Instrument equipment identification method, device, equipment and storage medium
By acquiring the ranging signal from the device and using a pre-trained model for feature extraction and recognition, the problem of data binding errors caused by manual recording was solved, and efficient and accurate identification of instruments and equipment was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 东莞信宝电子产品检测有限公司
- Filing Date
- 2026-05-08
- Publication Date
- 2026-06-09
AI Technical Summary
In laboratory or factory environments, manually operating multiple instruments and recording their models and test data can easily lead to data binding errors, affecting the accuracy of test results and wasting time.
By acquiring at least two device ranging signals, extracting multi-source signal features, and inputting them into a pre-trained instrument and equipment recognition model, the instrument and equipment recognition model learns the mapping relationship between the position features and equipment type features of the instrument and equipment to be identified based on the sample device ranging signal set, and then identifies it.
It improves the efficiency and accuracy of instrument and equipment identification, ensures the accuracy of identification results, and reduces human error.
Smart Images
Figure CN122174025A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, device, and storage medium for identifying instruments and equipment. Background Technology
[0002] In laboratory or factory environments, test engineers need to manually operate multiple instruments and manually record instrument models and test data. However, manual recording can lead to incorrect data binding with instruments, affecting the accuracy of test results, and is error-prone and time-consuming. Summary of the Invention
[0003] The purpose of this application is to provide a method, apparatus, device, and storage medium for identifying instruments and equipment, aiming to improve the efficiency and accuracy of identifying instruments and equipment.
[0004] This application provides a method for identifying instruments and equipment, including: Acquire at least two device ranging signals; the device ranging signals are signals generated by corresponding signal sources and detected at the location of the instrument or device to be identified. Feature extraction is performed on the ranging signal of the device to obtain multi-source signal features; The multi-source signal features are input into a pre-trained instrument and equipment recognition model. Based on the multi-source signal features, the instrument and equipment to be identified is identified to obtain the corresponding instrument and equipment recognition result. The instrument and equipment recognition model is obtained by learning the mapping relationship between the position features and equipment type features of the instrument and equipment to be identified based on the sample equipment ranging signal set.
[0005] In one embodiment, feature extraction of the device ranging signal includes: Extract the target features of the device ranging signal based on the signal type of the device ranging signal; The target features are normalized and encoded to obtain the first encoded features; The first coding features of the ranging signals from each of the aforementioned devices are spliced together to obtain the multi-source signal features.
[0006] In one embodiment, extracting the target features of the device ranging signal based on the signal type of the device ranging signal includes: When the device ranging signal is a WIFI signal, the signal strength characteristics and signal attenuation characteristics of the device ranging signal are extracted. When the device ranging signal is a Bluetooth signal, the signal strength discrete characteristics and frequency characteristics of the device ranging signal are extracted.
[0007] In one embodiment, prior to encoding the target feature, the method further includes: The confidence score of the device ranging signal is determined based on the target characteristics; At least two target device ranging signals are retained, and target features corresponding to non-target device ranging signals are eliminated; the target device ranging signals are the device ranging signals whose confidence scores meet the preset conditions.
[0008] In one embodiment, the instrument identification based on the multi-source signal features includes: The multi-source signal features are encoded to obtain the second encoded features; Feature extraction and nonlinear transformation are performed on the second encoded feature to obtain multi-source transformation features; The multi-source transformation features are activated, and the instruments and equipment are classified according to the activation results to obtain the instrument and equipment identification results.
[0009] In one embodiment, the instrument identification method further includes: Acquire the device positioning signal for the instrument to be identified; Feature extraction is performed on the device positioning signal, and the extracted features are then concatenated with the multi-source signal features.
[0010] In one embodiment, the instrument identification method further includes: When the device ranging signal is an NFC signal, the location of the instrument to be identified and the device type of the instrument to be identified are determined based on the device ranging signal.
[0011] This application also provides an instrument and equipment identification device, including: The first module is used to acquire at least two device ranging signals; the device ranging signals are signals generated by corresponding signal sources and detected at the location of the instrument or device to be identified. The second module is used to extract features from the ranging signal of the device to obtain multi-source signal features; The third module is used to input the multi-source signal features into a pre-trained instrument and equipment recognition model, and to perform instrument and equipment recognition on the instrument and equipment to be identified based on the multi-source signal features, so as to obtain the corresponding instrument and equipment recognition result; the instrument and equipment recognition model is obtained by learning the mapping relationship between the position features and equipment type features of the instrument and equipment to be identified based on the sample equipment ranging signal set.
[0012] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described instrument and equipment identification method.
[0013] This application also provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the above-described instrument and equipment identification method.
[0014] The beneficial effects of this application are as follows: At least two device ranging signals are acquired, and multi-source signal features are extracted from these signals. These multi-source signal features are then input into a pre-trained instrument recognition model. The pre-trained model identifies the instrument based on these multi-source signal features to obtain the corresponding identification result. Since the instrument recognition model is based on a set of sample device ranging signals, it learns the mapping relationship between the positional features and device type features of the instrument to be identified. By using at least two device ranging signals to locate the instrument and then using the multi-source signal features to identify it, the model can accurately identify instruments with a mapping relationship between positional features and device type features, thus improving the efficiency and accuracy of instrument recognition. Attached Figure Description
[0015] Figure 1 This is a flowchart of the instrument and equipment identification method provided in the embodiments of this application.
[0016] Figure 2 This is a flowchart of the specific method of step S102 provided in the embodiments of this application.
[0017] Figure 3 This is a flowchart of the specific method for step S104 provided in the embodiments of this application.
[0018] Figure 4 This is a schematic diagram of the structure of the instrument and equipment identification device provided in the embodiments of this application.
[0019] Figure 5 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0021] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and drawings are used to distinguish similar objects and are not used to describe a specific order or sequence.
[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.
[0023] Figure 1 This is a flowchart of the instrument and equipment identification method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S103.
[0024] Step S101: Acquire ranging signals from at least two devices.
[0025] The device ranging signal is generated by a corresponding signal source and detected at the location of the instrument to be identified. It can be understood that the location of the instrument to be identified is within the signal coverage area of at least two signal sources. Signal detection is performed at the location of the instrument to be identified, and the detected signal generated by the corresponding signal source is the device ranging signal. The device ranging signal represents the relative distance between the instrument to be identified and the signal source. The signal source can be deployed in the indoor environment where the instrument to be identified is located, providing signal coverage within that environment, such as a Wi-Fi hotspot or a Bluetooth module built into other devices. Alternatively, it can be built into the instrument to be identified, providing signal coverage centered on the instrument to be identified, such as a Bluetooth module built into the instrument to be identified.
[0026] Specifically, acquiring at least two device ranging signals can be achieved by setting a signal detection terminal at the location of the instrument to be identified, having the signal detection terminal detect at least two device ranging signals of the same or different signal types, and then acquiring at least two device ranging signals from the signal detection terminal.
[0027] Step S102: Extract features from the device ranging signal to obtain multi-source signal features.
[0028] Specifically, feature extraction of equipment ranging signals can be performed by extracting corresponding target features from the equipment ranging signals according to the signal type, and then fitting the target features extracted from each equipment ranging signal to obtain multi-source signal features.
[0029] Step S103: Input the multi-source signal features into the pre-trained instrument and equipment recognition model, and perform instrument and equipment recognition based on the multi-source signal features to obtain the corresponding instrument and equipment recognition results.
[0030] The instrument and equipment identification model is obtained by learning the mapping relationship between the positional features and equipment type features of the instrument and equipment to be identified based on the sample equipment ranging signal set.
[0031] Specifically, the instrument identification model is obtained by training a preset deep neural network model using a sample device ranging signal set. The sample device ranging signals from the set are input into the preset deep neural network model, which then identifies the instrument based on these signals. The model iteratively trains by calculating the deviation between the identified device type and the actual device type, gradually learning the mapping relationship between the location features and device type features of the instrument to be identified. In this embodiment, after inputting multi-source signal features into the instrument identification model, continuous feature extraction and nonlinear transformation are performed on the single-cell sequencing data within the model. Instrument identification is then performed based on the obtained multi-source transformed features, resulting in the corresponding instrument identification result.
[0032] The instrument and equipment identification method provided in this application acquires at least two device ranging signals and extracts multi-source signal features from these signals. These multi-source signal features are then input into a pre-trained instrument and equipment identification model. The pre-trained model identifies the instrument and equipment to be identified based on these multi-source signal features, thus obtaining the corresponding identification result. Since the instrument and equipment identification model is obtained by learning the mapping relationship between the position features and equipment type features of the instrument and equipment to be identified based on a set of sample device ranging signals, using at least two device ranging signals to locate the instrument and equipment to be identified and then using the instrument and equipment identification model to identify the instrument and equipment based on the multi-source signal features can accurately identify instruments and equipment with a mapping relationship between position features and equipment type features, improving the efficiency and accuracy of instrument and equipment identification.
[0033] See Figure 2 In one embodiment, step S102 may include steps S201 to S203.
[0034] Step S201: Extract the target features of the device ranging signal according to the signal type of the device ranging signal.
[0035] Specifically, extracting target features from the device ranging signal based on its signal type can be achieved by first identifying the signal type of the device ranging signal, then denoising the device ranging signal according to its signal type. For example, wavelet transform can be used to remove high-frequency noise for WIFI signals, and Kalman filtering can be used to smooth signal fluctuations for Bluetooth signals. Then, according to preset feature extraction rules, corresponding target features can be extracted from the denoised ranging signals obtained from device ranging signals of different signal types.
[0036] Step S202: Normalize and encode the target features to obtain the first encoded features.
[0037] Specifically, the target features are normalized and encoded. This can be done by using z-score normalization to normalize the target features, then using one-hot encoding to encode the normalized features and extract time-periodic features (such as hours and minutes) from the normalized features. The extracted time-periodic features are then converted into sine / cosine codes to eliminate the discontinuity of time span, thus obtaining the first encoded features.
[0038] Step S203: Assemble the first coded features of the ranging signals from each device to obtain multi-source signal features.
[0039] Specifically, according to the preset splicing template, the first encoded features of the ranging signals of each device are spliced into vector features to obtain multi-source signal features.
[0040] In one embodiment, extracting target features of the device ranging signal based on the signal type of the device ranging signal includes: when the device ranging signal is a WIFI signal, extracting signal strength features and signal attenuation features of the device ranging signal; and when the device ranging signal is a Bluetooth signal, extracting signal strength discrete features and frequency features of the device ranging signal.
[0041] When the detected device ranging signal is a Wi-Fi signal, this signal is obtained by detecting the Wi-Fi signal generated by the Wi-Fi hotspot at the location of the device to be identified. By extracting the signal strength and signal attenuation characteristics of the device ranging signal, the distance between the device to be identified and the Wi-Fi hotspot can be roughly determined. When the detected device ranging signal is a Bluetooth signal, this signal is obtained by detecting the Bluetooth signal generated by the Bluetooth module built into the device to be identified or the Bluetooth module built into other devices at the location of the device to be identified. By extracting the signal strength discrete characteristics and frequency characteristics of the device ranging signal, the distance between the signal detection terminal and the device to be identified, or the distance between the device to be identified and other devices, can be roughly determined.
[0042] In one embodiment, before encoding the target features, the method further includes: determining a confidence score for the device ranging signal based on the target features; retaining at least two target device ranging signals and discarding the target features corresponding to non-target device ranging signals. The target device ranging signal is a device ranging signal whose confidence score meets a preset condition.
[0043] Specifically, before encoding the target features, a confidence evaluation rule for the response is determined based on the signal type of the device ranging signal. The target features of the device ranging signal are then evaluated for confidence based on this rule to obtain corresponding confidence scores. At least two target features corresponding to the device ranging signals with the highest confidence scores are retained; these are the target features corresponding to the target device ranging signal. Target features corresponding to other device ranging signals are then proposed. For example, when the device ranging signal is a WIFI signal, the signal strength and signal attenuation features of the device ranging signal are compared with the corresponding signal strength and signal attenuation threshold features. Based on the comparison results, corresponding confidence scores are obtained. Device ranging signals with confidence scores greater than a preset confidence threshold score are considered to meet the preset condition and are designated as target device ranging signals. Device ranging signals with confidence scores less than or equal to the confidence threshold score are designated as non-target device ranging signals.
[0044] See Figure 3 In one embodiment, step S103 may include steps S301 to S303.
[0045] Step S301: Encode the features of the multi-source signal to obtain the second encoded features.
[0046] Step S302: Perform feature extraction and nonlinear transformation on the second encoded features to obtain multi-source transformation features.
[0047] Step S303: Activate the multi-source transformation features and classify the instruments and equipment according to the activation results to obtain the instrument and equipment identification results.
[0048] Steps S301 to S303 are completed using a multilayer perceptron. The second encoded feature is obtained by encoding the multi-source signal features into a feature vector of a preset dimension. Then, the second encoded feature is weighted and summed according to the signal type of the device ranging signal, and the weighted sum is nonlinearly activated to obtain the multi-source transformation feature. The multi-source transformation feature is then linearly or nonlinearly activated, and the instrument and equipment are classified according to the activation result to obtain the corresponding instrument and equipment identification result.
[0049] In one embodiment, the instrument and equipment identification method provided in this application further includes: acquiring the device positioning signal for the instrument and equipment to be identified; extracting features from the device positioning signal; and splicing the extracted features into multi-source signal features.
[0050] Specifically, when the signal detection terminal detects a device positioning signal for the device to be identified at its location, it extracts the latitude and longitude coordinate features of the device from the device ranging signal. Then, it concatenates the extracted latitude and longitude coordinate features with multi-source signal features as one of the references for determining the location of the device. The device positioning signal can be an outdoor positioning signal, generated by a signal source deployed in the outdoor environment to provide signal coverage in the area where the device to be identified is located. The signal source can be an outdoor positioning base station, and the outdoor positioning signal can be a GPS signal or a BeiDou positioning signal.
[0051] In one embodiment, the instrument and equipment identification method provided in this application further includes: when the device ranging signal is an NFC signal, determining the location of the instrument and equipment to be identified and determining the device type of the instrument and equipment to be identified based on the device ranging signal.
[0052] Specifically, when the device ranging signal is an NFC signal, that is, when the signal detection terminal detects the NFC signal generated by the device to be identified at the location of the device to be identified, the signal detection terminal extracts the identity information of the device to be identified from the device ranging signal and determines the device type of the device to be identified based on the identity information of the device to be identified.
[0053] Please see Figure 4 This application also provides an instrument and equipment identification device that can implement the above-described instrument and equipment identification method. The device includes: The first module 401 is used to acquire at least two device ranging signals; the device ranging signals are signals generated by corresponding signal sources and detected at the location of the instrument or device to be identified. The second module 402 is used to extract features from the device ranging signal to obtain multi-source signal features; The third module 403 is used to input multi-source signal features into the pre-trained instrument and equipment recognition model, and to perform instrument and equipment recognition based on the multi-source signal features to obtain the corresponding instrument and equipment recognition results; the instrument and equipment recognition model is obtained by learning the mapping relationship between the position features and equipment type features of the instrument and equipment to be recognized based on the sample equipment ranging signal set.
[0054] The specific implementation of this instrument and equipment identification device is basically the same as the specific implementation of the instrument and equipment identification method described above, and will not be repeated here.
[0055] Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment.
[0056] The following reference Figure 5 To describe an electronic device 500 according to such an embodiment of the present disclosure. Figure 5 The electronic device 500 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.
[0057] like Figure 5 As shown, the electronic device 500 is presented in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), a display unit 540, etc.
[0058] The storage unit stores program code, which can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the instrument and equipment identification method section of this specification according to various exemplary embodiments of this disclosure.
[0059] Storage unit 520 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include a read-only memory (ROM) 5203.
[0060] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.
[0061] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.
[0062] Electronic device 500 can also communicate with one or more external devices 500' (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. Network adapter 560 can communicate with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0063] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described instrument and equipment identification method.
[0064] The instrument and equipment identification method, apparatus, device, and storage medium provided in this application acquire at least two device ranging signals and extract multi-source signal features from these signals. These multi-source signal features are then input into a pre-trained instrument and equipment identification model. The pre-trained model identifies the instrument and equipment to be identified based on these multi-source signal features, thus obtaining the corresponding identification result. Since the instrument and equipment identification model is obtained by learning the mapping relationship between the position features and device type features of the instrument and equipment to be identified based on a set of sample device ranging signals, using at least two device ranging signals to locate the instrument and equipment to be identified and then using the instrument and equipment identification model to identify the instrument and equipment based on the multi-source signal features can accurately identify instruments and equipment with a mapping relationship between position features and device type features, improving the efficiency and accuracy of instrument and equipment identification.
[0065] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the methods described above according to the embodiments of this disclosure.
[0066] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0067] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.
[0068] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.
[0069] Exemplary embodiments of this disclosure have been specifically shown and described above. It should be understood that this disclosure is not limited to the detailed structures, arrangements, or implementations described herein; rather, this disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.
Claims
1. A method for identifying instruments and equipment, characterized in that, include: Acquire at least two device ranging signals; the device ranging signals are signals generated by corresponding signal sources and detected at the location of the instrument or device to be identified. Feature extraction is performed on the ranging signal of the device to obtain multi-source signal features; The multi-source signal features are input into a pre-trained instrument and equipment recognition model. Based on the multi-source signal features, the instrument and equipment to be identified is identified to obtain the corresponding instrument and equipment recognition result. The instrument and equipment recognition model is obtained by learning the mapping relationship between the position features and equipment type features of the instrument and equipment to be identified based on the sample equipment ranging signal set.
2. The instrument and equipment identification method according to claim 1, characterized in that, The feature extraction of the ranging signal from the device includes: Extract the target features of the device ranging signal based on the signal type of the device ranging signal; The target features are normalized and encoded to obtain the first encoded features; The first coding features of the ranging signals from each of the aforementioned devices are spliced together to obtain the multi-source signal features.
3. The instrument and equipment identification method according to claim 2, characterized in that, The step of extracting the target features of the device ranging signal based on the signal type of the device ranging signal includes: When the device ranging signal is a WIFI signal, the signal strength characteristics and signal attenuation characteristics of the device ranging signal are extracted. When the device ranging signal is a Bluetooth signal, the signal strength discrete characteristics and frequency characteristics of the device ranging signal are extracted.
4. The instrument and equipment identification method according to claim 2, characterized in that, Before encoding the target features, the method further includes: The confidence score of the device ranging signal is determined based on the target characteristics; At least two target device ranging signals are retained, and target features corresponding to non-target device ranging signals are eliminated; the target device ranging signals are the device ranging signals whose confidence scores meet the preset conditions.
5. The instrument and equipment identification method according to claim 1, characterized in that, The process of identifying the instrument or equipment based on the multi-source signal features includes: The multi-source signal features are encoded to obtain the second encoded features; Feature extraction and nonlinear transformation are performed on the second encoded feature to obtain multi-source transformation features; The multi-source transformation features are activated, and the instruments and equipment are classified according to the activation results to obtain the instrument and equipment identification results.
6. The instrument and equipment identification method according to claim 1, characterized in that, The instrument and equipment identification method further includes: Acquire the device positioning signal for the instrument to be identified; Feature extraction is performed on the device positioning signal, and the extracted features are then concatenated with the multi-source signal features.
7. The instrument and equipment identification method according to claim 1, characterized in that, The instrument and equipment identification method further includes: When the device ranging signal is an NFC signal, the location of the instrument to be identified and the device type of the instrument to be identified are determined based on the device ranging signal.
8. An instrument and equipment identification device, characterized in that, include: The first module is used to acquire at least two device ranging signals; the device ranging signals are signals generated by corresponding signal sources and detected at the location of the instrument or device to be identified. The second module is used to extract features from the ranging signal of the device to obtain multi-source signal features; The third module is used to input the multi-source signal features into a pre-trained instrument and equipment recognition model, and to perform instrument and equipment recognition on the instrument and equipment to be identified based on the multi-source signal features, so as to obtain the corresponding instrument and equipment recognition result; the instrument and equipment recognition model is obtained by learning the mapping relationship between the position features and equipment type features of the instrument and equipment to be identified based on the sample equipment ranging signal set.
9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the instrument and equipment identification method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the instrument and equipment identification method according to any one of claims 1 to 7.