Data preparation method and device for low-code GIS system use case recommendation

Abstract

The application provides a data preparation method and device for low-code GIS system use scheme recommendation, and relates to the technical field of geographic information systems. The structured data of the scheme generated by the method includes scheme classification, scheme description, coded data marked with user operation sequence, scheme coding structure, data corresponding to preset scheme execution efficiency indicators, data corresponding to preset scheme usage indicators, and the like. These rich data dimensions enable the preset large model to more accurately understand user needs after learning and training, find the most suitable scheme under the corresponding classification according to user needs, and optimize and improve the scheme according to the dialogue content.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geographic information system, and particularly relates to a data preparation method and device for use scheme recommendation of a low-code GIS system. BACKGROUND

[0002] With the wide application of GIS (Geographic Information System), a low-code GIS system greatly reduces the technical threshold of GIS application development by virtue of its "building block" type function combination mode, so that non-professional technical personnel can also participate in the creation of GIS applications. However, many problems are still exposed in actual application. Here, the low-code GIS system is an innovative platform combining the low-code development concept and GIS technology. It significantly reduces the coding workload required for traditional GIS application development through graphical interface, visual drag-and-drop operation and pre-installed function modules.

[0003] The low-code GIS system has complex functions and contains multiple modules, each of which is further divided into numerous sub-functions. Taking the calculation module as an example, it has a calculation function of pipeline processing of data, including point-to-surface generation, point fence judgment, attribution area generation, sales visit planning and other specific sub-functions. Different combination modes and structures exist between each function. Such a complex function system undoubtedly brings great challenges to the learning and use of users. Users often need to spend a lot of time to familiarize themselves with the purpose and mutual relationship of each module and sub-function before they can build a GIS application that meets their own needs.

[0004] In order to reduce the use cost of users, a large model can be used to make scheme recommendation. However, the existing data forms (such as low-code GIS system use tutorials or documents) are difficult for the large model to fully learn the historical use of the system, so as to give an efficient and accurate system use scheme. Therefore, in the face of the deficiencies of low-code GIS system in use scheme recommendation data preparation, it is urgent to propose a comprehensive and innovative data preparation method, aiming to optimize the learning and training data of the large model, so as to realize efficient and accurate system use scheme recommendation. SUMMARY

[0005] In view of the above problems, the present application is proposed in order to provide a low-code GIS system use scheme recommendation data preparation method and device, electronic equipment and storage medium which overcome the above problems or at least partially solve the above problems. The technical solution is as follows:

[0006] In a first aspect, a low-code GIS system use scheme recommendation data preparation method is provided, and the method comprises:

[0007] receiving a solution content submitted by a user, the solution content including a solution category and a solution description;

[0008] in response to an operation of the user on a solution editing interface of a low-code GIS system with respect to the solution content, encoding the operation on the solution editing interface of the low-code GIS system according to a pre-constructed basic encoding layer and an extended encoding layer, to generate encoded data marked with a user operation sequence, wherein the basic encoding layer includes a business process, a function module, a sub-function, and an operation type, and the extended encoding layer includes an operation time, a low-code GIS system version number, and a user role;

[0009] generating a solution encoding structure according to a structural relationship of the function module and the sub-function in the encoded data marked with the user operation sequence;

[0010] testing the solution using a pre-defined data set, executing the solution according to the encoded data marked with the user operation sequence, and collecting data corresponding to a pre-set solution execution efficiency indicator according to the pre-set solution execution efficiency indicator during the execution;

[0011] if the solution passes the test, receiving a solution publishing instruction submitted by the user, publishing the solution that passes the test, and collecting data corresponding to a pre-set solution usage indicator according to the pre-set solution usage indicator after the publishing;

[0012] structurally storing the solution category, the solution description, the encoded data marked with the user operation sequence, the solution encoding structure, the data corresponding to the pre-set solution execution efficiency indicator, and the data corresponding to the pre-set solution usage indicator, to generate solution structured data.

[0013] In a possible implementation, after the solution structured data is generated, the method further includes:

[0014] training a pre-set large model using the solution structured data as sample training data, to obtain a trained low-code GIS system use solution recommendation model;

[0015] receiving a user question of a user using the low-code GIS system, submitting the user question to the trained low-code GIS system use solution recommendation model, and outputting a low-code GIS system use recommendation solution corresponding to the user question.

[0016] In a possible implementation, the solution description is represented by natural language.

[0017] In a possible implementation, the pre-set solution execution efficiency indicator includes one or more of CPU usage, memory occupancy, and network traffic.

[0018] In a possible implementation, the preset scheme usage index includes one or more of the following: the number of times the scheme is used, the number of times the scheme is liked, and the scheme recommendation level.

[0019] In a possible implementation, if the scheme passes the test, the method further includes:

[0020] The collected preset scheme execution efficiency index data is bound and stored with the scheme classification, the scheme description, the encoded data marked with the user operation sequence, and the scheme encoding structure, and a test result is displayed, the test result including whether the user scheme runs successfully and details of each index.

[0021] In a possible implementation, the method further includes:

[0022] If the scheme test fails, prompt information that the user scheme has a problem is generated, and the user is guided to return to the scheme editing interface of the low-code GIS system for modification.

[0023] In a second aspect, a data preparation apparatus for low-code GIS system scheme recommendation is provided, and the apparatus includes:

[0024] A receiving unit is configured to receive scheme content submitted by a user, the scheme content including a scheme classification and a scheme description;

[0025] A first generating unit is configured to, in response to user operation on a scheme editing interface of a low-code GIS system with respect to the scheme content, encode the operation on the scheme editing interface of the low-code GIS system according to a pre-constructed basic encoding layer and an extended encoding layer, and generate encoded data marked with a user operation sequence, wherein the basic encoding layer includes a business process, a function module, a sub-function, and an operation type, and the extended encoding layer includes an operation time, a low-code GIS system version number, and a user role.

[0026] A second generating unit is configured to generate a scheme encoding structure according to a structural relationship between the function module and the sub-function in the encoded data marked with the user operation sequence.

[0027] A testing unit is configured to test the scheme using a predefined data set, execute the scheme according to the encoded data marked with the user operation sequence, and collect data corresponding to preset scheme execution efficiency indexes according to the preset scheme execution efficiency indexes during the execution.

[0028] A publishing unit is configured to, if the scheme passes the test, receive a scheme publishing instruction submitted by a user, publish the scheme that passes the test, and collect data corresponding to preset scheme usage indexes according to the preset scheme usage indexes after the publication.

[0029] The third generating unit is configured to store the scheme classification, the scheme description, the encoded data marked with the user operation sequence, the scheme encoding structure, the data corresponding to the preset scheme execution efficiency index, and the data corresponding to the preset scheme usage index in a structured manner, and generate scheme structured data.

[0030] In a third aspect, an electronic device is provided, which includes a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the data preparation method for low-code GIS system use scenario recommendation according to any one of the preceding aspects.

[0031] In a fourth aspect, a storage medium is provided, which stores a computer program, wherein the computer program is configured to perform the data preparation method for low-code GIS system use scenario recommendation according to any one of the preceding aspects when running.

[0032] By the technical solution, the data preparation method and device for low-code GIS system use scenario recommendation, the electronic equipment and the storage medium provided by the embodiment of the application, the data preparation method for low-code GIS system use scenario recommendation first receives the scenario content submitted by the user, the scenario content includes scenario classification and scenario description, which provides rich semantic information for the big model to understand the core meaning of the scenario, so that the big model can better grasp the overall intention of the scenario in the learning and training process, rather than being limited to the implementation level of the function; then, in response to the operation of the user on the scenario editing interface of the low-code GIS system for the scenario content, the operation of the scenario editing interface of the low-code GIS system is coded according to the pre-constructed basic coding layer and the extended coding layer, and the coding data marked with the user operation sequence is generated, from the learning efficiency and accuracy of the big model, the operation process coding greatly improves the learning efficiency of the big model; then, the scenario is tested using the pre-defined data set, the scenario is executed according to the coding data marked with the user operation sequence, and in the execution process, the data corresponding to the preset scenario execution efficiency index is collected according to the preset scenario execution efficiency index, so as to determine whether the scenario can normally run, and collect various indicators related to scenario execution efficiency, such as processing speed and various resource occupation conditions, and then bind these indicators with the coding combination, this step provides important data for the big model in terms of scenario performance, so that it can consider the feasibility and efficiency of the scenario when recommending the scenario; then, if the scenario test passes, the scenario publishing instruction submitted by the user is received, the scenario that passes the test is published, and after publishing, the data corresponding to the preset scenario usage index is collected according to the preset scenario usage index, only the scenario that has been tested to ensure correctness can be published, so that the collected coding data has practical significance and value, avoiding the interference of invalid or incorrect data on the learning of the big model, and collecting the user adoption related data such as the use amount of the user public scenario and the number of likes, since the scenario bottom layer is saved in the form of coding, that is, the use amount of the coding combination is collected, which helps the big model to understand which coding combination is more popular among users, so as to dig out efficient and practical scenario modes;Finally, the scheme classification, the scheme description, the coded data marked with the user operation sequence, the scheme coding structure, the data corresponding to the preset scheme execution efficiency index, and the data corresponding to the preset scheme usage index are stored in a structured manner to generate scheme structured data. In this way, the collected scheme related data and its coding are cleaned, unified and arranged, and saved as structured storage. The scheme structured data formed includes scheme classification, scheme description, coded data marked with user operation sequence, scheme coding structure, data corresponding to preset scheme execution efficiency index, and data corresponding to preset scheme usage index. These rich data dimensions enable the preset large model to more accurately understand user needs after learning and training, find the most suitable scheme under the corresponding classification according to user needs, and optimize and improve the scheme according to the dialogue content. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced.

[0034] Figure 1 A flow chart of a data preparation method for low-code GIS system use scheme recommendation provided by the embodiments of the present application is shown;

[0035] Figure 2 A schematic diagram of scheme structured data provided by the embodiments of the present application is shown;

[0036] Figure 3 A structural diagram of a data preparation device for low-code GIS system use scheme recommendation provided by the embodiments of the present application is shown;

[0037] Figure 4 A structural diagram of a data preparation device for low-code GIS system use scheme recommendation provided by another embodiment of the present application is shown;

[0038] Figure 5 A structural diagram of an electronic device provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0039] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present application can be more thoroughly understood and the scope of the present application can be accurately conveyed to those skilled in the art.

[0040] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0041] To address the aforementioned technical problems, this application provides a recommended data preparation method for low-code GIS system usage scenarios, such as... Figure 1 As shown, the data preparation method recommended by the low-code GIS system can include the following steps S101 to S106:

[0042] Step S101: Receive the solution content submitted by the user. The solution content includes solution category and solution description.

[0043] In this step, both the classification and description of the solutions can be expressed using natural language.

[0044] For example, the user interface of a low-code GIS system provides a dedicated entry point for creating solutions. After entering this entry point, the system guides the user to categorize the solution to be created. The categorization options can be preset to common business categories, such as "Insight Analysis," "Sales Route Planning," and "Data Standardization." Simultaneously, a text box is provided for users to write a solution description, detailing the solution's objectives, applicable scenarios, and other descriptive information. For instance, a user engaged in market analysis might create a solution for analyzing a specific regional market, selecting "Insight Analysis" in the category and writing in the description, "This solution aims to plan key areas for market promotion by analyzing data such as population density and consumption capacity in different areas of a city." The system saves the user's input of solution category and description information in real time.

[0045] Step S102: In response to the user's operation on the scheme editing interface of the low-code GIS system, the operation on the scheme editing interface of the low-code GIS system is encoded according to the pre-built basic coding layer and extended coding layer, generating coded data marked with the user's operation sequence. The basic coding layer includes business process, functional module, sub-function and operation type, and the extended coding layer includes operation time, low-code GIS system version number and user role.

[0046] Step S103: Generate the scheme coding structure based on the structural relationship between functional modules and sub-functions in the coded data marked with the user operation sequence.

[0047] Step S104, using the predefined data set to test the scheme, executing the scheme according to the encoded data marked with the user operation sequence, and collecting the data corresponding to the preset scheme execution efficiency index according to the preset scheme execution efficiency index during the execution.

[0048] In this step, the predefined data set can include data set 1, data set 2, data set 3, etc. For multiple schemes classified in the same scheme category, the same data set can be used for testing. For example, there are 3 schemes in scheme category a, and the 3 schemes are tested by using data set 1; there are 5 schemes in scheme category b, and the 5 schemes are tested by using data set 2. It should be noted that the examples listed here are only illustrative and do not limit the present embodiment.

[0049] The preset scheme execution efficiency index can include processing speed, CPU (Central Processing Unit) usage, memory usage, network traffic, etc., which is not limited in the present embodiment.

[0050] Step S105, if the scheme test is passed, receiving the scheme publishing instruction submitted by the user, publishing the test passed scheme, and after publishing, collecting the data corresponding to the preset scheme usage index according to the preset scheme usage index.

[0051] In this step, the preset scheme usage index can include the number of times the scheme is used, the number of times the scheme is liked, the scheme recommendation level, etc., which is not limited in the present embodiment.

[0052] Step S106, structurally storing the scheme category, scheme description, encoded data marked with the user operation sequence, scheme encoding structure, data corresponding to the preset scheme execution efficiency index, and data corresponding to the preset scheme usage index, to generate scheme structured data.

[0053] In the data preparation method for solution recommendation of the low-code GIS system provided by the embodiment, first, the solution content submitted by the user is received, the solution content including solution classification and solution description, which provides rich semantic information for the large model to understand the core meaning of the solution, so that the large model can better grasp the overall intention of the solution in the learning and training process, rather than being limited to the implementation level of the function; then, in response to the operation of the user on the solution editing interface of the low-code GIS system with respect to the solution content, the operation of the solution editing interface of the low-code GIS system is coded according to the pre-constructed basic coding layer and the extended coding layer, and coding data marked with the operation sequence of the user is generated, from the learning efficiency and accuracy of the large model, the learning efficiency of the large model is greatly improved through operation process coding; then, the solution is tested using a predefined data set, the solution is executed according to the coding data marked with the operation sequence of the user, and in the execution process, the data corresponding to the preset solution execution efficiency index is collected according to the preset solution execution efficiency index, to determine whether the solution can normally run, and the various indicators related to the execution efficiency of the solution, such as processing speed and various resource occupation, are collected, and then these indicators are bound with the coding combination, this step provides important data for the large model in terms of solution performance, so that it can consider the feasibility and efficiency of the solution when recommending the solution; after that, if the solution test is passed, the solution publishing instruction submitted by the user is received, the solution that passes the test is published, and after the publication, the data corresponding to the preset solution usage index is collected according to the preset solution usage index, only the solution that has been tested to ensure correctness can be published, so that the collected coding data has practical significance and value, avoiding the interference of invalid or incorrect data on the learning of the large model, and collecting the usage amount, the number of likes and other user adoption related data of the user's published solution, since the solution is saved in the form of coding at the bottom layer, that is, the usage amount of the coding combination is collected, which helps the large model to understand which coding combination is more popular among users, so as to dig out efficient and practical solution patterns; finally, the solution classification, the solution description, the coding data marked with the operation sequence of the user, the solution coding structure, the data corresponding to the preset solution execution efficiency index, and the data corresponding to the preset solution usage index are stored in a structured manner to generate solution structured data, so that the collected solution related data and its coding are cleaned, unified and arranged, saved as structured storage, and the solution structured data formed contains fields such as solution classification, solution description, coding data marked with operation sequence of the user, solution coding structure, data corresponding to preset solution execution efficiency index, and data corresponding to preset solution usage index, etc. These rich data dimensions enable the preset large model to more accurately understand user needs after learning and training, find the most suitable solution under the corresponding classification according to user needs, and optimize and improve the solution according to the dialogue content.

[0054] The embodiment of the present application provides a possible implementation manner, after the scheme structured data is generated in step S106, the method further includes the following steps A1 and A2:

[0055] Step A1, training the preset large model by taking the scheme structured data as sample training data, to obtain a trained low-code GIS system use scheme recommendation model.

[0056] The large model (Large Model, LM) in this step is first, as the name implies, large in scale, with network parameters reaching the scale of 10 billion, 100 billion, 1000 billion or even more; second, general-purpose, which means not limited to specific problems or fields; third, emergent, that is, new abilities beyond expectations.

[0057] Step A2, receiving a user problem of using the low-code GIS system, submitting the user problem to the trained low-code GIS system use scheme recommendation model, and outputting a low-code GIS system use recommendation scheme corresponding to the user problem.

[0058] In this embodiment, the scheme structured data is taken as sample training data to train the preset large model, and the scheme structured data includes scheme classification, scheme description, encoding data marked with user operation sequence, scheme encoding structure, data corresponding to preset scheme execution efficiency indicators, data corresponding to preset scheme use condition indicators and the like. These rich data dimensions enable the trained low-code GIS system use scheme recommendation model to more accurately understand user demand when having multiple rounds of dialogues with the user, find the most suitable scheme under the corresponding classification according to the user demand, and optimize and improve the scheme according to the dialogue content.

[0059] The embodiment of the present application provides a possible implementation manner, if the scheme test passes, the method further includes: binding and storing the collected data corresponding to the preset scheme execution efficiency indicators with the scheme classification, the scheme description, the encoding data marked with the user operation sequence and the scheme encoding structure, and displaying the test result, the test result including whether the user scheme runs successfully and details of each indicator.

[0060] The embodiment of the present application provides a possible implementation manner, if the scheme test fails, the method further includes: generating prompt information that the user scheme has a problem, and guiding the user to return to the scheme editing interface of the low-code GIS system for modification. In this way, through prompting and guiding, the user can more easily understand and correct the problems in the scheme.

[0061] The above introduces Figure 1Various implementation manners of each link of the illustrated embodiment will be further described below through specific embodiments of the data preparation method recommended by the low-code GIS system use scenario of the embodiments of the present application.

[0062] As previously introduced, the existing low-code GIS system has difficulties in preparing data for use scenario recommendation, and cannot provide ideal learning data for large models, thereby affecting the quality of use scenario recommendation. For example, the low-code GIS system uses tutorials or documents, which need to go through a complex semantic conversion process when being delivered to the large model. This not only increases the difficulty of learning for the large model, but also easily leads to inaccurate recommended solutions due to semantic understanding deviation. At the same time, compared with coding, textual expression has deficiencies in semantic explicitness, information accuracy, and reflecting user operation abstraction rules. In addition, although logs can also be used to record system content, they lack the advantages of structured and coded, and it is difficult to effectively organize and analyze user operations.

[0063] In this specific embodiment, the specific framework is as follows 1) to 6):

[0064] 1) Collecting scenario classification and description information data.

[0065] The user creates his own scenario and requires the user to classify the scenario and write detailed instructions and other description information. This initiative provides rich semantic information for the large model to understand the core meaning of the scenario, so that the large model can better grasp the overall intention of the scenario in the learning process, rather than being limited to the implementation level of the function.

[0066] 2) Collecting coding structure and sequence data corresponding to the scenario.

[0067] The user needs to complete the scenario content, specifically operating each module and sub-function to form a complete scenario. In this process, a set of coding rules is used to give each module and sub-function a unique and extensible coding identifier. The coding rules cover business process coding, function module coding, sub-function coding, and operation type coding. In addition, timestamps, version numbers, user roles, and other extended coding dimensions are added to the coding.

[0068] 3) Collecting various index data of test running.

[0069] The user tests the scenario using the pre-defined data set of the system to determine whether the scenario can run normally, collects various indexes related to the execution efficiency of the scenario such as processing speed and various resource occupation, and then binds these indexes with the coding. This step provides important data on the performance of the scenario for the large model, so that it can consider the feasibility and efficiency of the scenario when recommending the scenario.

[0070] 4) Mark the successfully published solution-related data as valid.

[0071] The user can publish the publishing range for the solution passed the test, only the solution that has passed the test to ensure correctness can be published, so that the collected coding data has practical significance and value, avoiding the interference of invalid or error data on the learning of the large model.

[0072] 5) Collect runtime data.

[0073] After the solution is published, the related usage data is collected. The usage of the user's public solution, the number of likes, and other user-adopted data are collected. Since the solution is saved in the form of code, the usage of the code combination is collected, which helps the large model understand which code combination is more popular among users, and thus excavate efficient and practical solution patterns.

[0074] 6) Store the valid solution-related data.

[0075] The collected solution-related data and its code are cleaned, unified, and arranged, and saved as structured storage. The structured data of the solution includes classifying the solution, describing the solution, code data marked with user operation sequence, solution code structure, data corresponding to preset solution execution efficiency indicators, and data corresponding to preset solution usage indicators. These rich data dimensions enable the preset large model to more accurately understand user needs after learning and training, find the most suitable solution under the corresponding classification according to user needs, and optimize and improve the solution according to the dialogue content.

[0076] The following will be further illustrated by examples:

[0077] (1) Solution creation and description:

[0078] In the operation interface of the low-code GIS system, a special solution creation entrance is provided for users. After the user enters the entrance, the system guides the user to classify the solution to be created. The classification options can be preset as common business categories such as "insight analysis", "sales route planning", "data standardization", etc. At the same time, a text box is provided for the user to write a solution description, detailing the goal, applicable scenario, and other description information of the solution. For example, a user engaged in market analysis creates a solution for analyzing the market of a specific area, selects "insight analysis" in the classification, and writes in the description "This solution aims to analyze the population density, consumer ability, and other data of different areas in a city to plan the key areas for market promotion". The system saves the user's input solution classification and description information in real time.

[0079] (2) Solution content completion and coding:

[0080] Coding rule application: When the user starts to select modules and sub-functions to build a solution, the system generates a code for each operation in the background according to the established coding rules. For example, if the user selects the "point data" sub-function in the "asset" module under the "insight analysis" business process and performs a create operation, the system generates the code "B001 - 01 - 001 - C". As the user continues to add other module and sub-function operations, such as selecting "point to generate circle" in the "model" module and creating, the code "B001 - 03 - 002 - C" is generated. At the same time, the system obtains the operation timestamp, such as "20250610143015", records the current system version number "V3", identifies the user role code "U015", and adds these information to the code to form the complete code "B001 - 03 - 002 - C | 20250610143015 - V3 - U015".

[0081] Structured processing: The system monitors user operation sequences in real time and records the coding sequence in the form of a sequential list. For example, after the user performs the above operations in sequence, the system generates a sequential list, i.e., a code data marked with the user operation sequence, where the order records the user operation sequence: [

[0083] {"code": "B001 - 01 - 001 - C", "order": "1"},

[0084] {"code": "B001 - 03 - 002 - C", "order": "2"}

[0085] ]。

[0086] For the structural relationship of functional modules and their sub-functions, the system records the structure. For example, if the "asset" module has "point data" and "surface asset" sub-functions, the system records the structure as follows:

[0087] {

[0088] "id": "0eefe9a8a74245f89d81c5f3b3d4811a", / / first marker

[0089] "B001 - 01": [ / / asset module

[0090] {

[0091] "id": "02da7f7143554ffca59e15c6c078a448", / / second marker

[0092] "B001 - 01 - 001": { / / point data

[0093] "desc": "point data description"

[0094] }

[0095] },

[0096] {

[0097] "id": "a90e812843a34ef5a5ed5ed30a68ba8e", / / third marker number

[0098] "B001 - 01 - 002": { / / surface data

[0099] "desc": "surface data description"

[0100] }

[0101] } ]

[0103] }.

[0104] (3) Scheme testing:

[0105] After the user completes the scheme construction, the system automatically pops up a scheme testing prompt, informing the user to use a pre-defined data set (fixed data set is beneficial for different schemes to have different performances) for scheme testing. The system selects appropriate data sets from the pre-set data sets according to the functions and data types involved in the scheme. For example, if the scheme involves the pull LBS (Location Based Services, Location Based Services) function of the model module, the system selects a data set containing a certain number of geographic coordinates and related attribute information. After the user confirms to start testing, the system runs the scheme and monitors various performance indicators in real time, such as CPU usage, memory occupation, network traffic of the server, and running time of the scheme. After the test is completed, the system binds and stores these performance indicator data with the code combination of the scheme, and displays the test results on the interface, informing the user whether the scheme runs successfully and the details of each indicator.

[0106] (4) Scheme publishing:

[0107] If the scheme test is passed, the system provides a scheme publishing setting interface for the user. The user can select the publishing range on this interface, such as personal (only himself can see), team (specific team members can see), and public (all users can see). After the user selects, clicks the publish button, the system stores the scheme and its related performance indicator data into the corresponding database. If the scheme test is not passed, the system prompts the user that the scheme has problems, and guides the user to return to the scheme editing interface for modification.

[0108] (5) Data collection for the scheme:

[0109] The system backend sets a counter. Whenever a user uses (such as viewing, copying, or performing secondary development based on the solution) a public solution, the usage counter for the corresponding solution is incremented by 1 and updated in the usage record table in the database.

[0110] (6) Data cleaning and structured storage:

[0111] (6.1) Data cleaning:

[0112] The system retrieves relevant solution data from the database, including codes, performance metrics, and usage. First, the codes are validated for format, checking if business process codes conform to the "BPPP" format and if functional module codes are two digits. Codes that do not conform to the format are corrected or marked as abnormal. For example, if a functional module code is found to be three digits, the administrator is prompted to manually confirm and correct it. Next, the format and rationality of extended dimensions such as timestamps, version numbers, and user role codes are checked. For example, are the timestamps valid? Do the version numbers correspond to the actual system version? Performance metric data is checked for outliers, such as CPU utilization exceeding 100% or negative runtime. Abnormal data is marked and corrected or deleted.

[0113] (6.2) Structured storage:

[0114] The cleaned data is stored according to a preset structured data format. A structured data table is created, containing fields such as solution category, solution description, coded data marked with the user's operation sequence, solution coding structure, data corresponding to preset solution execution efficiency indicators (extracted from performance indicator data), number of times the solution was used, number of times the solution was liked (collected through a user like function, initially 0), and solution recommendation level (initially set to the default level, subsequently adjusted according to a preset recommendation level algorithm). Data for each solution is then filled into the corresponding fields, such as... Figure 2 As shown.

[0115] exist Figure 2 The solution is categorized as "Insight Analysis," and described as "This solution aims to plan key marketing areas by analyzing data such as population density and spending power in different regions of a city." It includes coded data indicating the user's operation sequence, the solution's coding structure, and data corresponding to preset solution execution efficiency indicators (e.g., CPU utilization 30%, memory usage 512MB, runtime 10s). The solution has been used 10 times, received 4 likes, and its recommendation level is "Normal." It should be noted that... Figure 2 The examples provided are merely illustrative and do not limit the scope of this embodiment.

[0116] Coding rules:

[0117] Business processes: such as insight analysis, sales route planning, etc.

[0118] 1.1) Use "BPPP" format, where "B" represents business process, and "PPP" is a 3-digit number (001-999);

[0119] 1.2) Example: B001 = Insight Analysis, B002 = Sales Route Planning.

[0120] Function modules: corresponding to the main function modules in the system, such as maps, assets, models, etc.

[0121] 2.1) Use "MM" format, use 2-digit numbers (01-99);

[0122] 2.2) Example: 01 = Asset, 02 = Map, 03 = Model, 04 = Hanging, 05 = Application.

[0123] Sub-function: specific function points under the module, such as each calculation node under the model function, such as point generation circle, point fence judgment, etc.

[0124] 3.1) Use "SSS" format, "SSS" is a 3-digit number, representing sub-function (001-999);

[0125] 3.2) Example: 001 = Point Data.

[0126] Operation type: the type and details of specific operations.

[0127] 4.1) Use a single letter to represent: C = Create, R = Read / View, U = Update, D = Delete, E = Execute, B = Bind, etc.

[0128] This embodiment can achieve the following technical effects:

[0129] First, in terms of learning efficiency and accuracy of large models, the present embodiment greatly improves the learning efficiency of large models through operation process coding. Traditional data provided to large models in the form of text requires complex semantic conversion, which not only consumes time but also easily produces understanding bias. The present embodiment directly uses coded form, reducing the semantic conversion step, so that the large model can more directly learn the system history. For example, the large model does not need to perform complex semantic analysis on text descriptions such as "create a point generation circle function for insight analysis", but can directly understand the operation content through the simple code "B001 - 03 - 002 - C", thereby significantly improving the learning efficiency and more accurately grasping the user operation intention, laying a foundation for generating accurate use scheme recommendations.

[0130] Second, compared with text, coding has certain advantages in semantic clarity and information accuracy. The coding format is uniform and standardized, with large and accurate information, which can reflect the abstract process of user operations in a certain rule. Each coding combination not only clearly identifies the business process, function module, sub-function and operation type, but also integrates key information such as timestamp, version number and user role through extended coding dimensions. This enables the large model to fully and accurately understand the operation background and context relationship during the learning process. For example, through the code "B001 - 03 - 002 - C | 20250610143015 - V3 - U015", the large model can know the specific operation of the specific user role under the specific system version at a specific time, greatly reducing the information ambiguity and ambiguity compared with text description, and improving the quality of large model learning data.

[0131] In the field of AI (artificial intelligence) agent (intelligent agent) applications, the direct mapping of the present embodiment and the function brings the convenience of quick and automatic function invocation. When the agent determines the required function according to the keyword and context, it does not need to perform complex understanding and analysis like processing text descriptions, but directly maps to the specific function through the code. For example, when receiving an instruction related to "insight analysis - model module - point generation circle - create", the agent can quickly locate and invoke the corresponding function through the code "B001 - 03 - 002 - C", greatly improving the system response speed and automation level, and providing users with a smoother and more efficient use experience.

[0132] In addition, the embodiment can continuously accumulate the use combination modes and use skills of other users by encoding and effectively integrating. As the number of users of the system increases and the use time elapses, a large amount of encoding data is continuously gathered. Through analysis of these encoding combinations, various efficient and practical use modes and skills can be mined. For example, it is found that in certain specific business scenarios, certain encoding combinations can achieve more optimal data analysis effects or more convenient operation processes. These valuable experiences can be integrated into general use schemes and recommended to other users to promote knowledge sharing and improve the use level of the entire user group on the low-code GIS system.

[0133] Further, through these data accumulations, when the user has multiple rounds of dialogues with the large model, the large model can understand the user's needs, find the corresponding scheme under the classification, give the scheme that best meets the user's needs, and optimize and improve the scheme according to the content of the dialogue. For example, the schemes under a certain scheme classification all have the following steps: creating point data, generating a circle from the point, pulling LBS, and binding insight. These steps are the core steps, and according to the user's needs, steps such as creating a line chart can be added.

[0134] From the perspective of system operation analysis, the data preparation method of the embodiment is beneficial to directly and deeply analyze various situations of system operation and use. The structured and standardized encoding makes it very convenient to count the number and indicators of different encodings and encoding combinations. For example, by counting the use frequency of different business process encodings, it can be known which business scenarios are more common in actual application; analyzing the performance indicators (such as running time, resource occupation, etc.) of the encoding combinations of specific function modules can evaluate the efficiency and stability of each function module. These analysis results can provide a strong basis for system optimization, helping developers to improve system performance and perfect function design, and further improve the overall quality of the low-code GIS system and user satisfaction.

[0135] It should be noted that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. In actual application, all the above possible implementation manners can be combined in any combination to form possible embodiments of the present application, which will not be repeated here.

[0136] Based on the data preparation method for low-code GIS system use scheme recommendation provided in the above embodiments, based on the same inventive concept, the embodiment of the present application also provides a data preparation device for low-code GIS system use scheme recommendation.

[0137] Figure 3 is a structural diagram of the data preparation device for low-code GIS system use scheme recommendation provided by the embodiment of the present application. As shown inFigure 3 As shown, the low-code GIS system use scenario recommendation data preparation apparatus can specifically include a receiving unit 310, a first generating unit 320, a second generating unit 330, a testing unit 340, a publishing unit 350, and a third generating unit 360.

[0138] The receiving unit 310 is configured to receive scenario content submitted by a user, wherein the scenario content includes a scenario classification and a scenario description.

[0139] The first generating unit 320 is configured to, in response to user operations on a scenario editing interface of a low-code GIS system in relation to the scenario content, generate coding data marked with user operation sequences by coding operations on the scenario editing interface of the low-code GIS system according to a pre-constructed basic coding layer and an extended coding layer, wherein the basic coding layer includes a business process, a function module, a sub-function, and an operation type, and the extended coding layer includes an operation time, a low-code GIS system version number, and a user role.

[0140] The second generating unit 330 is configured to generate a scenario coding structure according to structural relationships of the function module and the sub-function in the coding data marked with user operation sequences.

[0141] The testing unit 340 is configured to test the scenario using a pre-defined data set, execute the scenario according to the coding data marked with user operation sequences, and collect data corresponding to a preset scenario execution efficiency index according to the preset scenario execution efficiency index during execution.

[0142] The publishing unit 350 is configured to, if the scenario passes the test, receive a scenario publishing instruction submitted by the user, publish the scenario that passes the test, and collect data corresponding to a preset scenario usage index according to the preset scenario usage index after publishing.

[0143] The third generating unit 360 is configured to store the scenario classification, the scenario description, the coding data marked with user operation sequences, the scenario coding structure, the data corresponding to the preset scenario execution efficiency index, and the data corresponding to the preset scenario usage index in a structured manner to generate scenario structured data.

[0144] The present application provides a possible implementation manner, as shown in the following Figure 4 As shown above Figure 3 The apparatus shown can further include a training unit 410 configured to train a preset large model using the scenario structured data as sample training data to obtain a trained low-code GIS system use scenario recommendation model.

[0145] Recommendation unit 420 is used to receive user questions about using the low-code GIS system, submit the user questions to the trained low-code GIS system usage recommendation model, and output the low-code GIS system usage recommendation scheme corresponding to the user questions.

[0146] This application provides a possible implementation method, and the solution description is expressed in natural language.

[0147] This application provides a possible implementation method, wherein the preset scheme execution efficiency indicators include one or more of CPU utilization, memory usage, and network traffic.

[0148] This application provides a possible implementation method, wherein the preset usage index of the scheme includes one or more of the following: the number of times the scheme is used, the number of times the scheme is liked, and the recommendation level of the scheme.

[0149] This application provides one possible implementation method, such as... Figure 4 As shown above, Figure 3 The device may also include a display unit 430, which, if the solution test passes, binds and stores the data corresponding to the preset solution execution efficiency index with the solution classification, the solution description, the coded data marked with the user operation sequence, and the solution coding structure, and displays the test results, including whether the user solution runs successfully and details of each index.

[0150] This application provides one possible implementation method, such as... Figure 4 As shown above, Figure 3 The device being demonstrated may also include a prompting unit 440, which generates a prompt message indicating a problem with the user's solution if the solution test fails, and guides the user back to the solution editing interface of the low-code GIS system for modification.

[0151] Based on the same inventive concept, embodiments of this application also provide an electronic device, including a processor and a memory, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the data preparation method recommended in any of the above embodiments for the use of a low-code GIS system.

[0152] In an exemplary embodiment, an electronic device is provided, such as Figure 5 As shown, Figure 5The electronic device 500 shown includes a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, for example, via a bus 502. Optionally, the electronic device 500 can also include a transceiver 504. It should be noted that the transceiver 504 is not limited to one in actual applications, and the structure of the electronic device 500 does not constitute a limitation on the embodiments of the present application.

[0153] The processor 501 can be a CPU, a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a FPGA or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure. The processor 501 can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc.

[0154] The bus 502 can include a path for transmitting information between the above-mentioned components. The bus 502 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 502 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 In the figure, only one thick line is used to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0155] The memory 503 can be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, a magnetic disk storage or other magnetic storage devices, or any other medium capable of storing desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0156] The memory 503 is configured to store computer program codes for implementing the scheme of the present application, and the processor 501 is configured to control the execution. The processor 501 is configured to execute the computer program codes stored in the memory 503 to implement the content shown in the foregoing method embodiments.

[0157] The electronic device includes, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet Personal Computer), a PMP (Portable Multimedia Player), a vehicle terminal (for example, a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. Figure 5 The electronic device shown is only an example, and should not bring any limitation to the function and use range of the embodiments of the present application.

[0158] Based on the same inventive concept, the embodiments of the present application also provide a storage medium having a computer program stored therein, wherein the computer program is configured to execute the data preparation method for recommending a low-code GIS system use scheme of any one of the above embodiments when running.

[0159] Those skilled in the art can clearly understand the specific working process of the system, device, and module described above, and can refer to the corresponding process in the foregoing method embodiments. For the sake of brevity, no further description is given here.

[0160] Those skilled in the art can understand that the technical solutions of the present application can be embodied in the form of software product essentially or partially, and the computer software product is stored in a storage medium, and includes a plurality of program instructions to make an electronic device (such as a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in the embodiments of the present application when the program instructions are run. The storage medium mentioned above includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0161] Alternatively, all or part of the steps of the foregoing method embodiments can be completed by program instruction related hardware (such as an electronic device of a personal computer, a server, or a network device, etc.), and the program instructions can be stored in a computer readable storage medium, and when the program instructions are executed by the processor of the electronic device, the electronic device executes all or part of the steps of the method described in the embodiments of the present application.

[0162] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that within the spirit and principle of the present application, the technical solutions recorded in the foregoing embodiments can still be modified, or part or all of the technical features can be replaced equivalently; and these modifications or replacements do not make the corresponding technical solutions deviate from the protection scope of the present application.

Claims

1. A data preparation method for low-code GIS system use case recommendation, characterized in that, The method comprises: receiving a user-submitted solution content, the solution content comprising a solution category and a solution description; wherein a dedicated solution creation portal is provided for the user on the operation interface of the low-code GIS system, the user creates his own solution, after the user enters the portal, the low-code GIS system guides the user to classify the solution to be created, and provides a text box for the user to write a solution explanation, which elaborates on the description information of the solution including the target and the applicable scenario; in response to the user's operation on the solution editing interface of the low-code GIS system for the solution content, encoding is performed for the operation of the solution editing interface of the low-code GIS system according to a pre-constructed basic coding layer and an extended coding layer, and coding data marked with user operation sequence is generated, wherein the basic coding layer comprises a business process, a function module, a sub-function and an operation type, and the extended coding layer comprises an operation time, a low-code GIS system version number and a user role; generating a solution coding structure according to the structural relationship of the function module and the sub-function in the coding data marked with user operation sequence; testing the solution using a pre-defined data set, executing the solution according to the coding data marked with user operation sequence, and collecting data corresponding to the preset solution execution efficiency index according to the preset solution execution efficiency index during execution; if the solution test is passed, receiving a solution publishing instruction submitted by the user, publishing the solution that passes the test, and after publishing, collecting data corresponding to the preset solution usage index according to the preset solution usage index; structurally storing the solution category, the solution description, the coding data marked with user operation sequence, the solution coding structure, the data corresponding to the preset solution execution efficiency index, and the data corresponding to the preset solution usage index, and generating solution structured data; if the solution test fails, generating a prompt information that the user's solution has a problem, and guiding the user to return to the solution editing interface of the low-code GIS system for modification.

2. The method of claim 1, wherein, After generating the solution structured data, the method further comprises: training a pre-set large model using the solution structured data as sample training data to obtain a trained low-code GIS system use solution recommendation model; receiving a user problem of using the low-code GIS system, submitting the user problem to the trained low-code GIS system use solution recommendation model, and outputting a low-code GIS system use recommendation solution corresponding to the user problem.

3. The method of claim 1, wherein, The solution description is expressed by natural language.

4. The method of claim 1, wherein, The preset solution execution efficiency index comprises one or more of CPU usage, memory occupancy, and network traffic.

5. The method of claim 1, wherein, The preset solution usage index comprises one or more of the number of times the solution is used, the number of times the solution is liked, and the solution recommendation level.

6. The method according to any one of claims 1 to 5, characterized in that, If the solution test is passed, the method further comprises: The collected data corresponding to the preset scheme execution efficiency indicators is bound and stored with the scheme classification, the scheme description, the encoded data marked with the user operation sequence, and the scheme encoding structure, and a test result is displayed, the test result including whether the user scheme is successfully run and details of each indicator.

7. A data preparation apparatus for low-code GIS system use case recommendation, characterized by, The device comprises: A receiving unit is configured to receive scheme content submitted by a user, the scheme content including a scheme classification and a scheme description; wherein a special scheme creation portal is provided for the user on an operation interface of a low-code GIS system, the user creates a scheme of his own, and after the user enters the portal, the low-code GIS system guides the user to classify the scheme to be created and provides a text box for the user to write a scheme description, which elaborates on the description information of the scheme including the target and applicable scenarios; A first generating unit is configured to, in response to an operation of the user on a scheme editing interface of the low-code GIS system with respect to the scheme content, encode the operation of the scheme editing interface of the low-code GIS system according to a pre-constructed basic encoding layer and an extended encoding layer, and generate encoded data marked with a user operation sequence, wherein the basic encoding layer includes a business process, a function module, a sub-function, and an operation type, and the extended encoding layer includes an operation time, a low-code GIS system version number, and a user role; A second generating unit is configured to generate a scheme encoding structure according to a structural relationship of the function module and the sub-function in the encoded data marked with the user operation sequence; A testing unit is configured to test a scheme using a pre-defined data set, execute the scheme according to the encoded data marked with the user operation sequence, and collect data corresponding to preset scheme execution efficiency indicators according to the preset scheme execution efficiency indicators during the execution; A publishing unit is configured to, if the scheme passes the test, receive a scheme publishing instruction submitted by the user, publish the scheme that passes the test, and after the publication, collect data corresponding to preset scheme usage indicators according to the preset scheme usage indicators; A third generating unit is configured to structure the scheme classification, the scheme description, the encoded data marked with the user operation sequence, the scheme encoding structure, the data corresponding to the preset scheme execution efficiency indicators, and the data corresponding to the preset scheme usage indicators, and generate scheme structured data; A prompting unit is configured to, if the scheme fails the test, generate prompt information that the user scheme has a problem and guide the user to return to the scheme editing interface of the low-code GIS system for modification.

8. An electronic device, comprising: A device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the data preparation method for low-code GIS system usage scheme recommendation of any one of claims 1 to 6.

9. A storage medium, characterized by The storage medium stores a computer program, wherein the computer program is configured to perform the data preparation method for low-code GIS system usage scheme recommendation of any one of claims 1 to 6 when running.

Images