Measurement tool recommendation method and device, electronic equipment and storage medium

Abstract

The invention provides a measurement tool recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: determining recommendation weights corresponding to a plurality of measurement tools according to the first historical selection information of a first user for the plurality of measurement tools; determining at least one recommended measurement tool from the plurality of measurement tools according to the recommended weight; in response to a measurement starting operation, displaying a starting control of the at least one recommended measurement tool; and in response to a selection operation on any starting control, starting a measuring tool corresponding to the starting control. According to the scheme, automatic recommendation of the measurement tools in the measurement system can be realized, and the efficiency of measurement work is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a measurement tool recommendation method, device, electronic device and storage medium. Background Art

[0002] In the CAD (Computer Aided Design) product engineering design process, product parameter detection is the most basic and important link. Currently, existing measurement systems usually have very cumbersome interface parameter configuration pages. With the continuous expansion of measurement functions, not only the custom configuration of measurement tools is becoming more and more complicated, but also the custom configuration often requires frequent large-scale movement and mouse clicks, which seriously affects the efficiency of designers' measurement work. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a measurement tool recommendation method, device, electronic device and storage medium, which can realize automatic recommendation of measurement tools in a measurement system, reduce the number of large-scale movements and mouse clicks, and improve the efficiency of measurement work.

[0004] According to a first aspect of an embodiment of the present application, a measurement tool recommendation method is provided, comprising: determining recommendation weights corresponding to the multiple measurement tools based on first historical selection information of a first user for the multiple measurement tools; determining at least one recommended measurement tool from the multiple measurement tools based on the recommendation weights; in response to starting a measurement operation, displaying a start control of the at least one recommended measurement tool; and in response to a selection operation of any of the start controls, starting the measurement tool corresponding to the start control.

[0005] In a possible implementation, determining recommendation weights corresponding to the multiple measurement tools based on first historical selection information of a first user for the multiple measurement tools includes: determining at least one reference user from the multiple second users based on the first historical selection information and second historical selection information of the multiple measurement tools by multiple second users; determining the first recommendation weights corresponding to the multiple measurement tools based on the second historical selection information of the at least one reference user; determining the first measurement tool last selected by the first user based on the first historical selection information; determining the second recommendation weights corresponding to the multiple measurement tools based on the first measurement tool and measurement sequence information corresponding to the first user, wherein the measurement sequence information is used to indicate the probability of the first user sequentially selecting any two measurement tools from the multiple measurement tools; determining the recommendation weights corresponding to the multiple measurement tools based on at least one of the first recommendation weight and the second recommendation weight.

[0006] In a possible implementation, determining at least one reference user from the multiple second users based on the first historical selection information and the second historical selection information of the multiple second users for the multiple measurement tools includes: determining a first text feature corresponding to the first user based on the first historical selection information; determining a second text feature corresponding to the second user based on the second historical selection information; determining the similarity between the first text feature and the second text as the first similarity between the first user and the second user; sorting the multiple second users in descending order corresponding to the first similarity; and determining at least one of the first second users as the reference user based on the sorting result of the second users.

[0007] In one possible implementation, determining the first recommendation weights corresponding to the multiple measurement tools based on the second historical selection information of the at least one reference user includes: converting the second historical selection information of the reference user into a corresponding measurement vector; normalizing the measurement vector to obtain a normalized first vector, wherein each element in the first vector corresponds to one measurement tool, and different elements correspond to different measurement tools; adding the first vectors to obtain a cumulative vector; and determining, based on the value of each element in the cumulative vector, the first recommendation weight of the measurement tool corresponding to the element.

[0008] In one possible implementation, the second recommendation weights corresponding to the multiple measurement tools are determined based on the measurement sequence information corresponding to the first measurement tool and the first user, including: taking each of the multiple measurement tools as a second measurement tool, and querying the probability of sequentially selecting the first measurement tool and the second measurement tool from the measurement sequence information as the second recommendation weight corresponding to the second measurement tool.

[0009] In a possible implementation, the method also includes: determining the number of times any two measurement tools are selected sequentially based on the second historical selection information corresponding to the multiple second users; determining the probability of the two measurement tools being selected sequentially based on the number of times the two measurement tools are selected sequentially; generating initial measurement sequence information including the probability of any two measurement tools being selected sequentially; and updating the initial measurement sequence information based on the first historical selection information to obtain the measurement sequence information.

[0010] In a possible implementation, determining the recommended weights corresponding to the multiple measurement tools based on at least one of the first recommended weight and the second recommended weight includes: determining the first recommended weight corresponding to the measurement tool as the recommended weight of the measurement tool; or determining the second recommended weight corresponding to the measurement tool as the recommended weight of the measurement tool.

[0011] In a possible implementation, determining the recommendation weights corresponding to the multiple measurement tools according to at least one of the first recommendation weight and the second recommendation weight includes: calculating the recommendation weight corresponding to each measurement tool in the multiple measurement tools according to the first recommendation weight and the second recommendation weight by using the following formula; is used to represent the recommendation weight corresponding to the i-th measurement tool among the multiple measurement tools, N is used to represent the number of the measurement tools, k j The first recommendation weight corresponding to the jth measurement tool is used to characterize the j,i Used to characterize the probability of sequentially selecting the jth measurement tool and the ith measurement tool.

[0012] In a possible implementation manner, the method further includes: in response to a selection operation on any of the start controls, updating the first historical selection information according to a measurement tool corresponding to the start control.

[0013] According to a second aspect of an embodiment of the present application, a measuring tool recommendation device is provided, comprising: a weight module, configured to determine the recommendation weights corresponding to the multiple measuring tools according to the historical selection information of the multiple measuring tools by the user; a selection module, configured to determine at least one recommended measuring tool from the multiple measuring tools according to the recommendation weights; a display module, configured to display the start control of the at least one recommended measuring tool in response to starting the measuring operation; and a start module, configured to display the start control of the at least one recommended measuring tool in response to starting the measuring operation. .

[0014] In one possible implementation, the weight module is used to: determine at least one reference user from the multiple second users based on the first historical selection information and the second historical selection information of the multiple measurement tools by the multiple second users; determine the first recommendation weights corresponding to the multiple measurement tools based on the second historical selection information of the at least one reference user; determine the first measurement tool last selected by the first user based on the first historical selection information; determine the second recommendation weights corresponding to the multiple measurement tools based on the first measurement tool and the measurement sequence information corresponding to the first user, wherein the measurement sequence information is used to indicate the probability of the first user sequentially selecting any two measurement tools from the multiple measurement tools; determine the recommendation weights corresponding to the multiple measurement tools based on at least one of the first recommendation weight and the second recommendation weight.

[0015] In a possible implementation, the weight module is used to: determine a first text feature corresponding to the first user based on the first historical selection information; determine a second text feature corresponding to the second user based on the second historical selection information; determine the similarity between the first text feature and the second text as the first similarity between the first user and the second user; sort the multiple second users in descending order corresponding to the first similarity; and determine at least one of the first second users as the reference user based on the sorting result of the second user.

[0016] In one possible implementation, the weight module is used to: convert the second historical selection information of the reference user into a corresponding measurement vector; normalize the measurement vector to obtain a normalized first vector, wherein each element in the first vector corresponds to a measurement tool, and different elements correspond to different measurement tools; add the first vectors to obtain a cumulative vector; and determine, based on the value of each element in the cumulative vector, a first recommendation weight of the measurement tool corresponding to the element.

[0017] In one possible implementation, the weight module is used to: use each of the multiple measurement tools as a second measurement tool, and query the probability of sequentially selecting the first measurement tool and the second measurement tool from the measurement sequence information as the second recommendation weight corresponding to the second measurement tool.

[0018] In one possible implementation, the weight module is used to: determine the number of times any two measurement tools are selected sequentially based on the second historical selection information corresponding to the multiple second users; determine the probability of the two measurement tools being selected sequentially based on the number of times the two measurement tools are selected sequentially; generate initial measurement sequence information including the probability of any two measurement tools being selected sequentially; and update the initial measurement sequence information based on the first historical selection information to obtain the measurement sequence information.

[0019] In a possible implementation, the weight module is used to: determine the first recommended weight corresponding to the measurement tool as the recommended weight of the measurement tool; or determine the second recommended weight corresponding to the measurement tool as the recommended weight of the measurement tool.

[0020] In a possible implementation, the weight module is used to: calculate the recommendation weight corresponding to each measurement tool in the multiple measurement tools according to the first recommendation weight and the second recommendation weight by using the following formula; ε i is used to represent the recommendation weight corresponding to the i-th measurement tool among the multiple measurement tools, N is used to represent the number of the measurement tools, k j The first recommendation weight corresponding to the jth measurement tool is used to characterize the j,i Used to characterize the probability of sequentially selecting the jth measurement tool and the ith measurement tool.

[0021] In a possible implementation manner, the device includes an updating module, and the updating module is used to: in response to a selection operation on any of the startup controls, update the first historical selection information according to a measurement tool corresponding to the startup control.

[0022] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising: at least one memory for storing instructions; and at least one processor for executing the measurement tool recommendation method provided by the above-mentioned first aspect or any possible implementation of the first aspect according to the instructions stored in the memory.

[0023] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the processor executes the measurement tool recommendation method provided by the first aspect or any possible implementation of the first aspect.

[0024] It can be seen from the above technical solution that the recommendation weight of the first user for different measurement tools can be determined based on the first historical selection information of the first user for multiple measurement tools, so as to assign a recommendation weight to each measurement tool, and use the recommendation weight to determine the measurement preference of the first user, so as to accurately recommend the measurement tool to the first user, thereby realizing automatic personalized recommendation of the measurement tool; by displaying the start control of the recommended measurement tool, the first user can select and start the required measurement tool more quickly, thereby reducing the number of large-scale movements and mouse clicks, and improving the efficiency of the measurement work. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of a method for recommending a measuring tool according to an embodiment of the present application;

[0026] Figure 2 is a flow chart of a method for determining a first recommendation weight according to an embodiment of the present application;

[0027] Figure 3 is a flow chart of a second recommendation weight determination method according to an embodiment of the present application;

[0028] Figure 4 is a schematic diagram of a measuring tool recommendation device according to an embodiment of the present application;

[0029] Figure 5 It is a schematic diagram of an electronic device including a memory and a processor according to the present application.

[0030] List of reference numerals:

[0031] 101-104: Method steps 201-202: Method steps 301-303: Method steps

[0032] 400: measurement tool recommendation device 401: weight module 402: selection module

[0033] 403: Display module 404: Start module 405: Update module

[0034] 500: electronic device 501: memory 502: processor DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should fall within the scope of protection of the embodiments of the present application.

[0036] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items. The nouns and pronouns about people in this patent application are not limited to specific genders.

[0037] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0038] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0039] Recommended methods for measuring tools

[0040] The present application embodiment provides a measurement tool recommendation method. Figure 1 A flow chart of the measurement tool recommendation method 100 is shown, as shown in FIG. Figure 1 As shown, the measurement tool recommendation method 100 includes the following steps:

[0041] Step 101: Determine recommendation weights corresponding to a plurality of measurement tools according to first historical selection information of a first user for the plurality of measurement tools.

[0042] In this embodiment, when the first user logs in to the measurement system, the first historical selection information of the first user for multiple measurement tools can be obtained in response to the login instruction of the first user. Taking the NX measurement system as an example, the historical usage information of all users for all measurement tools is stored in a prt file, and the corresponding first historical selection information can be obtained through the prt file corresponding to the first user, wherein the first user is used to indicate the user currently logged in to the measurement system.

[0043] In this embodiment, at least one reference user can be determined from multiple second users based on the first historical selection information corresponding to the first user and the second historical selection information of multiple second users for multiple measurement tools; and the first recommendation weights corresponding to the multiple measurement tools can be determined based on the second historical selection information of at least one reference user. Figure 2A flowchart of a first recommendation weight determination method 200 according to an embodiment of the present application is shown. Figure 2 As shown, the first recommendation weight determination method 200 includes the following steps:

[0044] Step 201: Determine at least one reference user from a plurality of second users according to first historical selection information and second historical selection information of a plurality of second users for a plurality of measurement tools.

[0045] After obtaining the first historical selection information corresponding to the first user, text processing may be performed on the first historical selection information, and the text feature obtained after the text processing may be determined as the first text feature corresponding to the first user. The first text feature may be used to indicate a measurement feature of the first user.

[0046] The second historical selection information of the plurality of second users on the plurality of measurement tools may be subjected to text processing, and the text features obtained after the text processing may be determined as the second text features corresponding to the second users. The second user is generally used to indicate at least one other user in the measurement system except the first user, and the second text features may be used to indicate the measurement features of the second user.

[0047] For each second user, a first similarity between a first text feature of the first user and a second text feature of the second user may be determined, the second users may be sorted in descending order according to the first similarity, and at least one first second user may be determined as a reference user according to the sorting result of the second users. The first similarity may be determined based on the cosine similarity between the first text feature and the second text feature, and the reference user finally obtained is a user with a higher similarity to the first user.

[0048] Step 202: Determine first recommendation weights corresponding to a plurality of measurement tools according to second historical selection information of at least one reference user.

[0049] After obtaining the reference user, the second historical selection information of the reference user can be converted into a corresponding measurement vector. As an example, each measurement tool can be quantized into a measurement vector of a numerical type by encoding, such as using One-Hot encoding, to create a corresponding feature vector for each measurement tool. Based on the different measurement tools and the number of times the measurement tools are used contained in each historical selection information, a measurement vector corresponding to each historical selection information is constructed.

[0050] The measurement vector may be normalized to obtain a first vector corresponding to the normalization. Each element in the first vector obtained after the normalization corresponds to a measurement tool, and different elements correspond to different measurement tools.

[0051] Perform vector addition on each first vector to obtain a cumulative vector; determine the first recommended weight of the measurement tool corresponding to each element according to the value of each element in the cumulative vector. As an example, the value of each element in the cumulative vector can be determined as the first recommended weight, or the first recommended weight can be determined according to the proportion of the value of each element in the cumulative vector to all elements. The specific method can be flexibly determined according to actual conditions.

[0052] In this embodiment, the second recommendation weights corresponding to the multiple measurement tools may be determined according to the first historical selection information and the measurement sequence information corresponding to the first user, wherein the measurement sequence information is used to indicate the probability of the first user sequentially selecting any two measurement tools from the multiple measurement tools. Figure 3 A flowchart of a second recommendation weight determination method 300 according to an embodiment of the present application is shown. Figure 3 As shown, the second recommendation weight determination method 300 includes the following steps:

[0053] Step 301: Determine measurement sequence information corresponding to a first user.

[0054] The number of times any two measurement tools are selected sequentially can be counted based on the second historical selection information corresponding to multiple second users; the probability of the two measurement tools being selected sequentially can be determined based on the number of times the two measurement tools are selected sequentially, and for each measurement tool, the probability of any other measurement tool being selected when the measurement tool is selected can be determined; based on the probability of each measurement tool being selected sequentially with the other measurement tools, initial measurement sequence information including the probability of any two measurement tools being selected sequentially is generated.

[0055] The initial measurement sequence information can be updated according to the first historical selection information to obtain the measurement sequence information. As an example, the number of times any two measurement tools in the first historical selection information are selected sequentially can be determined, and the probability of the two measurement tools being selected in the same order in the initial measurement sequence information can be updated according to the number of times the two measurement tools corresponding to the first historical selection information are selected sequentially, to obtain the updated measurement sequence information. For example, based on the two measurement tools corresponding to the first historical selection information, such as measurement tool A and measurement tool B, the number of times measurement tool A and measurement tool B are selected sequentially in the first historical selection information can be determined. The sequential selection here can characterize the existence of an adjacent order relationship between the two measurement tools when they are selected during the measurement process, such as the order relationship of selecting measurement tool A first and then selecting measurement tool B. The number of times a pair of measuring tools are selected sequentially, first selecting measuring tool A and then selecting measuring tool B, in the first historical selection information can be determined; the number can be weighted with the probability of sequential selection of measuring tool A and measuring tool B in the initial sequence information; or, the proportion of the number of times in the total number of times any two measuring tools are selected sequentially in all the first historical selection information can be determined, and a weighted operation can be performed based on the proportion and the probability of sequential selection of measuring tool A and measuring tool B in the initial sequence information; and the measurement sequence information can be updated based on the probability of the pair of measuring tools A and measuring tool B obtained after the weighted operation.

[0056] Step 302: Determine the first measurement tool last selected by the first user according to the first historical selection information.

[0057] The first measurement tool selected last time by the current first user may be determined according to the usage time of the measurement tool in the first historical selection information.

[0058] Step 303: Determine second recommendation weights corresponding to the plurality of measurement tools according to measurement sequence information corresponding to the first measurement tool and the first user.

[0059] Each of the multiple measurement tools is used as a second measurement tool, and the probability of sequentially selecting the first measurement tool and the second measurement tool is queried from the measurement sequence information as a second recommendation weight corresponding to the second measurement tool. As an example, for each second measurement tool, the probability of selecting the first measurement tool first and then the second measurement tool is determined from the measurement sequence information, and the probability is determined as the second recommendation weight corresponding to the second measurement tool.

[0060] In this embodiment, if the first user performs continuous measurement, the measurement sequence information currently corresponding to the first user can be updated based on the measurement tool selected by the first user last time, and the second recommendation weights corresponding to multiple measurement tools can be determined based on the updated measurement sequence information. For example, the first historical selection information corresponding to the first user can be updated based on the measurement tool selected by the first user last time, and the measurement sequence information can be directly updated based on the updated first historical selection information, without updating the initial measurement sequence information each time, and then determining the updated measurement sequence information based on the updated initial measurement sequence information. After being generated, the initial sequence information can be updated in response to a preset time threshold or a preset operation.

[0061] Step 102: Determine at least one recommended measurement tool from the multiple measurement tools according to the recommendation weight.

[0062] In this embodiment, the recommendation weights corresponding to the multiple measurement tools can be determined according to at least one of the first recommendation weight and the second recommendation weight. For example, the first recommendation weight corresponding to the measurement tool can be determined as the recommendation weight corresponding to the measurement tool; or the second recommendation weight corresponding to the measurement tool can be determined as the recommendation weight corresponding to the measurement tool; or, the recommendation weight corresponding to each measurement tool in the multiple measurement tools can be calculated according to the first recommendation weight and the second recommendation weight by the following formula (1).

[0063]

[0064] In formula (1), ε i It is used to represent the recommendation weight corresponding to the i-th measurement tool among multiple measurement tools, N is used to represent the number of measurement tools, and k j The first recommendation weight corresponding to the jth measurement tool is used to characterize the j,i Used to characterize the probability of sequentially selecting the jth measurement tool and the ith measurement tool.

[0065] As an example, the first user selects measuring tool A and measuring tool B for measurement, and the measuring tools in the second historical selection information of the second user include measuring tool A, measuring tool B, and measuring tool C. Based on the first historical selection information corresponding to the first user and the second historical selection information corresponding to the second user, it is assumed that the following is calculated by the above method: for the first user, the first recommendation weight corresponding to measuring tool A is k1, the first recommendation weight corresponding to measuring tool B is k2, and the third recommendation weight corresponding to measuring tool C is k3; the probability that the first user selects measuring tool B after selecting measuring tool A is ω 1,1 , the probability that the first user selects measurement tool A sequentially after selecting measurement tool A is ω 1,1 , the probability that the first user selects measurement tool B after selecting measurement tool A is ω1,2 , the probability that the first user selects measurement tool C after selecting measurement tool A is ω 1,3 ; The probability that the first user selects measurement tool A after selecting measurement tool B is ω 2,1 , the probability that the first user selects measurement tool B sequentially after selecting measurement tool B is ω 2,2 , the probability that the first user selects measurement tool C after selecting measurement tool B is ω 2,3 ; The probability that the first user selects measurement tool A after selecting measurement tool C is ω 3,1 , the probability that the first user selects measurement tool B after selecting measurement tool C is ω 3,2 , the probability that the first user selects measurement tool C sequentially after selecting measurement tool C is ω 3,3 For the first user, the recommendation weight of measuring tool A is: k1ω 1,1 +k2ω 2,1 +k3ω 3,1 ; The recommendation weight for recommending measurement tool B to the first user is: k1ω 1,2 +k2ω 2,2 +k3ω 3,2 ; The recommendation weight for recommending measurement tool C to the first user is: k1ω 1,3 +k2ω 2,3 +k3ω 3,3 .

[0066] In this embodiment, multiple measurement tools may be sorted from large to small based on the recommendation weights corresponding to the measurement tools, and the first at least one measurement tool after the sorting may be determined as the recommended measurement tool.

[0067] Step 103: In response to starting the measurement operation, displaying a start control of the at least one recommended measurement tool.

[0068] In this embodiment, a measurement operation can be initiated, such as by a right-click event of a mouse, and in response to the initiation of the measurement operation, a recommended measurement tool can be displayed at the mouse position. By displaying the recommended measurement tool in the menu option corresponding to the right-click of the mouse, a large range of movement and mouse clicks can be avoided when selecting a measurement tool, thereby improving the efficiency of the measurement work.

[0069] Step 104: In response to a selection operation on any of the start controls, the first historical selection information is updated according to a measurement tool corresponding to the start control.

[0070] In this embodiment, in response to the selection operation of any start control, when the mouse is moved over any start control, the start control is pre-selected with a highlighted color, and after selecting any start control with the mouse, releasing the mouse can start the measurement tool corresponding to the start control. In this way, it is possible to avoid moving the mouse back and forth between the command window and the 3D image window.

[0071] After starting the measurement tool corresponding to the control, the first historical selection information corresponding to the first user may be updated, and the recommendation weights corresponding to the multiple measurement tools may be re-determined based on the updated first historical selection information, and then at least one recommended measurement tool may be determined from the multiple measurement tools based on the recommendation weights.

[0072] In this embodiment, if the first user has not performed any measurement operation, the preset historical selection information is used as the first historical selection information to determine at least one recommended measurement tool for the first user.

[0073] Recommended measuring tools

[0074] Figure 4 FIG. 4 is a schematic diagram showing a measuring tool recommendation device 400 according to an embodiment of the present application. Figure 4 As shown, the measuring tool recommendation device 400 includes:

[0075] A weight module 401 is used to determine recommendation weights corresponding to the multiple measurement tools according to first historical selection information of the first user for the multiple measurement tools;

[0076] A selection module 402, configured to determine at least one recommended measurement tool from the plurality of measurement tools according to the recommendation weight;

[0077] A display module 403, configured to display a start control of the at least one recommended measurement tool in response to starting the measurement operation;

[0078] A starting module 404, configured to start a measurement tool corresponding to any of the starting controls in response to a selection operation on the starting controls;

[0079] The updating module 405 is used to update the first historical selection information in response to a selection operation on any of the startup controls according to a measurement tool corresponding to the startup control.

[0080] In an embodiment of the present application, the weight module 401 is used to determine at least one reference user from the multiple second users based on the first historical selection information and the second historical selection information of the multiple second users for the multiple measurement tools; determine the first recommendation weights corresponding to the multiple measurement tools based on the second historical selection information of the at least one reference user; determine the first measurement tool last selected by the first user based on the first historical selection information; determine the second recommendation weights corresponding to the multiple measurement tools based on the first measurement tool and the measurement sequence information corresponding to the first user, wherein the measurement sequence information is used to indicate the probability of the first user sequentially selecting any two measurement tools from the multiple measurement tools; determine the recommendation weights corresponding to the multiple measurement tools based on at least one of the first recommendation weight and the second recommendation weight.

[0081] In an embodiment of the present application, the weight module 401 is used to determine a first text feature corresponding to the first user based on the first historical selection information; determine a second text feature corresponding to the second user based on the second historical selection information; determine the similarity between the first text feature and the second text as the first similarity between the first user and the second user; sort the multiple second users in descending order according to the first similarity; and determine the first at least one second user as the reference user based on the sorting result of the second user.

[0082] In an embodiment of the present application, the weight module 401 is used to convert the second historical selection information of the reference user into a corresponding measurement vector; normalize the measurement vector to obtain a normalized first vector, wherein each element in the first vector corresponds to one measurement tool, and different elements correspond to different measurement tools; each of the first vectors is added to obtain a cumulative vector; and according to the value of each element in the cumulative vector, a first recommendation weight of the measurement tool corresponding to the element is determined.

[0083] In an embodiment of the present application, the weight module 401 is used to use each of the multiple measurement tools as a second measurement tool, and query the probability of sequentially selecting the first measurement tool and the second measurement tool from the measurement sequence information as the second recommendation weight corresponding to the second measurement tool.

[0084] In the embodiment of the present application, the weight module 401 is used to determine the number of times any two measurement tools are selected sequentially according to the second historical selection information corresponding to the multiple second users; determine the probability that the two measurement tools are selected sequentially according to the number of times the two measurement tools are selected sequentially; generate initial measurement sequence information including the probability that any two measurement tools are selected sequentially; update the initial measurement sequence information according to the first historical selection information to obtain the measurement sequence information;

[0085] In the embodiment of the present application, the weight module 401 is used to determine the first recommended weight corresponding to the measurement tool as the recommended weight of the measurement tool; or, determine the second recommended weight corresponding to the measurement tool as the recommended weight of the measurement tool.

[0086] In the embodiment of the present application, the weight module 401 is used to calculate the recommendation weight corresponding to each measurement tool in the multiple measurement tools according to the first recommendation weight and the second recommendation weight by the following formula: ε i is used to represent the recommendation weight corresponding to the i-th measurement tool among the multiple measurement tools, N is used to represent the number of the measurement tools, k j The first recommendation weight corresponding to the jth measurement tool is used to characterize the j,i Used to characterize the probability of sequentially selecting the jth measurement tool and the ith measurement tool.

[0087] It should be noted that, since the information interaction, execution process, etc. between the modules and sub-modules in the above-mentioned measurement tool recommendation device 400 are based on the same concept as the above-mentioned method embodiment, the specific contents can be found in the description of the above-mentioned method embodiment and will not be repeated here.

[0088] Electronic devices

[0089] Figure 5 A schematic diagram of an electronic device 500 according to an embodiment of the present application is shown. Figure 5 As shown, the measuring tool recommendation device 500 includes at least one memory 501 and at least one processor 502. The memory 501 is used to store instructions. The processor 502 is used to execute the measuring tool recommendation method described in any of the above embodiments according to the instructions stored in the memory 501.

[0090] Computer readable storage medium

[0091] The present disclosure also provides a computer-readable storage medium storing instructions for causing a machine to execute the measurement tool recommendation method as described herein. Specifically, a system or device equipped with a storage medium may be provided, on which a software program code for implementing the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device is enabled to read and execute the program code stored in the storage medium.

[0092] In this case, the program code itself read from the storage medium can realize the function of any one of the above embodiments, and thus the program code and the storage medium storing the program code constitute part of the present disclosure.

[0093] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0094] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the existence of other identical factors in the process, method, article or device including the elements.

[0095] Not all steps and modules in the above-mentioned processes and system structure diagrams are necessary, and some steps or modules can be ignored according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or some components in multiple independent devices may be implemented together.

[0096] In the above embodiments, the hardware module can be implemented mechanically or electrically. For example, a hardware module can include permanent dedicated circuits or logic (such as special processors, FPGA or ASIC) to complete the corresponding operation. The hardware module can also include programmable logic or circuits (such as general-purpose processors or other programmable processors), which can be temporarily set by software to complete the corresponding operation. The specific implementation method (mechanical method, or dedicated permanent circuit, or temporarily set circuit) can be determined based on cost and time considerations.

[0097] The present application is presented and described in detail above through the accompanying drawings and preferred embodiments; however, the present application is not limited to these disclosed embodiments, and those skilled in the art may know based on the above-mentioned multiple embodiments that the code review methods in the above-mentioned different embodiments may be combined to obtain more embodiments of the present application, and these embodiments are also within the protection scope of the present application.

Claims

1. A measurement tool recommendation method (100), characterized in that: The method comprises: Determining recommendation weights corresponding to the plurality of measurement tools according to first historical selection information of the first user for the plurality of measurement tools; determining at least one recommended measurement tool from the plurality of measurement tools according to the recommendation weight; In response to starting the measurement operation, displaying a start control of the at least one recommended measurement tool; In response to a selection operation on any of the start controls, a measurement tool corresponding to the start control is started.

2. The method according to claim 1, characterized in that The determining, according to first historical selection information of the first user for the plurality of measurement tools, the recommendation weights corresponding to the plurality of measurement tools comprises: determining at least one reference user from the plurality of second users according to the first historical selection information and second historical selection information of the plurality of second users on the plurality of measurement tools; determining first recommendation weights corresponding to the plurality of measurement tools according to the second historical selection information of the at least one reference user; determining the first measurement tool last selected by the first user according to the first historical selection information; Determining second recommendation weights corresponding to the multiple measurement tools according to measurement sequence information corresponding to the first measurement tool and the first user, wherein the measurement sequence information is used to indicate a probability that the first user sequentially selects any two measurement tools from the multiple measurement tools; Determine the recommendation weights corresponding to the plurality of measurement tools according to at least one of the first recommendation weight and the second recommendation weight.

3. The method according to claim 2, characterized in that The determining at least one reference user from the plurality of second users according to the first historical selection information and the second historical selection information of the plurality of second users on the plurality of measurement tools comprises: Determining a first text feature corresponding to the first user according to the first historical selection information; Determining a second text feature corresponding to the second user according to the second historical selection information; Determining the similarity between the first text feature and the second text as a first similarity between the first user and the second user; Sorting the plurality of second users in descending order corresponding to the first similarity; According to the ranking result of the second users, at least one of the first second users is determined as the reference user.

4. The method according to claim 2, characterized in that: The determining, according to the second historical selection information of the at least one reference user, first recommendation weights corresponding to the plurality of measurement tools comprises: converting the second historical selection information of the reference user into a corresponding measurement vector; Normalizing the measurement vector to obtain a normalized first vector, wherein each element in the first vector corresponds to one of the measurement tools, and different elements correspond to different measurement tools; Performing vector addition on each of the first vectors to obtain an accumulated vector; According to the value of each element in the accumulated vector, a first recommendation weight of the measurement tool corresponding to the element is determined.

5. The method according to claim 2, characterized in that: The determining, according to the measurement sequence information corresponding to the first measurement tool and the first user, second recommendation weights corresponding to the plurality of measurement tools includes: Each of the multiple measurement tools is used as a second measurement tool, and the probability of sequentially selecting the first measurement tool and the second measurement tool is queried from the measurement sequence information as the second recommendation weight corresponding to the second measurement tool.

6. The method according to claim 2, characterized in that The method further comprises: determining the number of times any two measurement tools are sequentially selected according to the second historical selection information corresponding to the plurality of second users; Determining the probability that the two measuring tools are selected sequentially according to the number of times the two measuring tools are selected sequentially; generating initial measurement sequence information including probabilities of any two measurement tools being selected sequentially; The initial measurement sequence information is updated according to the first history selection information to obtain the measurement sequence information.

7. The method according to claim 2, characterized in that The determining, according to at least one of the first recommended weight and the second recommended weight, the recommended weights corresponding to the plurality of measurement tools comprises: Determining the first recommended weight corresponding to the measurement tool as the recommended weight of the measurement tool; or, The second recommended weight corresponding to the measurement tool is determined as the recommended weight of the measurement tool.

8. The method according to claim 2, characterized in that: The determining, according to at least one of the first recommended weight and the second recommended weight, the recommended weights corresponding to the plurality of measurement tools comprises: According to the first recommendation weight and the second recommendation weight, a recommendation weight corresponding to each measurement tool in the multiple measurement tools is calculated by the following formula; ε i is used to represent the recommendation weight corresponding to the i-th measurement tool among the multiple measurement tools, N is used to represent the number of the measurement tools, k j The first recommendation weight corresponding to the jth measurement tool is used to characterize the j,i Used to characterize the probability of sequentially selecting the jth measurement tool and the ith measurement tool.

9. The method according to any one of claims 1 to 8, characterized in that: The method further comprises: In response to a selection operation on any of the start controls, the first historical selection information is updated according to a measurement tool corresponding to the start control.

10. A measuring tool recommendation device (400), characterized in that: The device comprises: A weight module (401) is used to determine the recommendation weights corresponding to the multiple measurement tools according to the historical selection information of the user for the multiple measurement tools; A selection module (402) is used to determine at least one recommended measurement tool from the plurality of measurement tools according to the recommendation weight; A display module (403), configured to display a start control of the at least one recommended measurement tool in response to starting the measurement operation; The start module (404) is used to start the measurement tool corresponding to any of the start controls in response to a selection operation on the start control.

11. An electronic device (500), comprising: at least one memory (501) for storing instructions; At least one processor (502) is configured to execute the measurement tool recommendation method according to any one of claims 1 to 9 according to instructions stored in the memory (501).

12. A computer-readable storage medium having computer instructions stored thereon, wherein when the computer instructions are executed by a processor, the processor is caused to execute the method according to any one of claims 1 to 9.

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