Intelligent terminal batch logistics delivery optimization system

Abstract

The present invention discloses an intelligent terminal batch logistics delivery optimization system, which relates to the field of logistics technology and includes: a data analysis module, wherein the data analysis module analyzes and obtains the user's delivery preference; an emergency identification module, wherein the emergency identification module obtains the cargo urgency coefficient; a distribution planning module, wherein the distribution planning module obtains the delivery time of batch cargo; a solution formation module, wherein the solution formation module forms a distribution solution; a congestion navigation module; a preference analysis module; a pop-up window setting module; a user prompt module; and a pop-up window optimization module, wherein the pop-up window optimization module optimizes the display design and pop-up condition setting of the pop-up window. By setting the data analysis module, the distribution planning module, the solution formation module, and the pop-up window optimization module, it is ensured that urgent cargo is delivered first, and the user experience effect during transportation is better, and the pop-up window is optimized to ensure that it can meet the user's usage habits.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to an intelligent terminal batch logistics delivery optimization system. Background Art

[0002] Currently, with the rapid development of the express delivery industry, the volume of express parcels is also increasing. This easily leads to a lack of effective balance between delivery efficiency and service quality in the final delivery link. Although the emergence of third-party express lockers and express stations has greatly alleviated the pressure of final delivery, it has also been accompanied by problems such as deteriorating service quality and an increase in customer complaints. Because the number, volume, and weight of goods vary, the difficulty of handling them varies from customer to customer, which can easily increase the difficulty of delivery and cause delays. In addition, during delivery, message reminders lack personalized settings, resulting in the timing and display method not meeting user needs. Summary of the Invention

[0003] In order to solve the above technical problems, an intelligent terminal batch logistics delivery optimization system is provided. This technical solution solves the problems raised in the above background technology.

[0004] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0005] Intelligent terminal batch logistics delivery optimization system, including:

[0006] A data analysis module, wherein the data analysis module obtains user behavior data and analyzes the user's delivery preferences based on the user behavior data;

[0007] An urgency identification module, wherein the urgency identification module obtains goods to be received by the user, identifies the urgency of the goods to be received by the user, and obtains an urgency coefficient of the goods;

[0008] A delivery planning module, which plans the batches of the user's incoming goods based on the user's delivery preferences and the urgency factor of the goods, and obtains the delivery time of the batches of goods;

[0009] A plan forming module, which forms a delivery plan based on the delivery time of the batched goods, the congestion of the goods route, and the delivery personnel's existing delivery tasks;

[0010] a congestion navigation module, which guides the delivery person through congestion when the delivery person is delivering according to the delivery plan;

[0011] A preference analysis module, which analyzes the user's information acceptance preferences based on user behavior data;

[0012] A pop-up window setting module, which designs the display of pop-up windows based on information reception preferences and sets pop-up conditions for pop-up windows;

[0013] A user prompt module, which provides pop-up feedback on the delivery status according to the pop-up display design and pop-up conditions;

[0014] A pop-up window optimization module optimizes the display design and pop-up condition settings of the pop-up window based on the user's subsequent behavior on the pop-up window.

[0015] Preferably, analyzing the user's delivery preferences based on the user behavior data includes the following steps:

[0016] Extracting evaluation information generated by the user after each delivery from the user behavior data to obtain at least one delivery evaluation information;

[0017] Extract keywords from the delivery evaluation information to obtain a keyword package;

[0018] Identify the attributes of keywords as dissatisfied and satisfied;

[0019] Based on the historical usage of keywords, obtain the usage percentage of keywords;

[0020] When the keyword attribute is dissatisfied, the opposite of the reciprocal of the occurrence ratio will be used as the keyword's satisfaction contribution;

[0021] When the keyword attribute is satisfaction, the inverse of the occurrence ratio will be used as the keyword's satisfaction contribution;

[0022] The satisfaction contribution of the keywords in the keyword package is averaged to obtain the evaluation satisfaction;

[0023] The ten receiving evaluation information with the highest evaluation satisfaction are used as the target receiving evaluation information;

[0024] According to the volume, weight and delivery time of the goods evaluated by at least one target delivery evaluation information, a volume preference range, a preferred weight range and a preferred delivery time range are formed respectively, and summarized as the user's delivery preference.

[0025] Preferably, the step of identifying the urgency of the goods to be received by the user and obtaining the goods urgency coefficient comprises the following steps:

[0026] Obtain user evaluation information on delivery time from user behavior data as urgent information;

[0027] Target urgent information about delayed delivery;

[0028] Obtain the evaluation dissatisfaction of the target emergency information, where the evaluation dissatisfaction is equal to the inverse of the evaluation satisfaction;

[0029] Obtain the delay time of the goods corresponding to the target emergency information, divide the dissatisfaction rating by the delay time to obtain the goods urgency coefficient, and pair the goods urgency coefficient with the goods corresponding to the target emergency information;

[0030] Extract features of the goods to obtain at least one item feature;

[0031] Extract features of the goods to be received by the user to obtain at least one feature of the goods;

[0032] The cargo urgency coefficient of the cargo that has the most overlap with the cargo characteristics of the cargo to be received by the user is used as the cargo urgency coefficient of the cargo to be received by the user.

[0033] Preferably, the batch planning of the goods to be received by the user and obtaining the delivery time of the goods in batches includes the following steps:

[0034] Sort the user's pending goods by their urgency coefficient from largest to smallest to obtain a goods sequence;

[0035] Randomly divide the goods sequence to obtain at least one division scheme, each of which corresponds to a division method of the goods sequence;

[0036] Divide the goods to be received by the user in the goods sequence into at least one group of goods in the same batch according to the division plan;

[0037] Uniformly select at least one time point within the preferred delivery time range as the delivery time. The number of time points is equal to the number of groups of the same batch of goods.

[0038] Allocate the receipt time to the same batch of goods in chronological order;

[0039] Calculate the proportion of the volume of goods in the same batch that exceeds the volume preference range as the volume excess ratio;

[0040] Calculate the proportion of the volume of goods in the same batch that exceeds the preferred weight range as the weight excess proportion;

[0041] The recognition coefficient is obtained by adding the average value of the volume excess ratio of the same batch of goods in the division scheme and the average value of the weight excess ratio of the same batch of goods in the division scheme;

[0042] The same batch of goods of the division scheme with the smallest identification coefficient is regarded as the divided goods, and the receipt time of the same batch of goods of the division scheme with the smallest identification coefficient is regarded as the receipt time of the divided goods.

[0043] Preferably, the forming of a delivery plan based on the delivery time of the batched goods, the congestion of the goods route, and the delivery personnel's existing delivery tasks includes the following steps:

[0044] Based on historical data, obtain the road congestion situation within the delivery range at the time of delivery of the batched goods. The delivery range is the area formed by the locations of the delivery personnel who accept the delivery task of the batched goods;

[0045] Estimate delivery time based on traffic congestion and the average speed of the delivery person.

[0046] The delivery time of the batched goods is obtained by subtracting the delivery time from the delivery time of the batched goods.

[0047] The idle deliveryman who is closest to the batch goods at the delivery time of the batch goods is regarded as the executor of the batch goods, and the delivery plan is summarized with the delivery time.

[0048] Preferably, when the delivery person is delivering according to the delivery plan, performing congestion navigation for the delivery person includes the following steps:

[0049] The shortest path between the delivery person's current location and the delivery destination is formed, and the congested path in the shortest path is regarded as the congested part;

[0050] Obtain the congestion length of the congested part and predict the congestion time based on the congestion length;

[0051] Obtain the shortest alternative route through the congested area on the map and estimate the travel time of the shortest alternative route based on the delivery person's speed;

[0052] When the congestion time does not exceed the driving time, the navigation delivery person will delay the delivery to the congested part. Otherwise, the shortest alternative route of the congested part will be used for delivery.

[0053] Preferably, analyzing the user's information acceptance preferences based on the user behavior data includes the following steps:

[0054] Obtain user actions related to pop-up windows from user behavior data as feature user actions;

[0055] Obtain the user's dwell time on the pop-up window and the user's completion of the pop-up window content from the characteristic user operation;

[0056] Use the hierarchical analysis method to form the weights of the user's stay time on the pop-up window and the user's completion of the pop-up window content;

[0057] Multiply the user's dwell time on the pop-up window and the user's completion of the pop-up window content with the corresponding weights and add them together to obtain the preference coefficient;

[0058] The pop-up windows corresponding to the 10 characteristic user operations with the largest preference coefficients are used as target pop-up windows;

[0059] Obtain the pop-up context of the target pop-up window, obtain the display characteristics of the target pop-up window, and summarize them into the user's information acceptance preference.

[0060] Preferably, the pop-up window display design based on information acceptance preference includes the following steps:

[0061] Randomly combining the display features of the target pop-up window to obtain at least one combination scheme;

[0062] Design a pop-up window display according to the display features in the combination scheme to obtain at least one preliminary display design;

[0063] Conduct user testing on the preliminary display designs, and select the 10 preliminary display designs with the largest preference coefficients as the pop-up display designs.

[0064] Preferably, the pop-up conditions for setting the pop-up window include the following steps:

[0065] Extract features of the pop-up context of the target pop-up window, analyze the extracted features, obtain the user's possible leisure time, and pop up the pop-up window during the possible leisure time.

[0066] Preferably, the optimization of the display design and pop-up condition setting of the pop-up window based on the user's subsequent behavior on the pop-up window includes the following steps:

[0067] Calculate the preference coefficient among the possible leisure times, and delete the possible leisure time with the smallest preference coefficient from the pop-up conditions;

[0068] The display design of the pop-up window with the highest preference coefficient is used as the target display design, and the display designs of the remaining pop-up windows are used as non-target display designs;

[0069] The features that are different between the target display design and the non-target display design are used as optimization features;

[0070] The addition of optimized features is performed in non-target display designs.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] By setting up a data analysis module, an emergency identification module, a distribution planning module, a plan formation module and a pop-up optimization module, goods are distributed in batches according to the user's delivery preferences and the urgency coefficient of the goods, thereby ensuring that urgent goods are delivered first, and that the user has a better experience during transportation and is not affected by too much goods, which leads to transportation difficulties. At the same time, the delivery time is planned, and congestion is taken into account during planning to prevent delivery delays. In addition, pop-ups are optimized to ensure that they can meet the user's usage habits, and pop-ups are displayed as much as possible when the user is idle to prevent interference with their work. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a flow chart of the intelligent terminal batch logistics delivery optimization system of the present invention;

[0074] Figure 2 This is a flow chart of analyzing and obtaining a user's delivery preferences based on user behavior data according to the present invention;

[0075] Figure 3 This is a flow chart of the present invention for identifying the urgency of goods to be received by a user and obtaining the goods urgency coefficient;

[0076] Figure 4 A schematic diagram of a process for planning the batches of goods to be received by a user and obtaining the delivery time of the batches of goods according to the present invention;

[0077] Figure 5 This is a flow chart of the present invention for forming a delivery plan based on the delivery time of batched goods, the congestion of the goods route, and the delivery personnel's existing delivery tasks;

[0078] Figure 6 This is a flow chart of the present invention for performing congestion navigation on a delivery person when the delivery person is delivering according to a delivery plan;

[0079] Figure 7 This is a flow chart of analyzing and obtaining a user's information acceptance preference based on user behavior data according to the present invention;

[0080] Figure 8 A schematic diagram of the process of designing a pop-up window display based on information acceptance preferences according to the present invention;

[0081] Figure 9 This is a flow chart of optimizing the display design and pop-up condition settings of a pop-up window based on the user's subsequent behavior on the pop-up window according to the present invention. DETAILED DESCRIPTION

[0082] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0083] Reference Figure 1 As shown, the intelligent terminal batch logistics delivery optimization system includes:

[0084] A data analysis module, wherein the data analysis module obtains user behavior data and analyzes the user's delivery preferences based on the user behavior data;

[0085] An urgency identification module, wherein the urgency identification module obtains goods to be received by the user, identifies the urgency of the goods to be received by the user, and obtains an urgency coefficient of the goods;

[0086] A delivery planning module, which plans the batches of the user's incoming goods based on the user's delivery preferences and the urgency factor of the goods, and obtains the delivery time of the batches of goods;

[0087] A plan forming module, which forms a delivery plan based on the delivery time of the batched goods, the congestion of the goods route, and the delivery personnel's existing delivery tasks;

[0088] a congestion navigation module, which guides the delivery person through congestion when the delivery person is delivering according to the delivery plan;

[0089] A preference analysis module, which analyzes the user's information acceptance preferences based on user behavior data;

[0090] A pop-up window setting module, which designs the display of pop-up windows based on information reception preferences and sets pop-up conditions for pop-up windows;

[0091] A user prompt module, which provides pop-up feedback on the delivery status according to the pop-up display design and pop-up conditions;

[0092] A pop-up window optimization module optimizes the display design and pop-up condition settings of the pop-up window based on the user's subsequent behavior on the pop-up window.

[0093] Batch delivery is usually not used for door-to-door delivery, because there is no difference between batch delivery and one-time delivery for door-to-door delivery. Therefore, batch delivery is mainly used for delivery at express delivery points. At the actual delivery end, users usually collect the express delivery by one-time transportation. Although it can be transported in batches, almost no one will adopt this operation. When the goods are large or the volume is large or the weight is heavy, it will produce a very bad experience. Therefore, in order to control this situation, the situation is controlled from the delivery end, so that the user has a better experience every time he receives the express delivery. Here, certain restrictions need to be placed on the delivery time, because some users have more stringent requirements on the delivery time. In addition, pop-up reminders need to be given to users during the delivery link so that they can pick up the goods in time, but different users have different preferences. Therefore, the effect of a unified pop-up window will inevitably not meet the needs of all users, which may result in the loss of some users. Therefore, the pop-up window also needs to be optimized.

[0094] Reference Figure 2 As shown, based on user behavior data, analyzing the user's delivery preferences includes the following steps:

[0095] Extracting evaluation information generated by the user after each delivery from the user behavior data to obtain at least one delivery evaluation information;

[0096] Extract keywords from the delivery evaluation information to obtain a keyword package;

[0097] Identify the attributes of keywords as dissatisfied and satisfied;

[0098] Based on the historical usage of keywords, obtain the usage percentage of keywords;

[0099] When the keyword attribute is dissatisfied, the opposite of the reciprocal of the occurrence ratio will be used as the keyword's satisfaction contribution;

[0100] When the keyword attribute is satisfaction, the inverse of the occurrence ratio will be used as the keyword's satisfaction contribution;

[0101] The satisfaction contribution of the keywords in the keyword package is averaged to obtain the evaluation satisfaction;

[0102] The ten receiving evaluation information with the highest evaluation satisfaction are used as the target receiving evaluation information;

[0103] According to the volume, weight and delivery time of the goods evaluated by at least one target delivery evaluation information, a volume preference range, a preferred weight range and a preferred delivery time range are formed respectively, and summarized as the user's delivery preference.

[0104] When calculating the satisfaction of the evaluation, it is necessary to identify the satisfaction level of the keywords. The usual semantic-based recognition requires the establishment of a very complex model for training, which is very time-consuming. Here, according to the normal distribution, very satisfied situations must rarely occur, and the closer the satisfaction level is to 0, the more situations it occurs. Similarly, very dissatisfied situations must rarely occur, and the closer the dissatisfaction level is to 0, the more situations it occurs. Dissatisfied situations are represented by negative numbers, so satisfaction and dissatisfaction can be considered together. Thus, by obtaining the probability, the satisfaction contribution of the keyword can be obtained. The smaller the probability, the greater the absolute value of the satisfaction contribution. Therefore, the inverse of the probability of the keyword is used for characterization. However, due to the different attributes of the keywords, it is also necessary to distinguish between positive and negative numbers.

[0105] Therefore, the user's delivery preferences can be screened out based on the calculation of evaluation satisfaction.

[0106] Reference Figure 3 As shown, identifying the urgency of the goods to be received by the user and obtaining the goods urgency coefficient includes the following steps:

[0107] Obtain user evaluation information on delivery time from user behavior data as urgent information;

[0108] Target urgent information about delayed delivery;

[0109] Obtain the evaluation dissatisfaction of the target emergency information, where the evaluation dissatisfaction is equal to the inverse of the evaluation satisfaction;

[0110] Obtain the delay time of the goods corresponding to the target emergency information, divide the dissatisfaction rating by the delay time to obtain the goods urgency coefficient, and pair the goods urgency coefficient with the goods corresponding to the target emergency information;

[0111] Extract features of the goods to obtain at least one item feature;

[0112] Extract features of the goods to be received by the user to obtain at least one feature of the goods;

[0113] The cargo urgency coefficient of the cargo that has the most overlap with the cargo characteristics of the cargo to be received by the user is used as the cargo urgency coefficient of the cargo to be received by the user.

[0114] The cargo urgency coefficient is mainly used for delivery sorting, that is, when delivering, priority is given to more urgent goods. The cargo urgency coefficient is mainly characterized by the result of dividing the evaluation dissatisfaction by the delay time. It cannot be characterized solely by the evaluation dissatisfaction, because the evaluation dissatisfaction of the same goods delayed for one day and ten days must be different. Therefore, the delay time needs to be taken into consideration, which is more reasonable.

[0115] Reference Figure 4 As shown, batch planning of the goods to be received by the user and obtaining the delivery time of the batched goods include the following steps:

[0116] Sort the user's pending goods by their urgency coefficient from largest to smallest to obtain a goods sequence;

[0117] Randomly divide the goods sequence to obtain at least one division scheme, each of which corresponds to a division method of the goods sequence;

[0118] Divide the goods to be received by the user in the goods sequence into at least one group of goods in the same batch according to the division plan;

[0119] Uniformly select at least one time point within the preferred delivery time range as the delivery time. The number of time points is equal to the number of groups of the same batch of goods.

[0120] Allocate the receipt time to the same batch of goods in chronological order;

[0121] Calculate the proportion of the volume of goods in the same batch that exceeds the volume preference range as the volume excess ratio;

[0122] Calculate the proportion of the volume of goods in the same batch that exceeds the preferred weight range as the weight excess proportion;

[0123] The recognition coefficient is obtained by adding the average value of the volume excess ratio of the same batch of goods in the division scheme and the average value of the weight excess ratio of the same batch of goods in the division scheme;

[0124] The same batch of goods of the division scheme with the smallest identification coefficient is regarded as the divided goods, and the receipt time of the same batch of goods of the division scheme with the smallest identification coefficient is regarded as the receipt time of the divided goods.

[0125] When making a division, each division corresponds to a delivery method, and the overall distribution will produce a delivery effect, which is characterized here by the recognition coefficient. The recognition coefficient mainly depends on the combination of the volume excess ratio and the weight excess ratio. For users, the volume preference range and the preferred weight range are the volume and weight with a better handling experience. Therefore, the larger the excess part, the worse the experience. Therefore, the same batch of goods with the division scheme with the smallest recognition coefficient is selected as the batched goods.

[0126] Reference Figure 5 As shown, the delivery plan is formed based on the delivery time of the batched goods, the congestion of the goods route, and the delivery personnel's existing delivery tasks, including the following steps:

[0127] Based on historical data, obtain the road congestion situation within the delivery range at the time of delivery of the batched goods. The delivery range is the area formed by the locations of the delivery personnel who accept the delivery task of the batched goods;

[0128] Estimate delivery time based on traffic congestion and the average speed of the delivery person.

[0129] The delivery time of the batched goods is obtained by subtracting the delivery time from the delivery time of the batched goods.

[0130] The idle deliveryman who is closest to the batch goods at the delivery time of the batch goods is regarded as the executor of the batch goods, and the delivery plan is summarized with the delivery time.

[0131] During delivery, there is a specific delivery time for batches of goods, but the deliveryman may not be able to deliver on time due to traffic reasons. Therefore, it is necessary to screen and get deliverymen with sufficient delivery time, and deliver to a location with ample time to ensure that there will be no delays even if congestion occurs. Because some users will directly collect the goods at the delivery time due to pop-up reminders, once a delay occurs, it will affect their psychological experience and may lead to user loss.

[0132] Reference Figure 6 As shown, when the delivery person is delivering according to the delivery plan, congestion navigation for the delivery person includes the following steps:

[0133] The shortest path between the delivery person's current location and the delivery destination is formed, and the congested path in the shortest path is regarded as the congested part;

[0134] Obtain the congestion length of the congested part and predict the congestion time based on the congestion length;

[0135] Obtain the shortest alternative route through the congested area on the map and estimate the travel time of the shortest alternative route based on the delivery person's speed;

[0136] When the congestion time does not exceed the driving time, the navigation delivery person will delay the delivery to the congested part. Otherwise, the shortest alternative route of the congested part will be used for delivery.

[0137] During delivery, the congestion situation cannot be exactly the same as the estimated situation. Therefore, timely navigation is required. Navigation is not about route navigation, but more about avoiding congestion, thereby ensuring that delivery can be completed in time.

[0138] Reference Figure 7 As shown, based on user behavior data, analyzing the user's information acceptance preferences includes the following steps:

[0139] Obtain user actions related to pop-up windows from user behavior data as feature user actions;

[0140] Obtain the user's dwell time on the pop-up window and the user's completion of the pop-up window content from the characteristic user operation;

[0141] Use the hierarchical analysis method to form the weights of the user's stay time on the pop-up window and the user's completion of the pop-up window content;

[0142] Multiply the user's dwell time on the pop-up window and the user's completion of the pop-up window content with the corresponding weights and add them together to obtain the preference coefficient;

[0143] The pop-up windows corresponding to the 10 characteristic user operations with the largest preference coefficients are used as target pop-up windows;

[0144] Obtain the pop-up context of the target pop-up window, obtain the display characteristics of the target pop-up window, and summarize them into the user's information acceptance preference.

[0145] The main function of pop-ups is to provide information reminders, which may require users to perform a series of operations. Therefore, preferences can be identified by the length of time the user stays and the degree of cooperation with the operational requirements, and based on this, the preferred features can be extracted to obtain the required design and pop-up settings.

[0146] Reference Figure 8 As shown, based on information acceptance preferences, the design of pop-up window display includes the following steps:

[0147] Randomly combining the display features of the target pop-up window to obtain at least one combination scheme;

[0148] Design a pop-up window display according to the display features in the combination scheme to obtain at least one preliminary display design;

[0149] Conduct user testing on the preliminary display designs, and select the 10 preliminary display designs with the largest preference coefficients as the pop-up display designs.

[0150] Setting pop-up conditions for a pop-up window includes the following steps:

[0151] Extract features of the pop-up context of the target pop-up window, analyze the extracted features, obtain the user's possible leisure time, and pop up the pop-up window during the possible leisure time.

[0152] Reference Figure 9 As shown, based on the user's subsequent behavior on the pop-up window, optimizing the pop-up window display design and pop-up condition settings includes the following steps:

[0153] Calculate the preference coefficient among the possible leisure times, and delete the possible leisure time with the smallest preference coefficient from the pop-up conditions;

[0154] The display design of the pop-up window with the highest preference coefficient is used as the target display design, and the display designs of the remaining pop-up windows are used as non-target display designs;

[0155] The features that are different between the target display design and the non-target display design are used as optimization features;

[0156] The addition of optimized features is performed in non-target display designs.

[0157] Since there is room for improvement in single settings, pop-up windows are optimized based on usage results.

[0158] Furthermore, the present solution also proposes a storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned intelligent terminal batch logistics delivery optimization system is run.

[0159] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD).

[0160] To sum up, the advantages of the present invention are: by setting up a data analysis module, an emergency identification module, a distribution planning module, a plan formation module and a pop-up optimization module, the goods are distributed in batches according to the user's delivery preferences and the goods urgency coefficient, thereby ensuring that urgent goods are delivered first, and the user experience during transportation is better, and is not affected by too much goods, resulting in transportation difficulties. At the same time, the delivery time is planned, and congestion is taken into account during planning, thereby preventing delivery delays. In addition, the pop-up window is optimized to ensure that it can meet the user's usage habits, and the pop-up window is displayed as much as possible when the user is idle to prevent interference with his work.

[0161] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent terminal batch logistics delivery optimization system, characterized by: include: A data analysis module, wherein the data analysis module obtains user behavior data and analyzes the user's delivery preferences based on the user behavior data; An urgency identification module, wherein the urgency identification module obtains goods to be received by the user, identifies the urgency of the goods to be received by the user, and obtains an urgency coefficient of the goods; A delivery planning module, which plans the batches of the user's incoming goods based on the user's delivery preferences and the urgency factor of the goods, and obtains the delivery time of the batches of goods; A plan forming module, which forms a delivery plan based on the delivery time of the batched goods, the congestion of the goods route, and the delivery personnel's existing delivery tasks; a congestion navigation module, which guides the delivery person through congestion when the delivery person is delivering according to the delivery plan; A preference analysis module, which analyzes the user's information acceptance preferences based on user behavior data; A pop-up window setting module, which designs the display of pop-up windows based on information reception preferences and sets pop-up conditions for pop-up windows; A user prompt module, which provides pop-up feedback on the delivery status according to the pop-up display design and pop-up conditions; A pop-up window optimization module, which optimizes the display design and pop-up condition settings of the pop-up window based on the user's subsequent behavior on the pop-up window; The batch planning of the goods to be received by the user and obtaining the delivery time of the goods in batches includes the following steps: Sort the user's pending goods by their urgency coefficient from largest to smallest to obtain a goods sequence; Randomly divide the goods sequence to obtain at least one division scheme, each of which corresponds to a division method of the goods sequence; Divide the goods to be received by the user in the goods sequence into at least one group of goods in the same batch according to the division plan; Uniformly select at least one time point within the preferred delivery time range as the delivery time. The number of time points is equal to the number of groups of the same batch of goods. Allocate the receipt time to the same batch of goods in chronological order; Calculate the proportion of the volume of goods in the same batch that exceeds the volume preference range as the volume excess ratio; Calculate the proportion of the volume of goods in the same batch that exceeds the preferred weight range as the weight excess proportion; The recognition coefficient is obtained by adding the average value of the volume excess ratio of the same batch of goods in the division scheme and the average value of the weight excess ratio of the same batch of goods in the division scheme; The same batch of goods of the division scheme with the smallest identification coefficient is regarded as the divided goods, and the receipt time of the same batch of goods of the division scheme with the smallest identification coefficient is regarded as the receipt time of the divided goods.

2. The intelligent terminal batch logistics delivery optimization system according to claim 1 is characterized in that: Analyzing the user's delivery preferences based on the user behavior data includes the following steps: Extracting evaluation information generated by the user after each delivery from the user behavior data to obtain at least one delivery evaluation information; Extract keywords from the delivery evaluation information to obtain a keyword package; Identify the attributes of keywords as dissatisfied and satisfied; Based on the historical usage of keywords, obtain the usage percentage of keywords; When the keyword attribute is dissatisfied, the opposite of the reciprocal of the occurrence ratio will be used as the keyword's satisfaction contribution; When the keyword attribute is satisfaction, the inverse of the occurrence ratio will be used as the keyword's satisfaction contribution; The satisfaction contribution of the keywords in the keyword package is averaged to obtain the evaluation satisfaction; The ten receiving evaluation information with the highest evaluation satisfaction are used as the target receiving evaluation information; According to the volume, weight and delivery time of the goods evaluated by at least one target delivery evaluation information, a volume preference range, a preferred weight range and a preferred delivery time range are formed respectively, and summarized as the user's delivery preference.

3. The intelligent terminal batch logistics delivery optimization system according to claim 2 is characterized in that: The step of identifying the urgency of the goods to be received by the user and obtaining the goods urgency coefficient comprises the following steps: Obtain user evaluation information on delivery time from user behavior data as urgent information; Target urgent information about delayed delivery; Obtain the evaluation dissatisfaction of the target emergency information, where the evaluation dissatisfaction is equal to the inverse of the evaluation satisfaction; Obtain the delay time of the goods corresponding to the target emergency information, divide the dissatisfaction rating by the delay time to obtain the goods urgency coefficient, and pair the goods urgency coefficient with the goods corresponding to the target emergency information; Extract features of the goods to obtain at least one item feature; Extract features of the goods to be received by the user to obtain at least one feature of the goods; The cargo urgency coefficient of the cargo that has the most overlap with the cargo characteristics of the cargo to be received by the user is used as the cargo urgency coefficient of the cargo to be received by the user.

4. The intelligent terminal batch logistics delivery optimization system according to claim 3 is characterized in that: The delivery plan is formed based on the delivery time of the batched goods, the congestion of the goods route and the delivery personnel's existing delivery tasks, including the following steps: Based on historical data, obtain the road congestion situation within the delivery range at the time of delivery of the batched goods. The delivery range is the area formed by the locations of the delivery personnel who accept the delivery task of the batched goods; Estimate delivery time based on road congestion and the average speed of the delivery person; The delivery time of the batched goods is obtained by subtracting the delivery time from the delivery time of the batched goods. The idle deliveryman who is closest to the batch goods at the delivery time of the batch goods is regarded as the executor of the batch goods, and the delivery plan is summarized with the delivery time.

5. The intelligent terminal batch logistics delivery optimization system according to claim 4 is characterized in that: When the delivery person is delivering according to the delivery plan, the congestion navigation for the delivery person includes the following steps: The shortest path between the delivery person's current location and the delivery destination is formed, and the congested path in the shortest path is regarded as the congested part; Obtain the congestion length of the congested part and predict the congestion time based on the congestion length; Obtain the shortest alternative route through the congested area on the map and estimate the travel time of the shortest alternative route based on the delivery person's speed; When the congestion time does not exceed the driving time, the navigation delivery person will delay the delivery to the congested part. Otherwise, the shortest alternative route of the congested part will be used for delivery.

6. The intelligent terminal batch logistics delivery optimization system according to claim 5 is characterized in that: Analyzing the user's information acceptance preferences based on the user behavior data includes the following steps: Obtain user actions related to pop-up windows from user behavior data as feature user actions; Obtain the user's dwell time on the pop-up window and the user's completion of the pop-up window content from the characteristic user operation; Use the hierarchical analysis method to form the weights of the user's stay time on the pop-up window and the user's completion of the pop-up window content; Multiply the user's dwell time on the pop-up window and the user's completion of the pop-up window content with the corresponding weights and add them together to obtain the preference coefficient; The pop-up windows corresponding to the 10 characteristic user operations with the largest preference coefficients are used as target pop-up windows; Obtain the pop-up context of the target pop-up window, obtain the display characteristics of the target pop-up window, and summarize them into the user's information acceptance preference.

7. The intelligent terminal batch logistics delivery optimization system according to claim 6 is characterized in that: The pop-up window display design based on information acceptance preference includes the following steps: Randomly combining the display features of the target pop-up window to obtain at least one combination scheme; Design a pop-up window display according to the display features in the combination scheme to obtain at least one preliminary display design; Conduct user testing on the preliminary display designs, and select the 10 preliminary display designs with the largest preference coefficients as the pop-up display designs.

8. The intelligent terminal batch logistics delivery optimization system according to claim 7 is characterized in that: The pop-up condition setting for the pop-up window includes the following steps: Extract features of the pop-up context of the target pop-up window, analyze the extracted features, obtain the user's possible leisure time, and pop up the pop-up window during the possible leisure time.

9. The intelligent terminal batch logistics delivery optimization system according to claim 8 is characterized in that: Optimizing the display design and pop-up condition settings of the pop-up window based on the user's subsequent behavior on the pop-up window includes the following steps: Calculate the preference coefficient among the possible leisure times, and delete the possible leisure time with the smallest preference coefficient from the pop-up conditions; The display design of the pop-up window with the highest preference coefficient is used as the target display design, and the display designs of the remaining pop-up windows are used as non-target display designs; The features that are different between the target display design and the non-target display design are used as optimization features; The addition of optimized features is performed in non-target display designs.

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