CN112416988A - Supply and demand matching method and device based on artificial intelligence and computer equipment - Google Patents
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Abstract
The invention discloses a supply and demand matching method and device based on artificial intelligence and computer equipment. The method comprises the following steps: the method comprises the steps of quantifying supply information from a first client to obtain supply characteristic information, classifying and storing the supply characteristic information, quantifying demand information from a second client to obtain demand characteristic information, obtaining alternative supply information matched with the demand information according to supply chain information, obtaining the correlation between the demand characteristic information and the supply characteristic information of each alternative supply information, sequencing to obtain a sequencing result, and sending the sequencing result to the second client. The invention is based on an artificial intelligence technology, belongs to the field of machine learning, acquires alternative supply information matched with demand information according to supply chain information, and intelligently matches the demand information with the supply information by introducing an attenuation formula, so that the matching efficiency and accuracy of the supply and demand information are improved, and the transaction efficiency of both supply and demand parties is improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, belongs to an application scene of intelligently matching supply and demand information of supply and demand parties in a smart city, and particularly relates to a supply and demand matching method and device based on artificial intelligence and computer equipment.
Background
When an enterprise selects a supplier or a cooperative object, the enterprise generally adopts the modes of telephone inquiry or bid inquiry, price inquiry and the like to investigate some enterprises meeting the requirements according to past experience or peer introduction, and finally determines the cooperative object and achieves the transaction intention. With the development of informatization, both sides of a transaction can reach a transaction agreement through the Internet so as to improve the transaction efficiency; although the technical method can improve the transaction efficiency, generally, a seller provides corresponding commodity information, a buyer browses the commodity information and then selects to complete the transaction, related screening operation still needs manual operation, and an enterprise often has difficulty in selecting products and cooperation objects meeting requirements through the selection mode, and the actual requirements of cooperation among enterprises in the actual application process are difficult to meet. Therefore, the prior art method has the problem that the supply and demand information of both supply and demand parties cannot be intelligently matched.
Disclosure of Invention
The embodiment of the invention provides a supply and demand matching method and device based on artificial intelligence, computer equipment and a storage medium, and aims to solve the problem that supply and demand information cannot be intelligently matched in the prior art.
In a first aspect, an embodiment of the present invention provides a supply and demand matching method based on artificial intelligence, which includes:
if receiving the supply information from the first client, acquiring supply characteristic information matched with the supply information according to a preset characteristic quantization rule;
storing the supply information and the supply characteristic information into a preset supply database in a classified manner according to the attribute information of the supply information;
if the demand information from the second client is received, acquiring demand characteristic information matched with the demand information according to the characteristic quantization rule;
acquiring alternative supply information matched with the demand information in the supply database according to preset supply chain information and attribute information of the demand information;
obtaining the correlation degree between the demand characteristic information and the supply characteristic information of each alternative supply information according to a preset correlation analysis model and a preset attenuation formula;
and sequencing the alternative supply information according to the relevancy and a preset sequencing rule, and sending a sequencing result to the second client.
In a second aspect, an embodiment of the present invention provides an artificial intelligence-based supply and demand matching apparatus, which includes:
a supply characteristic information obtaining unit, configured to, if supply information from the first client is received, obtain supply characteristic information that matches the supply information according to a preset characteristic quantization rule;
the information storage unit is used for storing the supply information and the supply characteristic information into a preset supply database in a classified manner according to the attribute information of the supply information;
the demand characteristic information acquisition unit is used for acquiring demand characteristic information matched with the demand information according to the characteristic quantization rule if the demand information from the second client is received;
the alternative supply information acquisition unit is used for acquiring alternative supply information matched with the demand information in the supply database according to preset supply chain information and attribute information of the demand information;
the correlation degree obtaining unit is used for obtaining the correlation degree between the demand characteristic information and the supply characteristic information of each alternative supply information according to a preset correlation analysis model and a preset attenuation formula;
and the sequencing result sending unit is used for sequencing the alternative supply information according to the relevancy and a preset sequencing rule and sending a sequencing result to the second client.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the supply and demand matching method based on artificial intelligence according to the first aspect is implemented.
In a fourth aspect, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the supply and demand matching method based on artificial intelligence according to the first aspect.
The embodiment of the invention provides a supply and demand matching method and device based on artificial intelligence, computer equipment and a storage medium. The method comprises the steps of quantifying supply information from a first client to obtain supply characteristic information, classifying and storing the supply characteristic information, quantifying demand information from a second client to obtain demand characteristic information, obtaining alternative supply information matched with the demand information according to supply chain information, obtaining the correlation between the demand characteristic information and the supply characteristic information of each alternative supply information, sequencing to obtain a sequencing result, and sending the sequencing result to the second client. By the method, the alternative supply information matched with the demand information is obtained according to the supply chain information, and the demand information and the supply information are intelligently matched by introducing the attenuation formula, so that the matching efficiency and accuracy between the supply and demand information are improved, and the transaction efficiency of both supply and demand parties is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a supply and demand matching method based on artificial intelligence according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an application scenario of the supply and demand matching method based on artificial intelligence according to an embodiment of the present invention;
FIG. 3 is a schematic sub-flow chart of a supply and demand matching method based on artificial intelligence according to an embodiment of the present invention;
FIG. 4 is a schematic view of another sub-flow chart of a supply and demand matching method based on artificial intelligence according to an embodiment of the present invention;
FIG. 5 is a schematic view of another sub-flow chart of a supply and demand matching method based on artificial intelligence according to an embodiment of the present invention;
FIG. 6 is a schematic view of another sub-flow chart of a supply and demand matching method based on artificial intelligence according to an embodiment of the present invention;
FIG. 7 is a schematic view of another sub-flow chart of a supply and demand matching method based on artificial intelligence according to an embodiment of the present invention;
FIG. 8 is a schematic flow chart of a supply and demand matching method based on artificial intelligence according to an embodiment of the present invention;
FIG. 9 is a schematic block diagram of an artificial intelligence based supply and demand matching apparatus provided by an embodiment of the present invention;
FIG. 10 is a schematic block diagram of a computer device provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic flowchart of a supply and demand matching method based on artificial intelligence according to an embodiment of the present invention, fig. 2 is a schematic diagram of an application scenario of the supply and demand matching method based on artificial intelligence according to an embodiment of the present invention, the supply and demand matching method based on artificial intelligence is applied to a management server 10, the method is executed by application software installed in the management server 10, the management server 10 is connected to a first client 20 and a second client 30 through a network to transmit data information, the management server 10 is a server side for executing the supply and demand matching method based on artificial intelligence to achieve intelligent matching of supply and demand information, the management server 10 may be a server set in an enterprise, the first client 10 and the second client 20 are terminal devices that establish a network connection with the management server 10 to transmit data information, such as a desktop computer, a notebook computer, a tablet computer, or a mobile phone. As shown in fig. 1, the method includes steps S110 to S160.
And S110, if the supply information from the first client is received, acquiring supply characteristic information matched with the supply information according to a preset characteristic quantization rule.
And if the supply information from the first client is received, acquiring the supply characteristic information matched with the supply information according to a preset characteristic quantization rule. The user of the first client can send supply information to the management server, the supply information comprises a plurality of items of information such as attribute information, supply quantity, price, timeliness, purity, region, sending time, sender, contact way, production permission and the like, the attribute information is specific information describing the attributes of the product in the supply information, and the attribute information comprises specific information such as product type, product name and the like. Part of the information contained in the provisioning information may be quantized according to a feature quantization rule to obtain provisioning feature information, which may be represented by quantization of the provisioning information through numerical information. The feature quantization rule includes a plurality of quantization items, the number of the quantization items is less than the number of information items included in the supply information, each quantization item can convert one corresponding item of information in the supply information into a vector value to represent, a plurality of vector values corresponding to one piece of supply information are combined to form the supply feature information of the supply information, the supply feature information can be represented as a multi-dimensional feature vector, and the range of quantization values obtained by quantizing one item of information of one piece of supply information is [0,1 ].
Specifically, in an embodiment, as shown in fig. 3, the step S110 includes the sub-steps of: s111, S112, S113 and S114.
S111, acquiring historical supply information of which the attribute information is matched with the supply information in the supply database; s112, calculating to obtain a middle value of each numerical quantization item in the characteristic quantization rule according to the historical supply information; s113, quantizing the data information matched with the numerical value quantization item in the supply information according to the numerical value quantization item and the intermediate value to obtain the first characteristic information; and S114, quantizing the data information matched with the non-numerical quantitative item in the supply information according to the non-numerical quantitative item to obtain the second characteristic information.
Specifically, the supply database stores a plurality of pieces of supply information, historical supply information identical to the attribute information can be obtained from the supply database according to the attribute information of the supply information, an intermediate value matched with each numerical quantization item in the characteristic quantization rule is obtained through calculation according to the historical supply information, and a plurality of pieces of information corresponding to the numerical quantization items in the supply information can be converted based on the intermediate value obtained through calculation. The feature quantization rule comprises a numerical quantization item and a non-numerical quantization item, and the supplied feature information comprises first feature information corresponding to the numerical quantization item and second feature information corresponding to the non-numerical quantization item.
Specifically, in the case where information corresponding to a quantized item is represented in a non-numeric manner, data corresponding to a keyword matched with the non-numeric in the non-numeric quantized item is directly acquired as a quantized value corresponding to the non-numeric.
For example, the non-numeric quantization item in the region in the feature quantization rule includes a keyword for each province, data corresponding to "jiangsu" is "0.15", data corresponding to "zhejiang" is "0.13", and the corresponding quantization value is "0.13" when the region in the supply information is zhejiang.
And for the condition that the information corresponding to the quantization item is represented in a numerical mode, the corresponding quantization rule in the characteristic quantization rule is an activation function and an intermediate value, and the intermediate value and one item of information of the numerical quantization item are calculated according to the activation function, so that the corresponding quantization value can be obtained.
For example, the activation function may be expressed as
Wherein x is an item of information corresponding to a numerical quantization item, and v is an intermediate value corresponding to the numerical quantization item. If the intermediate value corresponding to the numerical quantitative item of price is 350/kg and the price x in a certain piece of supply information is 320/kg, the corresponding quantitative value is 0.5214 calculated according to the activation function.
The intermediate value of the numerical quantification item can be preset by a user, and can also be obtained from a supply database, that is, the intermediate value of each numerical quantification item can be obtained from the supply database.
And S120, storing the supply information and the supply characteristic information into a preset supply database in a classified manner according to the attribute information of the supply information.
After the supply characteristic information of the supply information is acquired, the supply information and the corresponding supply characteristic information can be classified and stored in a preset supply database in a management server according to the attribute information of the supply information, wherein the attribute information of the supply information comprises a product type and a product name, the supply information and the obtained supply characteristic information can be classified and stored according to the product type and the product name, namely the supply information corresponding to the same product type and the same product name is placed in a classification for storage.
Specifically, in one embodiment, as shown in fig. 4, step S120 includes sub-steps S121 and S122.
S121, determining a standard name of the attribute information according to the attribute information of the supply information; s122, storing the supply information and the corresponding supply characteristic information into the supply database in a classified manner according to the standard name.
Since the same product may correspond to multiple different names, for example, the same product may contain multiple names such as standard chemical name (sodium bicarbonate), common name (baking soda), alias (sodium bicarbonate), and the like. In order to avoid errors in the process of classifying products due to different names, standard names matched with attribute information can be determined according to the attribute information of the supply information, and the same product corresponds to the same standard name. The standard name is used as a reference to store the supply information and the supply characteristic information corresponding to the supply information into the supply database in a classified manner, and other names can be used as auxiliary names to be associated with the standard name.
And S130, if the requirement information from the second client is received, acquiring requirement characteristic information matched with the requirement information according to the characteristic quantization rule.
And if the requirement information from the second client is received, acquiring the requirement characteristic information matched with the requirement information according to the characteristic quantization rule. The user of the second client can send demand information to the management server, the demand information comprises a plurality of items of information such as attribute information, demand, price, timeliness, purity, region, sending time, demanders and contact ways, the attribute information is specific information describing attributes of products in the demand information, and the attribute information comprises specific information such as product types and product names. Part of information contained in the demand information can be quantized according to the characteristic quantization rule to obtain the demand characteristic information, and the demand characteristic information can be represented in a quantization mode through numerical information. The obtained demand characteristic information can be represented as a multi-dimensional characteristic vector, and the range of quantized values obtained by quantizing one item of information of one piece of demand information is [0,1 ]. Specifically, the specific process of acquiring the demand characteristic information is the same as the specific process of acquiring the supply characteristic information, and is not described herein again.
S140, acquiring alternative supply information matched with the demand information in the supply database according to preset supply chain information and attribute information of the demand information.
And acquiring alternative supply information matched with the demand information in the supply database according to preset supply chain information and the attribute information of the demand information. In the purchasing process of an enterprise, only the required product may be purchased, or upstream raw materials of the required product may be purchased, and in order to meet the actual demand in the purchasing process of the enterprise, the product in the demand information of the supply chain information may be subjected to extended matching based on the product in the demand information to obtain the alternative supply information matched with the demand information in the database, and then the product in the demand information and the upstream product of the product are involved in the alternative supply information.
Specifically, in one embodiment, as shown in fig. 5, step S140 includes sub-steps S141, S142, and S143.
S141, judging whether the product in the demand information contains an upstream product in the supply chain information; and S142, if the product in the demand information contains an upstream product in the supply chain information, acquiring the upstream product matched with the attribute information of the demand information in the supply chain information.
The supply chain information comprises a supply chain formed by combining a plurality of products, the supply chain information comprises a plurality of nodes, each node corresponds to one product, the nodes are connected by a one-way arrow, and the product corresponding to the upstream node of a certain node can be used as the upstream product of the current node. Determining a current node matched with a product in the demand information according to the demand information, judging whether the current node comprises an upstream node or not based on the supply chain information, if so, indicating that the product in the demand information comprises the upstream product, and taking the product corresponding to the upstream node as the upstream product of the product in the demand information; if the product does not contain the upstream product, the product in the demand information does not contain the upstream product, and the matched alternative supply information in the supply database can be directly obtained according to the attribute information.
S143, obtaining the alternative supply information matched with the demand information in a supply database according to the attribute information of the upstream product and the attribute information of the demand information.
If the product in the demand information contains an upstream product, the alternative supply information matched with the demand information in the supply database can be obtained according to the attribute information of the upstream product and the attribute information of the demand information. And if each classification in the supply database correspondingly comprises at least one piece of supply information, determining the classification matched with the classification in the supply database according to the attribute information of the upstream product or the attribute information of the demand information, and acquiring the supply information of the corresponding classification as alternative supply information, wherein the alternative supply information at least comprises one piece of supply information.
S150, obtaining the correlation degree between the demand characteristic information and the supply characteristic information of each alternative supply information according to a preset correlation analysis model and a preset attenuation formula.
And acquiring the correlation between the demand characteristic information and the supply characteristic information of each alternative supply information according to a preset correlation analysis model and a preset attenuation formula. Each piece of supply information sent to the management server comprises a sending time, the supply information with different sending times has difference in supply effectiveness, in order to reflect the difference of the alternative supply information, the attenuation factor of each alternative supply information can be obtained through an attenuation formula and the sending time of the alternative supply information, the attenuation characteristic information of each alternative supply information is obtained through calculation based on the attenuation factor, and the attenuation formula is a calculation formula for obtaining the attenuation factor through calculation based on the current time and the sending time of the alternative supply information. The obtained attenuation characteristic information and demand characteristic information are input into a correlation analysis model, namely, the correlation between the attenuation characteristic information and the demand characteristic information can be obtained, the correlation can be used for quantitatively representing the correlation between the alternative supply information and the demand information, and the correlation analysis model is a model which is constructed based on a neural network and is used for intelligently correlating.
Specifically, in one embodiment, as shown in fig. 6, step S150 includes sub-steps S151, S152, and S153.
And S151, calculating the attenuation factor of each candidate supply information according to the sending time and the attenuation formula of each candidate supply information.
The sending time of each alternative supply information can be obtained, and the attenuation factor of each alternative supply information is calculated according to an attenuation formula and the current time, specifically, the attenuation formula can be represented by formula (1):
wherein, tiFor the transmission time of the ith alternative provisioning information, t0Is the current time, t0-tiIndicating the number of days, S, between the ith alternative offer and the current timeiAttenuation factor for the ith candidate provisioning information.
For example, if the transmission time of a certain candidate provisioning information is "2020-10-1" and the current time is "2020-10-4", the attenuation factor S obtained correspondinglyi＝0.8305。
S152, calculating attenuation characteristic information of each candidate supply information according to the attenuation factor of each candidate supply information and the corresponding supply characteristic information.
The attenuation characteristic information of each candidate supply information can be calculated according to the attenuation factors of the candidate supply information, specifically, the attenuation factors are multiplied by the quantized values corresponding to each numerical quantization item in the supply characteristic information of the corresponding candidate supply information, the quantized values corresponding to the non-numerical quantization items are not processed, and the attenuation characteristic information corresponding to the supply characteristic information can be obtained through the calculation.
S153, obtaining the correlation degree between the demand characteristic information and each attenuation characteristic information according to the correlation analysis model.
The correlation analysis model is composed of a weight layer, a plurality of input nodes, an output node and a full connection layer, each input node corresponds to one quantization value in attenuation characteristic information or demand characteristic information, the output node corresponds to the correlation between the demand characteristic information and currently input attenuation characteristic information, the full connection layer is arranged between the input nodes and the output nodes, the full connection layer comprises a plurality of characteristic units, a first formula group is arranged between the input nodes and the full connection layer, and a second formula group is arranged between the output nodes and the full connection layer. The first formula group comprises formulas from all input nodes to all feature cells, the formulas in the first formula group all use input node values as input values and feature cell values as output values, the second formula group comprises formulas from all output nodes to all feature cells, the formulas in the second formula group all use feature cell values as input values and output node values as output values, and each formula and weight layer in the correlation analysis model comprises corresponding parameter values. The output node value is also the correlation between the requirement characteristic information and the currently input attenuation characteristic information, the difference value between the requirement characteristic information and each quantization value of the attenuation characteristic information is calculated, the difference value between each quantization value is input into a correlation analysis model through the input node to calculate the correlation, and the value range of the obtained correlation is [0,1 ].
S160, the alternative supply information is sorted according to the relevance and a preset sorting rule, and a sorting result is sent to the second client.
And sequencing the alternative supply information according to the relevancy and a preset sequencing rule, and sending a sequencing result to the second client. After the relevancy between each alternative supply information and the requirement information is obtained, the alternative supply information can be ranked according to the relevancy and the ranking rule to obtain a ranking result, the ranking result is sent to the second client, and a user of the second client can check the ranking result and select corresponding selection information from the ranking result. Wherein the ordering rule includes a number of intercepts or a relevance threshold.
In one embodiment, as shown in fig. 7, step S160 includes sub-steps S161, S162, and S163.
S161, classifying the alternative supply information according to the product name of the alternative supply information; s162, sorting the alternative supply information from high to low according to the relevance of the classified alternative supply information; and S163, intercepting partial alternative supply information from the sorted alternative supply information according to the sorting rule to obtain the sorting result.
Specifically, the alternative supply information which is the same as the product in the demand information can be classified according to the product name of the alternative supply information, the alternative supply information which is different from the product in the demand information is classified into one type, the two types of alternative supply information are respectively sorted according to the relevance from high to low, and partial alternative supply information is respectively obtained by intercepting from the two types of alternative supply information after sorting according to a sorting rule and is used as a sorting result. If the sorting rule contains the interception number, respectively intercepting partial alternative supply information with the same interception number from the two types of alternative supply information after sorting to serve as a sorting result; if the sorting rule contains a relevancy threshold, intercepting partial alternative supply information with the relevancy not less than the relevancy threshold from the two kinds of alternative supply information after sorting as a sorting result. The sequencing result at least comprises one alternative supply information which is the same as the product in the demand information, and zero, one or more alternative supply information which is different from the product in the demand information.
In an embodiment, as shown in fig. 8, step S170 is further included after step S160.
S170, if selection information fed back by the second client according to the sorting result is received, training the correlation analysis model according to the selection information to obtain the trained correlation analysis model.
And if selection information fed back by the second client according to the sorting result is received, training the correlation analysis model according to the selection information to obtain the trained correlation analysis model. The method comprises the following steps that selection information fed back by a second client side can be received, the selection information is alternative supply information selected from a sequencing result, a correlation analysis model is trained based on the selection information, the process of training the correlation analysis model is that parameter values contained in the correlation analysis model are adjusted, and the specific steps can include (1) judging whether the selection information is matched with the alternative supply information with the highest correlation degree; (2) if the correlation values are matched with the parameter values, the parameter values in the correlation analysis model are not adjusted; (3) if not, acquiring a correlation difference value between the alternative supply information with the highest correlation and the alternative supply information corresponding to the selection information; (4) and calculating to obtain an updated value of each parameter in the correlation analysis model according to a gradient calculation formula of a threshold value and the correlation difference value, and updating the parameter value of each parameter.
Specifically, a calculated value obtained by calculating an input node value corresponding to the selection information by using a parameter in the correlation analysis model is input into the gradient calculation formula, and an updated value corresponding to the parameter is calculated by combining the correlation difference value, and the calculation process is also called gradient descent calculation.
Specifically, the gradient calculation formula can be expressed as:
And correspondingly updating the parameter value of each parameter in the correlation analysis model based on the calculated updated value, namely finishing a training process of the correlation analysis model.
The technical method can be applied to application scenes including intelligent matching of supply and demand information of supply and demand parties, such as intelligent logistics, intelligent transaction, intelligent shopping and the like, so that the construction of a smart city is promoted.
In the supply and demand matching method based on artificial intelligence provided by the embodiment of the invention, supply information from a first client is quantized to obtain supply characteristic information, the supply characteristic information is classified and stored, demand information from a second client is quantized to obtain demand characteristic information, alternative supply information matched with the demand information is obtained according to supply chain information, the correlation between the demand characteristic information and the supply characteristic information of each alternative supply information is obtained, and a sequencing result is obtained by sequencing and sent to the second client. By the method, the alternative supply information matched with the demand information is obtained according to the supply chain information, and the demand information and the supply information are intelligently matched by introducing the attenuation formula, so that the matching efficiency and accuracy between the supply and demand information are improved, and the transaction efficiency of both supply and demand parties is improved.
The embodiment of the invention also provides a supply and demand matching device based on artificial intelligence, which is used for executing any embodiment of the supply and demand matching method based on artificial intelligence. Specifically, referring to fig. 9, fig. 9 is a schematic block diagram of an artificial intelligence-based supply and demand matching apparatus according to an embodiment of the present invention. The supply and demand matching apparatus based on artificial intelligence may be configured in the management server 10.
As shown in fig. 9, the supply and demand matching apparatus 100 based on artificial intelligence includes a supply characteristic information acquisition unit 110, an information storage unit 120, a demand characteristic information acquisition unit 130, an alternative supply information acquisition unit 140, a correlation acquisition unit 150, and a ranking result transmission unit 160.
A provisioning feature information obtaining unit 110, configured to, if the provisioning information from the first client is received, obtain provisioning feature information matching the provisioning information according to a preset feature quantization rule.
In one embodiment, the supply feature information obtaining unit 110 includes sub-units: the device comprises a history supply information acquisition unit, an intermediate value calculation unit, a first characteristic information acquisition unit and a second characteristic information acquisition unit.
A historical supply information acquisition unit, configured to acquire historical supply information in which the attribute information matches the supply information in the supply database; the intermediate value calculation unit is used for calculating and obtaining the intermediate value of each numerical quantization item in the characteristic quantization rule according to the historical supply information; a first feature information obtaining unit, configured to quantize, according to the numerical quantization item and the intermediate value, data information that matches the numerical quantization item in the supply information to obtain first feature information; and the second characteristic information acquisition unit is used for quantizing the data information matched with the non-numerical quantitative items in the supply information according to the non-numerical quantitative items to obtain the second characteristic information.
An information storage unit 120, configured to store the provisioning information and the provisioning feature information into a preset provisioning database in a classified manner according to attribute information of the provisioning information.
In one embodiment, the information storage unit 120 includes sub-units: a standard name determining unit and a classification storage unit.
A standard name determination unit configured to determine a standard name of the attribute information based on the attribute information of the supply information; and the classification storage unit is used for classifying and storing the supply information and the corresponding supply characteristic information into the supply database according to the standard name.
A requirement characteristic information obtaining unit 130, configured to, if the requirement information from the second client is received, obtain requirement characteristic information matched with the requirement information according to the characteristic quantization rule.
An alternative supply information obtaining unit 140, configured to obtain alternative supply information matching the demand information in the supply database according to preset supply chain information and attribute information of the demand information.
In an embodiment, the alternative provisioning information obtaining unit 140 includes sub-units: the system comprises a demand information judging unit, an upstream product acquiring unit and an alternative supply information acquiring unit.
A demand information judging unit, configured to judge whether a product in the demand information includes an upstream product in the supply chain information; an upstream product obtaining unit, configured to obtain an upstream product in the supply chain information that matches the attribute information of the demand information if a product in the demand information includes an upstream product in the supply chain information; and the alternative supply information acquisition unit is used for acquiring alternative supply information matched with the demand information in a supply database according to the attribute information of the upstream product and the attribute information of the demand information.
A correlation obtaining unit 150, configured to obtain a correlation between the demand characteristic information and the supply characteristic information of each of the candidate supply information according to a preset correlation analysis model and a preset attenuation formula.
In an embodiment, the correlation obtaining unit 150 includes sub-units: the device comprises an attenuation factor acquisition unit, an attenuation characteristic information acquisition unit and a correlation analysis unit.
The attenuation factor acquisition unit is used for calculating the attenuation factor of each alternative supply information according to the sending time of each alternative supply information and an attenuation formula; the attenuation characteristic information acquisition unit is used for calculating attenuation characteristic information of each alternative supply information according to the attenuation factor of each alternative supply information and the corresponding supply characteristic information; and the correlation analysis unit is used for acquiring the correlation degree between the requirement characteristic information and each attenuation characteristic information according to the correlation analysis model.
A sorting result sending unit 160, configured to sort the alternative provisioning information according to the relevance and a preset sorting rule, and send a sorting result to the second client.
In one embodiment, the sorting result sending unit 160 includes sub-units: the device comprises an alternative supply information classification unit, a sorting unit and a sorting result acquisition unit.
The alternative supply information classification unit is used for classifying the alternative supply information according to the product name of the alternative supply information; the sorting unit is used for sorting the alternative supply information from high to low according to the relevance of the classified alternative supply information; and the sorting result acquisition unit is used for intercepting partial alternative supply information from the sorted alternative supply information according to the sorting rule to obtain the sorting result.
In an embodiment, the artificial intelligence based supply and demand matching apparatus 100 further comprises a sub-unit: and a feedback training unit.
And the feedback training unit is used for training the correlation analysis model according to the selection information to obtain the trained correlation analysis model if the selection information fed back by the second client according to the sequencing result is received.
The supply and demand matching device based on artificial intelligence provided by the embodiment of the invention applies the supply and demand matching method based on artificial intelligence, quantifies supply information from a first client to obtain supply characteristic information, performs classified storage, quantifies demand information from a second client to obtain demand characteristic information, obtains alternative supply information matched with the demand information according to supply chain information, obtains the correlation between the demand characteristic information and the supply characteristic information of each alternative supply information, performs sequencing to obtain a sequencing result, and sends the sequencing result to the second client. By the method, the alternative supply information matched with the demand information is obtained according to the supply chain information, and the demand information and the supply information are intelligently matched by introducing the attenuation formula, so that the matching efficiency and accuracy between the supply and demand information are improved, and the transaction efficiency of both supply and demand parties is improved.
The supply and demand matching apparatus based on artificial intelligence described above may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 10.
Referring to fig. 10, fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device can be a management server used for executing an artificial intelligence-based supply and demand matching method so as to intelligently match supply and demand information of supply and demand parties.
Referring to fig. 10, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, causes the processor 502 to perform an artificial intelligence based supply and demand matching method.
The processor 502 is used to provide computing and control capabilities that support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can be caused to execute an artificial intelligence based supply and demand matching method.
The network interface 505 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 10 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 500 to which aspects of the present invention may be applied, and that a particular computing device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The processor 502 is configured to run a computer program 5032 stored in the memory to implement the corresponding functions of the above supply and demand matching method based on artificial intelligence.
Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 10 does not constitute a limitation on the specific construction of the computer device, and that in other embodiments a computer device may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 10, and are not described herein again.
It should be understood that, in the embodiment of the present invention, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a non-volatile computer readable storage medium. The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps included in the artificial intelligence based supply and demand matching method described above.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a computer-readable storage medium, which includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage media comprise: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A supply and demand matching method based on artificial intelligence is applied to a management server, the management server is simultaneously connected with a first client and a second client through a network, and the method is characterized by comprising the following steps:
if receiving the supply information from the first client, acquiring supply characteristic information matched with the supply information according to a preset characteristic quantization rule;
storing the supply information and the supply characteristic information into a preset supply database in a classified manner according to the attribute information of the supply information;
if the demand information from the second client is received, acquiring demand characteristic information matched with the demand information according to the characteristic quantization rule;
acquiring alternative supply information matched with the demand information in the supply database according to preset supply chain information and attribute information of the demand information;
obtaining the correlation degree between the demand characteristic information and the supply characteristic information of each alternative supply information according to a preset correlation analysis model and a preset attenuation formula;
and sequencing the alternative supply information according to the relevancy and a preset sequencing rule, and sending a sequencing result to the second client.
2. The supply and demand matching method based on artificial intelligence according to claim 1, wherein the characteristic quantization rule includes a numerical quantization item and a non-numerical quantization item, the supply characteristic information includes first characteristic information corresponding to the numerical quantization item and second characteristic information corresponding to the non-numerical quantization item, and the obtaining of the supply characteristic information matching with the supply information according to a preset characteristic quantization rule includes:
acquiring historical supply information of which the attribute information is matched with the supply information in the supply database;
calculating to obtain a middle value of each numerical quantization item in the characteristic quantization rule according to the historical supply information;
quantizing the data information matched with the numerical value quantization item in the supply information according to the numerical value quantization item and the intermediate value to obtain the first characteristic information;
and quantizing the data information matched with the non-numerical quantitative item in the supply information according to the non-numerical quantitative item to obtain the second characteristic information.
3. The artificial intelligence based supply and demand matching method according to claim 1, wherein the storing the supply information and the supply characteristic information into a preset supply database according to the attribute information of the supply information in a classified manner comprises:
determining a standard name of the attribute information according to the attribute information of the supply information;
and storing the supply information and the corresponding supply characteristic information into the supply database according to the standard name in a classified manner.
4. The artificial intelligence-based supply and demand matching method according to claim 1, wherein the obtaining alternative supply information in the supply database that matches the demand information according to preset supply chain information and attribute information of the demand information comprises:
judging whether the product in the demand information contains an upstream product in the supply chain information;
if the product in the demand information contains an upstream product in the supply chain information, acquiring the upstream product matched with the attribute information of the demand information in the supply chain information;
and acquiring alternative supply information matched with the demand information in a supply database according to the attribute information of the upstream product and the attribute information of the demand information.
5. The artificial intelligence based supply and demand matching method according to claim 1, wherein the obtaining of the correlation between the demand characteristic information and the supply characteristic information of each alternative supply information according to a preset correlation analysis model and a preset attenuation formula comprises:
calculating an attenuation factor of each alternative supply information according to the sending time and an attenuation formula of each alternative supply information;
calculating attenuation characteristic information of each alternative supply information according to the attenuation factor of each alternative supply information and the corresponding supply characteristic information;
and acquiring the correlation degree between the demand characteristic information and each attenuation characteristic information according to the correlation analysis model.
6. The supply and demand matching method based on artificial intelligence according to claim 1, wherein the sorting the alternative supply information according to the relevancy and a preset sorting rule, and sending a sorting result to the second client comprises:
classifying the alternative supply information according to the product name of the alternative supply information;
sorting the alternative supply information from high to low according to the relevance of the classified alternative supply information;
and intercepting partial alternative supply information from the ordered alternative supply information according to the ordering rule to serve as the ordering result.
7. The supply and demand matching method based on artificial intelligence according to claim 1, wherein after the alternative supply information is sorted according to the relevancy and a preset sorting rule, and a sorting result is sent to the second client, the method further comprises:
and if selection information fed back by the second client according to the sorting result is received, training the correlation analysis model according to the selection information to obtain the trained correlation analysis model.
8. An artificial intelligence-based supply and demand matching device, comprising:
a supply characteristic information obtaining unit, configured to, if supply information from the first client is received, obtain supply characteristic information that matches the supply information according to a preset characteristic quantization rule;
the information storage unit is used for storing the supply information and the supply characteristic information into a preset supply database in a classified manner according to the attribute information of the supply information;
the demand characteristic information acquisition unit is used for acquiring demand characteristic information matched with the demand information according to the characteristic quantization rule if the demand information from the second client is received;
the alternative supply information acquisition unit is used for acquiring alternative supply information matched with the demand information in the supply database according to preset supply chain information and attribute information of the demand information;
the correlation degree obtaining unit is used for obtaining the correlation degree between the demand characteristic information and the supply characteristic information of each alternative supply information according to a preset correlation analysis model and a preset attenuation formula;
and the sequencing result sending unit is used for sequencing the alternative supply information according to the relevancy and a preset sequencing rule and sending a sequencing result to the second client.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the artificial intelligence based supply and demand matching method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to carry out the artificial intelligence based supply and demand matching method according to any one of claims 1 to 7.
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