Online car-hailing tenant marketing activity recommendation method and system

Abstract

The invention relates to the technical field of online car-hailing data recommendation, and discloses an online car-hailing tenant marketing activity recommendation method and system, and the method comprises the steps: obtaining the current position of a driver and the information of a city according to a marketing activity thermodynamic diagram query request, and querying a marketing activity list in which the driver can participate on the current day and a dynamic activity filtering rule corresponding to the city; thermodynamic diagram information is obtained according to the current position information; and inputting the marketing activity list and the dynamic activity filtering rule into a preset rule engine to obtain a to-be-displayed marketing activity, performing layer fusion on the to-be-displayed marketing activity and the thermodynamic diagram information to obtain target fusion data, and returning the target fusion data to a driver end for display. According to the invention, through dynamic configuration of the rule engine and the activity filtering rule, a series of rule interception can be carried out on the activity, visual promotion of the online car-hailing marketing activity and accuracy of personalized recommendation are ensured, so that the participation rate of a driver for the marketing activity is increased, and the benefit of an online car-hailing tenant platform is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of online car-hailing data recommendation, and in particular to a method and system for recommending marketing activities for online car-hailing tenants. Background Art

[0002] Currently, online ride-hailing platforms primarily promote marketing campaigns through listings and homepage push notifications. While these can inform drivers of promotions, they lack intuitiveness and personalized recommendations. When drivers receive promotional information, they may not immediately understand the relationship between these promotions and their own profitability. This leads to low participation rates, resulting in low order completion rates for drivers, and ultimately, negatively impacting the profitability of the entire online ride-hailing platform. Summary of the Invention

[0003] In view of this, the present invention provides a method and system for recommending marketing activities for online car-hailing tenants, so as to solve the problem of how to ensure the intuitiveness of the promotion of online car-hailing marketing activities and the accuracy of personalized recommendations, thereby increasing the participation rate of drivers in marketing activities and improving the efficiency of the online car-hailing tenant platform.

[0004] In a first aspect, the present invention provides a method for recommending marketing activities for online car-hailing tenants, which is applied to a server, and the method includes:

[0005] Obtaining a marketing activity heat map query request sent by the driver; the marketing activity heat map query request includes the driver's current location information and city information;

[0006] Based on the city information, query a preset activity database for a list of marketing activities that the driver can participate in on that day, and obtain dynamic activity filtering rules corresponding to the city from a preset rule configuration database;

[0007] According to the current location information, obtaining thermal map information from a preset thermal information database;

[0008] Inputting the marketing activity list and the dynamic activity filtering rules into a preset rule engine to obtain the marketing activities to be displayed;

[0009] The marketing activities to be displayed are layer-fused with the heat map information to obtain target fusion data, which is returned to the driver end for display.

[0010] In an embodiment of the present invention, a marketing activity heat map query request sent by a driver is obtained, including the driver's current location information and city information. Based on the city information, a list of marketing activities that the driver can participate in on that day is queried from an activity database, and dynamic activity filtering rules corresponding to the city are obtained from a rule configuration database, ensuring that the recommended activities are effective activities for the driver's city, thereby improving the relevance of marketing activity promotion; based on the driver's current location information, heat map information is obtained from a thermal information database, and the marketing activity list and dynamic activity filtering rules are input into a rule engine to obtain marketing activities to be displayed. The rule engine can intelligently filter out the marketing activities that are most suitable for the current driver based on real-time data and preset rules, realize personalized recommendations for the driver himself, and improve the accuracy of marketing goals; the marketing activities to be displayed are layer-fused with the heat map information to obtain target fusion data, and returned to the driver for display, so that the driver can not only see the activity itself, but also intuitively understand the success rate of participating in the marketing activity in combination with the heat map information of the current location, thereby improving the driver's enthusiasm for participating in the activity and the completion rate, thereby improving the efficiency of the online car-hailing tenant platform.

[0011] In an optional embodiment, the dynamic activity filtering rule includes multiple field information, and the field information includes at least one filtering type and at least one rule condition; the preset rule engine includes a first rule engine and a second rule engine;

[0012] Inputting the marketing activity list and the dynamic activity filtering rules into a preset rule engine to obtain the marketing activities to be displayed specifically includes:

[0013] Determining the filtering type of the dynamic activity filtering rule;

[0014] If the filtering type is the first type, all the corresponding rule conditions are input into the first rule engine; wherein the first type indicates that the marketing activities to be filtered must meet all the rule conditions;

[0015] If the filtering type is the second type, all the corresponding rule conditions are input into the second rule engine; wherein the second type indicates that the marketing activities to be filtered must meet any one of the rule conditions;

[0016] Based on the first rule engine or the second rule engine, each sub-information in the activity information corresponding to each marketing activity in the marketing activity list is matched using the rule condition to determine the filtered marketing activity as the marketing activity to be displayed.

[0017] In an embodiment of the present invention, when the filtering type is the first type, only activities that meet all the rule conditions will be recommended; when it is the second type, only any one condition needs to be met. Different marketing activity information can be flexibly configured, and the rule engine is used to intelligently match the sub-information in the activity information. The marketing activities to be displayed are not all activities, but activities that are more in line with the requirements of the city and location. The marketing activities to be displayed are filtered according to the first rule engine or the second rule engine, which flexibly adapts to the driver's personal situation, thereby improving the driver's participation rate.

[0018] In an optional embodiment, the activity information of each marketing activity includes multiple sub-information;

[0019] Based on the first rule engine, each condition of each marketing activity in the marketing activity list is matched by using the rule condition, and a filtered marketing activity is determined as the marketing activity to be displayed, specifically comprising: parsing the set value of each condition in the rule condition, traversing the activity information of all marketing activities, during the traversal process, obtaining the actual value of each sub-information corresponding to the activity information of each marketing activity, and using a comparison operator to compare the actual value with the set value; if the comparison result of the actual value and the set value of each sub-information is both logically true, then the corresponding current marketing activity is selected as the marketing activity to be displayed;

[0020] Based on the second rule engine, each condition of each marketing activity in the marketing activity list is matched using the rule condition, and the filtered marketing activity is determined as the marketing activity to be displayed, specifically including: parsing the set value of each condition in the rule condition, traversing the activity information of all marketing activities during the traversal process, obtaining the actual value of each sub-information corresponding to the activity information of each marketing activity during the traversal process, and using the comparison operator to compare the actual value with the set value; if the comparison result of the actual value and the set value of any sub-information is logically true, the corresponding current marketing activity will be used as the marketing activity to be displayed.

[0021] In an embodiment of the present invention, a first rule engine is used to parse rule conditions, traverse all activity information, and compare the actual value of each sub-information with the set value. Only when all conditions are met will the activity be recommended, ensuring that the activity relevance is high enough to be recommended to the driver side; a second rule engine is used to parse rule conditions, traverse all activity information, and compare the actual value of each sub-information with the set value. When any comparison operation result is logically true, it can be displayed. By adapting the rule engine according to different filtering types, all marketing activities to be displayed can be determined in one traversal process, thereby improving the filtering speed and providing an accurate data basis for the superposition of subsequent layers.

[0022] In an optional embodiment, before the step of performing layer fusion of the marketing activity information to be displayed and the heat map information to obtain target fusion data and returning the data to the driver terminal for display, the step further includes:

[0023] Setting a corresponding display priority score according to the activity information of the marketing activity to be displayed specifically includes: obtaining each sub-information in the activity information corresponding to each marketing activity; according to each sub-information, obtaining a weight value corresponding to each sub-information from a preset sorting weight; and summing up all the sub-information weight values ​​to obtain a display priority score for the marketing activity.

[0024] The embodiment of the present invention obtains sub-information of each marketing activity, obtains the corresponding weight value from the preset sorting weight, and sums the weight values ​​to calculate the display priority score, so as to more accurately identify the activities most relevant to specific drivers, thereby making personalized recommendations and improving the driver's enthusiasm for participating in the activities.

[0025] In an optional embodiment, before the step of performing layer fusion of the marketing activity information to be displayed and the heat map information to obtain target fusion data and returning the data to the driver terminal for display, the step further includes:

[0026] Performing location screening on the marketing activities to be displayed based on the current location information of the driver and the activity information of the marketing activities to be displayed to obtain screened marketing activities to be displayed; specifically including:

[0027] Obtain the geo-fenced area of ​​the activity address of each marketing activity to be displayed, as well as the display area of ​​each marketing activity to be displayed on the page;

[0028] If the display area is a bubble area, then the spatial relationship between the driver terminal and the geo-fence area of ​​the activity address of each marketing activity to be displayed is calculated based on the driver's current location information and the map scale;

[0029] According to the spatial relationship, marketing activities in which the driver's terminal is within the range of the active electronic fence or the driver's terminal is within a preset distance threshold from the active electronic fence are filtered out as the filtered marketing activities to be displayed.

[0030] In an embodiment of the present invention, the electronic fence area of ​​a marketing activity and the display area type of the activity on the page are obtained, and the specific spatial position relationship between the driver and the activity area is calculated in combination with the map scale of the driver's current location. Ultimately, two types of activities are retained, namely, the driver is already within the activity electronic fence or the driver is within a preset threshold distance from the activity area. This is equivalent to only pushing activities that are geographically feasible, avoiding pushing activities that are too far away and cannot actually be participated in, thereby improving the practicality of the recommendation and further increasing the driver's willingness to participate in the activities, thereby improving the driver's completion rate.

[0031] In an optional embodiment, the layer fusion of the marketing activity to be displayed and the heat map information to obtain target fusion data, and returning the target fusion data to the driver terminal for display, specifically includes:

[0032] Determine a display area on the page for each marketing activity to be displayed;

[0033] If the display area is a carousel area, the order of each marketing activity to be displayed is determined in descending order according to the display priority score; wherein the order is positively correlated with the display priority score; and a carousel area display layer is generated according to the order based on a preset carousel component;

[0034] If the display area is a bubble area, generating a bubble area display layer for the electronic fence area of ​​the activity address of the filtered marketing activity to be displayed;

[0035] The carousel area display layer and the bubble area display layer are superimposed on the heat map information to obtain target fusion data, which is returned to the driver end for display.

[0036] In the embodiment of the present invention, activities are sorted according to display priority, and activities with high priority are displayed first. A carousel component is used for carousel area display, and multiple activities are automatically displayed in a cycle, providing dynamic visual effects and enhancing appeal. Activities are associated with electronic fence areas, and activity locations are marked in the form of bubbles on the map. Bubble area display is performed to intuitively display the geographical scope of the activity and help drivers quickly judge the distance. The carousel layer, bubble layer and heat map are superimposed to obtain a multi-dimensional information fusion display, which solves the problems of insufficient intuitiveness and low personalization in the promotion of related marketing activities, thereby increasing the driver's participation rate in activities, promoting order completion, and ensuring the benefits of the tenant platform.

[0037] In a second aspect, the present invention provides a system for recommending marketing activities for online car-hailing tenants, the system comprising:

[0038] A request receiving module is used to obtain a marketing activity heat map query request sent by a driver; the marketing activity heat map query request includes the driver's current location information and city information;

[0039] An activity query module is used to query a list of marketing activities that the driver can participate in on that day from a preset activity database based on the city information, and to obtain dynamic activity filtering rules corresponding to the city from a preset rule configuration database;

[0040] A thermal map acquisition module, configured to acquire thermal map information from a preset thermal information database according to the current location information;

[0041] A rule engine processing module, configured to input the marketing activity list and the dynamic activity filtering rules into a preset rule engine to obtain marketing activities to be displayed;

[0042] The data fusion module is used to perform layer fusion on the marketing activities to be displayed and the heat map information to obtain target fusion data, and return it to the driver end for display.

[0043] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, computer instructions being stored in the memory, and the processor executing the computer instructions to execute the method for recommending online car-hailing tenant marketing activities according to the first aspect or any corresponding embodiment thereof.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for recommending online car-hailing tenant marketing activities according to the first aspect or any corresponding embodiment thereof.

[0045] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, the computer instructions being used to enable a computer to execute the method for recommending online car-hailing tenant marketing activities according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 1 is a flowchart of a method for recommending marketing activities for online ride-hailing tenants according to an embodiment of the present invention;

[0048] Figure 2 1 is a schematic diagram of an activity list query process according to a method for recommending marketing activities for online ride-hailing tenants according to an embodiment of the present invention;

[0049] Figure 3 is a flowchart of another method for recommending marketing activities for online ride-hailing tenants according to an embodiment of the present invention;

[0050] Figure 4 2. Schematic diagram of rule engine filtering according to a method for recommending marketing activities for online ride-hailing tenants according to an embodiment of the present invention;

[0051] Figure 5 is a flowchart of another method for recommending marketing activities for online ride-hailing tenants according to an embodiment of the present invention;

[0052] Figure 6 1. A schematic diagram of a carousel display area according to a method for recommending marketing activities for online ride-hailing tenants according to an embodiment of the present invention;

[0053] Figure 7 2. Schematic diagram of a bubble display area according to a method for recommending marketing activities for online ride-hailing tenants according to an embodiment of the present invention;

[0054] Figure 8 This is a structural block diagram of a system for recommending marketing activities for online ride-hailing tenants according to an embodiment of the present invention;

[0055] Figure 9 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0057] With the rapid development of the sharing economy, online ride-hailing platforms have become a vital component of modern transportation. To incentivize drivers to actively complete orders, online ride-hailing platforms (such as Didi and Zhu) often launch a series of marketing incentives, including commission-free programs, volume rewards, and time-based incentives. These initiatives aim to boost driver enthusiasm and response rates. While these incentives are effective to a certain extent, effectively promoting and disseminating these programs, increasing driver completion rates, and ensuring platform profitability remain challenges.

[0058] Related technologies mainly use a single page to display marketing activities, such as activity lists and homepage push notifications. When drivers receive activity information, they may not be able to understand the relationship between these activities and their own income in a timely manner, resulting in a low participation rate in the activities.

[0059] An embodiment of the present invention provides a method for recommending marketing activities for online car-hailing tenants. By obtaining a marketing activity heat map query request sent by the driver side, the list of marketing activities that the driver can participate in on that day, the corresponding dynamic activity filtering rules, and the heat map information are queried from the database according to the information in the query request. The list of marketing activities is filtered according to the preset rule engine to obtain the marketing activities to be displayed. The marketing activities to be displayed are layer-fused with the heat map information to obtain target fusion data, which is returned to the driver side for display. This allows drivers to know more intuitively where they can make more money. Combined with the background's dynamic configuration of the rule engine and activity filtering rules, a series of rule interceptions can be performed on the activities, thereby making the provided activity promotion conversion rate higher.

[0060] According to an embodiment of the present invention, an embodiment of a method for recommending marketing activities for online car-hailing tenants is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0061] In this embodiment, a method for recommending marketing activities for online car-hailing tenants is provided, which can be used in the above-mentioned computer. Figure 1 is a flow chart of a method for recommending marketing activities for online car-hailing tenants according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0062] Step S101: Obtain a marketing activity heat map query request sent by the driver. The marketing activity heat map query request includes the driver's current location information and city information.

[0063] It's important to note that a marketing campaign heat map query request refers to a specific data request sent by the driver's app to the server to obtain heat map data containing marketing campaign information. The driver's current location information includes latitude and longitude coordinates, while the city information refers to the name or city code of the city the driver is currently in.

[0064] Specifically, you can use HTTP requests to obtain the driver's precise city location information, or use GPS or base station positioning to obtain the driver's precise location. In actual application, you first need to integrate positioning functionality into the driver's application and obtain the current latitude and longitude coordinates by calling the device's positioning service. Secondly, based on the obtained coordinate information, determine the driver's city, package it into a standard request, and send it to the server to query the background data. The purpose is to provide the necessary basic data for subsequent marketing campaign screening and heat map generation, ensuring that recommended activities and heat map information are relevant to the driver's current location from the outset, thereby improving the targeted nature of marketing activities.

[0065] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0066] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the operation requested will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operations of the disclosed technical solution based on the prompt message.

[0067] As an optional but non-limiting implementation, in response to receiving a user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0068] It is understood that the above notification and user authorization process is merely illustrative and does not limit the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure. The data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws and regulations and relevant provisions.

[0069] Step S102: Based on the city information, a list of marketing activities that the driver can participate in on that day is searched from a preset activity database, and dynamic activity filtering rules corresponding to the city are obtained from a preset rule configuration database.

[0070] It should be noted that the pre-set activity database refers to the database that stores all marketing activity information, including fields such as activity ID, activity type, activity time, and activity city. The rule configuration database refers to the database that stores dynamic activity filtering rules, which can be dynamically configured by tenants in the backend. The list of marketing activities that a driver can participate in on that day refers to all valid activities that meet the conditions of the current date and the driver's city. Dynamic activity filtering rules are a series of conditions used to further filter and sort activities.

[0071] Specifically, it is necessary to construct database query conditions based on the driver's city information, where the query conditions should include city matching, activity validity check (available on the same day), and other screening conditions (optional); execute database query to obtain a list of activities that meet the conditions; obtain dynamic activity filtering rules related to the city from the rule configuration database. The actual query process can be referred to Figure 2 , first query the activity cache. If there is no activity cache, query the service of the corresponding activity type, and then store the current driver activity information in redis.

[0072] Exemplarily, the query result is a marketing activity list and activity filtering rules; wherein the marketing activity list includes a set of activity IDs of all queried marketing activities; and the activity filtering rules are activity filtering rules corresponding to each activity ID.

[0073] In the embodiment of the present invention, the activity filtering rules can be stored in JSON format. The example structure is as follows:

[0074] {

[0075] "tenantId":1,

[0076] "city":"Beijing",

[0077] "conditionType":2,

[0078] "effectDate":"2024-02-08 00:00:00",

[0079] "conditionList":[

[0080] {

[0081] "field":"act_id",

[0082] "condition":6,

[0083] "value":"[1305283,1305239,1305288,1305402]",

[0084] "valueType":"Array",

[0085] "whiteType":2

[0086] },

[0087] {

[0088] "field":"award",

[0089] "condition":2,

[0090] "valueType":"2.00",

[0091] "type":"Decimal",

[0092] "whiteType":1

[0093] } ]

[0095] }

[0096] Step S103: acquiring thermal map information from a preset thermal information database according to the current location information.

[0097] It should be noted that heat map information refers to visual data that reflects the intensity distribution of a specific activity within a specific geographic area, such as order heat, nearby gas station heat, and charging station heat. The preset heat information database is a database that stores heat values ​​corresponding to various geographic locations. The current location information includes the driver's precise latitude and longitude coordinates.

[0098] Specifically, query conditions are constructed based on the driver's current location, and relevant data is retrieved from the thermal information database. The driver's current location information is converted into a format suitable for database queries, such as converting longitude and latitude coordinates into a geographic grid index. Secondly, a database query statement is constructed to retrieve thermal data covering the driver's current location and a certain range around it. The retrieved thermal data is layered to form thermal map information that can be used for subsequent layer fusion. This ensures that the thermal map presented to the driver accurately reflects the activity intensity distribution in their area (order thermal, nearby gas station thermal, charging station thermal).

[0099] Step S104: input the marketing activity list and dynamic activity filtering rules into a preset rule engine to obtain marketing activities to be displayed.

[0100] It should be noted that the preset rule engine refers to software that can process data and make decisions based on predefined rules. In this embodiment, since a real-time query interface is required and the heat map needs to include operations such as dynamic refresh, the interface PV is relatively high. Therefore, the lightweight EasyRule is used as the rule engine (with the advantages of small memory usage, low development cost, and high flexibility). The marketing activities to be displayed refer to the list of activities that are finally determined to be displayed to drivers after being processed by the rule engine.

[0101] Among them, Easy Rules comes with three CompositeRule implementations:

[0102] The first type: UnitRuleGroup, either all rules are applied or no rules are applied (AND logic);

[0103] The second type: ActivationRuleGroup, which triggers the first applicable rule and ignores the other rules in the group (XOR logic);

[0104] The third type: ConditionalRuleGroup, if the rule with the highest priority evaluates to true, the remaining rules are triggered.

[0105] In this embodiment of the present invention, applying the rules of UnitRuleGroup forms a rule group. The UnitRuleGroup returns true only if all rules in the rule group pass (return true after a logical operation). Applying the rules of ActivationRuleGroup forms a rule group. ActivationRuleGroup returns true if any rule in the rule group passes (returns true after a logical operation). The conditionType field specifies the use of the rule. Currently, AND logic (conditionType=1) and XOR logic (conditionType=2) are mainly used for implementation.

[0106] Specifically, the dynamic activity filtering rules need to be parsed into rule objects that can be recognized by the rule engine; each activity in the marketing activity list is input into the rule engine in turn and evaluated using the rule object; and eligible activities are filtered out based on the rule evaluation results.

[0107] For example, using step S102 as an example, the rules engine parses this rule into two rule objects: Rule Object 1, which checks whether the campaign ID is not in the specified list, and Rule Object 2, which checks whether the reward amount is greater than 2.00. Since conditionType is 2, indicating the use of XOR logic, as long as at least one of the rules is met, the rules engine will execute both rules for each campaign. If the campaign passes the rule check, it will be added to the list of marketing campaigns to be displayed.

[0108] An example of rule object 1 is as follows:

[0109] {

[0110] "field":"act_id",

[0111] "condition":6,

[0112] "value":"[1305283,1305239,1305288,1305402]",

[0113] "valueType":"Array",

[0114] "whiteType":2

[0115] }

[0116] An example of rule object 2 is as follows:

[0117] {

[0118] "field":"award",

[0119] "condition":2,

[0120] "valueType":"2.00",

[0121] "type":"Decimal",

[0122] "whiteType":1

[0123] }

[0124] It is understandable that the rules engine is used to intelligently filter and sort activities, with the aim of ensuring that the activities displayed to drivers are the most relevant and attractive, thereby increasing the participation rate of the activities.

[0125] Step S105: The marketing activity to be displayed is layer-fused with the heat map information to obtain target fusion data, which is returned to the driver side for display.

[0126] In the embodiment of the present invention, layer fusion refers to the display of marketing campaign information and heat map information in the form of different layers on the same map. Target fusion data refers to the comprehensive data generated after layer fusion, including heat map and campaign information.

[0127] Specifically, you can use the multi-layer overlay function provided by the map SDK to overlay the heat map and activity markers as different layers, or convert the geographic location information of the activity into specific coordinate points on the map, and convert the thermal information in the heat map into coordinate points on the same coordinate system to form a new layer, etc.

[0128] In an embodiment of the present invention, a marketing activity heat map query request sent by a driver is obtained, including the driver's current location information and city information. Based on the city information, a list of marketing activities that the driver can participate in on that day is queried from an activity database, and dynamic activity filtering rules corresponding to the city are obtained from a rule configuration database, ensuring that the recommended activities are effective activities for the driver's city, thereby improving the relevance of marketing activity promotion; based on the driver's current location information, heat map information is obtained from a thermal information database, and the marketing activity list and dynamic activity filtering rules are input into a rule engine to obtain marketing activities to be displayed. The rule engine can intelligently filter out the marketing activities that are most suitable for the current driver based on real-time data and preset rules, realize personalized recommendations for the driver himself, and improve the accuracy of marketing goals; the marketing activities to be displayed are layer-fused with the heat map information to obtain target fusion data, and returned to the driver for display, so that the driver can not only see the activity itself, but also intuitively understand the success rate of participating in the marketing activity in combination with the heat map information of the current location, thereby improving the driver's enthusiasm for participating in the activity and the completion rate, thereby improving the efficiency of the online car-hailing tenant platform.

[0129] In this embodiment, a method for recommending marketing activities for online car-hailing tenants is provided, which can be used in the above-mentioned computer. Figure 3 is a flow chart of a method for recommending marketing activities for online car-hailing tenants according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0130] Step S301: Obtain a marketing activity heat map query request sent by the driver. The marketing activity heat map query request includes the driver's current location information and city information. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0131] Step S302: Based on the city information, query the list of marketing activities that the driver can participate in on that day from the preset activity database, and obtain the dynamic activity filtering rules corresponding to the city from the preset rule configuration database. Figure 1Step S102 of the illustrated embodiment will not be described in detail here.

[0132] Step S303: Obtain thermal map information from a preset thermal information database based on the current location information. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0133] Step S304: input the marketing activity list and dynamic activity filtering rules into a preset rule engine to obtain marketing activities to be displayed.

[0134] Specifically, the above step S304 includes:

[0135] Step S3041: Determine the filtering type of the dynamic activity filtering rule.

[0136] For example, the content in step S102 is used as an example for explanation, "conditionType" represents the filter type, "conditionList" represents the rule object array, wherein "field", "condition", "value", "valueType" and the like represent multiple rule conditions.

[0137] Step S3042: If the filtering type is the first type, all corresponding rule conditions are input into the first rule engine; wherein the first type indicates that the marketing activities to be filtered must meet all rule conditions.

[0138] Exemplarily, conditionType is 1 (first type), indicating that all rule conditions need to be met when filtering marketing activities, and all corresponding rule conditions are input into the first rule engine.

[0139] Step S3043: If the filtering type is the second type, all corresponding rule conditions are input into the second rule engine; wherein the second type indicates that the marketing activities to be filtered must meet any one of the rule conditions.

[0140] Exemplarily, conditionType is 2 (second type), which means that any one rule condition needs to be met when filtering marketing activities, and all corresponding rule conditions are input into the second rule engine.

[0141] Step S3044: Based on the first rule engine or the second rule engine, each sub-information in the activity information corresponding to each marketing activity in the marketing activity list is matched using the rule condition to determine the filtered marketing activity as the marketing activity to be displayed.

[0142] The activity information of each marketing activity contains multiple sub-information;

[0143] The above step S3044 may specifically include the following two methods:

[0144] Method 1, based on the first rule engine, uses the rule conditions to match each condition of each marketing activity in the marketing activity list, and determines the filtered marketing activities as the marketing activities to be displayed, specifically including: parsing the set value of each condition in the rule conditions, traversing the activity information of all marketing activities, and during the traversal process, obtaining the actual value of each sub-information corresponding to the activity information of each marketing activity, and using the comparison operator to compare the actual value and the set value; if the comparison operation results of the actual value and the set value of each sub-information are both logically true, then the corresponding current marketing activity will be used as the marketing activity to be displayed.

[0145] Specifically, Method 1 involves iterating through each marketing campaign and obtaining each sub-information within its campaign information, such as the campaign ID and reward amount. The rule condition is then parsed for each condition, e.g., the campaign ID must not be in a certain list, the reward amount must be greater than 2.00, and so on. The actual value of each sub-information is compared with the set value using a comparison operator. Only when all sub-information values ​​compare to the set value and the result is logically true is the campaign selected for display.

[0146] Method 2, based on the second rule engine, uses the rule conditions to match each condition of each marketing activity in the marketing activity list, and determines the filtered marketing activities as the marketing activities to be displayed, specifically including: parsing the set value of each condition in the rule conditions, traversing the activity information of all marketing activities during the traversal process, obtaining the actual value of each sub-information corresponding to the activity information of each marketing activity during the traversal process, and using the comparison operator to compare the actual value and the set value; if the comparison result of the actual value and the set value of any sub-information is logically true, the corresponding current marketing activity will be used as the marketing activity to be displayed.

[0147] Specifically, Method 2 involves iterating through each marketing campaign and obtaining each sub-information within its campaign information, such as the campaign ID and reward amount. The rule conditions are then parsed for their set values, e.g., the campaign ID must not be in a certain list, the reward amount must be greater than 2.00, and so on. Comparison operators are then used to compare the actual value of each sub-information with the set value. If any of the sub-information values ​​compare to the set value and the result is logically true, the campaign is selected as a marketing campaign to be displayed.

[0148] For example, refer to Figure 4If conditionType=1, the first rule engine is used and the rule is encapsulated as a UnitRuleGroup (AND logic group). For each rule in conditionList: read the field field to determine the activity information field that needs to be verified. Parse value (set value), condition (comparison operator), and valueType (value type) and assemble these elements into a specific rule logic code. For example, if value=3, condition=less than, and valueType=int, the corresponding rule after logical assembly is $var<3, where $var is the variable value. Since conditionType=1, both rules (conditionList[1] and conditionList[2]) must pass for the rule verification to pass.

[0149] In an embodiment of the present invention, a first rule engine is used to parse rule conditions, traverse all activity information, and compare the actual value of each sub-information with the set value. Only when all conditions are met will the activity be recommended, ensuring that the activity relevance is high enough to be recommended to the driver side; a second rule engine is used to parse rule conditions, traverse all activity information, and compare the actual value of each sub-information with the set value. When any comparison operation result is logically true, it can be displayed. By adapting the rule engine according to different filtering types, all marketing activities to be displayed can be determined in one traversal process, thereby improving the filtering speed and providing an accurate data basis for the superposition of subsequent layers.

[0150] Step S305: The marketing activities to be displayed are layer-fused with the heat map information to obtain target fusion data, which is then returned to the driver terminal for display. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0151] In the embodiment of the present invention, when the filtering type is the first type, only activities that meet all the rule conditions will be recommended; when it is the second type, only any one condition needs to be met. Different marketing activity information can be flexibly configured, and the rule engine is used to intelligently match the sub-information in the activity information. The marketing activities to be displayed are not all activities, but activities that are more in line with the requirements of the city and location. The marketing activities to be displayed are obtained by filtering according to the first rule engine or the second rule engine, which flexibly adapts to the driver's personal situation, thereby improving the driver's participation rate.

[0152] This embodiment provides a method for recommending marketing activities for online car-hailing tenants, which can be used in the above-mentioned computer. Figure 5 is a flow chart of a method for recommending marketing activities for online car-hailing tenants according to an embodiment of the present invention. Figure 5 As shown, the process includes the following steps:

[0153] Step S501: Obtain a marketing activity heat map query request sent by the driver. The marketing activity heat map query request includes the driver's current location information and city information. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0154] Step S502: Based on the city information, query the list of marketing activities that the driver can participate in on that day from the preset activity database, and obtain the dynamic activity filtering rules corresponding to the city from the preset rule configuration database. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0155] Step S503: Obtain thermal map information from a preset thermal information database based on the current location information. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0156] Step S504: Input the marketing activity list and dynamic activity filtering rules into the preset rule engine to obtain the marketing activities to be displayed. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0157] Step S505: setting a corresponding display priority score according to the activity information of the marketing activity to be displayed.

[0158] Specifically, the above step S505 includes:

[0159] Step S5051: Obtain each item of sub-information in the activity information corresponding to each marketing activity, and obtain a weight value corresponding to each item of sub-information from a preset sorting weight according to each item of sub-information.

[0160] Specifically, detailed information for each activity is extracted from the activity database. Based on a pre-set ranking weight strategy, the weight value mapped to the sub-information is found from the ranking weight strategy. Ranking weights can be derived through historical data analysis or manually set by the operations team based on market strategies.

[0161] It is understandable that the principle of this step is to quantify the various characteristics of the activity into comparable numerical values, with the aim of providing basic data for the subsequent calculation of the overall display priority, thereby achieving more accurate activity sorting.

[0162] Step S5052: Sum up all sub-information weight values ​​to obtain a display priority score for the marketing activity.

[0163] For example, if the activity type is a bonus for placing an order, the background configuration weight is 60; if the activity is channel investment, the background configuration weight is 50. If there are no other weight factors, then the final weight of this activity is 110.

[0164] The embodiment of the present invention obtains sub-information of each marketing activity, obtains the corresponding weight value from the preset sorting weight, and sums the weight values ​​to calculate the display priority score, so as to more accurately identify the activities most relevant to specific drivers, thereby making personalized recommendations and improving the driver's enthusiasm for participating in the activities.

[0165] Step S506 , performing location screening on the marketing activities to be displayed according to the current location information of the driver and the activity information of the marketing activities to be displayed, and obtaining the screened marketing activities to be displayed.

[0166] Specifically, the above step S506 includes:

[0167] Step S5061: Acquire the activity address geo-fence area of ​​each marketing activity to be displayed, and the display area of ​​each marketing activity to be displayed on the page.

[0168] It should be noted that the activity address electronic fence area refers to the range of activities that can be participated in by the user, which is displayed as an area on the map. The display area refers to the display location of the activity information on the driver's application interface. Figure 6 Middle top carousel position and Figure 7 There are two types of bubble activities.

[0169] In step S5062, if the display area is a bubble area, the spatial relationship between the driver's side and the electronic fence area of ​​the activity address of each marketing activity to be displayed is calculated based on the driver's current location information and the map scale. Based on the spatial relationship, marketing activities whose driver's side is within the range of the activity electronic fence or whose driver's side is within a preset distance threshold from the activity electronic fence are selected as the filtered marketing activities to be displayed.

[0170] It should be noted that bubbles are used to mark nearby activities. In actual implementation, spatial geometry calculation technology can be used to determine the spatial relationship between the driver and the activity area based on information such as the driver's current latitude and longitude, the coordinate set of the active geo-fence, and the current map scale.

[0171] Specifically, the driver's current latitude and longitude coordinates and map scale level are obtained; the positional relationship between the driver's position and the electronic fence is calculated to determine whether it is outside; if it is outside, the shortest distance from the driver's position to the boundary of the electronic fence is calculated.

[0172] For example, when the driver is at a certain location, it will determine whether the point is within the electronic fence polygon composed of multiple coordinate points. If it is not inside, the distance from the point to the fence boundary will be calculated. If the distance is within a preset threshold (for example, 50 meters), the activity will be retained.

[0173] In an embodiment of the present invention, the electronic fence area of ​​a marketing activity and the display area type of the activity on the page are obtained, and the specific spatial position relationship between the driver and the activity area is calculated in combination with the map scale of the driver's current location. Ultimately, two types of activities are retained, namely, the driver is already within the activity electronic fence or the driver is within a preset threshold distance from the activity area. This is equivalent to only pushing activities that are geographically feasible, avoiding pushing activities that are too far away and cannot actually be participated in, thereby improving the practicality of the recommendation and further increasing the driver's willingness to participate in the activities, thereby improving the driver's completion rate.

[0174] Step S507: The marketing activity to be displayed is layer-fused with the heat map information to obtain target fusion data, which is returned to the driver side for display.

[0175] Specifically, the above step S507 includes:

[0176] Step S5071: Determine the display area of ​​each marketing activity to be displayed on the page.

[0177] It should be noted that in the display area, the top carousel position is mainly used to display large-scale activities (such as district-level and city-level activities), and the map bubble area is mainly used to display activities with a specific geographical scope (such as a certain business district).

[0178] For example, if a city-level event doesn't have a specific geo-fence, its display area is determined to be the top carousel. If a specific geo-fence is configured for the event, its display area is determined to be the map bubble area. It can be understood that choosing the appropriate display method for different types of events can improve the intuitiveness of marketing event presentations.

[0179] Step S5072: If the display area is a carousel area, the order of each marketing activity to be displayed is determined in descending order according to the display priority score; wherein the order is positively correlated with the display priority score; and a carousel area display layer is generated according to the order based on the preset carousel image component.

[0180] For example, if there are three carousel area activities, and their priority scores are 110 points (order bonus + channel investment), 90 points (regional incentive), and 70 points (newcomer reward), they will be displayed in the carousel component in a loop in the order of 110 points, 90 points, and 70 points.

[0181] Step S5073: If the display area is a bubble area, a bubble area display layer is generated for the filtered activity address geo-fence area of ​​the marketing activity to be displayed.

[0182] Specifically, the filtered bubble area activity information is obtained, including a set of electronic fence coordinate points, the electronic fence coordinate points are converted into map visualization polygons, a bubble mark is generated at the center of the electronic fence area, and the polygon area and the bubble mark are combined into a display layer.

[0183] Step S5074: superimpose the carousel area display layer and the bubble area display layer with the heat map information to obtain target fusion data, and return it to the driver side for display.

[0184] Specifically, determine the display order of each layer, fix the carousel area display layer at the top of the page, overlay the bubble area display layer on the map base layer, blend the heat map information layer with other layers with transparency, and generate the final fusion display data.

[0185] For example, in the final display effect, the activity carousel information with the highest weight is displayed at the top, and the supply and demand information of the heat map and the activity bubble mark are displayed on the map at the same time. When the driver user clicks the bubble, he can view the specific activity information.

[0186] In the embodiment of the present invention, activities are sorted according to display priority, and activities with high priority are displayed first. A carousel component is used for carousel area display, and multiple activities are automatically displayed in a cycle, providing dynamic visual effects and enhancing appeal. Activities are associated with electronic fence areas, and activity locations are marked in the form of bubbles on the map. Bubble area display is performed to intuitively display the geographical scope of the activity and help drivers quickly judge the distance. The carousel layer, bubble layer and heat map are superimposed to obtain a multi-dimensional information fusion display, which solves the problems of insufficient intuitiveness and low personalization in the promotion of related marketing activities, thereby increasing the driver's participation rate in activities, promoting order completion, and ensuring the benefits of the tenant platform.

[0187] In this embodiment, a system for recommending marketing activities for online car-hailing tenants is also provided. The system is used to implement the above-mentioned embodiments and preferred embodiments, and the details that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0188] This embodiment provides a car-hailing tenant marketing activity recommendation system. Figure 8 As shown, including:

[0189] The request receiving module 801 is used to obtain the marketing activity heat map query request sent by the driver; the marketing activity heat map query request includes the driver's current location information and the city information;

[0190] The activity query module 802 is used to query the list of marketing activities that the driver can participate in on that day from the preset activity database based on the city information, and obtain the dynamic activity filtering rules corresponding to the city from the preset rule configuration database;

[0191] The heat map acquisition module 803 is used to obtain heat map information from a preset heat information database according to the current location information;

[0192] The rule engine processing module 804 is used to input the marketing activity list and dynamic activity filtering rules into the preset rule engine to obtain the marketing activities to be displayed;

[0193] The data fusion module 805 is used to perform layer fusion of the marketing activities to be displayed and the heat map information to obtain target fusion data, and return it to the driver end for display.

[0194] In some optional implementations, the dynamic activity filtering rule includes multiple field information, each field information including at least one filtering type and at least one rule condition; the preset rule engine includes a first rule engine and a second rule engine; and the rule engine processing module 804 specifically includes:

[0195] A filter type determination unit, configured to determine the filter type of a dynamic activity filter rule;

[0196] a first-type rule engine unit, configured to input all corresponding rule conditions into the first rule engine if the filtering type is the first type; wherein the first type indicates that the marketing activities to be filtered must meet all the rule conditions;

[0197] A second-type rule engine unit, configured to input all corresponding rule conditions into a second rule engine if the filtering type is the second type; wherein the second type indicates that the marketing activities to be filtered must satisfy any one of the rule conditions;

[0198] The activity information matching unit is used to match each sub-information in the activity information corresponding to each marketing activity in the marketing activity list based on the first rule engine or the second rule engine using the rule conditions, and determine the filtered marketing activity as the marketing activity to be displayed.

[0199] In some optional implementations, the activity information of each marketing activity includes multiple sub-information;

[0200] The activity information matching unit specifically includes: a first rule engine sub-unit, configured to parse the set value of each condition in the rule condition, traverse the activity information of all marketing activities, obtain the actual value of each sub-information corresponding to the activity information of each marketing activity, and compare the actual value with the set value using a comparison operator; if the comparison result of the actual value and the set value of each sub-information is logically true, then the corresponding current marketing activity is selected as the marketing activity to be displayed;

[0201] The activity information matching unit specifically includes: a second rule engine sub-unit, which is used to parse the set value of each condition in the rule condition, and traverse the activity information of all marketing activities during the traversal process. During the traversal process, the actual value of each sub-information corresponding to the activity information of each marketing activity is obtained, and the actual value and the set value are compared using a comparison operator; if the comparison result of the actual value and the set value of any sub-information is logically true, the corresponding current marketing activity will be used as the marketing activity to be displayed.

[0202] In some optional embodiments, the above system further includes:

[0203] The marketing activity priority scoring module is used to set the corresponding display priority score according to the activity information of the marketing activity to be displayed, specifically including: obtaining each sub-information in the activity information corresponding to each marketing activity; according to each sub-information, obtaining the weight value corresponding to each sub-information from the preset sorting weight; summing up the weight values ​​of all sub-information to obtain the display priority score of the marketing activity.

[0204] In some optional embodiments, the above system further includes:

[0205] The marketing activity location screening module is used to perform location screening on the marketing activities to be displayed based on the driver's current location information and the activity information of the marketing activities to be displayed, and obtain the screened marketing activities to be displayed; specifically, it includes: obtaining the activity address electronic fence area of ​​each marketing activity to be displayed, and the display area of ​​each marketing activity to be displayed on the page; if the display area is a bubble area, then according to the driver's current location information and the map scale, the spatial relationship between the driver's side and the activity address electronic fence area of ​​each marketing activity to be displayed is calculated; according to the spatial relationship, the marketing activities whose driver's side is within the activity electronic fence range or the driver's side is within a preset distance threshold from the activity electronic fence are screened out as the screened marketing activities to be displayed.

[0206] In some optional implementations, the data fusion module 805 specifically includes:

[0207] A display area determination unit, used to determine the display area of ​​each marketing activity to be displayed on the page;

[0208] A carousel area display sorting unit, for determining the order of each marketing activity to be displayed in a descending order based on the display priority score if the display area is a carousel area; wherein the order is positively correlated with the display priority score; and generating a carousel area display layer according to the order based on a preset carousel image component;

[0209] The bubble area display layer unit is used to generate a bubble area display layer for the activity address geo-fence area of ​​the filtered marketing activity to be displayed if the display area is a bubble area;

[0210] The target fusion data unit is used to superimpose the carousel area display layer and the bubble area display layer with the heat map information to obtain target fusion data, and return it to the driver side for display.

[0211] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0212] The online car-hailing tenant marketing activity recommendation system in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0213] The embodiment of the present invention also provides a computer device having the above Figure 8 The ride-hailing tenant marketing campaign recommendation system shown.

[0214] See also Figure 9 , Figure 9 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 9As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 9 A processor 10 is taken as an example.

[0215] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0216] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0217] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0218] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0219] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0220] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0221] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0222] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the defined scope.

Claims

1. A method for recommending marketing activities for online car-hailing tenants, characterized in that: Applied to the server, the method includes: Obtaining a marketing activity heat map query request sent by the driver; the marketing activity heat map query request includes the driver's current location information and city information; According to the city information, query the list of marketing activities that the driver can participate in on that day from the preset activity database, and obtain the dynamic activity filtering rules corresponding to the city from the preset rule configuration database; According to the current position information, obtaining thermal map information from a preset thermal information database; Inputting the marketing activity list and the dynamic activity filtering rules into a preset rule engine to obtain the marketing activities to be displayed; The marketing activity to be displayed is layer-fused with the heat map information to obtain target fusion data, which is returned to the driver end for display.

2. The method according to claim 1, characterized in that The dynamic activity filtering rule includes a plurality of field information, wherein the field information includes at least one filtering type and at least one rule condition; the preset rule engine includes a first rule engine and a second rule engine; The step of inputting the marketing activity list and the dynamic activity filtering rules into a preset rule engine to obtain the marketing activities to be displayed specifically includes: Determining the filtering type of the dynamic activity filtering rule; If the filtering type is the first type, all the corresponding rule conditions are input into the first rule engine; wherein the first type indicates that the marketing activities to be filtered must meet all the rule conditions; If the filtering type is the second type, all the corresponding rule conditions are input into the second rule engine; wherein the second type indicates that the marketing activities to be filtered must satisfy any one of the rule conditions; Based on the first rule engine or the second rule engine, each sub-information in the activity information corresponding to each marketing activity in the marketing activity list is matched by using the rule condition to determine the filtered marketing activity as the marketing activity to be displayed.

3. The method according to claim 2, characterized in that The activity information of each marketing activity contains multiple sub-information; Based on the first rule engine, each condition of each marketing activity in the marketing activity list is matched by using the rule condition, and the filtered marketing activity is determined as the marketing activity to be displayed, specifically including: parsing the set value of each condition in the rule condition, traversing the activity information of all marketing activities, and in the traversal process, obtaining the actual value of each sub-information corresponding to the activity information of each marketing activity, and using a comparison operator to compare the actual value with the set value; if the comparison result of the actual value and the set value of each sub-information is logically true, the corresponding current marketing activity is used as the marketing activity to be displayed; Based on the second rule engine, each condition of each marketing activity in the marketing activity list is matched by using the rule condition to determine the filtered marketing activities as the marketing activities to be displayed, specifically including: parsing the set value of each condition in the rule condition, traversing the activity information of all marketing activities, and during the traversal process, obtaining the actual value of each sub-information corresponding to the activity information of each marketing activity, and using a comparison operator to compare the actual value with the set value; if the comparison result of the actual value and the set value of any sub-information is logically true, the corresponding current marketing activity is used as the marketing activity to be displayed.

4. The method according to claim 2 or 3, characterized in that: Before the step of fusing the marketing activity information to be displayed with the heat map information to obtain target fusion data and returning the data to the driver end for display, the step further includes: The corresponding display priority score is set according to the activity information of the marketing activity to be displayed, specifically including: obtaining each sub-information in the activity information corresponding to each marketing activity; according to each sub-information, obtaining the weight value corresponding to each sub-information from the preset sorting weight; and summing up all the sub-information weight values ​​to obtain the display priority score of the marketing activity.

5. The method according to claim 4, characterized in that Before the step of fusing the marketing activity information to be displayed with the heat map information to obtain target fusion data and returning the data to the driver end for display, the step further includes: Performing location screening on the marketing activities to be displayed according to the current location information of the driver and the activity information of the marketing activities to be displayed to obtain the screened marketing activities to be displayed; specifically including: Obtain the activity address geo-fenced area of ​​each marketing activity to be displayed, as well as the display area of ​​each marketing activity to be displayed on the page; If the display area is a bubble area, the spatial relationship between the driver terminal and the electronic fence area of ​​the activity address of each marketing activity to be displayed is calculated according to the current location information of the driver and the map scale; According to the spatial relationship, marketing activities in which the driver's end is within the range of the active electronic fence or the driver's end is within a preset distance threshold from the active electronic fence are filtered out as filtered marketing activities to be displayed.

6. The method according to claim 5, characterized in that The step of fusing the marketing activity to be displayed with the heat map information to obtain target fusion data, and returning the data to the driver terminal for display, specifically includes: Determine a display area of ​​each marketing activity to be displayed on the page; If the display area is a carousel area, the order of each marketing activity to be displayed is determined in a descending order according to the display priority score; wherein the order is positively correlated with the display priority score; and a carousel area display layer is generated according to the order based on a preset carousel image component; If the display area is a bubble area, generating a bubble area display layer from the activity address electronic fence area of ​​the filtered marketing activity to be displayed; The carousel area display layer and the bubble area display layer are superimposed on the heat map information to obtain target fusion data, which is returned to the driver end for display.

7. A marketing activity recommendation system for online car-hailing tenants, characterized in that: The system comprises: A request receiving module, used to obtain a marketing activity heat map query request sent by a driver; the marketing activity heat map query request includes the driver's current location information and the city information; An activity query module, used to query a list of marketing activities that the driver can participate in on that day from a preset activity database according to the city information, and to obtain dynamic activity filtering rules corresponding to the city from a preset rule configuration database; A thermal map acquisition module, used to acquire thermal map information from a preset thermal information database according to the current position information; A rule engine processing module, used for inputting the marketing activity list and the dynamic activity filtering rules into a preset rule engine to obtain the marketing activities to be displayed; The data fusion module is used to perform layer fusion on the marketing activities to be displayed and the heat map information to obtain target fusion data, and return it to the driver end for display.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for recommending marketing activities for online car-hailing tenants according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for recommending online car-hailing tenant marketing activities as described in any one of claims 1-6.

10. A computer program product, characterized in that It includes computer instructions, which are used to enable a computer to execute the online car-hailing tenant marketing activity recommendation method described in any one of claims 1-6.

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