A device selection method and computing device

Abstract

The embodiment of the application provides a device selection method and a computing device, relates to the technical field of PCB design, and solves the problems of low efficiency and accuracy of device selection. The method comprises the following steps: obtaining device selection conditions, and obtaining candidate devices satisfying the device selection conditions; the device selection conditions comprise specification attributes used for screening target devices; the specification attributes of the candidate devices satisfy the specification attributes screened by the device selection conditions; from a pre-constructed recommended rule library, a target recommended rule matched with the specification attributes of the candidate devices and a recommended weight of the target recommended rule are obtained; the recommended rule library comprises a plurality of recommended rules and a recommended weight of each recommended rule; the plurality of recommended rules comprise the target recommended rule; according to the target recommended rule and the recommended weight of the target recommended rule, a recommended index of the candidate device is determined, and the candidate device satisfying a recommended index condition is determined as the target device.

Description

Technical Field

[0001] This application relates to the field of printed circuit board (PCB) design technology, and in particular to a device selection method and computing device. Background Technology

[0002] In PCB design, the construction of hardware circuits is the core step. Hardware circuits refer to combinations of electrical signal paths made up of components (such as resistors, capacitors, integrated circuits, etc.) connected according to a specific topology to achieve specific functions such as signal processing and power conversion. The PCB is the physical carrier of the hardware circuits, which uses conductive lines and pads on the board to fix the components in the hardware circuits and realize the electrical connections between them.

[0003] With the rapid development of electronic product technology, hardware circuit design has become an indispensable part of many semiconductor integrated circuit R&D institutions. Engineers need to select appropriate specific components for each location in the circuit based on the functional requirements of the hardware circuit; this process is called component selection.

[0004] The selection of components directly determines the performance, cost, reliability, and feasibility of subsequent production of hardware circuits. Therefore, how to quickly and accurately select components is a pressing technical problem that needs to be solved. Summary of the Invention

[0005] This application provides a device selection method and computing device, which solves the problem of low efficiency and accuracy in device selection.

[0006] In a first aspect, embodiments of this application provide a device selection method applied to a computing device. The method includes: obtaining device selection conditions and obtaining candidate devices that meet the device selection conditions; the device selection conditions include specification attributes for screening target devices; the specification attributes of the candidate devices meet the specification attributes screened by the device selection conditions; obtaining target recommendation rules and recommendation weights of the target recommendation rules that match the specification attributes of the candidate devices from a pre-built recommendation rule library; the recommendation rule library includes multiple recommendation rules and recommendation weights of each recommendation rule; the multiple recommendation rules include target recommendation rules; determining the recommendation index of the candidate devices based on the target recommendation rules and the recommendation weights of the target recommendation rules, and determining the candidate devices that meet the recommendation index conditions as target devices.

[0007] As can be seen from the above, the embodiments of this application obtain device selection conditions and candidate devices that meet the device selection conditions, thereby achieving preliminary screening of devices that meet the specifications and attributes required by the device selection conditions. This allows for the rapid and accurate screening of candidate devices that meet the specifications and attributes, significantly improving the efficiency and accuracy of the initial screening stage.

[0008] Secondly, this embodiment of the application determines the recommendation index of candidate devices through target recommendation rules and the recommendation weights of the target recommendation rules, and identifies candidate devices that meet the recommendation index conditions as target devices, thereby realizing intelligent and multi-dimensional comprehensive evaluation of candidate devices. By using preset recommendation rules and weights, these factors are quantified into calculable recommendation indices, so that the selection results no longer rely on historical experience, but are based on unified and quantifiable evaluation standards, which greatly improves the objectivity, accuracy and consistency of device selection.

[0009] Furthermore, the embodiments of this application are applied to computing devices, that is, the selection of target devices can be realized through computing devices, realizing automated device selection and avoiding errors such as inefficiency or omissions in manual operation.

[0010] In some embodiments, obtaining candidate devices that meet the device selection criteria includes: obtaining target constraint rules corresponding to the specification attributes selected by the device selection criteria from a pre-built constraint rule library; the constraint rule library includes multiple constraint rules and the specification attributes constrained by each constraint rule; the multiple constraint rules include target constraint rules; the target constraint rules are used to constrain the attribute values ​​of the corresponding specification attributes; and obtaining candidate devices when the attribute values ​​of the specification attributes selected by the device selection criteria meet the target constraint rules.

[0011] In this embodiment, by obtaining the target constraint rules corresponding to the specification attributes selected by the device selection conditions from the constraint rule library, and obtaining candidate devices when the attribute values ​​of the specification attributes meet the target constraint rules, compliance verification of device selection conditions is realized. This enables selection conditions that do not comply with basic design specifications to be identified and processed in the early stage, avoiding the problem that subsequent screening results are unusable due to improper selection conditions.

[0012] In some embodiments, the recommendation rule base also includes constraint rules corresponding to each recommendation rule; obtaining target recommendation rules and recommendation weights of target recommendation rules that match the specification attributes of candidate devices from the pre-built recommendation rule base includes: determining the recommendation rules in the recommendation rule base that correspond to the target constraint rules as target recommendation rules, and obtaining the recommendation weights of the target recommendation rules.

[0013] In this embodiment, by determining the recommended rule in the recommended rule base that corresponds to the target constraint rule as the target recommended rule, an association relationship between the constraint rule and the recommended rule is established, so that the determination of the recommended rule can be based on the constraint rule that has passed the verification.

[0014] In some embodiments, the method further includes: obtaining current status information of candidate devices; the current status information includes: current inventory and / or current price; if the change in the current status information of candidate devices is greater than a preset threshold, adjusting the recommendation weight of the recommendation rules associated with the current status information of candidate devices in the recommendation rule base according to the change in the current status information of candidate devices.

[0015] In this embodiment, by acquiring the current status information of candidate devices and adjusting the recommendation weight of the recommendation rule associated with the current status information when the change in the current status information exceeds a preset threshold, the dynamic response of the recommendation weight to real-time status changes is realized, so that the recommendation results can reflect the changes in dynamic factors such as inventory and price, and improve the adaptability of the selection results to the actual production supply chain status.

[0016] In some embodiments, the device selection criteria further include: device types for screening target devices; obtaining candidate devices that meet the device selection criteria, including: obtaining candidate devices from a pre-built historical recommendation library according to the device types screened by the device selection criteria; the historical recommendation library includes multiple historical recommended devices and the device type of each historical recommended device; the multiple historical recommended devices include candidate devices; the method further includes: determining the candidate device as the target device if the matching degree between the attribute value of the specification attribute of the candidate device and the attribute value of the specification attribute screened by the device selection criteria is greater than a preset matching degree.

[0017] In this embodiment, candidate devices are obtained from the historical recommendation library based on device type, and when the matching degree between the specification attribute values ​​of the candidate device and the specification attribute values ​​selected by the device selection conditions is greater than the preset matching degree, the candidate device is determined as the target device. This realizes the effective reuse of historical selection experience, enabling historical devices that are highly matched with the current selection needs to be quickly identified and recommended, thereby improving selection efficiency.

[0018] In some embodiments, candidate devices are obtained from a pre-built historical recommendation library based on device types selected according to device selection criteria. This includes: obtaining a set of candidate devices from the historical recommendation library based on device types selected according to device selection criteria; multiple historical recommended devices include the set of candidate devices; obtaining input subject information for device selection criteria; the input subject information includes user information and / or time information for inputting device selection criteria; obtaining selected subject information for each candidate device in the set of candidate devices; the selected subject information includes user information and / or time information for selecting the candidate device; and identifying candidate devices whose similarity to the input subject information is greater than a preset similarity as candidate devices.

[0019] In this embodiment, by acquiring the input subject information of the device selection conditions and the selected subject information of the candidate devices, and determining the candidate devices corresponding to the selected subject information with a similarity greater than a preset similarity with the input subject information as candidate devices, the historical recommendation screening can take into account factors such as user identity and selection time, and realize personalized historical recommendations based on user preferences and timeliness.

[0020] In some embodiments, when the matching degree between the attribute value of the specification attribute of the candidate device and the attribute value of the specification attribute selected by the device selection conditions is greater than a preset matching degree, the candidate device is determined as the target device, including: when the matching degree between the attribute value of the specification attribute of the candidate device and the attribute value of the specification attribute selected by the device selection conditions is greater than a preset matching degree, and there are multiple candidate devices, obtaining the selection frequency of each candidate device in a historical time period; and determining the candidate device whose selection frequency in the historical time period is greater than the preset frequency as the target device.

[0021] In this embodiment, when there are multiple candidate devices and the matching degree of each device is greater than the preset matching degree, the frequency of selection of each candidate device in a historical time period is obtained, and the candidate devices with a selection frequency greater than the preset frequency are determined as target devices. This allows the final result of historical recommendations to reflect the actual usage popularity of the devices in past projects, thereby improving the credibility and practicality of the recommendation results.

[0022] In some embodiments, obtaining candidate devices that meet the device selection criteria includes: obtaining candidate devices from a pre-built device library based on the specifications and attributes selected by the device selection criteria; the device library includes multiple devices and the specifications and attributes of each device; the multiple devices include candidate devices.

[0023] In this embodiment, candidate devices are obtained from a pre-built device library based on the specifications and attributes selected according to the device selection criteria, providing a basic data source for subsequent automatic and historical recommendations.

[0024] In some embodiments, the recommendation rule base includes at least two of the following recommendation rules: business compatibility level rule, device usage rule, historical selection frequency rule, inventory level rule, price rule, applicable product type rule, and risk level rule.

[0025] In this embodiment, by configuring the recommendation rules to include at least two of the following: business compatibility level rules, device usage rules, historical selection frequency rules, inventory level rules, price rules, applicable product type rules, and risk level rules, the evaluation of candidate devices can be quantified from multiple dimensions, thereby improving the comprehensiveness and refinement of the recommendation results.

[0026] In a second aspect, embodiments of this application provide a computing device including a processor and a memory; the processor is coupled to the memory; the memory is used to store computer instructions, which are loaded and executed by the processor to enable the computing device to implement the methods provided in the first aspect and its possible embodiments described above.

[0027] Thirdly, embodiments of this application provide a computer-readable storage medium comprising: computer software instructions; when the computer software instructions are executed in a computing device, they cause the computing device to implement the methods provided in the first aspect and its possible embodiments.

[0028] Fourthly, embodiments of this application provide a computer program product that, when run on a computing device, causes the computing device to execute the steps of the relevant method described in the first aspect above, so as to implement the method of the first aspect above.

[0029] The beneficial effects of the second to fourth aspects mentioned above can be referred to the corresponding description of the first aspect, and will not be repeated here. Attached Figure Description

[0030] Figure 1 A schematic diagram of a device selection system provided in this application embodiment; Figure 2 A schematic flowchart illustrating a device selection method provided in an embodiment of this application; Figure 3 A flowchart illustrating another device selection method provided in an embodiment of this application; Figure 4 A flowchart illustrating another device selection method provided in an embodiment of this application; Figure 5 A flowchart illustrating another device selection method provided in an embodiment of this application; Figure 6 A flowchart illustrating another device selection method provided in an embodiment of this application; Figure 7 A flowchart illustrating another device selection method provided in an embodiment of this application; Figure 8 A flowchart illustrating another device selection method provided in an embodiment of this application; Figure 9 This is a schematic diagram of the architecture of a computing device provided in an embodiment of this application. Detailed Implementation

[0031] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0032] In the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can mean A or B. "And / or" in this application is merely a description of the relationship between the related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. A and B can be singular or plural.

[0033] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0034] Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" are not necessarily different. Meanwhile, in the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of terms such as "exemplary" or "for example" is intended to present related concepts in a concrete manner for ease of understanding.

[0035] It should be noted that in the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0036] The following provides an exemplary description of the application scenarios of the embodiments of this application.

[0037] As described in the background section, component selection is a crucial step connecting hardware circuit design and physical implementation in the PCB design process. After engineers complete the schematic design of the hardware circuit, they need to assign a specific, procureable, and manufacturable physical device to each component symbol in the circuit.

[0038] Specifically, engineers can first extract the key constraints of components based on the functional requirements of the hardware circuit. For example, for a capacitor in a power supply circuit, engineers need to specify its capacitance, voltage rating, package size, operating temperature range, and other specifications; for a resistor in a signal link, they need to specify its resistance, accuracy, power rating, and other specifications. These specifications form the basic basis for selecting target components.

[0039] Secondly, engineers need to search for components that meet the above specifications from various information sources. These sources may include: datasheets provided by component suppliers, internally accumulated bills of material (BOM) tables, third-party component selection platforms, or historical project component records scattered across multiple Excel files. Engineers typically need to review these information sources one by one and compare the parameters of different components to see if they meet the design requirements.

[0040] After identifying a pool of candidate devices that meet the required specifications, engineers need to consider other factors to determine the final target device. These factors may include: device price, supply stability, inventory status, whether it is supplied by multiple vendors, its performance in previous projects, whether it meets environmental requirements, and whether it has relevant safety certifications. Engineers need to consider factors such as project budget, production plan, and supply chain risks to select the final target device from the candidate devices for use.

[0041] Next, the engineer will enter the selected target device information into the PCB design tool, complete the binding of schematic components, and continue with the subsequent layout and routing design.

[0042] The above process is a typical scenario for component selection recommendation (CSR). Component selection recommendation refers to a systematic decision-making process during PCB design, where the most suitable component is selected from a variety of options based on the functional requirements, operating environment, and technical specifications of the circuit system. In this embodiment, component selection recommendation refers to providing the user with a list of recommended components based on the user-input component selection criteria and recommendation rules.

[0043] Based on this, embodiments of this application provide a device selection method applied to a computing device. The computing device can obtain device selection criteria and acquire candidate devices that meet the criteria. The device selection criteria include specifications for screening target devices; the specifications of the candidate devices meet the specifications selected by the device selection criteria.

[0044] Next, the computing device can obtain target recommendation rules and recommendation weights that match the specification attributes of the candidate device from the pre-built recommendation rule library, and determine the recommendation index of the candidate device based on the target recommendation rules and their recommendation weights, and identify the candidate device that meets the recommendation index conditions as the target device.

[0045] The recommendation rule base includes multiple recommendation rules and the recommendation weight of each rule; the multiple recommendation rules include target recommendation rules.

[0046] As can be seen from the above, the embodiments of this application obtain device selection conditions and candidate devices that meet the device selection conditions, thereby achieving preliminary screening of devices that meet the specifications and attributes required by the device selection conditions. This allows for the rapid and accurate screening of candidate devices that meet the specifications and attributes, significantly improving the efficiency and accuracy of the initial screening stage.

[0047] Secondly, this embodiment of the application determines the recommendation index of candidate devices through target recommendation rules and the recommendation weights of the target recommendation rules, and identifies candidate devices that meet the recommendation index conditions as target devices, thereby realizing intelligent and multi-dimensional comprehensive evaluation of candidate devices. By using preset recommendation rules and weights, these factors are quantified into calculable recommendation indices, so that the selection results no longer rely on historical experience, but are based on unified and quantifiable evaluation standards, which greatly improves the objectivity, accuracy and consistency of device selection.

[0048] Furthermore, the embodiments of this application are applied to computing devices, that is, the selection of target devices can be realized through computing devices, realizing automated device selection and avoiding errors such as inefficiency or omissions in manual operation.

[0049] The system architecture of the embodiments of this application will be described below as an example.

[0050] This application provides a system architecture for a device selection system, such as... Figure 1 As shown, the device selection system may include: computing device 101 and storage device 102.

[0051] The computing device 101 is communicatively connected to the storage device 102.

[0052] The computing device 101 is used to execute the device selection method provided in the embodiments of this application. The storage device 102 is used to provide the computing device 101 with the data required to implement the device selection method in the embodiments of this application.

[0053] For example, storage device 102 may store a pre-established recommendation rule base. Computing device 101 can obtain the target recommendation rule and the recommendation weight of the target recommendation rule required to implement the device selection method of the embodiments of this application from the recommendation rule base on storage device 102. The specific implementation process can be referred to the description below, and will not be repeated here.

[0054] For example, storage device 102 may store at least one of a pre-established constraint rule library, a historical recommendation library, and a device library. Computing device 101 can obtain the data required to implement the device selection method of the embodiments of this application from the various libraries on storage device 102. The specific implementation process can be referred to the description below, and will not be repeated here.

[0055] This application does not impose any restrictions on the specific form of the computing device 101. For example, the computing device 101 can be a server. The server can be a single server, or it can be a server cluster composed of multiple servers. In some embodiments, the server cluster can also be a distributed cluster, which is not limited in this application. As another example, the computing device 101 can specifically be a terminal device. The terminal device can be referred to as: terminal, user equipment (UE), terminal device, access terminal, user unit, user station, mobile station, remote station, remote terminal, mobile device, user terminal, wireless communication device, user agent, or user equipment, etc. Specifically, the terminal device can be a mobile phone, augmented reality (AR) device, virtual reality (VR) device, tablet computer, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), etc.

[0056] This application does not impose any restrictions on the specific form of the storage device 102. For example, the storage device 102 may be a non-volatile memory deployed locally on the computing device 101, a server deployed in the cloud, or other types of storage devices. This application does not limit these possibilities.

[0057] It should be pointed out that, Figure 1 The system architecture shown does not constitute a limitation on the device selection system, except Figure 1 In addition to the devices shown, the device selection system may include more or fewer devices than shown, or a combination of certain devices, or different device arrangements.

[0058] For example, the computing device 101 and the storage device 102 described above can be integrated into the same device.

[0059] When computing device 101 and storage device 102 are integrated into the same device, the communication method between computing device 101 and storage device 102 is the communication between internal modules of the device.

[0060] The device selection method provided in the embodiments of this application will be described in detail below.

[0061] like Figure 2 The diagram shown is a flowchart illustrating a device selection method provided in an embodiment of this application. Figure 2 The method shown can be applied to the above. Figure 1 The computing device 101 is included. The device selection method includes: S201. Obtain device selection criteria for computing equipment.

[0062] The specifications of the candidate devices meet the specifications selected by the device selection criteria.

[0063] During the PCB design process, after completing the schematic design of the hardware circuit, the user needs to assign a specific physical device to each component symbol in the circuit. At this point, the user can input the component selection criteria required for this selection through the interactive interface provided by the computing device.

[0064] The device selection criteria include the specifications used to screen target devices.

[0065] Specification attributes are specific parameters that describe the electrical performance and physical characteristics of a device. Different types of devices correspond to different specification attributes.

[0066] For example, specifications for capacitors may include capacitance, voltage rating, package size, and operating temperature range. For resistors, specifications may include resistance, accuracy, and power rating. For integrated circuits, specifications may include operating voltage, number of pins, and package type.

[0067] Users can input one or more specifications and their desired attribute values ​​or ranges based on the functional requirements of the hardware circuit, which will serve as the basis for selecting target devices.

[0068] In one possible implementation, the computing device can also provide the user with selection criteria for different types of devices for the user to choose from. That is, after the user inputs the desired device type, the computing device can display the corresponding selection criteria for that type of device on a prompt page for the user to select from.

[0069] For example, when the user selects a varistor, the computing device can display the device selection criteria, including at least one of the following: resistance value, rated power, temperature range, and accuracy.

[0070] For example, when the user selects a surface mount device (SMD) ceramic capacitor, the computing device can display the device selection criteria, including at least one of the following: nominal capacitance, rated operating voltage, and frequency.

[0071] For example, when the user selects a PIN diode (positive-intrinsic-negative diode, PIN), the computing device can display the device selection criteria, including at least one of the following: reverse working voltage, forward current, forward resistance, junction capacitance, and operating temperature range.

[0072] For example, when the user selects a transistor as the device type, the computing device can display the device selection criteria, including at least one of the following: the breakdown voltage between the collector and emitter, the maximum plate current, the power, and the operating temperature range.

[0073] For example, when the user selects a signal air-core coil as the device type, the computing device can display the device selection criteria, including at least one of the following: inductance, temperature rise rated current, inductance error, and DC resistance.

[0074] S202. The computing device obtains candidate devices that meet the device selection criteria.

[0075] After obtaining the above device selection criteria, the computing device can obtain candidate devices that meet the device selection criteria.

[0076] In some embodiments, a method for a computing device to obtain candidate devices that meet device selection criteria specifically includes: The computing device retrieves candidate devices from a pre-built device library based on the specifications and attributes selected according to the device selection criteria.

[0077] In other words, computing devices can search for and obtain candidate devices that meet the device selection criteria from a pre-built device library.

[0078] The device library includes multiple devices and their specifications. These devices include candidate devices.

[0079] In other words, the component library includes multiple components and the specifications of each component, which form the data basis for all subsequent selection operations.

[0080] There are various ways to build a device library. In one feasible approach, computing devices can associate and store the device's specifications and attributes (such as manufacturer information, environmental requirements, safety certifications, applicable product descriptions, etc.) with the corresponding device through methods such as adding new pages, editing pages, or importing from Excel, thus forming a standardized device database, i.e., a device library.

[0081] In the process of obtaining the specific implementation of candidate devices based on the specifications selected according to the device selection criteria, since the device selection criteria input by the user are usually in natural language text, the computing device needs to convert the natural language text input by the user into machine language that the computing device can recognize. This process is called parameter format conversion (PFC).

[0082] Parameter format conversion is the process of transforming data from one format or structure to another to meet the needs of different systems or applications. In this embodiment, parameter format conversion refers to extracting input parameter formats that conform to basic rule verification and selection recommendations by converting user-input device selection conditions. In this case, the computing device can convert user-input device selection conditions into structured query statements.

[0083] For example, users can enter the following device selection criteria on the computing device selection interface: Product: XXX; Product type: XXX; Device type: Low-voltage, low-capacitance ceramic capacitor; Nominal capacitance: 100-500uf; Rated voltage: 50; Frequency: 25000 Hz.

[0084] After obtaining the above-mentioned device selection criteria, the computing device can convert them into a structured query statement: Product=xxx; productType=xxx; categoryCode=xxxxxx (This code corresponds to the device type of low-voltage, low-capacitance ceramic capacitor). nominalValue>=100&&nominalValue<=500; ratedVoltage=50; Frequency=25000.

[0085] Through this format conversion, the computing device transforms the device selection criteria input by the user, which may contain units or natural language descriptions, into key-value pairs and logical expressions that can be retrieved from the database, laying the foundation for subsequent candidate device screening.

[0086] Next, the computing device can iterate through each device in the device library, comparing the device's specifications with the specifications selected by the device selection criteria. If a device's specifications meet the specifications selected by the device selection criteria, such as its capacitance value being within the range of the user-input capacitance value and its withstand voltage value not being lower than the user-input withstand voltage requirement, then the device is identified as a candidate device.

[0087] Through the above process, the computing device obtains one or more candidate devices that meet the specifications and attributes selected by the device selection criteria, which will be used as the objects for further evaluation.

[0088] S203. The computing device obtains the target recommendation rule and the recommendation weight of the target recommendation rule that match the specification attributes of the candidate device from the pre-built recommendation rule library.

[0089] After identifying candidate devices that meet the specifications and attributes selected by the device selection criteria, the computing device needs to conduct a further comprehensive evaluation of these candidate devices to determine which one or more candidate devices are superior and more suitable for the current design requirements. To this end, the computing device can obtain target recommendation rules and their recommendation weights that match the specifications and attributes of the candidate devices from a pre-built recommendation rule base.

[0090] The recommendation rule base includes multiple recommendation rules and the recommendation weight for each rule. These recommendation rules include a target recommendation rule.

[0091] In other words, the recommendation rule base is a set of rules used to evaluate the merits of devices. The recommendation rule base includes multiple recommendation rules and a recommendation weight for each rule. Each recommendation rule corresponds to an evaluation dimension, used to measure the performance of the candidate device in that dimension; each recommendation weight indicates the importance of the recommendation rule in the overall evaluation, with higher weight values ​​indicating a greater impact of the rule on the final recommendation result. The multiple recommendation rules include the target recommendation rule obtained in this study.

[0092] In one feasible approach, the computing device acquires target recommendation rules that match the specification attributes of candidate devices. This means selecting recommendation rules applicable to the candidate device from a recommendation rule base based on its own specification attribute characteristics. Different device types or specification attributes may correspond to different sets of recommendation rules.

[0093] For example, for capacitors, it may be necessary to evaluate whether their equivalent series resistance (ESR) is low enough. For power devices, it may be necessary to evaluate their thermal performance or power rating. For general-purpose devices, it may be necessary to evaluate whether they meet environmental requirements and whether they are supplied by multiple vendors.

[0094] The computing device analyzes the specifications of candidate devices (such as device type, package form, electrical parameters, etc.) and automatically matches the recommended rules related to the device, which are then used as the target recommended rules for subsequent scoring.

[0095] In the recommendation rule base, each recommendation rule can correspond to a specific evaluation dimension.

[0096] In some embodiments, the recommendation rule base includes at least two of the following recommendation rules: business suitability level rule, device usage rule, historical selection frequency rule, inventory level rule, price rule, applicable product type rule, and risk level rule. Each recommendation rule is associated with a recommendation weight, which indicates the importance of the rule in the recommendation decision.

[0097] For example, a company's recommendation rule library can be configured with the following rules: Rule 1: Prioritize devices with high company preference level (i.e. business compatibility level), with a recommended weight of 80.

[0098] Rule 2: Prioritize components with high actual usage (i.e., high component usage) in existing products, with a recommended weight of 100.

[0099] Rule 3: Prioritize devices that have been selected more times in historical recommendations (i.e., the number of times they have been selected in history), with a recommendation weight of 90.

[0100] Rule 4: Prioritize components with sufficient inventory, with a recommended weight of 70.

[0101] Rule 5: Prioritize lower-priced components, with a recommended weight of 60.

[0102] Rule 6: Match devices based on the type of product they are applicable to, with a recommended weight of 80.

[0103] Rule 7: Prioritize devices with lower risk levels (e.g., no risk of production stoppage, no reliability issues), with a recommended weight of 100.

[0104] In one feasible approach, the aforementioned recommendation rules could also include localization, exclusive supply, etc.

[0105] For example: Rule 8: Prioritize domestically produced components, with a recommended weight of 90%.

[0106] Rule 9: Prioritize components that are not exclusively supplied (i.e., supplied by multiple vendors), with a recommended weight of 90.

[0107] The above weight values ​​can be pre-configured and dynamically adjusted according to the company's business needs, project requirements, or supply chain conditions.

[0108] In one possible implementation, the computing device can acquire the target recommendation rule by associating it with the constraint rules described in subsequent embodiments. For example, the recommendation rule base may also include the constraint rules corresponding to each recommendation rule. When the computing device has acquired the target constraint rule corresponding to the device selection conditions, it can determine the recommendation rule in the recommendation rule base corresponding to the target constraint rule as the target recommendation rule. The specific implementation process can be referred to the description below, and will not be repeated here.

[0109] In another possible implementation, the recommendation rule base can also include a mapping between each recommendation rule and the specification attributes of the device of its corresponding type. In this case, the computing device can directly retrieve, from the recommendation rule base, the target recommendation rule that matches the specification attributes of the candidate device and the recommendation weight of the target recommendation rule, based on this mapping.

[0110] S204. The computing device determines the recommendation index of the candidate devices based on the target recommendation rules and the recommendation weights of the target recommendation rules, and determines the candidate devices that meet the recommendation index conditions as the target devices.

[0111] After obtaining the target recommendation rules that match the specifications of the candidate devices and the recommendation weights corresponding to each target recommendation rule, the computing device can perform quantitative evaluation of each candidate device based on these rules and weights, determine its recommendation index, and select the final target device from multiple candidate devices according to the recommendation index.

[0112] The recommendation index refers to the quantitative scoring of each candidate device's performance across various recommendation rule dimensions, combined with the weights of each rule, to obtain the final recommendation index for that candidate device.

[0113] In one feasible approach, for each candidate device, the computing device first evaluates its conformity to each target recommendation rule based on its specifications and other relevant information, obtaining a dimensional score. For example: Regarding the service compatibility level rule, computing devices can query the service compatibility level corresponding to the device; the higher the level, the higher the score.

[0114] Regarding the rule of device usage, the computing device can count the cumulative number of devices used in existing products; the greater the usage, the higher the score.

[0115] Regarding the rule of historical selection count, the computing device can query the number of times the device was ultimately selected by the user in the historical selection recommendations. The more times it is selected, the higher the score.

[0116] Regarding the inventory rule, the computing device can obtain the current inventory information of the device; the more abundant the inventory, the higher the score.

[0117] Regarding the price rule, the computing device can obtain the current price of the device; the lower the price, the higher the score.

[0118] Regarding the rule of applicable product type, the computing device can determine whether the device is applicable to the product type of the current design, and if it is applicable, it will receive a high score.

[0119] Regarding the risk level rule, computing devices can be assessed based on information such as the risk of device discontinuation and reliability issues; the lower the risk, the higher the score.

[0120] Regarding the rule of whether something is domestically produced, the computing equipment can make a judgment based on the country of origin of the component. Domestically produced components receive a high score, while non-domestically produced components receive a low score or no score.

[0121] Regarding the rule of exclusive supply, computing devices can determine this based on the number of suppliers for the device. Devices with multiple suppliers receive a higher score, while devices with an exclusive supplier receive a lower score.

[0122] After obtaining the dimensional scores of each candidate device under each recommendation rule, the computing device combines the recommendation weights corresponding to each recommendation rule and performs a weighted summation to obtain the recommendation index of the candidate device. The calculation formula can be expressed as: Recommendation index = Σ (dimensional score × recommendation weight).

[0123] In one feasible approach, before calculating the weighted sum, the computing device can also normalize the recommendation weights of each recommendation rule so that the sum of the weights is 1 or 100%, to ensure the reasonableness and comparability of the calculation results.

[0124] Meanwhile, to ensure that the recommendation results do not have obvious shortcomings in key dimensions, the computing device can also set weight thresholds and attribute score thresholds to filter out rules that are below the preset weight threshold or directly exclude candidate devices that are below the preset attribute score threshold, so as to ensure that the finally recommended devices perform well in all aspects.

[0125] In another possible implementation, to more clearly describe the calculation process of the above recommendation index, this application introduces the following algorithm definition: Assume that the set of all pre-built devices in the device library is D, which is the pre-constructed device library. The candidate devices obtained through S201 are a subset of D, that is, those devices that meet the specifications and attributes selected by the device selection criteria.

[0126] Suppose that the set of query conditions obtained after format conversion using S201 is C={C1,C2,…,C…} n}, where each C i This represents a specific query condition. For example: nominal capacitance ≥ 100 microfarads (μF) or rated voltage = 50 volts (V).

[0127] The computing device retrieves devices from the device library D that satisfy all query conditions C, which are then considered as candidate devices. This process can be represented by the matching function Match(D,C).

[0128] When there are multiple recommendation dimensions, let the recommendation weight vector be W={W1,W2,…,W2}. n}, where each W i The recommendation weight corresponds to a recommendation rule. The target recommendation rule and its recommendation weight obtained by the computing device constitute the rule set and weight vector used in this step.

[0129] The specific algorithm steps for the computing device to determine the recommendation index of candidate devices and to sort and filter them based on the recommendation index are as follows: First, the computing device transforms the device selection criteria input by the user into a structured set of query criteria C.

[0130] Next, the computing device finds all devices in the device library D that match the query condition C, obtaining a candidate device set. This candidate device set is a subset of D, denoted as D_candidate.

[0131] Next, for each target recommendation rule, the computing device evaluates the performance of the current candidate device on that rule dimension, and obtains the attribute score d for that dimension. i .

[0132] For example, for inventory quantity rules, a score can be calculated based on the current inventory quantity of the device; for historical selection frequency rules, a score can be calculated based on the number of times the device has been selected in historical recommendations. Each d i The value range can be set according to actual needs, such as 0-100 points.

[0133] In one feasible approach, to ensure that the weights of each recommendation rule are calculated under the same dimension, the computing device can normalize the recommendation weights of each rule. Let the original weights be w. i The normalized weights are w i The normalization formula is: w i '=w i / Σw j (j=1,2,...,n).

[0134] Where Σw j This represents the sum of the weights of all target recommendation rules involved in the calculation. Through normalization, the sum of all normalized weights is 1, facilitating subsequent weighted summation calculations.

[0135] In one feasible approach, to ensure that the recommendation results do not have significant weaknesses in key dimensions, the computing device can set weight thresholds and attribute score thresholds for filtering. Specifically, this includes weight threshold filtering and attribute score threshold filtering.

[0136] Weighted threshold filtering refers to filtering the normalized weights w. i Recommended rules with weights below the preset weight threshold (threshold_w) can be filtered out from the calculation or have their weights reset to zero. This avoids unnecessary interference from rules with excessively low weights in the calculation results.

[0137] Attribute score threshold filtering refers to filtering the attribute score d. i Candidate devices whose scores fall below the preset attribute score threshold (threshold_d) can be directly excluded from the candidate device set. This ensures that the final recommended devices meet the basic requirements in every important dimension, avoiding devices with high overall scores but extremely poor performance in a particular key dimension.

[0138] After the above normalization and threshold filtering processes, the computing device performs a weighted summation of the remaining recommendation rules and their scores to obtain the recommendation index S of the current candidate device. The weighted summation formula is: S=Σ(di ×w i (i=1,2,...,n).

[0139] Where, d i Let w be the attribute score of the i-th recommendation rule. i ' represents the normalized recommendation weight of the i-th recommendation rule.

[0140] Then, the computing device repeats the above calculation process for each candidate device in the candidate device set D_candidate to obtain the recommendation index S for each candidate device.

[0141] Subsequently, the computing device sorts the candidate device set in descending order based on the recommendation index S of each candidate device, resulting in a sorted device list. This sorted device list represents the recommendation results, ranked from highest to lowest based on the overall score.

[0142] After obtaining the recommendation index for each candidate device and sorting the candidate devices, the computing device determines the candidate devices that meet the recommendation index conditions as the target devices.

[0143] The recommendation index conditions can be flexibly configured according to actual needs. Common configuration methods include, but are not limited to, at least one of the following: the highest score principle, the threshold principle, and the ranking principle.

[0144] The highest score principle identifies the candidate device with the highest recommendation index as the target device. This approach is suitable for scenarios where a unique optimal solution needs to be selected, such as choosing the most suitable device for a critical position.

[0145] The threshold principle identifies all candidate devices whose recommendation index exceeds a preset threshold as the target device. This approach is suitable for scenarios that require providing multiple alternatives, such as offering users a recommendation list for them to make a final selection based on other factors.

[0146] The ranking principle is to determine the top N candidate devices in the recommendation index as the target devices. For example, the top three in the ranked device list are taken as the recommendation results.

[0147] In practice, the computing device can output candidate devices that meet the recommendation index criteria as the final target devices to the user for confirmation or direct use. If the user is not satisfied with the recommendation results, they can manually filter through subsequent classification and selection functions, forming a complete selection loop.

[0148] For example, such as Figure 3 As shown, after obtaining candidate devices that meet the device selection criteria, the computing device needs to conduct a comprehensive evaluation of these candidate devices to determine the final recommended list.

[0149] Figure 3 The upper part illustrates the input of device selection criteria and the set of candidate devices matched based on those criteria. The product, device code, and specific parameters input by the user constitute the device selection criteria. The computing device uses these criteria to filter candidate devices from the device library that meet the specification requirements, such as device 1, device 2, device 3, and device 4 in the figure.

[0150] After acquiring candidate devices, the computing device retrieves target recommendation rules and their recommendation weights that match the specification attributes of the candidate devices from a pre-built recommendation rule library. Figure 3 In the illustrated embodiment, the target recommendation rule includes the following four evaluation dimensions: Preferred Rank Score: This score corresponds to the company's preferred rank recommendation rule and is used to assess the priority level of a device within the company. The higher the rank, the higher the score.

[0151] Actual usage score: This score corresponds to the recommended rule of actual device usage, which is used to evaluate the cumulative number of devices used in existing products. The higher the usage, the higher the score.

[0152] Recommended Selection Frequency Score: This score corresponds to the historical selection frequency, used to evaluate how many times a device has been ultimately selected by the user in historical selection recommendations. The more times a device is selected, the higher the score.

[0153] Inventory score: This recommendation rule, corresponding to inventory level, is used to assess the current adequacy of device inventory. The more abundant the inventory, the higher the score.

[0154] Each recommendation rule corresponds to a recommendation weight, which indicates the importance of that rule in the overall evaluation. Figure 3 In the illustrated embodiment, the weights of each recommendation rule can be pre-configured. For example, the weight of the preferred level is 90, the weight of actual usage is 80, the weight of the number of recommended selections is 100, and the weight of inventory is 90. These weight values ​​can be dynamically adjusted according to the company's strategic orientation, project needs, or supply chain conditions.

[0155] The computing device evaluates the performance of each candidate device across the four recommendation rule dimensions mentioned above, obtaining a score for each dimension. Figure 3 The four candidate devices shown are examples: Component 1: Preferred grade score 90, actual usage score 80, recommended selection frequency score 100, inventory score 90; Component 2: Preferred grade score 90, actual usage score 70, recommended selection frequency score 70, inventory score 80; Component 3: Preferred grade score 100, actual usage score 90, recommended selection frequency score 90, inventory score 100; Component 4: Preferred grade score 80, actual usage score 60, recommended selection frequency score 60, inventory score 90.

[0156] After obtaining the scores for each dimension, the computing device combines the recommendation weights of each recommendation rule to calculate the recommendation index for each candidate device. The recommendation index can be calculated using a weighted summation method: multiplying the score for each dimension by its corresponding weight, and then summing the weighted values ​​for all dimensions to obtain the comprehensive recommendation index. Figure 3 Taking the weight configuration shown as an example, the recommendation index for each candidate device is calculated as follows: Recommendation index for device 1 = 90×90 + 80×80 + 100×100 + 90×90 = 8100 + 6400 + 10000 + 8100 = 32600, which is approximately 90 points after normalization (90 points is shown in the figure). Recommendation index for device 2 = 90×90 + 70×80 + 70×100 + 80×90 = 8100 + 5600 + 7000 + 7200 = 27900, which is approximately 77.5 points after normalization (77.5 points is shown in the figure). Recommendation index for device 3 = 100×90 + 90×80 + 90×100 + 100×90 = 9000 + 7200 + 9000 + 9000 = 34200, which is approximately 95 points after normalization (95 points is shown in the figure). Recommendation index for device 4 = 80×90 + 60×80 + 60×100 + 90×90 = 7200 + 4800 + 6000 + 8100 = 26100, which is approximately 72.5 points after normalization (72.5 points is shown in the figure).

[0157] After calculating the recommendation index for each candidate device, the computing device integrates and sorts the candidate devices according to the recommendation index, resulting in a list of devices with recommendation indices from high to low. Figure 3 In the illustrated embodiment, device 3 has the highest recommendation index (95 points), ranking first; device 1 is second (90 points); device 2 is third (77.5 points); and device 4 has the lowest (72.5 points), ranking fourth.

[0158] The computing device outputs the sorted list of devices as a recommendation list to the user, such as Figure 3 As shown, the recommended list displays the device's code, specific parameters, and recommendation index, allowing users to make a final selection based on their actual needs. Users can directly select the top-ranked devices in the recommended list, or view detailed information on other devices for comparison.

[0159] When determining target devices, the computing device can filter them based on preset recommendation index conditions. For example: If the recommendation index condition is set to "take the highest score", the computing device will identify device 3 as the target device. If the recommendation index condition is set to "recommendation index greater than 90 points", then both device 3 and device 1 will be identified as target devices.

[0160] If the recommendation index condition is set to "take the top two", then device 3 and device 1 are identified as target devices.

[0161] Another example is the selection of a filter capacitor in a power supply circuit. Suppose that the computing device obtains three candidate devices A, B, and C, and obtains three target recommendation rules and their recommendation weights: rule 1 is sufficient inventory with a weight of 70, rule 2 is that it has been selected many times in the past with a weight of 90, and rule 3 is that the price is low with a weight of 60.

[0162] The computing device first performs a dimensional evaluation of the three candidate devices, obtaining scores for each dimension: Component A: Ample stock (score 95), average number of times selected in history (score 60), moderate price (score 70). Component B: Average inventory (score 60), high historical selection frequency (score 95), low price (score 90); Component C: Ample inventory (score 95), low historical selection frequency (score 40), high price (score 30).

[0163] The computing device performs weight normalization processing: Σw j =70+90+60=220, and the normalized weights are: w1'=70 / 220≈0.318, w2'=90 / 220≈0.409, w3'=60 / 220≈0.273.

[0164] Assuming the preset weight threshold_w is 0.1, the normalized weights of all three rules are higher than the threshold, so no filtering is needed. The preset attribute score threshold_d is 50. Device C's historical selection count score is 40, which is lower than the threshold. Therefore, device C is directly excluded and will not participate in the subsequent weighted summation.

[0165] Next, the computing device performs a weighted summation on device A and device B: Recommendation index for component A = 95 × 0.318 + 60 × 0.409 + 70 × 0.273 = 30.21 + 24.54 + 19.11 = 73.86; Recommendation index for device B = 60 × 0.318 + 95 × 0.409 + 90 × 0.273 = 19.08 + 38.86 + 24.57 = 82.51.

[0166] The computing device sorts the candidate devices according to the recommendation index, resulting in a sorted device list = [device B, device A]. If the recommendation index condition is set to take the highest score, the computing device identifies device B as the target device; if the recommendation index condition is set to a recommendation index exceeding 75, only device B meets the condition and is identified as the target device; if the recommendation index condition is set to take the top two, both device B and device A are identified as target devices for further selection by the user.

[0167] Through the above process, the computing device, based on quantified rules and weights, combined with normalization processing, threshold filtering, and weighted summation algorithms, realizes intelligent evaluation and screening of candidate devices, providing users with objective, accurate, and interpretable selection recommendation results, i.e., target devices.

[0168] The process of verifying the device selection conditions in the embodiments of this application is described below with reference to the accompanying drawings.

[0169] In some embodiments, the device selection criteria obtained by the computing device include specifications for screening target devices, such as nominal capacitance, rated voltage, and frequency. However, not all specifications entered by the user are reasonable or feasible. In actual hardware circuit design, there are some basic design specifications and constraints. The values ​​of certain specifications must meet specific constraints; otherwise, it may lead to abnormal circuit operation, device damage, or even safety hazards.

[0170] To address this, this application introduces a constraint rule verification mechanism. Before entering the candidate device screening process, the compliance of the device selection conditions themselves is first verified. The computing device can verify whether the device selection conditions are compliant based on the target constraint rules corresponding to the specification attributes selected by the device selection conditions.

[0171] In this case, combination Figure 2 ,like Figure 4 As shown, in S202 above, the computing device obtains candidate devices that meet the device selection criteria, including: S401. The computing device obtains the target constraint rules corresponding to the specification attributes selected by the device selection conditions from the pre-built constraint rule library.

[0172] The constraint rule library includes multiple constraint rules and the specification attributes constrained by each constraint rule. These constraint rules include target constraint rules. Target constraint rules are used to constrain the attribute values ​​of the corresponding specification attributes.

[0173] In other words, a constraint rule base is a pre-built set of rules used to store various design specification constraints. The constraint rule base includes multiple constraint rules, each corresponding to one or more specification attributes, used to constrain the conditions that the attribute values ​​of these specification attributes must meet. The constraint rule base can be built based on various sources such as industry standards, enterprise design specifications, and accumulated engineering experience, and can be pre-configured in the computing device through methods such as adding new rules on the page or batch importing.

[0174] For example, a complete PCB design system can contain the following types of constraint rules in the constraint rule library: Prohibition Rules: Certain types of devices are prohibited from use in specific application scenarios. For example, prohibiting the use of MnO2 tantalum capacitors in power supply circuits is a typical constraint rule because MnO2 tantalum capacitors may catch fire in failure modes, posing a safety hazard.

[0175] Selection Guidelines: In certain application scenarios, specific types of devices should be prioritized. For example, the requirement for multi-layer ceramic capacitors (MLCCs) with an ESR below 50 milliohms (mΩ) and an equivalent series inductance (ESL) below 0.3 nanohenries (nH) in high-frequency circuits is a constraint rule that specifies the threshold conditions that the ESR and ESL of capacitors in high-frequency circuits must meet.

[0176] Capacitance and voltage matching rules: Based on the capacitance size and voltage application, the available device types are constrained. For example, ceramic capacitors are preferred for small capacitance (≤100 microfarads (μF)); liquid aluminum capacitors are preferred for large capacitance (≥100 microfarads (μF)) in high voltage applications (≥24 volts (V)) and polymer capacitors are preferred in low voltage applications. This is a composite constraint rule that restricts the range of available device types based on both capacitance and voltage specifications.

[0177] Environmental compliance rules: For example, unless otherwise specified, the requirement to select environmentally friendly capacitors is a constraint rule that requires the environmental properties of devices to meet specific requirements.

[0178] Voltage application rule: For example, in low voltage applications (<24 volts (V)), the preferred use of solid capacitors (ceramic capacitors, polymer tantalum capacitors, polymer aluminum capacitors) is a constraint rule that restricts the type of device that can be selected based on the voltage application.

[0179] The computing device retrieves target constraint rules from a pre-built constraint rule library that correspond to the specification attributes selected by the device selection criteria. Here, "correspondence" means that the computing device searches the constraint rule library for rules that constrain the same specification attributes based on the specification attributes appearing in the device selection criteria.

[0180] For example, suppose the device selection criteria entered by the user include: device type is capacitor, nominal capacitance is 220 microfarads (μF), rated voltage is 50 volts (V), and application is power supply circuit.

[0181] The computing device analyzes these specifications and attributes, identifying properties such as nominal capacitance, rated voltage, device type, and application. Then, the computing device traverses the constraint rule base, searching for rules that constrain attributes such as nominal capacitance and rated voltage. In the example above, the following constraint rules might be matched: For small-capacity (≤100 μF) capacitors, ceramic capacitors are preferred; for large-capacity (≥100 μF) capacitors, liquid aluminum capacitors are preferred in high-voltage applications (≥24 V), and polymer capacitors are preferred in low-voltage applications. This rule applies to both nominal capacitance and rated voltage.

[0182] MnO2 tantalum capacitors are prohibited in power supply circuits. This rule restricts both the application and the device type.

[0183] The two rules mentioned above are the target constraint rules corresponding to the specification attributes selected by the current device selection conditions.

[0184] Target constraint rules are used to constrain the attribute values ​​of corresponding specification attributes, that is, to specify the conditions that these specification attribute values ​​must meet. After the computing device obtains the target constraint rules, it can compare the attribute values ​​of the specification attributes in the user-input device selection conditions with the conditions specified in the target constraint rules to determine whether the currently input device selection conditions comply with the basic design specifications.

[0185] If the device selection criteria meet all target constraints, the user-input device selection criteria are compliant, and subsequent candidate device screening can continue. If the device selection criteria do not meet a certain target constraint, for example, if the user inputs a MnO2 tantalum capacitor as the device type in the power supply circuit, the computing device can promptly issue a prompt or warning to the user to help optimize the selection criteria and avoid potential design problems.

[0186] Through this verification mechanism, the embodiments of this application can detect and correct non-compliant inputs early in the selection process, avoid the unusable subsequent screening results due to improper selection conditions, or the design risks of the finally selected devices, thereby improving the reliability and security of device selection.

[0187] S402. The computing device obtains candidate devices when the attribute values ​​of the specifications selected by the device selection conditions meet the target constraint rules.

[0188] After obtaining the target constraint rules corresponding to the specifications selected by the device selection criteria, the computing device needs to further determine whether the attribute values ​​of the specifications in the device selection criteria meet the requirements of these target constraint rules. Only if all target constraint rules are met will the computing device continue to execute the candidate device acquisition operation; if there are any unmet requirements, appropriate prompts or warnings need to be issued.

[0189] To more clearly describe the mapping relationship between device selection conditions and rules and the subsequent algorithm logic in the embodiments of this application, the relevant mathematical model definitions are given below: The computing device can transform user-input device selection criteria into a structured data mapping. Let the set of device selection criteria be C = {c1, c2, ..., c...} n}, which is converted into machine-readable language. Among them, each component selection condition c i It can be represented as a triple: c i =(k,op,v), where k represents the specification attribute name (e.g., nominal capacitance), v represents the specification attribute value (e.g., 100), and op represents the operator (e.g., ≥, =, ≤), used to define the comparison relationship between the specification attribute value and the target value.

[0190] Suppose the rule set is R = {r} i |r i =(condition i action i )}. Wherein, condition i It is a Boolean expression used to define design constraints or recommended evaluation conditions; action i This refers to the action executed when a rule is triggered. Actions may include, but are not limited to: assigning scores by dimension, disabling the selection of devices, and adjusting recommendation weights.

[0191] Computing devices can establish a mapping relationship between a set of device selection conditions C and a set of rules R, defined as a mapping model M(C,R)={(c i ,r j )|c i ∈C,r j ∈R, and c i With r j There is a correlation. Through this mapping model, computing devices can quickly locate rules associated with the current device selection criteria, laying the foundation for subsequent verification and recommendations.

[0192] The computing device is selected based on the component selection criteria c i Matching rule r in rule set R j When the device selection condition c i Satisfy rule r j condition j When this happens, the rule is triggered and the corresponding action is executed. j For example, when the specification attribute value in the device selection criteria violates a certain constraint rule, the action of prompting a warning or prohibiting selection is executed; when a certain recommendation rule is met, the action of assigning scores by dimension is executed.

[0193] In some embodiments, the computing device can also pre-establish the correspondence between constraint rules and recommendation rules. Each recommendation rule corresponds to one or more constraint rules. This association enables the computing device to quickly locate the relevant recommendation rules based on the already acquired target constraint rules when making recommendations, thereby organically combining design specification constraints with optimal recommendation strategies.

[0194] In one feasible approach, the correspondence between recommendation rules and constraint rules can be pre-configured when the rule base is built.

[0195] For example, for a complete PCB design system, the following relationships can be established within the system: When the constraint rule requires the selection of MLCCs with ESR and ESL for high-frequency circuits, the associated recommended rules may include at least one of the following: Recommendation Rule 1: When selecting MLCC capacitors, prioritize environmentally friendly capacitors, with a recommended weight of 80%.

[0196] Recommendation Rule 2: When selecting MLCC capacitors, prioritize non-exclusive suppliers, with a recommended weight of 90%.

[0197] Recommendation Rule 3: When selecting MLCC capacitors, give priority to domestically produced components, with a recommended weight of 90%.

[0198] Recommendation Rule 4: When selecting MLCC capacitors, prioritize the actual usage of existing products, with a recommended weight of 100.

[0199] Recommendation Rule 5: When selecting MLCC capacitors, prioritize components with the highest preferred grade from the company, with a recommended weight of 100.

[0200] Recommendation Rule 6: When selecting MLCC capacitors, prioritize devices that have been selected more frequently in historical recommendations, with a recommendation weight of 90%.

[0201] As the examples above demonstrate, a single constraint rule (high-frequency circuits require the selection of MLCC capacitors with low ESR and low ESL) can be associated with multiple recommendation rules. These recommendation rules further optimize devices that meet the constraint rule based on different evaluation dimensions (environmental protection, supply, domestic production, usage volume, preference level, and historical selection frequency). Through this correlation, computing devices can conduct multi-dimensional and comprehensive evaluations of candidate devices while meeting basic design specifications.

[0202] Based on the above relationships, the recommendation rule base also includes constraint rules corresponding to each recommendation rule. For example... Figure 4 As shown, in S203 above, the method by which the computing device obtains the target recommendation rule and the recommendation weight of the target recommendation rule that match the specification attributes of the candidate device from the pre-built recommendation rule base specifically includes: S403. The computing device determines the recommendation rule corresponding to the target constraint rule in the recommendation rule base as the target recommendation rule and obtains the recommendation weight of the target recommendation rule.

[0203] The computing device first obtains the target constraint rules. The target constraint rules are those constraint rules that correspond to the specification attributes selected by the device selection criteria, representing the basic design specifications that must be met in this selection.

[0204] For example, in the example above, assuming the user inputs the selection of MLCC capacitors in a high-frequency circuit, the target constraint rule could include the requirement that the high-frequency circuit should select MLCC capacitors with low ESR and low ESL.

[0205] Next, the computing device traverses the recommendation rule base, searching for recommendation rules that correspond to the target constraint rules. Specifically, the computing device identifies the recommendation rules in the recommendation rule base that correspond to the target constraint rules as the target recommendation rules.

[0206] For example, in the example above, the six recommended rules corresponding to the constraint that high-frequency circuits need to select MLCC capacitors with low ESR and low ESL are determined as the target recommended rules for this round of selection.

[0207] Through the above method, this application embodiment achieves deep coupling between constraint rules and recommendation rules. Since the target recommendation rule is determined based on the already validated target constraint rules, the subsequent recommendation process is based on meeting basic design specifications. The recommended devices not only meet design requirements in terms of specifications, but their device type, application scenario, etc., also conform to the company's design red lines and engineering specifications.

[0208] While determining the target recommendation rules, the computing device also obtains the recommendation weights corresponding to these target recommendation rules.

[0209] In some embodiments, the recommendation weights corresponding to the target recommendation rule may change based on the current state information of the candidate devices. This dynamic adjustment mechanism enables computing devices to perceive real-time supply chain status and market changes, thereby providing users with recommendation results that are more in line with current production realities and economic considerations.

[0210] In this case, the method provided in the embodiments of this application further includes: Obtain the current state information of the candidate device, and if the change in the current state information of the candidate device is greater than a preset threshold, adjust the recommendation weight of the recommendation rule associated with the current state information of the candidate device in the recommendation rule base according to the change in the current state information of the candidate device.

[0211] The current status information includes: current inventory and / or current price.

[0212] Current inventory refers to the available inventory of candidate components at the current point in time. Inventory data can come from enterprise resource planning (ERP) systems, material management systems, or real-time inventory interfaces provided by suppliers. Inventory levels are typically a dynamically changing value, fluctuating with procurement receipts and production usage.

[0213] The current price refers to the unit price of the candidate device at the current point in time. Price data can be obtained from the procurement system, supplier quotations, or market information. Prices may fluctuate due to various factors such as market supply and demand, raw material costs, and purchase volume.

[0214] Optionally, in addition to inventory levels and prices, current status information can also include dynamically changing factors such as delivery dates (procurement lead times), production stoppage warnings, information on alternative products, and recent quality feedback. This information also has a significant impact on selection decisions.

[0215] Computing devices can interface with a company's supply chain management system and material management system to obtain current status information in real time or periodically, and associate and store or cache it with candidate devices for use in selection and recommendation.

[0216] After acquiring the current status information of candidate devices, the computing device needs to monitor changes in this information and determine whether the magnitude of the change exceeds a preset threshold. This magnitude of change can be relative to the status at the time of the last selection, or it can be relative to a benchmark value (such as average inventory level or historical average price).

[0217] For example, a threshold can be set for inventory levels, such as a decrease in inventory exceeding 50% or an inventory level falling below the safety stock level. If the current inventory of a candidate device has decreased by 60% since the last query, the change is considered to exceed the preset threshold.

[0218] For prices, a threshold for the magnitude of change can be set, such as a price increase exceeding 10% or a price fluctuation exceeding ±5%. If the current price of a candidate device increases by 15% compared to the average price of the previous month, the change is considered to exceed the preset threshold.

[0219] The preset threshold can be flexibly configured based on factors such as device type, project importance, and corporate procurement strategy. For critical devices or scarce materials, a smaller threshold can be set to make them more sensitive to changes; for general-purpose devices or materials with sufficient supply, a larger threshold can be set to avoid overly frequent adjustments.

[0220] If the change in the current state information of a candidate device exceeds a preset threshold, the computing device adjusts the recommendation weights of the recommendation rules associated with the current state information of the candidate device in the recommendation rule base according to the change in the current state information of the candidate device.

[0221] Recommendation rules associated with the current state information refer to those recommendation rules whose evaluation dimensions are directly related to the state information.

[0222] For example, a recommendation rule base typically contains one or more inventory-related rules, such as prioritizing devices with sufficient inventory. The recommendation weight of this rule can be associated with the current inventory level of the device. When the inventory level of a candidate device drops significantly, or even faces the risk of stockout, the computing device can reduce the weight of that device on the sufficient inventory rule, so that it no longer has an advantage in the overall score; or increase the weight of its risk level-related rules, so that risk factors account for a larger proportion in the evaluation.

[0223] For example, the recommendation rule base typically includes price-related rules such as prioritizing lower-priced devices or prioritizing devices with high cost-performance ratios. When the price of a candidate device increases by more than a preset threshold, the computing device can reduce the device's score on price-related rules or adjust the weight of price rules to weaken their impact on the final recommendation index.

[0224] When the computing device adjusts the recommendation weights of the recommendation rules associated with the current state information of candidate devices in the recommendation rule base, it can make linear or non-linear adjustments based on the magnitude of the change.

[0225] For example, an adjustment function can be set: Weight Adjustment Amount = Baseline Weight × Change Amount × Adjustment Coefficient. When inventory decreases by 30%, the weight of the inventory rule is reduced by 10%; when inventory decreases by 50%, the weight of the inventory rule is reduced by 20%. A tiered adjustment strategy can also be set: when inventory falls below the safety stock level, the weight of the inventory rule is directly reduced to zero, meaning that the factor of sufficient inventory is no longer considered.

[0226] The process of determining the target device through a historical recommendation library according to the embodiments of this application is described below with reference to the accompanying drawings.

[0227] In some embodiments, to further improve the efficiency of device selection and fully utilize the experience data accumulated in historical selection processes, this application introduces a mechanism of a historical recommendation library. In this case, the device selection criteria also include: the device type used to filter target devices. For example... Figure 5 As shown, the method for a computing device to obtain candidate devices that meet the device selection criteria specifically includes: S501. The computing device obtains candidate devices from a pre-built historical recommendation library based on the device types selected according to the device selection criteria.

[0228] The historical recommendation library includes multiple historical recommended devices and the device type for each historical recommended device. These historical recommended devices include candidate devices.

[0229] In other words, the historical recommendation library is a pre-built collection of data used to store historical selection records. Every selection recommendation operation performed by a user, including information such as the device information that was ultimately determined as the target device, the selection conditions at the time, the selection time, and the user making the selection, can be recorded and stored in the historical recommendation library.

[0230] The historical recommendation database includes multiple historical recommended devices and their respective device types. Furthermore, it stores information such as historical selection criteria, the number of times a device was selected, and historical usage feedback for each device. This historical data, forming the historical recommendation database, reflects the actual performance of different devices in past projects and user preferences.

[0231] As can be seen from the above, the device selection criteria include not only the specifications used to screen target devices, but also the device type used to screen target devices.

[0232] Device type refers to the category to which a device belongs, such as MLCC, aluminum electrolytic capacitor, tantalum capacitor, thin film resistor, etc. Device type usually determines the basic characteristics and application scenarios of the device, and is an important screening dimension in the selection process.

[0233] The computing device can search for historically recommended devices with the same or similar device types as the currently input device type from a pre-built historical recommendation library based on the device type selected by the device selection criteria, and use these historically recommended devices as candidate devices.

[0234] For example, suppose the user's current input device selection criteria include a MLCC (Multi-Layer Ceramic Capacitor) type, a nominal capacitance of 100 microfarads (μF), and a rated voltage of 50 volts (V). The computing device first filters through the historical recommendation library to select all historically recommended MLCC devices, forming a candidate device set. This candidate device set includes various MLCC capacitors that have been recommended or ultimately selected in similar scenarios in the past.

[0235] S502. If the matching degree between the attribute value of the candidate device's specification attribute and the attribute value of the specification attribute selected by the device selection conditions is greater than the preset matching degree, the computing device shall determine the candidate device as the target device.

[0236] After filtering the candidate device set by device type, the computing device needs to further determine the degree of match between these historically recommended devices and the current selection requirements. This is because, although the device types are the same, the specifications (such as nominal capacitance, rated voltage, package size, etc.) of different historically recommended devices may differ from the specifications currently input. Only those historically recommended devices whose specifications highly match the current requirements have direct reuse value.

[0237] To this end, the computing device calculates the degree of match between the attribute values ​​of the specifications of each candidate device and the attribute values ​​of the specifications selected by the device selection criteria. The degree of match is a quantitative indicator that measures the closeness between the two and can be calculated in various ways.

[0238] In one feasible approach, the computing device can determine the degree of matching between the attribute values ​​of the candidate device's specifications and the attribute values ​​of the specifications selected by the device selection criteria, based on the number of times the selection criteria are matched in historical recommendation records.

[0239] Specifically, the computing device obtains the currently input device selection criteria and compares them with the historical selection criteria corresponding to each historically recommended device in the candidate device set, calculating the number of matching selection criteria. Here, "matching selection criteria" means that the specification attribute items and their attribute values ​​or attribute value ranges that are the same as the current device selection criteria in the historical selection criteria are consistent.

[0240] For example, current device selection criteria include: device type is MLCC, nominal capacitance is 100 microfarads (μF), rated voltage is 50 volts (V), and package is SMD-0805.

[0241] The computing device iterates through each historically recommended device in the candidate device set, examining its corresponding historical selection criteria. Assume the candidate device set contains the following historically recommended devices and their historical selection criteria: Device A: The historical selection criteria were [MLCC, 100 μF, 50 V, SMD-0805], which are exactly the same as the current selection criteria, with a hit rate of 4 items.

[0242] Device B: The historical selection criteria were [MLCC, 100 microfarads (μF), 50 volts (V), SMD-0603]. Compared with the current selection criteria, the first three items are the same but the package is different, and the number of matching items is 3.

[0243] Device C: The historical selection criteria were [MLCC, 100 μF, 25 V, SMD-0805]. Compared with the current selection criteria, the rated voltage is different, and the number of hits is 3.

[0244] Device D: The historical selection criteria were [MLCC, 220 μF, 50 V, SMD-0805]. Compared with the current selection criteria, the nominal capacitance is different, and the number of matching criteria is 3.

[0245] The computing device sorts the devices in the candidate device set according to the number of hits, with devices having a higher number of hits ranked higher. In the example above, device A has 4 hits, ranking first; devices B, C, and D all have 3 hits, tying for second place.

[0246] For example, such as Figure 6 As shown, the historical recommendation database stores multiple historical recommendation records. Each row represents a historical recommendation record, containing the device corresponding to that recommendation, the device type, and the selection criteria used at that time. The historical recommendation records illustrated in the diagram include: Device xxxxxx01, device type is light-emitting diode, corresponding historical selection conditions include selection condition 3; Device xxxxxx02 is a semiconductor sensor, and its historical selection criteria include selection criterion 1 and selection criterion 2. Device xxxxxx03 is a semiconductor sensor, and its historical selection criteria include selection criteria 1, selection criteria 2, and selection criteria 4. Device xxxxxx04 is a semiconductor sensor, and its historical selection criteria include selection criteria 1, 2, 3, and 4.

[0247] When the user's current input device selection criteria include four items: selection criterion 1, selection criterion 2, selection criterion 3, and selection criterion 4, and the device type is a semiconductor sensor, the computing device filters historical recommended devices of the semiconductor sensor type from the historical recommendation library, namely xxxxxx02, xxxxxx03, and xxxxxx04. Then, the computing device calculates the number of matches between the historical selection criteria and the current selection criteria for each of these three devices. The historical selection criteria for xxxxxx02 include selection criteria 1 and selection criteria 2, and the number of identical selection criteria is 2. The historical selection criteria for xxxxxx03 include selection criteria 1, selection criteria 2, and selection criteria 4, with 3 criteria matching the current selection criteria. The historical selection criteria for xxxxxx04 include selection criteria 1, selection criteria 2, selection criteria 3, and selection criteria 4, which are exactly the same as the current selection criteria, and the number of hits is 4.

[0248] The computing device sorts these devices according to the number of hits, with devices having a higher number of hits ranked higher. Therefore, xxxxxx04 (4 hits) ranks first, xxxxxx03 (3 hits) ranks second, and xxxxxx02 (2 hits) ranks third. The computing device outputs the sorted device list as a recommendation list, which includes these devices and their specific parameter information, such as... Figure 6 As shown below, the recommendation list displays xxxxxx04, xxxxxx03, and xxxxxx02 and their corresponding parameters in sequence.

[0249] In another possible approach, for numerical specification attributes, the computing device can also determine the degree of matching based on the relative or absolute error between the attribute values ​​of the candidate device's specification attributes and the attribute values ​​of the specification attributes selected by the device selection criteria.

[0250] For example, assuming the current device selection criteria require a nominal capacitance of 100 microfarads (μF), and candidate device A has a nominal capacitance of 100 microfarads (μF), then the matching degree is 100%; candidate device B has a nominal capacitance of 105 microfarads (μF), then the matching degree can be calculated as (1-|105-100| / 100)×100%=95%.

[0251] In another possible approach, for enumerated specification attributes such as package type and operating temperature range, computing devices can also use exact matching or inclusion matching. For example, if the current device selection criteria require a package of "SMD-0805", then only candidate devices with a package of "SMD-0805" will achieve a 100% match in that category.

[0252] The computing device can weight and summarize the matching degree of multiple specifications and attributes to obtain an overall matching degree score. For example, the weight of nominal capacitance can be set to 40%, rated voltage to 30%, and package to 30%. The matching degree of each individual item can be weighted and summed to obtain the overall matching degree of the candidate device with the current device selection conditions.

[0253] After calculating the matching degree of each candidate device, the computing device identifies those with a matching degree greater than a preset matching degree threshold as target devices that meet the requirements. The preset matching degree threshold can be configured according to actual needs, for example, it can be set to 80%, 90%, or 95%. A higher threshold indicates a stricter matching requirement for historically recommended devices, and only those historical devices that are highly consistent with the current needs will be reused; a lower threshold allows for a wider range of historical devices to be considered, but may require further screening and confirmation by the user.

[0254] In this way, computing devices can quickly filter out components from a historical recommendation library that highly match the current selection requirements and directly recommend them to the user as target components. This method is particularly suitable for selection scenarios with high repetition and little change, such as when the same or similar components are needed in multiple similar projects.

[0255] When a target device with a high hit rate and matching degree can be found through the historical recommendation library, the computing device can directly output these devices as selection results without entering the subsequent automatic recommendation process (i.e., S203 and S204). This greatly improves selection efficiency and achieves the effect of "select once, reuse multiple times".

[0256] If the target device fails to find a number of hits or a matching degree that meets the requirements through the historical recommendation library, for example, if the candidate device set is empty, or if the number of hits of all candidate devices is too low and the matching degree is lower than the preset threshold, the computing device can switch to the automatic recommendation process and make a comprehensive evaluation and recommendation based on the recommendation rules and recommendation weights in the manner described in S203-S204.

[0257] In some embodiments, the devices recommended in the historical recommendation library may be strongly correlated with the user and / or time at that time. In this case, the method by which the aforementioned computing device obtains candidate devices from the pre-built historical recommendation library based on the device types selected according to device selection criteria specifically includes: The computing device obtains the input information for the device selection criteria.

[0258] The input subject information includes: user information and / or time information for input device selection conditions.

[0259] User information can include the user's identity, department, job level, and historical selection preferences. This information can reflect different users' selection habits and priorities.

[0260] Time information can include the timestamp of this selection process, the current project stage, and the current date. Time information can reflect the exact moment the selection occurred, as well as any potential seasonal or time-sensitive factors.

[0261] The computing device obtains a set of candidate devices from the historical recommendation library, based on the device selection criteria and the selected entity information for each candidate device in the set.

[0262] The historical recommended devices include a set of candidate devices. The selected entity information includes: user information and / or time information for selecting the candidate device.

[0263] Specifically, the computing device, based on the device selection criteria, filters all historically recommended devices matching the selected device type from the historical recommendation database, forming a candidate device set. This candidate device set includes all devices historically recommended or selected under similar device types. Simultaneously, the computing device obtains the selection entity information corresponding to each candidate device in the candidate device set from the historical recommendation records.

[0264] The selected entity information refers to the relevant entity information from the historical selection process when the device was recommended and ultimately selected, including user information and / or time information. For example, if a device was selected by an engineer in a power supply department three months ago, then the user identifier and timestamp corresponding to that selection constitute the selected entity information for that device.

[0265] Next, the computing device identifies the candidate devices corresponding to the selected subject information whose similarity to the input subject information is greater than a preset similarity as candidate devices.

[0266] Optionally, different measurement methods can be used to calculate similarity depending on the specific application scenario: In one feasible approach, computing devices can calculate the similarity between users based on user identifiers, departmental attributes, historical selection records, and other factors. For example, users from the same department or project team have high user similarity; users who frequently choose similar devices also have high user similarity.

[0267] In another possible approach, computing devices can calculate temporal proximity based on timestamps. For example, recently selected devices should have a higher temporal similarity than devices selected much earlier, reflecting the timeliness of selection trends. Temporal similarity can be designed as a time decay function, with higher similarity the more recent the time.

[0268] In another possible approach, the computing device can weight and combine user similarity and temporal similarity to obtain a comprehensive similarity score.

[0269] The computing device identifies candidate devices corresponding to selected subject information with a similarity greater than a preset similarity threshold. In other words, only devices that were historically selected by users with high similarity to the current user within a similar timeframe will be included in the candidate device set for subsequent matching and recommendation.

[0270] By introducing a similarity filter between the input subject information and the selected subject information, this application embodiment can recommend devices that are more suitable for the user's habits based on the user's personal historical preferences or the commonly used devices in their department.

[0271] Furthermore, if the same user enters the same device selection criteria at different times, the computing device will prioritize recommending devices that have been widely used recently due to the different time information, and avoid recommending older models that are outdated or have supply risks.

[0272] In some embodiments, when there are multiple candidate devices obtained from the historical recommendation library, and the matching degree between the attribute values ​​of the specification attributes of these candidate devices and the attribute values ​​of the specification attributes selected by the device selection criteria is greater than a preset matching degree, the computing device can further determine the final target device based on the selection frequency of these candidate devices within a historical time period. That is, when the matching degree between the attribute values ​​of the specification attributes of the candidate devices and the attribute values ​​of the specification attributes selected by the device selection criteria is greater than a preset matching degree, the method by which the computing device determines the candidate device as the target device specifically includes: If the matching degree between the attribute value of the candidate device's specification attribute and the attribute value of the specification attribute selected by the device selection conditions is greater than the preset matching degree, and there are multiple candidate devices, obtain the selection frequency of each candidate device in the historical time period, and determine the candidate device whose selection frequency in the historical time period is greater than the preset frequency as the target device.

[0273] The historical time period refers to the time range within which the frequency of device selection is statistically analyzed. This time period can be flexibly configured according to actual application needs, such as being set to the most recent year, the most recent six months, or the most recent three months. By limiting the historical time period, it is ensured that the statistical results reflect the recent usage popularity of devices, avoiding the influence of too much early data on the selection of recently popular devices.

[0274] The computing device retrieves data from the historical recommendation database, counting the number of times each candidate device was ultimately selected by the user within a preset historical time period; this is known as the selection frequency. The historical recommendation database records each selection recommendation operation, including information such as the selected target device, the selection time, and the user making the selection. The computing device can then retrieve the number of records in the historical recommendation database for each candidate device that it was selected as the target device within the historical time period.

[0275] For example, for candidate device A, the computing device searches the historical recommendation library for all records where the selected device is device A and the selection time falls within the most recent year. The total number of records obtained is the selection frequency of device A in the most recent year.

[0276] After obtaining the selection frequency of each candidate device, the computing device identifies the candidate devices whose selection frequency is greater than a preset frequency threshold as the target devices. The preset frequency threshold can be set according to actual needs, for example, it can be set to 10 times, 20 times, or it can be set to a dynamic value, such as selecting the top 3 devices by frequency.

[0277] If the preset frequency threshold is 10 times, only candidate devices selected more than 10 times will be identified as target devices. This approach is suitable for situations where it is desirable to ensure that the selected devices have a sufficient historical verification basis.

[0278] Of course, computing devices can also choose not to set a fixed threshold, but instead determine the N candidate devices with the highest selection frequency as the target devices. For example, the top three devices with the highest selection frequency can be selected as the target devices and output to the user.

[0279] When the selection frequency of multiple candidate devices is greater than a preset threshold, the computing device can identify all of these devices as target devices, or it can sort them by frequency and output the results for the user to make a final selection based on other factors.

[0280] By introducing a screening mechanism based on historical selection frequency, this application's embodiments can further optimize the results of historical recommendations. On the one hand, devices with high selection frequency usually indicate that they have been fully validated in past projects, possessing high reliability and acceptance; recommending such devices can reduce selection risk. On the other hand, frequency statistics combined with historical time periods can dynamically reflect changes in device selection trends, avoiding the recommendation of outdated or obsolete devices. This mechanism, while ensuring selection accuracy, further enhances the practicality and credibility of the recommendation results.

[0281] The overall process of the device selection method provided in the embodiments of this application will be described below with reference to the accompanying drawings.

[0282] In some embodiments, such as Figure 7As shown, the device selection method provided in this application includes: S701. Obtain the device selection criteria.

[0283] S702. Obtain the target constraint rule corresponding to the specification attribute selected by the device selection conditions from the pre-built constraint rule library.

[0284] S703. If the attribute values ​​of the specifications selected by the device selection criteria meet the target constraint rules, candidate devices are obtained from the pre-built historical recommendation library according to the device types selected by the device selection criteria.

[0285] S704. If the matching degree between the attribute value of the candidate device's specification attribute and the attribute value of the specification attribute selected by the device selection conditions is greater than the preset matching degree, the candidate device shall be determined as the target device.

[0286] S705. If the matching degree between the attribute value of the specification attribute of the candidate device and the attribute value of the specification attribute selected by the device selection conditions is less than or equal to the preset matching degree, the recommendation rule in the recommendation rule base that corresponds to the target constraint rule is determined as the target recommendation rule, and the recommendation weight of the target recommendation rule is obtained.

[0287] S706. Determine the recommendation index of candidate devices based on the target recommendation rule and the recommendation weight of the target recommendation rule.

[0288] S707. If the recommendation index of a candidate device meets the recommendation index condition, the candidate device that meets the recommendation index condition shall be determined as the target device.

[0289] S708. If none of the candidate devices meet the recommendation index conditions, the computing device displays the categories of each device to the user through the classification and selection function, and in response to the user's selection operation, determines the device selected by the selection operation as the target device.

[0290] When the automatic recommendation process (S705-S707) fails to generate target devices that meet the recommendation index conditions, such as when the recommendation index of all candidate devices is lower than the preset threshold, or when the result returned by the automatic recommendation is empty, this application embodiment also provides a classification selection function as a supplementary means to manual selection, to ensure that users can complete device selection under any circumstances.

[0291] The categorized selection function is a manual selection method independent of the automatic recommendation. Its core lies in organizing the components in the existing library according to preset category columns, allowing users to quickly locate the required components through browsing and filtering. The specific implementation process is as follows: First, the computing device displays a categorized navigation interface for the device library to the user. The devices in the library are pre-categorized according to multiple classification dimensions; common category sections include, but are not limited to: Classified by device type: such as capacitors, resistors, inductors, diodes, transistors, integrated circuits, etc.

[0292] Classified by package type: such as surface mount device (SMD), through-hole device, and specific package codes (such as SMD-0805, SMD-0603, TO-220, etc.).

[0293] Classified by application area: for example, power management, signal processing, radio frequency communication, automotive electronics, etc.

[0294] Users can quickly narrow down their browsing scope by clicking into the corresponding category based on their selection needs. For example, if they need to select an MLCC, they can directly click on the MLCC category within the capacitor category, and the computing device will display a list of all devices under that category.

[0295] After a user selects a category, the computing device provides filtering functionality for that category. The filtering criteria dynamically adapt to different device types to help users more accurately locate the desired device. For example: When users enter the MLCC capacitor category, the filtering criteria can include nominal capacitance, rated voltage, package size, temperature characteristics, accuracy class, etc.

[0296] When users enter the transistor category, the filtering criteria can include collector-emitter breakdown voltage, maximum collector current, power, operating temperature range, etc.

[0297] When a user enters the resistor category, the filtering criteria can include resistance value, rated power, accuracy, temperature coefficient, etc.

[0298] This dynamic filtering mechanism guides users to input the specifications most relevant to the current device type, avoiding wasting time on irrelevant attributes.

[0299] After the user inputs filter criteria, the computing device filters from the component library under the current category in real time based on these criteria and displays a list of components that meet the criteria. The list can display key specifications of the components, such as model number, main parameters, inventory status, and price, allowing users to quickly browse.

[0300] To help users make more accurate choices among different components, the categorization and selection function also provides a component comparison feature. Users can select multiple components of interest from the list and trigger a comparison. The computing device will display detailed specifications and attributes of these components side-by-side, including but not limited to manufacturer, package, electrical parameters, operating temperature, environmental certifications, and supply information, allowing users to intuitively compare the differences between components and make more informed decisions.

[0301] Ultimately, users browse, filter, and compare to identify one or more suitable devices and select them as the target device through a selection action (such as clicking a "select" button). The computing device responds to the user's selection, designating the selected device as the final target device for this selection process. It can also input the device's information into the design tool and update the historical recommendation library for future reuse.

[0302] The categorized selection function serves as a fallback solution to the automatic recommendation system, ensuring that users can still efficiently complete the selection process manually when automatic recommendations fail to meet their needs. Simultaneously, the user actions and final choices accumulated during the categorized selection process can serve as feedback data to optimize the automatic recommendation rules, forming a closed-loop evolution of the selection system.

[0303] In some embodiments, such as Figure 8 As shown, when the computing device executes the automatic recommendation process described in S705 to S707, it can comprehensively call relevant data provided by multiple data sources such as the product library, the device specification library, and the recommendation rule library to conduct a multi-dimensional comprehensive evaluation of candidate devices and finally select the target device. Figure 8 The diagram illustrates the data flow relationship between the computing device and each data source. The following section provides a detailed explanation of the data retrieval process in each step, using the diagram as a reference.

[0304] The product database is a pre-built collection of data used to store information about a company's existing products and the usage of their components. It records information such as product model, product type, and the actual list and quantity of components used in each product. In the automated recommendation process, the product database primarily provides data support for the recommendation rules, including dimensions such as the actual usage of components and applicable product types.

[0305] Specifically, after the computing device determines the target recommendation rule in S705, it needs to retrieve relevant information from the product library when calculating the dimensional scores of each candidate device in S706. For example, for the rule of actual device usage, the computing device can query the product library to count the cumulative usage of a candidate device among all existing products; the higher the usage, the higher the score for that dimension. For the rule of applicable product type, the computing device can query the product library to determine whether the candidate device is applicable to the product type corresponding to the current design; if applicable, a higher score is given.

[0306] The device specification library is a pre-built collection of data used to store detailed specifications and attributes of various devices. It includes multiple devices and their specifications, such as nominal capacitance, rated voltage, package size, operating temperature range, manufacturer information, environmental requirements, and safety certifications. The device specification library is the fundamental data source for obtaining candidate devices in S201 and a crucial basis for calculating scores across various recommendation rule dimensions in S706.

[0307] In S705, after the computing device determines the target recommendation rule based on the target constraint rule, it needs to obtain a list of candidate devices that meet the specification attribute requirements from the device specification library.

[0308] In S706, for each candidate device, the computing device needs to extract information related to each recommendation rule from its specification attributes to calculate the dimensional score. For example, for the rule of "company preference level," the preference level information of the device needs to be obtained from the device specification library; for the rule of "whether it is domestically produced," the place of origin information of the device needs to be obtained from the device specification library; for the rule of "inventory quantity," although inventory quantity is dynamic information, the device specification library usually stores device identifiers, which can be used to query real-time inventory data.

[0309] The recommendation rule base is a pre-built data collection used to store recommendation rules and their recommendation weights. For a detailed description, please refer to the description above, which will not be repeated here.

[0310] Through the collaborative work of these multiple data sources, computing devices can comprehensively consider various dimensions such as the inherent attributes of components, historical performance, real-time status information, and corporate strategic orientation, to achieve accurate and comprehensive intelligent selection recommendations. This data fusion mechanism ensures that the recommended results not only conform to design specifications and align with actual production, but also take into account the company's procurement strategy and supply chain conditions, significantly improving the quality and efficiency of selection.

[0311] The above embodiments mainly use the component selection scenario in PCB design as an example to illustrate the technical solution provided in this application in detail. However, those skilled in the art should understand that the technical concept of this application can also be extended to other scenarios that require multi-dimensional data management and intelligent recommendation.

[0312] This application embodiment integrates and categorizes the common basic attributes of devices (such as specifications, manufacturer information, environmental requirements, safety certifications, etc.) to form a standardized device library, achieving a unified management method that can be extended to other scenarios requiring multi-dimensional data management. Through unified standardized data processing, the overall system's collaborative capabilities can be improved.

[0313] For example, in the field of smart manufacturing, similar methods can be used to standardize the management of various equipment, materials and process parameters, achieve cross-system compatibility and data interoperability, and improve production efficiency and collaboration.

[0314] In the field of logistics and supply chain management, logistics equipment and warehousing facilities can be classified according to unified attributes to establish a standardized resource database, thereby optimizing resource allocation and inventory management and improving supply chain operation efficiency.

[0315] In the field of digital asset management, enterprise IT assets (such as servers, network equipment, software licenses, etc.) can be classified and standardized for management, a unified asset information database can be established, and the efficiency and accuracy of asset management and maintenance can be improved.

[0316] Furthermore, the method in this embodiment, which calculates a device recommendation index based on the modeled rules and identifies devices that meet the recommendation index conditions as target devices, can also be extended to other scenarios requiring device selection or recommendation. Simply modify the PCB device selection rules to match those of other scenarios and associate them with the corresponding basic data to automatically perform intelligent recommendations.

[0317] For example, in the field of smart homes, the recommendation method of this application can be applied to scenarios such as smart device selection and smart scene configuration, and recommend suitable smart home product combinations to users based on user needs and device attributes.

[0318] In the field of automotive electronics, the recommended methods of this application can be applied to scenarios such as the selection of automotive electronic components and the configuration of vehicle systems, and recommend the optimal electronic component solutions for R&D engineers based on vehicle positioning, functional requirements and supply chain conditions.

[0319] The above-described application extensions are merely exemplary embodiments of this application and do not constitute a limitation on the scope of protection of this application. Any solution that utilizes the technical concepts described in the embodiments of this application to perform data standardization governance or intelligent recommendation in similar scenarios falls within the scope of protection of this application.

[0320] In an exemplary embodiment, this application also provides a device selection apparatus. This device selection apparatus may be a computing device that performs the aforementioned device selection method, or it may be a processor within the computing device. The device selection apparatus may include one or more functional modules for implementing the device selection method of the above method embodiments.

[0321] This application also provides a computing device. Figure 9 This is a schematic diagram of the architecture of a computing device provided in an embodiment of this application. Figure 9As shown, the computing device 100 includes: one or more memories 120, one or more processors 110, a communication bus 140, and a communication interface 130. The processors 110 and memories 120 are connected via the communication bus 140; the one or more memories 120 are used to store computer program code, which includes computer instructions; when the one or more processors 110 execute the computer instructions, the computing device 100 performs the device selection method provided in this embodiment.

[0322] Optionally, the memory 120 may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. The embodiments of this application do not impose any limitations on this.

[0323] The processor 110 may be a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof, and the embodiments of this application do not impose any limitations on this.

[0324] The communication bus 140 can be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. This communication bus 140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 9 It is represented by a single thick line, but this does not mean that there is only one bus or one type of communication bus.

[0325] Communication interface 130 uses any transceiver-like device for communicating with other devices or communication networks, such as control systems, radio access networks (RAN), wireless local area networks (WLAN), etc.

[0326] It should be noted that the system architecture and application scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0327] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware; for example, the related hardware can be a processor of a computing device. The program instructions can be stored in the above-mentioned computer-readable storage medium, and when executed, the processes of the above method embodiments can be implemented. The computer-readable storage medium can be memory. The above-mentioned computer-readable storage medium can also be an external storage device, such as a hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Further, the above-mentioned computer-readable storage medium can include both memory and external storage devices. The above-mentioned computer-readable storage medium is used to store the above-mentioned computer program instructions and other programs and data required by the above-mentioned device selection method.

[0328] This application also provides a computer program product, which includes a computer program that, when run on a computing device, causes the computing device to execute any of the device selection methods provided in the above embodiments.

[0329] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0330] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

[0331] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A device selection method, characterized in that, Applied to computing devices, including: Obtain device selection criteria and candidate devices that meet the device selection criteria; the device selection criteria include specification attributes for screening target devices; the specification attributes of the candidate devices meet the specification attributes screened by the device selection criteria; From a pre-built recommendation rule library, a target recommendation rule matching the specification attributes of the candidate device and the recommendation weight of the target recommendation rule are obtained; the recommendation rule library includes multiple recommendation rules and the recommendation weight of each recommendation rule; the multiple recommendation rules include the target recommendation rule; Based on the target recommendation rule and the recommendation weight of the target recommendation rule, the recommendation index of the candidate device is determined, and the candidate device that meets the recommendation index condition is determined as the target device.

2. The method according to claim 1, characterized in that, The process of obtaining candidate devices that meet the device selection criteria includes: From a pre-built constraint rule library, a target constraint rule corresponding to the specification attribute selected by the device selection conditions is obtained; the constraint rule library includes multiple constraint rules and the specification attribute constrained by each constraint rule; the multiple constraint rules include the target constraint rule; the target constraint rule is used to constrain the attribute value of the corresponding specification attribute. If the attribute values ​​of the specifications selected by the device selection criteria satisfy the target constraint rules, the candidate device is obtained.

3. The method according to claim 2, characterized in that, The recommendation rule base also includes constraint rules corresponding to each recommendation rule; obtaining the target recommendation rule and the recommendation weight of the target recommendation rule that match the specification attributes of the candidate device from the pre-built recommendation rule base includes: The recommendation rule in the recommendation rule base that corresponds to the target constraint rule is determined as the target recommendation rule, and the recommendation weight of the target recommendation rule is obtained.

4. The method according to any one of claims 1-3, characterized in that, The method further includes: Obtain the current status information of the candidate device; the current status information includes: current inventory and / or current price; If the change in the current state information of the candidate device exceeds a preset threshold, the recommendation weight of the recommendation rule associated with the current state information of the candidate device in the recommendation rule base is adjusted according to the change in the current state information of the candidate device.

5. The method according to any one of claims 1-4, characterized in that, The device selection criteria further include: device types for screening the target device; obtaining candidate devices that meet the device selection criteria includes: Candidate devices are obtained from a pre-built historical recommendation library based on the device selection criteria and the selected device types. The historical recommendation library includes multiple historical recommended devices and the device type of each historical recommended device. The multiple historical recommended devices include the candidate devices. The method further includes: If the matching degree between the attribute value of the candidate device's specification attribute and the attribute value of the specification attribute selected by the device selection conditions is greater than a preset matching degree, the candidate device is determined as the target device.

6. The method according to claim 5, characterized in that, The step of obtaining candidate devices from a pre-built historical recommendation library based on the device selection criteria includes: From the historical recommendation library, a set of candidate devices is obtained based on the device selection criteria and the selected device types; the multiple historical recommended devices include the set of candidate devices. Obtain the input subject information for the device selection criteria; the input subject information includes: user information and / or time information for inputting the device selection criteria; Obtain the selected entity information for each candidate device in the candidate device set; the selected entity information includes: user information and / or time information for selecting the candidate device; The candidate devices are those whose similarity to the input subject information is greater than a preset similarity.

7. The method according to claim 5 or 6, characterized in that, The step of determining the candidate device as the target device when the matching degree between the attribute value of the candidate device's specification attribute and the attribute value of the specification attribute selected by the device selection criteria is greater than a preset matching degree includes: If the matching degree between the attribute value of the specification attribute of the candidate device and the attribute value of the specification attribute selected by the device selection conditions is greater than the preset matching degree, and there are multiple candidate devices, the selection frequency of each candidate device in the historical time period is obtained. Candidate devices whose selection frequency is greater than a preset frequency in historical time periods are identified as the target devices.

8. The method according to any one of claims 1-7, characterized in that, The process of obtaining candidate devices that meet the device selection criteria includes: Candidate devices are obtained from a pre-built device library based on the specifications and attributes selected according to the device selection criteria; the device library includes multiple devices and the specifications and attributes of each device; the multiple devices include the candidate devices.

9. The method according to any one of claims 1-8, characterized in that, The recommendation rule base includes at least two of the following recommendation rules: business compatibility level rule, device usage rule, historical selection frequency rule, inventory level rule, price rule, applicable product type rule, and risk level rule.

10. A computing device, characterized in that, The computing device includes a processor and a memory; the processor is coupled to the memory. The memory is used to store computer instructions; The computer instructions are loaded and executed by the processor to enable the computing device to perform the method as described in any one of claims 1-9.

Images