Report searching method and device, equipment and storage medium

Abstract

This disclosure provides a report search method, apparatus, device, and storage medium, relating to the field of battery manufacturing technology. The method includes, in response to obtaining the current user's login information, displaying an interactive page, the interactive page including multiple controls and a recommendation area; in response to listening to a target event triggered on a first control on the interactive page, displaying first search information on the first control and displaying recommended search information in the recommendation area, the recommended search information being generated based on global behavior data and / or the first search information; in response to listening to a selection operation on the recommended search information, controlling the focus to enter a second control corresponding to the recommended search information; in response to listening to a target event triggered on the second control, displaying second search information on the second control; and in response to listening to the fulfillment of a recommendation stop condition, performing a search based on the first and second search information and displaying the search results, which can improve interaction efficiency and search efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of battery manufacturing technology, and in particular to a report search method, a report search device, an electronic device, and a computer-readable storage medium. Background Technology

[0002] In related technologies, production reporting systems are used to visualize and analyze data collected by Manufacturing Execution Systems (MES). These systems have search and filtering functions, allowing users to query data based on specific criteria. However, production reporting systems suffer from problems such as inefficient search interaction, inability to adapt to user roles and contexts, and error-prone and time-consuming input processes. Summary of the Invention

[0003] This disclosure provides a report search method, apparatus, device, and storage medium, which at least to some extent overcomes the problems of low search interaction efficiency in related technologies.

[0004] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0005] According to one aspect of this disclosure, a report search method is provided, applied to a client, the method comprising: in response to obtaining login information of the current user, displaying an interactive page, the interactive page including multiple controls and a recommendation area; in response to listening to a target event triggered on a first control in the interactive page, displaying first search information on the first control and displaying recommended search information in the recommendation area, wherein the recommended search information is generated based on global behavior data and / or the first search information; in response to listening to a selection operation on the recommended search information, controlling a second control corresponding to the recommended search information to gain focus, displaying second search information on the second control, the second search information being generated in response to the target event triggered on the second control gaining focus; and in response to listening to a recommendation stop condition being met, displaying search results.

[0006] According to another aspect of this disclosure, a report search method is provided, applied to a server. The method includes: generating an interactive page based on login information input by a current user, wherein the interactive page includes multiple controls and a recommendation area; obtaining first search information, wherein the first search information is generated when the current user operates on a first control in the interactive page and triggers a target event; generating recommended search information based on global behavior data and / or the first search information, and sending the recommended search information to a client; obtaining second search information, wherein the second search information is generated when the current user operates on a second control in the interactive page based on the recommended search information and triggers the target event; and if a recommendation stop condition is met, performing a search based on the first search information and the second search information to obtain search results, and returning the search results to the client.

[0007] According to another aspect of this disclosure, a report search device is provided, applied to a client, the device comprising: a first display module, configured to display an interactive page in response to obtaining the current user's login information, the interactive page including multiple controls and a recommendation area; a second display module, configured to display first search information on the first control and recommended search information in the recommendation area in response to listening to a target event triggered on a first control in the interactive page, wherein the recommended search information is generated based on global behavior data and / or the first search information; a focus acquisition module, configured to control a second control corresponding to the recommended search information to gain focus in response to listening to a selection operation on the recommended search information; a third display module, configured to display second search information on the second control in response to listening to the target event triggered on the second control; and a fourth display module, configured to display search results in response to listening to a recommendation stop condition being met.

[0008] According to another aspect of this disclosure, a report search device is also provided, applied to a server. The device includes: a page generation module, used to generate an interactive page based on login information input by the current user, wherein the interactive page includes multiple controls and a recommendation area; a first acquisition module, used to acquire first search information, wherein the first search information is generated when the current user operates on a first control in the interactive page and triggers a target event; a recommendation generation module, used to generate recommended search information based on global behavior data and / or the first search information, and send the recommended search information to a client; a second acquisition module, used to acquire second search information, wherein the second search information is generated when the current user operates on a second control in the interactive page based on the recommended search information and triggers a target event; and a search execution module, used to execute a search based on the first search information and the second search information if a recommendation stop condition is met, obtain search results, and return the search results to the client.

[0009] According to another aspect of this disclosure, an electronic device is also provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the report search method described in any of the preceding claims by executing the executable instructions.

[0010] According to another aspect of this disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the report search method described in any of the preceding claims.

[0011] According to another aspect of this disclosure, a computer program product is also provided, including a computer program that, when executed by a processor, implements the report search method described in any of the preceding claims.

[0012] In this embodiment, by displaying an interactive page based on the current user's login information and dynamically generating a concise interactive page, search conditions can be pre-filtered and sorted, reducing the user's search and judgment time and avoiding cognitive search among a large number of irrelevant search options, thus reducing the user's cognitive burden. By making intelligent recommendations based on the user's initial search information and displaying recommended search information in the recommendation area, the user's search time is further reduced. Through intelligent recommendations and quick confirmation, the user's manual input is reduced, effectively lowering the data entry error rate and improving search efficiency.

[0013] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0015] Figure 1 This diagram illustrates the structure of an energy storage system provided in an embodiment of the present disclosure.

[0016] Figure 2 A flowchart illustrating a report search method executed by a client according to an embodiment of this disclosure is shown.

[0017] Figure 3 This diagram illustrates the structure of an interactive page provided in an embodiment of the present disclosure.

[0018] Figure 4 This diagram illustrates the structure of another interactive page provided in an embodiment of the present disclosure.

[0019] Figure 5 This diagram illustrates the structure of yet another interactive page provided in an embodiment of the present disclosure.

[0020] Figure 6 The flowchart illustrates a report search method executed by a server according to an embodiment of this disclosure.

[0021] Figure 7 A flowchart of an interactive page generation method provided in an embodiment of this disclosure is shown.

[0022] Figure 8 A flowchart of the recommended search information determination method provided in an embodiment of this disclosure is shown.

[0023] Figure 9 A flowchart illustrating the historical score determination method for candidate search information provided in an embodiment of this disclosure is shown.

[0024] Figure 10 A flowchart illustrating the real-time score determination method for candidate search information provided in an embodiment of this disclosure is shown.

[0025] Figure 11 A flowchart illustrating the method for determining the context score of candidate search information provided in an embodiment of this disclosure is shown.

[0026] Figure 12 This diagram illustrates an example flowchart of a report search method provided in an embodiment of this disclosure.

[0027] Figure 13 This diagram illustrates the structure of a report search device provided in an embodiment of the present disclosure.

[0028] Figure 14 This diagram illustrates the structure of another report search device provided in an embodiment of the present disclosure.

[0029] Figure 15 This diagram illustrates a structural block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0030] Because the energy we need is highly time- and space-dependent, in order to utilize energy rationally and improve energy efficiency, it is necessary to store one form of energy in the same way or by converting it into another, and then release it in a specific energy form based on future application needs. Currently, the main way to generate green electricity is to develop green energy sources such as photovoltaics and wind power to replace fossil fuels.

[0031] Currently, the generation of green electricity generally relies on solar, wind, and hydropower. However, wind and solar power are generally characterized by strong intermittency and large fluctuations, which can cause grid instability, insufficient power during peak demand periods, and excessive power during off-peak periods. Unstable voltage can also damage the power grid. Therefore, insufficient electricity demand or insufficient grid capacity may lead to the problem of "wind and solar curtailment." Solving these problems requires energy storage. This involves converting electrical energy into other forms of energy through physical or chemical means and storing it. When needed, this energy can be converted back into electrical energy and released. Simply put, energy storage is like a large "power bank," storing electrical energy when solar and wind power are abundant and releasing the stored electricity when needed.

[0032] Taking electrochemical energy storage as an example, this solution provides an energy storage device 110, which is applied to the energy storage system 100. The energy storage device 110 is equipped with a set of chemical batteries, which mainly use the chemical elements in the batteries as energy storage medium. The charging and discharging process is accompanied by the chemical reaction or change of the energy storage medium. Simply put, the electrical energy generated by wind and solar energy is stored in the chemical batteries. When the use of external electrical energy reaches its peak, the stored electrical energy is released for use, or transferred to places with a shortage of electricity for use.

[0033] Current energy storage applications are quite widespread, including generation-side energy storage, grid-side energy storage, and consumption-side energy storage. The corresponding types of energy storage devices 110 include: (1) Large-scale energy storage power stations (including multiple prefabricated energy storage modules) applied to wind power and photovoltaic power stations can help renewable energy power generation meet grid connection requirements and improve the utilization rate of renewable energy. As a high-quality active / reactive power regulation power source on the power supply side, energy storage power stations can achieve load matching of power in time and space, enhance the renewable energy absorption capacity, reduce instantaneous power changes, reduce the impact on the power grid, improve the absorption of new energy power generation, and are of great significance in power grid system backup, alleviating peak load power supply pressure and peak regulation and frequency regulation. (2) The energy storage prefabricated cabin applied on the grid side mainly functions as peak regulation, frequency regulation and grid congestion relief. In terms of peak regulation, it can realize peak shaving and valley filling of electricity load, that is, charging the energy storage battery when the electricity load is low and releasing the stored electricity during the peak electricity load period, thereby achieving a balance between power production and consumption. (3) Small energy storage cabinets applied to the electricity consumption side mainly function as self-consumption of electricity, peak-valley price arbitrage, capacity cost management, and improvement of power supply reliability. Depending on the application scenario, electricity consumption side energy storage can be divided into industrial and commercial energy storage cabinets, household energy storage devices, energy storage charging piles, etc., which are generally used in conjunction with distributed photovoltaics. Industrial and commercial users can use energy storage for peak-valley price arbitrage and capacity cost management. In the electricity market implementing peak-valley pricing, by charging the energy storage system when the electricity price is low and discharging the energy storage system when the electricity price is high, peak-valley price arbitrage can be achieved, reducing electricity costs. In addition, industrial enterprises subject to two-part tariffs can use energy storage systems to store energy during off-peak hours and discharge during peak loads, thereby reducing peak power and the maximum demand declared, achieving the goal of reducing capacity charges. Household photovoltaics with energy storage can improve the level of self-consumption of electricity. Due to high electricity prices and poor power supply stability, the demand for household photovoltaic installations is driven. Given that photovoltaic power generation occurs during the day, while current user loads are generally higher at night, configuring energy storage can better utilize photovoltaic power, improve self-consumption levels, and reduce electricity costs. Furthermore, energy storage is needed in areas such as communication base stations and data centers for backup power.

[0034] In some embodiments, see Figure 1 , Figure 1 This is a schematic diagram of the structure of an energy storage system 100 according to an embodiment of this application. Figure 1 And this application Figure 1 The embodiments are illustrated using a shared energy storage scenario on the generation / distribution side as an example. The energy storage device in this application is not limited to a prefabricated energy storage module in a generation / distribution energy storage scenario.

[0035] This application provides an energy storage system 100, which includes: a high-voltage cable 120, a first power conversion device 130, a second power conversion device 140, and an energy storage device 110 provided in this application. In some embodiments of the power generation scenario, the second power conversion device 140 can be a wind power conversion device. Since the electricity generated by wind power conversion is volatile, random, and intermittent, the unstable electricity output by the wind power conversion device can be stored in the energy storage device through grid connection. The energy storage device is connected to the high-voltage cable 120 and outputs smooth electricity to the power consumption side of the distribution network, realizing peak shaving and frequency regulation, and ensuring stable grid operation; or, the wind power conversion device... The device is always connected to the high-voltage cable 120. Under normal power generation conditions, the power output of the wind power conversion device is supplied to the power consumption side of the distribution network through the high-voltage cable. When the current power load is low and the wind power conversion device generates excess power, the excess power is first stored in the energy storage device 110 to reduce wind and solar curtailment rates and improve the problem of new energy power generation consumption. When the power load is high, the power grid issues an instruction to transmit the power stored in the energy storage device 110 together with the high-voltage cable 120 in grid-connected mode to supply power to the power consumption side. This provides the power grid with various services such as peak shaving, frequency regulation, and backup, giving full play to the peak shaving role of the power grid, promoting peak shaving and valley filling, and alleviating the power supply pressure of the power grid.

[0036] In some embodiments on the distribution network side, the first power conversion device 130 can be a photovoltaic panel, and the energy storage device 110 is connected to the high-voltage cable 120 and installed downstream of the high-voltage cable 120 and between the current user load. The electrical energy output by the photovoltaic panel is stored in the energy storage device 110, which can respond in a timely manner to act as a backup power source when the power grid / distribution network fails; or, it can provide power supply support to alleviate line congestion when the high-voltage cable 120 transmission line is blocked, and to delay the economic pressure caused by the expansion of the power grid / distribution capacity when the power grid is planned to be expanded.

[0037] The first power conversion device 130 may include, but is not limited to, a photovoltaic panel, and the second power conversion device 140 may include, but is not limited to, a wind power conversion device. The first power conversion device 130 and the second power conversion device 140 can convert at least one of solar energy, light energy, wind energy, thermal energy, tidal energy, biomass energy and mechanical energy into electrical energy.

[0038] The energy storage device 110 may include, but is not limited to, energy storage applications such as energy storage power stations, hydropower / thermal / wind power generation systems, solar power generation systems, mobile power systems, smart home systems, or temporary power supply systems. It is also used in multiple fields such as data centers, military equipment, aerospace, charging piles, and electric vehicles.

[0039] The energy storage device 110 may include battery modules, battery packs, battery clusters, mobile power supplies, energy storage cabinets / prefabricated energy storage compartments, and other battery integrated systems composed of individual batteries. The actual application form of the energy storage device 110 provided in this application embodiment may be, but is not limited to, the listed products, and may also be other application forms. This application embodiment does not strictly limit the application form of the energy storage device 110.

[0040] The single cell is not limited to at least one of cylindrical, prismatic, prismatic, or other shaped batteries. Optionally, the single cell can be a rechargeable battery, which refers to a single cell that can be recharged after discharge to activate the active materials and continue to be used. The single cell can be a lithium-ion battery, sodium-ion battery, sodium-lithium-ion battery, lithium metal battery, sodium metal battery, lithium-sulfur battery, magnesium-ion battery, nickel-metal hydride battery, nickel-cadmium battery, lead-acid battery, etc., and this application does not specifically limit it.

[0041] The report search method disclosed herein can be applied to report search in production report systems during battery production, as well as in production report systems during module production. It can also be applied to report search in production report systems in other product manufacturing industries (such as semiconductors, solar cells, etc.). In this disclosure, the report search method is described in detail using a specific application scenario of report search in production report systems in the battery manufacturing industry as an example.

[0042] The Manufacturing Execution System (MES) collects and stores data from the entire production process, including multiple dimensions such as time, equipment, materials, personnel, process parameters, and quality inspection results, providing crucial data for production monitoring, quality traceability, and efficiency analysis.

[0043] To make efficient use of the data collected by MES (Manufacturing Execution System), a production reporting system is typically used for visualization and data analysis. This system has search and filtering functions, allowing users to query data based on specific search criteria. Search criteria are provided to users in form format, where they input or select criteria values ​​to limit the search scope, ultimately generating the required reports.

[0044] The aforementioned production report function meets basic data query needs. However, with the increasing complexity of production models and the rapid increase in data dimensions, the shortcomings of the above report search methods have become increasingly apparent, mainly in the following aspects: 1. Numerous and static search conditions. A production report may contain more than ten fixed search conditions, requiring users to manually search, fill in, and select from all the available conditions, resulting in complex human-computer interaction steps. 2. Complex relationships between search conditions. There are complex relationships between different search conditions, such as a product being produced only on a specific production line, but the production report system cannot utilize this relationship for intelligent guidance. 3. Differences in user roles' focus. Users with different roles focus on different data dimensions, but the production report system cannot utilize this relationship for intelligent guidance. 4. High query efficiency requirements. In the production field, the timeliness of decision-making is crucial to production efficiency, and the cumbersome search configuration process prolongs data acquisition time. It is evident that the report search methods in related technologies suffer from low search interaction efficiency, inability to adapt to user roles and contexts, and error-prone and time-consuming input processes.

[0045] To address at least some of the aforementioned technical problems, the report search method provided in this disclosure displays an interactive page based on the current user's login information, dynamically generating a concise interactive page. This allows for pre-filtering and sorting of search conditions, reducing user search and judgment time, avoiding cognitive searches among numerous irrelevant options, and lowering the user's cognitive burden. Furthermore, by providing intelligent recommendations based on the user's initial search information and displaying recommended search information in the recommendation area, the user's search time is further reduced. Intelligent recommendations and quick confirmation reduce manual input, effectively lowering the data entry error rate and improving search efficiency.

[0046] This disclosure provides a report search method, which can be executed by any electronic device with computing capabilities. In some embodiments, the report search method provided by this disclosure can be executed by a client; in some embodiments, the report search method provided by this disclosure can be executed by a server; in some embodiments, it can also be implemented through interaction between the client and the server.

[0047] Figure 2 This diagram illustrates a report search method executed by a client, according to an embodiment of this disclosure. Figure 2 As shown, the report search method provided in this embodiment of the present disclosure is applied to a client, and the method includes the following steps S202~S210.

[0048] Specifically: S202, in response to obtaining the current user's login information, displays an interactive page, which includes multiple controls and a recommendation area.

[0049] In one embodiment, the current user's login information is obtained through an application (such as a production reporting system) installed on the terminal device. The terminal device can be various electronic devices, including but not limited to smartwatches, tablets, laptops, desktop computers, wearable devices, etc. The clients of the applications installed on different terminal devices are the same, or clients of the same type of application based on different operating systems. Depending on the terminal platform, the specific form of the application client can also differ; for example, the client can be a mobile client, a PC client, etc.

[0050] The server can be a server providing various services, such as a backend management server. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, and big data and artificial intelligence platforms. The server can interact with the client via a network.

[0051] The current user is the user currently logged into the production report system to perform report searches. Users can log in to the report generation system by entering their username and password as a key, or they can use other methods; this disclosure does not impose any restrictions.

[0052] The interactive page is the page displayed to the current user in the production reporting system. For example... Figure 3 As shown, the interactive page 200 may include a search navigation area 210, a results display area 220, and a recommendation area 230. The results display area 220 is used to display search results to the user, which can be displayed in a list, chart, or other format. The recommendation area 230 is used to display recommended search information to the current user, enabling intelligent recommendations and displaying recommended search information in a more intuitive way. For example, Figure 3 The recommended search information shown is the next recommendation: [Process Segment], popular options [Electrode Segment - Anode, Electrode Segment - Cathode].

[0053] The search navigation area 210 is used to display search configuration controls corresponding to candidate search information from multiple dimensions to the user. The number of controls in the search navigation area 210 can be determined according to actual needs. Figure 3 In the search navigation area 210, 14 search configuration controls are displayed, which correspond to the selection of dimensions such as date, time range, shift, base, factory, workshop / line, process segment, process, equipment, product model, work order type, PN, work order number, and defect code. In each search configuration control, corresponding candidate conditions or candidate values ​​can be set, that is, the candidate search information can include candidate conditions and / or candidate values.

[0054] The search navigation area 210 may also include analysis dimension controls and grouping dimension controls. The analysis dimension controls may include controls for dimensions such as base, factory, workshop / production line, process segment, process, and equipment. When one analysis dimension control is selected, the corresponding search configuration control gains focus, allowing further selection of search candidate conditions and / or candidate values ​​from that control. For example, when the analysis dimension control for the base dimension is selected, the corresponding base search configuration control gains focus, allowing configuration of search conditions to be completed within the base search configuration control.

[0055] Grouping dimension controls can include controls for dimensions such as product model, PN, work order type, and shift. Selecting one of the grouping dimension controls allows you to choose the corresponding grouping candidate conditions and / or candidate values ​​from the controls associated with that grouping dimension. For example, when the product model dimension grouping control is selected, the corresponding product model search configuration control gains focus, allowing you to configure the grouping conditions within that control.

[0056] In one embodiment, the result display area 220 can be used as the recommendation area 230 before the search is performed; alternatively, a floating window can be used above the result display area 220 as the recommendation area 230. This disclosure does not limit the implementation of the recommendation area 230.

[0057] S204. In response to listening to a target event triggered on a first control in the interactive page, display first search information on the first control and display recommended search information in the recommendation area, wherein the recommended search information is generated based on global behavior data and / or the first search information.

[0058] The first control on the interactive page is the search configuration control corresponding to the analysis dimension control or grouping dimension control that the user actively selects on the interactive page. For example, in Figure 3 In the current user selection, the analysis dimension control for the base and factory dimensions and the grouping dimension control for the product model dimension are selected. At this time, the search configuration control corresponding to the base dimension, factory dimension and product model dimension is the first control 211. The search configuration control corresponding to the dimensions other than the above dimensions (workshop / line, process segment, process, equipment, PN, work order type, shift) is the second control 212.

[0059] Target events can include, but are not limited to, events such as the value change of the first control, the loss of focus, or the selection of an item from the completion list. When the current user interacts with the interactive page, events triggered by the first control on the interactive page are monitored in real time. For example, target events include the Change event, the Blur event, or the Select event. The Change event is triggered when the current user selects a value from the dropdown list of the first control; the Blur event is triggered when the dropdown list of the first control loses focus; and the Select event is triggered when the current user selects an item from the completion list.

[0060] The first search information may include candidate conditions and / or candidate values ​​that the current user has configured in the first control.

[0061] Recommended search information is generated by the server based on overall behavioral data and / or initial search information. Recommended search information can include recommendations from one dimension or multiple dimensions. Overall behavioral data can include at least one of historical behavioral data, real-time production data, and contextual data. Historical behavioral data can include the current user's historical behavioral data and the historical behavioral data of users with the same role identifier as the current user. Real-time production data is real-time data such as production line status, work-in-process information, and anomaly alarms obtained from external systems such as EMS through API interfaces or database polling. Contextual data can include the current user's current operation flow data. The specific generation method of recommended search information will be elaborated later.

[0062] For example, when the current user selects a grouping dimension control or an analysis dimension control, the corresponding first control on the interactive page gains focus. The current user can click on the first control to display its drop-down list and select candidate conditions and / or candidate values ​​from the drop-down list using at least one method such as clicking, using arrow keys, or pressing Enter. When listening for changes in the value of the first control or events where the focus is lost, the target event triggered on the first control is detected.

[0063] S206. In response to the listening operation of selecting recommended search information, control the second control corresponding to the recommended search information to gain focus, and display the second search information on the second control. The second search information is generated in response to the target event triggered by the second control that gains focus.

[0064] In one embodiment, the selection operation of recommended search information may include clicking, swiping, long-pressing, double-clicking, voice-triggered operation, specific keyboard commands (such as the Tab key), etc. In this disclosure, the Tab key is used as an example for illustration.

[0065] The second search information includes the recommended candidate conditions and / or recommended candidate values ​​configured by the current user in the second control that has gained focus.

[0066] Triggering a target event on a second control that gains focus can include, but is not limited to, a value change event of the second control, a focus loss event of the second control, or selecting an item from the completion list. The implementation method for triggering a target event on a second control that gains focus is similar to the implementation method for triggering a target event on a first control in the aforementioned embodiments, and will not be repeated here.

[0067] It should be noted that, in response to a detected "cancel recommendation" action, the search results obtained by applying the first search condition are displayed. The "cancel recommendation" action can be either the current user closing the recommended area, or the current user pressing the ESC key to exit when the recommended area has focus.

[0068] S208. In response to the detection that the recommended stopping condition is met, perform a search based on the first search information and the second search information, and display the search results.

[0069] In one embodiment, the recommendation stopping condition includes at least one of the following: the current user cancels the search recommendation, there is no recommended content, or the current user does not select candidate search information in the recommended area within a preset time period. In response to the current user closing the recommended area when it receives focus, or the current user pressing the ESC key to exit, it is determined that the current user has canceled the search recommendation. It should be noted that cancelling the search recommendation can mean cancelling the search recommendation for the current dimension. After the current user configures the first control for another dimension, the system can then recommend search information for the next dimension. Canceling the recommendation can also mean cancelling the search recommendations for other dimensions during the current search process. "No recommended content" means that the server has completed search recommendations for all dimensions.

[0070] The aforementioned preset time period can be pre-configured, and the value of the preset time period can be determined according to actual needs. For example, if the current user does not select candidate search information in the recommended area within 3 seconds, it means that the recommendation stopping condition has been met.

[0071] In one embodiment, when the recommended stopping condition is met and the selection operation of the query control in the interactive page is triggered, a search is performed based on the first search information and the second search information, and the search results are displayed in the results display area.

[0072] Search results may be displayed in the form of charts, lists, text, etc., and this disclosure does not impose any specific restrictions on this.

[0073] In this embodiment, by displaying an interactive page based on the current user's login information and dynamically generating a concise interactive page, search conditions can be pre-filtered and sorted, reducing the user's search and judgment time and avoiding cognitive search among a large number of irrelevant search options, thus reducing the user's cognitive burden. Furthermore, by intelligently recommending based on the user's initial search information and displaying recommended search information in the recommendation area, the user's search time is further reduced. The recommendation and quick confirmation of search information greatly reduce the user's manual input, effectively lowering the data entry error rate caused by spelling, memory errors, etc., and improving search efficiency.

[0074] In one embodiment, S206, in response to detecting a selection operation on the recommended search information, controls the second control corresponding to the recommended search information to gain focus, and displays the second search information on the second control, including: S206A. In response to the second control having multiple recommended candidate values, display the option list of the second control; S206B, In response to detecting a confirmation operation on at least one recommended candidate value in the option list, a second search information is displayed in a second control, the second search information including the at least one recommended candidate value.

[0075] In one embodiment, the recommended search information includes recommended candidate conditions and / or recommended candidate values. When the recommended search information includes multiple recommended candidate values, they are displayed in the option list of the second control. The confirmation operation for at least one recommended candidate value in the option list may include, but is not limited to, the current user selecting using the arrow keys and confirming using the Enter key or the Tab key.

[0076] exist Figure 3 In the current scenario, after the user selects the analysis dimension controls corresponding to the base and factory, configures the first search information in the corresponding first control 211, and presses the Tab key, the recommended search information ([workshop / line]) for the candidate region 230 is selected; for example... Figure 4 As shown, the analysis dimension control corresponding to the workshop / line is selected, and the second control 212 corresponding to the workshop / line gains focus. According to the candidate area 230, the recommended search information includes two recommended candidate values, which are displayed in the option list 2121 of the second control 212. The current user selects a recommended candidate value using the arrow keys and confirms it by pressing Enter or Tab. The second control displays the second search information confirmed by the current user and shows the recommended search information for the next dimension in the recommended area, such as... Figure 5 The next recommended option shown is: [Process Segment], and popular options are: [Electrode Segment - Anode, Electrode Segment - Cathode].

[0077] In this embodiment, when the focus enters the second control, multiple recommended candidate values ​​are displayed through an option list, and interaction is achieved through shortcut keys. This simplifies the combination of multiple mouse clicks and keyboard inputs into a continuous stream of keyboard commands, significantly reducing the number of operation steps and total time required to complete a complex query. It also reduces the user's search and judgment time. The recommended candidate values ​​and quick confirmation method reduce manual input, effectively lowering the data entry error rate caused by spelling, memory errors, etc., and improving search accuracy and efficiency.

[0078] It should be noted that the recommended search information disclosed herein can be multi-dimensional search information. In response to the listening operation of selecting recommended search information in the recommended area, the second control corresponding to the first dimension in the recommended search information is controlled to gain focus. After the sub-search information is displayed in the second control, the current user presses the Tab key to control the second control corresponding to the second dimension in the recommended search information to gain focus. The current user can configure the search in the second control until the recommendation stop condition is met, and the sub-search information configured in each second control is determined as the second search information.

[0079] The recommended search information disclosed herein can be single-dimensional search information. After the current user has configured the recommended search information for this dimension, the system will proceed to the next dimension of search recommendations until the recommendation stop condition is met.

[0080] In one embodiment, the method further includes: in response to detecting that the recommendation stop condition has not been met, displaying next-dimensional recommendation search information in the recommendation area, wherein the next-dimensional recommendation search information is generated based on global behavioral data and / or first search information and second search information. The next-dimensional recommendation search information is generated in the same way as the recommendation search information generated based on the first search information and / or global behavioral data, thereby achieving efficient configuration of search information and improving report search efficiency.

[0081] Figure 6 This diagram illustrates a report search method executed by a server according to an embodiment of this disclosure. Figure 6 As shown, in one embodiment, the report search method provided by this disclosure is applied to a server, and the method includes S602~S610.

[0082] Specifically, S602 generates an interactive page based on the login information entered by the current user. The interactive page includes multiple controls and a recommendation area.

[0083] The login information entered by the current user may include a username and / or password, mobile phone number, and verification code. The interactive page is the page displayed to the current user in the production reporting system. Controls on the interactive page are used to configure search information; one control can correspond to one search dimension, and this disclosure does not specify a particular limit on the number of controls. The recommendation area is used to display recommended search information to the user.

[0084] S604. Obtain the first search information, wherein the first search information is generated when the current user operates on the first control in the interactive page and triggers the target event.

[0085] The first control is the control that the user actively configures for searching information on the interactive page. The target events triggered on the first control may include, but are not limited to, events such as value change, focus loss, or selecting an item from the completion list. The server obtains the first search information, and correspondingly, the client sends the first search information to the server.

[0086] S606. Based on the overall behavior data and / or the first search information, generate recommended search information and send the recommended search information to the client.

[0087] Recommended search information is generated by the server based on overall behavioral data and / or initial search information. The recommended search information includes recommendations from at least one dimension. Correspondingly, the client receives the recommended search information sent by the server.

[0088] S608. Obtain second search information, wherein the second search information is generated when the current user operates on the second control in the interactive page based on the recommended search information and triggers the target event.

[0089] The second search information includes the recommended candidate conditions and / or recommended candidate values ​​configured by the current user in the second control that has focus. Triggering target events on the second control with focus may include, but is not limited to, the value change event of the second control, the focus loss event of the second control, selecting an item from the completion list, etc. The server obtains the second search information, and correspondingly, the client sends the second search information to the server.

[0090] S610. If the recommended stopping condition is met, then perform a search based on the first search information and the second search information, obtain the search results, and return the search results to the client.

[0091] The server returns the search results to the client, and the client receives the search results sent by the server.

[0092] In one embodiment, the recommendation stopping condition includes at least one of the following: the current user cancels the search recommendation; there is no recommended content; the current user does not select candidate search information in the recommended area within a preset time period. By setting recommendation stopping conditions, a search chain from user input, intelligent recommendation, recommendation confirmation, and search jump can be constructed, effectively improving interaction efficiency and search efficiency.

[0093] In one embodiment, the method further includes: if the recommendation stopping condition is not met, generating next-dimensional recommendation search information based on the global behavior data, the first search information, and the second search information, until the recommendation stopping condition is met.

[0094] In this disclosure, multiple dimensions of recommended search information can be recommended at once, or one dimension can be recommended at a time. When recommending one dimension of recommended search information at a time, after the current user has configured the recommended search information for the current dimension, the server generates recommended search information for the next dimension based on the configured search information. The current user then configures the recommended search information for the next dimension as the recommended search information for the current dimension, and the server continues to generate recommended search information for the next dimension, and so on, until the recommendation stopping condition is met. Thus, through intelligent recommendation, the user's search and judgment time is reduced, cognitive load and error rate are lowered, and search efficiency is improved.

[0095] In this embodiment, by generating an interactive page based on the current user's login information, a concise interactive page can be dynamically generated. This allows for pre-filtering and sorting of search conditions, reducing the user's search and judgment time, avoiding cognitive searches among numerous irrelevant options, and lowering the user's cognitive burden. Furthermore, by generating recommended search information based on the first search information and / or global behavioral data, intelligent recommendations further reduce the user's search time. The recommendation and rapid confirmation of search information significantly reduce manual input, effectively lowering the data entry error rate caused by spelling, memory errors, etc., and improving search efficiency.

[0096] Figure 7 This diagram illustrates a flowchart of an interactive page generation method provided by an embodiment of the present disclosure. Figure 7 As shown, in one embodiment, the interactive page is a page corresponding to the current user's role identifier; wherein, the above-mentioned S602 generates the interactive page based on the login information entered by the current user, including: S702. Determine the current user's role identifier based on the login information entered by the current user; S704. Based on the preset mapping relationship, determine the target search information template corresponding to the role identifier; S706. Parse the target search information template and generate an interactive page.

[0097] The current user's role identifier refers to their identity information within the enterprise. Role identifiers can include roles such as production supervisor and quality inspector, used to distinguish different user identities within the company. When a user registers in the production reporting system, a role identifier is configured for them, and the association between each user's role identifier and login information is stored on the server. When the production reporting system obtains the login information entered by the current user through the user login session or identity authentication module, the server determines the current user's role identifier based on the pre-stored association between role identifiers and login information.

[0098] The aforementioned preset mapping relationship represents the mapping relationship between role identifiers and search information templates. This preset mapping relationship can be stored in the configuration library of the production reporting system. The configuration library is stored in the form of data tables, where each data entry represents a one-to-one mapping relationship between a set of role identifiers and search information templates. The target search information template is the search information template in the data table corresponding to the current role identifier.

[0099] The target search information template defines the set of search criteria to be displayed, the subordinate groups of each search criterion, and the initial sorting. Subordinate groups include primary criteria, secondary criteria, etc. The server parses the target search information template to obtain the parsing results and sends the parsing results to the client. The client framework dynamically renders a customized interactive page based on the parsing results, where the search criteria are laid out according to the subordinate groups and the initial sorting.

[0100] In this embodiment, by determining the current user's role identifier and the target search information template corresponding to the role identifier, parsing the target search information template, and dynamically generating a customized interactive page containing structured groupings, the search conditions are filtered and sorted, further reducing the user's search and judgment time, improving interaction efficiency, and dynamically generating a concise interactive page, avoiding the user's cognitive search among a large number of irrelevant candidates, thus reducing cognitive load.

[0101] Figure 8 This diagram illustrates a flowchart of a method for generating recommended search information according to an embodiment of this disclosure. Figure 8 As shown, in one embodiment, S606 generates recommended search information based on the global behavior data and / or the first search information, and sends the recommended search information to the client, including: S802. Calculate the historical score of each candidate search information based on historical behavior data; S804. Calculate the real-time score of each candidate search information based on real-time production data; S806. Based on the context data and the first search information, calculate the context score for each candidate search information; S808. Determine the total score for each candidate search information based on at least one of the historical score, real-time score, and context score; S810. Determine the recommended search information based on the total score of each candidate search information.

[0102] In one embodiment, historical behavior data may include the historical behavior data of the current user and the historical behavior data of users with the same role identifier as the current user. Historical scores characterize the degree to which the historical behavior of the current user and users with the same role identifier influences candidate search information.

[0103] Real-time production data consists of real-time data such as production line status, work-in-process information, and anomaly alarms obtained from external systems like EMS via API interfaces or database polling. The real-time score characterizes the degree of influence of current real-time hotspots within the generated production data on candidate search information.

[0104] Context data can include the current user's current action flow data. The context score represents the degree of influence of the current user's current action flow (such as the action flow of the first search information) and the context unit on the candidate search information.

[0105] Calculate historical scores Real-time scores and context score The total score of the candidate search information is obtained by weighted summation of at least one of the items in the above list. It is expressed by the following formula: (Formula 1) in, , , These are the weight values ​​corresponding to the historical score, real-time score, and context score of candidate search information i, respectively. The values ​​of these weight values ​​are not limited; for example, =0.3, , .

[0106] In one embodiment, S810 determines recommended search information based on the total score of each candidate search information, including at least one of the following: determining candidate search information with a total score greater than a preset score threshold as recommended search information; sorting each candidate search information in descending order based on the total score, and taking a preset number of candidate search information at the top of the sort as recommended search information, thereby quickly determining recommended search information and providing strong data support for report search.

[0107] Recommended search information can include multiple candidate values ​​under the same dimension. For example, if the recommended search information is workshop / line drawing - workshop A and workshop B, the recommended search information includes two candidate values ​​under the workshop / line drawing dimension.

[0108] Recommended search information can also include multiple recommended candidate conditions under the same dimension. For example, if the recommended search information is the process segment - electrode segment and winding segment, the recommended search information includes two recommended search conditions under the process segment dimension.

[0109] The aforementioned preset score thresholds and preset quantities can be determined according to actual needs, and this disclosure does not impose specific limitations. For example, candidate search information with a total score greater than 0.6 can be used as recommended search information, that is, if 5 out of 10 candidate search information have a total score greater than 0.6, then these 5 candidate search information with a total score greater than 0.6 can be used as recommended search information; the top N (e.g., N=5) candidate search information can be used as recommended search information, that is, the top 5 candidate search information can be selected as recommended search information.

[0110] In this embodiment, the total score of each candidate search information is determined by historical score, real-time score and context score. The interaction logic can be dynamically adjusted according to user role, historical habits, context information and real-time status of production line, so that the production report system can better adapt to the real-time data query needs of different users and different production scenarios, improve search efficiency and provide a better user experience.

[0111] Figure 9 A flowchart illustrating the historical score determination method for candidate search information provided in an embodiment of this disclosure is shown. Figure 9 As shown, in one embodiment, S802 calculates the historical score of each candidate search information based on historical behavior data, including: S902, Obtain the real-time count value of the current user for each candidate search information, and the maximum count value for the candidate search information; S904. Obtain the weighted count value of each candidate search information for users with the same role identifier as the current user, and the total weighted count value of all candidate search information for users with the same role identifier as the current user. S906. Calculate the historical score of each candidate search information based on the real-time count value, the maximum count value, the weighted count value, and the total weighted count value.

[0112] In one embodiment, by analyzing a historical database and employing a frequency statistics algorithm or a time decay weighted algorithm, the count values ​​of each candidate search condition and / or candidate search value based on the current user's usage frequency and co-occurrence pattern are calculated to obtain the aforementioned real-time count value and maximum count value. The calculated values ​​of each candidate search condition and / or candidate search value based on the usage frequency and co-occurrence pattern of users with the same role identifier as the current user are also calculated to obtain the aforementioned weighted count value and total weighted count value.

[0113] Historical scores of each candidate search information i This can be expressed by the following formula: (Formula 2) in, This is a normalization function used to calculate the minimum-maximum normalization within the candidate search information set; This is the real-time count value of the current user u for the candidate search information i; This represents the maximum count of candidate search information for the current user u. The weighted count of candidate search information i for all users with the same role identifier r as the current user u; The total weighted count of all candidate search information for all users with role identifier r; fusion coefficient The older the person, the more they tend to focus on personal history.

[0114] The normalization function can be represented as follows: (Formula 3) in, The current value in the candidate search information set. , These are the minimum and maximum values ​​in the candidate search information set, respectively. >0, to prevent the denominator from being 0.

[0115] (Formula 4)

[0116] in, Let be the time decay function. "now" represents the current time, and "t" represents the time when the user uses the candidate search information. The time decay constant, >0; The behavior weight for type. In this disclosure, the various parameters can be pre-configured, for example, =14 days; Views: 0.5, Clicks: 1, Uses: 3, Favorites: 5; .

[0117] In this embodiment, by analyzing the historical habits of the current user and the historical habits of all users with the same role identifier as the current user, the interaction logic is dynamically adjusted so that the production report system can better meet the data query needs of the current user and improve the user experience.

[0118] Figure 10 A flowchart illustrating a real-time score determination method for candidate search information provided in an embodiment of this disclosure is shown. Figure 10 As shown, in one embodiment, the above-described S804 calculates the real-time score of each candidate search information based on real-time production data, including: S1002. Based on real-time production data, determine the real-time hotspots within a preset time period and calculate the correlation between each candidate search information and the real-time hotspots. S1004. Calculate the time interval between the current moment and the time when the real-time hotspot was generated, and calculate the time decay weight based on the time interval; S1006. Calculate the real-time score of each candidate search information based on relevance and time decay weight.

[0119] In one embodiment, the preset time period can be determined according to actual needs, such as the past 3 days, the past 7 days, the past 14 days, etc. Real-time hotspots include alarm devices, abnormal work orders, and emergencies within the preset time period.

[0120] The correlation Rel(i,h) between candidate search information i and real-time hotspot h takes the value [0,1]. This correlation can be calculated based on business rules. A mapping rule between candidate search information and real-time hotspots is pre-built, and one candidate search information can correspond to at least one real-time hotspot. For example, when the candidate search information and the real-time hotspot are exactly matched (same device or same work order), the correlation is 1; when they belong to the same production line or the same area, the correlation value is 0.6 to 0.9.

[0121] The correlation between candidate search information and real-time hot topics can also be calculated using a pre-trained artificial intelligence model.

[0122] The time interval Δt is the difference between the current time now and the time when the real-time hotspot h was generated. The difference between them, Δt≥0.

[0123] The time decay weight is expressed as: (Formula 5) in, , is the real-time decay time constant, which controls the time sensitivity; the smaller it is, the more sensitive it is.

[0124] Real-time scores of candidate search information It is expressed as follows: (Formula 6) Where Normalize is the normalization function. The correlation between candidate search information i and real-time hotspot h. This is the time decay weight.

[0125] In this embodiment, by identifying high-frequency, abnormal, and high-value real-time hotspots from real-time production data, quantifying the correlation between real-time hotspots and candidate search conditions, and calculating real-time scores in conjunction with a time decay mechanism, the system uses these scores as the basis for filtering and recommending search information. This achieves a shift from passive filtering to proactive and precise push notifications, improves the efficiency of search condition configuration, and shortens anomaly response time.

[0126] Figure 11 A flowchart illustrating the context score determination method for candidate search information provided in an embodiment of this disclosure is shown. Figure 11 As shown, in one embodiment, the above-described S806 calculates the context score of each candidate search information based on context data and first search information, including: S1102. Calculate the matching degree between each candidate search information and the first search information; S1104. Determine the context step interval based on the candidate search information and the first search information; S1106. Calculate the context decay weight based on the context step size interval; S1108. Calculate the context score for each candidate search information based on the matching degree and context decay weight.

[0127] Matching degree between candidate search information and first search information This refers to the matching degree between candidate search information i and the current context unit j that generated the first search information. Context unit j can include the current task, query, session topic, page module, scene tag, etc. Matching degree The value can be calculated through rules or models. It should be noted that the matching degree... The calculation method is similar to that of the correlation degree Rel(i,h) in the previous embodiment, and will not be repeated here.

[0128] The context step interval Δs refers to the number of interaction steps within a session since the last context event, the number of industry levels or focus switches. If there is no concept of steps, a time interval can be used instead.

[0129] Context loss weights It can be represented as: (Formula 7) in, The context decay constant, Context step interval smaller or The larger the value, the longer the impact of the action corresponding to the first search result lasts.

[0130] The context score of candidate search information can be represented as: (Formula 8) in, For normalization functions, such as min-max normalization within the candidate search information set; This refers to the degree of matching between candidate search information i and the current context unit j that generated the first search information; Context loss weights.

[0131] In this embodiment, by quantifying the contextual matching degree between the first search information and the candidate search information, and combining it with a time decay mechanism to calculate the context score, a shift from passive filtering to proactive and precise adaptation is achieved, significantly improving search efficiency.

[0132] To deepen the understanding of the search method for this public report, the following will combine... Figure 12 Specific examples will be provided. For instance... Figure 12 As shown, the report search method disclosed herein includes: S1201. The current user has logged in or entered the production report system; S1202. Generate a customized interactive page based on the current user's role identifier; S1203, Listen for keyboard events of the current user on controls in the interactive page; S1204. Determine if it is a Tab key. If yes, execute S1205; otherwise, execute S1209. S1205. Confirm the current candidate value; S1206. Determine whether the recommended stopping condition is met. If not, proceed to S1207; if yes, proceed to S1211. S1207. Combine historical scores, real-time scores, and contextual scores to calculate the next dimension of recommended search information; S1208. Display the next dimension of recommended search information to the current user, and return to S1203; S1209. Determine if it is an arrow key. If yes, execute S1210. If no, determine it as another operation and do not process it. S1210. Navigate through the options list and return to S1203; S1211. Perform a search and display the search results.

[0133] It should be noted that in S1204, when the system listens for a Tab key press, it performs a compound operation: preventing the default behavior, adopting a recommendation, and focusing. Preventing the default behavior prevents the default behavior of using the Tab key to switch to the next element in the DOM flow; adopting a recommendation means the system adopts the next recommended search information that has been displayed; focusing means calling a DOM API interface (such as element.focus()) to immediately set the focus on the corresponding control. This disclosure reduces user search and judgment time by generating interactive pages based on role identification and intelligent recommendations, and significantly reduces the operation steps and time required to complete a complex query through shortcut key interaction; by dynamically generating concise interactive pages and intelligently predicting the next operation, it avoids users having to perform cognitive searches among irrelevant options, reducing the cognitive burden on users; recommended candidate values ​​and quick confirmation reduce manual input and lower the data entry error rate; and by dynamically adjusting the interaction logic according to user roles, historical habits, contextual data, and the real-time status of the production line, the production report system can better adapt to the real-time data query needs of different users and different production scenarios, thus improving the user experience.

[0134] Based on the same inventive concept, this disclosure also provides a report search device, as shown in the following embodiment. Since the principle by which this device embodiment solves the problem is similar to that of the above-described method embodiment, the implementation of this device embodiment can refer to the implementation of the above-described method embodiment, and repeated details will not be elaborated further.

[0135] Figure 13 This diagram illustrates a report search device provided in an embodiment of the present disclosure. Figure 13As shown, in one embodiment, the report search device is applied to a client and includes a first display module 1310, a second display module 1320, a focus acquisition module 1330, a third display module 1340, and a fourth display module 1350. The first display module 1310 is used to display an interactive page in response to obtaining the current user's login information. The interactive page includes multiple controls and a recommendation area. The second display module 1320 is used to display first search information on the first control and recommended search information in the recommendation area in response to listening to a target event triggered on a first control in the interactive page. The recommended search information is generated based on global behavior data and / or the first search information. The third display module 1330 is used to control a second control corresponding to the recommended search information to gain focus in response to listening to a selection operation on the recommended search information, and displays second search information on the second control. The second search information is generated in response to a target event triggered on the second control that has gained focus. The fourth display module 1350 is used to perform a search based on the first and second search information and display the search results in response to listening to the fulfillment of a recommendation stop condition.

[0136] The third display module 1330 is configured to display an option list of the second control in response to the second control having multiple recommended candidate values; and to display second search information, including a recommended candidate value, in the second control in response to a confirmation operation of a recommended candidate value in the option list.

[0137] The second display module 1320 is also used to display the next dimension of recommended search information in the recommendation area in response to the detection that the recommendation stop condition has not been met. The next dimension of recommended search information is generated based on the global behavior data, the first search information, and / or the second search information.

[0138] The recommendation suspension condition includes at least one of the following: the current user cancels the search recommendation; there is no recommended content; the user does not select candidate search information in the recommended area within the preset time period.

[0139] The target event includes at least one of the following: the current user selects a candidate value corresponding to a candidate option from the option list of the first or second control; the first or second control loses focus; the current user selects an entry from the completion list.

[0140] In one embodiment, global behavioral data includes at least one of historical behavioral data, real-time production data, and contextual data.

[0141] Figure 14 A schematic diagram of another report search device provided in an embodiment of this disclosure is shown. Figure 14As shown, in one embodiment, the report search device of this disclosure is applied to a server. The device includes a page generation module 1410, a first acquisition module 1420, a recommendation generation module 1430, a second acquisition module 1440, and a search execution module 1450. The page generation module 1410 is used to generate an interactive page based on the login information input by the current user. The interactive page includes multiple controls and a recommendation area. The first acquisition module 1420 is used to acquire first search information, which is generated when the current user operates on a first control in the interactive page and triggers a target event. The recommendation generation module 1430 is used to generate recommended search information based on global behavior data and / or the first search information, and send the recommended search information to the client. The second acquisition module 1440 is used to acquire second search information, which is generated when the current user operates on a second control in the interactive page based on the recommended search information and triggers a target event. The search execution module 1450 is used to execute a search based on the first and second search information if a recommendation stopping condition is met, obtain search results, and return the search results to the client.

[0142] The overall behavioral data includes at least one of historical behavioral data, real-time production data, and contextual data; wherein, the recommendation generation module 1430 is used to calculate the historical score of each candidate search information based on the historical behavioral data; calculate the real-time score of each candidate search information based on the real-time production data; calculate the contextual score of each candidate search information based on the contextual data and the first search information; determine the total score of each candidate search information based on at least one of the historical score, real-time score, and contextual score; and determine the recommended search information based on the total score of each candidate search information.

[0143] The recommendation generation module 1430 is used to obtain the real-time count value of the current user for each candidate search information and the maximum count value for each candidate search information; obtain the weighted count value of users with the same role identifier as the current user for each candidate search information and the total weighted count value of users with the same role identifier as the current user for all candidate search information; and calculate the historical score of each candidate search information based on the real-time count value, the maximum count value, the weighted count value, and the total weighted count value.

[0144] The recommendation generation module 1430 is used to determine real-time hotspots within a preset time period based on real-time production data, and calculate the correlation between each candidate search information and the real-time hotspots; calculate the time interval between the current time and the time when the real-time hotspots were generated, and calculate the time decay weight based on the time interval; and calculate the real-time score of each candidate search information based on the correlation and the time decay weight.

[0145] The recommendation generation module 1430 is used to calculate the matching degree between each candidate search information and the first search information; determine the context step interval based on the candidate search information and the first search information; calculate the context decay weight based on the context step interval; and calculate the context score of each candidate search information based on the matching degree and the context decay weight.

[0146] The recommendation generation module 1430 is used to determine candidate search information with a total score greater than a preset score threshold as recommended search information; and / or to sort each candidate search information in descending order based on the total score, and to use the preset number of candidate search information at the top of the sort as recommended search information.

[0147] The interactive page is the page corresponding to the current user's role identifier; the page generation module 1410 is used to determine the current user's role identifier based on the login information entered by the current user; determine the target search information template corresponding to the role identifier based on the preset mapping relationship; parse the target search information template and generate the interactive page.

[0148] The recommendation suspension condition includes at least one of the following: the current user cancels the search recommendation; there is no recommended content; the user does not select candidate search information in the recommended area within the preset time period.

[0149] The recommendation generation module 1430 is also used to generate recommendation search information for the next dimension based on the global behavior data, the first search information, and the second search information if the recommendation stopping condition is not met, until the recommendation stopping condition is met.

[0150] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0151] The following reference Figure 15 To describe an electronic device 1500 according to such an embodiment of the present disclosure. Figure 15 The illustrated electronic device 1500 is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein. In one embodiment, the electronic device 1500 includes a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to perform the report search method of the above embodiments by executing the executable instructions.

[0152] like Figure 15As shown, the electronic device 1500 is manifested in the form of a general-purpose computing device. The components of the electronic device 1500 may include, but are not limited to: at least one processing unit 1510, at least one storage unit 1520, and a bus 1530 connecting different system components (including the storage unit 1520 and the processing unit 1510). The storage unit stores program code that can be executed by the processing unit 1510, causing the processing unit 1510 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0153] Storage unit 1520 may include readable media in the form of volatile storage units, such as random access memory (RAM) 15201 and / or cache memory 15202, and may further include read-only memory (ROM) 15203. Storage unit 1520 may also include a program / utility 15204 having a set (at least one) of program modules 15205, such program modules 15205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0154] Bus 1530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0155] Electronic device 1500 can also communicate with one or more external devices 1540 (e.g., keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable the current user to interact with electronic device 1500, and / or any device that enables electronic device 1500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 1550. Furthermore, electronic device 1500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 1560. Network adapter 1560 communicates with other modules of electronic device 1500 via bus 1530. Although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 1500, including but not limited to: microcode, device drivers, redundant processing units, tape drives, and data backup storage systems.

[0156] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer program product, which includes a computer program that, when executed by a processor, implements the steps described in the "Exemplary Methods" section above according to various exemplary implementations of the present disclosure.

[0157] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, having a computer program stored thereon that, when executed by a processor, implements the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of this disclosure. This computer-readable storage medium may be a readable signal medium or a readable storage medium.

[0158] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0159] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0160] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A report search method characterized by comprising: Applied to a client, the method includes: In response to obtaining the current user's login information, an interactive page is displayed, which includes multiple controls and a recommendation area; In response to detecting a target event triggered on a first control in the interactive page, first search information is displayed on the first control, and recommended search information is displayed in the recommended area, wherein the recommended search information is generated based on global behavior data and / or the first search information; In response to the detection of a selection operation on the recommended search information, the second control corresponding to the recommended search information is controlled to gain focus, and the second search information is displayed on the second control. The second search information is generated by the current user triggering the target event on the second control. In response to the detection that the recommended stopping condition is met, a search is performed based on the first search information and the second search information, and the search results are displayed.

2. The method of claim 1, wherein, The step of responding to a selected operation on the recommended search information, controlling the second control corresponding to the recommended search information to gain focus, and displaying the second search information on the second control, includes: In response to the second control having multiple recommended candidate values, a list of options for the second control is displayed; In response to a confirmed action on at least one recommended candidate value in the list of options, the second search information, including the at least one recommended candidate value, is displayed in the second control.

3. The method according to claim 1 or 2, characterized in that, The method further includes: In response to the detection that the recommendation stop condition has not been met, the next dimension of recommended search information is displayed in the recommendation area. The next dimension of recommended search information is generated based on the global behavior data, the first search information, and / or the second search information.

4. The method of claim 1, wherein, The recommended stopping condition includes at least one of the following: The current user cancels search recommendations; No recommended content; The current user does not select any candidate search information in the recommended area within the preset time period.

5. The method of claim 1, wherein, The target event includes at least one of the following: The value change event of the first control or the second control; The focus event of the first control or the second control being lost; The current user selects an entry from the completion list.

6. The method of claim 1, wherein, The global behavioral data includes at least one of historical behavioral data, real-time production data, and contextual data.

7. A report search method, characterized in that, Applied to the server side, the method includes: An interactive page is generated based on the login information entered by the current user. The interactive page includes multiple controls and a recommendation area. Obtain first search information, wherein the first search information is generated by the current user interacting with the first control on the interactive page and triggering a target event; Based on the overall behavior data and / or the first search information, generate recommended search information and send the recommended search information to the client; Obtain second search information, wherein the second search information is generated by the current user interacting with the second control on the interactive page based on the recommended search information and triggering the target event; If the recommended stopping condition is met, then a search is performed based on the first search information and the second search information to obtain the search results, and the search results are returned to the client.

8. The method according to claim 7, characterized in that, The global behavior data includes at least one of historical behavior data, real-time production data, and contextual data. The step of generating recommended search information based on the overall behavior data and / or the first search information, and sending the recommended search information to the client, includes: Based on the historical behavior data, calculate the historical score for each candidate search item; Based on the real-time production data, calculate the real-time score for each candidate search information; Based on the context data and the first search information, calculate the context score for each candidate search information; The total score for each candidate search item is determined based on at least one of the historical score, the real-time score, and the context score. The recommended search information is determined based on the total score of each candidate search information.

9. The method according to claim 8, characterized in that, The step of calculating the historical score of each candidate search information based on the historical behavior data includes: Obtain the real-time count value of the current user for each candidate search information, as well as the maximum count value for the candidate search information; Obtain the weighted count values ​​of users with the same role identifier as the current user for each candidate search information, and the total weighted count value of users with the same role identifier as the current user for all candidate search information; The historical score of each candidate search information is calculated based on the real-time count value, the maximum count value, the weighted count value, and the total weighted count value.

10. The method according to claim 8, characterized in that, The calculation of the real-time score for each candidate search information based on the real-time production data includes: Based on the real-time production data, determine the real-time hotspots within a preset time period, and calculate the correlation between each candidate search information and the real-time hotspots; Calculate the time interval between the current moment and the time when the real-time hotspot was generated, and calculate the time decay weight based on the time interval; The real-time score of each candidate search information is calculated based on the relevance and the time decay weight.

11. The method of claim 8, wherein, The step of calculating the context score for each candidate search information based on the context data and the first search information includes: Calculate the matching degree between each of the candidate search information and the first search information; The context step interval is determined based on the candidate search information and the first search information; Calculate the context decay weight based on the context step size interval; Calculate the context score for each candidate search information based on the matching degree and the context decay weight.

12. The method according to claim 8, characterized in that, The step of determining the recommended search information based on the total score of each candidate search information includes at least one of the following: Candidate search information with a total score greater than a preset score threshold is selected as the recommended search information; Based on the total score, the candidate search information is sorted in descending order, and a preset number of candidate search information at the top of the sorted list are used as the recommended search information.

13. The method according to claim 7, characterized in that, The interactive page is the page corresponding to the current user's role identifier; The step of generating an interactive page based on the login information entered by the current user includes: Based on the login information entered by the current user, determine the role identifier of the current user; Based on a preset mapping relationship, a target search information template corresponding to the role identifier is determined; The target search information template is parsed to generate the interactive page.

14. The method according to claim 7, characterized in that, The recommended stopping condition includes at least one of the following: The current user cancels search recommendations; No recommended content; The current user does not select any candidate search information in the recommended area within the preset time period.

15. The method of claim 7, wherein, The method further includes: If the recommendation stopping condition is not met, then based on the global behavior data, the first search information, and the second search information, the next dimension of recommended search information is generated until the recommendation stopping condition is met.

16. A report search device, characterized in that, Applied to a client, the device includes: The first display module is used to display an interactive page in response to obtaining the current user's login information. The interactive page includes multiple controls and a recommendation area. The second display module is configured to respond to a target event triggered by a first control in the interactive page, display first search information in the first control, and display recommended search information in the recommended area, wherein the recommended search information is generated based on global behavior data and / or the first search information; The third display module is used to respond to the listening operation of selecting the recommended search information, control the second control corresponding to the recommended search information to gain focus, and display the second search information on the second control. The second search information is generated in response to the second control gaining focus and triggering a target event. The fourth display module is used to respond to the detection that the recommended stopping condition is met, perform a search based on the first search information and the second search information, and display the search results.

17. A report search device, characterized in that, Applied to the server side, the device includes: The page generation module is used to generate an interactive page based on the login information entered by the current user. The interactive page includes multiple controls and a recommendation area. The first acquisition module is used to acquire first search information, wherein the first search information is generated by the current user operating the first control in the interactive page and triggering a target event; The recommendation generation module is used to generate recommended search information based on the global behavior data and / or the first search information, and send the recommended search information to the client; The second acquisition module is used to acquire second search information, wherein the second search information is generated by the current user operating the second control in the interactive page based on the recommended search information and triggering a target event; The search execution module is used to perform a search based on the first search information and the second search information if the recommended stopping condition is met, obtain the search results, and return the search results to the client.

18. An electronic device, comprising: include: processor; A memory for storing executable instructions of the processor; wherein the processor is configured to execute the report search method of any one of claims 1 to 6, or the report search method of any one of claims 7 to 15, by executing the executable instructions.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the report search method according to any one of claims 1 to 6, or the report search method according to any one of claims 7 to 15.

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