Dealer sales volume prediction method and system, computer equipment and storage medium

Abstract

The embodiment of the invention belongs to the technical field of data prediction, and relates to a dealer sales volume prediction method, which comprises the following steps: acquiring comprehensive implantation data of a target hospital about a target product; according to the comprehensive implantation data, determining the implantation demand of the target hospital about the target product; acquiring historical dealer sales volume data of the target hospital about the target product; analyzing the historical dealer sales volume data to obtain a dealer sales volume trend of the target hospital about the target product; and according to the implantation demand quantity and the dealer sales volume trend, calculating to obtain a dealer sales volume prediction value of the target hospital about the target product. The invention further provides a dealer sales volume prediction system, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the comprehensive implantation data, the implantation demand, the historical dealer sales volume data, the dealer sales volume trend and the dealer sales volume prediction value can be stored in a block chain. The invention aims to improve the accuracy and prediction efficiency of sales prediction.

Description

Technical Field

[0001] The present application relates to the field of data prediction technology, and is applied to the scenario of predicting the sales volume of distributors in the medical device industry, and in particular to a method, system, computer device and storage medium for predicting the sales volume of distributors. Background Art

[0002] With the introduction of various policies for medical devices, lean management has become one of the key capabilities for comprehensive competition of enterprises. Usually, there are many distributors in the medical device industry, and the commercial behavior of distributors cannot be captured. There are problems such as irregular and volatile sales of distributors, and it is easy for distributors to have empty sales in different time ranges, which is not conducive to the development of the industry. Therefore, it is necessary to predict the sales of distributors in a timely manner for scientific management.

[0003] At present, most traditional solutions for dealer sales forecasting use a single forecasting model to forecast single historical data. This solution uses a small amount of data, which makes the sales data predicted less accurate, resulting in a large discrepancy between the forecast results and the actual results, and the sales forecast efficiency is low. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a distributor sales forecasting method, system, computer device and storage medium to solve the technical problems of insufficient accuracy and low efficiency when forecasting the sales of distributors in the medical device industry.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a dealer sales forecasting method, which adopts the following technical solution:

[0006] A dealer sales forecasting method comprises the following steps:

[0007] Obtain comprehensive implantation data of target products in target hospitals;

[0008] Determining the implantation demand of the target hospital for the target product according to the comprehensive implantation data;

[0009] Obtaining historical distributor sales data of the target product in the target hospital;

[0010] Analyze the historical distributor sales data to obtain the distributor sales trend of the target product in the target hospital;

[0011] The predicted sales value of the target product to the target hospital is calculated based on the implant demand and the dealer sales trend.

[0012] Furthermore, the step of obtaining the comprehensive implantation data of the target hospital on the target product specifically includes:

[0013] Obtaining historical implantation data of the target hospital regarding the target product;

[0014] establishing an implantation prediction model based on the historical implantation data;

[0015] Generating predicted implantation data of the target hospital regarding the target product according to the output result of the implantation prediction model;

[0016] The historical implantation data and the predicted implantation data are used as the comprehensive implantation data.

[0017] Furthermore, the step of establishing an implantation prediction model based on the historical implantation data specifically includes:

[0018] Establish an initial machine learning model based on a preset machine learning algorithm;

[0019] Preprocessing and feature extraction are performed on the historical implantation data to obtain a feature data set;

[0020] Performing data segmentation on the feature data set to obtain a training set and a test set;

[0021] Performing model training on the initial machine learning model according to the training set to obtain a trained first model;

[0022] The first model is tested according to the test set to obtain a second model after the test, which is used as the implantation prediction model.

[0023] Furthermore, the step of analyzing the historical distributor sales data to obtain the distributor sales trend of the target product in the target hospital specifically includes:

[0024] Determine the sales volume share data of each distributor of the target product in the target hospital based on the distributor's historical sales volume data;

[0025] The sales volume share data is input into a preset sales volume trend calculation model to obtain the dealer sales volume trend.

[0026] Furthermore, the step of obtaining the target hospital's historical distributor sales data on the target product specifically includes:

[0027] Determine the distributor replacement relationship corresponding to the target hospital according to the maintenance information associated with the target hospital;

[0028] The historical dealer sales data is calculated based on the dealer substitution relationship.

[0029] Furthermore, the step of determining the implantation demand of the target hospital for the target product according to the comprehensive implantation data specifically includes:

[0030] Summarizing characteristic index values ​​of the target product from the comprehensive implantation data;

[0031] The implantation requirement is determined according to the characteristic indicator value.

[0032] Furthermore, after the step of calculating the predicted sales value of the target hospital's distributor for the target product according to the implantation demand and the distributor's sales trend, the method further includes:

[0033] The comprehensive implantation data, the implantation demand, the historical dealer sales data, the dealer sales trend and the dealer sales forecast value are stored in a preset blockchain node.

[0034] In order to solve the above technical problems, the embodiment of the present application also provides a dealer sales forecasting system, which adopts the following technical solution:

[0035] A dealer sales forecasting system, comprising:

[0036] The first acquisition module is used to obtain comprehensive implantation data of the target hospital on the target product;

[0037] A determination module, used for determining the implantation demand of the target hospital for the target product according to the comprehensive implantation data;

[0038] A second acquisition module is used to acquire the historical distributor sales data of the target product in the target hospital;

[0039] An analysis module is used to analyze the historical distributor sales data to obtain the distributor sales trend of the target product in the target hospital;

[0040] A calculation module is used to calculate the predicted sales value of the target product for the target hospital according to the implant demand and the sales trend of the distributor.

[0041] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0042] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the dealer sales forecasting method as described above when executing the computer-readable instructions.

[0043] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0044] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the dealer sales forecasting method as described above.

[0045] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0046] The dealer sales forecasting method disclosed in the present application obtains the comprehensive implantation data of the target hospital on the target product; and determines the implantation demand of the target hospital on the target product based on the comprehensive implantation data; then obtains the historical dealer sales data of the target hospital on the target product; and then analyzes the historical dealer sales data to obtain the dealer sales trend of the target hospital on the target product; and thus calculates the dealer sales forecast value of the target hospital on the target product based on the implantation demand and the dealer sales trend. By combining the implantation demand data of the hospital, the historical sales data of the dealer, and the dealer substitution relationship, the present application can effectively solve the problems of data missing, dealer changes, etc., accurately predict the product sales of the target hospital, thereby improving the accuracy and prediction efficiency of sales forecasting. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0049] Figure 2 is a flow chart of an embodiment of a dealer sales forecasting method according to the present application;

[0050] Figure 3 This is a dealer replacement rule scenario diagram according to an embodiment of the dealer sales forecasting method of the present application;

[0051] Figure 4 A flow chart of dealer sales forecasting according to one embodiment of the dealer sales forecasting method of the present application;

[0052] Figure 5is a schematic diagram of the structure of an embodiment of a dealer sales forecasting system according to the present application;

[0053] Figure 6 A schematic structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0055] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0057] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0058] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0059] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0060] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0061] It should be noted that the dealer sales forecasting method provided in the embodiment of the present application is generally executed by a server, and accordingly, the dealer sales forecasting system is generally set in the server.

[0062] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0063] Continue to refer Figure 2 , shows a flow chart of an embodiment of a dealer sales forecasting method according to the present application. The dealer sales forecasting method comprises the following steps:

[0064] Step S201, obtaining comprehensive implantation data of the target hospital regarding the target product.

[0065] In this embodiment, the electronic device (eg Figure 1 The server / terminal device shown in the figure can send or receive data through a wired connection or a wireless connection. It should be noted that the above wireless connection methods may include but are not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0066] In this embodiment, historical implantation data is first obtained from the medical equipment management system of the target hospital, such as the hospital's past product demand, purchase frequency, etc. These data usually contain the product demand information of various departments in the hospital.

[0067] Step S202: Determine the implantation demand of the target hospital for the target product based on the comprehensive implantation data.

[0068] In this embodiment, first, by summarizing various characteristic indicators in the comprehensive implantation data, such as product type, implantation frequency, usage cycle, etc., the demand for each product in the hospital is determined; then, based on the summarized characteristic data, the actual implantation demand of the target hospital is obtained.

[0069] Step S203, obtaining the historical distributor sales data of the target product of the target hospital.

[0070] In this embodiment, it is determined whether there is a dealer substitution relationship based on the dealer information of the target hospital. For example, after a dealer is merged, renamed or replaced, the continuity of the sales data may be affected. Assuming that the original dealers G100 and G150 of the target hospital are merged into G200, the merged G200 needs to re-count its historical sales data. Therefore, by analyzing the maintenance information of the target hospital, the substitution relationship between G100 and G150 after the merger is confirmed, and the merged sales data is summarized. Then, through the dealer substitution rule, the historical sales data is merged to ensure the integrity and continuity of the sales data, so as to obtain the dealer historical sales data corresponding to the target hospital. Assuming that the original sales of G100 are 200 units and the sales of G150 are 180 units, the historical sales of the merged G200 should be 200+180=380 units.

[0071] What needs to be explained is that according to the maintained dealer substitution relationship, it is determined whether there is dealer substitution and what kind of scenario the substitution relationship is (direct substitution, one-to-many substitution, many-to-one substitution), so as to calculate the dealer's historical sales data. Figure 3 As shown, Figure 3 This is a scenario diagram for dealer substitution rules. The substitution relationship can have the following scenarios:

[0072] Scenario 1: G150, G155, and G100 are in a substitution relationship, so the sales data of these three dealers are grouped together. So G100 is a new dealer, and the sales of G150 and G155 are also aggregated to G100. Then the historical sales of G100 = the historical sales of G150 + the historical sales of G155.

[0073] Scenario 2: The sales data of G140, G139, and G159 are grouped together. So G159 is a new dealer, and the sales of G140 and G139 are also aggregated to G159. Then the historical sales of G159 = sales of G140 + sales of G139.

[0074] Scenario 3: For a one-to-many replacement relationship, select any distributor as a replacement, such as G157 replacing G138, and group G157 and G138 together. Therefore, G138 is the new distributor, and the sales of G157 are also aggregated to G138. Then the historical sales of G138 = the historical sales of G157.

[0075] Step S204: Analyze the historical distributor sales data to obtain the distributor sales trend of the target product in the target hospital.

[0076] In this embodiment, the sales share of the target dealer in the target product is calculated based on the historical sales data of the target dealer. The sales share formula is: sales of a dealer / total sales of all dealers. Assuming that the sales of dealer G200 is 380 units and the total sales of all dealers is 1500 units, the sales share of G200 is 380 / 1500=0.2533, that is, 25.33%. Then, the sales share data is input into the sales trend calculation model to calculate the sales trend. Common trend calculation methods include time series analysis. By inputting the sales share of G200 and the historical sales data, the sales trend is obtained using time series analysis. Assume that the model calculates that the sales of G200 will gradually increase in the next three months, and predicts that the sales in April will be 250 units, 260 units in May, and 270 units in June.

[0077] Step S205, calculating the predicted distributor sales value of the target product in the target hospital based on the implantation demand and the distributor sales trend.

[0078] In this embodiment, the predicted value of the dealer's sales volume is calculated based on the implantation demand of the target hospital and the sales volume share of the dealer. Assuming that the sales volume share of G200 is 25.33%, and the demand of hospital A in April is 55 units, the predicted sales volume of G200 is: 55 units * 25.33% = 13.94 units.

[0079] For explanation, see Figure 4 As shown, Figure 4This is a flow chart for dealer sales forecasting. The data is based on the hospital's historical sales and trend forecasts, reflecting the hospital's possible future demand for a certain product. Hospital implant forecast data is generated by analyzing the hospital's historical implant data, industry trends, and factors that may affect demand. These data are particularly important for sales forecasting and can help dealers better plan inventory and production. Dealers may change for various reasons (such as mergers, name changes, etc.). Substitution rules help ensure that even if dealers change, the system can still correctly process historical data and ensure the continuity of sales forecasts. For example, if two dealers merge, the merged new dealer will be able to inherit the sales data of the original dealer. This part may involve confirming whether it is necessary to adjust its historical sales data based on dealer changes (such as substitution or name change), and judging which data should be classified as new dealers and which should remain the same to ensure the accuracy of sales forecasts. UPN's share of historical sales in each dealer in the past three months means that for a specific product (usually using UPN as an identifier), the sales proportion accounted for by different dealers in the past three months is analyzed. Through this data, we can determine the market share of each distributor and provide a basis for subsequent sales forecasts. For example, if a distributor's sales share in the past three months is 30%, its forecast for future sales will have a weight of 30%. Summarize the hospital implant demand (HOSPITAL SALES) under the same UPN and the same-DS This step summarizes the demand for the product by all hospitals under the same UPN (product code) and the same DS (sales channel or region), which helps to more accurately predict the future demand for the product by each hospital. The sales forecast value of each distributor of UPN is the sales forecast value of each distributor for a certain product (UPN) calculated based on the hospital's demand forecast and the historical sales share of each distributor. This value reflects the contribution of each distributor to the future sales of the product based on the predicted demand and market share. UPN's sales share of new distributors in the past 3 months For new distributors (for example, newly joined distributors or distributors that have undergone replacement changes), analyzing their sales share of a certain product (UPN) in the past three months helps to adjust future sales forecasts based on the market performance of new distributors. By conducting a detailed analysis of each dealer's performance over different time periods, we can ensure that accurate sales forecasts can still be made in the event of a change in dealers.

[0080] This application can effectively solve problems such as missing data and dealer changes by combining the hospital's implant demand data, the distributor's historical sales data and the distributor substitution relationship, and accurately predict the product sales of the target hospital, thereby improving the accuracy and efficiency of sales forecasts.

[0081] In some optional implementations of this embodiment, the step of obtaining the comprehensive implantation data of the target hospital on the target product specifically includes:

[0082] Obtaining historical implantation data of the target hospital regarding the target product;

[0083] establishing an implantation prediction model based on the historical implantation data;

[0084] Generating predicted implantation data of the target hospital regarding the target product according to the output result of the implantation prediction model;

[0085] The historical implantation data and the predicted implantation data are used as the comprehensive implantation data.

[0086] In this embodiment, first, the historical implantation data of the target product is obtained from the medical equipment management system of the target hospital. These data include the installation frequency of the product, the demand of each department, the replacement cycle of the product, etc. It is assumed that the implantation data of hospital A for product X in the past 12 months are: 50 units in January, 45 units in February, 60 units in March, etc.; then, using the historical implantation data, an implantation demand prediction model is established through machine learning algorithms (such as regression analysis, neural networks, etc.), and the model can predict future demand based on the historical demand trend of the hospital. It is assumed that the historical implantation data of hospital A is used to establish a regression analysis model to obtain a demand forecast for the next three months. For example, the model predicts that the future demand is: 55 units in April, 58 units in May, and 60 units in June; finally, based on the output result of the implantation prediction model and combined with the historical implantation data, the predicted implantation data of the target hospital is generated, and used together with the historical data as comprehensive implantation data. It is assumed that the predicted data of hospital A is 55 units in April, 58 units in May, and 60 units in June. After integrating the historical data, a complete comprehensive implantation data set is obtained.

[0087] This application generates comprehensive implantation data by combining historical implantation data with the output of the prediction model, which can more accurately reflect the future implantation needs of the target hospital and provide accurate input data for sales forecasting.

[0088] In some optional implementations of this embodiment, the step of establishing an implantation prediction model based on the historical implantation data specifically includes:

[0089] Establish an initial machine learning model based on a preset machine learning algorithm;

[0090] Preprocessing and feature extraction are performed on the historical implantation data to obtain a feature data set;

[0091] Performing data segmentation on the feature data set to obtain a training set and a test set;

[0092] Performing model training on the initial machine learning model according to the training set to obtain a trained first model;

[0093] The first model is tested according to the test set to obtain a second model after the test, which is used as the implantation prediction model.

[0094] In this embodiment, first, an appropriate machine learning algorithm (such as linear regression, support vector machine, neural network, etc.) is selected to establish a preliminary machine learning model. Assuming that a linear regression algorithm is used, an initial machine learning model based on historical implant data is established; then, the historical implant data is preprocessed, including data cleaning, filling missing values, standardization, etc., and then key features are extracted, such as seasonal fluctuations, product life cycle, etc. For example, the historical implant data of Hospital A is preprocessed to remove outliers and normalize the demand data to make the data suitable for the machine learning model; then, the processed feature data is used as the input data. The data set is divided into a training set and a test set. The training set is used to train the model, and the test set is used to verify the accuracy of the model. Assume that the historical implant data of Hospital A is divided into a training set and a test set in a ratio of 7:3, 80% of the data is used to train the model, and 20% of the data is used to verify the model; then, the training set is used to train the initial machine learning model, adjust the model parameters, and optimize the model. For example, the training set data is used to iteratively optimize the machine learning model to obtain a trained preliminary model; finally, the trained model is verified with the test set to evaluate the prediction effect of the model. If the model accuracy is insufficient, it is necessary to adjust the parameters or select other algorithms. Assuming that the model is verified using the test set data, the test results show that the prediction error of the model is 5%, and the model accuracy can be improved through further optimization.

[0095] This application uses machine learning algorithms to extract effective feature information from historical data and generate accurate implant prediction models, effectively improving the accuracy of implant data prediction.

[0096] In some optional implementations of this embodiment, the step of analyzing the historical distributor sales data to obtain the distributor sales trend of the target product in the target hospital specifically includes:

[0097] Determine the sales volume share data of each distributor of the target product in the target hospital based on the distributor's historical sales volume data;

[0098] The sales volume share data is input into a preset sales volume trend calculation model to obtain the dealer sales volume trend.

[0099] In this embodiment, the sales volume share of each distributor in the target hospital is determined by analyzing the historical sales volume data of the distributors of the target hospital. Assuming that the sales volume of the G200 distributor is 380 units and the total sales volume is 1,500 units, the sales volume share of G200 is 380 / 1,500=25.33%. The sales volume share data of each distributor is input into the preset sales volume trend calculation model to calculate the sales volume trend. Assuming that the sales volume share data is input through the trend analysis model, the model calculates that the sales volume of G200 will gradually increase in the next three months, and the sales volume is predicted to be 250 units in April, 260 units in May, and 270 units in June.

[0100] By analyzing historical sales data and sales share, this application can reveal the sales trend of each dealer, providing more detailed and accurate data support for sales forecasts.

[0101] In some optional implementations of this embodiment, the step of obtaining the target hospital's historical distributor sales data on the target product specifically includes:

[0102] Determine the distributor replacement relationship corresponding to the target hospital according to the maintenance information associated with the target hospital;

[0103] The historical dealer sales data is calculated based on the dealer substitution relationship.

[0104] In this embodiment, based on the maintenance information of the target hospital and the distributor, it is determined whether there is a distributor substitution relationship. For example, distributor mergers, renaming, etc. may cause data breaks, so sales need to be recalculated. Based on the distributor substitution relationship, historical sales data are integrated to obtain the historical sales data corresponding to the target hospital.

[0105] This application ensures the accuracy and continuity of historical sales data by processing dealer substitution relationships, and avoids sales data distortion caused by dealer changes.

[0106] In some optional implementations of this embodiment, the step of determining the implantation demand of the target hospital for the target product based on the comprehensive implantation data specifically includes:

[0107] Summarizing characteristic index values ​​of the target product from the comprehensive implantation data;

[0108] The implantation requirement is determined according to the characteristic indicator value.

[0109] In this embodiment, characteristic index values ​​related to the product are extracted from the comprehensive implantation data, such as product specifications, hospital demand frequency, etc. It is assumed that characteristic data of product X is extracted from the comprehensive implantation data to obtain relevant indicators of demand; the implantation demand of the target hospital is determined based on the summarized characteristic index values. By analyzing the comprehensive implantation data of hospital A, it is obtained that the implantation demand in the next three months is: 55 units in April, 58 units in May, and 60 units in June.

[0110] This application can accurately calculate the implant demand of the target hospital by summarizing the characteristic indicators of the implant data, providing an accurate data basis for sales forecasting.

[0111] In some optional implementations of this embodiment, after the step of calculating the predicted sales value of the target hospital for the target product according to the implantation demand and the dealer sales trend, the method further includes:

[0112] The comprehensive implantation data, the implantation demand, the historical dealer sales data, the dealer sales trend and the dealer sales forecast value are stored in a preset blockchain node.

[0113] In this embodiment, all relevant data (including comprehensive implantation data, implantation demand, historical sales data, sales trends, and sales forecasts) are stored in a preset blockchain node to ensure that the data cannot be tampered with and is traceable. For example, the comprehensive implantation data, forecasted sales, and dealer sales trends of Hospital A are stored in the blockchain node, making the entire data storage process verifiable and with complete audit records.

[0114] This application ensures the immutability and transparency of data by storing all data in blockchain nodes, thereby enhancing the credibility and security of the data and helping to improve the reliability of sales forecasting and decision-making processes.

[0115] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned comprehensive implantation data, implantation demand, historical dealer sales data, dealer sales trends and dealer sales forecast values, the above-mentioned comprehensive implantation data, implantation demand, historical dealer sales data, dealer sales trends and dealer sales forecast values ​​can also be stored in a node of a blockchain.

[0116] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0117] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. 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.

[0118] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0119] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0120] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0121] Further references Figure 5 , as a response to the above Figure 2 The present application provides an embodiment of a dealer sales forecasting system, which is similar to Figure 2 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0122] like Figure 5 As shown, the dealer sales forecasting system 300 described in this embodiment includes: a first acquisition module 301, a determination module 302, a second acquisition module 303, an analysis module 304 and a calculation module 305. Among them:

[0123] The first acquisition module 301 is used to acquire the comprehensive implantation data of the target hospital about the target product;

[0124] A determination module 302 is used to determine the implantation demand of the target hospital for the target product according to the comprehensive implantation data;

[0125] The second acquisition module 303 is used to acquire the historical distributor sales data of the target product of the target hospital;

[0126] An analysis module 304 is used to analyze the historical distributor sales data to obtain the distributor sales trend of the target product in the target hospital;

[0127] The calculation module 305 is used to calculate the predicted sales value of the target product for the target hospital according to the implantation demand and the sales trend of the distributor.

[0128] The dealer sales forecasting system provided in this application can effectively solve problems such as missing data and dealer changes by combining the hospital's implant demand data, the dealer's historical sales data and the dealer substitution relationship, and accurately predict the product sales of the target hospital, thereby improving the accuracy and efficiency of sales forecasts.

[0129] In some optional implementations of this embodiment, the first acquisition module 301 is further used to:

[0130] Obtaining historical implantation data of the target hospital regarding the target product;

[0131] establishing an implantation prediction model based on the historical implantation data;

[0132] Generating predicted implantation data of the target hospital regarding the target product according to the output result of the implantation prediction model;

[0133] The historical implantation data and the predicted implantation data are used as the comprehensive implantation data.

[0134] The dealer sales forecasting system provided in this application generates comprehensive implantation data by combining historical implantation data with the output results of the forecasting model, which can more accurately reflect the future implantation needs of the target hospital and provide accurate input data for sales forecasting.

[0135] In some optional implementations of this embodiment, the first acquisition module 301 is further used to:

[0136] Establish an initial machine learning model based on a preset machine learning algorithm;

[0137] Preprocessing and feature extraction are performed on the historical implantation data to obtain a feature data set;

[0138] Performing data segmentation on the feature data set to obtain a training set and a test set;

[0139] Performing model training on the initial machine learning model according to the training set to obtain a trained first model;

[0140] The first model is tested according to the test set to obtain a second model after the test, which is used as the implantation prediction model.

[0141] The dealer sales forecasting system provided in this application extracts effective feature information from historical data through machine learning algorithms and generates an accurate implantation prediction model, effectively improving the accuracy of implantation data prediction.

[0142] In some optional implementations of this embodiment, the analysis module 304 is further configured to:

[0143] Determine the sales volume share data of each distributor of the target product in the target hospital based on the distributor's historical sales volume data;

[0144] The sales volume share data is input into a preset sales volume trend calculation model to obtain the dealer sales volume trend.

[0145] The dealer sales forecasting system provided in this application can reveal the sales trend of each dealer by analyzing historical sales data and sales share, providing more detailed and accurate data support for sales forecasting.

[0146] In some optional implementations of this embodiment, the second acquisition module 303 is further used to:

[0147] Determine the distributor replacement relationship corresponding to the target hospital according to the maintenance information associated with the target hospital;

[0148] The historical dealer sales data is calculated based on the dealer substitution relationship.

[0149] The dealer sales forecasting system provided in this application ensures the accuracy and continuity of historical sales data by processing dealer substitution relationships, and avoids the problem of sales data distortion caused by dealer changes.

[0150] In some optional implementations of this embodiment, the calculation module 305 is further used to:

[0151] Summarizing characteristic index values ​​of the target product from the comprehensive implantation data;

[0152] The implantation requirement is determined according to the characteristic indicator value.

[0153] The dealer sales forecasting system provided in this application can accurately calculate the implantation demand of the target hospital by summarizing the characteristic indicators of the implantation data, providing an accurate data basis for sales forecasting.

[0154] In some optional implementations of this embodiment, the calculation module 305 is further used to:

[0155] The comprehensive implantation data, the implantation demand, the historical dealer sales data, the dealer sales trend and the dealer sales forecast value are stored in a preset blockchain node.

[0156] The dealer sales forecasting system provided by this application ensures the immutability and transparency of the data by storing all data in blockchain nodes, thereby enhancing the credibility and security of the data and helping to improve the reliability of sales forecasting and decision-making processes.

[0157] To solve the above technical problems, the present application also provides a computer device. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0158] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0159] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0160] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the dealer sales forecasting method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0161] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for running the dealer sales forecasting method.

[0162] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0163] The computer equipment provided in this application can effectively solve problems such as missing data and dealer changes by combining the hospital's implant demand data, the dealer's historical sales data and dealer substitution relationships, and accurately predict the product sales of the target hospital, thereby improving the accuracy and efficiency of sales forecasts.

[0164] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the dealer sales forecasting method as described above.

[0165] The computer-readable storage medium provided in this application can effectively solve problems such as missing data and dealer changes by combining the hospital's implant demand data, the dealer's historical sales data and the dealer substitution relationship, and accurately predict the product sales of the target hospital, thereby improving the accuracy and prediction efficiency of sales forecasts.

[0166] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0167] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A dealer sales forecasting method, characterized in that: The steps include: Obtain comprehensive implantation data of target products in target hospitals; Determining the implantation demand of the target hospital for the target product according to the comprehensive implantation data; Obtaining historical distributor sales data of the target product in the target hospital; Analyze the historical distributor sales data to obtain the distributor sales trend of the target product in the target hospital; The predicted sales value of the target product to the target hospital is calculated based on the implant demand and the dealer sales trend.

2. The dealer sales forecasting method according to claim 1, characterized in that: The step of obtaining the comprehensive implantation data of the target hospital on the target product specifically includes: Obtaining historical implantation data of the target hospital regarding the target product; establishing an implantation prediction model based on the historical implantation data; Generating predicted implantation data of the target hospital regarding the target product according to the output result of the implantation prediction model; The historical implantation data and the predicted implantation data are used as the comprehensive implantation data.

3. The dealer sales forecasting method according to claim 2, characterized in that: The step of establishing an implantation prediction model according to the historical implantation data specifically includes: Establish an initial machine learning model based on a preset machine learning algorithm; Preprocessing and feature extraction are performed on the historical implantation data to obtain a feature data set; Performing data segmentation on the feature data set to obtain a training set and a test set; Performing model training on the initial machine learning model according to the training set to obtain a trained first model; The first model is tested according to the test set to obtain a second model after the test, which is used as the implantation prediction model.

4. The dealer sales forecasting method according to claim 1, characterized in that: The step of analyzing the historical distributor sales data to obtain the distributor sales trend of the target product in the target hospital specifically includes: Determine the sales volume share data of each distributor of the target product in the target hospital based on the distributor's historical sales volume data; The sales volume share data is input into a preset sales volume trend calculation model to obtain the dealer sales volume trend.

5. The dealer sales forecasting method according to claim 1, characterized in that: The step of obtaining the target hospital's historical distributor sales data on the target product specifically includes: Determine the distributor replacement relationship corresponding to the target hospital according to the maintenance information associated with the target hospital; The historical dealer sales data is calculated based on the dealer substitution relationship.

6. The dealer sales forecasting method according to claim 1, characterized in that: The step of determining the implantation demand of the target hospital for the target product according to the comprehensive implantation data specifically includes: Summarizing characteristic index values ​​of the target product from the comprehensive implantation data; The implantation requirement is determined according to the characteristic indicator value.

7. The dealer sales forecasting method according to any one of claims 1 to 6, characterized in that: After the step of calculating the predicted sales value of the target hospital for the target product according to the implantation demand and the sales trend of the distributor, the method further includes: The comprehensive implantation data, the implantation demand, the historical dealer sales data, the dealer sales trend and the dealer sales forecast value are stored in a preset blockchain node.

8. A dealer sales forecasting system, characterized in that: include: The first acquisition module is used to obtain comprehensive implantation data of the target hospital on the target product; A determination module, used for determining the implantation demand of the target hospital for the target product according to the comprehensive implantation data; A second acquisition module is used to acquire the historical distributor sales data of the target product in the target hospital; An analysis module is used to analyze the historical distributor sales data to obtain the distributor sales trend of the target product in the target hospital; A calculation module is used to calculate the predicted sales value of the target product for the target hospital according to the implant demand and the sales trend of the distributor.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the dealer sales forecasting method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the dealer sales forecasting method according to any one of claims 1 to 7 are implemented.

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