A cable project data management method and system

Abstract

The present invention relates to a big data processing method, and discloses a cable project data management method and system, including: judging whether the customer has historical data, and if the historical data exists, judging whether the first product is the same as the second product of the historical data of the customer's purchase of the product; configuring the fourth product corresponding to the third configuration parameter that is greater than the luxury threshold of the first configuration parameter in the first echelon from low to high according to the third configuration parameter, and configuring other fourth products in the second echelon according to the price from low to high, and generating a recommendation list according to the appearance of the first echelon first and then the second echelon; if it does not exceed, generating a recommendation list according to the price difference between the fourth product and the second product of the historical data from small to large; and outputting the recommendation list. This method can automatically retrieve the cable project data of this time, and provide customers with an automated middle-end robot, which can maximize the profit margin and transaction rate while meeting customer needs and inventory needs.

Description

Technical Field

[0001] The present invention relates to a big data processing method, and in particular to a method for managing cable project data. Background Art

[0002] In cable factories, the cooperation of the front desk, middle desk, and back desk is usually required to complete related projects. The front desk can be understood as the sales end, the middle desk can be understood as the product manager end that assists in sales and matches the corresponding products, and the back desk can be understood as the production, processing, transportation, and sales end of the corresponding products for customers.

[0003] So, for cable projects, the middle office should not only manage the above cable projects and realize the corresponding value, but also assist customers in selecting corresponding products, combining product profit margins, inventory pressure and other considerations to meet more needs of customers and provide better value for cable factories.

[0004] Traditionally, the middle office needs more experienced personnel to take on the role. This will invisibly increase the cost of the cable factory. However, the younger front desk staff lack the corresponding knowledge and are not qualified for the role of the middle office.

[0005] Therefore, there is a need for a cable project data management method that can directly assist the front desk to complete the tasks of the front desk and the middle desk at the same time, so as to more intelligently increase the profit margin of the cable factory and reduce the inventory pressure of the cable factory. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a cable project data management method that can directly assist the front desk to complete the tasks of the front desk and the middle desk at the same time, so as to more intelligently increase the profit margin of the cable factory and reduce the inventory pressure of the cable factory.

[0007] Subject: Automated middle office management.

[0008] In order to match customers with cable equipment that is more suitable for customers and also suitable for production automation platforms, the inventory pressure is reduced and the user experience of matching products is improved.

[0009] In a first aspect, the present invention provides a cable project data management method, comprising:

[0010] Acquire customer intention data, historical data of the customer's purchase of products, and inventory data of each product, wherein the customer intention data includes a first product, a first configuration parameter, and a first quantity, the historical data includes a second product, a second configuration parameter, and a second quantity, and the inventory data includes a third product, a third configuration data, and a third quantity;

[0011] The third product in the inventory data whose third configuration parameter and third quantity are respectively greater than or equal to the first configuration parameter and first quantity of the first product in the customer's intention data is configured as the fourth product, and it is determined whether there is historical data for the customer.

[0012] If there is no historical data, the fourth product is configured from low to high price to generate a recommendation list;

[0013] If there is historical data, determining whether the first product is the same as the second product in the historical data of the product purchased by the customer;

[0014] If they are the same, the first tier is configured from high to low according to the purchasing habits of the second product that is the same as the fourth product, and the other fourth products are configured from low to high according to the price. The recommendation list is generated according to the first tier appearing first and then the second tier;

[0015] If they are different, it is determined whether the second configuration parameter of the historical data purchased by the customer exceeds the luxury threshold (this luxury threshold should be the first configuration parameter of the historical purchase). If it exceeds, the fourth products corresponding to the third configuration parameters that are greater than the luxury threshold of the first configuration parameter are configured in the first tier from low to high according to the third configuration parameters, and other fourth products are configured in the second tier according to the price from low to high, and the recommendation list is generated according to the first tier appearing first and then the second tier; if it does not exceed, the recommendation list is generated according to the price difference between the fourth products and the second products in the historical data from small to large;

[0016] Output a list of recommendations.

[0017] The present invention provides a cable project data management method, wherein, if the second product is the same as the fourth product, the first echelon is configured from high to low according to the purchasing habits of the second product that is the same as the fourth product, including:

[0018] S331. Generate a range interval corresponding to the first product based on the pre-stored range coefficient corresponding to the first product.

[0019] S332. In each purchase record of the historical data of the customer purchasing the product, the number of purchase records in which the second quantity corresponding to the first product is within the second range is the first matching quantity, and the quantity in all purchase records of the customer is the second matching quantity. It is determined whether the quotient of the first quantity divided by the second quantity is greater than the habitual threshold. If so, the fourth product that is the same as the second product within the range is arranged in the first echelon from closest to least similar according to the purchase quantity in each purchase record.

[0020] S333. If it is not greater than, the second product identical to the fourth product is arranged in the first echelon according to the purchase quantity in each purchase record from closest to least similar.

[0021] The present invention provides a cable project data management method, wherein, if greater than, the second product identical to the fourth product within the range is configured in the first echelon from closest to least close according to the purchase quantity in each purchase record, including:

[0022] S3321. If it is greater than, then the second products that are the same as the fourth product within the range are arranged in a first candidate list from closest to least similar according to the purchase quantity in each purchase record.

[0023] S3322. Determine whether the number of items in the first candidate list exceeds a reference threshold. If so, convert the first candidate list into the first echelon. If not, expand the range coefficient, jump to step S331, and record the number of jumps. When the number of jumps exceeds 3 times, the fourth product that is the same as the second product will be configured in the first echelon according to the purchase quantity in each purchase record from closest to least close.

[0024] The present invention provides a cable project data management method, wherein if different, determining whether the second configuration parameter of the historical data purchased by the customer exceeds the luxury threshold comprises:

[0025] S341. The database pre-stores a luxury threshold corresponding to the first configuration parameter of each first product.

[0026] S342. Determine whether the purchase record of the historical data is greater than 1. If not, determine whether the second configuration parameter of the second product in the purchase record exceeds the luxury threshold of the first product in the purchase record of the historical data. If it exceeds the luxury threshold, it is deemed to exceed the luxury threshold. If it does not exceed the luxury threshold, it is deemed not to exceed the luxury threshold.

[0027] If it is greater than, then the second product corresponding to the second configuration parameter of each purchase record in more than 1 purchase record that exceeds the luxury threshold of the first product of this purchase record in historical data is regarded as 1 positive quantity; the second product corresponding to the second configuration parameter that does not exceed the luxury threshold of the first product of this purchase record in historical data is regarded as 1 negative quantity; consecutive positive numbers are regarded as a group, and consecutive negative numbers are regarded as a group, and the X-axis is configured from the past to the present, with each unit of the X-axis as a group, and the Y-axis is the number of consecutive positive numbers on the upper side of the X-axis, and the number of consecutive negative numbers on the lower side of the Y-axis to construct a bar chart.

[0028] Draw a line graph based on the vertex line of each column of the above bar graph, count the upper area surrounded by the line between the X-axis and the upper side of the X-axis and the lower area of ​​the line between the X-axis and the lower side of the X-axis, and determine whether the upper area in the first time period before obtaining the customer intention data is smaller than the lower area. If so, it is deemed not to exceed the luxury threshold; if not, it is deemed to exceed the luxury threshold.

[0029] The present invention provides a cable project data management method, wherein, if exceeded, the fourth product corresponding to the third configuration parameter greater than the luxury threshold of the first configuration parameter is configured in the first echelon from low to high according to the third configuration parameter, including:

[0030] S3421. If exceeded, the fourth product corresponding to the third configuration parameter that is greater than the luxury threshold of the first configuration parameter is configured in the first candidate list from low to high according to the third configuration parameter.

[0031] S3422. Determine whether the number of items in the first candidate list exceeds a reference threshold. If so, convert the first candidate list into the first echelon. If not, expand the range coefficient, jump to step S331, and record the number of jumps. When the number of jumps exceeds 3 times, the fourth product that is the same as the second product will be configured in the first echelon according to the purchase quantity in each purchase record from closest to least close.

[0032] The present invention provides a cable project data management method, wherein it is judged whether the upper side area in the first time period before obtaining the customer intention data is smaller than the lower side area, if it is smaller, it is deemed not to exceed the luxury threshold; if it is not smaller, it is deemed to exceed the luxury threshold, including:

[0033] S3431. Determine whether the absolute value of the difference between the upper area and the lower area in the first time period before obtaining the customer intention data is greater than the fuzzy threshold. If so, determine whether the upper area in the first time period before obtaining the customer intention data is smaller than the lower area. If it is smaller, it is deemed not to exceed the luxury threshold; if it is not smaller, it is deemed to exceed the luxury threshold; if not, lower the luxury threshold and jump to step S341, and record the number of jumps. When the number of jumps is greater than 4, delete the image corresponding to the first time period before the customer intention data of the line graph, and use the last time of the line graph as the time of the customer intention data, and rerun S3421.

[0034] The present invention provides a cable project data management method, wherein, if not, the luxury threshold is lowered and the process jumps to step S341, and the number of jumps is recorded. When the number of jumps is greater than 4, the image corresponding to the first time period before the current customer intention data of the line graph is deleted, and the last time of the line graph is used as the time of the current customer intention data, and S3431 is re-run, including:

[0035] If not, lower the luxury threshold and jump to step S341, and record the number of jumps, where the reference threshold of S3422 is lowered for every 2 jumps. When the number of jumps is greater than 4, delete the corresponding image in the first time period before the customer intention data of the line chart, and use the last time of the line chart as the time of the customer intention data, and rerun S3431.

[0036] In a second aspect, a cable project data management system includes:

[0037] An input module is used to obtain customer intention data, historical data of the customer's purchase of products, and inventory data of each product, wherein the customer intention data includes a first product, a first configuration parameter, and a first quantity, the historical data includes a second product, a second configuration parameter, and a second quantity, and the inventory data includes a third product, a third configuration data, and a third quantity.

[0038] a processing module, which is used to configure the third product whose third configuration parameter and third quantity in the inventory data are respectively greater than or equal to the first configuration parameter and first quantity of the first product in the customer's intention data as the fourth product, and to determine whether the customer has historical data. If no historical data exists, the fourth product is configured in order of price from low to high to generate a recommendation list; if historical data exists, it is determined whether the first product is the same as the second product in the historical data of the customer's purchase of the product; if the same, the first echelon is configured in order of high to low according to the purchase habits of the second product that is the same as the fourth product, and other fourth products are configured in order of price from low to high to generate a recommendation list. To the high-configuration second tier, generate a recommendation list according to the first tier appearing first and then the second tier; if they are different, determine whether the second configuration parameter of the historical data purchased by the customer exceeds the luxury threshold. If it exceeds, then the fourth product corresponding to the third configuration parameter that is greater than the luxury threshold of the first configuration parameter will be configured in the first tier from low to high according to the third configuration parameter, and other fourth products will be configured in the second tier according to price from low to high, and generate a recommendation list according to the first tier appearing first and then the second tier; if it does not exceed, then generate a recommendation list according to the price gap between the fourth product and the second product in the historical data from small to large.

[0039] The output module is used to output the recommendation list.

[0040] In a third aspect, a computer program product comprises a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

[0041] In a fourth aspect, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

[0042] The cable project data management method of the present invention is different from the prior art in that the cable project data management method of the present invention classifies users first, and takes the third configuration data and the third quantity of the inventory data as the prerequisite for selling to customers, thereby reducing inventory pressure and meeting customers' purchasing needs; then distinguishing customers who have purchased and customers who have not purchased by customer portrait, customers who have not purchased are guided by cost performance, and a recommendation list is generated from low to high according to the unit price of the inventory data that meets the first configuration parameter of the customer's intention data; customers who have purchased should give priority to recommending purchased products according to their purchasing habits, so as to facilitate customers to repurchase; however, due to factors such as production capacity, production cycle, inventory cycle, etc., the products that may have been purchased may not meet the customer's purchasing needs this time, so it is necessary to analyze the customer portrait to increase the profit margin as much as possible while ensuring the success rate of this sale. Specifically, if the customer has a high second configuration parameter for historical data and has exceeded the luxury threshold, then this time, products with higher configuration parameters should be recommended as much as possible as a recommendation list output to customers in priority; if it does not exceed the luxury threshold, then, among the fourth products that meet customer needs, the fourth product with a price closer to the second product of historical data should be recommended to the customer's recommendation list. Therefore, this method can automatically retrieve the cable project data and provide customers with an automated middle-office robot to maximize the profit margin and transaction rate while meeting customer needs and inventory needs.

[0043] A cable project data management method of the present invention is further described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of a cable project data management method.

[0045] Figure 2 It is a schematic diagram of a bar chart and a discount chart. DETAILED DESCRIPTION

[0046] like Figure 1 As shown, the present invention provides a cable project data management method, comprising:

[0047] Acquire customer intention data, historical data of the customer's purchase of products, and inventory data of each product, wherein the customer intention data includes a first product, a first configuration parameter, and a first quantity, the historical data includes a second product, a second configuration parameter, and a second quantity, and the inventory data includes a third product, a third configuration data, and a third quantity.

[0048] The third product in the inventory data whose third configuration parameter and third quantity are respectively greater than or equal to the first configuration parameter and first quantity of the first product in the customer's intention data is configured as the fourth product, and it is determined whether there is historical data for the customer.

[0049] If there is no historical data, the fourth product is configured from low to high price to generate a recommendation list;

[0050] If there is historical data, determining whether the first product is the same as the second product in the historical data of the product purchased by the customer;

[0051] If they are the same, the first tier is configured from high to low according to the purchasing habits of the second product that is the same as the fourth product, and the other fourth products are configured from low to high according to the price. The recommendation list is generated according to the first tier appearing first and then the second tier;

[0052] If they are different, it is determined whether the second configuration parameter of the historical data purchased by the customer exceeds the luxury threshold (this luxury threshold should be the first configuration parameter of the historical purchase). If it exceeds, the fourth products corresponding to the third configuration parameters that are greater than the luxury threshold of the first configuration parameter are configured in the first tier from low to high according to the third configuration parameters, and other fourth products are configured in the second tier according to the price from low to high, and the recommendation list is generated according to the first tier appearing first and then the second tier; if it does not exceed, the recommendation list is generated according to the price difference between the fourth products and the second products in the historical data from small to large;

[0053] Output a list of recommendations.

[0054] The present invention classifies users first, and takes the third configuration data and the third quantity of inventory data as the prerequisite for selling to customers, thereby reducing inventory pressure and meeting customers' purchasing needs; then distinguishes customers who have purchased and customers who have not purchased by customer portraits, and customers who have not purchased are guided by cost performance, and a recommendation list is generated from low to high according to the unit price of the inventory data that meets the first configuration parameter of the customer's intention data; customers who have purchased should give priority to recommending purchased products according to their purchasing habits, so as to facilitate customers to repurchase; however, due to factors such as production capacity, production cycle, inventory cycle, etc., the purchased products may not meet the customer's purchasing needs this time, so it is necessary to analyze the customer portrait to increase the profit margin as much as possible while ensuring the success rate of this sale. Specifically, if the customer has a higher second configuration parameter for historical data and has exceeded the luxury threshold, then this time, products with higher configuration parameters should be recommended as much as possible as a recommendation list output to the customer in priority; if it does not exceed the luxury threshold, then, among the fourth products that meet the customer's needs, the fourth product with a price closer to the second product of historical data should be recommended to the customer's recommendation list. Therefore, this method can automatically retrieve the cable project data and provide customers with an automated middle-office robot to maximize the profit margin and transaction rate while meeting customer needs and inventory needs.

[0055] In the above “configure the third product whose third configuration parameter and third quantity in the inventory data are respectively greater than or equal to the first configuration parameter and first quantity of the first product in the customer intention data as the fourth product”, the third configuration parameter is greater than the first configuration parameter, which can be understood as the third configuration parameter being better and stricter than the first configuration parameter. To a certain extent, if the third configuration parameter is greater than the first configuration parameter, the third product should be more expensive than the first product.

[0056] Similarly, "then determine whether the second configuration parameter of the user's purchase history data exceeds the luxury threshold" means that the second configuration parameter is better and stricter than the luxury threshold. To a certain extent, your second product corresponding to the second configuration parameter should be more expensive than the product corresponding to the luxury threshold.

[0057] That is, if a configuration parameter in the entire text is greater than or exceeds another configuration parameter, it means that this configuration parameter is better, more difficult to achieve, more stringent, and represents a higher specification or standard.

[0058] The configuration parameters can be:

[0059] Rated voltage: The rated voltage indicates the maximum voltage that the cable can withstand during normal operation. Common rated voltages include 300 / 500V, 450 / 750V and 0.6 / 1kV.

[0060] Insulation resistance: Insulation resistance is an important indicator to measure the insulation performance of cables. At 20°C, after stable charging for 1 minute with a DC 500V voltage test, the insulation resistance should be no less than a certain value (such as 2500MΩ·km).

[0061] Withstand voltage: This parameter ensures the safety and reliability of the cable in a high-voltage environment. The cable cores should be able to withstand a 50Hz AC 2000V voltage test for 5 minutes without breakdown.

[0062] Conductor cross-sectional area: The conductor cross-sectional area determines the current carrying capacity of the cable. Common conductor cross-sectional areas include 0.5mm², 1.0mm², 1.5mm², 2.5mm², 4mm², 6mm², etc.

[0063] Insulation thickness: The deviation of the insulation thickness is also an important indicator of the extrusion process level. The thickness of the insulation layer should meet certain standards to ensure the insulation performance of the cable.

[0064] Sheath thickness: The thickness of the sheath should be sufficient to provide adequate mechanical strength and protection. The sheath is an important structural part that protects the cable from damage by the external environment.

[0065] Minimum bending radius: This parameter ensures that the cable will not be damaged when it is bent. For cables without armor, the minimum bending radius should be less than 6 times the outer diameter of the cable; for cables with armor, the minimum bending radius should not be less than 12 times the outer diameter of the cable.

[0066] Working temperature: Cables used in high temperature environments need to use special materials and structures to ensure their safety and reliability. The long-term working temperature of the cable is 70℃.

[0067] Conductor material: Common conductor materials are copper and aluminum. Copper conductor has good conductivity and mechanical strength, and is a commonly used conductor material in cables; aluminum conductor has light weight and low cost.

[0068] Insulation materials: Common insulation materials include PVC (polyvinyl chloride), PE (polyethylene), XLPE (cross-linked polyethylene), etc. These materials have good insulation properties and aging resistance.

[0069] Armor layer: The armor layer is an important structural part that protects the cable from external mechanical damage. Common armor materials include steel strips and steel wires.

[0070] Certification standards: The cable should comply with relevant national and international standards, such as GB (China), IEC (International), UL (USA), CE (Europe), etc. These standards ensure the performance, safety and reliability of the cable.

[0071] Among them, customer intention data represents the application scenarios that customers actually need.

[0072] The historical data of the customer's product purchases represents the customer's previous purchase records, which can be one purchase or multiple purchases.

[0073] The inventory data for each product represents the same product as the customer purchase intention data.

[0074] The first configuration parameter, the second configuration parameter, and the third configuration parameter in the entire text can all take 300 / 500V as an example.

[0075] For example, if the first configuration parameter is 300V, and after the middle office issues a recommended list for the customer to choose, the customer actually purchases a 500V product, then when this customer makes a purchase next time, we will observe that the second configuration parameter purchased by this customer exceeds the luxury threshold, and we should configure the 500V product in the first tier of the recommended list for him next time as the output recommended list. Only products with a third configuration parameter of at least 300V can be used as the fourth product.

[0076] Among them, the first quantity, the second quantity, and the third quantity in the full text can all be measured in pieces. For example, if the first quantity is 100 cables, the third quantity should be at least greater than 100 to be used as the fourth product.

[0077] Among them, the fourth product represents the product that can meet the conditions for selling to customers and can be selected by customers, or in other words, the fourth product should be all the products that appear in the recommended list. All the methods in the whole article are to adjust the order of the fourth products in the recommended list in order to improve the transaction rate and increase the profit margin.

[0078] As for profit margins, customers purchasing products with higher configuration parameters will inevitably bring greater profits to the company.

[0079] Generally speaking, the higher the configuration parameters, the higher the price. However, there are no exceptions, so in some cases, the actual price is used as the sorting standard.

[0080] It should be noted that the price of the product of the present invention refers to the unit price, that is, the unit price. For example, each cable is sold for 100 yuan.

[0081] Among them, judging whether there is historical data for the customer can be understood as judging whether the customer has purchased cables.

[0082] If there is no historical data, the fourth product will be configured in order of price from low to high to generate a recommendation list. This can be understood as: this customer has never bought cables, so we can output the fourth product in the recommendation list in order of unit price from cheap to expensive. This is convenient for customers to close deals and choose the fourth product.

[0083] If there is historical data, it is determined whether the first product is the same as the second product in the historical data of the customer's purchase of the product. This can be understood as: this customer has bought a cable, and it is determined whether the first product that the customer needs to buy this time is the same as the second product that appeared in the historical data. This is to see whether the customer directly wants to repurchase this cable product.

[0084] Among them, if they are the same, the first tier is configured from high to low according to the purchasing habits of the second product that is the same as the fourth product, and other fourth products are configured in the second tier according to the price from low to high. The recommendation list is generated according to the appearance of the first tier first and then the second tier. It can be understood as: if they are the same, it means that the customer has purchased and may want to repurchase the product. In this case, we can configure the first tier according to the purchasing habits, configure the second tier for the others, and output the recommendation list.

[0085] If they are different, then we determine whether the second configuration parameter of the historical data purchased by the customer exceeds the luxury threshold (this luxury threshold should be aligned with the first configuration parameter of the historical purchase). This can be understood as follows: if the first product in the customer's intention data this time, that is, the first product the customer wants to buy this time, is different from the first product in the customer's previous purchase record, it means that the customer is not repurchasing, but wants to buy our other products. Then, we need to trace the second configuration parameter of the second product corresponding to the first configuration parameter of the first product proposed by the customer in the historical purchase record to see if it exceeds the luxury threshold; in other words, determine whether the second configuration parameter is greater than the quotient of the corresponding first configuration parameter multiplied by the luxury threshold. In this way, we can understand whether the user purchased cable products based on the principle of higher reliability requirements in the last purchase record, and thus, by analogy, we can understand whether this purchase should also purchase products with higher reliability.

[0086] Among them, if it exceeds, the fourth product corresponding to the third configuration parameter that is greater than the luxury threshold of the first configuration parameter will be configured in the first tier from low to high according to the third configuration parameter, and the fourth product corresponding to the third configuration parameter that is less than the luxury threshold of the first configuration parameter will be configured in the second tier according to price from low to high, and a recommendation list is generated according to the first tier appearing first and then the second tier, including: If it exceeds, it means that the product was purchased with a relatively high reliability index requirement last time. This user portrait is a relatively wealthy and relatively demanding customer. Then, when promoting other products, high-reliability products that exceed the luxury threshold should also be recommended, so as to provide customers with products suitable for them, thereby increasing profit margins and transaction rates.

[0087] Furthermore, the quotient of the first configuration parameter multiplied by the luxury threshold is compared with the third configuration parameters of all current fourth products, and the third configuration parameters that exceed the above quotient are configured in the first tier from low to high, and the fourth products that are less than the above quotient are configured in the second tier according to price from low to high, and then the configuration list is output in this way. This can output a configuration list that better meets customer needs, thereby increasing profit margins, transaction rates, and reducing customer selection costs.

[0088] Among them, if it does not exceed, then a recommendation list is generated according to the price difference between the fourth product and the second product in the historical data from small to large, including: if it does not exceed, it means that the historical data is the second product purchased with normal, non-high reliability requirements, then the fourth product is directly arranged from small to large according to the price difference with the second product in the historical data and a recommendation list is generated. It should be noted that when the historical data is not unique, the price of the second product in this historical data can be the average price each time. That is, the historical data is divided into 3 times, a cable with a unit price of 5 yuan, a cable with a unit price of 10 yuan for the second time, and a cable with a unit price of 15 yuan for the third time. Then, the fourth product whose unit price is closer to 10 yuan this time should be ranked higher, thereby reducing the customer's selection cost and increasing the success rate.

[0089] The luxury threshold can be 1.1 to 10, preferably 1.5. That is to say, if the configuration parameter in the customer's intention data is 300V, if a cable with a configuration parameter of more than 450V is purchased, it means that the customer's historical data exceeds the luxury threshold.

[0090] See also Figure 1 In some embodiments, if they are the same, the first echelon is configured from high to low according to the purchasing habits of the second product that is the same as the fourth product, including:

[0091] S331. Generate a range interval corresponding to the first product based on the pre-stored range coefficient corresponding to the first product.

[0092] S332. In each purchase record of the historical data of the customer purchasing the product, the number of purchase records in which the second quantity corresponding to the first product is within the second range is the first matching quantity, and the quantity in all purchase records of the customer is the second matching quantity. It is determined whether the quotient of the first quantity divided by the second quantity is greater than the habitual threshold. If so, the fourth product that is the same as the second product within the range is arranged in the first echelon from closest to least similar according to the purchase quantity in each purchase record.

[0093] S333. If it is not greater than, the second product identical to the fourth product is arranged in the first echelon according to the purchase quantity in each purchase record from closest to least similar.

[0094] By comparing the quotient of the first quantity divided by the second quantity with the customary threshold, the present invention can clearly understand whether the main quantity is more concentrated or the quantity of each order is more dispersed, thereby further defining the customer portrait. That is, the main quantity is larger and more concentrated, which must represent customers who need to make large-scale purchases and regular purchases. In this case, we only recommend the second product that the main purchase is made to the customer, that is, the second product that only appears in the range interval as the first echelon, so as to facilitate communication with the user and increase the probability of transaction. If the purchase quantity of the user is more dispersed and more jumpy, then in most cases, it is a customer portrait that needs to replenish when using it. In this case, we can display all the second products to the customer, which is convenient for the replenishment customer to choose, thereby increasing the possibility of more transactions.

[0095] Among them, S331, based on the pre-stored range coefficient corresponding to the first product, generates a range interval corresponding to the first product, which can be understood as: the range coefficient is plus or minus 1%~200%, and the pre-stored range coefficient of each product can be different. The range coefficient can be preferably 20%. For example, the first quantity of the first product of the customer intention data this time is 100, then the first range interval should be (1-20%)*100~(1+20%)*100, that is, 80~120.

[0096] Among them, S332, in each purchase record of the historical data of the customer's purchase of products, the number of purchase records whose second quantity corresponding to the first product is within the second range is the first matching number, and the number of all purchase records of the customer is the second matching number, which can be understood as: the purchase record can be 50 for the first time, 90 for the second time, and 150 for the third time, for a total of 3 times. Then, the first matching number is 1, and the second matching number is 3. In other words, the first matching number is the number of transactions within the first range, and the second matching number is the total number of transactions.

[0097] Among them, judging whether the quotient of the first number divided by the second number is greater than the habitual threshold can be understood as follows: the quotient of the first number divided by the second number can be understood as the above-mentioned 1 divided by 3, that is, 33%, and the habitual threshold can be 10% to 90%, preferably 50%. The above-mentioned quotient is to be compared with the habitual threshold, that is, 33% is compared with 50%.

[0098] Among them, if it is greater than, then the fourth product that is the same as the second product within the range will be arranged in the first tier from closest to least close according to the purchase quantity in each purchase record. It can be understood that: if the above quotient is greater than the habitual threshold, then it means that the customer's purchase quantity is relatively stable. The quantity can be used as a guide, and the average value of the second quantity in the purchase record can be used as a reference object. The first tier can be arranged according to the third quantity of the fourth object from close to the average value of the second quantity to the average value not close to the second quantity.

[0099] For example, since the cable has 300V, 350V, and 400V fourth products, and the purchase quantity is within the range of 80 to 120, and the customer intention data this time is the 300V first product, then if the quotient is greater than the customary threshold, only this 300V product will be promoted as the first echelon. If there is a sub-series of this 300V, the first echelon will be configured in such a way that the second quantity of each order of the sub-series is closest to the midpoint of the range to the second quantity of each order of the sub-series is least close to the midpoint of the range. That is, the first echelon can have only the fourth product that is the same as the second product, or it can have multiple sub-series of the fourth product that is the same as the second product.

[0100] Among them, S333, if it is not greater than, the second product that is the same as the fourth product will be configured in the first echelon according to the purchase quantity in each purchase record from the closest to the least close. It can be understood as: for example, since the cable has 300V, 350V, and 400V fourth products, the purchase quantity is in the range of 80~120, and the customer intention data this time is the first product of 300V, then, if the quotient is not greater than the habitual threshold, all the fourth products corresponding to the purchase quantity of each purchase record will be configured as the first echelon. Among them, the sorting of the first echelon is configured according to the closest purchase record of the second product corresponding to each fourth product, and the focus of this range interval is configured from the closest to the least close.

[0101] See also Figure 1 , in some embodiments, if it is greater than, then the second product that is the same as the fourth product within the range is arranged in the first echelon according to the purchase quantity in each purchase record from closest to least close, including:

[0102] S3321. If it is greater than, then the second products that are the same as the fourth product within the range are arranged in a first candidate list from closest to least similar according to the purchase quantity in each purchase record.

[0103] S3322. Determine whether the number of items in the first candidate list exceeds a reference threshold. If so, convert the first candidate list into the first echelon. If not, expand the range coefficient, jump to step S331, and record the number of jumps. When the number of jumps exceeds 3 times, the fourth product that is the same as the second product will be configured in the first echelon according to the purchase quantity in each purchase record from closest to least close.

[0104] Through the above method, the present invention can output the number of fourth products in the first echelon that is greater than a reference threshold, that is, it meets the requirement that the output number is greater than the reference threshold, so that the number of fourth products in the first echelon is not too small, which makes the range of possible choices for users too small, thereby reducing the number of products that customers may purchase. At the same time, the first echelon appears in the recommendation list as a more accurate product recommended to customers, thereby improving the customer's transaction rate.

[0105] Among them, S3321, if it is greater than, then the second product that is the same as the fourth product within the range will be configured in the first candidate list from closest to least close according to the purchase quantity in each purchase record, including: the present invention first uses the first candidate list as a transit station for the first echelon, which can facilitate further analysis and processing thereof.

[0106] Among them, S3322, judging whether the number of the first candidate list exceeds the reference threshold, if it exceeds, converting the first candidate list into the first echelon, including: by comparing the number of the first candidate list with the reference threshold, it can be avoided that the number of the first candidate list is too small, and the opportunity to place in a better priority position in the recommendation list is lost; that is, if the number of the first candidate list is too small, the first echelon is too small, so that it is not easy for users to find other products that are more suitable for them, reducing the transaction success rate and profit rate. Among them, the reference threshold can be 2 to 20, preferably 3.

[0107] For example, when the first candidate list consists of 4 persons and the reference threshold is 3, the first candidate list can be directly converted into the first echelon.

[0108] Wherein, if it is not exceeded, the range coefficient is expanded, and the jump to step S331 is performed and the number of jumps is recorded. When the number of jumps exceeds 3 times, the fourth product, which is the same as the second product, is configured in the first echelon from the closest to the least close according to the purchase quantity in each purchase record, including: wherein, the range coefficient can be expanded by 1% to 100% each time, preferably 10%, that is, each time it is not exceeded, the range coefficient is increased by 10%, so that the end point position of the range interval is adjusted. For example, the original 20% range coefficient is increased by 10% to 22%, and the two end points of 100 are changed to 78~122. When the number is not exceeded, that is, the number of jumps exceeds 3 times, all the matching fourth products are directly configured as the first echelon, so that the number of the first echelon can be expanded as much as possible. Wherein, the configuration method of the purchase quantity in each purchase record from the closest to the least close is the same in the full text of the present invention.

[0109] Of course, when the number of jumps exceeds 3 times, the reference threshold may be lowered and the process may jump to S311.

[0110] The reference threshold may be reduced by directly subtracting 1, that is, the original reference threshold is 3, and becomes 2 after being reduced once.

[0111] See also Figure 1 , 2 In some embodiments, if different, determining whether the second configuration parameter of the historical data purchased by the customer exceeds the luxury threshold includes:

[0112] S341. The database pre-stores a luxury threshold corresponding to the first configuration parameter of each first product.

[0113] S342. Determine whether the purchase record of the historical data is greater than 1. If not, determine whether the second configuration parameter of the second product in the purchase record exceeds the luxury threshold of the first product in the purchase record of the historical data. If it exceeds the luxury threshold, it is deemed to exceed the luxury threshold. If it does not exceed the luxury threshold, it is deemed not to exceed the luxury threshold.

[0114] If it is greater than, then the second product corresponding to the second configuration parameter of each purchase record in more than 1 purchase record that exceeds the luxury threshold of the first product of this purchase record in historical data is regarded as 1 positive quantity; the second product corresponding to the second configuration parameter that does not exceed the luxury threshold of the first product of this purchase record in historical data is regarded as 1 negative quantity; consecutive positive numbers are regarded as a group, and consecutive negative numbers are regarded as a group, and the X-axis is configured from the past to the present, with each unit of the X-axis as a group, and the Y-axis is the number of consecutive positive numbers on the upper side of the X-axis, and the number of consecutive negative numbers on the lower side of the Y-axis to construct a bar chart.

[0115] Draw a line graph based on the vertex line of each column of the above bar graph, count the upper area surrounded by the line between the X-axis and the upper side of the X-axis and the lower area of ​​the line between the X-axis and the lower side of the X-axis, and determine whether the upper area in the first time period before obtaining the customer intention data is smaller than the lower area. If so, it is deemed not to exceed the luxury threshold; if not, it is deemed to exceed the luxury threshold.

[0116] The present invention constructs a line graph after constructing a continuous bar graph, which will inevitably increase the weight of continuously exceeding the luxury threshold or continuously not exceeding the luxury threshold in the most recent first time period, while the weight of repeatedly fluctuating time periods for whether the luxury threshold is exceeded will be reduced. From then on, the purchase intention of this customer is accurately counted in the form of a combination of numbers and shapes, and whether it is necessary to give priority to recommending products that exceed the luxury threshold, have higher reliability, and higher security, thereby improving the profit rate of the transaction while ensuring the success rate of the transaction.

[0117] It should be noted that if the first time period involves a part of one of the columns in a group, then the earliest time of the group to which this column belongs should be within this first time period.

[0118] Among them, S341, the database pre-stores a luxury threshold corresponding to the first configuration parameter of each first product, including: first, the luxury threshold can be 1.1~10, preferably 1.5. That is to say, if the configuration parameter in the customer's intention data is 300V, if a cable with a configuration parameter of more than 450V is purchased, it means that the customer's historical data exceeds the luxury threshold. Secondly, each first product can pre-store a luxury threshold corresponding to it, and the above luxury threshold can be adjusted.

[0119] Among them, S342, determine whether the purchase record of the historical data is greater than 1, if not, determine whether the second configuration parameter of the second product in the purchase record exceeds the luxury threshold of the first product in the purchase record of the historical data, if it exceeds the luxury threshold, it is deemed to exceed the luxury threshold, if it does not exceed the luxury threshold, it is deemed not to exceed the luxury threshold, including: determine whether the customer has only purchased the first product that is the same as the second product once. If so, then you can simply consider the relationship between this second product and the first product when the second product was purchased at that time to see if it exceeds the luxury threshold. In other words, whether the quotient of the first product when the second product was purchased in the one-to-one historical record and the luxury threshold exceeds the second product can clearly and intuitively know whether the customer should also recommend the fourth product that exceeds the luxury threshold before purchasing other products.

[0120] Among them, if it is greater than, then the second product corresponding to the second configuration parameter of each purchase record greater than 1 that exceeds the luxury threshold of the first product of this purchase record of historical data is regarded as 1 positive quantity; the second product corresponding to the second configuration parameter that does not exceed the luxury threshold of the first product of this purchase record of historical data is regarded as 1 negative quantity; consecutive positive numbers are regarded as a group, and consecutive negative numbers are regarded as a group, and the X-axis is configured from the past to the present, each unit of the X-axis is a group, and the Y-axis is the number of consecutive positive numbers on the upper side of the X-axis, and the number of consecutive negative numbers on the lower side of the Y-axis to construct a bar chart, including: See Figure 2 , Figure 2 The bar chart is constructed, and its data is derived from whether the number of purchases in the purchase record exceeds the luxury threshold. If it exceeds, it is a positive number, and if it does not exceed, it is a negative number. From the earliest statistical time to the acquisition of customer intention data is the time axis, which is the basic configuration direction of the X-axis. However, the difference is that the present invention combines the time axis of continuous positive and negative numbers and classifies them into 1 group. Each unit of the X-axis of the present invention is a group.

[0121] Among them, a line graph is drawn according to the vertex line of each column of the above bar graph, and the upper area surrounded by the line between the X-axis and the upper side of the X-axis and the lower area of ​​the line between the X-axis and the lower side of the X-axis are counted to determine whether the upper area in the first time period before obtaining the customer intention data is smaller than the lower area. If it is smaller, it is considered that it does not exceed the luxury threshold; if it is not smaller, it is considered that it exceeds the luxury threshold, including: reference Figure 2 , the above discount starts from the far point, connects the middle position of the upper vertex or lower vertex of each bar graph, and ends with the final position of the X-axis of the bar graph to draw a broken line. And the broken lines on the upper and lower sides of the broken line and the X-axis are used to construct the upper and lower areas shown in the shadow in the figure. And in this way, the time when the customer intention data is obtained is the initial end time of the first time period. It should be noted that if the initial start time of the first time period is the start time of one part of the bar graph, then the earliest time of the group where this column is located is used as the earliest time of the first time period. In other words, although the first time period is a fixed time, the duration of each first time period actually obtained may not be fixed. In this way, the results that exceed the luxury threshold more continuously in the recent period and do not exceed the luxury threshold are magnified, and the weight of the upper area or the lower area is increased, so as to understand the customer's recent purchasing habits to adapt to the first echelon and recommendation list that are more in line with the customer's purchasing habits.

[0122] See also Figure 1 , 2 In some embodiments, if exceeded, the fourth product corresponding to the third configuration parameter that is greater than the luxury threshold of the first configuration parameter is configured in the first echelon from low to high according to the third configuration parameter, including:

[0123] S3421. If exceeded, the fourth product corresponding to the third configuration parameter that is greater than the luxury threshold of the first configuration parameter is configured in the first candidate list from low to high according to the third configuration parameter.

[0124] S3422. Determine whether the number of items in the first candidate list exceeds a reference threshold. If so, convert the first candidate list into the first echelon. If not, expand the range coefficient, jump to step S331, and record the number of jumps. When the number of jumps exceeds 3 times, the fourth product that is the same as the second product will be configured in the first echelon according to the purchase quantity in each purchase record from closest to least close.

[0125] Through the above method, the present invention can output the number of fourth products in the first echelon that is greater than a reference threshold, that is, it meets the requirement that the output number is greater than the reference threshold, so that the number of fourth products in the first echelon is not too small, which makes the range of possible choices for users too small, thereby reducing the number of products that customers may purchase. At the same time, the first echelon appears in the recommendation list as a more accurate product recommended to customers, thereby improving the customer's transaction rate.

[0126] The present invention first uses the first candidate list as a transfer station for the first echelon, which can facilitate further analysis and processing thereof.

[0127] See also Figure 1 , 2 In some embodiments, it is determined whether the upper area in the first time period before obtaining the customer intention data is smaller than the lower area. If it is smaller, it is considered that the luxury threshold is not exceeded; if it is not smaller, it is considered that the luxury threshold is exceeded, including:

[0128] S3431. Determine whether the absolute value of the difference between the upper area and the lower area in the first time period before obtaining the customer intention data is greater than the fuzzy threshold. If so, determine whether the upper area in the first time period before obtaining the customer intention data is smaller than the lower area. If it is smaller, it is deemed not to exceed the luxury threshold; if it is not smaller, it is deemed to exceed the luxury threshold; if not, lower the luxury threshold and jump to step S341, and record the number of jumps. When the number of jumps is greater than 4, delete the image corresponding to the first time period before the customer intention data of the line graph, and use the last time of the line graph as the time of the customer intention data, and rerun S3421.

[0129] Through the above steps, the present invention can output a relatively complete user portrait with a luxury threshold with a larger difference as much as possible, and use a clearer user portrait to influence the list of the first echelon output to the user, thereby affecting the content of the recommendation list. In addition, since the lowered luxury threshold still cannot make the user portrait meet the assessment of the fuzzy threshold, then the user portrait is deemed to be an unclear user portrait in this first time period. In this case, the assessment of the user portrait in the previous first time period should be traced back, and finally a clearer user portrait that can meet the fuzzy threshold assessment is configured in the first echelon, thereby affecting the recommendation list. This method can make the user portrait clearer and more specific, so that the intelligent middle platform method can better guarantee the success rate and profit rate of matching products to facilitate transactions.

[0130] The present invention uses the above-mentioned fuzzy threshold as the size of the inevitable difference between the upper area and the lower area, so that for users with large fluctuations and vague portraits of whether to purchase above the luxury threshold, by adjusting the luxury threshold, a clearer user portrait can be obtained, thereby recommending products with clearer positioning to them, so that the intelligent middle platform method can better guarantee the success rate and profit margin of matching products to facilitate transactions.

[0131] The fuzzy threshold may be 30% of the sum of the upper area and the lower area, that is, for example, if the upper area is 100 square centimeters and the lower area is 50 square centimeters, then the fuzzy threshold is 45 square centimeters. Then the absolute value of the difference between the upper area and the lower area is greater than the fuzzy threshold.

[0132] Among them, it is determined whether the absolute value of the difference between the upper area and the lower area in the first time period before obtaining the customer intention data is greater than the fuzzy threshold. If so, it is determined whether the upper area in the first time period before obtaining the customer intention data is smaller than the lower area. If it is smaller, it is deemed not to exceed the luxury threshold; if it is not smaller, it is deemed to exceed the luxury threshold. It can be understood as follows: if the difference between the upper area and the lower area is large, and the difference is greater than the fuzzy threshold, it means that the difference between the upper area and the lower area is obvious and not fuzzy. Then, it can be directly determined according to which of the upper and lower sides is larger to determine whether the luxury threshold is exceeded.

[0133] If not, the luxury threshold is lowered and the process jumps to step S341, and the number of jumps is recorded. When the number of jumps is greater than 4, the image corresponding to the first time period before the customer intention data of this time is deleted, and the last time of the line graph is used as the time of the customer intention data of this time, and S3431 is re-run. It can be understood that if the difference between the upper area and the lower area is small, and the difference is less than the fuzzy threshold, it means that the user's willingness to buy products exceeding the luxury threshold is relatively vague, so the luxury threshold should be appropriately lowered and the above judgment logic should be re-run. If it is still not clear whether the user is willing to buy products exceeding the luxury threshold after multiple jumps, this fuzzy time period is deleted, and it is equivalent to considering the customer's purchase intention of the previous first time period, and the above steps are re-executed, and the corresponding results are output, so as to obtain a judgment that can be relatively clear in the near future whether the customer is willing to buy products exceeding the luxury threshold. For example, if the customer is relatively vague recently, but was willing to buy products exceeding the luxury threshold before, then the products exceeding the luxury threshold will still be recommended as the first echelon, thereby improving the profit margin.

[0134] See also Figure 1 , 2 In some embodiments, if not, the luxury threshold is lowered and the process jumps to step S341, and the number of jumps is recorded. When the number of jumps is greater than 4, the image corresponding to the first time period before the current customer intention data of the line graph is deleted, and the last time of the line graph is used as the time of the current customer intention data, and S3431 is re-run, including:

[0135] If not, lower the luxury threshold and jump to step S341, and record the number of jumps, where the reference threshold of S3422 is lowered for every 2 jumps. When the number of jumps is greater than 4, delete the corresponding image in the first time period before the customer intention data of the line chart, and use the last time of the line chart as the time of the customer intention data, and rerun S3431.

[0136] The present invention reduces the reference threshold of S3422 by one every time it jumps twice, thereby relatively reducing the number of customers presented in the first echelon. This allows customers whose prices fluctuate greatly over the luxury threshold to reduce the number of customers who may be presented in the first echelon while adjusting the luxury threshold, so that customers with greater fluctuations can refer to a more accurate number of customers in the first echelon, thereby increasing the probability that customers may be traded in the first echelon, and increasing profit margins and transaction rates.

[0137] Among them, lowering the luxury threshold may be 10% of the luxury threshold, and lowering the reference threshold of S3422 may be the reference threshold minus one.

[0138] The present invention provides a cable project data management system, comprising:

[0139] An input module is used to obtain customer intention data, historical data of the customer's purchase of products, and inventory data of each product, wherein the customer intention data includes a first product, a first configuration parameter, and a first quantity, the historical data includes a second product, a second configuration parameter, and a second quantity, and the inventory data includes a third product, a third configuration data, and a third quantity.

[0140] a processing module, which is used to configure the third product whose third configuration parameter and third quantity in the inventory data are respectively greater than or equal to the first configuration parameter and first quantity of the first product in the customer's intention data as the fourth product, and to determine whether the customer has historical data. If no historical data exists, the fourth product is configured in order of price from low to high to generate a recommendation list; if historical data exists, it is determined whether the first product is the same as the second product in the historical data of the customer's purchase of the product; if the same, the first echelon is configured in order of high to low according to the purchase habits of the second product that is the same as the fourth product, and other fourth products are configured in order of price from low to high to generate a recommendation list. To the high-configuration second tier, generate a recommendation list according to the first tier appearing first and then the second tier; if they are different, determine whether the second configuration parameter of the historical data purchased by the customer exceeds the luxury threshold. If it exceeds, then the fourth product corresponding to the third configuration parameter that is greater than the luxury threshold of the first configuration parameter will be configured in the first tier from low to high according to the third configuration parameter, and other fourth products will be configured in the second tier according to price from low to high, and generate a recommendation list according to the first tier appearing first and then the second tier; if it does not exceed, then generate a recommendation list according to the price gap between the fourth product and the second product in the historical data from small to large.

[0141] The output module is used to output the recommendation list.

[0142] The cable project data management method of the present invention is different from the prior art in that the cable project data management method of the present invention classifies users first, and takes the third configuration data and the third quantity of the inventory data as the prerequisite for selling to customers, thereby reducing inventory pressure and meeting customers' purchasing needs; then distinguishing customers who have purchased and customers who have not purchased by customer portrait, customers who have not purchased are guided by cost performance, and a recommendation list is generated from low to high according to the unit price of the inventory data that meets the first configuration parameter of the customer's intention data; customers who have purchased should give priority to recommending purchased products according to their purchasing habits, so as to facilitate customers to repurchase; however, due to factors such as production capacity, production cycle, inventory cycle, etc., the products that may have been purchased may not meet the customer's purchasing needs this time, so it is necessary to analyze the customer portrait to increase the profit margin as much as possible while ensuring the success rate of this sale. Specifically, if the customer has a high second configuration parameter for historical data and has exceeded the luxury threshold, then this time, products with higher configuration parameters should be recommended as much as possible as a recommendation list output to customers in priority; if it does not exceed the luxury threshold, then, among the fourth products that meet customer needs, the fourth product with a price closer to the second product of historical data should be recommended to the customer's recommendation list. Therefore, this method can automatically retrieve the cable project data and provide customers with an automated middle-office robot to maximize the profit margin and transaction rate while meeting customer needs and inventory needs.

[0143] The present invention provides a computer program product, comprising a computer program, wherein the steps of the method described above are implemented when the computer program is executed by a processor.

[0144] The present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described above are implemented.

[0145] The embodiments described above are merely descriptions of preferred implementation modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A cable project data management method, characterized in that: include Acquire customer intention data, historical data of the customer's purchase of products, and inventory data of each product, wherein the customer intention data includes a first product, a first configuration parameter, and a first quantity, the historical data includes a second product, a second configuration parameter, and a second quantity, and the inventory data includes a third product, a third configuration data, and a third quantity; The third product in the inventory data whose third configuration parameter and third quantity are respectively greater than or equal to the first configuration parameter and first quantity of the first product in the customer's intention data is configured as the fourth product, and it is determined whether there is historical data for the customer. If there is no historical data, the fourth product is configured from low to high price to generate a recommendation list; If there is historical data, determining whether the first product is the same as the second product in the historical data of the product purchased by the customer; If they are the same, the first tier is configured from high to low according to the purchasing habits of the second product that is the same as the fourth product, and the other fourth products are configured from low to high according to the price. The recommendation list is generated according to the first tier appearing first and then the second tier; If they are different, it is determined whether the second configuration parameter of the historical data purchased by the customer exceeds the luxury threshold. If so, the fourth products corresponding to the third configuration parameters that are greater than the luxury threshold of the first configuration parameter are configured in the first tier from low to high according to the third configuration parameters, and other fourth products are configured in the second tier from low to high according to the price, and a recommendation list is generated according to the first tier appearing first and then the second tier; if not, a recommendation list is generated according to the fourth products according to the price difference from the second product in the historical data from small to large; Output a list of recommendations.

2. A cable project data management method according to claim 1, characterized in that: If they are the same, the first tier is configured from high to low according to the purchasing habits of the second product that is the same as the fourth product, including: S331, generating a range interval corresponding to the first product according to a pre-stored range coefficient corresponding to the first product; S332: In each purchase record of the historical data of the customer's purchase of products, the number of purchase records in which the second quantity corresponding to the first product is within the second range is the first matching quantity, and the quantity in all purchase records of the customer is the second matching quantity, and it is determined whether the quotient of the first quantity divided by the second quantity is greater than the habitual threshold. If so, the fourth product that is the same as the second product within the range is arranged in the first echelon according to the purchase quantity in each purchase record from closest to least similar. S333. If it is not greater than, the second product identical to the fourth product is arranged in the first echelon according to the purchase quantity in each purchase record from closest to least similar.

3. A cable project data management method according to claim 2, characterized in that: If it is greater, the second products that are the same as the fourth product within the range are arranged in the first tier according to the purchase quantity in each purchase record from closest to least, including: S3321. If it is greater than, then the second products that are the same as the fourth product within the range are arranged in a first waiting list according to the purchase quantity in each purchase record from closest to least similar; S3322. Determine whether the number of items in the first candidate list exceeds a reference threshold. If so, convert the first candidate list into the first echelon. If not, expand the range coefficient, jump to step S331, and record the number of jumps. When the number of jumps exceeds 3 times, the fourth product that is the same as the second product will be configured in the first echelon according to the purchase quantity in each purchase record from closest to least close.

4. A cable project data management method according to claim 3, characterized in that: If they are different, determining whether the second configuration parameter of the historical data purchased by the customer exceeds the luxury threshold includes: S341. Pre-store in a database a luxury threshold value corresponding to a first configuration parameter of each first product; S342, determining whether the purchase record of the historical data is greater than 1, if not, determining whether the second configuration parameter of the second product in the current purchase record exceeds the luxury threshold of the first product in the current purchase record of the historical data, if it exceeds the luxury threshold, it is deemed to exceed the luxury threshold, if it does not exceed the luxury threshold, it is deemed not to exceed the luxury threshold; If it is greater than, the second product corresponding to the second configuration parameter of each purchase record greater than 1 that exceeds the luxury threshold of the first product of this purchase record of historical data is regarded as 1 positive quantity; the second product corresponding to the second configuration parameter that does not exceed the luxury threshold of the first product of this purchase record of historical data is regarded as 1 negative quantity; consecutive positive numbers are regarded as a group, consecutive negative numbers are regarded as a group, and the X-axis is configured from the past to the present, with each unit of the X-axis as a group, and the Y-axis is the number of consecutive positive numbers on the upper side of the X-axis, and the number of consecutive negative numbers on the lower side of the Y-axis to construct a bar chart; Draw a line graph based on the vertex line of each column of the above bar graph, count the upper area surrounded by the line between the X-axis and the upper side of the X-axis and the lower area of ​​the line between the X-axis and the lower side of the X-axis, and determine whether the upper area in the first time period before obtaining the customer intention data is smaller than the lower area. If so, it is deemed not to exceed the luxury threshold; if not, it is deemed to exceed the luxury threshold.

5. A cable project data management method according to claim 4, characterized in that: If it exceeds, the fourth product corresponding to the third configuration parameter that is greater than the luxury threshold of the first configuration parameter will be configured in the first echelon from low to high according to the third configuration parameter, including: S3421. If exceeded, the fourth product corresponding to the third configuration parameter that is greater than the luxury threshold of the first configuration parameter is configured in the first candidate list from low to high according to the third configuration parameter; S3422. Determine whether the number of items in the first candidate list exceeds a reference threshold. If so, convert the first candidate list into the first echelon. If not, expand the range coefficient, jump to step S331, and record the number of jumps. When the number of jumps exceeds 3 times, the fourth product that is the same as the second product will be configured in the first echelon according to the purchase quantity in each purchase record from closest to least close.

6. A cable project data management method according to claim 5, characterized in that: Determine whether the upper area in the first time period before obtaining the customer intention data is smaller than the lower area. If so, it is deemed that the luxury threshold is not exceeded; If it is not less than, it is considered to exceed the luxury threshold, including: S3431. Determine whether the absolute value of the difference between the upper area and the lower area in the first time period before obtaining the customer intention data is greater than the fuzzy threshold. If so, determine whether the upper area in the first time period before obtaining the customer intention data is smaller than the lower area. If it is smaller, it is deemed not to exceed the luxury threshold; if it is not smaller, it is deemed to exceed the luxury threshold; if not, lower the luxury threshold and jump to step S341, and record the number of jumps. When the number of jumps is greater than 4, delete the image corresponding to the first time period before the customer intention data of the line graph, and use the last time of the line graph as the time of the customer intention data, and rerun S3421.

7. A cable project data management method according to claim 6, characterized in that: If not, lower the luxury threshold and jump to step S341, and record the number of jumps. When the number of jumps is greater than 4, delete the image corresponding to the first time period before the current customer intention data of the line graph, and use the last time of the line graph as the time of the current customer intention data, and re-run S3431, including: If not, lower the luxury threshold and jump to step S341, and record the number of jumps, where the reference threshold of S3422 is lowered for every 2 jumps. When the number of jumps is greater than 4, delete the corresponding image in the first time period before the customer intention data of the line chart, and use the last time of the line chart as the time of the customer intention data, and rerun S3431.

8. A cable project data management system, characterized by: include An input module, which is used to obtain customer intention data, historical data of the customer's purchase of products, and inventory data of each product, wherein the customer intention data includes a first product, a first configuration parameter, and a first quantity, the historical data includes a second product, a second configuration parameter, and a second quantity, and the inventory data includes a third product, a third configuration data, and a third quantity; a processing module, which is used to configure the third product whose third configuration parameter and third quantity in the inventory data are respectively greater than or equal to the first configuration parameter and first quantity of the first product in the customer's intention data as the fourth product, and to determine whether the customer has historical data. If no historical data exists, the fourth product is configured in order of price from low to high to generate a recommendation list; if historical data exists, it is determined whether the first product is the same as the second product in the historical data of the customer's purchase of the product; if the same, the first echelon is configured in order of high to low according to the purchase habits of the second product that is the same as the fourth product, and other fourth products are configured in order of price from low to high to generate a recommendation list. To the high-configuration second echelon, generate a recommendation list according to the appearance of the first echelon first and then the second echelon; if they are different, determine whether the second configuration parameter of the historical data purchased by the customer exceeds the luxury threshold. If so, configure the fourth product corresponding to the third configuration parameter that is greater than the luxury threshold of the first configuration parameter in the first echelon from low to high according to the third configuration parameter, and configure other fourth products in the second echelon according to the price from low to high, and generate a recommendation list according to the appearance of the first echelon first and then the second echelon; if not, generate a recommendation list according to the price difference between the fourth product and the second product in the historical data from small to large; The output module is used to output the recommendation list.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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