A smart pharmacy service evaluation method and system

Abstract

The present invention provides a smart pharmacy service evaluation method and system, the method comprising: obtaining a first service platform set of a first smart pharmacy, constructing a first user usage information database; classifying the first user usage information database according to a first category and a second category to obtain a first classification result; obtaining a first preset evaluation rule by a cloud-based intelligent evaluation system; constructing a first intelligent evaluation model according to the first preset evaluation rule, inputting the first category database and the second category database into the first intelligent evaluation model respectively, obtaining a first evaluation result and a second evaluation result; obtaining a first service level of the first smart pharmacy according to the evaluation result; obtaining first identification information according to the first service level; and displaying the first identification information on each service platform in the first service platform set. The technical problems of inaccurate service evaluation of smart pharmacies and low evaluation efficiency in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence related technologies, and in particular to a smart pharmacy service evaluation method and system. Background Art

[0002] The smart pharmacy project has innovated the medical service and management model, bringing together information resources from hospitals, medical insurance, and pharmaceutical companies’ pharmacies, and realizing the interconnection of information from medical institutions (doctors’ prescriptions) at all levels, medical insurance settlement information, and drug purchase, sales, and inventory information, providing big data services for the synchronous smart supervision of prescriptions, settlements, and purchase, sales, and inventory by health, medical insurance, market supervision, and other management departments. It provides a foundation for resource sharing for the construction of smart pharmacies and the realization of synchronous integrated services for medical treatment, drug purchase, and settlement, as well as for the construction of Internet hospitals and joint cost control.

[0003] Compared with traditional pharmacies that only provide drug sales services, smart pharmacies also provide prescription information, medication reminders, medication follow-up, and even online consultation and prescription, as well as the establishment of chronic disease files. Although smart pharmacies have broad development prospects, their current development still encounters many problems. For example, the current market is mixed with good and bad. In order to optimize the services of smart pharmacies and distinguish the pros and cons of services, the evaluation of smart pharmacy services is particularly important. At present, it mainly relies on the sales data of smart pharmacies, the number of prescriptions, and manual evaluation of smart pharmacy services.

[0004] However, the inventors of this application found that the above technology has at least the following technical problems in the process of implementing the technical solution of the invention in the embodiment of this application:

[0005] There are technical problems in the existing technology that the service evaluation of smart pharmacies is not accurate enough and the evaluation efficiency is low due to the lack of feedback information from the user side and manual evaluation. Summary of the invention

[0006] The embodiment of the present application provides a smart pharmacy service evaluation method, which solves the technical problem in the prior art that the service evaluation of smart pharmacies is not accurate enough and the evaluation efficiency is low due to the lack of feedback information from the user end and manual evaluation. The method achieves the technical effect of building a user information database, classifying and managing user information, and inputting the classified user information into an intelligent model to score the smart pharmacy service under the preset evaluation rules, and further grading the smart pharmacy service based on the score, and displaying the service level on each service platform, thereby enhancing the role of user feedback information, and using the intelligent model for evaluation to obtain a more comprehensive evaluation result more efficiently.

[0007] In view of the above problems, the embodiments of the present application provide a smart pharmacy service evaluation method and system.

[0008] In a first aspect, an embodiment of the present application provides a smart pharmacy service evaluation method, which is applied to a cloud-based intelligent evaluation system, wherein the method includes: obtaining a first service platform set of a first smart pharmacy; obtaining data information of each service platform in the first service platform set, and constructing a first user usage information database; classifying the first user usage information database according to a first category and a second category to obtain a first classification result, wherein the first classification result includes a first category database and a second category database; obtaining a first preset evaluation rule by the cloud-based intelligent evaluation system; constructing a first intelligent evaluation model according to the first preset evaluation rule, and inputting the first category database and the second category database into the first intelligent evaluation model respectively to obtain a first evaluation result and a second evaluation result; obtaining a first service level of the first smart pharmacy according to the first evaluation result and the second evaluation level; obtaining first identification information according to the first service level; and displaying the first identification information on each service platform in the first service platform set.

[0009] On the other hand, an embodiment of the present application provides a smart pharmacy service evaluation system, wherein the system includes: a first obtaining unit, the first obtaining unit is used to obtain a first service platform set of a first smart pharmacy; a second obtaining unit, the second obtaining unit is used to obtain data information of each service platform in the first service platform set, and construct a first user usage information database; a first classification unit, the first classification unit is used to classify the data of the first user usage information database according to a first category and a second category, and obtain a first classification result, wherein the first classification result includes a first category database and a second category database; a third obtaining unit, the third obtaining unit is used to obtain a first preset evaluation rule from a cloud-based intelligent evaluation system; a fourth obtaining unit, the fourth obtaining unit is used to construct a first intelligent evaluation model according to the first preset evaluation rule, and the first category database and the second category database are respectively input into the first intelligent evaluation model to obtain a first evaluation result and a second evaluation result; a fifth obtaining unit, the fifth obtaining unit is used to obtain a first service level of the first smart pharmacy according to the first evaluation result and the second evaluation level; a sixth obtaining unit, the sixth obtaining unit is used to obtain first identification information according to the first service level; a first display unit, the first display unit is used to display the first identification information on each service platform in the first service platform set.

[0010] In a third aspect, an embodiment of the present application provides a smart pharmacy service evaluation system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the first aspect when executing the program.

[0011] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0012] The invention adopts the following technical schemes: obtaining a first service platform set of a first smart pharmacy; obtaining data information of each service platform in the first service platform set to construct a first user usage information database; classifying the first user usage information database according to the first category and the second category to obtain a first classification result, wherein the first classification result includes a first category database and a second category database; obtaining a first preset evaluation rule by the cloud-based intelligent evaluation system; constructing a first intelligent evaluation model according to the first preset evaluation rule, inputting the first category database and the second category database into the first intelligent evaluation model respectively to obtain a first evaluation result and a second evaluation result; obtaining a first service level of the first smart pharmacy according to the first evaluation result and the second evaluation level; obtaining first identification information according to the first service level; and displaying the first identification information on each service platform in the first service platform set. The invention achieves the technical effect of classifying and managing user information by constructing a user information database, and inputting the classified user information into the intelligent model to score the smart pharmacy service under the preset evaluation rules, further grading the smart pharmacy service according to the score, and displaying the service level on each service platform, thereby enhancing the role of user feedback information. In addition, the intelligent model is used for evaluation to obtain a more comprehensive and efficient evaluation result.

[0013] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a smart pharmacy service evaluation method according to an embodiment of the present application;

[0015] Figure 2 This is a schematic diagram of the structure of a smart pharmacy service evaluation system according to an embodiment of the present application;

[0016] Figure 3 It is a schematic diagram of the structure of an exemplary electronic device according to an embodiment of the present application.

[0017] Explanation of the figure marks: first obtaining unit 11, second obtaining unit 12, first classification unit 13, third obtaining unit 14, fourth obtaining unit 15, fifth obtaining unit 16, sixth obtaining unit 17, first display unit 18, electronic device 300, memory 301, processor 302, communication interface 303, bus architecture 304. DETAILED DESCRIPTION

[0018] The embodiment of the present application provides a smart pharmacy service evaluation method, which solves the technical problem in the prior art that the service evaluation of smart pharmacies is not accurate enough and the evaluation efficiency is low due to the lack of feedback information from the user end and manual evaluation. The method achieves the technical effect of building a user information database, classifying and managing user information, and inputting the classified user information into an intelligent model to score the smart pharmacy service under the preset evaluation rules, and further grading the smart pharmacy service based on the score, and displaying the service level on each service platform, thereby enhancing the role of user feedback information, and using the intelligent model for evaluation to obtain a more comprehensive and efficient evaluation result.

[0019] Application Overview

[0020] The smart pharmacy project has innovated the medical service and management model, bringing together information resources from hospitals, medical insurance, and pharmaceutical companies, and realizing the interconnection of information from medical institutions (doctor prescriptions) at all levels, medical insurance settlement information, and drug purchase, sales, and inventory information, providing big data services for the synchronous smart supervision of prescriptions, settlements, and purchase, sales, and inventory by management departments such as health, medical insurance, and market supervision. It provides a basis for resource sharing for the construction of smart pharmacies and the realization of synchronous integrated services for medical treatment, drug purchase, and settlement, as well as for the construction of Internet hospitals and joint cost control. Compared with traditional pharmacies that only provide drug sales services, smart pharmacies also provide prescription information, medication reminders, medication follow-up, and even online consultation and prescription, and the establishment of chronic disease archives. Although smart pharmacies have broad development prospects, their current development still encounters many problems. For example, the current market is mixed with good and bad. In order to optimize the services of smart pharmacies and distinguish the advantages and disadvantages of services, the evaluation of smart pharmacy services is particularly important. At present, it mainly relies on the sales data of smart pharmacies, the number of prescriptions, and manual evaluation of smart pharmacy services. However, the existing technology has technical problems such as inaccurate service evaluation and low evaluation efficiency of smart pharmacies due to lack of feedback information from the user side and manual evaluation.

[0021] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0022] The embodiment of the present application provides a smart pharmacy service evaluation method, which is applied to a cloud-based intelligent evaluation system, wherein the method includes: obtaining a first service platform set of a first smart pharmacy; obtaining data information of each service platform in the first service platform set, and constructing a first user usage information database; classifying the first user usage information database according to a first category and a second category to obtain a first classification result, wherein the first classification result includes a first category database and a second category database; obtaining a first preset evaluation rule by the cloud-based intelligent evaluation system; constructing a first intelligent evaluation model according to the first preset evaluation rule, and inputting the first category database and the second category database into the first intelligent evaluation model respectively to obtain a first evaluation result and a second evaluation result; obtaining a first service level of the first smart pharmacy according to the first evaluation result and the second evaluation level; obtaining first identification information according to the first service level; and displaying the first identification information on each service platform in the first service platform set. The result is achieved by building a user information database, classifying and managing user information, and inputting the classified user information into an intelligent model to score the smart pharmacy service under preset evaluation rules. The smart pharmacy services are further graded based on the scores, and the service levels are displayed on various service platforms, thereby enhancing the role of user feedback information. In addition, the use of intelligent models for evaluation has achieved the technical effect of obtaining more comprehensive and efficient evaluation results.

[0023] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0024] Embodiment 1

[0025] like Figure 1 As shown, the embodiment of the present application provides a smart pharmacy service evaluation method, which is applied to a cloud-based intelligent evaluation system, wherein the method includes:

[0026] S100: Get the first service platform set of the first smart pharmacy;

[0027] Specifically, the first smart pharmacy is a pharmacy that needs to evaluate its services, and the first service platform set refers to the major platforms used by the first smart pharmacy to serve users, such as offline automatic drug vending points, online drug purchase platforms, etc. Strictly collecting all service platform information belonging to the first smart pharmacy can ensure the integrity and comprehensiveness of the information.

[0028] S200: Acquire data information of each service platform in the first service platform set, and construct a first user usage information database;

[0029] Specifically, the data information of each service platform in the set of the first service platform refers to the basic information related to the service effect of each service platform, including but not limited to the record information of the whole process of using the smart pharmacy, such as user purchase order information, user evaluation information, evaluation information on the efficacy and use experience after purchasing medicines. Further, these collected information are stored, managed to form the first user use information database, and wait for call, and the construction form is an example without limitation here: the order volume of each platform, the return visit volume of old users and other service platform statistical feedback information in the data information of each service platform are stored as one category; the user-side feedback statistics such as user purchase evaluation and service experience evaluation of the medication process in the data information of each service platform are stored as one category. By classifying and storing the data information of each service platform to form a database, it is convenient to call efficiently when processing data.

[0030] S300: Classifying the first user usage information database according to the first category and the second category to obtain a first classification result, where the first classification result includes a first category database and a second category database;

[0031] Specifically, the first category and the second category refer to the categories in which the data in the first user usage information database is stored. Still taking the above example as a guideline, the first category refers to the information statistically fed back by each service platform, such as sales volume, etc.; the second category refers to the information statistically fed back by the user end, such as user evaluation, etc. The first classification result refers to the classification of the first user usage information database based on the first category and the second category. After the classification is completed, the data that meets the first category constitutes the first category database, and the data that meets the second category constitutes the second category database. By dividing the first user usage information database into two databases according to category, it is avoided that information of different categories is too redundant in the same data, and it is convenient to call and analyze them separately, thereby achieving the technical effect of making the data processing process less error-prone.

[0032] S400: Obtaining a first preset evaluation rule by the cloud-based intelligent evaluation system;

[0033] Specifically, the cloud-based intelligent evaluation system refers to a virtual intelligent platform built based on application virtualization and cloud computing technology. Cloud computing provides powerful computing power, big data provides huge data support, and AI provides a high degree of learning and decision-making ability. Terminal devices do not need to be equipped with powerful computing power. They only need to access the Internet or a dedicated network to obtain computing power through the cloud. Intelligent technology can be uniformly deployed in the cloud, which is not only convenient for upgrading and adjustment, but also does not require expensive high-end chips and other hardware for terminal devices; the first preset evaluation rule refers to the evaluation criteria for the data information of each service platform set by the cloud-based intelligent evaluation system. For example: the evaluation criteria are preset based on the data information in the first category database and the second category database, and the service level is preset to be evaluated by scoring, with a full score of 10 points and 6 points as qualified. The sales volume is preset according to the local flow of people. If the actual feedback sales volume is lower than the preset sales volume, the service level is unqualified; a 5% threshold is set for the negative evaluation of the user's experience of purchasing medicines. If it exceeds 5%, the service level score is unqualified. Evaluation through the cloud-based intelligent evaluation system reduces costs and enhances feasibility.

[0034] S500: constructing a first intelligent evaluation model according to the first preset evaluation rule, inputting the first category database and the second category database into the first intelligent evaluation model respectively, and obtaining a first evaluation result and a second evaluation result;

[0035] Specifically, the first intelligent evaluation model is trained by calling data based on the first preset evaluation rule. Further, the first evaluation result information and the second evaluation result information are the scoring results obtained by inputting the first category database and the second category database into the first intelligent evaluation model for intelligent analysis, respectively. The first intelligent evaluation model is established on the basis of a neural network model and has the characteristics of a neural network model, wherein an artificial neural network is proposed and developed on the basis of modern neuroscience, and is an abstract mathematical model intended to reflect the structure and function of the human brain. A neural network is an operation model composed of a large number of nodes (or neurons) connected to each other, each node represents a specific output function called an excitation function, and the connection between each two nodes represents a weighted value for the signal passing through the connection, called a weight, which is equivalent to the memory of the artificial neural network. The output of the network is in accordance with the connection mode of the network, which is an expression of a logical strategy. The first intelligent evaluation model established based on the neural network model can output accurate information on the first evaluation result and the second evaluation result, thereby having a strong analytical and computational capability, and achieving accurate and efficient technical effects.

[0036] S600: Obtaining a first service level of the first smart pharmacy according to the first evaluation result and the second evaluation result;

[0037] S700: Obtaining first identification information according to the first service level;

[0038] S800: Display the first identification information on each service platform in the first service platform set.

[0039] Specifically, the first service level of the first smart pharmacy refers to the level evaluation of the service level of the first smart pharmacy based on the comprehensive evaluation of the first evaluation result and the second evaluation result. Still based on the first preset evaluation rule in the above example: the comprehensive score of the first evaluation result and the second evaluation result preferably removes part of the highest score and part of the lowest score of each information, and the remaining score is used to calculate the average score as the evaluation result, and then the score of the first evaluation result and the score of the second evaluation result are weighted to calculate the average, and the service level is evaluated according to the comprehensive score, such as 9.5-10 points corresponding to the first service level, 8.5-9.5 points corresponding to the second service level, 7.5-8.5 points corresponding to the third service level, 6-7.5 points corresponding to the fourth service level, and <6 points corresponding to the fifth service level is also an unqualified level. Further, the first service level pair information is used as the first identification information and the corresponding service platforms are identified, and the first identification information is displayed on each service platform in the first service platform set. The technical effect of efficient and intelligent evaluation of the first smart pharmacy service is achieved.

[0040] Further, based on obtaining the first service level of the first smart pharmacy according to the first evaluation result and the second evaluation level, step S600 further includes:

[0041] S610: Obtain a first smart pharmacy set in a first area;

[0042] S620: Obtain a user usage information database of each smart pharmacy in the first smart pharmacy set;

[0043] S630: Classify the user usage information database according to the first category and the second category to obtain a second classification result, where the second classification result includes a third category database and a fourth category database;

[0044] S640: Obtaining first usage popularity data of each smart pharmacy in the first smart pharmacy set;

[0045] S650: Establishing mapping relationships between the third category database and the fourth category database and the first usage heat data respectively, to obtain a first mapping relationship and a second mapping relationship, wherein the first mapping relationship is a mapping relationship between the third category database and the first usage heat data, and the second mapping relationship is a mapping relationship between the fourth category database and the first usage heat data;

[0046] S660: Obtain a first weight coefficient from the first mapping relationship, where the first weight coefficient is used to identify the degree of influence of the first evaluation result on the first service level;

[0047] S670: Obtain a second weight coefficient from the second mapping relationship, where the second weight coefficient is used to identify the influence of the second evaluation result on the first service level;

[0048] S680: Determine the first service level by combining the first weight coefficient and the second weight coefficient.

[0049] Specifically, the weight data of the first evaluation result and the second evaluation result when determining the first service level are determined in the following manner: the first set of smart pharmacies refers to all smart pharmacies in the service level area to be evaluated; the user usage information of each smart pharmacy in the first set of smart pharmacies is called to reconstruct the user usage information database, and the second classification result refers to the classification of the newly constructed user usage information database into the third category database and the fourth category database according to the first category and the second category based on the same principle as the first classification result. Further, the first usage heat data of each smart pharmacy refers to the usage rate of each smart pharmacy, preferably determined based on sales volume and number of users; the first mapping relationship refers to the functional relationship constructed based on the sales volume, order volume, and return visit volume of the service-side feedback in the third category database on the first usage heat data; the second mapping relationship refers to the functional relationship constructed based on the user-side evaluation in the fourth category database, such as the number of negative reviews on the first usage heat; the first weight coefficient and the second weight coefficient refer to the weight ratio constructed by comparing the first mapping relationship and the second mapping relationship to the first usage heat data. The first weight coefficient and the second weight coefficient are the weight proportions of the first evaluation result and the second evaluation result respectively, so as to determine the first service level. By adding weight data to the first evaluation result and the second evaluation result in combination with the mapping relationship, the evaluation result is more accurate.

[0050] Furthermore, based on obtaining the first usage popularity data of each smart pharmacy in the first smart pharmacy set, step S640 further includes:

[0051] S641: Obtain first distribution information of each smart pharmacy in the first smart pharmacy set;

[0052] S642: Obtaining the first user information of each smart pharmacy;

[0053] S643: Determine a first data normalization method based on the first distribution information;

[0054] S644: Standardize the first user information according to the first data standardization method to obtain the first usage popularity data.

[0055] Specifically, the first distribution information of each smart pharmacy refers to the distribution location of each smart pharmacy in the area where the service level needs to be evaluated and the total number of people in the distribution area; the first user information refers to the number of users of each smart pharmacy within a fixed time. According to an unrestricted example, such as the flow of people within a week, each smart pharmacy and its corresponding first user information are stored one by one; the first data standardization method means that because the total number of distributed people in each area is different, it is necessary to unify the distributed number of people and the first user information within the influence range of each distribution position when determining the first usage heat data. An optional example is: calculate and store the proportion of the first user information in the total number of people in the distribution location of the corresponding smart pharmacy The valuation information is unified, and then the unified first user information can be calculated based on the proportion, and the first usage heat data can be established based on this standard.

[0056] Furthermore, the method further includes step S900;

[0057] S910: Establishing a third mapping relationship between the first distribution information and the first user-times information;

[0058] S920: Obtaining a first usage heat probability distribution of each smart pharmacy in the first smart pharmacy set according to the third mapping relationship;

[0059] S930: Obtaining a first expected usage heat of the first smart pharmacy;

[0060] S940: Obtaining a first ideal distribution point of the first smart pharmacy based on the first expected usage heat and the first usage heat probability distribution;

[0061] S950: Obtain a first distribution point of the first smart pharmacy;

[0062] S960: Determine whether the first distribution point matches the first ideal distribution point;

[0063] S970: If the first distribution point does not match the first ideal distribution point, obtain a first sending instruction, and send the first ideal distribution point to the cloud-based intelligent evaluation system.

[0064] Specifically, the third mapping relationship refers to a relationship that characterizes the degree of association between the first distribution information and the first user information, which is constructed according to the changing trend of the number of users at different distribution locations in the first distribution information; the first usage heat probability distribution refers to the possible usage heat probability distribution of each smart pharmacy obtained according to the change of the flow of people at the different distribution locations, that is, the third mapping relationship. The method for determining the usage heat probability can be optionally: using the proportion of the number of users at the distribution location within one month in the flow of people as the first usage heat probability; further, for each smart pharmacy at different distribution locations, a preset first expected usage heat is established, and the first expected usage heat of each smart pharmacy is compared with the first usage heat probability to obtain the first usage heat probability that matches the first expected usage heat, and the distribution position corresponding to the first usage heat probability is stored as the first ideal distribution point of the first smart pharmacy; further, the first ideal distribution point is compared with the corresponding first distribution point. If the first distribution point is not the first ideal distribution point, based on the first sending instruction, the first ideal distribution point of the first smart pharmacy is sent to the cloud-based intelligent evaluation system. By sending the first ideal distribution point to the cloud-based intelligent evaluation system, the service level of the first smart pharmacy can be optimized. For example, it can be determined whether there is a pharmacy at the first ideal distribution point. If not, the first pharmacy can be built at the first ideal distribution point.

[0065] Furthermore, based on the matching of the first distribution point with the first ideal distribution point, the method further includes S1000:

[0066] S1010: Obtaining a first radiation range of the first smart pharmacy according to the first distribution point;

[0067] S1020: Obtaining first pedestrian flow change data within the first radiation range;

[0068] S1030: Obtain a first time threshold;

[0069] S1040: within the first time threshold, performing data analysis on the pedestrian flow change data to obtain a first potential usage heat;

[0070] S1050: Performing feature analysis on a fixed population within the first radiation range to obtain a first stable usage popularity;

[0071] S1060: Obtain a second expected usage heat by combining the first potential usage heat and the first stable usage heat, and modify the first expected usage heat according to the second expected usage heat.

[0072] Specifically, the first radiation range of the first smart pharmacy refers to the preset service range of the first smart pharmacy at its distribution location. The radiation area can be optionally constructed with the first smart pharmacy as the center for representation, and the radiation radius is determined by the scale of the first smart pharmacy; the first traffic flow change data refers to the traffic flow change information within the first radiation range within a fixed time, such as storing traffic flow changes for four months in four seasons of the year; the first time threshold refers to the fixed time set above; the first potential use heat refers to the potential number of users who may use the first smart pharmacy based on the change trend and number of traffic flow, and the greater the traffic flow, the greater the potential number of users; the first stable use heat refers to the statistical stable number of people using the first smart pharmacy within the first radiation range; the second expected use heat refers to the expected use heat obtained by adding a certain proportion of the first potential use heat to the original first stable use heat, and the first expected use heat is corrected based on the second expected heat. If the first distribution point matches the first ideal distribution point, the expected heat obtained according to the first potential use heat and the first stable use heat is the ideal use heat target of the first smart pharmacy. The technical effect of providing an optimization plan based on the intelligent evaluation results of the first smart pharmacy has been achieved.

[0073] Furthermore, the method step S1060 also includes:

[0074] S1061: Obtain the second radiation range of the second smart pharmacy;

[0075] S1062: Determine whether the second radiation range overlaps with the first radiation range;

[0076] S1063: If the second radiation range overlaps with the first radiation range, obtaining a first overlapping area;

[0077] S1064: performing a potential usage heat analysis and a stable usage heat analysis on the first overlapping area to obtain a first correction coefficient;

[0078] S1065: Correct the first stable usage heat according to the first correction coefficient to obtain a second expected usage heat.

[0079] Specifically, the second radiation range of the second smart pharmacy refers to the preset service range of the second smart pharmacy at its distribution location. The radiation area is constructed with the second smart pharmacy as the center for characterization, and the radiation radius is determined by the scale of the second smart pharmacy. Compare the first radiation range with the second radiation range. If the second radiation range overlaps with the first radiation range, call the location information of the overlapping area to form the first overlapping area; the first correction coefficient refers to the possible conversion ratio of the potential use heat in the first overlapping area. The optional determination method is: determine the potential conversion ratio by counting the conversion ratio of traffic flow within the past six months, and obtain the specific conversion value of the potential use heat. Further, the first stable use heat is corrected according to the first correction coefficient, and the above example is still applicable: the possible conversion specific value of the potential use heat is added to the first stable use heat to form the second expected use heat. By calculating the expected use heat in the overlapping area between adjacent smart pharmacies, the situation of regional intersection when counting the expected use heat of each smart pharmacy is avoided, and the evaluation result obtained will also be more accurate.

[0080] Furthermore, the method step S500 further includes:

[0081] S510: inputting the first category database into the first intelligent evaluation model, where the first intelligent evaluation model is obtained by training multiple sets of training data, where each set of training data in the multiple sets includes: the first category database and identification information used to identify the first evaluation result;

[0082] S520: Obtain a first output result of the first intelligent evaluation model, wherein the first output result is the first evaluation result for the first category database.

[0083] Specifically, the first intelligent evaluation model is also a neural network model, which is a neural network model in machine learning. It reflects many basic characteristics of human brain functions and is a highly complex nonlinear dynamic learning system. Among them, it can continuously perform self-training and learning according to training data, and each group of training data in the multiple groups includes: the first category database and identification information used to identify the first evaluation result. The first intelligent evaluation model continuously corrects itself, and when the output information of the first intelligent evaluation model reaches a predetermined accuracy / convergence state, the supervised learning process ends. By training the first intelligent evaluation model with data, the first intelligent evaluation model processes the input data more accurately, and then the output of the first evaluation result information is also more accurate, achieving the technical effect of accurately obtaining data information and improving the intelligence of the evaluation results.

[0084] In summary, the smart pharmacy service evaluation method and system provided in the embodiments of the present application have the following technical effects:

[0085] 1. The invention adopts the following technical schemes: obtaining a first service platform set of a first smart pharmacy; obtaining data information of each service platform in the first service platform set to construct a first user usage information database; classifying the first user usage information database according to a first category and a second category to obtain a first classification result, wherein the first classification result includes a first category database and a second category database; obtaining a first preset evaluation rule by the cloud-based intelligent evaluation system; constructing a first intelligent evaluation model according to the first preset evaluation rule, inputting the first category database and the second category database into the first intelligent evaluation model respectively, obtaining a first evaluation result and a second evaluation result; obtaining a first service level of the first smart pharmacy according to the first evaluation result and the second evaluation level; obtaining first identification information according to the first service level; and displaying the first identification information on each service platform in the first service platform set. The invention achieves the technical effect of constructing a user information database, classifying and managing user information, and inputting the classified user information into an intelligent model to score smart pharmacy services under preset evaluation rules, further grading smart pharmacy services according to the scores, and displaying the service levels on each service platform, thereby enhancing the role of user feedback information, and using an intelligent model for evaluation to obtain a more comprehensive evaluation result more efficiently.

[0086] 2. By using multiple groups of the first category databases and identification information used to identify the first evaluation results to train a neural network model, the first intelligent evaluation model processes the first category database and the second category database separately, and can efficiently output the first evaluation results and the second evaluation results, providing an accurate data basis for further comprehensive evaluation of the services of the first smart pharmacy.

[0087] Embodiment 2

[0088] Based on the same inventive concept as the smart pharmacy service evaluation method in the aforementioned embodiment, Figure 2 As shown, the embodiment of the present application provides a smart pharmacy service evaluation system, wherein the system includes:

[0089] A first obtaining unit 11, wherein the first obtaining unit 11 is used to obtain a first service platform set of a first smart pharmacy;

[0090] A second obtaining unit 12, the second obtaining unit 12 is used to obtain data information of each service platform in the first service platform set and build a first user usage information database;

[0091] A first classification unit 13, the first classification unit 13 is used to classify the first user usage information database according to a first category and a second category to obtain a first classification result, wherein the first classification result includes a first category database and a second category database;

[0092] A third obtaining unit 14, the third obtaining unit 14 is used to obtain a first preset evaluation rule from a cloud-based intelligent evaluation system;

[0093] A fourth obtaining unit 15, the fourth obtaining unit 15 is used to construct a first intelligent evaluation model according to the first preset evaluation rule, input the first category database and the second category database into the first intelligent evaluation model respectively, and obtain a first evaluation result and a second evaluation result;

[0094] a fifth obtaining unit 16, wherein the fifth obtaining unit 16 is configured to obtain a first service level of the first smart pharmacy according to the first evaluation result and the second evaluation level;

[0095] a sixth obtaining unit 17, wherein the sixth obtaining unit 17 is configured to obtain first identification information according to the first service level;

[0096] The first display unit 18 is used to display the first identification information on each service platform in the first service platform set.

[0097] Furthermore, the system also includes:

[0098] A seventh obtaining unit, the seventh obtaining unit is used to obtain a first smart pharmacy set in the first area;

[0099] an eighth obtaining unit, the eighth obtaining unit being used to obtain a user usage information database of each smart pharmacy in the first smart pharmacy set;

[0100] a ninth obtaining unit, the ninth obtaining unit being used to classify the user usage information database according to the first category and the second category to obtain a second classification result, wherein the second classification result includes a third category database and a fourth category database;

[0101] a tenth obtaining unit, the tenth obtaining unit being used to obtain first usage popularity data of each smart pharmacy in the first smart pharmacy set;

[0102] an eleventh obtaining unit, the eleventh obtaining unit being used to establish mapping relationships between the third category database and the fourth category database and the first usage heat data respectively, to obtain a first mapping relationship and a second mapping relationship, wherein the first mapping relationship is a mapping relationship between the third category database and the first usage heat data, and the second mapping relationship is a mapping relationship between the fourth category database and the first usage heat data;

[0103] a first identification unit, the first identification unit being used to obtain a first weight coefficient from the first mapping relationship, the first weight coefficient being used to identify the influence of the first evaluation result on the first service level;

[0104] a second identification unit, the second identification unit being used to obtain a second weight coefficient from the second mapping relationship, the second weight coefficient being used to identify the influence degree of the second evaluation result on the first service level;

[0105] A first determining unit, wherein the first determining unit is configured to determine the first service level by combining the first weight coefficient and the second weight coefficient.

[0106] Furthermore, the system also includes:

[0107] A first centralized unit, the first centralized unit is used to obtain first distribution information of each smart pharmacy in the first smart pharmacy set;

[0108] a twelfth obtaining unit, the twelfth obtaining unit being used to obtain the first user information of each smart pharmacy;

[0109] a second determining unit, the second determining unit being configured to determine a first data normalization method according to the first distribution information;

[0110] A thirteenth obtaining unit is used to perform standardization processing on the first user information according to the first data standardization method to obtain the first usage heat data.

[0111] Furthermore, the system also includes:

[0112] A first construction unit, the first construction unit is used to establish a third mapping relationship between the first distribution information and the first user-times information;

[0113] A fourteenth obtaining unit, the fourteenth obtaining unit is used to obtain a first usage heat probability distribution of each smart pharmacy in the first smart pharmacy set according to the third mapping relationship;

[0114] a fifteenth obtaining unit, the fifteenth obtaining unit being used to obtain a first expected usage heat of the first smart pharmacy;

[0115] a sixteenth obtaining unit, the sixteenth obtaining unit being configured to obtain a first ideal distribution point of the first smart pharmacy according to the first expected usage heat and the first usage heat probability distribution;

[0116] A seventeenth obtaining unit, the seventeenth obtaining unit is used to obtain a first distribution point of the first smart pharmacy;

[0117] a first judging unit, configured to judge whether the first distribution point matches the first ideal distribution point;

[0118] An eighteenth obtaining unit, wherein the eighteenth obtaining unit is used to obtain a first sending instruction if the first distribution point does not match the first ideal distribution point, and send the first ideal distribution point to the cloud-based intelligent evaluation system.

[0119] Furthermore, the system also includes:

[0120] A nineteenth obtaining unit, the nineteenth obtaining unit is used to obtain a first radiation range of the first smart pharmacy according to the first distribution point;

[0121] a twentieth obtaining unit, the twentieth obtaining unit being used to obtain first human flow change data within the first radiation range;

[0122] A twenty-first obtaining unit, the twenty-first obtaining unit is used to obtain a first time threshold;

[0123] A twenty-second obtaining unit, the twenty-second obtaining unit is used to perform data analysis on the pedestrian flow change data within the first time threshold to obtain a first potential usage heat;

[0124] A twenty-third obtaining unit, the twenty-third obtaining unit is used to perform feature analysis on a fixed group of people within the first radiation range to obtain a first stable usage popularity;

[0125] A first correction unit, wherein the first correction unit is used to obtain a second expected usage heat by combining the first potential usage heat and the first stable usage heat, and to correct the first expected usage heat according to the second expected usage heat.

[0126] Furthermore, the system also includes:

[0127] A twenty-fourth obtaining unit, the twenty-fourth obtaining unit is used to obtain a second radiation range of the second smart pharmacy;

[0128] a second judging unit, the second judging unit being used to judge whether the second radiation range overlaps with the first radiation range;

[0129] a twenty-fifth obtaining unit, the twenty-fifth obtaining unit being configured to obtain a first overlapping area if the second radiation range overlaps with the first radiation range;

[0130] a twenty-sixth obtaining unit, the twenty-sixth obtaining unit being used to perform a potential usage heat analysis and a stable usage heat analysis on the first overlapping area to obtain a first correction coefficient;

[0131] The twenty-seventh obtaining unit is used to correct the first stable usage heat according to the first correction coefficient to obtain a second expected usage heat.

[0132] Furthermore, the system also includes:

[0133] A first training unit, the first training unit is used to input the first category database into the first intelligent evaluation model, the first intelligent evaluation model is obtained by training multiple sets of training data, each set of training data in the multiple sets includes: the first category database and identification information used to identify the first evaluation result;

[0134] A first output unit, wherein the first output unit is used to obtain a first output result of the first intelligent evaluation model, wherein the first output result is the first evaluation result for the first category database.

[0135] Exemplary Electronic Devices

[0136] Reference below Figure 3 To describe the electronic device of the embodiment of the present application,

[0137] Based on the same inventive concept as the smart pharmacy service evaluation method in the aforementioned embodiment, the embodiment of the present application also provides a smart pharmacy service evaluation system, including: a processor, the processor is coupled to a memory, the memory is used to store a program, when the program is executed by the processor, the system executes the method described in any one of the first aspects

[0138] The electronic device 300 includes: a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, the processor 302, and the memory 301 may be interconnected via the bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0139] The processor 302 may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the present application.

[0140] The communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), wired access networks, etc.

[0141] The memory 301 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor via the bus architecture 304. The memory may also be integrated with the processor.

[0142] The memory 301 is used to store computer-executable instructions for executing the solution of the present application, and the execution is controlled by the processor 302. The processor 302 is used to execute the computer-executable instructions stored in the memory 301, thereby realizing a smart pharmacy service evaluation method provided in the above embodiment of the present application.

[0143] Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application code, which is not specifically limited in the embodiments of the present application.

[0144] The embodiment of the present application provides a smart pharmacy service evaluation method, which is applied to a cloud-based intelligent evaluation system, wherein the method includes: obtaining a first service platform set of a first smart pharmacy; obtaining data information of each service platform in the first service platform set, and constructing a first user usage information database; classifying the first user usage information database according to a first category and a second category to obtain a first classification result, wherein the first classification result includes a first category database and a second category database; obtaining a first preset evaluation rule by the cloud-based intelligent evaluation system; constructing a first intelligent evaluation model according to the first preset evaluation rule, and inputting the first category database and the second category database into the first intelligent evaluation model respectively to obtain a first evaluation result and a second evaluation result; obtaining a first service level of the first smart pharmacy according to the first evaluation result and the second evaluation level; obtaining first identification information according to the first service level; and displaying the first identification information on each service platform in the first service platform set. The result is achieved by building a user information database, classifying and managing user information, and inputting the classified user information into an intelligent model to score the smart pharmacy service under preset evaluation rules. The smart pharmacy services are further graded based on the scores, and the service levels are displayed on various service platforms, thereby enhancing the role of user feedback information. In addition, the use of intelligent models for evaluation has achieved the technical effect of obtaining more comprehensive and efficient evaluation results.

[0145] Those of ordinary skill in the art will appreciate that the various digital numbers such as the first and second involved in the present application are only for the convenience of description, and are not used to limit the scope of the embodiments of the present application, nor do they represent the order of precedence. "And / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one" refers to one or more. At least two refers to two or more. "At least one", "any one" or similar expressions refer to any combination of these items, including any combination of single items (individuals) or plural items (individuals). For example, at least one item (individual, kind) of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0146] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server, data center, etc. that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0147] The various illustrative logic units and circuits described in the embodiments of the present application can be implemented or operated by a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or the design of any combination of the above functions. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any traditional processor, controller, microcontroller or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration to implement.

[0148] The steps of the method or algorithm described in the embodiments of the present application can be directly embedded in hardware, a software unit executed by a processor, or a combination of the two. The software unit can be stored in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC, and the ASIC can be arranged in a terminal. Optionally, the processor and the storage medium can also be arranged in different components in the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0149] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. A smart pharmacy service evaluation method, applied to a cloud-based intelligent evaluation system, wherein the method comprises: Get the first service platform collection of the first smart pharmacy; Acquire data information of each service platform in the first service platform set, and construct a first user usage information database; Classifying the first user usage information database according to the first category and the second category. After the classification is completed, the data that meets the first category constitutes the first category database, and the data that meets the second category constitutes the second category database, to obtain a first classification result, the first classification result includes the first category database and the second category database, the first category is the information statistically fed back by each service platform, and the second category is the information statistically fed back by the user end; Obtaining a first preset evaluation rule from the cloud-based intelligent evaluation system; A first intelligent evaluation model is constructed according to the first preset evaluation rule, wherein the first intelligent evaluation model is established based on a neural network model, and the first category database and the second category database are respectively input into the first intelligent evaluation model to obtain a first evaluation result and a second evaluation result, wherein the first intelligent evaluation model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the first category database and identification information used to identify the first evaluation result; Obtaining a first service level of the first smart pharmacy according to the first evaluation result and the second evaluation result; Obtaining first identification information according to the first service level; Displaying the first identification information on each service platform in the first service platform set; The obtaining of the first service level of the first smart pharmacy according to the first evaluation result and the second evaluation result also includes: Get the first smart pharmacy collection in the first area; Obtaining a user usage information database of each smart pharmacy in the first smart pharmacy set; Classifying the user usage information database according to the first category and the second category to obtain a second classification result, wherein the second classification result includes a third category database and a fourth category database, and the second classification result is to classify the user usage information database into the third category database and the fourth category database according to the first category and the second category based on the same principle as the first classification result; Obtaining first usage popularity data of each smart pharmacy in the first smart pharmacy set, where the first usage popularity data of each smart pharmacy is a usage rate of each smart pharmacy; A mapping relationship is established between the third category database and the fourth category database and the first usage heat data respectively, to obtain a first mapping relationship and a second mapping relationship, wherein the first mapping relationship is a mapping relationship between the sales volume, order volume, and return visit volume fed back by the server in the third category database and the first usage heat data, and the second mapping relationship is a mapping relationship between the user-side evaluation in the fourth category database and the first usage heat data; Obtaining a first weight coefficient from the first mapping relationship, where the first weight coefficient is used to identify the degree of influence of the first evaluation result on the first service level; Obtaining a second weight coefficient from the second mapping relationship, where the second weight coefficient is used to identify the degree of influence of the second evaluation result on the first service level; Determine the first service level by combining the first weight coefficient and the second weight coefficient; The obtaining of the first usage popularity data of each smart pharmacy in the first smart pharmacy set further includes: Obtaining first distribution information of each smart pharmacy in the first smart pharmacy set, wherein the first distribution information of each smart pharmacy is the distribution location of each smart pharmacy in the area where the service level is evaluated and the total number of people in the distribution area; Obtaining first user-time information of each smart pharmacy, where the first user-time information is information about the number of users of each smart pharmacy within a fixed time period; Determining a first data standardization method based on the first distribution information, the first data standardization method is to calculate and store the proportion of the first user information in the total number of people in the distribution location corresponding to the smart pharmacy, and unify the total number of people in all distribution areas; The first user information is standardized according to the first data standardization method to obtain the first usage heat data.

2. The method of claim 1, wherein: The method comprises: Establishing a third mapping relationship between the first distribution information and the first user-times information, wherein the third mapping relationship is a relationship characterizing the degree of association between the first distribution information and the first user-times information, which is constructed according to a change trend of the number of users at different distribution positions in the first distribution information; According to the third mapping relationship, a first usage heat probability distribution of each smart pharmacy in the first smart pharmacy set is obtained, wherein the first usage heat probability distribution is based on the change of the flow of people at the different distribution locations, that is, the usage heat probability distribution of each smart pharmacy is obtained by the third mapping relationship, and the usage heat probability is determined by using the proportion of the number of users of the distribution location in the flow of people within one month as the first usage heat probability; Obtaining a first expected usage heat of the first smart pharmacy; Obtaining a first ideal distribution point of the first smart pharmacy based on the first expected usage heat and the first usage heat probability distribution; Obtaining a first distribution point of the first smart pharmacy; Determining whether the first distribution point matches the first ideal distribution point; If the first distribution point does not match the first ideal distribution point, a first sending instruction is obtained to send the first ideal distribution point to the cloud-based intelligent evaluation system.

3. The method of claim 2, wherein: If the first distribution point matches the first ideal distribution point, the method further includes: Obtaining a first radiation range of the first smart pharmacy according to the first distribution point; Obtaining first human flow change data within the first radiation range; Get the first time threshold; Within the first time threshold, the first human flow change data is analyzed to obtain a first potential usage heat, where the first potential usage heat is the potential number of users who use the first smart pharmacy predicted based on the change trend and amount of human flow, and the greater the human flow, the greater the potential number of users; Performing feature analysis on a fixed population within the first radiation range to obtain a first stable usage popularity, where the first stable usage popularity is a statistically stable number of people using the first smart pharmacy within the first radiation range; A second expected usage heat is obtained by combining the first potential usage heat and the first stable usage heat, and the first expected usage heat is corrected according to the second expected usage heat.

4. The method of claim 3, wherein: The method further comprises: Obtain the second radiation range of the second smart pharmacy; Determining whether the second radiation range overlaps with the first radiation range; If the second radiation range overlaps with the first radiation range, a first overlapping area is obtained; Performing a potential usage heat analysis and a stable usage heat analysis on the first overlapping area to obtain a first correction coefficient, where the first correction coefficient is a conversion ratio of the potential usage heat in the first overlapping area; The first stable usage heat is corrected according to the first correction coefficient to obtain a second expected usage heat.

5. The method of claim 1, wherein: The method comprises: inputting the first category database into the first intelligent assessment model; A first output result of the first intelligent evaluation model is obtained, wherein the first output result is the first evaluation result for the first category database.

6. A smart pharmacy service evaluation system, wherein: The system comprises: A first obtaining unit, wherein the first obtaining unit is used to obtain a first service platform set of a first smart pharmacy; a second obtaining unit, the second obtaining unit being used to obtain data information of each service platform in the first service platform set and construct a first user usage information database; A first classification unit, the first classification unit is used to classify data in the first user usage information database according to a first category and a second category. After the classification is completed, the data that conforms to the first category constitutes a first category database, and the data that conforms to the second category constitutes a second category database, and a first classification result is obtained. The first classification result includes a first category database and a second category database. The first category is information statistically fed back by each service platform, and the second category is information statistically fed back by the user end; A third obtaining unit, the third obtaining unit is used to obtain a first preset evaluation rule from a cloud-based intelligent evaluation system; A fourth obtaining unit, the fourth obtaining unit is used to construct a first intelligent evaluation model according to the first preset evaluation rule, the first intelligent evaluation model is established based on a neural network model, the first category database and the second category database are respectively input into the first intelligent evaluation model to obtain a first evaluation result and a second evaluation result, the first intelligent evaluation model is obtained by training with multiple groups of training data, and each group of training data in the multiple groups includes: the first category database and identification information used to identify the first evaluation result; a fifth obtaining unit, configured to obtain a first service level of the first smart pharmacy according to the first evaluation result and the second evaluation result; a sixth obtaining unit, configured to obtain first identification information according to the first service level; a first display unit, the first display unit being configured to display the first identification information on each of the service platforms in the first service platform set; The system further comprises: A seventh obtaining unit, the seventh obtaining unit is used to obtain a first smart pharmacy set in the first area; an eighth obtaining unit, the eighth obtaining unit being used to obtain a user usage information database of each smart pharmacy in the first smart pharmacy set; a ninth obtaining unit, the ninth obtaining unit being used to classify the user usage information database according to the first category and the second category to obtain a second classification result, the second classification result including a third category database and a fourth category database, the second classification result being based on the same principle as the first classification result to classify the user usage information database into the third category database and the fourth category database according to the first category and the second category; a tenth obtaining unit, the tenth obtaining unit being used to obtain first usage heat data of each smart pharmacy in the first smart pharmacy set, wherein the first usage heat data of each smart pharmacy is a usage rate of each smart pharmacy; an eleventh obtaining unit, the eleventh obtaining unit being used to establish mapping relationships between the third category database and the fourth category database and the first usage heat data, respectively, to obtain a first mapping relationship and a second mapping relationship, wherein the first mapping relationship is a mapping relationship between the sales volume, order volume, and return visit volume fed back by the server in the third category database and the first usage heat data, and the second mapping relationship is a mapping relationship between the user-side evaluation in the fourth category database and the first usage heat data; a first identification unit, the first identification unit being used to obtain a first weight coefficient from the first mapping relationship, the first weight coefficient being used to identify the influence of the first evaluation result on the first service level; a second identification unit, the second identification unit being used to obtain a second weight coefficient from the second mapping relationship, the second weight coefficient being used to identify the influence degree of the second evaluation result on the first service level; a first determining unit, the first determining unit being configured to determine the first service level by combining the first weight coefficient and the second weight coefficient; A first concentration unit, the first concentration unit is used to obtain first distribution information of each smart pharmacy in the first smart pharmacy set, wherein the first distribution information of each smart pharmacy is the distribution location of each smart pharmacy in the area where the service level is evaluated and the total number of people in the distribution area; a twelfth obtaining unit, the twelfth obtaining unit being used to obtain first user-times information of each smart pharmacy, the first user-times information being information of the number of users of each smart pharmacy within a fixed time period; A second determination unit, the second determination unit is used to determine a first data standardization method based on the first distribution information, the first data standardization method is to calculate and store the proportion of the first user information in the total number of people in the distribution location corresponding to the smart pharmacy, and unify the total number of people in all distribution areas; A thirteenth obtaining unit is used to perform standardization processing on the first user information according to the first data standardization method to obtain the first usage heat data.

7. A smart pharmacy service evaluation system, comprising: A processor, the processor is coupled to a memory, the memory is used to store a program, when the program is executed by the processor, the system is enabled to perform the method according to any one of claims 1 to 5.

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