Signal-oriented instrument and equipment recommendation method and device

Abstract

The invention discloses a signal-oriented instrument and equipment recommendation method and device, and belongs to the technical field of instrument and equipment recommendation. According to the method, signal decomposition is carried out on a signal-oriented signal model of a tested piece and a signal-oriented signal model of instrument and equipment which meet the IEEE 1641 standard, and the signal-oriented signal model of the tested piece is obtained on the basis of a genetic algorithm; the method comprises the following steps: establishing a class of a tested piece signal and a class of an instrument equipment signal, taking the highest score of an instrument equipment combination as a target, constructing a signal-oriented adaptation algorithm model, determining constraint conditions, calculating and completing the adaptation of a tested piece signal demand and an instrument equipment signal capability, and giving a recommended score of a specific instrument equipment (combination). The method overcomes the defects of high labor cost, low efficiency, long consumed time and the like of traditional instrument adaptation, has the characteristics of short adaptation time, high precision and the like, and can greatly improve the model selection efficiency of instrument equipment, thereby improving the design efficiency of an automatic test solution and reducing the development cost of an automatic test system.

Description

Technical Field

[0001] The present invention belongs to the technical field of instrument recommendation, and in particular relates to a signal-oriented instrument recommendation method and device. Background Art

[0002] At present, automatic test technology has been widely used in important fields such as aerospace, national defense, transportation, and energy, playing an important role in all aspects from product development, production, storage to use and maintenance. In order to solve the problems of instrument interchangeability and test program set reusability and portability, the existing technology proposed the signal-oriented design concept and introduced standards such as ATLAS. After decades of development, signal-oriented design has been widely recognized and matured. With the release of IEEE1641 and IEEE1671 standards, manufacturers can use common XML files as data interaction media, making the development of signal-oriented automatic test systems more mature.

[0003] The IEEE1641 standard is used for signal and test definition, which solves the problem of test description standardization and instrument interchangeability, but cannot solve the problem of test program migration. For this reason, IEEE has developed the ATML standard family since 2002, resulting in the IEEE1671 series of standards, which use XML language to describe test information. This standard family references the IEEE1641 standard and uses signals to describe the test requirements, instrument capabilities, test descriptions, and other information of the unit under test (UUT). Figure 1 As shown, the IEEE1641 standard includes a signal modeling language layer, a basic signal component layer, a test signal framework layer, and a test process language layer, and each layer is built on the basis of the previous layer.

[0004] BSC (Basic Signal Component) is a set of components defined by IEEE1641, which provides the most basic building blocks for creating signals. BSC defines an extension mechanism. Signals not defined in the most basic signal library can be interconnected by multiple BSC modules according to the standard framework to generate complex signals to meet test requirements and form a standard TSF layer (test signal framework layer). The process of using BSC to create a complex signal model is as follows: Figure 2 shown.

[0005] By analyzing the test requirements, instrument capabilities, test descriptions and other signal-oriented description models that comply with the IEEE1641 standard, test developers can obtain the signal requirements of the UUT and the signal capabilities of the instrument equipment that reference the TSF and BSC signal libraries. Figure 2 As shown on the right, the basic signals that the signal consists of can be obtained through the inverse relationship between the signal requirements of the device under test and the signal capabilities of the instrument and equipment, thereby completing the adaptation process.

[0006] However, the inventors have discovered that the correspondence between the signal requirements of the device under test and the signal capabilities of the instrument after decomposition is a complex many-to-many relationship. Therefore, manually adapting the signal requirements of the device under test and the signal capabilities of the instrument has many shortcomings, such as high cost, low efficiency, long time consumption, and almost impossible to adapt when the signal complexity is high.

[0007] In addition, with a complete model, the current method of adapting the signal requirements of the device under test to the signal capabilities of the instrument equipment has the following shortcomings:

[0008] (1) The adaptation process needs to be completed based on the tester’s experience, which is time-consuming and labor-intensive.

[0009] (2) Software developers are required to develop signal mapping software using programming methods based on fixed DUTs and instruments. Moreover, when additional DUTs or instruments are added, the software needs to be redeveloped, resulting in poor reusability. Summary of the invention

[0010] The purpose of the present invention is to overcome the deficiencies in the above-mentioned prior art and to provide a method and device for recommending instruments and equipment based on signal orientation. The method decomposes the signal demand of the device under test that conforms to the IEEE1641 standard into a signal-oriented model and an instrument and equipment signal capability-oriented model. Based on a genetic algorithm, classes of device under test signals and classes of instrument and equipment signals are created. With the highest score of the instrument and equipment combination as the goal, a signal-oriented adaptation algorithm model is constructed and constraints are determined. The adaptation of the signal demand of the device under test and the signal capability of the instrument and equipment is calculated and completed, and a recommendation score for a specific instrument and equipment (combination) is given. The method overcomes the disadvantages of high labor cost, low efficiency and long time consumption of traditional instrument adaptation, and has the characteristics of short adaptation time and high precision, which can greatly improve the efficiency of instrument and equipment selection, thereby improving the design efficiency of automatic test solutions and reducing the development cost of automatic test systems.

[0011] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0012] In a first aspect, the technical solution of the present invention provides a method for recommending instruments and equipment based on signals, comprising:

[0013] Obtain the signal requirement signal of the device under test and the signal capability signal of the instrument and equipment;

[0014] Decomposing the acquired DUT signal requirement signal to obtain a DUT signal requirement decomposition signal set; Decomposing the instrument equipment signal capability signal to obtain an instrument equipment signal capability decomposition signal set;

[0015] Establishing a class of instrument and equipment signals according to the decomposed instrument and equipment signal capability signal set, wherein the class of instrument and equipment signals contains instrument and equipment signal capability meta-signals and instrument and equipment attribute information; establishing a class of device under test signals according to the decomposed device under test signal requirement signal set, wherein the class of device under test signals contains at least a device under test signal requirement meta-signal;

[0016] Based on the classes of the instrument and equipment signals and the classes of the measured device signals, a signal-oriented adaptation algorithm model is constructed with the goal of maximizing the score of the instrument and equipment combination, and the constraint rules of the signal-oriented adaptation algorithm model are determined; based on the genetic algorithm, the signal-oriented adaptation algorithm model is solved by the adaptation algorithm, and the optimal solution for the instrument and equipment combination is output.

[0017] In at least one embodiment, obtaining the DUT signal requirement signal and the instrument signal capability signal is specifically as follows:

[0018] Based on the test requirement-oriented signal description model of the device under test that complies with the IEEE1641 standard, the signal requirement signal of the device under test is obtained; based on the instrument and equipment capability-oriented signal model that complies with the IEEE1641 standard, the instrument and equipment signal capability signal is obtained.

[0019] In at least one embodiment, the DUT test requirement oriented signal description model is specifically an XML file containing a complex signal describing the DUT test requirement;

[0020] The instrument capability signal-oriented model is an XML file containing complex signals describing the instrument capability.

[0021] In at least one embodiment, performing signal decomposition on the acquired DUT signal requirement signal to obtain a DUT signal requirement decomposition signal set specifically includes:

[0022] Based on the IEEE1641-BSC basic signal library and the TSF signal library, the acquired DUT signal demand signal is decomposed according to the signal decomposition algorithm to obtain the meta-signals and the number of the decomposed DUT signal demand signal, which are the basic signals constituting the DUT signal demand signal and are included in the BSC basic signal library or the TSF signal library; according to the obtained meta-signals and the number, the meta-signals are combined, the same items are merged, and the signals of the same basic type are taken as the union to obtain the DUT signal demand decomposition signal set;

[0023] The signal decomposition of the instrument equipment signal capability signal to obtain the instrument equipment signal capability decomposition signal set specifically includes:

[0024] Based on the IEEE1641-BSC basic signal library and TSF signal library, the obtained instrument and equipment signal capability signals are decomposed according to the signal decomposition algorithm to obtain the meta-signals and numbers of the decomposed instrument and equipment signal capability signals. The meta-signals are the basic signals that constitute the instrument and equipment signal capability signals and are included in the BSC basic signal library or TSF signal library. According to the meta-signals and numbers obtained, the meta-signals are combined, the same items are merged, and the meta-signals of the same basic type are taken as the union to obtain the signal requirement decomposition signal set of the device under test.

[0025] In at least one embodiment, the instrument and equipment attribute information specifically includes instrument and equipment cost, instrument and equipment accuracy, and instrument and equipment localization attribute information.

[0026] In at least one embodiment, the constraint rules specifically include: the constraint rules include hard constraint rules and soft constraint rules;

[0027] The hard constraint rule is: randomly obtain at least one instrument from the instrument population composed of all instruments in the instrument signal class, and after the signal set is combined, it must cover all the meta-signals in the class of the device signal to be a feasible solution;

[0028] The soft constraint rule is: the fewer the number of instruments m, the higher the final score, and the soft constraint parameter coefficients of the objective function are adjusted as needed;

[0029] The soft constraint parameters specifically include instrument and equipment cost, instrument and equipment accuracy, and instrument and equipment localization.

[0030] In at least one embodiment, performing an adaptation algorithm on the signal-oriented adaptation algorithm model based on a genetic algorithm and outputting an optimal solution for the instrument and equipment combination specifically includes:

[0031] All the instruments and equipment in the class of the instrument and equipment signal form an initial population S of instruments and equipment, and randomly generate instrument and equipment combinations in the initial population S as the first generation instrument and equipment combination G of the population;

[0032] The fitness of each individual in the first generation instrument combination is calculated according to the hard constraint rules and the soft constraint rules, that is, the score of each individual; according to the score, some candidate solutions are retained and other candidate solutions are abandoned;

[0033] Perform a crossover operation on the first-generation instrument and equipment combination G to generate a new instrument and equipment combination, perform a mutation operation on an instrument in the new instrument and equipment combination to obtain a new-generation instrument and equipment combination G+1; let G=G+1, and determine whether the reproduction generation has reached the set generation GN. If not, continue to calculate. If so, output the best instrument and equipment combination plan, that is, the instrument and equipment combination with the highest score.

[0034] In a second aspect, the technical solution of the present invention provides a signal-oriented instrument recommendation device, comprising:

[0035] The signal acquisition module is configured to: acquire a signal requirement signal of the device under test and a signal capability signal of the instrument and equipment;

[0036] The signal decomposition module is configured to: perform signal decomposition on the acquired DUT signal demand signal to obtain a DUT signal demand decomposition signal set; perform signal decomposition on the instrument equipment signal capability signal to obtain an instrument equipment signal capability decomposition signal set;

[0037] Establishing a related class module, which is configured to: establish a class of instrument and equipment signals according to the decomposed instrument and equipment signal capability signal set, wherein the class of instrument and equipment signals contains instrument and equipment signal capability meta-signals and instrument and equipment attribute information; constructing a class of device under test signals according to the decomposed device under test signal requirement signal set, wherein the class of device under test signals contains at least a device under test signal requirement meta-signal;

[0038] The model adaptation module is configured as follows: based on the class of the instrument and equipment signal and the class of the test device signal, with the goal of maximizing the instrument and equipment combination score, construct a signal-oriented adaptation algorithm model and determine the constraint rules of the signal-oriented adaptation algorithm model; based on the genetic algorithm, solve the signal-oriented adaptation algorithm model with an adaptation algorithm and output the best solution for the instrument and equipment combination.

[0039] The beneficial effects of the technical solution of the present invention are as follows:

[0040] 1) The present invention proposes a recommendation method for solving the problem of signal capability of instrument equipment according to signal decomposition adaptation under the condition of a given description model of the signal requirements of the device under test. The method is divided into four parts: signal acquisition, signal decomposition, establishment of related classes, and model adaptation. The method uses a genetic algorithm to match many-to-many basic element signals, and gives a recommendation method for instrument equipment (combination) scores by calculating soft constraints. It overcomes the disadvantages of high labor cost, low efficiency, and long time consumption of traditional instrument adaptation, and has the characteristics of short adaptation time and high precision. It can greatly improve the efficiency of instrument equipment selection, thereby improving the design efficiency of automatic test solutions and reducing the development cost of automatic test systems.

[0041] 2) In actual applications, the overall code can be reused by incrementally adding instrument signal capabilities or DUT signal requirements without redeveloping internal logic, greatly improving the reusability of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0043] Figure 1 It is a schematic diagram of a hierarchical structure model of the IEEE1641 standard in the prior art;

[0044] Figure 2 It is a schematic diagram of the process of using BSC to create a complex signal model in the prior art;

[0045] Figure 3 It is a flow chart of a method for signal-oriented instrument and equipment recommendation provided by the present invention;

[0046] Figure 4 It is a process schematic diagram of a method for recommending instruments and equipment based on signals provided by the present invention;

[0047] Figure 5 It is a schematic diagram of the cooperative relationship of each module provided by the present invention;

[0048] Figure 6 It is a schematic diagram of the signal decomposition module flow provided by the present invention;

[0049] Figure 7 It is a schematic diagram of establishing a related class flow provided by the present invention;

[0050] Figure 8 It is a schematic diagram of the model adaptation process provided by the present invention; DETAILED DESCRIPTION

[0051] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0052] SML: Signal Modeling Language;

[0053] BSC: Basic Signaling Component;

[0054] TSF: Test Signal Framework;

[0055] TPL: Test Procedure Language;

[0056] Example 1

[0057] In a typical embodiment of the present invention, Figure 3 , Figure 5 As shown, this embodiment discloses a method for recommending instruments and equipment based on signals, including:

[0058] S1. Obtain the signal requirement signal of the device under test and the signal capability signal of the instrument and equipment;

[0059] S2. Decompose the acquired DUT signal demand signal to obtain a DUT signal demand decomposition signal set; decompose the instrument signal capability signal to obtain an instrument signal capability decomposition signal set;

[0060] S3. Establishing a class of instrument and equipment signals according to the decomposed instrument and equipment signal capability signal set, wherein the class of instrument and equipment signals contains instrument and equipment signal capability element signals and instrument and equipment attribute information; constructing a class of DUT signals according to the decomposed DUT signal requirement signal set, wherein the class of DUT signals contains at least a DUT signal requirement element signal;

[0061] S4. Based on the class of the instrument and equipment signal and the class of the test device signal, with the goal of maximizing the score of the instrument and equipment combination, a signal-oriented adaptation algorithm model is constructed, and the constraint rules of the signal-oriented adaptation algorithm model are determined; based on the genetic algorithm, the signal-oriented adaptation algorithm model is solved by the adaptation algorithm, and the optimal solution for the instrument and equipment combination is output.

[0062] The signal-oriented instrument and equipment recommendation method provided in this embodiment includes four components. The first part is signal acquisition, including the acquisition of the signal requirement signal of the device under test and the acquisition of the signal capability signal of the instrument and equipment; the second part is signal decomposition, including the decomposition of the signal requirement signal of the device under test and the decomposition of the signal capability of the instrument and equipment; the third part is the establishment of related classes, including the class of the device under test signal and the class of the instrument and equipment signal; the fourth part is model adaptation, including the signal-oriented adaptation algorithm model matching of the class of the device under test signal and the class of the instrument and equipment signal to give an adaptation result.

[0063] Among them, model adaptation is the core module of the present invention. Its principle is to automatically transform the signal adaptation problem into a single-objective planning problem with instrument adaptation as the goal. According to signal matching, instrument cost, instrument accuracy, and instrument localization as constraints, the instrument adaptation problem is transformed into a linear programming problem, and the optimal solution is obtained through a genetic algorithm.

[0064] The detailed description is given below.

[0065] S1. Obtain the signal requirement signal of the device under test and the signal capability signal of the instrument equipment.

[0066] Based on the IEEE1641 and IEEE1671 standards, it is believed that the test requirements of the DUT, instrument and equipment capabilities, test descriptions, etc. can be expressed in a signal-oriented manner. Therefore, test developers can obtain the DUT signal requirements and instrument and equipment signal capabilities that reference the TSF and BSC signal libraries by analyzing the DUT test requirements signal-oriented description model, instrument and equipment capability signal-oriented description model, and test description signal-oriented description model that comply with the IEEE1641 standard.

[0067] In this embodiment, the signal requirement signal of the device under test and the signal capability signal of the instrument equipment are obtained, specifically: the signal requirement signal of the device under test is obtained based on the signal description model oriented to the test requirement of the device under test that conforms to the IEEE1641 standard; the instrument equipment signal capability signal is obtained based on the signal description model oriented to the instrument equipment capability that conforms to the IEEE1641 standard.

[0068] The signal-oriented description model for the test requirements of the device under test is specifically an XML file containing complex signals describing the test requirements of the device under test; and the signal-oriented model for the instrument and equipment capability is an XML file containing complex signals describing the instrument and equipment capability.

[0069] S2. Decompose the acquired DUT signal requirement signal to obtain a DUT signal requirement decomposition signal set; decompose the instrument and equipment signal capability signal to obtain an instrument and equipment signal capability decomposition signal set.

[0070] In this embodiment, the signal requirements of the device under test and the signal capabilities of the instrument and equipment are represented by the signal sets (elementary signals and their numbers) in the TSF signal library and the BSC basic signal library. Figure 6 As shown, in this embodiment, the signal decomposition specifically includes:

[0071] Based on the IEEE1641-BSC basic signal library and TSF signal library, the acquired DUT signal demand signal is decomposed according to the signal decomposition algorithm to obtain the meta-signals and number of the decomposed DUT signal demand signal. The meta-signal is the basic signal constituting the DUT signal demand signal and is included in the BSC basic signal library or TSF signal library. According to the obtained meta-signals and number, the meta-signals are combined, the same items are merged, and the signals of the same basic type are taken as the union to obtain the DUT signal demand decomposition signal set. For example, two complex signals are decomposed to obtain {sine wave, square wave, triangle wave} and {sine wave, pulse}, and the union is taken as {sine wave, square wave, triangle wave, pulse}.

[0072] Similarly, based on the IEEE1641-BSC basic signal library and TSF signal library, the acquired instrument and equipment signal capability signals are decomposed according to the signal decomposition algorithm to obtain the meta-signals and numbers of the decomposed instrument and equipment signal capability signals. The meta-signals are the basic signals constituting the instrument and equipment signal capability signals and are included in the BSC basic signal library or TSF signal library. According to the meta-signals and numbers obtained, the meta-signals are combined, the same items are merged, and the meta-signals of the same basic type are taken as the union to obtain the signal requirement decomposition signal set of the device under test.

[0073] In this embodiment, the signal decomposition algorithm can adopt an existing signal decomposition algorithm, such as the decomposition method of the synthetic signal disclosed in the patent document with application number 202010843267.2, which will not be repeated in this embodiment.

[0074] S3. Establishing a class of instrument signal based on the decomposed instrument signal capability signal set, wherein the class of instrument signal contains instrument signal capability element signal and instrument attribute information;

[0075] Constructing a class of the device under test signal according to the device under test signal requirement signal set obtained by decomposition, wherein the class of the device under test signal at least contains a device under test signal requirement element signal;

[0076] like Figure 7 As shown, in this embodiment, first, a class of instrument and equipment signals is established according to the decomposed signal set of the instrument and equipment signal capability, and the class of the instrument and equipment signal contains the instrument and equipment signal capability meta-signal and the instrument and equipment attribute information. Among them, the instrument and equipment attribute information specifically includes attribute information such as instrument and equipment cost, instrument and equipment accuracy, and instrument and equipment localization. All instruments and equipment in the class of the instrument and equipment signal constitute the initial population S.

[0077] According to the DUT signal requirements, the signal set is decomposed to construct a DUT signal class, wherein the DUT signal class contains at least n DUT signal requirement element signals.

[0078] S4. Based on the class of the instrument and equipment signal and the class of the test device signal, with the goal of maximizing the score of the instrument and equipment combination, a signal-oriented adaptation algorithm model is constructed, and the constraint rules of the signal-oriented adaptation algorithm model are determined; based on the genetic algorithm, the signal-oriented adaptation algorithm model is solved by the adaptation algorithm, and the optimal solution for the instrument and equipment combination is output.

[0079] The objective function of the signal adaptation algorithm model is specifically to use the highest instrument and equipment combination score as the objective function of the model adaptation, so as to output the best instrument and equipment combination plan.

[0080] In order to ensure the rationality and practicality of the model adaptation results when solving the signal adaptation algorithm model for the instrument signal class and the test device signal class, and to improve the interpretability and robustness of the model adaptation, it is necessary to create constraints for the model adaptation process. Constraints are divided into hard constraints and soft constraints. Hard constraints are inviolable constraints and are necessary conditions to ensure feasible solutions; soft constraints are conditions that may not be met but will affect the excellence of the adaptation results.

[0081] In this embodiment, all the instruments and equipment in the class of instrument and equipment signals form the initial population S, and the class of the measured device signal contains n element signals. Therefore, the hard constraint rule is created as follows: m instruments are randomly obtained from the population S, and after the signal set is combined, it must cover the n element signals in the class of the measured device signal to be a feasible solution. The soft constraint rule is: the fewer the number of instruments m, the higher the final score, and the soft constraint parameter coefficient of the objective function is adjusted as needed, where the soft constraint parameters specifically include instrument and equipment cost, instrument and equipment accuracy, and instrument and equipment localization.

[0082] In this embodiment, after determining the objective function and constraint rules of the signal-oriented adaptation algorithm model, the signal-oriented adaptation algorithm model is adapted and solved based on a genetic algorithm to output an optimal solution for the combination of instruments and equipment.

[0083] Genetic algorithms use the principles of biological evolution to optimize by imitating natural selection and genetic processes. They can effectively search for potential optimal solutions without relying on problem structure and derivative information. The basic features mainly include the following aspects:

[0084] (1) Population search strategy: Genetic algorithms do not start from a single solution and gradually optimize, but maintain a population of solutions, each of which becomes an individual or chromosome.

[0085] (2) Fitness evaluation: No information about the gradient or other derivatives of the objective function is required. Only a simple fitness function is needed to evaluate the individual’s ability to solve the problem.

[0086] (3) Probability-driven. Each operation in the genetic algorithm is based on probability, which means that the algorithm does not search for the optimal solution along a deterministic path, but allows excellent characteristics to be inherited and mutated through probability.

[0087] (4) Self-adaptation capability. Adaptive search can be achieved by dynamically adjusting parameters. As the search progresses, the algorithm can adjust the search strategy on its own.

[0088] (5) Parallelism and distributed processing. Multiple individuals in a population can be evaluated and evolved in parallel, which to some extent reflects the advantages of parallel computing, especially in large-scale computing environments.

[0089] (6) Robustness and versatility. Genetic algorithms show good robustness and versatility for various types of optimization problems and are particularly suitable for solving complex, nonlinear, high-dimensional, and multimodal problems.

[0090] like Figure 8 As shown, in this embodiment, based on the genetic algorithm, the adaptation algorithm is solved for the signal-oriented adaptation algorithm model to perform model adaptation and solution, and the optimal solution for outputting the instrument and equipment combination specifically includes:

[0091] All the instruments and equipment in the class of the instrument and equipment signal form an initial population S of instruments and equipment, and randomly generate instrument and equipment combinations in the initial population S as the first generation instrument and equipment combination G of the population;

[0092] The fitness of each individual in the first generation instrument combination is calculated according to the hard constraint rules and the soft constraint rules, that is, the score of each individual; according to the score, some candidate solutions are retained and other candidate solutions are abandoned;

[0093] Perform a crossover operation on the first-generation instrument and equipment combination G to generate a new instrument and equipment combination, perform a mutation operation on an instrument in the new instrument and equipment combination to obtain a new-generation instrument and equipment combination G+1; let G=G+1, and determine whether the reproduction generation has reached the set generation GN. If not, continue to calculate. If so, output the best instrument and equipment combination plan, that is, the instrument and equipment combination with the highest score.

[0094] This embodiment proposes a signal-oriented instrument and equipment recommendation method, and proposes a recommendation method for adapting the signal capability of instrument and equipment according to signal decomposition under a given description model of the signal requirements of the device under test. The method is divided into four parts: signal acquisition, signal decomposition, establishment of related classes, and model adaptation. A genetic algorithm is used to match many-to-many basic meta-signals, and a recommendation method for giving instrument and equipment (combination) scores is given by calculating soft constraints.

[0095] The core of this embodiment is to propose a genetic algorithm module, which gives the recommended score of the instrument equipment combination by calculation when the relevant description model of the signal requirements of the measured device is input. The traditional extremely difficult work of solving instrument adaptation based on manual experience is transformed into the optimal instrument equipment combination obtained by genetic algorithm calculation, which can greatly improve the efficiency of instrument equipment selection.

[0096] Example 2

[0097] In a typical implementation of the present invention, this embodiment discloses a signal-oriented instrument recommendation device, comprising:

[0098] The signal acquisition module is configured to: acquire a signal requirement signal of the device under test and a signal capability signal of the instrument and equipment;

[0099] The signal decomposition module is configured to: perform signal decomposition on the acquired DUT signal demand signal to obtain a DUT signal demand decomposition signal set; perform signal decomposition on the instrument equipment signal capability signal to obtain an instrument equipment signal capability decomposition signal set;

[0100] Establishing a related class module, which is configured to: establish a class of instrument and equipment signals according to the decomposed instrument and equipment signal capability signal set, wherein the class of instrument and equipment signals contains instrument and equipment signal capability meta-signals and instrument and equipment attribute information; constructing a class of device under test signals according to the decomposed device under test signal requirement signal set, wherein the class of device under test signals contains at least a device under test signal requirement meta-signal;

[0101] The model adaptation module is configured as follows: based on the class of the instrument and equipment signal and the class of the test device signal, with the goal of maximizing the instrument and equipment combination score, construct a signal-oriented adaptation algorithm model and determine the constraint rules of the signal-oriented adaptation algorithm model; based on the genetic algorithm, solve the signal-oriented adaptation algorithm model with an adaptation algorithm and output the best solution for the instrument and equipment combination.

[0102] Example 3

[0103] In a typical implementation of the present invention, an embodiment of the present invention further provides a computing device, which includes a memory, a processor, and a bus.

[0104] The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via a bus. When the machine-readable instructions are executed by the processor, the steps of a method for recommending instrument equipment based on signal orientation in the method embodiment shown in Example 1 can be executed. The specific implementation method can be found in Method Embodiment 1, which will not be repeated here.

[0105] Example 4

[0106] In a typical embodiment of the present invention, the embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for signal-oriented instrument and equipment recommendation in the above-mentioned method embodiment 1 are executed.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for recommending instruments and equipment based on signals, characterized in that: include: Obtain the signal requirement signal of the device under test and the signal capability signal of the instrument and equipment; Decomposing the acquired DUT signal demand signal to obtain a DUT signal demand decomposition signal set; Decomposing the instrument and equipment signal capability signal to obtain an instrument and equipment signal capability decomposition signal set; Establishing a class of instrument and equipment signals according to the decomposed instrument and equipment signal capability signal set, wherein the class of instrument and equipment signals contains instrument and equipment signal capability meta-signals and instrument and equipment attribute information; establishing a class of device under test signals according to the decomposed device under test signal requirement signal set, wherein the class of device under test signals contains at least a device under test signal requirement meta-signal; Based on the classes of the instrument and equipment signals and the classes of the measured device signals, a signal-oriented adaptation algorithm model is constructed with the goal of maximizing the score of the instrument and equipment combination, and the constraint rules of the signal-oriented adaptation algorithm model are determined; based on the genetic algorithm, the signal-oriented adaptation algorithm model is solved by the adaptation algorithm, and the optimal solution for the instrument and equipment combination is output.

2. The method for recommending instruments and equipment based on signals according to claim 1, characterized in that: Obtain the DUT signal requirement signal and the instrument signal capability signal, specifically: Based on the test requirement-oriented signal description model of the device under test that complies with the IEEE1641 standard, the signal requirement signal of the device under test is obtained; based on the instrument and equipment capability-oriented signal model that complies with the IEEE1641 standard, the instrument and equipment signal capability signal is obtained.

3. The method for recommending instruments and equipment based on signals as claimed in claim 2, characterized in that: The signal-oriented description model for the test requirement of the device under test is specifically an XML file containing complex signals describing the test requirement of the device under test; The instrument capability signal-oriented model is an XML file containing complex signals describing the instrument capability.

4. The method for recommending instruments and equipment based on signals according to claim 1, characterized in that: The acquired DUT signal requirement signal is decomposed to obtain a DUT signal requirement decomposition signal set, which specifically includes: Based on the IEEE1641-BSC basic signal library and the TSF signal library, the acquired DUT signal demand signal is decomposed according to the signal decomposition algorithm to obtain the meta-signals and the number of the decomposed DUT signal demand signal, which are the basic signals constituting the DUT signal demand signal and are included in the BSC basic signal library or the TSF signal library; according to the obtained meta-signals and the number, the meta-signals are combined, the same items are merged, and the signals of the same basic type are taken as the union to obtain the DUT signal demand decomposition signal set; The signal decomposition of the instrument equipment signal capability signal to obtain the instrument equipment signal capability decomposition signal set specifically includes: Based on the IEEE1641-BSC basic signal library and TSF signal library, the obtained instrument and equipment signal capability signals are decomposed according to the signal decomposition algorithm to obtain the meta-signals and numbers of the decomposed instrument and equipment signal capability signals. The meta-signals are the basic signals that constitute the instrument and equipment signal capability signals and are included in the BSC basic signal library or TSF signal library. According to the meta-signals and numbers obtained, the meta-signals are combined, the same items are merged, and the meta-signals of the same basic type are taken as the union to obtain the signal requirement decomposition signal set of the device under test.

5. The method for recommending instruments and equipment based on signals according to claim 1, characterized in that: The instrument and equipment attribute information specifically includes instrument and equipment cost, instrument and equipment accuracy, and instrument and equipment localization attribute information.

6. The method for recommending instruments and equipment based on signals according to claim 1, characterized in that: The constraint rules specifically include: the constraint rules include hard constraint rules and soft constraint rules; The hard constraint rule is: randomly obtain at least one instrument from the instrument population composed of all instruments in the instrument signal class, and after the signal set is combined, it must cover all the meta-signals in the class of the device signal to be a feasible solution; The soft constraint rule is: the fewer the number of instruments m, the higher the final score, and the soft constraint parameter coefficients of the objective function are adjusted as needed; The soft constraint parameters specifically include instrument and equipment cost, instrument and equipment accuracy, and instrument and equipment localization.

7. The method for recommending instruments and equipment based on signals according to claim 1, characterized in that: The method of solving the signal-oriented adaptation algorithm model based on the genetic algorithm and outputting the best solution of the instrument and equipment combination specifically includes: All the instruments and equipment in the class of the instrument and equipment signal form an initial population S of instruments and equipment, and randomly generate instrument and equipment combinations in the initial population S as the first generation instrument and equipment combination G of the population; The fitness of each individual in the first generation instrument combination is calculated according to the hard constraint rules and the soft constraint rules, that is, the score of each individual; according to the score, some candidate solutions are retained and other candidate solutions are abandoned; Perform a crossover operation on the first-generation instrument and equipment combination G to generate a new instrument and equipment combination, perform a mutation operation on an instrument in the new instrument and equipment combination to obtain a new-generation instrument and equipment combination G+1; let G=G+1, and determine whether the reproduction generation has reached the set generation GN. If not, continue to calculate. If so, output the best instrument and equipment combination plan, that is, the instrument and equipment combination with the highest score.

8. A signal-oriented instrument recommendation device, characterized in that: include: The signal acquisition module is configured to: acquire a signal requirement signal of the device under test and a signal capability signal of the instrument and equipment; The signal decomposition module is configured to: perform signal decomposition on the acquired DUT signal demand signal to obtain a DUT signal demand decomposition signal set; perform signal decomposition on the instrument equipment signal capability signal to obtain an instrument equipment signal capability decomposition signal set; Establishing a related class module, which is configured to: establish a class of instrument and equipment signals according to the decomposed instrument and equipment signal capability signal set, wherein the class of instrument and equipment signals contains instrument and equipment signal capability meta-signals and instrument and equipment attribute information; constructing a class of device under test signals according to the decomposed device under test signal requirement signal set, wherein the class of device under test signals contains at least a device under test signal requirement meta-signal; The model adaptation module is configured as follows: based on the class of the instrument and equipment signal and the class of the test device signal, with the goal of maximizing the instrument and equipment combination score, construct a signal-oriented adaptation algorithm model and determine the constraint rules of the signal-oriented adaptation algorithm model; based on the genetic algorithm, solve the signal-oriented adaptation algorithm model with an adaptation algorithm and output the best solution for the instrument and equipment combination.

9. A computing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the methods described in claims 1 to 7 when executing the program.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are performed.

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