Ultrasonic equipment refined management system and method

Abstract

The present invention relates to the technical field of ultrasonic equipment management, and specifically discloses a refined management system and method for ultrasonic equipment. The system comprises: a data acquisition module, which is used for all-round collection of ultrasonic equipment operation data; a cross-category combination module, which is used for cross-category combination mining of ultrasonic equipment operation data, and behavioral classification and project analysis matching of ultrasonic equipment to obtain behavioral classification and project analysis matching results; a model construction module, which is used for building an equipment-side evaluation model by using a deep learning algorithm and a big data analysis method; an equipment evaluation module, which is used for using the equipment-side evaluation model to perform deep mining and analysis on ultrasonic equipment operation data and behavioral classification and project analysis matching results to predict equipment-side evaluation results; a report generation module, which is used for customized generation of equipment management data reports based on equipment-side evaluation results; and refined management of ultrasonic equipment is realized, the efficiency and quality of equipment management are improved, and it is helpful to extend the service life of equipment and reduce maintenance costs.

Description

Technical Field

[0001] The present invention relates to the technical field of ultrasonic equipment management, and in particular to a refined management system and method for ultrasonic equipment. Background Art

[0002] In the modern medical field, ultrasound equipment plays an indispensable role as an important diagnostic tool. With the continuous advancement of medical technology and the continuous growth of medical needs, the number and types of ultrasound equipment in medical institutions are increasing, and its application scope is becoming more and more extensive, covering many aspects from routine physical examinations to complex disease diagnosis. In order to ensure the stable operation and efficient use of ultrasound equipment, effective management is essential.

[0003] However, the current management of ultrasound equipment faces many challenges. In terms of data collection, traditional management methods can usually only obtain limited, single-dimensional information, and cannot achieve all-round operation data collection, making it difficult to fully reflect the actual operation status of the equipment. For the collected data, there is a lack of cross-category combined mining capabilities, making it difficult to accurately classify the behavior of the equipment and match project analysis, and unable to gain in-depth insights into the use patterns and potential needs of the equipment. In terms of evaluation model construction, existing methods mostly rely on simple statistical analysis or empirical judgment, and fail to fully utilize the advantages of deep learning algorithms and big data analysis, resulting in insufficient accuracy and adaptability of the evaluation model, making it difficult to accurately evaluate the equipment. Moreover, in the equipment evaluation link, due to the lack of deep mining and analysis capabilities, it is impossible to fully utilize the collected data and classification analysis results, making it difficult to accurately predict the status and potential problems of the equipment. In addition, in terms of report generation, existing systems often cannot be customized based on the evaluation results, and the generated report content lacks pertinence and practicality, making it difficult to meet the needs of medical institutions for refined equipment management.

[0004] Therefore, the present invention proposes a refined management system and method for ultrasonic equipment. Summary of the invention

[0005] The present invention provides a refined management system and method for ultrasonic equipment, which can use the data acquisition module to collect ultrasonic equipment operation data in all directions, providing a comprehensive and rich data basis for subsequent analysis and evaluation. The cross-class combination module deeply understands the operating status and usage of ultrasonic equipment through cross-class combined mining, behavior classification and project analysis matching. The model construction module uses the device-side evaluation model constructed by deep learning algorithms and big data analysis methods to improve the accuracy and scientificity of the evaluation. The equipment evaluation module uses the evaluation model to conduct in-depth mining and analysis of the data, and can accurately predict the equipment-side evaluation results. The report generation module generates a customized equipment management data report based on the evaluation results, providing an intuitive and targeted decision-making basis for equipment management. On the whole, refined management of ultrasonic equipment is achieved, the efficiency and quality of equipment management are improved, and it helps to extend the service life of the equipment and reduce maintenance costs.

[0006] The present invention provides a refined management system for ultrasonic equipment, comprising:

[0007] Data acquisition module, used to collect all-round operation data of ultrasonic equipment;

[0008] The cross-category combination module is used to conduct cross-category combination mining on the operation data of ultrasonic equipment, classify the behavior of ultrasonic equipment and conduct project analysis and matching to obtain the behavior classification and project analysis and matching results;

[0009] Model building module, which is used to build device-side evaluation models using deep learning algorithms and big data analysis methods;

[0010] The equipment evaluation module is used to use the equipment-side evaluation model to conduct in-depth mining and analysis on the ultrasonic equipment operation data, behavior classification, and project analysis matching results, and predict the equipment-side evaluation results;

[0011] The report generation module is used to generate customized device management data reports based on device-side evaluation results.

[0012] Preferably, in the refined management system of ultrasound equipment, the ultrasound equipment operation data includes ultrasound equipment power supply and consumption data, ultrasound equipment pairing data, ultrasound probe movement data, and ultrasound equipment operation status data.

[0013] Preferably, the data acquisition module includes:

[0014] The power supply and consumption data acquisition submodule is used to obtain the power supply health of the ultrasound equipment host and the terminal power consumption data of the ultrasound equipment host as the power supply and consumption data of the ultrasound equipment;

[0015] The pairing monitoring submodule is used to obtain the pairing list of the main body and the probe of the ultrasound device in real time as the pairing data of the ultrasound device;

[0016] A motion monitoring submodule, used to obtain the motion data of the ultrasound probe based on a high-sensitivity motion sensor;

[0017] The status record monitoring submodule is used to obtain the host operation status record and probe usage record of the ultrasound device as the ultrasound device operation status data.

[0018] Preferably, the power supply and consumption data acquisition submodule includes:

[0019] A power supply monitoring unit, used to monitor the power connection stability of the ultrasonic device using a photoelectric detection sensor, and obtain the power supply health of the ultrasonic device host based on the power connection stability analysis;

[0020] The power consumption monitoring unit is used to obtain the terminal power consumption data of the ultrasound equipment host.

[0021] Preferably, the motion monitoring submodule includes:

[0022] An action judgment result acquisition unit is used to acquire the action feature quantity of the ultrasonic probe in real time based on the high-sensitivity motion sensor, and obtain the action judgment result of the ultrasonic probe based on the action feature quantity of the ultrasonic probe, wherein the action feature quantity includes action direction, action speed, action acceleration, and action angle;

[0023] An action duration statistics unit is used to count the duration of each action in the action judgment result;

[0024] The ultrasonic probe motion data includes the motion judgment result of the ultrasonic probe and the duration of each motion in the motion judgment result.

[0025] Preferably, the cross-class combination module includes:

[0026] A power supply behavior classification submodule, used to obtain power supply behavior classification information of the ultrasonic device based on the power supply health of the ultrasonic device host;

[0027] The state pairing submodule is used to perform pairing calibration on the operation state data of the ultrasonic device based on the pairing data of the ultrasonic device, and obtain the operation state synchronization pairing thread of the host and probe of the ultrasonic device;

[0028] An action thread generation submodule, used to generate at least one ultrasound probe action recording thread based on ultrasound probe motion data;

[0029] The total thread fitting submodule is used to synchronize the pairing threads based on the operating status of the host and probe of the ultrasound device to sort and fit the total thread of all currently acquired valid ultrasound probe recording threads to obtain the total thread of ultrasound device behavior records;

[0030] A motion behavior classification submodule is used to classify the motion behavior of the ultrasonic device based on the total thread of the ultrasonic device behavior record to obtain the motion behavior classification information of the ultrasonic device;

[0031] The project analysis submodule is used to perform project analysis and matching based on the motion behavior classification information of the ultrasonic device and the terminal power consumption data of the ultrasonic device host to obtain the project analysis and matching results;

[0032] The behavior classification and project analysis matching results include behavior classification results and project analysis matching results, and the behavior classification results include power supply behavior classification information and motion behavior classification information.

[0033] Preferably, the motor behavior classification submodule includes:

[0034] A behavior track pairing generation unit, used for acquiring all valid behavior tracks corresponding to the ultrasound probe based on all valid ultrasound probe recording threads in the total ultrasound device behavior recording thread;

[0035] A behavior similarity analysis unit, used for measuring the behavior similarity between the two ultrasonic probe behaviors corresponding to the two effective ultrasonic probe recording threads based on all effective ultrasonic probe recording threads and all effective behavior trajectories in the total ultrasonic device behavior recording thread;

[0036] The motion behavior classification unit is used to calibrate all ultrasonic probe behaviors recorded in the total thread of ultrasonic equipment behavior records into the same category based on the behavioral similarity between pairwise ultrasonic probe behaviors, obtain record sequences of multiple ultrasonic probe behavior categories, and classify the motion behaviors of ultrasonic equipment based on the confidence of the record sequences of multiple ultrasonic probe behavior categories to obtain motion behavior classification information of ultrasonic equipment.

[0037] Preferably, the sports behavior classification unit includes:

[0038] The same type behavior identification subunit is used to determine all ultrasound probe behaviors whose behavior similarity between two ultrasound probe behaviors exceeds a similarity threshold as belonging to the same type and calibrate them, and to record the ordinal values ​​of the valid ultrasound probe record threads corresponding to all ultrasound probe behaviors of the same type recorded in the ultrasound equipment behavior record total thread in sequence in the ultrasound equipment behavior record total thread, so as to obtain a record sequence of each ultrasound probe behavior category;

[0039] A confidence determination subunit, used to determine the confidence of the record sequence of each ultrasonic probe behavior category based on the behavior similarity between any two ultrasonic probe behaviors in all ultrasonic probe behaviors of each ultrasonic probe behavior category;

[0040] The behavior classification subunit is used to input the record sequences and corresponding confidences of all ultrasound probe behavior categories into the motion behavior classification model to obtain the motion behavior classification information of the ultrasound device.

[0041] Preferably, the confidence determination subunit comprises:

[0042] An average similarity calculation end, used for calculating the average similarity of all ultrasound probe behaviors of each ultrasound probe behavior category based on the behavior similarity between any two ultrasound probe behaviors in all ultrasound probe behaviors of each ultrasound probe behavior category;

[0043] A standard deviation calculation end, used to calculate the similarity standard deviation of all ultrasound probe behaviors of each ultrasound probe behavior category based on the behavior similarity between any two ultrasound probe behaviors in all ultrasound probe behaviors of each ultrasound probe behavior category;

[0044] The confidence calculation end is used to determine the confidence of the record sequence of each ultrasound probe behavior category based on the average similarity and similarity standard deviation of all ultrasound probe behaviors of each ultrasound probe behavior category.

[0045] The present invention provides a method for fine management of ultrasonic equipment, which is applied to any of the above-mentioned fine management systems of ultrasonic equipment, and comprises:

[0046] S1: Comprehensive collection of ultrasound equipment operation data;

[0047] S2: Conduct cross-category combined mining on the operation data of the ultrasonic equipment, conduct behavioral classification and project analysis matching on the ultrasonic equipment, and obtain the behavioral classification and project analysis matching results;

[0048] S3: Use deep learning algorithms and big data analysis methods to build device-side evaluation models;

[0049] S4: Use the device-side evaluation model to conduct in-depth mining and analysis on the ultrasound equipment operation data, behavior classification, and project analysis matching results to predict the device-side evaluation results;

[0050] S5: Generate customized device management data reports based on device-side evaluation results.

[0051] The beneficial effects of the present invention compared to the prior art are as follows: the data acquisition module can be used to collect the operation data of the ultrasonic equipment in all directions, providing a comprehensive and rich data basis for subsequent analysis and evaluation. The cross-category combination module deeply understands the operation status and usage of the ultrasonic equipment through cross-category combination mining, behavior classification and project analysis matching. The model construction module uses the deep learning algorithm and big data analysis method to construct the device-side evaluation model, which improves the accuracy and scientificity of the evaluation. The equipment evaluation module uses the evaluation model to conduct in-depth mining and analysis of the data, and can accurately predict the equipment-side evaluation results. The report generation module generates customized equipment management data reports based on the evaluation results, providing intuitive and targeted decision-making basis for equipment management. On the whole, it realizes the refined management of ultrasonic equipment, improves the efficiency and quality of equipment management, helps to extend the service life of the equipment and reduce maintenance costs.

[0052] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in this application document.

[0053] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0055] Figure 1 It is a schematic diagram of a refined management system for ultrasound equipment in an embodiment of the present invention;

[0056] Figure 2 It is a schematic diagram of the data processing flow in the process of ultrasonic equipment data categories and refined management in an embodiment of the present invention;

[0057] Figure 3 The figure is a flow chart of a method for fine management of ultrasound equipment in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0059] Example 1

[0060] The present invention provides a refined management system for ultrasonic equipment. Figure 1 and Figure 2 ,include:

[0061] Data acquisition module, used to collect all-round operation data of ultrasonic equipment;

[0062] The cross-category combination module is used to conduct cross-category combination mining on the operation data of ultrasonic equipment, classify the behavior of ultrasonic equipment and conduct project analysis and matching to obtain the behavior classification and project analysis and matching results;

[0063] Model building module, which is used to build device-side evaluation models using deep learning algorithms and big data analysis methods;

[0064] The equipment evaluation module is used to use the equipment-side evaluation model to conduct in-depth mining and analysis on the ultrasonic equipment operation data, behavior classification, and project analysis matching results, and predict the equipment-side evaluation results;

[0065] The report generation module is used to generate customized device management data reports based on device-side evaluation results.

[0066] In this embodiment, a device-side evaluation model is constructed using a deep learning algorithm and a big data analysis method: this refers to using relevant deep learning algorithms and a large amount of data analysis and processing methods to create a model that can evaluate the status of ultrasound equipment. This model can output evaluation information for the equipment based on relevant input data, such as equipment operation data, behavior classification results, etc. For example: by analyzing a large amount of past ultrasound equipment operation data and classification results, a deep learning algorithm is used to train an evaluation model that can predict the possibility of equipment failure.

[0067] In this embodiment, the device-side evaluation model is used to conduct in-depth mining and analysis of the ultrasonic equipment operation data, behavior classification, and project analysis matching results to predict the device-side evaluation results: using the already constructed device-side evaluation model, in-depth exploration of the ultrasonic equipment operation data, behavior classification, and project analysis matching results and other information, through the calculation and analysis of the model, the evaluation prediction results of the equipment status are obtained. For example: the newly collected ultrasonic equipment operation data, classification, and analysis results are input into the model. After complex calculations and reasoning, the model gives evaluation results such as whether the current performance of the equipment is good and whether maintenance is required.

[0068] In this embodiment, the device-side evaluation result is the conclusion on the condition of the ultrasound device obtained after processing and analyzing the relevant data through the device-side evaluation model, which may include evaluation information on the performance status, potential problems, maintenance requirements, etc. of the device. For example, the evaluation result may be "the device is performing well, but it is expected to require routine maintenance within the next month."

[0069] In this embodiment, a customized device management data report is generated based on the device-side evaluation results: Based on the evaluation results obtained by the device-side evaluation model, a management data report specifically for the ultrasound device is generated according to specific requirements and formats. The report content may include detailed evaluation conditions of the device, recommended management measures, etc. For example: Based on the evaluation results, a report is generated that includes the specific fault location of the device, maintenance recommendations, and subsequent maintenance plans.

[0070] The beneficial effects of the above technologies are as follows: the data acquisition module can be used to collect all-round operating data of ultrasonic equipment, providing a comprehensive and rich data foundation for subsequent analysis and evaluation. The cross-category combination module deeply understands the operating status and usage of ultrasonic equipment through cross-category combination mining, behavior classification and project analysis matching. The model building module uses deep learning algorithms and big data analysis methods to construct a device-side evaluation model, which improves the accuracy and scientificity of the evaluation. The equipment evaluation module uses the evaluation model to conduct in-depth mining and analysis of the data, and can accurately predict the equipment-side evaluation results. The report generation module generates customized equipment management data reports based on the evaluation results, providing intuitive and targeted decision-making basis for equipment management. On the whole, it realizes the refined management of ultrasonic equipment, improves the efficiency and quality of equipment management, and helps to extend the service life of equipment and reduce maintenance costs.

[0071] Example 2

[0072] Based on Example 1, the ultrasound equipment operation data includes ultrasound equipment power supply and consumption data, ultrasound equipment pairing data, ultrasound probe movement data, and ultrasound equipment operation status data.

[0073] The beneficial effects of the above technologies include: covering various types of ultrasonic equipment operation data, making the monitoring and analysis of the equipment more comprehensive and in-depth. Power supply and consumption data helps to understand the energy consumption and power stability of the equipment. Pairing data can reflect the coordination of the equipment with other components or systems. Probe motion data can reflect the accuracy and flexibility of equipment operation. Operation status data directly reflects the overall working condition of the equipment. The combination of these data provides rich information support for comprehensive evaluation of equipment performance and refined management. Overall, it improves the accuracy and comprehensiveness of the grasp of the operating status of ultrasonic equipment, providing a solid data foundation for subsequent management decisions.

[0074] Example 3

[0075] Based on Example 1, the ultrasonic equipment refined management system, the data acquisition module, includes:

[0076] The power supply and consumption data acquisition submodule is used to obtain the power supply health of the ultrasound equipment host and the terminal power consumption data of the ultrasound equipment host as the power supply and consumption data of the ultrasound equipment;

[0077] The pairing monitoring submodule is used to obtain the pairing list of the main body and the probe of the ultrasound device in real time as the pairing data of the ultrasound device;

[0078] A motion monitoring submodule, used to obtain the motion data of the ultrasound probe based on a high-sensitivity motion sensor;

[0079] The status record monitoring submodule is used to obtain the host operation status record and probe usage record of the ultrasound device as the ultrasound device operation status data.

[0080] In this embodiment, the power supply health of the ultrasound equipment host refers to the goodness of the power supply condition of the ultrasound equipment host, including a comprehensive evaluation of voltage stability, whether the current is normal, whether there is leakage, etc. For example: if the voltage fluctuation is small, the current is stable and there is no leakage, the power supply health is high; conversely, if there is a large voltage fluctuation or leakage, the power supply health is low.

[0081] In this embodiment, the terminal power consumption data of the ultrasound equipment host: the specific power consumption value and related information of the ultrasound equipment host during use, such as power consumption, power consumption, power consumption time, etc. For example: at a certain moment, the power consumption of the ultrasound equipment host is 1000 watts, and the power consumption is 10 degrees per day. These are all terminal power consumption data.

[0082] In this embodiment, the ultrasound device main body and probe pairing list: a list recording the pairing of the ultrasound device main body with different probes, including the pairing time, the probe model used, etc. For example, the list may show that at 10 am, the main body is paired with a probe of model A.

[0083] In this embodiment, the ultrasonic probe motion data refers to information related to the motion of the ultrasonic probe during operation, such as the direction, speed, acceleration, angle of the motion, and duration of each motion, etc. For example, the probe moves to the left within a certain period of time, with a speed of 5 cm per second and an acceleration of 1 cm per second, etc.

[0084] In this embodiment, the host operation status record is a record of various status conditions of the ultrasound device host when it is working, such as whether it is normally started, whether there are any faults or errors during operation, whether the temperature is normal, etc. For example, the host operation status record may show that an overheating alarm occurs at 2 pm.

[0085] In this embodiment, the probe usage record is a detailed record of the use of the ultrasound probe, including the start and end time of use, the operation during use, whether there are any abnormalities, etc. For example, the probe usage record shows that the probe was used from 9 am to 10 am and the operation was normal.

[0086] The beneficial effects of the above technologies include: the power supply and consumption data acquisition submodule can obtain the power supply health and terminal power consumption data, fully grasp the power status of the ultrasonic equipment, and ensure the stable operation of the equipment. The pairing monitoring submodule obtains the pairing list in real time to ensure the correct matching of the equipment body and the probe, improving work efficiency and accuracy. The motion monitoring submodule uses a highly sensitive motion sensor to obtain probe motion data, accurately monitor probe operation, and help evaluate the quality of operation. The status record monitoring submodule obtains the host operation status record and probe usage record, providing a basis for equipment maintenance and troubleshooting. Overall, multi-dimensional and accurate collection of ultrasonic equipment operation data is achieved, providing reliable data support for refined management.

[0087] Example 4

[0088] Based on Example 3, the power supply and consumption data acquisition submodule includes:

[0089] A power supply monitoring unit, used to monitor the power connection stability of the ultrasonic device using a photoelectric detection sensor, and obtain the power supply health of the ultrasonic device host based on the power connection stability analysis;

[0090] The power consumption monitoring unit is used to obtain the terminal power consumption data of the ultrasound equipment host.

[0091] In this embodiment, the stability of the power connection of the ultrasonic device is monitored by using a photoelectric detection sensor: the photoelectric detection sensor is used to detect whether the power connection of the ultrasonic device is stable, continuous and normal, including whether the contact between the plug and the socket is good, whether the line is loose or interrupted, etc. For example: the photoelectric detection sensor detects that the contact between the plug and the socket of the ultrasonic device is tight and the current transmission is stable, indicating that the power connection is stable.

[0092] In this embodiment, the power supply health of the ultrasound equipment host is obtained based on the power connection stability analysis: further analysis and judgment are performed based on the detection result of whether the power connection of the ultrasound equipment is stable, so as to obtain the health status of the power supply of the ultrasound equipment host. For example: if the power connection has been stable without fluctuations or interruptions, then the power supply health can be considered high; if the connection is often unstable or even short-term power outages occur, it may be judged that the power supply health is low.

[0093] The beneficial effects of the above technologies include: the power supply monitoring unit monitors the stability of the power connection and obtains the health of the power supply through a photoelectric detection sensor, which can promptly detect potential problems in the power supply and ensure the reliability of the equipment's power supply. The power consumption monitoring unit obtains the terminal power consumption data, which helps to fully understand the power consumption of the equipment and provide data support for energy consumption management and fault diagnosis. The combination of the two can more comprehensively and accurately grasp the power supply and consumption status of the ultrasonic equipment, prevent power supply failures in advance, and optimize power management. Overall, the accuracy and reliability of monitoring the power supply and consumption data of ultrasonic equipment have been improved, providing a strong guarantee for the stable operation and energy-saving optimization of the equipment.

[0094] Example 5

[0095] Based on Example 3, the motion monitoring submodule includes:

[0096] An action judgment result acquisition unit is used to acquire the action feature quantity of the ultrasonic probe in real time based on the high-sensitivity motion sensor, and obtain the action judgment result of the ultrasonic probe based on the action feature quantity of the ultrasonic probe, wherein the action feature quantity includes action direction, action speed, action acceleration, and action angle;

[0097] An action duration statistics unit is used to count the duration of each action in the action judgment result;

[0098] The ultrasonic probe motion data includes the motion judgment result of the ultrasonic probe and the duration of each motion in the motion judgment result.

[0099] In this embodiment, obtaining the action judgment result of the ultrasonic probe based on the action feature quantity of the ultrasonic probe refers to determining what kind of action the ultrasonic probe is performing, such as translation, rotation, fast movement or slow movement, based on the numerical values ​​and changes of the features such as the action direction, action speed, action acceleration, action angle, etc. shown by the ultrasonic probe in the action. For example: if the action feature quantity of the ultrasonic probe shows that the action direction is horizontal to the right, the action speed is fast, the action acceleration is small, and the action angle is 0 degrees, then the action judgment result may be that the probe is rapidly horizontally translating to the right.

[0100] The beneficial effects of the above technology include: the action judgment result acquisition unit obtains the action feature quantity and obtains the action judgment result through a high-sensitivity motion sensor, and can accurately perceive the various action details of the probe. The monitoring of feature quantities such as action direction, speed, acceleration and angle fully reflects the motion state of the probe. The action duration statistics unit counts the duration of each action, providing important data for evaluating the continuity and efficiency of the operation. The ultrasonic probe motion data includes the action judgment result and duration, making the analysis of the probe motion more comprehensive and in-depth. On the whole, high-precision monitoring and analysis of the ultrasonic probe movement is achieved, which helps to improve the standardization of operations and the use effect of the equipment.

[0101] Example 6

[0102] On the basis of Example 1, the cross-class combination module includes:

[0103] A power supply behavior classification submodule, used to obtain power supply behavior classification information of the ultrasonic device based on the power supply health of the ultrasonic device host;

[0104] The state pairing submodule is used to perform pairing calibration on the operation state data of the ultrasonic device based on the pairing data of the ultrasonic device, and obtain the operation state synchronization pairing thread of the host and probe of the ultrasonic device;

[0105] An action thread generation submodule, used to generate at least one ultrasound probe action recording thread based on ultrasound probe motion data;

[0106] The total thread fitting submodule is used to synchronize the pairing threads based on the operating status of the host and probe of the ultrasound device to sort and fit the total thread of all currently acquired valid ultrasound probe recording threads to obtain the total thread of ultrasound device behavior records;

[0107] A motion behavior classification submodule is used to classify the motion behavior of the ultrasonic device based on the total thread of the ultrasonic device behavior record, and obtain the motion behavior classification information of the ultrasonic device;

[0108] The project analysis submodule is used to perform project analysis and matching based on the motion behavior classification information of the ultrasonic device and the terminal power consumption data of the ultrasonic device host to obtain the project analysis and matching results;

[0109] The behavior classification and project analysis matching results include behavior classification results and project analysis matching results, and the behavior classification results include power supply behavior classification information and motion behavior classification information.

[0110] In this embodiment, the power supply behavior classification information of the ultrasonic device is obtained based on the power supply health of the ultrasonic device host: according to the health of the power supply of the ultrasonic device host (for example, stable, unstable, occasional fluctuations, etc.), the power supply behavior of the device is classified and summarized, such as normal power supply behavior, potential risk power supply behavior, etc.

[0111] In this embodiment, the operating status data of the ultrasonic device is paired and calibrated based on the pairing data of the ultrasonic device to obtain a synchronous pairing thread for the operating status of the host and probe of the ultrasonic device: the pairing data of the ultrasonic device body and the probe are used to mark and associate the data of the device operating status, thereby obtaining a series of data combinations that can reflect the synchronization of the operating status of the host and the probe, that is, the operating status synchronization pairing thread.

[0112] In this embodiment, the operating status synchronization pairing thread of the host and probe of the ultrasound device is a data sequence that combines and associates the operating status related data of the host and probe of the ultrasound device according to specific rules, and is used to represent the synchronization relationship between the operating status of the two.

[0113] In this embodiment, at least one ultrasound probe motion recording thread is generated based on the ultrasound probe motion data: one or more threads specifically used for recording the probe motion conditions are created according to the acquired ultrasound probe motion related data.

[0114] In an embodiment, a synchronization pairing thread based on the operating status of the host and the probe of the ultrasonic device sorts and fits the total thread of all currently acquired valid ultrasonic probe recording threads to obtain the total thread of ultrasonic device behavior recording: according to the rules of the synchronization pairing thread of the operating status of the host and the probe, the acquired valid ultrasonic probe recording threads are arranged in sequence, and these threads are integrated and fitted together through a certain algorithm to form a total thread that comprehensively reflects the behavior of the ultrasonic device.

[0115] In this embodiment, the ultrasound device behavior record overall thread is a comprehensive data thread that integrates multiple information such as the ultrasound device host, the operating status of the probe, and the probe action, and can fully display the behavior of the ultrasound device.

[0116] In this embodiment, the motion behavior classification information of the ultrasound device: information obtained by classifying and summarizing the motion behavior of the ultrasound device (such as movement and operation of the probe), such as fast operation behavior, slow and precise operation behavior, etc.

[0117] In this embodiment, project analysis and matching are performed based on the motion behavior classification information of the ultrasonic device and the terminal power consumption data of the ultrasonic device host to obtain the project analysis and matching result: the motion behavior classification information of the ultrasonic device is combined with the terminal power consumption data of the host, and a comprehensive analysis and comparison is performed to obtain the type of project performed by the ultrasonic device at this time according to the association and matching between them. Assume that the motion behavior classification information of the ultrasonic device indicates that the ultrasonic probe moves frequently and at a fast speed for a period of time, and the operation is relatively complex. At the same time, the terminal power consumption data of the host shows that the power consumption during this period is large and fluctuates significantly. Comprehensively analyze and compare these two sets of data: if it is found that this motion behavior and power consumption pattern usually appear in high-resolution, long-term organ scanning projects, and match the data characteristics of such projects in the past, then it can be concluded that the type of project performed by the ultrasonic device this time may be high-resolution organ scanning. For example, the motion behavior classification information shows that the probe movement is relatively simple and slow, while the terminal power consumption data shows that the power consumption is low and stable. After analysis and comparison, this may be consistent with the characteristics of conventional physical examination projects, so that the project type may be a conventional physical examination. Through such comprehensive analysis, the correlation and matching between the motion behavior classification information and the terminal power consumption data can be found, and the type of project being performed by the ultrasonic equipment can be inferred.

[0118] The beneficial effects of the above technologies include: the power supply behavior classification submodule obtains power supply behavior classification information based on the health of the power supply, which helps to optimize the power supply strategy in a targeted manner. The state pairing submodule obtains the operation state synchronization pairing thread through pairing calibration, which can more accurately grasp the overall operation of the equipment. The action thread generation submodule generates the ultrasound probe action record thread, providing a detailed data basis for subsequent analysis. The total thread fitting submodule sorts and fits the relevant threads to obtain the total thread of behavior records, realizing data integration and unification. The motion behavior classification submodule classifies motion behaviors based on the total thread, making the analysis of motion behaviors more organized. The project analysis submodule performs project analysis matching based on classification information and terminal power consumption data, providing a comprehensive decision-making basis for equipment management. On the whole, the deep integration and analysis of ultrasonic equipment operation data is achieved, which improves the refinement and scientificity of equipment management.

[0119] Embodiment 7:

[0120] Based on Example 6, the sports behavior classification submodule includes:

[0121] A behavior track pairing generation unit, used for acquiring all valid behavior tracks corresponding to the ultrasound probe based on all valid ultrasound probe recording threads in the total ultrasound device behavior recording thread;

[0122] A behavior similarity analysis unit, used for measuring the behavior similarity between the two ultrasonic probe behaviors corresponding to the two effective ultrasonic probe recording threads based on all effective ultrasonic probe recording threads and all effective behavior trajectories in the total ultrasonic device behavior recording thread;

[0123] The motion behavior classification unit is used to calibrate all ultrasonic probe behaviors recorded in the total thread of ultrasonic equipment behavior records into the same category based on the behavioral similarity between pairwise ultrasonic probe behaviors, obtain record sequences of multiple ultrasonic probe behavior categories, and classify the motion behaviors of ultrasonic equipment based on the confidence of the record sequences of multiple ultrasonic probe behavior categories to obtain motion behavior classification information of ultrasonic equipment.

[0124] In this embodiment, the effective ultrasound probe recording thread refers to the relevant actions and states performed when the ultrasound probe is paired with the currently managed ultrasound device, and is a probe recording data thread that has actual value and function in subsequent analysis, so as to facilitate the analysis of the project types performed by the currently managed ultrasound device during this period.

[0125] In this embodiment, the effective behavior trajectory refers to the movement path of the ultrasound probe corresponding to the effective ultrasound probe recording thread.

[0126] In this embodiment, the behavior similarity between the two ultrasonic probe behaviors corresponding to the two valid ultrasonic probe recording threads is measured based on all valid ultrasonic probe recording threads and all valid behavior trajectories in the total ultrasonic device behavior recording thread:

[0127] Assume that there is a total thread of ultrasound equipment behavior records There are effective ultrasound probe recording threads, denoted as , and the corresponding behavior trajectories are ;

[0128] For any two ultrasound probe recording threads and ( ), and their corresponding behavior trajectories are and ,in is the number of data points in the behavior trajectory (assuming that each behavior trajectory has the same length. If the actual length is different, appropriate normalization can be performed).

[0129] First, calculate the feature vector of each behavior trajectory:

[0130] For the trajectory , whose eigenvector ,

[0131] in It can be calculated as follows:

[0132]

[0133] Here is the behavior trajectory data point Some feature extraction function, for example, if Contains location and time information, This could be the average speed or acceleration over a specific period of time, etc. is the corresponding weight coefficient, indicating that different data points have different The importance of a feature;

[0134] Then, calculate the behavior similarity:

[0135]

[0136] in, represents the minimum sum of the corresponding elements of two eigenvectors, It represents the sum of the maximum values ​​of the corresponding elements of two eigenvectors, which partially reflects the similarity based on the eigenvectors. represents the sum of the absolute differences between the two behavior trajectories at each data point, is a normalization constant used to map the difference and to the interval [0,1], is the number of behavior trajectory data points. This part reflects the similarity of the behavior trajectory itself.

[0137] In this way, the behavioral similarity between pairwise ultrasound probe behaviors is measured by comprehensively considering the feature vector of the behavior trajectory and the differences in the trajectory data points themselves.

[0138] The specific functions and parameters in the above formula (such as , and ) can be further adjusted and determined according to the actual ultrasound probe behavior data characteristics and application requirements, for example:

[0139] Assume that the ultrasound probe behavior trajectory data points Including time and the position coordinates in three-dimensional space For the function , if we want to calculate the average speed in a time window as a feature, let the time window size be For speed To calculate the direction, first find all the data points in the time window that satisfy For all points, then calculate:

[0140]

[0141] in is the starting point of the time window Coordinates. Similarly, we can calculate and The speed and acceleration in the direction are used as other features. To calculate the direction, we can first calculate the velocity sequence, then derive the velocity sequence to get the acceleration sequence, and then take the average value as the feature. , assuming that we believe that the data points close to the center of the time window are more important, let the center of the time window be , the weight coefficient can be defined as:

[0142]

[0143] in is a parameter that controls the speed of weight decay. For example, (unit of time).

[0144] For the normalization constant , assuming that the maximum possible range of position variation of the ultrasound probe in normal operation is , , , then you can take for:

[0145]

[0146] In practical applications, adjustments and optimizations can be made based on the specific working characteristics and data features of the ultrasonic equipment.

[0147] In this embodiment, the behavior similarity between the behaviors of two ultrasonic probes is: the behaviors of any two ultrasonic probes are compared to obtain the similarity between them in terms of action characteristics, movement patterns, etc.

[0148] In this embodiment, the ultrasound probe behavior category: various behaviors of the ultrasound probe are divided into different categories or types according to certain standards or characteristics.

[0149] In this embodiment, the recording sequence of the ultrasound probe behavior categories is a sequence formed by recording the ultrasound probe behavior categories in chronological order.

[0150] In this embodiment, the confidence of the recording sequence of the ultrasound probe behavior category is a numerical value indicating the reliability or trustworthiness of the recording sequence of the ultrasound probe behavior category. The higher the confidence, the greater the accuracy and reliability of the recording sequence.

[0151] The beneficial effects of the above technologies include: the behavior trajectory pairing generation unit obtains effective behavior trajectories, providing a specific data basis for subsequent analysis. The behavior similarity analysis unit measures the behavior similarity and can quantify the similarity between different probe behaviors. The motion behavior classification unit performs similarity calibration and classification based on similarity, making the classification of motion behaviors more accurate and scientific. Classification is performed in combination with the confidence of the recorded sequence, which improves the reliability and stability of the classification results. The obtained motion behavior classification information helps to gain a deeper understanding of the motion patterns and laws of the ultrasonic equipment. Overall, the accuracy and effectiveness of the classification of the motion behavior of the ultrasonic equipment are improved, providing strong support for further equipment management and optimization.

[0152] Embodiment 8:

[0153] On the basis of Example 7, the sports behavior classification unit includes:

[0154] The same type behavior identification subunit is used to determine all ultrasound probe behaviors whose behavior similarity between two ultrasound probe behaviors exceeds a similarity threshold as belonging to the same type and calibrate them, and to record the ordinal values ​​of the valid ultrasound probe record threads corresponding to all ultrasound probe behaviors of the same type recorded in the ultrasound equipment behavior record total thread in sequence in the ultrasound equipment behavior record total thread, so as to obtain a record sequence of each ultrasound probe behavior category;

[0155] A confidence determination subunit, used to determine the confidence of the record sequence of each ultrasonic probe behavior category based on the behavior similarity between any two ultrasonic probe behaviors in all ultrasonic probe behaviors of each ultrasonic probe behavior category;

[0156] The behavior classification subunit is used to input the record sequences and corresponding confidences of all ultrasound probe behavior categories into the motion behavior classification model to obtain the motion behavior classification information of the ultrasound device.

[0157] In this embodiment, the similarity threshold is a pre-set numerical standard used to determine whether the similarity between the behaviors of two ultrasound probes reaches a level that allows them to be classified into the same category. If the similarity between the two behaviors exceeds this threshold, they are considered similar and can be classified into the same category; if it is lower than the threshold, they are considered dissimilar and cannot be classified into the same category. For example, if the similarity threshold is set to 80%, when the similarity between the behaviors of two ultrasound probes reaches 85%, they are considered to belong to the same category.

[0158] In this embodiment, the motion behavior classification model is a model constructed through data training, which is used to classify the motion behavior of the ultrasound device. It outputs the category of the motion behavior of the ultrasound device according to certain algorithms and rules based on the input relevant data, such as the record sequence of all ultrasound probe behavior categories and the corresponding confidence. For example: the record sequence of all ultrasound probe behavior categories of the ultrasound probe and the corresponding confidence are input into the motion behavior classification model, and the model outputs the result that the motion behavior belongs to the "fast scanning category" after calculation and analysis.

[0159] The beneficial effects of the above technology include: the similar behavior identification subunit calibrates similar behaviors by setting similarity thresholds, clarifying the standards for behavior classification. Recording the ordinal values ​​in sequence to obtain the record sequence helps to intuitively understand the distribution and order of similar behaviors. The confidence determination subunit determines the confidence based on the behavior similarity, providing a quantitative assessment of the reliability of the classification results. The behavior classification subunit inputs the record sequence and confidence into the motion behavior classification model, realizes intelligent classification, and improves the accuracy and efficiency of classification. The obtained motion behavior classification information can provide a more accurate basis for the management and optimization of ultrasonic equipment. On the whole, the classification of ultrasonic equipment motion behavior is made more scientific, accurate and reliable, which improves the level and quality of equipment management.

[0160] Embodiment 9:

[0161] On the basis of Example 8, the confidence determination subunit includes:

[0162] An average similarity calculation end, used for calculating the average similarity of all ultrasound probe behaviors of each ultrasound probe behavior category based on the behavior similarity between any two ultrasound probe behaviors in all ultrasound probe behaviors of each ultrasound probe behavior category;

[0163] A standard deviation calculation end, used to calculate the similarity standard deviation of all ultrasound probe behaviors of each ultrasound probe behavior category based on the behavior similarity between any two ultrasound probe behaviors in all ultrasound probe behaviors of each ultrasound probe behavior category;

[0164] The confidence calculation end is used to determine the confidence of the record sequence of each ultrasound probe behavior category based on the average similarity and similarity standard deviation of all ultrasound probe behaviors of each ultrasound probe behavior category.

[0165] Assume that there is a certain ultrasound probe behavior category record sequence The ultrasound probe behavior, and its corresponding pairwise behavior similarity matrix is ,in Indicates and The behavioral similarity between the behaviors of ultrasound probes ( ,and );

[0166] First, calculate the average similarity:

[0167]

[0168] Then, calculate the standard deviation of the similarity:

[0169]

[0170] Finally, determine the confidence level:

[0171]

[0172] if , a very small default value (such as 0.001) can be set to avoid the situation where the denominator is 0. This method determines the confidence by analyzing the distribution of similarities between similar ultrasound probe behaviors. The average similarity reflects the overall similarity of this type of behavior, and the standard deviation reflects the degree of dispersion of the similarity. The confidence measures the reliability of the record sequence of this behavior category through the relationship between the two. If the standard deviation is small, it means that the behavior similarity is more concentrated, and the confidence will be higher; conversely, if the standard deviation is large, the confidence will be lower.

[0173] The beneficial effects of the above technology include: the average similarity calculation end reflects the overall level of behavioral similarity within the category by calculating the average similarity. The similarity standard deviation calculated by the standard deviation calculation end reflects the degree of discreteness of behavioral similarity and measures the stability of the data. The confidence calculation end determines the confidence based on the average similarity and standard deviation, making the calculation of confidence more scientific and accurate. Accurate confidence can more reliably evaluate the reliability of the recording sequence of the ultrasound probe behavior category. It provides a more valuable reference for the classification of motion behavior, which helps to improve the accuracy and credibility of the classification. Overall, it enhances the scientificity and reliability of the classification of ultrasound equipment motion behavior, and provides stronger support for equipment management decisions.

[0174] Embodiment 10:

[0175] The present invention provides a method for fine management of ultrasonic equipment, which is applied to any one of the fine management systems of ultrasonic equipment in embodiments 1 to 9, referring to Figure 3 ,include:

[0176] S1: Comprehensive collection of ultrasound equipment operation data;

[0177] S2: Conduct cross-category combined mining on the operation data of the ultrasonic equipment, conduct behavioral classification and project analysis matching on the ultrasonic equipment, and obtain the behavioral classification and project analysis matching results;

[0178] S3: Use deep learning algorithms and big data analysis methods to build device-side evaluation models;

[0179] S4: Use the device-side evaluation model to conduct in-depth mining and analysis on the ultrasound equipment operation data, behavior classification, and project analysis matching results to predict the device-side evaluation results;

[0180] S5: Generate customized device management data reports based on device-side evaluation results.

[0181] The beneficial effects of the above technology include: Step S1 can comprehensively collect relevant data on the operation of ultrasonic equipment, providing rich and accurate original information for subsequent analysis and management. Step S2 uses cross-category combined mining and classification analysis matching to gain an in-depth understanding of the equipment's operating behavior and project requirements, laying the foundation for precise management. The equipment-side evaluation model constructed in step S3 uses deep learning algorithms and big data analysis to improve the scientificity and accuracy of the evaluation. Step S4 uses the model for deep mining and analysis to accurately predict the equipment-side evaluation results and provide a strong basis for management decisions. Step S5 generates customized reports based on the evaluation results, making management data more targeted and practical, and facilitating managers to make scientific decisions. Overall, the refinement and intelligence of the entire process of ultrasonic equipment management has been achieved, which has improved management efficiency and quality, helped extend equipment life, reduced maintenance costs, and improved the use efficiency of equipment.

[0182] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. Ultrasonic equipment refined management system, characterized by: include: Data acquisition module, used to collect all-round operation data of ultrasonic equipment; The cross-category combination module is used to conduct cross-category combination mining on the operation data of ultrasonic equipment, classify the behavior of ultrasonic equipment and conduct project analysis and matching to obtain the behavior classification and project analysis and matching results; Model building module, which is used to build device-side evaluation models using deep learning algorithms and big data analysis methods; The equipment evaluation module is used to use the equipment-side evaluation model to conduct in-depth mining and analysis on the ultrasonic equipment operation data, behavior classification, and project analysis matching results, and predict the equipment-side evaluation results; Report generation module, used to generate customized device management data reports based on device-side evaluation results; Among them, cross-category combination modules include: A power supply behavior classification submodule, used to obtain power supply behavior classification information of the ultrasonic device based on the power supply health of the ultrasonic device host; The state pairing submodule is used to perform pairing calibration on the operation state data of the ultrasonic device based on the pairing data of the ultrasonic device, and obtain the operation state synchronization pairing thread of the host and probe of the ultrasonic device; An action thread generation submodule, used to generate at least one valid ultrasound probe recording thread based on ultrasound probe motion data; The total thread fitting submodule is used to synchronize the pairing threads based on the operating status of the host and probe of the ultrasound device to sort and fit the total thread of all currently acquired valid ultrasound probe recording threads to obtain the total thread of ultrasound device behavior records; A motion behavior classification submodule is used to classify the motion behavior of the ultrasonic device based on the total thread of the ultrasonic device behavior record, and obtain the motion behavior classification information of the ultrasonic device; The project analysis submodule is used to perform project analysis and matching based on the motion behavior classification information of the ultrasonic device and the terminal power consumption data of the ultrasonic device host to obtain the project analysis and matching results; The behavior classification and project analysis matching results include behavior classification results and project analysis matching results, and the behavior classification results include power supply behavior classification information and motion behavior classification information.

2. The ultrasonic equipment refined management system according to claim 1, characterized in that: The ultrasonic equipment operation data includes ultrasonic equipment power supply and consumption data, ultrasonic equipment pairing data, ultrasonic probe movement data, and ultrasonic equipment operation status data.

3. The ultrasonic equipment refined management system according to claim 1, characterized in that: Data acquisition module, including: The power supply and consumption data acquisition submodule is used to obtain the power supply health of the ultrasound equipment host and the terminal power consumption data of the ultrasound equipment host as the power supply and consumption data of the ultrasound equipment; The pairing monitoring submodule is used to obtain the pairing list of the main body and the probe of the ultrasound device in real time as the pairing data of the ultrasound device; A motion monitoring submodule, used to obtain the motion data of the ultrasound probe based on a high-sensitivity motion sensor; The status record monitoring submodule is used to obtain the host operation status record and probe usage record of the ultrasound device as the ultrasound device operation status data.

4. The ultrasonic equipment refined management system according to claim 3, characterized in that: The power supply and consumption data acquisition submodule includes: A power supply monitoring unit, used to monitor the power connection stability of the ultrasonic device using a photoelectric detection sensor, and obtain the power supply health of the ultrasonic device host based on the power connection stability analysis; The power consumption monitoring unit is used to obtain the terminal power consumption data of the ultrasound equipment host.

5. The ultrasonic equipment refined management system according to claim 3, characterized in that: Motion monitoring submodule, including: An action judgment result acquisition unit is used to acquire the action feature quantity of the ultrasonic probe in real time based on the high-sensitivity motion sensor, and obtain the action judgment result of the ultrasonic probe based on the action feature quantity of the ultrasonic probe, wherein the action feature quantity includes action direction, action speed, action acceleration, and action angle; An action duration statistics unit is used to count the duration of each action in the action judgment result; The ultrasonic probe motion data includes the motion judgment result of the ultrasonic probe and the duration of each motion in the motion judgment result.

6. The ultrasonic equipment refined management system according to claim 1, characterized in that: Movement behavior classification submodule, including: A behavior track pairing generation unit, used for acquiring all valid behavior tracks corresponding to the ultrasound probe based on all valid ultrasound probe recording threads in the total ultrasound device behavior recording thread; A behavior similarity analysis unit, used for measuring the behavior similarity between the two ultrasonic probe behaviors corresponding to the two effective ultrasonic probe recording threads based on all effective ultrasonic probe recording threads and all effective behavior trajectories in the total ultrasonic device behavior recording thread; The motion behavior classification unit is used to calibrate all ultrasonic probe behaviors recorded in the total thread of ultrasonic equipment behavior records into the same category based on the behavioral similarity between pairwise ultrasonic probe behaviors, obtain record sequences of multiple ultrasonic probe behavior categories, and classify the motion behaviors of ultrasonic equipment based on the confidence of the record sequences of multiple ultrasonic probe behavior categories to obtain motion behavior classification information of ultrasonic equipment.

7. The ultrasonic equipment refined management system according to claim 6, characterized in that: Movement behavior classification units include: The same type behavior identification subunit is used to determine all ultrasound probe behaviors whose behavior similarity between two ultrasound probe behaviors exceeds a similarity threshold as belonging to the same type and calibrate them, and to record the ordinal values ​​of the valid ultrasound probe record threads corresponding to all ultrasound probe behaviors of the same type recorded in the ultrasound equipment behavior record total thread in sequence in the ultrasound equipment behavior record total thread, so as to obtain a record sequence of each ultrasound probe behavior category; A confidence determination subunit, used to determine the confidence of the record sequence of each ultrasonic probe behavior category based on the behavior similarity between any two ultrasonic probe behaviors in all ultrasonic probe behaviors of each ultrasonic probe behavior category; The behavior classification subunit is used to input the record sequences and corresponding confidences of all ultrasound probe behavior categories into the motion behavior classification model to obtain the motion behavior classification information of the ultrasound device.

8. The ultrasonic equipment refined management system according to claim 7, characterized in that: The confidence determination subunit includes: An average similarity calculation end, used for calculating the average similarity of all ultrasound probe behaviors of each ultrasound probe behavior category based on the behavior similarity between any two ultrasound probe behaviors in all ultrasound probe behaviors of each ultrasound probe behavior category; A standard deviation calculation end, used to calculate the similarity standard deviation of all ultrasound probe behaviors of each ultrasound probe behavior category based on the behavior similarity between any two ultrasound probe behaviors in all ultrasound probe behaviors of each ultrasound probe behavior category; The confidence calculation end is used to determine the confidence of the record sequence of each ultrasound probe behavior category based on the average similarity and similarity standard deviation of all ultrasound probe behaviors of each ultrasound probe behavior category.

9. A refined management method for ultrasonic equipment, characterized in that: The ultrasonic equipment refined management system applied to any one of claims 1 to 8 comprises: S1: Comprehensive collection of ultrasound equipment operation data; S2: Conduct cross-category combined mining on the operation data of the ultrasonic equipment, conduct behavioral classification and project analysis matching on the ultrasonic equipment, and obtain the behavioral classification and project analysis matching results; S3: Use deep learning algorithms and big data analysis methods to build device-side evaluation models; S4: Use the device-side evaluation model to conduct in-depth mining and analysis on the ultrasound equipment operation data, behavior classification, and project analysis matching results to predict the device-side evaluation results; S5: Generate customized device management data reports based on device-side evaluation results.

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