabilities; second, it enables a gradient utilization of merchant resources, preventing highly qualified teams from being trapped in the inefficiency of low-value orders; and third, it establishes a precise mapping of demand and capabilities, directing custom orders to merchants with innovative designs and diverting basic orders to merchants with cost control. Industry benchmarking data shows that this mechanism can reduce the order adaptation error rate, while improving the order acceptance quality of highly qualified merchants, resulting in a dual improvement in service quality and operational efficiency.

[0095] Step S305 includes:

[0096] SE1: The calculation formula of the matching score is S=N×F1+M×F2;

[0097] SE2: Among them: F1 is the historical praise rate, F2 is the completion rate of the project period;

[0098] SE3: N and M are dynamic weights, and N+M=1, which are adjusted according to the characteristics of the order demand category:

[0099] SE4: When the order requirement category feature is a “custom design” order, N=N1, M=M1, N1, and M1 are preset parameters;

[0100] SE5: When the order demand category feature is a “basic decoration” order, N=N2, M=M2, N2, and M2 are preset parameters.

[0101] In this embodiment, a digital representation system for home improvement orders is constructed through multi-dimensional feature parsing and vectorized modeling techniques, achieving efficient conversion of unstructured text information into structured data. This system uses natural language processing to deeply parse order text, accurately identifying location keywords and converting them into spatial coordinates, forming a quantitative basis for service coverage. It also extracts demand category keywords to construct service type labels, distinguishing between scenarios such as customized design and standardized construction, providing a basis for differentiated service matching. Furthermore, pattern recognition techniques are used to extract physical attributes such as floor area and floor plan. Combined with budget range divisions, a composite feature model is constructed that incorporates spatial attributes, functional requirements, physical characteristics, and cost constraints. Finally, feature concatenation and vectorized encoding techniques are used to integrate discrete features into continuous numerical vectors, forming an order representation in a high-dimensional feature space. This system effectively eliminates natural language ambiguity, improves data computability, and provides high-quality input for subsequent merchant screening and service matching. Field tests have shown that it can shorten order processing cycles and improve feature parsing completeness, laying the foundation for the digital upgrade of the home improvement service chain.

[0102] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0103] In one embodiment, a home improvement order processing system is provided. The home improvement order processing system corresponds to the home improvement order processing method in the above embodiment. The home improvement order processing system includes:

[0104] The first module: when receiving a home improvement order from a client, extract key features to generate an order feature vector;

[0105] The second module: based on the order feature vector and the preset merchant service radius library, screening out a first set of merchants that can serve the target area;

[0106] The third module: Based on the order feature vector, the preset merchant capability model and the collaborative filtering algorithm, the merchants in the first merchant set are matched and analyzed in terms of construction capability and historical service evaluation, and a matching merchant list is generated;

[0107] The fourth module: sending an order task notification to the merchant end in the matching merchant list;

[0108] The fifth module: When the merchant confirms the order, the order status is updated and a construction plan is generated;

[0109] Module 6: Send order confirmation to the client and schedule construction.

[0110] Optionally, also include:

[0111] Module 7: extracting geographic location keywords from the home improvement order and outputting the order geographic location features;

[0112] Module 8: extracting demand category keywords from the home improvement order and outputting order demand category features;

[0113] Module 9: extracting keywords such as house area and house type from the home improvement order and outputting basic house features;

[0114] Module 10: extracting budget keywords from the home improvement order and outputting budget features;

[0115] Module 11: Integrate the order geographic location features, order demand category features, house basic features, and budget features into the order feature vector and output it.

[0116] Optionally, also include:

[0117] Module 12: Output the order's latitude and longitude coordinates based on the order's geographic location features;

[0118] Module 13: Query the preset merchant service radius library and output the merchant's latitude and longitude coordinates and maximum service distance. The service radius library stores the mapping relationship between merchant ID and maximum service distance;

[0119] Module 14: Calculate the distance between the order's geographic location and each merchant's location based on the order's longitude and latitude coordinates and the merchant's longitude and latitude coordinates;

[0120] Module 15: Filter merchants whose distance is ≤ the maximum service distance and generate the first merchant set.

[0121] Optionally, also include:

[0122] Module 16: The preset merchant capability model includes hard capability indicators and soft evaluation indicators;

[0123] Module 17: The hard ability indicators include qualification level and project experience type;

[0124] Module 18: The soft evaluation indicators include historical praise rate and construction period achievement rate;

[0125] Module 19: Based on the order demand category characteristics, screen merchants that meet the hard capability indicators from the first merchant set to generate a candidate merchant set;

[0126] Module 20: Based on soft evaluation indicators and order feature vectors, a collaborative filtering algorithm is used to calculate the matching score between each merchant in the candidate merchant set and the order;

[0127] Module 21: Generate a list of matching merchants sorted by matching scores.

[0128] Optionally, also include:

[0129] Module 22: The order demand category features include "custom design" orders and "basic decoration" orders;

[0130] Module 2 and 3: When the order requirement category is "custom design" orders, screen merchants with qualification level ≥ A and experience in "customization" projects;

[0131] Module 24: When the order requirement category feature is a "basic decoration" order, merchants with qualification level ≥ B will be screened.

[0132] Optionally, also include:

[0133] Module 25: The calculation formula of the matching score is S=N×F1+M×F2;

[0134] Module 26: Among them: F1 is the historical praise rate, F2 is the completion rate of the project period;

[0135] 27 module: N and M are dynamic weights, and N+M=1, which are adjusted according to the characteristics of the order demand category:

[0136] 28 module: When the order requirement category feature is a "custom design" order, N=N1, M=M1, N1, M1 are preset parameters;

[0137] Module 29: When the order demand category feature is a "basic decoration" order, N=N2, M=M2, N2, M2 are preset parameters.

[0138] For the specific definition of a home improvement order processing system, please refer to the definition of a home improvement order processing method above and will not be repeated here. Each module in the above-mentioned home improvement order processing system can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0139] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store a first merchant list. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for processing home improvement orders is implemented.

[0140] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for processing a home improvement order is implemented.

[0141] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a method for processing a home improvement order is provided.

[0142] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0143] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A method for processing home improvement orders, characterized in that: include: When receiving a home improvement order from a client, key features are extracted to generate an order feature vector; Based on the order feature vector and a preset merchant service radius library, screening out a first set of merchants that can serve the target area; Based on the order feature vector, the preset merchant capability model and the collaborative filtering algorithm, performing a matching analysis on the construction capabilities and historical service evaluations of merchants in the first merchant set to generate a matching merchant list; Sending an order acceptance task notification to the merchant terminals in the matching merchant list; When the merchant confirms the order, the order status is updated and a construction plan is generated; Send order confirmation to the client and schedule construction.

2. A method for processing home improvement orders according to claim 1, characterized in that: The step of extracting key features and generating an order feature vector when receiving a home improvement order from a client includes: Extracting geographic location keywords from the home improvement order and outputting order geographic location features; Extract demand category keywords from the home improvement order and output order demand category features; Extract house area and house type keywords from the home improvement order and output basic house features; extracting budget keywords from the home improvement order and outputting budget features; The order location features, order demand category features, house basic features, and budget features are integrated into the order feature vector and output.

3. A method for processing home improvement orders according to claim 1, characterized in that: The step of screening out a first set of merchants that can serve the target area based on the geographic location features in the order feature vector and a preset merchant service radius library includes: Based on the geographic location characteristics of the order, output the latitude and longitude coordinates of the order; Query a preset merchant service radius library and output the merchant's latitude and longitude coordinates and maximum service distance. The service radius library stores a mapping relationship between merchant ID and maximum service distance. Based on the order's longitude and latitude coordinates and the merchant's longitude and latitude coordinates, calculate the distance between the order's geographic location and each merchant's location; Filter merchants whose distance is ≤ the maximum service distance to generate the first merchant set.

4. A method for processing home improvement orders according to claim 1, characterized in that: The step of performing a matching analysis of the construction capabilities and historical service evaluations of merchants in the first merchant set based on the order feature vector, the preset merchant capability model, and the collaborative filtering algorithm to generate a matching merchant list includes: The preset merchant capability model includes hard capability indicators and soft evaluation indicators; The hard ability indicators include qualification level and project experience type; The soft evaluation indicators include historical praise rate and construction period achievement rate; Based on the order demand category characteristics, screening merchants that meet the hard capability indicators from the first merchant set to generate a candidate merchant set; Based on the soft evaluation index and the order feature vector, a collaborative filtering algorithm is used to calculate the matching score between each merchant in the candidate merchant set and the order; Generate a list of matching merchants sorted by matching scores.

5. A method for processing home improvement orders according to claim 4, characterized in that: The step of screening merchants that meet the hard capability indicators from the first merchant set based on the order demand category characteristics to generate a candidate merchant set includes: The order demand category features include "custom design" orders and "basic decoration" orders; When the order requirement category is "Custom Design," merchants with a qualification level ≥ A and experience in "Custom Design" projects are screened. When the order requirement category feature is "basic decoration" orders, merchants with qualification level ≥ B will be screened.

6. A method for processing home improvement orders according to claim 4, characterized in that: The step of calculating the matching score between each merchant in the candidate merchant set and the order through a collaborative filtering algorithm based on the soft evaluation index and the order feature vector includes: The calculation formula of the matching score is S=N×F1+M×F2; Among them: F1 is the historical praise rate, F2 is the completion rate of the project period; N and M are dynamic weights, and N+M=1, which are adjusted according to the characteristics of the order demand category: When the order requirement category feature is "custom design" order, N = N1, M = M1, N1, M1 are preset parameters; When the order requirement category feature is "Basic Decoration" order, N=N2, M=M2, N2, and M2 are preset parameters.

7. A home improvement order processing system, characterized in that: include: The first module: when receiving a home improvement order from a client, extract key features to generate an order feature vector; The second module: based on the order feature vector and the preset merchant service radius library, screening out a first set of merchants that can serve the target area; The third module: based on the order feature vector, the preset merchant capability model and the collaborative filtering algorithm, the first merchant; Module 4: Perform matching analysis on the construction capabilities and historical service evaluation of merchants in the collection and generate a list of matching merchants; The fifth module: sending an order task notification to the merchant end in the matching merchant list; Module 6: When the merchant confirms the order, the order status is updated and a construction plan is generated; Module 7: Send order confirmation to the client and schedule construction.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the home improvement order processing method according to claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the home improvement order processing method according to claims 1-6.

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