Operation strategy execution method and device based on edge node, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence, and provides an operation strategy execution method and device based on edge nodes, equipment and a medium, which can obtain an operation conversion rate prediction model according to historical operation data of a target merchant, a marked operation strategy and an operation conversion rate training decision tree model. According to the method, the to-be-adjusted operation strategy is input into the operation conversion rate prediction model, and the target operation strategy is obtained by performing sectional parameter adjustment on the to-be-adjusted operation strategy, so that the optimal operation strategy is automatically predicted based on an artificial intelligence means, the making efficiency is high, and unreasonable making of the operation strategy caused by human factors can be avoided; according to the method, the target price strategy is deployed on the plurality of nodes with the edge computing capability on the block chain, so that the target merchant is assisted to execute the target operation strategy in a distributed manner based on the edge nodes, the execution efficiency is improved, errors are not easy to occur, and meanwhile, the labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an edge node-based operation strategy execution method, device, equipment and medium. Background Art

[0002] At present, the operation of merchants usually requires manual decision-making by operators, which is highly subjective and requires high business capabilities of operators. It is easy for operational errors to be introduced due to human factors, causing unnecessary losses to merchants.

[0003] Furthermore, merchant operations are multifaceted, involving the formulation of pricing strategies (such as price calculation methods), information display strategies (such as advertising display strategies and push notification display strategies), product strategies (such as menu selection and product quality control processes), service strategies, store design plans, and customer relationship management strategies. Therefore, manually formulating operational strategies can be extremely labor-intensive.

[0004] At the same time, the execution of operational strategies also needs to be driven manually, which not only has low execution efficiency but also a high error rate. Summary of the Invention

[0005] In view of the above, it is necessary to provide an edge node-based operation strategy execution method, device, equipment and medium, aiming to solve the problems of high execution cost, low execution efficiency and easy error of operation strategy.

[0006] An edge node-based operation strategy execution method, the edge node-based operation strategy execution method comprising:

[0007] Obtain historical operating data of target merchants;

[0008] Marking the operation strategy and operation conversion rate of the historical operation data to obtain training samples;

[0009] Using the training samples to train a decision tree model to obtain an operation conversion rate prediction model;

[0010] Responding to the target merchant's operation strategy adjustment instruction, obtaining the operation strategy to be adjusted;

[0011] Inputting the to-be-adjusted operating strategy into the operating conversion rate prediction model, and performing segmented parameter adjustment on the to-be-adjusted operating strategy until the operating conversion rate output by the operating conversion rate prediction model reaches the configured conversion rate, stopping the adjustment, and determining the adjusted to-be-adjusted operating strategy as the target operating strategy;

[0012] Sending the target operation strategy to the target merchant for confirmation;

[0013] When a confirmation signal of the target merchant for the target operation strategy is received, a target price strategy and a target information display strategy are extracted from the target operation strategy, and the target price strategy is encapsulated as a component;

[0014] Creating a smart contract for invoking the component, deploying the smart contract to multiple nodes with edge computing capabilities on a blockchain, and storing the target information display strategy in the multiple nodes; wherein the multiple nodes correspond to multiple edge nodes of the target merchant;

[0015] The target merchants are assisted in executing the target operation strategy in a distributed manner based on the multiple edge nodes.

[0016] According to a preferred embodiment of the present invention, the stepwise parameter adjustment of the operation strategy to be adjusted includes:

[0017] For the price strategy in the operation strategy to be adjusted, obtaining a non-uniform sequence configured to store multiple deduction steps; adjusting the price strategy using the non-uniform sequence; and / or

[0018] For the information display strategy in the operation strategy to be adjusted, a knowledge graph for storing the mapping relationship between user attributes and information display strategies is obtained, and the information display strategy is adjusted according to the knowledge graph.

[0019] According to a preferred embodiment of the present invention, encapsulating the target price strategy into components includes:

[0020] Abstracting the target price strategy into a function, and encapsulating the function into the component;

[0021] Wherein, the function includes a non-uniform deduction value sequence and a cycle period corresponding to the cyclic segmentation model;

[0022] The elements of the non-uniform deduction value sequence are a plurality of deduction values that are not of equal decreasing value, and the value of the first element in the non-uniform deduction value sequence is greater than the value of the last element.

[0023] According to a preferred embodiment of the present invention, assisting the target merchant in distributed execution of the target operation strategy based on the multiple edge nodes includes:

[0024] When a scanning signal of a barcode corresponding to any edge node among the plurality of edge nodes is received, obtaining information to be displayed from a configuration database according to the target information display strategy, and sending the information to be displayed to a terminal that scans the barcode corresponding to the any edge node;

[0025] The arbitrary edge node is used to collect order information, and the component corresponding to the arbitrary edge node is called to process the collected order information to obtain a first consumption amount, and payment prompt information based on the first consumption amount is sent to a terminal that scans the barcode corresponding to the arbitrary edge node.

[0026] According to a preferred embodiment of the present invention, assisting the target merchant in distributed execution of the target operation strategy based on the multiple edge nodes further includes:

[0027] When a start signal of a designated applet is received, the information to be displayed is obtained from the configuration database according to the target information display strategy, and the information to be displayed is displayed on the display interface of the designated applet;

[0028] Obtain current location information according to the designated mini program, and select a target node from the multiple edge nodes according to the current location information; use the target node to collect order information, and call the component corresponding to the target node to process the collected order information to obtain a second consumption amount, and send payment prompt information based on the second consumption amount to the designated mini program.

[0029] According to a preferred embodiment of the present invention, after the target merchant is assisted in executing the target operation strategy in a distributed manner based on the multiple edge nodes, the method further includes:

[0030] When receiving abnormal feedback, obtaining abnormal orders from the blockchain according to the abnormal feedback;

[0031] Perform exception tracing based on the abnormal order.

[0032] According to a preferred embodiment of the present invention, the method further comprises:

[0033] Collecting new operating data of the target merchant at preset time intervals;

[0034] The operation conversion rate prediction model is optimized and trained based on the newly added operation data.

[0035] An edge node-based operation strategy execution device, the edge node-based operation strategy execution device comprising:

[0036] An acquisition unit, used to acquire historical operating data of a target merchant;

[0037] a marking unit, configured to mark the operation strategy and operation conversion rate of the historical operation data to obtain training samples;

[0038] A training unit, configured to train a decision tree model using the training samples to obtain an operation conversion rate prediction model;

[0039] The acquisition unit is further configured to acquire the operation strategy to be adjusted in response to the operation strategy adjustment instruction of the target merchant;

[0040] an adjustment unit, configured to input the operation strategy to be adjusted into the operation conversion rate prediction model, and perform stepwise parameter adjustment on the operation strategy to be adjusted until the operation conversion rate output by the operation conversion rate prediction model reaches the configured conversion rate, stop the adjustment, and determine the adjusted operation strategy to be adjusted as the target operation strategy;

[0041] a sending unit, configured to send the target operation strategy to the target merchant for confirmation;

[0042] an encapsulation unit, configured to extract a target price strategy and a target information display strategy from the target operation strategy upon receiving a confirmation signal from the target merchant regarding the target operation strategy, and encapsulate the target price strategy into a component;

[0043] a deployment unit, configured to create a smart contract for invoking the component, deploy the smart contract to multiple nodes with edge computing capabilities on a blockchain, and store the target information display policy in the multiple nodes; wherein the multiple nodes correspond to multiple edge nodes of the target merchant;

[0044] An execution unit is configured to assist the target merchant in distributively executing the target operation strategy based on the multiple edge nodes.

[0045] A computer device, comprising:

[0046] a memory storing at least one instruction; and

[0047] The processor executes the instructions stored in the memory to implement the edge node-based operation policy execution method.

[0048] A computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the edge node-based operation policy execution method.

[0049] It can be seen from the above technical solutions that the present invention can obtain an operation conversion rate prediction model based on the historical operation data, marked operation strategies and operation conversion rate training decision tree model of the target merchant, and input the operation strategy to be adjusted into the operation conversion rate prediction model, and obtain the target operation strategy by performing segmented parameter adjustment on the operation strategy to be adjusted, thereby automatically predicting the optimal operation strategy based on artificial intelligence means, which not only has high formulation efficiency, but also can avoid unreasonable formulation of operation strategies due to human factors; the target price strategy is deployed on multiple nodes with edge computing capabilities on the blockchain, thereby assisting the target merchants in distributed execution of the target operation strategy based on the edge nodes, thereby improving execution efficiency, making it less prone to errors, and reducing labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a preferred embodiment of the edge node-based operation strategy execution method of the present invention.

[0051] Figure 2 It is a functional module diagram of a preferred embodiment of the edge node-based operation strategy execution device of the present invention.

[0052] Figure 3 It is a structural diagram of a computer device of a preferred embodiment of the present invention for implementing an edge node-based operation strategy execution method. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] like Figure 1 FIG. 1 is a flow chart of a preferred embodiment of the method for executing an operation strategy based on an edge node of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different requirements.

[0055] The edge node-based operation strategy execution method is applied to one or more computer devices, which are devices that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Their hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0056] The computer device may be any electronic product that can interact with a user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an interactive network television (IPTV), a smart wearable device, etc.

[0057] The computer device may also include a network device and / or a user device, wherein the network device includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0058] The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0059] Among them, Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0060] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0061] The network where the computer device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0062] S10, obtaining historical operating data of the target merchant.

[0063] In this embodiment, the target merchants may include catering merchants, supermarket merchants, etc.

[0064] In this embodiment, the historical operation data may include the target merchant's historical operating products, historical prices, historical promotion methods, historical service methods, historical store designs, historical customer relationship management methods, etc.

[0065] S11, marking the operation strategy and operation conversion rate of the historical operation data to obtain a training sample.

[0066] In this embodiment, the historical operation data may be labeled according to the operation strategy and operation conversion rate of the historical operation data to mark the operation strategy and operation conversion rate of the historical operation data to obtain the training sample.

[0067] S12: Using the training samples to train a decision tree model to obtain an operation conversion rate prediction model.

[0068] In this embodiment, the training samples may be further divided into a training set and a validation set. The training set is used to train the decision tree model, and the validation set is used to validate the training results.

[0069] When the trained model passes the verification, the current model is determined as the operation conversion rate prediction model.

[0070] S13: Responding to the target merchant's operation strategy adjustment instruction, obtaining the operation strategy to be adjusted.

[0071] In this embodiment, the operation strategy adjustment instruction can be triggered by the operation personnel according to actual needs, or can be triggered at a fixed time.

[0072] In this embodiment, the operation strategy to be adjusted may be the operation strategy currently being used by the target merchant, or may be the operation strategy initially formulated by the operation personnel as a basis for model adjustment.

[0073] S14, input the operation strategy to be adjusted into the operation conversion rate prediction model, and perform segmented parameter adjustment on the operation strategy to be adjusted until the operation conversion rate output by the operation conversion rate prediction model reaches the configured conversion rate, stop the adjustment, and determine the adjusted operation strategy to be adjusted as the target operation strategy.

[0074] In this embodiment, the stepwise parameter adjustment of the operation strategy to be adjusted includes:

[0075] For the price strategy in the operation strategy to be adjusted, obtaining a non-uniform sequence configured to store multiple deduction steps; adjusting the price strategy using the non-uniform sequence; and / or

[0076] For the information display strategy in the operation strategy to be adjusted, a knowledge graph for storing the mapping relationship between user attributes and information display strategies is obtained, and the information display strategy is adjusted according to the knowledge graph.

[0077] The deduction step can be 10, 5, etc., and the original price strategy is deducted one by one according to the deduction step. For example, when the original price strategy includes a price of 100 yuan, if the corresponding deduction step is 10, the adjusted price is 100-10=90.

[0078] The knowledge graph can use user attributes and information display strategies as nodes, and the mapping relationship between the two as edges. By constructing the knowledge graph, the relationship between user attributes and information display strategies can be more reasonably reflected, so that more appropriate information display strategies can be formulated according to different types of users.

[0079] The information display strategy may include advertisement display, push display, etc.

[0080] The configuration conversion rate can be configured based on experiments. The configuration conversion rate is used to characterize the rationality of the formulated operation strategy. The higher the operation conversion rate output by the operation conversion rate prediction model, the higher the availability of the corresponding operation strategy.

[0081] S15: Send the target operation strategy to the target merchant for confirmation.

[0082] In this embodiment, in order to ensure the rationality of the operation strategy formulated by the model, the target operation strategy is further sent to the target merchant for confirmation, so as to improve the availability of the operation strategy formulated by the model.

[0083] S16 , when a confirmation signal of the target merchant for the target operation strategy is received, a target price strategy and a target information display strategy are extracted from the target operation strategy, and the target price strategy is encapsulated as a component.

[0084] In this embodiment, encapsulating the target price strategy into components includes:

[0085] Abstracting the target price strategy into a function, and encapsulating the function into the component;

[0086] Wherein, the function includes a non-uniform deduction value sequence and a cycle period corresponding to the cyclic segmentation model;

[0087] The elements of the non-uniform deduction value sequence are a plurality of deduction values that are not of equal decreasing value, and the value of the first element in the non-uniform deduction value sequence is greater than the value of the last element.

[0088] For example, the non-uniform deduction value sequence may be (50 30 20 20 20); and the cycle period is 500.

[0089] That is, if the purchase amount reaches 100, the discount value is used to deduct one amount. That is, for every 500 spent, the discount value is 50 + 30 + 20 + 20 + 20 = 140. In this process, if the purchase amount is 100, the discount is 50; if the purchase amount is 150 but less than 200, the discount is 50; if the purchase amount is 200, the discount is 50 + 30 = 80, and so on.

[0090] Of course, the non-uniform deduction value sequence and the cycle period can be customized according to actual needs, and the present invention is not limited thereto.

[0091] In the above embodiment, a cyclic segmentation model is adopted to ensure that users are attracted by high discount values in the front end and continuous incentives are provided in the back end to complete the cycle, which can effectively increase the user's repurchase rate, improve customer stickiness, and effectively control costs.

[0092] Experimental verification shows that the above pricing strategy can effectively increase the repurchase rate for both chain merchants and takeaway merchants.

[0093] S17, creating a smart contract for calling the component, deploying the smart contract to multiple nodes with edge computing capabilities on the blockchain, and storing the target information display strategy to the multiple nodes; wherein the multiple nodes correspond to multiple edge nodes of the target merchant.

[0094] Through the above embodiments, the security of data can be improved by combining with blockchain, and the data processing efficiency can be improved through edge nodes.

[0095] S18: Assisting the target merchant in executing the target operation strategy in a distributed manner based on the multiple edge nodes.

[0096] In this embodiment, assisting the target merchant in distributively executing the target operation strategy based on the multiple edge nodes includes:

[0097] When a scanning signal of a barcode corresponding to any edge node among the plurality of edge nodes is received, obtaining information to be displayed from a configuration database according to the target information display strategy, and sending the information to be displayed to a terminal that scans the barcode corresponding to the any edge node;

[0098] The arbitrary edge node is used to collect order information, and the component corresponding to the arbitrary edge node is called to process the collected order information to obtain a first consumption amount, and payment prompt information based on the first consumption amount is sent to a terminal that scans the barcode corresponding to the arbitrary edge node.

[0099] The configuration database may be a locally deployed database to improve data reading efficiency.

[0100] The information to be displayed may include, but is not limited to: advertisements, push notifications, prompt information, etc.

[0101] For example, the above execution method can be used for users who scan and order in stores and place orders.

[0102] In this embodiment, assisting the target merchant in executing the target operation strategy in a distributed manner based on the multiple edge nodes further includes:

[0103] When a start signal of a designated applet is received, the information to be displayed is obtained from the configuration database according to the target information display strategy, and the information to be displayed is displayed on the display interface of the designated applet;

[0104] Obtain current location information according to the designated mini program, and select a target node from the multiple edge nodes according to the current location information; use the target node to collect order information, and call the component corresponding to the target node to process the collected order information to obtain a second consumption amount, and send payment prompt information based on the second consumption amount to the designated mini program.

[0105] The target node may be an edge node closest to the current positioning information.

[0106] For example: For users who place orders through mini programs, the above execution method can be used.

[0107] In the above embodiments, targeted processing can be performed in combination with different triggering methods of users.

[0108] In this embodiment, after the target merchant is assisted in executing the target operation strategy in a distributed manner based on the multiple edge nodes, the method further includes:

[0109] When receiving abnormal feedback, obtaining abnormal orders from the blockchain according to the abnormal feedback;

[0110] Perform exception tracing based on the abnormal order.

[0111] Through the above embodiments, safe tracing of anomalies can be achieved based on the tamper-proof nature of blockchain data.

[0112] In this embodiment, the method further includes:

[0113] Collecting new operating data of the target merchant at preset time intervals;

[0114] The operation conversion rate prediction model is optimized and trained based on the newly added operation data.

[0115] The preset time interval can be configured according to actual needs, such as 3 months.

[0116] Through the above embodiments, the real-time availability of the model can be effectively guaranteed, while the accuracy of the operation strategy predicted by the model is guaranteed.

[0117] It can be seen from the above technical solutions that the present invention can obtain an operation conversion rate prediction model based on the historical operation data, marked operation strategies and operation conversion rate training decision tree model of the target merchant, and input the operation strategy to be adjusted into the operation conversion rate prediction model, and obtain the target operation strategy by performing segmented parameter adjustment on the operation strategy to be adjusted, thereby automatically predicting the optimal operation strategy based on artificial intelligence means, which not only has high formulation efficiency, but also can avoid unreasonable formulation of operation strategies due to human factors; the target price strategy is deployed on multiple nodes with edge computing capabilities on the blockchain, thereby assisting the target merchants in distributed execution of the target operation strategy based on the edge nodes, thereby improving execution efficiency, making it less prone to errors, and reducing labor costs.

[0118] like Figure 2 , which is a functional module diagram of a preferred embodiment of an edge node-based operation policy execution device of the present invention. The edge node-based operation policy execution device 11 includes an acquisition unit 110, a marking unit 111, a training unit 112, an adjustment unit 113, a sending unit 114, an encapsulation unit 115, a deployment unit 116, and an execution unit 117. The modules / units referred to in the present invention refer to a series of computer program segments that can be executed by a processor and can perform fixed functions, which are stored in a memory. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0119] The acquisition unit 110 is used to acquire the historical operation data of the target merchant;

[0120] The marking unit 111 is used to mark the operation strategy and operation conversion rate of the historical operation data to obtain training samples;

[0121] The training unit 112 is configured to train a decision tree model using the training samples to obtain an operation conversion rate prediction model;

[0122] The acquisition unit 110 is further configured to acquire the operation strategy to be adjusted in response to the operation strategy adjustment instruction of the target merchant;

[0123] The adjustment unit 113 is configured to input the to-be-adjusted operation strategy into the operation conversion rate prediction model, and perform step-by-step parameter adjustment on the to-be-adjusted operation strategy until the operation conversion rate output by the operation conversion rate prediction model reaches the configured conversion rate, then stop the adjustment, and determine the adjusted to-be-adjusted operation strategy as the target operation strategy;

[0124] The sending unit 114 is used to send the target operation strategy to the target merchant for confirmation;

[0125] The encapsulation unit 115 is configured to extract a target price strategy and a target information display strategy from the target operation strategy upon receiving a confirmation signal from the target merchant regarding the target operation strategy, and encapsulate the target price strategy into a component;

[0126] The deployment unit 116 is configured to create a smart contract for invoking the component, deploy the smart contract to multiple nodes with edge computing capabilities on the blockchain, and store the target information display strategy in the multiple nodes; wherein the multiple nodes correspond to multiple edge nodes of the target merchant;

[0127] The execution unit 117 is configured to assist the target merchant in executing the target operation strategy in a distributed manner based on the multiple edge nodes.

[0128] It can be seen from the above technical solutions that the present invention can obtain an operation conversion rate prediction model based on the historical operation data, marked operation strategies and operation conversion rate training decision tree model of the target merchant, and input the operation strategy to be adjusted into the operation conversion rate prediction model, and obtain the target operation strategy by performing segmented parameter adjustment on the operation strategy to be adjusted, thereby automatically predicting the optimal operation strategy based on artificial intelligence means, which not only has high formulation efficiency, but also can avoid unreasonable formulation of operation strategies due to human factors; the target price strategy is deployed on multiple nodes with edge computing capabilities on the blockchain, thereby assisting the target merchants in distributed execution of the target operation strategy based on the edge nodes, thereby improving execution efficiency, making it less prone to errors, and reducing labor costs.

[0129] like Figure 3 , which is a structural diagram of a computer device according to a preferred embodiment of the present invention for implementing an edge node-based operation strategy execution method.

[0130] The computer device 1 may include a memory 12 , a processor 13 , and a bus, and may further include a computer program stored in the memory 12 and executable on the processor 13 , such as an edge node-based operation policy execution program.

[0131] Those skilled in the art will understand that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation on the computer device 1. The computer device 1 may have either a bus structure or a star structure. The computer device 1 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components. For example, the computer device 1 may also include input and output devices, network access devices, etc.

[0132] It should be noted that the computer device 1 is only an example. Other existing or future electronic products that are suitable for the present invention should also be included in the scope of protection of the present invention and included here by reference.

[0133] Among them, the memory 12 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the computer device 1, such as a mobile hard disk of the computer device 1. In other embodiments, the memory 12 can also be an external storage device of the computer device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 1. Furthermore, the memory 12 can also include both an internal storage unit of the computer device 1 and an external storage device. The memory 12 can not only be used to store application software and various types of data installed on the computer device 1, such as the code of the edge node-based operation policy execution program, but can also be used to temporarily store data that has been output or is to be output.

[0134] In some embodiments, the processor 13 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 13 is the control core (Control Unit) of the computer device 1, connecting the various components of the entire computer device 1 using various interfaces and lines. It executes the various functions and processes data of the computer device 1 by running or executing programs or modules stored in the memory 12 (such as executing an edge node-based operation policy execution program) and calling data stored in the memory 12.

[0135] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-mentioned embodiments of the operation strategy execution method based on edge nodes, for example Figure 1 Steps shown.

[0136] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to implement the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into an acquisition unit 110, a labeling unit 111, a training unit 112, an adjustment unit 113, a sending unit 114, a packaging unit 115, a deployment unit 116, and an execution unit 117.

[0137] The above-mentioned integrated unit implemented in the form of a software functional module can be stored in a computer-readable storage medium. The above-mentioned software functional module stored in a storage medium includes a number of instructions for causing a computer device (which can be a personal computer, computer device, or network device, etc.) or a processor to execute the portion of the edge node-based operation policy execution method described in various embodiments of the present invention.

[0138] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the processes in the above-mentioned method embodiments by instructing relevant hardware devices through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments.

[0139] The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory, etc.

[0140] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0141] Blockchain, as used in this article, refers to a novel application model for computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Blockchain is essentially a decentralized database, a series of data blocks generated using cryptographic methods. Each block contains information about a batch of online transactions, used to verify the validity of this information (to prevent counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, the platform product service layer, and the application service layer.

[0142] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The figure shows that only one straight line is used, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 12 and at least one processor 13.

[0143] Although not shown, the computer device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 13 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power supply may also include one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components. The computer device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be detailed here.

[0144] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used to establish a communication connection between the computer device 1 and other computer devices.

[0145] Optionally, the computer device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or a display unit, and is used to display information processed in the computer device 1 and to display a visual user interface.

[0146] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0147] Figure 3 Only the computer device 1 having components 12-13 is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the computer device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0148] Combine Figure 1 The memory 12 in the computer device 1 stores a plurality of instructions to implement an edge node-based operation strategy execution method, and the processor 13 can execute the plurality of instructions to implement:

[0149] Obtain historical operating data of target merchants;

[0150] Marking the operation strategy and operation conversion rate of the historical operation data to obtain training samples;

[0151] Using the training samples to train a decision tree model to obtain an operation conversion rate prediction model;

[0152] Responding to the target merchant's operation strategy adjustment instruction, obtaining the operation strategy to be adjusted;

[0153] Inputting the to-be-adjusted operating strategy into the operating conversion rate prediction model, and performing segmented parameter adjustment on the to-be-adjusted operating strategy until the operating conversion rate output by the operating conversion rate prediction model reaches the configured conversion rate, stopping the adjustment, and determining the adjusted to-be-adjusted operating strategy as the target operating strategy;

[0154] Sending the target operation strategy to the target merchant for confirmation;

[0155] When a confirmation signal of the target merchant for the target operation strategy is received, a target price strategy and a target information display strategy are extracted from the target operation strategy, and the target price strategy is encapsulated as a component;

[0156] Creating a smart contract for invoking the component, deploying the smart contract to multiple nodes with edge computing capabilities on a blockchain, and storing the target information display strategy in the multiple nodes; wherein the multiple nodes correspond to multiple edge nodes of the target merchant;

[0157] The target merchants are assisted in executing the target operation strategy in a distributed manner based on the multiple edge nodes.

[0158] Specifically, the specific implementation method of the processor 13 for the above instructions can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0159] It should be noted that the data involved in this case were all obtained legally.

[0160] In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical functional division, and actual implementation may employ other division methods.

[0161] The present invention can be used in a wide variety of general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0162] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0163] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0164] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0165] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0166] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in the present invention may also be implemented by a single unit or device through software or hardware. Terms such as first and second are used to indicate names and do not imply any particular order.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for executing an operation strategy based on an edge node, characterized in that: The edge node-based operation strategy execution method includes: Obtain historical operating data of target merchants; Marking the operation strategy and operation conversion rate of the historical operation data to obtain training samples; Using the training samples to train a decision tree model to obtain an operation conversion rate prediction model; Responding to the target merchant's operation strategy adjustment instruction, obtaining the operation strategy to be adjusted; Inputting the to-be-adjusted operating strategy into the operating conversion rate prediction model, and performing segmented parameter adjustment on the to-be-adjusted operating strategy until the operating conversion rate output by the operating conversion rate prediction model reaches the configured conversion rate, stopping the adjustment, and determining the adjusted to-be-adjusted operating strategy as the target operating strategy; Sending the target operation strategy to the target merchant for confirmation; When a confirmation signal of the target merchant for the target operation strategy is received, a target price strategy and a target information display strategy are extracted from the target operation strategy, and the target price strategy is encapsulated as a component; Creating a smart contract for invoking the component, deploying the smart contract to multiple nodes with edge computing capabilities on a blockchain, and storing the target information display strategy in the multiple nodes; wherein the multiple nodes correspond to multiple edge nodes of the target merchant; The target merchants are assisted in executing the target operation strategy in a distributed manner based on the multiple edge nodes.

2. The edge node-based operation strategy execution method according to claim 1, characterized in that: The stepwise parameter adjustment of the operation strategy to be adjusted includes: For the price strategy in the operation strategy to be adjusted, obtaining a non-uniform sequence configured to store multiple deduction steps; adjusting the price strategy using the non-uniform sequence; and / or For the information display strategy in the operation strategy to be adjusted, a knowledge graph for storing the mapping relationship between user attributes and information display strategies is obtained, and the information display strategy is adjusted according to the knowledge graph.

3. The edge node-based operation strategy execution method according to claim 1, characterized in that: The encapsulating the target price strategy into components includes: Abstracting the target price strategy into a function, and encapsulating the function into the component; Wherein, the function includes a non-uniform deduction value sequence and a cycle period corresponding to the cyclic segmentation model; The elements of the non-uniform deduction value sequence are a plurality of deduction values that are not of equal decreasing value, and the value of the first element in the non-uniform deduction value sequence is greater than the value of the last element.

4. The edge node-based operation strategy execution method according to claim 1, characterized in that: The step of assisting the target merchant in distributively executing the target operation strategy based on the multiple edge nodes includes: When a scanning signal of a barcode corresponding to any edge node among the plurality of edge nodes is received, obtaining information to be displayed from a configuration database according to the target information display strategy, and sending the information to be displayed to a terminal that scans the barcode corresponding to the any edge node; The arbitrary edge node is used to collect order information, and the component corresponding to the arbitrary edge node is called to process the collected order information to obtain a first consumption amount, and payment prompt information based on the first consumption amount is sent to a terminal that scans the barcode corresponding to the arbitrary edge node.

5. The edge node-based operation strategy execution method according to claim 4, characterized in that: The step of assisting the target merchant in distributively executing the target operation strategy based on the multiple edge nodes further includes: When a start signal of a designated applet is received, the information to be displayed is obtained from the configuration database according to the target information display strategy, and the information to be displayed is displayed on the display interface of the designated applet; Obtain current location information according to the designated mini program, and select a target node from the multiple edge nodes according to the current location information; use the target node to collect order information, and call the component corresponding to the target node to process the collected order information to obtain a second consumption amount, and send payment prompt information based on the second consumption amount to the designated mini program.

6. The edge node-based operation strategy execution method according to claim 1, characterized in that: After the target merchant is assisted in executing the target operation strategy in a distributed manner based on the multiple edge nodes, the method further includes: When receiving abnormal feedback, obtaining abnormal orders from the blockchain according to the abnormal feedback; Perform exception tracing based on the abnormal order.

7. The edge node-based operation strategy execution method according to claim 1, characterized in that: The method further comprises: At predetermined time intervals, new operational data of the target merchant is collected; The operation conversion rate prediction model is optimized and trained based on the newly added operation data.

8. An edge node-based operation strategy execution device, characterized in that: The edge node-based operation strategy execution device includes: An acquisition unit, used to acquire historical operation data of a target merchant; a marking unit, configured to mark the operation strategy and operation conversion rate of the historical operation data to obtain training samples; A training unit, configured to train a decision tree model using the training samples to obtain an operation conversion rate prediction model; The acquisition unit is further configured to acquire the operation strategy to be adjusted in response to the operation strategy adjustment instruction of the target merchant; an adjustment unit, configured to input the operation strategy to be adjusted into the operation conversion rate prediction model, and perform stepwise parameter adjustment on the operation strategy to be adjusted until the operation conversion rate output by the operation conversion rate prediction model reaches the configured conversion rate, stop the adjustment, and determine the adjusted operation strategy to be adjusted as the target operation strategy; a sending unit, configured to send the target operation strategy to the target merchant for confirmation; an encapsulation unit, configured to extract a target price strategy and a target information display strategy from the target operation strategy upon receiving a confirmation signal from the target merchant regarding the target operation strategy, and encapsulate the target price strategy into a component; a deployment unit, configured to create a smart contract for invoking the component, deploy the smart contract to multiple nodes with edge computing capabilities on a blockchain, and store the target information display policy in the multiple nodes; wherein the multiple nodes correspond to multiple edge nodes of the target merchant; An execution unit is configured to assist the target merchant in distributively executing the target operation strategy based on the multiple edge nodes.

9. A computer device, characterized in that: The computer device comprises: a memory storing at least one instruction; and A processor executes instructions stored in the memory to implement the edge node-based operation policy execution method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the edge node-based operation policy execution method according to any one of claims 1 to 7.

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