Resource recommendation method, resource recommendation device, equipment and computer storage medium

Abstract

The invention provides a resource recommendation method, a resource recommendation device, resource recommendation equipment and a computer storage medium. The resource recommendation method comprises the steps of obtaining a target planting parameter group and candidate resources; obtaining an adaptability value of the candidate resource and each planting parameter in the target planting parameter group; performing statistics on a resource adaptability score of the candidate resource based on the adaptability value of each planting parameter; and generating a target resource recommendation result according to the resource applicability score of the candidate resource. Through the resource recommendation method, a more flexible, accurate and personalized agricultural material product recommendation method is provided, and the overall service level of an online agricultural transaction platform is greatly improved.

Description

Technical Field

[0001] The present application relates to the field of agricultural informatization and e-commerce technology, and in particular to a resource recommendation method, a resource recommendation device, a resource recommendation equipment and a computer storage medium. Background Art

[0002] With the rapid development of the Internet, online agricultural materials trading platforms have become an important bridge connecting agricultural producers and suppliers, greatly promoting the optimal allocation and circulation efficiency of agricultural resources. However, in the process of recommending agricultural materials (such as seeds, pesticides, fertilizers, etc.), traditional methods based on keyword matching or single condition screening have problems such as recommended products not meeting user planting needs, inaccurate recommendation results, and high requirements for farmers' experience.

[0003] At present, online agricultural materials trading platforms mainly adopt two mainstream strategies when recommending agricultural materials and their suppliers: one is keyword-based matching recommendation, which directly searches for product names through keywords entered by users and recommends them according to the degree of matching. However, this method ignores the deep differences in user needs, and when there is no direct match, users may face the embarrassing situation of having no products to choose from; the other is screening recommendation based on user selection conditions, such as seed variety, moisture content and other specific parameters. Although it increases the accuracy of screening, it is also limited by the integrity of the platform's product database. Once the conditions set by the user are too specific or there are no fully matching products on the platform, it cannot be effectively recommended. Moreover, this method requires a high level of experience and knowledge from farmers, and requires farmers to understand the meaning and scope of application of each parameter. Summary of the invention

[0004] In order to solve the above technical problems, the present application proposes a resource recommendation method, a resource recommendation device, a resource recommendation equipment and a computer storage medium.

[0005] In order to solve the above technical problems, the present application proposes a resource recommendation method, which includes:

[0006] Obtaining a target planting parameter group and candidate resources;

[0007] Obtaining the suitability value of each planting parameter in the candidate resource and the target planting parameter group;

[0008] Calculating resource suitability scores of the candidate resources based on the suitability values ​​of each planting parameter;

[0009] According to the resource suitability scores of the candidate resources, a target resource recommendation result is generated.

[0010] The step of generating a target resource recommendation result according to the resource suitability score of the candidate resource includes:

[0011] The candidate resources are sorted according to the resource suitability scores to generate target resource recommendation results.

[0012] Wherein, the target planting parameter group includes target planting discrete parameters and target planting continuous parameters;

[0013] The obtaining of the suitability value of each planting parameter in the candidate resource and the target planting parameter group includes:

[0014] Obtaining a discrete parameter applicability table of the candidate resources and each type of planting discrete parameters;

[0015] Determining a discrete suitability value according to the discrete parameter suitability table corresponding to each planting parameter in the target planting parameter group;

[0016] Get the value range of each type of planting continuous parameter;

[0017] Obtaining the optimal value of the continuous parameter of each type of planting of the candidate resource;

[0018] A continuous suitability value is determined according to the value range, the optimal value and the target planting continuous parameter.

[0019] Wherein, the resource recommendation method further includes:

[0020] Generate an initial discrete parameter table for each resource and each type of parameter, wherein the initial discrete parameter table includes all planting parameters of each type of parameter and an initialization fitness value;

[0021] According to the resource applicability parameters configured by the resource provider, the initialization applicability values ​​in the initial discrete parameter table are configured with applicability values ​​to generate the discrete parameter applicability table.

[0022] The calculating of the resource suitability score of the candidate resource based on the suitability value of each planting parameter includes:

[0023] Based on the fitness value of each planting parameter, the fitness scores of all candidate resources of each supplier are calculated to obtain the supplier fitness score of the supplier;

[0024] The resource recommendation method further includes:

[0025] A target supplier recommendation result is generated based on the supplier fitness score.

[0026] The step of generating a target supplier recommendation result according to the supplier fitness score includes:

[0027] Obtaining a supplier fitness score for each target planting parameter group among a plurality of target planting parameter groups;

[0028] The target supplier fitness scores for all target planting parameter groups were calculated;

[0029] A target supplier recommendation result is generated according to the target supplier fitness score.

[0030] The step of generating a target supplier recommendation result according to the target supplier fitness score includes:

[0031] All suppliers are sorted according to the target supplier fitness scores to generate target supplier recommendation results.

[0032] In order to solve the above technical problems, the present application also proposes a resource recommendation device, which includes: an acquisition module, a calculation module, a scoring module, and a recommendation module; wherein:

[0033] The acquisition module is used to acquire a target planting parameter group and candidate resources;

[0034] The calculation module is used to obtain the suitability value of each planting parameter in the candidate resource and the target planting parameter group;

[0035] The scoring module is used to calculate the resource suitability score of the candidate resource based on the suitability value of each planting parameter;

[0036] The recommendation module is used to generate a target resource recommendation result according to the resource suitability scores of the candidate resources.

[0037] To solve the above technical problems, the present application also proposes a resource recommendation device, which includes a memory and a processor coupled to the memory; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the resource recommendation method as described above.

[0038] In order to solve the above technical problems, the present application also proposes a computer storage medium, wherein the computer storage medium is used to store program data, and when the program data is executed by a computer, it is used to implement the above resource recommendation method.

[0039] Compared with the prior art, the beneficial effects of the present application are as follows: the resource recommendation device obtains the target planting parameter group and candidate resources; obtains the suitability value of each planting parameter in the candidate resource and the target planting parameter group; calculates the resource suitability score of the candidate resource based on the suitability value of each planting parameter; and generates the target resource recommendation result according to the resource suitability score of the candidate resource. Through the above resource recommendation method, a more flexible, accurate and personalized agricultural product recommendation method is provided, which greatly improves the overall service level of the online agricultural trading platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] in:

[0042] Figure 1 It is a flowchart of an embodiment of a resource recommendation method provided by the present application;

[0043] Figure 2 It is a schematic diagram of the overall process of the resource recommendation method provided by this application;

[0044] Figure 3 It is a flowchart of the basic data configuration provided by this application;

[0045] Figure 4 It is a flowchart of the single resource recommendation solution provided by this application;

[0046] Figure 5 yes Figure 1 The specific flow chart of step S12 of the resource recommendation method shown;

[0047] Figure 6 It is a flowchart of another embodiment of the resource recommendation method provided by the present application;

[0048] Figure 7 It is a flow chart of the supplier recommendation scheme provided in this application;

[0049] Figure 8 It is a structural diagram of an embodiment of a resource recommendation device provided by the present application;

[0050] Fig. 9 It is a structural diagram of an embodiment of a resource recommendation device provided by the present application;

[0051] Fig.10It is a structural diagram of an embodiment of a computer storage medium provided by the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0054] Existing technologies have the following shortcomings in agricultural product recommendation and supply-demand matching:

[0055] (1) Limitations of the keyword matching method: A common recommendation method currently used by online agricultural trading platforms is the keyword matching method. Users enter keywords in the search box, and the system matches the keywords with product names, descriptions, and other information, and returns search results based on the degree of match. The advantage of this method is that it is simple and intuitive, but the disadvantage is that it has a superficial understanding of user needs and cannot fully consider farmers' planting information. It is difficult to provide personalized recommendations, and it is easy for the recommended products to be inappropriate for farmers' planting conditions.

[0056] (2) Rigidity of conditional screening: Another common recommendation method is conditional screening, which allows users to filter products based on a series of preset parameters (such as seed variety, moisture content, etc.). Although this method can narrow the selection range to a certain extent and provide products that better meet user needs, it requires users to clearly know their needs, and when the preset conditions are more stringent, there may be no products that meet the conditions, leaving users with no choice. In addition, using this method for querying requires users to understand the meaning and scope of application of the selection parameters, which requires high experience and technical skills of farmers.

[0057] (3) Lack of supplier recommendation method: Currently, most platforms recommend suppliers based on supplier reputation ratings or evaluation scores, without considering the user's planting situation. The recommended suppliers often do not meet the actual needs of farmers, resulting in low communication efficiency and reduced shopping experience and efficiency.

[0058] This application proposes a more intelligent and accurate online agricultural trading platform resource recommendation method by fully considering the farmers' planting information, so as to improve the user experience and promote the accurate connection of agricultural transactions.

[0059] This application is aimed at recommending agricultural trading platform products and matching supply and demand, and has the following advantages:

[0060] (1) Recommended resources are more accurate:

[0061] The intelligent comprehensive scoring mechanism proposed in this application is based on the user's selection of product categories, comprehensively considering the planting information of the plots configured by farmers, and the recommended products are more in line with the user's actual needs.

[0062] (2) The recommendation mechanism is more flexible:

[0063] This application breaks the traditional conditional screening method. Based on the planting information configured by the user, all the products and suppliers on the platform are scored, and recommendations are made from high to low according to the scoring results, which reduces the frustration of users who cannot find suitable products and improves the transaction rate and procurement efficiency.

[0064] (3) Dynamic recommendations and personalized services:

[0065] Farmers can dynamically configure planting parameter groups according to the planting stage. At different planting stages, the platform will automatically recommend agricultural products that meet the stage. In addition, the recommendation mechanism provided by this application can comprehensively consider various user information, including the type of crops planted, the planting area, the planting area, etc., to provide more personalized recommendation services.

[0066] The technologies involved in the resource recommendation method of this application include but are not limited to:

[0067] Agricultural supplies: various resources needed in the agricultural production process, including planting, pesticides, fertilizers, etc.

[0068] Platform: Online trading mall that provides agricultural inputs configuration, display, ordering and other functions.

[0069] Farmers: The purchasing party in agricultural machinery transactions.

[0070] Supplier: The seller in an agricultural machinery transaction.

[0071] Discrete parameters: their values ​​are isolated point sets, such as soil types, whose values ​​include loam, clay, sand, and gravel.

[0072] Continuous parameter: its value is a continuous numerical sequence, such as soil pH: its value range is a continuous number between 0-14.

[0073] Please refer to Figure 1 and Figure 2 , Figure 1 is a flowchart of an embodiment of a resource recommendation method provided by the present application. Figure 2 It is a schematic diagram of the overall process of the resource recommendation method provided by this application.

[0074] The camera-based resource recommendation method of the present application is applied to a resource recommendation device, wherein the resource recommendation device of the present application can be a server, a terminal device, or a system in which a server and a terminal device cooperate with each other. Accordingly, the various parts included in the resource recommendation device, such as various units, sub-units, modules, and sub-modules, can all be set in the server, can all be set in the terminal device, or can be set in the server and the terminal device respectively.

[0075] Furthermore, the above-mentioned server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules, such as software or software modules used to provide distributed servers, or it can be implemented as a single software or software module, which is not specifically limited here.

[0076] like Figure 2 As shown, the overall process of the resource recommendation method of the present application includes at least the following three parts: basic parameter configuration, resource recommendation, and supplier recommendation. Among them, resource recommendation and supplier recommendation are the purposes of the resource recommendation method provided by the present application, and basic parameter configuration is the prerequisite for realizing the resource recommendation method provided by the present application.

[0077] For details, please continue to see Figure 3 , Figure 3 It is a flowchart of the basic data configuration provided by this application.

[0078] like Figure 3 As shown, the basic data configuration scheme of this application specifically includes the following three parts: defining parameters, farmers configuring planting parameter groups, and suppliers configuring applicable parameters.

[0079] Among them, the details of the solution for defining parameters: the agricultural trading platform defines parameters according to the type of operating resources. For example, for agricultural products such as seeds, pesticides, and fertilizers, soil information, planting information, pest and disease information, etc. can be defined. Soil information may include but is not limited to: soil quality (loam, clay, sandy soil, gravel soil), soil fertility (extremely high, high, medium, low, extremely low), soil pH, etc.; planting information includes: planting crops (wheat, corn, soybeans, rice, etc.), species categories (Longmai 30, Zhengmai 9023, Yumai 34, etc.) and other information; pest and disease information includes: disease types (wheat rust, ergot, wheat powdery mildew), pest categories (wheat spiders, wireworms, nematodes), etc.

[0080] Among them, the details of the planting parameter group configuration plan are as follows: farmers configure their own planting parameter groups, including discrete parameters and continuous parameters. For discrete parameters, the agricultural trading platform provides options for users to choose from. For continuous parameters, the agricultural trading platform provides parameter value ranges, and users enter actual parameter values.

[0081] Taking the above preset parameters as an example, if the user selects soil type: clay; soil fertility: high; soil pH: 7.6; crop: wheat; crop category: Longmai 30; disease type: none; pest type: wheat spider, then the parameter group is [clay, high, 7.6, wheat, Longmai 30, null, wheat spider].

[0082] One user can configure multiple sets of planting information. For example, if a user plants corn and soybeans separately, the user can configure two planting parameter groups.

[0083] Let t represent the number of planting information sets configured by farmers, m represent the number of configurable parameters, and C represent the parameters configured by farmers. Then C ki represents the parameter value of the i-th parameter in the k-th group of parameters (k is 1, 2, 3...t) (i is 1, 2, 3...m), then the user configuration information can be expressed as:

[0084] [C 11 , C 12 , C 13 ...C 1m ]

[0085] [C 21 , C 22 , C 23 ...C 2m ]

[0086] [C t1 , C t2 , C t3 ...C tm ]

[0087] If the user does not configure a parameter, the parameter value is null.

[0088] Among them, the solution details about applicable parameter configuration:

[0089] The resource applicable parameters are configured by the resource supplier. The applicable parameters are consistent with the planting parameters, and also include discrete applicable parameters and continuous applicable parameters.

[0090] (1) Discrete applicability parameters Taking wheat seeds as an example, the suitable planting areas can be configured, from high to low in terms of suitability: South China, North China, Central China, East China, Northeast China, Northwest China, and Southwest China; the suitable soil types, from high to low in terms of suitability: loam, clay, sandy soil, and gravel soil; the suitable fertility, from high to low in terms of suitability: medium, low, and very low. The applicable parameters are consistent with the preset parameters, but the applicable parameter value is not a single option or fixed value. It is a suitability table, that is, for a certain parameter, different values ​​correspond to different suitabilities.

[0091] Taking Longmai 30 as an example, its soil suitability can be expressed in the following table:

[0092] Soil quality Applicability Loam 10 clay 8 sand 2 Gravel soil 0

[0093] It should be noted that all types of fitness tables involved in this application are fitness relationship tables between two different parameters. For example, the above table can be the fitness relationship between a certain type of farmer's resource, such as Longmai 30, and soil quality. For another type of farmer's resource, the agricultural trading platform maintains another soil quality suitability table.

[0094] The applicability ranges from 0 to 10. The higher the applicability, the more applicable the resource is to the current parameter value, that is, the expected effect after use is better. When the applicability is 0, it means that the resource is completely unsuitable for the parameter value.

[0095] (2) The continuous applicable parameter needs to be configured with the interval (a, b) of the parameter, where a is the lower limit of the parameter value, b is the upper limit of the parameter value, and the optimal value d needs to be configured. For the soil pH parameter, a=0; b=14, d=7 can be configured, indicating that the soil pH parameter range is 0-14. When the soil pH=7, the resource can play the best effect.

[0096] Figure 2 Please continue to refer to the resource recommendation plan for details. Figure 4 , Figure 4 It is a flowchart of a single resource recommendation scheme provided by this application. Resource recommendation refers to recommending a certain type of resource when a farmer selects a parameter group.

[0097] like Figure 1 As shown, the specific steps are as follows:

[0098] Step S11: Obtain a target planting parameter group and candidate resources.

[0099] like Figure 4 As shown, the resource recommendation device selects a planting parameter group according to the user's planting needs, and the basic data of the crop recommendation of the selected planting parameter group participates in the subsequent resource suitability score calculation. For example, the user intends to purchase pesticides suitable for the planting parameters of [clay, high, 7.6, wheat, Longmai 30, null, wheat spider]. After selecting this group of parameters, the user can search under the pesticide classification, and the agricultural trading platform automatically recommends pesticide resources that serve the planting parameters. Among them, the candidate resources are all tradable pesticide resources provided by the agricultural trading platform.

[0100] Specifically, the target planting parameter group selected by the user includes target planting discrete parameters and target planting continuous parameters. The definitions of discrete parameters and continuous parameters have been described in the above introduction and will not be repeated here.

[0101] Step S12: Obtain the suitability value of each planting parameter in the candidate resource and target planting parameter group.

[0102] In the embodiment of the present application, the resource recommendation device calculates the suitability value of each candidate resource provided by the agricultural trading platform and each planting parameter in the target planting parameter group. According to the discrete parameters and continuous parameters in the planting parameters, the resource recommendation device adopts different suitability value calculation schemes. For details, please refer to Figure 5 , Figure 5 yes Figure 1 The specific flow chart of step S12 of the resource recommendation method is shown.

[0103] like Figure 5 As shown, the specific steps are as follows:

[0104] Step S21: Obtain a discrete parameter applicability table of candidate resources and each type of planting discrete parameters.

[0105] In the embodiment of the present application, the resource recommendation device extracts the discrete parameter applicability table of each candidate resource and each type of planting discrete parameter maintained in advance from the agricultural trading platform. For the expression form of the discrete parameter applicability table, please refer to the discrete applicability parameter applicability table, which is not listed here one by one.

[0106] Step S22: Determine the discrete suitability value according to the discrete parameter suitability table corresponding to each planting parameter in the target planting parameter group.

[0107] In the embodiment of the present application, taking the target planting parameter group of [clay, high, 7.6, wheat, long wheat 30, null, wheat spider] as an example, the resource recommendation device extracts the suitability table between soil quality and each candidate resource, the suitability table between soil fertility and each candidate resource, the suitability table between soil pH and each candidate resource, etc. from the agricultural trading platform.

[0108] Then, the resource recommendation device further determines the discrete suitability value between viscosity and each candidate resource from the suitability table between soil quality and each candidate resource, and so on until all required discrete suitability values ​​are obtained.

[0109] The specific process is as follows:

[0110] A. According to the discrete parameter applicability table, find the parameter value C selected by the user ki The corresponding applicability.

[0111] B. Use S to represent the fitness value, then SC ki Indicates that the current resource is in the parameter value C ki , the suitability value of the resource.

[0112] C. When C ki = null, SC ki =0.

[0113] Step S23: Obtain the value range of each type of planting continuous parameter.

[0114] Step S24: Obtain the optimal value of the continuous parameter of each type of planting of the candidate resource.

[0115] Step S25: Determine the continuous suitability value according to the value range, the optimal value and the target planting continuous parameter.

[0116] In the embodiment of the present application, the resource recommendation device obtains the value range of each type of planting continuous parameter of the target planting parameter group, the upper limit of the continuous parameter value is a, the lower limit is b, and the optimal value is d.

[0117] Among them, C ki is the parameter value configured by the user. Then the continuous parameter applicability SC ki for:

[0118]

[0119] SC ki =0C ki =null

[0120] For example, the soil pH value range is 0-14, that is, a = 0, b = 14, and the resource is best used when the pH value is 7, that is, d = 7. When the user configures the soil pH value to be 7.5 (Cki = 7.5), the suitability is: [1-|7.5-7| / (14-0)]*10≈9.64.

[0121] Step S13: calculating the resource suitability score of the candidate resources based on the suitability value of each planting parameter.

[0122] In the embodiment of the present application, the resource recommendation device uses H to represent the resource suitability score, then H k Indicates the score of the resource when k sets of parameters are selected:

[0123]

[0124] Where m is the number of parameters in the k parameter group; i represents the i-th parameter in the k parameter group; SC ki is the calculated single parameter fitness of the i-th parameter in the k-th parameter group.

[0125] Step S14: Generate target resource recommendation results based on the resource suitability scores of the candidate resources.

[0126] In the embodiment of the present application, the resource recommendation device makes recommendations from high to low suitability according to the resource suitability scores calculated in the above steps S11 to S13. For example, if the user selects pesticides and the planting parameter group selects 2, the platform will calculate the suitability scores for all pesticide resources. The calculation results are sorted from high to low, and the sorting is the resource recommendation result.

[0127] In this application, the resource recommendation device obtains a target planting parameter group and candidate resources; obtains the suitability values ​​of the candidate resources and each planting parameter in the target planting parameter group; calculates the resource suitability score of the candidate resources based on the suitability value of each planting parameter; and generates a target resource recommendation result based on the resource suitability score of the candidate resources. Through the above resource recommendation method, a more flexible, accurate and personalized agricultural product recommendation method is provided, which greatly improves the overall service level of the online agricultural trading platform.

[0128] If a farmer has multiple planting plots or types of crops, he may configure multiple planting parameter groups. For different parameter groups, the platform will recommend different agricultural products, but these agricultural products may not belong to the same supplier. In this case, farmers need to communicate with multiple suppliers, and the procurement and communication efficiency will be low. Therefore, this application further provides a resource recommendation method for supplier recommendation, which recommends the supplier that best meets the needs of farmers based on the multiple planting parameter groups selected by farmers.

[0129] Please continue reading Figure 6 and Figure 7 , Figure 6 is a flow chart of another embodiment of the resource recommendation method provided by the present application. Figure 7 It is a flowchart of the supplier recommendation scheme provided in this application.

[0130] like Figure 6 As shown, the specific steps are as follows:

[0131] Step S31: Obtaining a supplier fitness score for each target planting parameter group in a plurality of target planting parameter groups.

[0132] In the embodiments of the present application, Figure 7 As shown, the resource recommendation device selects multiple planting parameter groups according to the user's planting needs, and the basic data of the crop recommendation of the selected planting parameter group participates in the subsequent resource suitability score calculation. For example, the user intends to purchase fertilizers suitable for the following parameter groups:

[0133] [Clay, high, 7.6, wheat, dragon wheat 30, null, wheat spider]

[0134] [clay, medium, 7.2, corn, corn 30, scab, null]

[0135] If you select the first set of parameters alone, fertilizers from supplier A may be recommended. If you select the second set of parameters alone, fertilizers from supplier B may be recommended. However, if the user wants to purchase from the same supplier, he or she can select both sets of parameters and make supplier recommendations. At this time, the platform can combine the two sets of parameters and recommend suppliers that meet the user's needs.

[0136] The resource recommendation device uses x to represent the number of supplier resources and z to represent the zth resource of the supplier. For each planting parameter group selected by the user, the suitability score of all resources of the supplier is calculated. If the user selects the kth planting parameter group, the suitability score is H kz :

[0137]

[0138] Among them, H kz Score the suitability of the supplier's zth resource for the farmer's kth set of parameters; SC ki is the fitness of the i-th parameter in the k-th parameter group. m represents the number of parameters in the k-th parameter group.

[0139] For the kth group of planting parameters, through the above resource suitability calculation, x suitability scores can be obtained, and the maximum value is taken to obtain the optimal suitability score H of the kth group of planting parameters. k :

[0140] H k =max(H kz )

[0141] Where: H kz It is the suitability score of the supplier's z-th resource for the farmer's k-th group of parameters; z takes values ​​(1, 2, 3...x), and x is the number of supplier resources.

[0142] Step S32: Count the target supplier fitness scores of all target planting parameter groups.

[0143] In the embodiment of the present application, the resource recommendation device obtains the suitability score H of each supplier according to the statistics of the optimal suitability score of each group of planting parameters calculated in step S31:

[0144]

[0145] Among them, H k The supplier's optimal suitability score for the kth set of implant parameters; k represents the kth set of parameters selected by the user; t represents the number of parameter sets selected by the user.

[0146] Step S33: Generate target supplier recommendation results based on the target supplier fitness score.

[0147] In the embodiment of the present application, after the user selects multiple groups of planting parameters, the resource recommendation device calculates the suitability scores of all suppliers under the user's selected category and recommends them from high to low in terms of suitability. For example, if the user selects pesticides and selects planting parameter groups 1, 2, and 3, the platform will calculate the suitability scores of all pesticide suppliers. The calculation results are sorted from high to low, and the sorting is the supplier recommendation result.

[0148] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0149] To implement the above resource recommendation method, this application also proposes a resource recommendation device, please refer to Figure 8 , Figure 8 It is a structural diagram of an embodiment of a resource recommendation device provided by the present application.

[0150] The resource recommendation device 500 of this embodiment includes: an acquisition module 51 , a calculation module 52 , a scoring module 53 , and a recommendation module 54 .

[0151] The acquisition module 51 is used to acquire a target planting parameter group and candidate resources.

[0152] The calculation module 52 is used to obtain the suitability value of each planting parameter in the candidate resource and the target planting parameter group.

[0153] The scoring module 53 is used to calculate the resource suitability score of the candidate resource based on the suitability value of each planting parameter.

[0154] The recommendation module 54 is used to generate a target resource recommendation result according to the resource suitability scores of the candidate resources.

[0155] To implement the above resource recommendation method, this application also proposes a resource recommendation device, please refer to Fig. 9 , Fig. 9 It is a structural diagram of an embodiment of a resource recommendation device provided by the present application.

[0156] The resource recommendation device 400 of this embodiment includes a processor 41 , a memory 42 , an input / output device 43 , and a bus 44 .

[0157] The processor 41 , the memory 42 , and the input / output device 43 are respectively connected to the bus 44 . The memory 42 stores program data, and the processor 41 is used to execute the program data to implement the resource recommendation method described in the above embodiment.

[0158] In the embodiment of the present application, the processor 41 may also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip having the ability to process signals. The processor 41 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or the processor 41 may also be any conventional processor, etc.

[0159] This application also provides a computer storage medium, please continue to refer to Fig.10 , Fig.10 It is a structural diagram of an embodiment of a computer storage medium provided in the present application. The computer storage medium 600 stores a computer program 61. When the computer program 61 is executed by a processor, it is used to implement the resource recommendation method of the above embodiment.

[0160] When the embodiments of the present application are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0161] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A resource recommendation method, characterized in that: The resource recommendation method comprises: Obtaining a target planting parameter group and candidate resources; Obtaining the suitability value of each planting parameter in the candidate resource and the target planting parameter group; Calculating resource suitability scores of the candidate resources based on the suitability values ​​of each planting parameter; According to the resource suitability scores of the candidate resources, a target resource recommendation result is generated.

2. The resource recommendation method according to claim 1, characterized in that: Generating a target resource recommendation result according to the resource suitability score of the candidate resource includes: The candidate resources are sorted according to the resource suitability scores to generate target resource recommendation results.

3. The resource recommendation method according to claim 1, characterized in that: The target planting parameter group includes target planting discrete parameters and target planting continuous parameters; The obtaining of the suitability value of each planting parameter in the candidate resource and the target planting parameter group includes: Obtaining a discrete parameter applicability table of the candidate resources and each type of planting discrete parameters; Determining a discrete suitability value according to the discrete parameter suitability table corresponding to each planting parameter in the target planting parameter group; Get the value range of each type of planting continuous parameter; Obtaining the optimal value of the continuous parameter of each type of planting of the candidate resource; A continuous suitability value is determined according to the value range, the optimal value and the target planting continuous parameter.

4. The resource recommendation method according to claim 3, characterized in that: The resource recommendation method further includes: Generate an initial discrete parameter table for each resource and each type of parameter, wherein the initial discrete parameter table includes all planting parameters of each type of parameter and an initialization fitness value; According to the resource applicability parameters configured by the resource provider, the initialization applicability values ​​in the initial discrete parameter table are configured with applicability values ​​to generate the discrete parameter applicability table.

5. The resource recommendation method according to claim 1, characterized in that: The calculating of the resource suitability score of the candidate resource based on the suitability value of each planting parameter includes: Based on the fitness value of each planting parameter, the fitness scores of all candidate resources of each supplier are calculated to obtain the supplier fitness score of the supplier; The resource recommendation method further includes: A target supplier recommendation result is generated based on the supplier fitness score.

6. The resource recommendation method according to claim 5, characterized in that: Generating a target supplier recommendation result according to the supplier fitness score includes: Obtaining a supplier fitness score for each target planting parameter group among a plurality of target planting parameter groups; The target supplier fitness scores for all target planting parameter groups were calculated; A target supplier recommendation result is generated according to the target supplier fitness score.

7. The resource recommendation method according to claim 6, characterized in that: Generating a target supplier recommendation result according to the target supplier fitness score includes: All suppliers are sorted according to the target supplier fitness scores to generate target supplier recommendation results.

8. A resource recommendation device, characterized in that: The resource recommendation device comprises: an acquisition module, a calculation module, a scoring module, and a recommendation module; wherein, The acquisition module is used to acquire a target planting parameter group and candidate resources; The calculation module is used to obtain the suitability value of each planting parameter in the candidate resource and the target planting parameter group; The scoring module is used to calculate the resource suitability score of the candidate resource based on the suitability value of each planting parameter; The recommendation module is used to generate a target resource recommendation result according to the resource suitability scores of the candidate resources.

9. A resource recommendation device, characterized in that: The resource recommendation device includes a memory and a processor coupled to the memory; The memory is used to store program data, and the processor is used to execute the program data to implement the resource recommendation method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium is used to store program data, and when the program data is executed by a computer, it is used to implement the resource recommendation method according to any one of claims 1 to 7.

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