Intelligent prediction method and device for demand information of entrusting party

Abstract

The invention discloses an intelligent prediction method and device for demand information of an entrusting party, and the method comprises the steps: obtaining a historical cargo parameter set of a target cargo in a preset historical time period from an order management platform, and carrying out the prediction of the demand information of the entrusting party according to the historical cargo parameter set of the target cargo and a historical order record of the entrusting party for the target cargo; determining a historical transaction trend condition of the entrusting party for the target goods; and predicting target cargo demand information of the entrusting party according to the current cargo parameter and the historical transaction trend condition of the target cargo associated with the entrusting party in the preset order management platform obtained in real time. Visibly, the method can analyze the historical transaction trend condition of the entrusting party for the target cargo, and then predicts the target cargo demand information of the entrusting party in combination with the current cargo parameters of the target cargo, so that the demand information of the entrusting party can be intelligently predicted, the prediction efficiency and accuracy of the demand information of the entrusting party are improved, and the prediction efficiency and accuracy of the demand information of the entrusting party are improved. And thus, efficient management of the supply chain can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain procurement, and in particular to an intelligent prediction method and device for client demand information. Background Art

[0002] The supply chain is a system that includes multiple entities such as suppliers, manufacturers, warehousers, transporters and clients. Supply chain management is the various activities and processes for planning, coordinating, operating, controlling and optimizing the entire supply chain system. Among them, predicting the client's demand information is a very core part of supply chain management.

[0003] At present, the existing client demand information forecasting methods mostly rely on manually established historical databases, and then relevant operators analyze historical order records to predict the client's recent relevant demand information. However, information forecasting through manual methods not only requires a lot of manpower and material resources, but also because the timeliness of information and the reliability of the operator's analysis methods are difficult to guarantee, the accuracy of the predicted client demand information is not high, thus affecting the efficient management of the supply chain. It can be seen that it is particularly important to provide a method that can improve the accuracy of predicting client demand information. Summary of the invention

[0004] The present invention provides an intelligent prediction method and device for client demand information, which can intelligently predict client demand information, improve the prediction efficiency and accuracy of client demand information, and thus facilitate efficient management of the supply chain.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses an intelligent prediction method for client demand information, the method comprising:

[0006] Real-time acquisition of current cargo parameters of the target cargo associated with the entrusting party in a preset order management platform; the current cargo parameters of the target cargo include at least one of current cargo quantity parameters, current cargo model parameters, current cargo price parameters, current cargo transportation parameters, current cargo quality parameters and current cargo booking parameters of the target cargo;

[0007] Acquire a set of historical commodity parameters of the target commodity within a preset historical time period from the order management platform, and determine a historical transaction trend of the client for the target commodity based on the set of historical commodity parameters of the target commodity and the client's historical order record for the target commodity;

[0008] The target goods demand information of the client is predicted based on the current goods parameters of the target goods and the historical transaction trends; the target goods demand information of the client includes the first goods demand information of the client for the target goods and / or the second goods demand information of the client for similar goods similar to the target goods.

[0009] As an optional implementation manner, in the first aspect of the present invention, determining the historical transaction trend of the client for the target goods according to the historical goods parameter set of the target goods and the historical order record of the client for the target goods includes:

[0010] Determine, from the historical cargo parameter set of the target cargo, the historical cargo parameter corresponding to each sub-historical time period contained in the historical time period; the historical cargo parameter corresponding to each sub-historical time period includes at least one of the historical cargo quantity parameter, historical cargo model parameter, historical cargo price parameter, historical cargo transportation parameter, historical cargo quality parameter and historical cargo booking parameter corresponding to the sub-historical time period;

[0011] Determine the historical fluctuation trend of the target goods in the historical time period according to the historical goods parameters corresponding to all the sub-historical time periods;

[0012] According to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, the historical transaction trend of the client for the target goods is determined; the historical order records of the client for the target goods include the historical order records of the client for the target goods in each sub-historical time period.

[0013] As an optional implementation manner, in the first aspect of the present invention, determining the historical transaction trend of the client for the target goods according to the historical fluctuation trend of the target goods and the historical order record of the client for the target goods includes:

[0014] Obtaining the historical situation of the entrusting party in each of the sub-historical time periods; the historical situation includes at least one of historical capital situation, historical cargo demand situation, historical cargo entrustment situation, historical cargo transportation situation and historical management structure situation;

[0015] Determine the historical changes of the client in the historical time period according to the historical situations of the client in all the sub-historical time periods;

[0016] Determine a target association relationship matching the client according to the historical changes of the client in the historical time period, the historical fluctuation trend of the target goods, and the historical order records of the client for the target goods;

[0017] According to the target association relationship, the historical transaction trend of the client for the target goods is determined.

[0018] As an optional implementation manner, in the first aspect of the present invention, determining the target association relationship matching the client according to the historical changes of the client in the historical time period, the historical fluctuation trend of the target goods, and the historical order records of the client for the target goods includes:

[0019] According to the historical changes of the client in the historical time period and the historical order records of the client for the target goods, a first association relationship is established between the historical changes and the historical order records of the client for the target goods;

[0020] According to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, a second association relationship between the historical fluctuation trend and the historical order records of the client for the target goods is established;

[0021] A target association relationship matching the client is determined according to the first association relationship and the second association relationship.

[0022] As an optional implementation manner, in the first aspect of the present invention, predicting the target goods demand information of the client according to the current goods parameters of the target goods and the historical transaction trend includes:

[0023] Determine the market environment that matches the client; the market environment includes market competition environment and / or market cooperation environment;

[0024] According to the market environment, predict the target situation of the client; the target situation includes at least one of the target funding situation, the target goods demand situation, the target goods entrustment situation, the target goods transportation situation and the target management structure situation;

[0025] According to the target situation of the client, the current cargo parameters of the target cargo and the historical transaction trend situation, the target cargo demand information of the client is predicted.

[0026] As an optional implementation manner, in the first aspect of the present invention, predicting the target goods demand information of the client according to the target situation of the client, the current goods parameters of the target goods and the historical transaction trend situation includes:

[0027] Acquire current goods parameters of similar goods similar to the target goods in the order management platform; the current goods parameters of the similar goods include at least one of current goods quantity parameters, current goods model parameters, current goods price parameters, current goods transportation parameters, current goods quality parameters and current goods booking parameters of the similar goods;

[0028] Determining a current cargo difference parameter between the target cargo and the similar cargo according to the current cargo parameter of the target cargo and the current cargo parameter of the similar cargo;

[0029] Determining, according to the current goods difference parameter between the target goods and the similar goods and the target situation of the client, the goods demand ratio parameter between the target goods and the similar goods of the client;

[0030] The target goods demand information of the client is predicted according to the goods demand ratio parameter and the historical transaction trend.

[0031] As an optional implementation manner, in the first aspect of the present invention, determining the cargo demand ratio parameter of the client for the target cargo and the similar cargo according to the current cargo difference parameter between the target cargo and the similar cargo and the target situation of the client includes:

[0032] Determining the cargo function requirement parameters of the client for the target cargo, and determining the cargo function parameters of the similar cargo;

[0033] Determining, according to the cargo function requirement parameters of the client for the target cargo and the cargo function parameters of the similar cargo, the cargo function requirement difference parameters of the client for the target cargo and the similar cargo;

[0034] The cargo demand ratio parameter between the target cargo and the similar cargo of the client is determined according to the cargo function demand difference parameter between the target cargo and the similar cargo of the client, the current cargo difference parameter between the target cargo and the similar cargo and the target situation of the client.

[0035] The second aspect of the present invention discloses an intelligent prediction device for client demand information, the device comprising:

[0036] an acquisition module, used for acquiring in real time the current cargo parameters of the target cargo associated with the entrusting party in a preset order management platform; the current cargo parameters of the target cargo include at least one of the current cargo quantity parameters, current cargo model parameters, current cargo price parameters, current cargo transportation parameters, current cargo quality parameters and current cargo booking parameters of the target cargo; and acquiring a set of historical cargo parameters of the target cargo within a preset historical time period from the order management platform;

[0037] A determination module, configured to determine a historical transaction trend of the client for the target goods according to a historical goods parameter set of the target goods and a historical order record of the client for the target goods;

[0038] The prediction module is used to predict the target goods demand information of the client according to the current goods parameters of the target goods and the historical transaction trend; the target goods demand information of the client includes the first goods demand information of the client for the target goods and / or the second goods demand information of the client for similar goods similar to the target goods.

[0039] As an optional implementation, in the second aspect of the present invention, the determination module determines the historical transaction trend of the target goods by the client according to the historical goods parameter set of the target goods and the historical order record of the client for the target goods, specifically including:

[0040] Determine, from the historical cargo parameter set of the target cargo, the historical cargo parameter corresponding to each sub-historical time period contained in the historical time period; the historical cargo parameter corresponding to each sub-historical time period includes at least one of the historical cargo quantity parameter, historical cargo model parameter, historical cargo price parameter, historical cargo transportation parameter, historical cargo quality parameter and historical cargo booking parameter corresponding to the sub-historical time period;

[0041] Determine the historical fluctuation trend of the target goods in the historical time period according to the historical goods parameters corresponding to all the sub-historical time periods;

[0042] According to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, the historical transaction trend of the client for the target goods is determined; the historical order records of the client for the target goods include the historical order records of the client for the target goods in each sub-historical time period.

[0043] As an optional implementation, in the second aspect of the present invention, the determination module determines the historical transaction trend of the target goods by the client according to the historical fluctuation trend of the target goods and the historical order record of the client for the target goods, specifically including:

[0044] Obtaining the historical situation of the entrusting party in each of the sub-historical time periods; the historical situation includes at least one of historical capital situation, historical cargo demand situation, historical cargo entrustment situation, historical cargo transportation situation and historical management structure situation;

[0045] Determine the historical changes of the client in the historical time period according to the historical situations of the client in all the sub-historical time periods;

[0046] Determine a target association relationship matching the client according to the historical changes of the client in the historical time period, the historical fluctuation trend of the target goods, and the historical order records of the client for the target goods;

[0047] According to the target association relationship, the historical transaction trend of the client for the target goods is determined.

[0048] As an optional implementation, in the second aspect of the present invention, the determination module determines the target association relationship matching the client according to the historical changes of the client in the historical time period, the historical fluctuation trend of the target goods, and the historical order records of the client for the target goods, specifically including:

[0049] According to the historical changes of the client in the historical time period and the historical order records of the client for the target goods, a first association relationship is established between the historical changes and the historical order records of the client for the target goods;

[0050] According to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, a second association relationship between the historical fluctuation trend and the historical order records of the client for the target goods is established;

[0051] A target association relationship matching the client is determined according to the first association relationship and the second association relationship.

[0052] As an optional implementation, in the second aspect of the present invention, the prediction module predicts the target goods demand information of the client according to the current goods parameters of the target goods and the historical transaction trend, specifically including:

[0053] Determine the market environment that matches the client; the market environment includes market competition environment and / or market cooperation environment;

[0054] According to the market environment, predict the target situation of the client; the target situation includes at least one of the target funding situation, the target goods demand situation, the target goods entrustment situation, the target goods transportation situation and the target management structure situation;

[0055] According to the target situation of the client, the current cargo parameters of the target cargo and the historical transaction trend situation, the target cargo demand information of the client is predicted.

[0056] As an optional implementation, in the second aspect of the present invention, the prediction module predicts the target goods demand information of the client according to the target situation of the client, the current goods parameters of the target goods and the historical transaction trend situation, specifically including:

[0057] Acquire current goods parameters of similar goods similar to the target goods in the order management platform; the current goods parameters of the similar goods include at least one of current goods quantity parameters, current goods model parameters, current goods price parameters, current goods transportation parameters, current goods quality parameters and current goods booking parameters of the similar goods;

[0058] Determining a current cargo difference parameter between the target cargo and the similar cargo according to the current cargo parameter of the target cargo and the current cargo parameter of the similar cargo;

[0059] Determining, according to the current goods difference parameter between the target goods and the similar goods and the target situation of the client, the goods demand ratio parameter between the target goods and the similar goods of the client;

[0060] The target goods demand information of the client is predicted according to the goods demand ratio parameter and the historical transaction trend.

[0061] As an optional implementation, in the second aspect of the present invention, the prediction module determines the cargo demand ratio parameter of the client for the target cargo and the similar cargo according to the current cargo difference parameter between the target cargo and the similar cargo and the target situation of the client, specifically including:

[0062] Determining the cargo function requirement parameters of the client for the target cargo, and determining the cargo function parameters of the similar cargo;

[0063] Determining, according to the cargo function requirement parameters of the client for the target cargo and the cargo function parameters of the similar cargo, the cargo function requirement difference parameters of the client for the target cargo and the similar cargo;

[0064] The cargo demand ratio parameter between the target cargo and the similar cargo of the client is determined according to the cargo function demand difference parameter between the target cargo and the similar cargo of the client, the current cargo difference parameter between the target cargo and the similar cargo and the target situation of the client.

[0065] The third aspect of the present invention discloses another intelligent prediction device for client demand information, the device comprising:

[0066] A memory storing executable program code;

[0067] a processor coupled to the memory;

[0068] The processor calls the executable program code stored in the memory to execute the intelligent prediction method for client demand information disclosed in the first aspect of the present invention.

[0069] The fourth aspect of the present invention discloses a computer storage medium, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the intelligent prediction method of client demand information disclosed in the first aspect of the present invention.

[0070] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0071] In the embodiment of the present invention, a historical cargo parameter set of the target cargo within a preset historical time period is obtained from the order management platform, and the historical transaction trend of the target cargo by the entrusting party is determined based on the historical cargo parameter set of the target cargo and the historical order record of the entrusting party for the target cargo; the target cargo demand information of the entrusting party is predicted based on the current cargo parameters and historical transaction trend of the target cargo associated with the entrusting party in the preset order management platform obtained in real time. It can be seen that the implementation of the present invention can analyze the historical transaction trend of the entrusting party for the target cargo, and then predict the target cargo demand information of the entrusting party in combination with the current cargo parameters of the target cargo. In this way, the entrusting party's demand information can be intelligently predicted, and the prediction efficiency and accuracy of the entrusting party's demand information can be improved, thereby facilitating the efficient management of the supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0073] Figure 1 It is a flow chart of an intelligent prediction method of client demand information disclosed in an embodiment of the present invention;

[0074] Figure 2 It is a flow chart of another intelligent prediction method of client demand information disclosed in an embodiment of the present invention;

[0075] Figure 3 It is a structural schematic diagram of an intelligent prediction device for client demand information disclosed in an embodiment of the present invention;

[0076] Figure 4 It is a structural schematic diagram of another intelligent prediction device for client demand information disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0077] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0079] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0080] The present invention discloses an intelligent prediction method and device for client demand information, which can intelligently predict client demand information, improve the prediction efficiency and accuracy of client demand information, and thus facilitate efficient management of the supply chain.

[0081] Embodiment 1

[0082] See also Figure 1 , Figure 1 The figure is a flowchart of a method for intelligently predicting client demand information disclosed in an embodiment of the present invention. Optionally, the method can be implemented by a demand information prediction device, which can be integrated into a smart device, such as a smart phone, a smart computer, a smart tablet, etc. When the demand information prediction device exists independently, it can also be a local server or a cloud server for processing the client demand information prediction process, which is not limited by the embodiment of the present invention. Figure 1 As shown, the intelligent prediction method of the client's demand information may include the following operations:

[0083] 101. Obtain in real time the current cargo parameters of the target cargo associated with the client in the preset order management platform.

[0084] In an embodiment of the present invention, optionally, the current cargo parameters of the target cargo include at least one of the current cargo quantity parameters, current cargo model parameters, current cargo price parameters, current cargo transportation parameters, current cargo quality parameters and current cargo booking parameters of the target cargo, wherein the target cargo can be understood as the cargo that the entrusting party has ever ordered, and the target cargo can be determined by searching the entrusting party's historical electronic warehouse receipt collection in the order management platform.

[0085] 102. Obtain a set of historical goods parameters of the target goods within a preset historical time period from the order management platform, and determine the historical transaction trend of the client for the target goods based on the set of historical goods parameters of the target goods and the client's historical order records for the target goods.

[0086] In an embodiment of the present invention, optionally, the historical transaction trend may include historical transaction quantity trend, historical transaction price trend, historical transaction goods model trend, historical transaction goods quality trend, historical transaction goods transportation trend, etc. For example, in the historical goods parameter set of the target goods, the price of goods A in the target goods is a in January, and the price of goods B in May is b. Then, combined with the historical order records of the client for the target goods, it is analyzed that the client usually places an order for a batch of goods A in January and uses a large truck for transportation, and places an order for a batch of goods B in May and uses a medium-sized truck for transportation, etc., thereby obtaining the client's historical transaction trend for goods A and goods B.

[0087] 103. Based on the current cargo parameters and historical transaction trends of the target cargo, predict the client's target cargo demand information.

[0088] In an embodiment of the present invention, optionally, the target goods demand information of the entrusting party includes the first goods demand information of the entrusting party for the target goods and / or the second goods demand information of the entrusting party for similar goods similar to the target goods, wherein similar goods similar to the target goods can be understood as goods that can meet the functional requirements of the entrusting party for the goods and have never been ordered.

[0089] It can be seen that the implementation of the embodiment of the present invention can analyze the historical transaction trends of the client for the target goods, and then predict the client's target goods demand information in combination with the current goods parameters of the target goods. In this way, the client's demand information can be intelligently predicted, and the prediction efficiency and accuracy of the client's demand information can be improved, which is conducive to achieving efficient management of the supply chain.

[0090] In an optional embodiment, the step 102 of determining the historical transaction trend of the client for the target goods according to the historical goods parameter set of the target goods and the historical order record of the client for the target goods includes:

[0091] Determine, from the historical cargo parameter set of the target cargo, the historical cargo parameter corresponding to each sub-historical time period contained in the historical time period;

[0092] According to the historical cargo parameters corresponding to all sub-historical time periods, determine the historical fluctuation trend of the target cargo within the historical time period;

[0093] According to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, the historical transaction trend of the client for the target goods is determined.

[0094] In this optional embodiment, optionally, the historical goods parameter corresponding to each sub-historical time period includes at least one of the historical goods quantity parameter, historical goods model parameter, historical goods price parameter, historical goods transportation parameter, historical goods quality parameter and historical goods booking parameter corresponding to the sub-historical time period. Further optionally, the historical fluctuation trend of the target goods may include historical transaction price fluctuation trend, historical quantity fluctuation trend, historical quality fluctuation trend, etc.

[0095] Furthermore, the historical order records of the client for the target goods include the historical order records of the client for the target goods in each sub-historical time period, wherein the historical order records of the client for the target goods can be obtained from the historical electronic warehouse receipt set of the client.

[0096] For example, if the transaction price of the target commodity in January is m1, the transaction price in February is m2, the transaction price in March is m3, and the transaction price in April is m4, then the historical transaction price volatility of the target commodity in April can be [m4-(m1+m2+m3) / 3] / m4, and then the transaction volatility of the target commodity in historical months such as May and June is calculated in this way, thereby obtaining the historical fluctuation trend of the target commodity in the historical time period.

[0097] It can be seen that this optional embodiment can analyze the historical fluctuation trend of the target goods within the historical time period according to the historical goods parameters corresponding to each sub-historical time period, and then determine the historical transaction trend of the client for the target goods in combination with the historical order records of the client for the target goods. In this way, the reliability and accuracy of the analysis of the historical fluctuation trend of the target goods can be improved, and then the reliability and accuracy of the determination of the historical transaction trend of the client for the target goods can be improved, thereby improving the subsequent prediction efficiency and accuracy of the client's target goods demand information.

[0098] In another optional embodiment, the above step of determining the historical transaction trend of the client for the target goods according to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods includes:

[0099] Obtain the historical situation of the client in each sub-historical time period;

[0100] According to the historical situation of the client in all sub-historical time periods, determine the historical changes of the client in the historical time period;

[0101] Determine the target association relationship that matches the client based on the client's historical changes in the historical time period, the historical fluctuation trend of the target goods, and the client's historical order records for the target goods;

[0102] Based on the target association relationship, determine the client's historical transaction trends for the target goods.

[0103] In this optional embodiment, the historical situation optionally includes at least one of historical financial situation, historical goods demand situation, historical goods entrustment situation (such as historical goods entrustment situation of the entrusting party under each order management platform), historical goods transportation situation and historical management structure situation (such as personnel changes, company size changes, etc.).

[0104] In this optional embodiment, further, the above step of determining the target association relationship matching the client according to the historical changes of the client in the historical time period, the historical fluctuation trend of the target goods, and the historical order records of the client for the target goods includes:

[0105] According to the historical changes of the client in the historical time period and the historical order records of the client for the target goods, a first association relationship between the historical changes and the historical order records of the client for the target goods is established;

[0106] According to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, a second association relationship between the historical fluctuation trend and the historical order records of the client for the target goods is established;

[0107] According to the first association relationship and the second association relationship, a target association relationship matching the client is determined.

[0108] In this optional embodiment, starting from the correlation between the client's historical order records for the target goods and its own changes, and the correlation between the client's historical order records for the target goods and the historical fluctuation trend of the target goods, the target correlation relationship between the target goods, the client's own situation and the client's order situation is analyzed.

[0109] It can be seen that this optional embodiment can further analyze the target association relationship corresponding to the principal based on the historical changes of the principal in the historical time period, the historical fluctuation trend of the target goods and the historical order records of the principal for the target goods, and then determine the historical transaction trend of the principal for the target goods. This is conducive to improving the reliability and accuracy of the analysis of the target association relationship corresponding to the principal, and then conducive to improving the reliability and accuracy of the determination of the historical transaction trend of the principal for the target goods, so as to facilitate the subsequent efficient and intelligent prediction of the principal's demand information.

[0110] Embodiment 2

[0111] See also Figure 2 , Figure 2 The figure is a flowchart of a method for intelligently predicting client demand information disclosed in an embodiment of the present invention. Optionally, the method can be implemented by a demand information prediction device, which can be integrated into a smart device, such as a smart phone, a smart computer, a smart tablet, etc. When the demand information prediction device exists independently, it can also be a local server or a cloud server for processing the client demand information prediction process, which is not limited by the embodiment of the present invention. Figure 2 As shown, the intelligent prediction method of the client's demand information may include the following operations:

[0112] 201. Obtain in real time current cargo parameters of target cargo associated with the client in a preset order management platform.

[0113] 202. Obtain a set of historical goods parameters of the target goods within a preset historical time period from the order management platform, and determine the historical transaction trend of the client for the target goods based on the set of historical goods parameters of the target goods and the client's historical order records for the target goods.

[0114] 203. Determine the market environment that matches the client.

[0115] In the embodiment of the present invention, optionally, the market environment conditions include market competition environment conditions and / or market cooperation environment conditions.

[0116] 204. Predict the client’s objectives based on the market environment.

[0117] In an embodiment of the present invention, the target situation includes target capital situation, target goods demand situation (such as demand situation of multiple types of goods: the predicted demand for goods C is c, the predicted demand for goods D is d, etc.), target goods entrustment situation (such as entrustment situation of multiple types of goods: the predicted entrustment quantity of goods C in company A is f, the predicted entrustment quantity in company B is g, and the predicted entrustment quantity of goods D in company A is h, the predicted entrustment quantity in company B is i, etc.), target goods transportation situation and at least one of the target management architecture situation.

[0118] 205. Predict the client's target goods demand information based on the client's target situation, current goods parameters of the target goods, and historical transaction trends.

[0119] In the embodiment of the present invention, it can be understood that the client's target goods demand information is predicted and derived based on the client's target situation and current goods parameters of the target goods, combined with the first association relationship and the second association relationship contained in the historical transaction trend situation.

[0120] In the embodiment of the present invention, for other descriptions of step 201 and step 202, please refer to the detailed description of step 101 and step 102 in the first embodiment, and the embodiment of the present invention will not be repeated.

[0121] It can be seen that the implementation of the embodiment of the present invention can predict the target situation of the entrusting party according to the market environment corresponding to the entrusting party, and then predict the target goods demand information of the entrusting party according to the target situation of the entrusting party, the current goods parameters of the target goods and the historical transaction trends. In this way, the prediction reliability and accuracy of the target goods demand information of the entrusting party can be improved, and then the management efficiency of the target goods by the corresponding operators can be improved, which is conducive to realizing efficient management of the supply chain.

[0122] In an optional embodiment, the step 205 above predicts the client's target goods demand information based on the client's target situation, current goods parameters of the target goods, and historical transaction trends, including:

[0123] Obtain current goods parameters of similar goods in the order management platform that are similar to the target goods;

[0124] Determine the current cargo difference parameters between the target cargo and the similar cargo according to the current cargo parameters of the target cargo and the current cargo parameters of the similar cargo;

[0125] Determine the cargo demand ratio parameter between the target cargo and similar cargo by the client according to the current cargo difference parameter between the target cargo and similar cargo and the client's target situation;

[0126] According to the cargo demand ratio parameters and historical transaction trends, the client's target cargo demand information is predicted.

[0127] In this optional embodiment, the current goods parameters of similar goods may include at least one of the current goods quantity parameters, current goods model parameters, current goods price parameters, current goods transportation parameters, current goods quality parameters and current goods booking parameters of similar goods. Further, the current goods difference parameters may include the current goods quantity difference parameters, the current goods model difference parameters, the current goods price difference parameters, the current goods transportation difference parameters, the current goods quality difference parameters and the current goods booking difference parameters, etc.

[0128] It can be seen that this optional embodiment can further determine the cargo demand ratio parameters between the target cargo and similar cargo of the client according to the current cargo difference parameters between similar cargo and the target cargo and the target situation of the client, and then predict the target cargo demand information of the client. In this way, the reliability and accuracy of the analysis of the cargo demand ratio parameters can be improved, and then the reliability and accuracy of the prediction of the demand information corresponding to the target cargo and similar cargo of the client can be improved, which is beneficial for relevant operators to realize efficient management of the target cargo and similar cargo.

[0129] In another optional embodiment, the above step of determining the cargo demand ratio parameter between the target cargo and similar cargo according to the current cargo difference parameter between the target cargo and similar cargo and the target situation of the client includes:

[0130] Determine the cargo function requirement parameters of the client for the target cargo, and determine the cargo function parameters of similar cargo;

[0131] Determine the difference parameters of the entrusting party's cargo function requirements between the target cargo and the similar cargo based on the entrusting party's cargo function requirements parameters for the target cargo and the cargo function parameters for the similar cargo;

[0132] The cargo demand ratio parameters between the target cargo and similar cargo are determined according to the cargo function demand difference parameters between the target cargo and similar cargo, the current cargo difference parameters between the target cargo and similar cargo, and the target situation of the client.

[0133] In this optional embodiment, the client's demand information for the target goods and similar goods is analyzed based on the client's target situation and in combination with the client's goods function demand difference parameters between the target goods and similar goods and the current goods difference parameters.

[0134] It can be seen that this optional embodiment can further determine the difference parameters of the entrusting party's cargo function requirements between the target goods and similar goods, and then analyze the entrusting party's cargo demand ratio parameters between the target goods and similar goods according to the entrusting party's target situation, the difference parameters of the entrusting party's cargo function requirements between the target goods and similar goods, and the current cargo difference parameters. In this way, the reliability and accuracy of the analysis of the cargo demand ratio parameters between the target goods and similar goods can be further improved, which is conducive to further improving the prediction reliability and accuracy of the entrusting party's target goods demand information, thereby facilitating better efficient management of the supply chain.

[0135] Embodiment 3

[0136] See also Figure 3 , Figure 3 Schematic diagram of the structure of an intelligent prediction device for client demand information disclosed in an embodiment of the present invention. Figure 3 As shown, the intelligent prediction device for the client's demand information may include:

[0137] The acquisition module 301 is used to acquire the current cargo parameters of the target cargo associated with the entrusting party in the preset order management platform in real time; and to acquire the historical cargo parameter set of the target cargo in the preset historical time period from the order management platform;

[0138] A determination module 302 is used to determine the historical transaction trend of the client for the target goods according to the historical goods parameter set of the target goods and the historical order records of the client for the target goods;

[0139] The prediction module 303 is used to predict the target goods demand information of the client according to the current goods parameters and historical transaction trends of the target goods.

[0140] In an embodiment of the present invention, the current cargo parameters of the target cargo include at least one of the current cargo quantity parameters, current cargo model parameters, current cargo price parameters, current cargo transportation parameters, current cargo quality parameters and current cargo booking parameters of the target cargo; the target cargo demand information of the entrusting party includes the first cargo demand information of the entrusting party for the target cargo and / or the second cargo demand information of the entrusting party for similar cargo similar to the target cargo.

[0141] It can be seen that implementation Figure 3 The described intelligent prediction device for the client's demand information can analyze the client's historical transaction trends for the target goods, and then predict the client's target goods demand information in combination with the current goods parameters of the target goods. In this way, the client's demand information can be intelligently predicted, and the prediction efficiency and accuracy of the client's demand information can be improved, which is conducive to the efficient management of the supply chain.

[0142] In an optional embodiment, the determination module 302 determines the historical transaction trend of the target goods by the client according to the historical goods parameter set of the target goods and the historical order record of the client for the target goods, specifically including:

[0143] Determine, from the historical cargo parameter set of the target cargo, the historical cargo parameter corresponding to each sub-historical time period contained in the historical time period;

[0144] According to the historical cargo parameters corresponding to all sub-historical time periods, determine the historical fluctuation trend of the target cargo within the historical time period;

[0145] According to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, the historical transaction trend of the client for the target goods is determined.

[0146] In this optional embodiment, the historical goods parameters corresponding to each sub-historical time period include at least one of the historical goods quantity parameters, historical goods model parameters, historical goods price parameters, historical goods transportation parameters, historical goods quality parameters and historical goods booking parameters corresponding to the sub-historical time period; the historical order records of the client for the target goods include the historical order records of the client for the target goods in each sub-historical time period.

[0147] It can be seen that implementation Figure 3 The described intelligent prediction device for the client's demand information can analyze the historical fluctuation trend of the target goods within the historical time period according to the historical goods parameters corresponding to each sub-historical time period, and then determine the historical transaction trend of the client for the target goods in combination with the client's historical order records for the target goods. In this way, the reliability and accuracy of the analysis of the historical fluctuation trend of the target goods can be improved, and then the reliability and accuracy of the determination of the historical transaction trend of the client for the target goods can be improved, thereby improving the subsequent prediction efficiency and accuracy of the client's target goods demand information.

[0148] In another optional embodiment, the determination module 302 determines the historical transaction trend of the target goods by the client according to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, specifically including:

[0149] Obtain the historical situation of the client in each sub-historical time period;

[0150] According to the historical situation of the client in all sub-historical time periods, determine the historical changes of the client in the historical time period;

[0151] Determine the target association relationship that matches the client based on the client's historical changes in the historical time period, the historical fluctuation trend of the target goods, and the client's historical order records for the target goods;

[0152] Based on the target association relationship, determine the client's historical transaction trends for the target goods.

[0153] In this optional embodiment, the historical situation includes at least one of historical funding situation, historical cargo demand situation, historical cargo entrustment situation, historical cargo transportation situation and historical management structure situation.

[0154] In this optional embodiment, further, the determination module 302 determines the target association relationship matching the client according to the historical changes of the client in the historical time period, the historical fluctuation trend of the target goods, and the historical order records of the client for the target goods, specifically including:

[0155] According to the historical changes of the client in the historical time period and the historical order records of the client for the target goods, a first association relationship between the historical changes and the historical order records of the client for the target goods is established;

[0156] According to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, a second association relationship between the historical fluctuation trend and the historical order records of the client for the target goods is established;

[0157] According to the first association relationship and the second association relationship, a target association relationship matching the client is determined.

[0158] It can be seen that implementation Figure 3 The described intelligent prediction device for the client's demand information can further analyze the target association relationship corresponding to the client based on the historical changes of the client in the historical time period, the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, and then determine the historical transaction trend of the client for the target goods. This is conducive to improving the reliability and accuracy of the analysis of the target association relationship corresponding to the client, and then conducive to improving the reliability and accuracy of the determination of the historical transaction trend of the client for the target goods, so as to facilitate the subsequent efficient and intelligent prediction of the client's demand information.

[0159] In another optional embodiment, the prediction module 303 predicts the client's target goods demand information according to the current goods parameters and historical transaction trends of the target goods in the following manner:

[0160] Determine the market environment that matches the client;

[0161] Predict the client's target situation based on the market environment;

[0162] According to the client's target situation, the current cargo parameters of the target cargo and the historical transaction trends, the client's target cargo demand information is predicted.

[0163] In this optional embodiment, the market environment conditions include market competition environment conditions and / or market cooperation environment conditions; the target conditions include at least one of target funding conditions, target cargo demand conditions, target cargo entrustment conditions, target cargo transportation conditions and target management structure conditions.

[0164] It can be seen that implementation Figure 3 The described intelligent prediction device for the client's demand information can predict the client's target situation according to the client's corresponding market environment, and then predict the client's target goods demand information according to the client's target situation, the current goods parameters of the target goods and the historical transaction trends. In this way, the prediction reliability and accuracy of the client's target goods demand information can be improved, and then the management efficiency of the target goods by the corresponding operators can be improved, which is conducive to the efficient management of the supply chain.

[0165] In another optional embodiment, the prediction module 303 predicts the client's target goods demand information according to the client's target situation, the current goods parameters of the target goods, and the historical transaction trend situation, specifically including:

[0166] Obtain current goods parameters of similar goods in the order management platform that are similar to the target goods;

[0167] Determine the current cargo difference parameters between the target cargo and the similar cargo according to the current cargo parameters of the target cargo and the current cargo parameters of the similar cargo;

[0168] Determine the cargo demand ratio parameter between the target cargo and similar cargo by the client according to the current cargo difference parameter between the target cargo and similar cargo and the client's target situation;

[0169] According to the cargo demand ratio parameters and historical transaction trends, the client's target cargo demand information is predicted.

[0170] In this optional embodiment, the current goods parameters of similar goods include at least one of current goods quantity parameters, current goods model parameters, current goods price parameters, current goods transportation parameters, current goods quality parameters and current goods booking parameters of similar goods.

[0171] It can be seen that implementation Figure 3The described intelligent prediction device for the client's demand information can further determine the client's cargo demand ratio parameters between the target cargo and similar cargo based on the current cargo difference parameters between similar cargo and the target cargo and the client's target situation, and then predict the client's target cargo demand information. In this way, the reliability and accuracy of the analysis of the cargo demand ratio parameters can be improved, and then the reliability and accuracy of the prediction of the client's demand information corresponding to the target cargo and similar cargo can be improved, which is conducive to the relevant operators to achieve efficient management of the target cargo and similar cargo.

[0172] In another optional embodiment, the prediction module 303 determines the cargo demand ratio parameter between the target cargo and similar cargo according to the current cargo difference parameter between the target cargo and similar cargo and the target situation of the client, specifically including:

[0173] Determine the cargo function requirement parameters of the client for the target cargo, and determine the cargo function parameters of similar cargo;

[0174] Determine the difference parameters of the entrusting party's cargo function requirements between the target cargo and the similar cargo based on the entrusting party's cargo function requirements parameters for the target cargo and the cargo function parameters for the similar cargo;

[0175] The cargo demand ratio parameters between the target cargo and similar cargo are determined according to the cargo function demand difference parameters between the target cargo and similar cargo, the current cargo difference parameters between the target cargo and similar cargo, and the target situation of the client.

[0176] It can be seen that implementation Figure 3 The described intelligent prediction device for the client's demand information can further determine the difference parameters of the client's functional requirements for target goods and similar goods, and then analyze the client's demand ratio parameters for target goods and similar goods based on the client's target situation, the difference parameters of the client's functional requirements for target goods and similar goods, and the current goods difference parameters. In this way, the reliability and accuracy of the analysis of the demand ratio parameters for target goods and similar goods can be further improved, which is conducive to further improving the reliability and accuracy of the prediction of the client's target goods demand information, thereby facilitating better efficient management of the supply chain.

[0177] Embodiment 4

[0178] See also Figure 4 , Figure 4 FIG. 1 is a schematic diagram of the structure of another intelligent prediction device for client demand information disclosed in an embodiment of the present invention. Figure 4As shown, the intelligent prediction device for the client's demand information may include:

[0179] A memory 401 storing executable program codes;

[0180] a processor 402 coupled to the memory 401;

[0181] The processor 402 calls the executable program code stored in the memory 401 to execute the steps of the intelligent prediction method of the client demand information described in the first embodiment of the present invention or the second embodiment of the present invention.

[0182] Embodiment 5

[0183] An embodiment of the present invention discloses a computer storage medium storing computer instructions. When the computer instructions are called, they are used to execute the steps of the intelligent prediction method of client demand information described in Embodiment 1 or Embodiment 2 of the present invention.

[0184] Embodiment 6

[0185] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the intelligent prediction method of client demand information described in Example 1 or Example 2.

[0186] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0187] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0188] Finally, it should be noted that the intelligent prediction method and device for client demand information disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent prediction method for client demand information, characterized in that: The method comprises: Real-time acquisition of current cargo parameters of the target cargo associated with the entrusting party in a preset order management platform; the current cargo parameters of the target cargo include at least one of current cargo quantity parameters, current cargo model parameters, current cargo price parameters, current cargo transportation parameters, current cargo quality parameters and current cargo booking parameters of the target cargo; Acquire a set of historical commodity parameters of the target commodity within a preset historical time period from the order management platform, and determine a historical transaction trend of the client for the target commodity based on the set of historical commodity parameters of the target commodity and the client's historical order record for the target commodity; The target goods demand information of the client is predicted based on the current goods parameters of the target goods and the historical transaction trends; the target goods demand information of the client includes the first goods demand information of the client for the target goods and / or the second goods demand information of the client for similar goods similar to the target goods.

2. The intelligent prediction method for client demand information according to claim 1, characterized in that: The determining, based on the historical commodity parameter set of the target commodity and the historical order record of the client for the target commodity, the historical transaction trend of the client for the target commodity includes: Determine, from the historical cargo parameter set of the target cargo, the historical cargo parameter corresponding to each sub-historical time period contained in the historical time period; the historical cargo parameter corresponding to each sub-historical time period includes at least one of the historical cargo quantity parameter, historical cargo model parameter, historical cargo price parameter, historical cargo transportation parameter, historical cargo quality parameter and historical cargo booking parameter corresponding to the sub-historical time period; Determine the historical fluctuation trend of the target goods in the historical time period according to the historical goods parameters corresponding to all the sub-historical time periods; According to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, the historical transaction trend of the client for the target goods is determined; the historical order records of the client for the target goods include the historical order records of the client for the target goods in each sub-historical time period.

3. The intelligent prediction method for client demand information according to claim 2, characterized in that: Determining the historical transaction trend of the target goods by the client according to the historical fluctuation trend of the target goods and the historical order record of the client for the target goods includes: Obtaining the historical situation of the entrusting party in each of the sub-historical time periods; the historical situation includes at least one of historical capital situation, historical cargo demand situation, historical cargo entrustment situation, historical cargo transportation situation and historical management structure situation; Determine the historical changes of the client in the historical time period according to the historical situations of the client in all the sub-historical time periods; Determine a target association relationship matching the client according to the historical changes of the client in the historical time period, the historical fluctuation trend of the target goods, and the historical order records of the client for the target goods; According to the target association relationship, the historical transaction trend of the client for the target goods is determined.

4. The intelligent prediction method of client demand information according to claim 3, characterized in that: The determining of a target association relationship matching the client according to the historical changes of the client in the historical time period, the historical fluctuation trend of the target goods, and the historical order records of the client for the target goods includes: According to the historical changes of the client in the historical time period and the historical order records of the client for the target goods, a first association relationship is established between the historical changes and the historical order records of the client for the target goods; According to the historical fluctuation trend of the target goods and the historical order records of the client for the target goods, a second association relationship between the historical fluctuation trend and the historical order records of the client for the target goods is established; A target association relationship matching the client is determined according to the first association relationship and the second association relationship.

5. The intelligent prediction method for client demand information according to any one of claims 1 to 4, characterized in that: The predicting of the target goods demand information of the client according to the current goods parameters of the target goods and the historical transaction trend includes: Determine the market environment that matches the client; the market environment includes market competition environment and / or market cooperation environment; According to the market environment, predict the target situation of the client; the target situation includes at least one of the target funding situation, the target goods demand situation, the target goods entrustment situation, the target goods transportation situation and the target management structure situation; According to the target situation of the client, the current cargo parameters of the target cargo and the historical transaction trend situation, the target cargo demand information of the client is predicted.

6. The intelligent prediction method of client demand information according to claim 5, characterized in that: The predicting of the target goods demand information of the client according to the target situation of the client, the current goods parameters of the target goods and the historical transaction trend situation includes: Acquire current goods parameters of similar goods similar to the target goods in the order management platform; the current goods parameters of the similar goods include at least one of current goods quantity parameters, current goods model parameters, current goods price parameters, current goods transportation parameters, current goods quality parameters and current goods booking parameters of the similar goods; Determining a current cargo difference parameter between the target cargo and the similar cargo according to the current cargo parameter of the target cargo and the current cargo parameter of the similar cargo; Determining, according to the current goods difference parameter between the target goods and the similar goods and the target situation of the client, the goods demand ratio parameter between the target goods and the similar goods of the client; The target goods demand information of the client is predicted according to the goods demand ratio parameter and the historical transaction trend.

7. The intelligent prediction method of client demand information according to claim 6, characterized in that: The determining, according to the current goods difference parameter between the target goods and the similar goods and the target situation of the entrusting party, the goods demand ratio parameter between the target goods and the similar goods of the entrusting party comprises: Determining the cargo function requirement parameters of the client for the target cargo, and determining the cargo function parameters of the similar cargo; Determining, according to the cargo function requirement parameters of the client for the target cargo and the cargo function parameters of the similar cargo, the cargo function requirement difference parameters of the client for the target cargo and the similar cargo; The cargo demand ratio parameter between the target cargo and the similar cargo of the client is determined according to the cargo function demand difference parameter between the target cargo and the similar cargo of the client, the current cargo difference parameter between the target cargo and the similar cargo and the target situation of the client.

8. An intelligent prediction device for client demand information, characterized in that: The device comprises: an acquisition module, used for acquiring in real time the current cargo parameters of the target cargo associated with the entrusting party in a preset order management platform; the current cargo parameters of the target cargo include at least one of the current cargo quantity parameters, current cargo model parameters, current cargo price parameters, current cargo transportation parameters, current cargo quality parameters and current cargo booking parameters of the target cargo; and acquiring a set of historical cargo parameters of the target cargo within a preset historical time period from the order management platform; A determination module, configured to determine a historical transaction trend of the client for the target goods according to a historical goods parameter set of the target goods and a historical order record of the client for the target goods; The prediction module is used to predict the target goods demand information of the client according to the current goods parameters of the target goods and the historical transaction trend; the target goods demand information of the client includes the first goods demand information of the client for the target goods and / or the second goods demand information of the client for similar goods similar to the target goods.

9. An intelligent prediction device for client demand information, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent prediction method for client demand information as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, which, when called, are used to execute the intelligent prediction method for client demand information as described in any one of claims 1 to 7.

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