Product searching method and device, network equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a product searching method and device, network equipment and a storage medium. The product search method comprises the steps that under the condition that a first product corresponding to product information and searched through an A star algorithm does not meet product requirements, adjustment information of a search mode is obtained, and the adjustment information comprises parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A star algorithm; adjusting the first search information by adopting the adjustment information to obtain second search information corresponding to the product information; and in a state space, searching by adopting the second search information and the product information to obtain a second product meeting the product requirement. According to the invention, the product determination efficiency and accuracy can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to a product search method, device, network equipment and storage medium. Background Art

[0002] With the development of science and technology, users' lives are becoming more and more convenient. For example, users can protect their own interests by purchasing insurance products. However, the products purchased by users are recommended based on manual experience, which lacks systematicity, resulting in low efficiency and accuracy of product recommendations and cannot meet user needs. Summary of the invention

[0003] The present disclosure provides a product search method, device, network device and storage medium to improve the efficiency and accuracy of product determination. The technical solution of the present disclosure is as follows:

[0004] According to a first aspect of an embodiment of the present disclosure, a product search method is provided, including:

[0005] When the first product corresponding to the product information searched by using the A-star algorithm does not meet the product requirements, obtaining adjustment information of the search method, wherein the adjustment information includes parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm;

[0006] Using the adjustment information to adjust the first search information, and obtaining second search information corresponding to the product information;

[0007] In the state space, the second search information and the product information are used to search and obtain a second product that meets the product requirement.

[0008] According to some embodiments, the method further comprises:

[0009] According to the product information, the A-star algorithm is used to perform an iterative search in the state space to obtain a target node corresponding to the product information, wherein the state space includes at least one node;

[0010] The product corresponding to the target node is used as the first product corresponding to the product information.

[0011] According to some embodiments, the method further comprises:

[0012] Obtaining a node set in the state space;

[0013] Obtaining a cost function between a first node and a second node in the node set, wherein the first node and the second node are adjacent nodes in the node set;

[0014] Obtaining a heuristic function between the first node and the second node;

[0015] The A-star algorithm is generated according to the cost function, the first weight corresponding to the cost function, the heuristic function, and the second weight corresponding to the heuristic function.

[0016] According to some embodiments, the method further comprises:

[0017] Get application scenario information;

[0018] The state space is modified according to the application scenario information to obtain a modified state space.

[0019] According to some embodiments, the method further comprises:

[0020] Modifying the cost function according to historical data to obtain a modified cost function;

[0021] and / or

[0022] According to the application scenario information and the evaluation information, the first weight and the second weight are modified at preset time intervals to obtain a modified first weight and a modified second weight.

[0023] According to some embodiments, the adjustment information includes search strategy adjustment information, and the using the adjustment information to adjust the first search information to obtain the second search information corresponding to the product information includes at least one of the following:

[0024] In a case where the search strategy adjustment information is used to indicate adjustment of the number of iterative searches, modifying the number of iterative searches to obtain a modified number of iterative searches, wherein the modified number of iterative searches is used to indicate the number of searches in the state space;

[0025] In the case where the search strategy adjustment information is used to indicate adjustment of the iterative search direction, the iterative search direction is modified to obtain a modified search direction.

[0026] According to some embodiments, modifying the iterative search number to obtain a modified number of iterations includes:

[0027] Acquire a performance indicator corresponding to a search process, wherein the search process is a process of searching for a first product corresponding to the product information using the A-star algorithm;

[0028] The performance indicator is used to modify the number of iterative searches to obtain a modified number of iterative searches. According to a second aspect of an embodiment of the present disclosure, a product search device is provided, comprising:

[0029] an information acquisition unit, configured to acquire adjustment information of the search method when the first product corresponding to the product information searched by using the A-star algorithm does not meet the product requirements, wherein the adjustment information includes parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm;

[0030] The information acquisition unit is further configured to adjust the first search information using the adjustment information to acquire second search information corresponding to the product information;

[0031] The product search unit is used to search in the state space using the second search information and the product information to obtain a second product that meets the product requirement.

[0032] According to a third aspect of an embodiment of the present disclosure, a network device is provided, including:

[0033] processor;

[0034] a memory for storing instructions executable by the processor;

[0035] The processor is configured to execute the instructions to implement the product search method described in any one of the aforementioned aspects.

[0036] According to a fourth aspect of an embodiment of the present disclosure, a storage medium is provided. When instructions in the storage medium are executed by a processor of a network device, the network device can execute the product search method described in any one of the preceding aspects.

[0037] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements any one of the methods described in the preceding aspects when executed by a processor.

[0038] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0039] In some or related embodiments, when the first product corresponding to the product information searched by the A-star algorithm does not meet the product requirements, the adjustment information of the search method is obtained, wherein the adjustment information includes the parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm; the first search information is adjusted by the adjustment information to obtain the second search information corresponding to the product information; in the state space, the second search information and the product information are used to search to obtain the second product that meets the product requirements. Therefore, by adjusting the original search information, the situation where the product that meets the product requirements cannot be obtained can be reduced, and there is no need for manual experience determination, which reduces the reliance on manual experience and intuition, enhances the systematicness and scientificity of product determination, and can improve the matching of search information and products, and improve the efficiency and accuracy of product determination.

[0040] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0042] Figure 1 is a flow chart of a product search method according to an exemplary embodiment;

[0043] Figure 2 is a flow chart of a product search method according to an exemplary embodiment;

[0044] Figure 3 is a schematic diagram showing an example of a product search method according to an exemplary embodiment;

[0045] Figure 4 is a block diagram of a product search device according to an exemplary embodiment;

[0046] Figure 5 The invention is a block diagram of a network device according to an exemplary embodiment. DETAILED DESCRIPTION

[0047] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0048] The embodiments of the present disclosure provide a product search method, device, network device, and storage medium. In some embodiments, the terms product search method, information processing method, communication method, etc. can be replaced with each other, the terms product search device, information processing device, communication device, etc. can be replaced with each other, and the terms information processing system, communication system, etc. can be replaced with each other.

[0049] The embodiments of the present disclosure are not exhaustive, but are only illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined, for example, some or all of the steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0050] In each embodiment of the present disclosure, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between the embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form a new embodiment based on their internal logical relationships.

[0051] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0052] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular form, such as "a", "an", "the", "above", "said", "aforementioned", "this", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun after the article may be understood as a singular expression or a plural expression.

[0053] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0054] In some embodiments, the terms “at least one,” “one or more,” “a plurality of,” “multiple,” etc. may be used interchangeably.

[0055] In some embodiments, "at least one of A and B", "A and / or B", "A in one case, B in another case", "in response to one case A, in response to another case B", etc., may include the following technical solutions according to the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). When there are more branches such as A, B, C, etc., the above is also similar.

[0056] In some embodiments, devices and equipment may be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they may also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc.

[0057] In some embodiments, "terminal" or "terminal device" can be referred to as "user equipment (UE)", "user terminal" "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc.

[0058] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0059] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure 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 interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0060] Figure 1 is a flowchart of a product search method according to an exemplary embodiment. Figure 1 As shown, the following steps are included:

[0061] In step S11, when the first product corresponding to the product information searched by the A-star algorithm does not meet the product requirements, the adjustment information of the search method is obtained, wherein the adjustment information includes parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm;

[0062] According to some embodiments, the execution subject of the embodiments of the present disclosure may be, for example, a network device or an electronic device. The embodiments of the present disclosure do not limit this. Wherein, when the execution subject is a network device, the network device does not specifically refer to a fixed network device. For example, when the device identifier corresponding to the network device changes, the network device may also change accordingly.

[0063] According to some embodiments, the A-star algorithm may be, for example, an algorithm that has been generated and can perform product search in the state space. The A-star algorithm does not specifically refer to a fixed algorithm. For example, when any weight information in the A-star algorithm changes, the A-star algorithm may also change accordingly. For example, when the cost function or heuristic function corresponding to the A-star algorithm changes, the A-star algorithm may also change accordingly.

[0064] In some embodiments, the product information may be, for example, product demand information for input. The product information does not specifically refer to a fixed information. For example, when the input time point of the product information changes, the product information may also change accordingly. For example, when any information in the product information changes, the product information may also change accordingly.

[0065] According to some embodiments, the first product may be, for example, a product corresponding to the product information searched by the A-star algorithm. The first product does not specifically refer to a fixed product. For example, when the product information changes, the first product may also change accordingly. For example, when the A-star algorithm changes, the first product may also change accordingly.

[0066] In some embodiments, the product requirement may be, for example, a requirement for determining whether the searched product is a product required by the user. The product requirement does not specifically refer to a fixed requirement. For example, when a modification instruction for the product requirement is received, the product requirement may also change accordingly. For example, different application scenarios may correspond to different product requirements.

[0067] In some embodiments, the search method may be, for example, a method of searching products using the A-star algorithm. The search method includes, but is not limited to, the data structure, search direction, search times, search duration, etc. The search method disclosed herein does not specifically refer to a fixed method.

[0068] In some embodiments, the adjustment information may be used to adjust the search method. The adjustment information does not specifically refer to a fixed information. For example, when the adjustment information is for any search method in the search method, when the any search method changes, the adjustment information may also change accordingly.

[0069] In some embodiments, when the first product corresponding to the product information searched using the A-star algorithm does not meet the product requirements, adjustment information of the search method is obtained, wherein the adjustment information includes parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm.

[0070] In step S12, the first search information is adjusted using the adjustment information to obtain second search information corresponding to the product information;

[0071] In some embodiments, the second search information may be, for example, information used to search for product information after adjustment. The second search information is used to indicate a method for searching in the state space. The second search information does not specifically refer to a fixed information. For example, when the search method changes, the second search information may also change accordingly.

[0072] In some embodiments, the first search information may be, for example, search information for obtaining a first product that does not meet the product requirement. The first in the first search information is used to distinguish from the remaining search information. The first search information does not specifically refer to a fixed information. For example, when the search method corresponding to the first search information changes, the first search information may also change accordingly.

[0073] In some embodiments, the second search information may be, for example, search information adjusted from the first search information. For example, when the first search information changes or the adjustment information changes, the second search information may also change accordingly.

[0074] In some embodiments, when the adjustment information is obtained, the adjustment information may be used to adjust the first search information to obtain second search information corresponding to the product information.

[0075] In step S13, in the state space, the second search information and the product information are used to search to obtain a second product that meets the product requirement.

[0076] In some embodiments, the state space may be, for example, a space for searching. The state space does not specifically refer to a fixed space. For example, when the number of nodes corresponding to the state space changes, the state space may also change accordingly. For example, when the node information of any node in the state space changes, the state space may also change accordingly.

[0077] In some embodiments, the second search information and the product information may be used to search in the state space to obtain a second product that meets the product requirement.

[0078] In some or related embodiments, when the first product corresponding to the product information searched by the A-star algorithm does not meet the product requirements, the adjustment information of the search method is obtained, wherein the adjustment information includes the parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm; the first search information is adjusted by the adjustment information to obtain the second search information corresponding to the product information; in the state space, the second search information and the product information are used to search to obtain the second product that meets the product requirements. Therefore, by adjusting the original search information, the situation where the product that meets the product requirements cannot be obtained can be reduced, and there is no need for manual experience determination, which reduces the reliance on manual experience and intuition, enhances the systematicness and scientificity of product determination, and can improve the matching of search information and products, and improve the efficiency and accuracy of product determination.

[0079] Figure 2 is a flowchart of a product search method according to an exemplary embodiment. Figure 2 As shown, the following steps are included:

[0080] In step S21, according to the product information, the A-star algorithm is used to perform an iterative search in the state space to obtain a target node corresponding to the product information, wherein the state space includes at least one node;

[0081] The specific process is as described above and will not be repeated here.

[0082] According to some embodiments, for example, the A-star algorithm may be used to perform an iterative search in the state space to determine the best search path for insurance products. For example, starting from an initial node, the next node to be explored may be selected according to the cost function and heuristic function in the A-star algorithm until a target node that meets the node requirements is found. For example, the initial node may be a basic node, and then through continuous iterative search, a target node that meets market demand and risk assessment requirements is found.

[0083] In some embodiments, based on the product information, the A-star algorithm is used to perform an iterative search in the state space to obtain a target node corresponding to the product information, wherein the state space includes at least one node.

[0084] According to some embodiments, the method further comprises:

[0085] Obtaining a node set in the state space;

[0086] Obtaining a cost function between a first node and a second node in the node set, wherein the first node and the second node are adjacent nodes in the node set;

[0087] Obtaining a heuristic function between the first node and the second node;

[0088] The A-star algorithm is generated according to the cost function, the first weight corresponding to the cost function, the heuristic function, and the second weight corresponding to the heuristic function. Therefore, the A-star algorithm can be determined according to various parameters, which can improve the accuracy of obtaining the A-star algorithm and the accuracy of determining the first product.

[0089] In one embodiment of the present disclosure, a node set may be, for example, a collection of at least one node. The node set does not specifically refer to a fixed collection. For example, when any node in the node set changes, the node set may also change accordingly. For example, when the number of nodes corresponding to the node set changes, the node set may also change accordingly.

[0090] According to some embodiments, Figure 3is a flowchart of a product search method according to an exemplary embodiment. Figure 3 As shown, taking insurance products as an example, the product information of each product can be obtained, and the product information can be abstracted into a node in the state space, for example, each node corresponds to a product plan. The product information includes but is not limited to type, term, amount, etc.

[0091] According to some embodiments, for example, an edge between nodes can be defined in the state space, and the edge can represent the cost from one node to another node. The cost function can be, for example, the cost between a first node and a second node, wherein the first node and the second node can be, for example, any adjacent nodes in a node set. The first node and the second node do not specifically refer to a fixed node. For example, when the node identifier of the first node or the second node changes, the first node and the second node can also change accordingly.

[0092] The heuristic function is used to estimate the optimal path cost from the current node to the target node. In this patent, the heuristic function can be adjusted or determined based on historical data, market trends and other factors. For example, if historical data shows that a certain type of insurance product has performed well in the market in the past, then the heuristic function may tend to select this type of node.

[0093] In some embodiments, when the A-star algorithm is obtained, the A-star algorithm can be verified, for example, based on historical data. When the output product of the A-star algorithm meets the product requirements, the A-star algorithm can be used to search for products. When the output product of the A-star algorithm does not meet the product requirements, the A-star algorithm can be updated.

[0094] According to some embodiments, the method further comprises:

[0095] Get application scenario information;

[0096] The state space is modified according to the application scenario information to obtain a modified state space.

[0097] According to some embodiments, the method further comprises:

[0098] Modifying the cost function according to historical data to obtain a modified cost function;

[0099] and / or

[0100] According to the application scenario information and the evaluation information, the first weight and the second weight are modified at preset intervals to obtain a modified first weight and a modified second weight. Therefore, the accuracy of the A-star algorithm determination can be improved, the matching of the A-star algorithm and the product search can be improved, and the accuracy of product determination can be improved.

[0101] The historical data includes but is not limited to market demand, risk assessment results, product price determination strategy, etc. The cost function can be modified according to the historical data to reduce the cost of product search and improve the efficiency of product determination.

[0102] Among them, for example, taking automobile insurance products as an example, factors such as market demand, risk assessment and pricing strategy can be comprehensively considered. For example, the acquired node set may include information such as premiums, coverage, deductibles, etc., and the cost function is designed according to market demand and risk assessment results, and the heuristic function is designed based on historical data and market trends. The A-star algorithm is used to search for the optimal insurance product design path in the state space. Starting from the initial node (a basic design plan), the algorithm continuously selects the next node to be explored according to the cost function and heuristic function until the target node that meets the market demand and risk assessment requirements is found.

[0103] The node information includes at least one of the following:

[0104] Insurance types: life insurance, health insurance, property insurance, liability insurance;

[0105] Insurance period: one year, five years, ten years, lifetime, etc.;

[0106] Insurance amount (insurance transaction information);

[0107] insurance rates and premiums;

[0108] Insurance calculation and payment methods;

[0109] Insurance liability;

[0110] Exclusions of liability;

[0111] Observation period;

[0112] Claim conditions;

[0113] Surrender conditions;

[0114] Additional terms;

[0115] Target customer group;

[0116] Sales channels;

[0117] Market competitiveness;

[0118] Contract termination and modification.

[0119] In some embodiments, the A-star algorithm can determine the first product or the second product through an evaluation function, where the evaluation function can be, for example, F(n)=G(n)+H(n), where G(n) represents the actual cost from the starting point to the current node n, which can be calculated based on the considered insurance product information; H(n) represents the estimated cost from the current node n to the target node, which is usually a heuristic function used to predict future costs.

[0120] In some embodiments, G(n) can be calculated based on the determined insurance product information, for example:

[0121] Insurance type: Different types of insurance products may have different base costs, for example, health insurance may be more complex than life insurance and therefore have a higher base cost.

[0122] Policy duration: Long-term policies may require more administration and risk assessment and therefore be more expensive.

[0123] Sum insured: The higher the sum insured, the greater the risk the insurance company bears, and therefore the higher the cost.

[0124] Premium calculation and payment methods: Different calculation and payment methods may affect the operating costs and cash flow of the insurance company, thereby affecting the cost.

[0125] Coverage and Exclusions: Broader coverage and fewer exclusions may increase the insurer's risk of claims and therefore be more costly.

[0126] In some embodiments, H(n) is a heuristic function used to estimate the cost from the current node to the target node. In insurance product search or state space determination, H(n) can be estimated based on the following factors:

[0127] Market demand: Predict whether the current design elements meet market demand. The higher the degree of compliance, the lower the estimated cost.

[0128] Risk assessment: Predict the possible compensation risk caused by the current design elements. The lower the risk, the lower the estimated cost.

[0129] Pricing strategy: Predict whether the current design elements comply with the company's pricing strategy. The higher the degree of compliance, the lower the estimated cost.

[0130] Long-term development goals: Predict whether the current design elements will help achieve the company's long-term development goals, such as market share growth, brand image enhancement, etc. Design elements that help achieve the goals are estimated to be less costly.

[0131] For example, in one embodiment of the present disclosure, the A-star algorithm is used to search for the optimal path from the start node to the target node in the state space. In each step, the A-star algorithm can select the next node to explore based on the value of the evaluation function F(n)=G(n)+H(n), for example.

[0132] Start your search:

[0133] Initialize the Open List and the Closed List. Add the starting node (basic insurance product design) to the Open List. Set the characteristics of the target node (optimal design) so that it can be identified during the search process.

[0134] Select Node:

[0135] Select the node with the smallest evaluation function value F(n) from the open list. Evaluation function F(n) = G(n) + H(n), where G(n) is the actual cost from the start node to the current node, and H(n) is the heuristic estimated cost from the current node to the target node. Remove the selected node from the open list and add it to the closed list.

[0136] Extension Node:

[0137] According to all neighboring nodes of the current node, for each neighboring node, calculate its evaluation function value F(n). If the neighboring node is already in the closed list, ignore it.

[0138] Processing neighboring nodes:

[0139] If the neighboring node is not in the open list, calculate its G(n), H(n), and F(n) values ​​and add it to the open list.

[0140] If the neighboring node is already in the open list, check whether the path to the neighboring node through the current node is better (i.e., the G(n) value is smaller). If so, update the G(n) value of the neighboring node and the parent node to the current node.

[0141] Loop search:

[0142] Repeat the above steps until the target node is found (i.e. the optimal path that meets the node requirements and constraints), or the open list is empty.

[0143] Find the optimal solution, that is, find the target node:

[0144] The search ends when the target node is added to the closed list. The shortest path can be constructed by tracing back from the target node along the parent nodes to the start node.

[0145] In step S22, the product corresponding to the target node is used as the first product corresponding to the product information;

[0146] In some embodiments, for example, the product corresponding to the target node may be used as the first product corresponding to the product information. For example, the corresponding first product may be output according to the searched best path. The product information of the first product may include product information input by a user. For example, the product information of the first product may include coverage, premium, and deductible, and the product information input by the user may include coverage and premium.

[0147] In step S23, when the first product corresponding to the product information searched by using the A-star algorithm does not meet the product requirements, the adjustment information of the search method is obtained, wherein the adjustment information includes parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm;

[0148] The specific process is as described above and will not be repeated here.

[0149] The fact that the first product corresponding to the product information searched by using the A-star algorithm does not meet the product requirements may also include, for example, a situation where the first product is not obtained, that is, a situation where the target node is not obtained.

[0150] In step S24, the first search information is adjusted using the adjustment information to obtain second search information corresponding to the product information;

[0151] The specific process is as described above and will not be repeated here.

[0152] According to some embodiments, in some embodiments, the first search information may further include, for example, a data structure and a search strategy, wherein the search strategy may include, for example, the number of iterative searches and the iterative search direction.

[0153] In one embodiment of the present disclosure, the adjustment information includes search strategy adjustment information, and the step of adjusting the first search information by using the adjustment information to obtain second search information corresponding to the product information includes at least one of the following:

[0154] In a case where the search strategy adjustment information is used to indicate adjustment of the number of iterative searches, modifying the number of iterative searches to obtain a modified number of iterative searches, wherein the modified number of iterative searches is used to indicate the number of searches in the state space;

[0155] When the search strategy adjustment information is used to indicate the adjustment of the iterative search direction, the iterative search direction is modified to obtain a modified search direction. Therefore, the search information can be adjusted to improve the search efficiency and search quality, and improve the accuracy of product acquisition.

[0156] According to some embodiments, the first search information may include, for example, the number of iterative searches and the iterative search direction. The number of iterative searches may be, for example, the number of iterative searches performed when searching for products corresponding to the product information. The number of iterative searches does not specifically refer to a fixed number. For example, the number of iterative searches may be modified according to a number modification instruction. The adjustment of the number of iterative searches may be, for example, increasing the number of iterative searches.

[0157] In some embodiments, the iterative search direction may be used to indicate the direction of product search, and the iterative search direction does not specifically refer to a fixed direction. The iterative search direction may be, for example, searching from a starting point, or from an end point, or searching from both the starting point and the end point simultaneously until the two meet. The iterative search may gradually increase the search depth to find the product most relevant to the product information.

[0158] According to some embodiments, modifying the iterative search number to obtain a modified number of iterations includes:

[0159] Acquire a performance indicator corresponding to a search process, wherein the search process is a process of searching for a first product corresponding to the product information using the A-star algorithm;

[0160] The performance index is used to modify the iterative search times to obtain a modified iterative search times, which can improve the matching of the iterative search times with the application scenario, and improve the product search efficiency and search quality.

[0161] In some embodiments, adjusting the data structure may be, for example, managing an open list and a closed list of a queue, which may improve search efficiency.

[0162] According to some embodiments, adjusting the first search information using the adjustment information to obtain second search information corresponding to the product information may include:

[0163] Get application scenario information;

[0164] The state space is modified according to the application scenario information to obtain a modified state space.

[0165] In some embodiments, the state space may be modified according to market demand, for example, when the selection rate of a certain node is less than a selection rate threshold, the node may be removed from the state space, which may reduce search complexity and improve search efficiency.

[0166] In some embodiments, for example, when new product information needs to be added, nodes can be added to the state space to improve the matching of product determination.

[0167] According to some embodiments, using the adjustment information to adjust the first search information to obtain second search information corresponding to the product information includes:

[0168] Modifying the cost function according to historical data to obtain a modified cost function;

[0169] and / or

[0170] According to the application scenario information and the evaluation information, the first weight and the second weight are modified at preset intervals to obtain a modified first weight and a modified second weight. Therefore, by adjusting the first weight and the second weight, the search speed and the search quality can be balanced, the matching of the A-star algorithm and the product search can be improved, and the accuracy of product determination can be improved.

[0171] In some embodiments, for example, the parameters in the cost function and the heuristic function may be adjusted, for example, by adjusting the parameters based on the feedback information for the first product or the second product.

[0172] In some embodiments, for example, the evaluation function is updated, and the accuracy and timeliness of the search results can be improved by updating the parameters and weights in the evaluation function. For example, the G(n) value in the evaluation function can be updated according to the feedback results. For example, when new historical data is obtained, G(n) can be reconfigured to obtain a new evaluation function, that is, the unupdated evaluation function is the first evaluation function, and the updated evaluation function can be the second evaluation function: F'(n) = G'(n) + H'(n).

[0173] In step S25, in the state space, the second search information and the product information are used to search to obtain a second product that meets the product requirement.

[0174] The specific process is as described above and will not be repeated here.

[0175] In some or related embodiments, according to the product information, the A-star algorithm is used to perform an iterative search in the state space to obtain the target node corresponding to the product information, and the product corresponding to the target node is used as the first product corresponding to the product information. Therefore, by determining the first product through the A-star algorithm, product recommendations can be made through data-driven and algorithm optimization, without the need for manual experience, reducing reliance on manual experience and intuition, enhancing the systematic and scientific nature of product determination, improving the matching of search information and products, and improving product determination efficiency and accuracy. Secondly, in the case where the first product does not meet the product requirements, the search process can be adjusted to improve the flexibility and scalability of product search.

[0176] Figure 4 is a block diagram of a product search device according to an exemplary embodiment. Figure 4 , the product search device 400 comprises:

[0177] The information acquisition unit 401 is used to acquire adjustment information of the search method when the first product corresponding to the product information searched by using the A-star algorithm does not meet the product requirements, wherein the adjustment information includes parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm;

[0178] The information acquisition unit 401 is further configured to adjust the first search information using the adjustment information to acquire second search information corresponding to the product information;

[0179] The product search unit 402 is used to search in the state space using the second search information and the product information to obtain a second product that meets the product requirement.

[0180] According to some embodiments, the product search unit 402 is further configured to:

[0181] According to the product information, the A-star algorithm is used to perform an iterative search in the state space to obtain a target node corresponding to the product information, wherein the state space includes at least one node;

[0182] The product corresponding to the target node is used as the first product corresponding to the product information.

[0183] According to some embodiments, the product search unit 402 is further configured to:

[0184] Obtaining a node set in the state space;

[0185] Obtaining a cost function between a first node and a second node in the node set, wherein the first node and the second node are adjacent nodes in the node set;

[0186] Obtaining a heuristic function between the first node and the second node;

[0187] The A-star algorithm is generated according to the cost function, the first weight corresponding to the cost function, the heuristic function, and the second weight corresponding to the heuristic function.

[0188] According to some embodiments, the product search unit 402 is further configured to:

[0189] Get application scenario information;

[0190] The state space is modified according to the application scenario information to obtain a modified state space.

[0191] According to some embodiments, the product search unit 402 is further configured to:

[0192] Modifying the cost function according to historical data to obtain a modified cost function;

[0193] and / or

[0194] According to the application scenario information and the evaluation information, the first weight and the second weight are modified at preset time intervals to obtain a modified first weight and a modified second weight.

[0195] According to some embodiments, the adjustment information includes search strategy adjustment information, and the information acquisition unit 401 is used to use the adjustment information to adjust the first search information, and when acquiring the second search information corresponding to the product information, it is specifically used for at least one of the following:

[0196] In a case where the search strategy adjustment information is used to indicate adjustment of the number of iterative searches, modifying the number of iterative searches to obtain a modified number of iterative searches, wherein the modified number of iterative searches is used to indicate the number of searches in the state space;

[0197] In the case where the search strategy adjustment information is used to indicate adjustment of the iterative search direction, the iterative search direction is modified to obtain a modified search direction.

[0198] According to some embodiments, the information acquisition unit 401 is used to modify the iterative search times, and when the modified iterative search times are obtained, specifically to:

[0199] Acquire a performance indicator corresponding to a search process, wherein the search process is a process of searching for a first product corresponding to the product information using the A-star algorithm;

[0200] The performance indicator is used to modify the number of iterative searches to obtain a modified number of iterative searches.

[0201] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0202] In some or related embodiments, an information acquisition unit is used to obtain adjustment information of the search method when the first product corresponding to the product information searched by the A-star algorithm does not meet the product requirements, wherein the adjustment information includes parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm; the information acquisition unit is also used to adjust the first search information by using the adjustment information to obtain the second search information corresponding to the product information; the product search unit is used to search in the state space by using the second search information and the product information to obtain the second product that meets the product requirements. Therefore, by adjusting the original search information, the situation where the product that meets the product requirements cannot be obtained can be reduced, and there is no need for manual experience determination, which reduces the reliance on manual experience and intuition, enhances the systematicness and scientificity of product determination, and can improve the matching of search information and products, and improve the efficiency and accuracy of product determination.

[0203] Figure 5 is a block diagram of a network device 500 provided in an embodiment of the present disclosure. For example, the network device 500 may be provided as a network side device. Figure 5 The network device 500 includes a processing component 522, which further includes at least one processor, and a memory resource represented by a memory 532 for storing instructions executable by the processing component 522, such as an application. The application stored in the memory 532 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 522 is configured to execute instructions to perform any method of the aforementioned method applied to the network side device.

[0204] The network device 500 may also include a power supply component 527 configured to perform power management of the network device 500, a wired or wireless network interface 550 configured to connect the network device 500 to a network, and an input / output (I / O) interface 558. The network device 500 may operate based on an operating system stored in the memory 532, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM or the like.

[0205] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0206] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable product search device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0207] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0208] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0209] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0210] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0211] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0212] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A product search method, characterized in that: include: When the first product corresponding to the product information searched by using the A-star algorithm does not meet the product requirements, obtaining adjustment information of the search method, wherein the adjustment information includes parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm; Using the adjustment information to adjust the first search information, and obtaining second search information corresponding to the product information; In the state space, the second search information and the product information are used to search and obtain a second product that meets the product requirement.

2. The method according to claim 1, characterized in that The method further comprises: According to the product information, the A-star algorithm is used to perform an iterative search in the state space to obtain a target node corresponding to the product information, wherein the state space includes at least one node; The product corresponding to the target node is used as the first product corresponding to the product information.

3. The method according to claim 2, characterized in that The method further comprises: Obtaining a node set in the state space; Obtaining a cost function between a first node and a second node in the node set, wherein the first node and the second node are adjacent nodes in the node set; Obtaining a heuristic function between the first node and the second node; The A-star algorithm is generated according to the cost function, the first weight corresponding to the cost function, the heuristic function, and the second weight corresponding to the heuristic function.

4. The method according to claim 3, characterized in that The method further comprises: Get application scenario information; The state space is modified according to the application scenario information to obtain a modified state space.

5. The method according to claim 3, characterized in that: The method further comprises: Modifying the cost function according to historical data to obtain a modified cost function; and / or According to the application scenario information and the evaluation information, the first weight and the second weight are modified at preset time intervals to obtain a modified first weight and a modified second weight.

6. The method according to claim 1, characterized in that in, The adjustment information includes search strategy adjustment information, and the step of using the adjustment information to adjust the first search information to obtain second search information corresponding to the product information includes at least one of the following: In a case where the search strategy adjustment information is used to indicate adjustment of the number of iterative searches, modifying the number of iterative searches to obtain a modified number of iterative searches, wherein the modified number of iterative searches is used to indicate the number of searches in the state space; In the case where the search strategy adjustment information is used to indicate adjustment of the iterative search direction, the iterative search direction is modified to obtain a modified search direction.

7. The method according to claim 6, characterized in that The step of modifying the iterative search times to obtain a modified number of iterations includes: Acquire a performance indicator corresponding to a search process, wherein the search process is a process of searching for a first product corresponding to the product information using the A-star algorithm; The performance indicator is used to modify the number of iterative searches to obtain a modified number of iterative searches.

8. A product search device, characterized in that: include: an information acquisition unit, configured to acquire adjustment information of the search method when the first product corresponding to the product information searched by using the A-star algorithm does not meet the product requirements, wherein the adjustment information includes parameter adjustment information, data structure adjustment information or search strategy adjustment information of the A-star algorithm; The information acquisition unit is further configured to adjust the first search information using the adjustment information to acquire second search information corresponding to the product information; The product search unit is used to search in the state space using the second search information and the product information to obtain a second product that meets the product requirement.

9. A network device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the product search method according to any one of claims 1 to 7.

10. A storage medium, when instructions in the storage medium are executed by a processor of a network device, the network device is enabled to execute the product search method according to any one of claims 1 to 7.

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