Ultrasonic parameter monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of parameter monitoring, and discloses an ultrasonic parameter monitoring method and device, computer equipment and a storage medium, and the method comprises the steps: extracting ultrasonic parameters, and carrying out the division and classification of the ultrasonic parameters, and obtaining a classified parameter structure; generating a parameter tree by the classified parameter structure according to a node layering mode; performing node comparison on the parameter nodes stored in the parameter tree and parameter nodes backed up in advance to obtain a comparison result; according to the method, the ultrasonic parameters are extracted and classified, the classified parameter structure generates the parameter tree according to a node layering mode, the parameter tree is used for storing the parameter nodes, and the hierarchical relation of the parameter tree is used for monitoring the influence of the upper layer parameter change on the lower layer parameter change. And the parameter nodes stored in the parameter tree are compared with the parameter nodes which are backed up in advance, so that the ultrasonic parameters are automatically monitored.

Description

Technical Field

[0001] The present invention relates to the technical field of parameter monitoring, and in particular to an ultrasonic parameter monitoring method, device, computer equipment and storage medium. Background Art

[0002] At present, medical ultrasound imaging is widely used in departments such as radiology, cardiac surgery, gynecology and obstetrics. Ultrasound imaging equipment emits ultrasound waves, which pass through the human body surface. During the ultrasound imaging process, the ultrasound system generates a large number of parameters. Doctors or related staff can use ultrasound parameters to calculate related indicators, and related staff can also call parameters for analysis.

[0003] In the prior art, relevant staff usually manually organize ultrasound parameters, sort out the linkage range of parameter changes, and monitor parameter changes. However, this method relies on highly professional talents and consumes a lot of manpower and time costs. Summary of the invention

[0004] In view of this, the present invention provides an ultrasonic parameter monitoring method, device, computer equipment and storage medium to solve the problem in the prior art of manually monitoring parameter changes, which results in a large amount of manpower cost and time cost.

[0005] In a first aspect, the present invention provides an ultrasonic parameter monitoring method, the method comprising:

[0006] Extracting ultrasound parameters, dividing and classifying the ultrasound parameters, and obtaining a classified parameter structure;

[0007] Generate a parameter tree by hierarchical node structure based on the classified parameter structure;

[0008] Compare the parameter nodes stored in the parameter tree with the pre-backed-up parameter nodes to obtain a comparison result;

[0009] The changes of ultrasound parameters are automatically monitored based on the comparison results.

[0010] The present invention divides and classifies the extracted ultrasonic parameters, generates a parameter tree according to the node layering method for the classified ultrasonic parameters, uses the parameter tree to store parameter nodes, and uses the hierarchical structure of the parameter tree itself to monitor the impact of upper-layer parameter changes on lower-layer parameter changes. By comparing the parameter nodes stored in the parameter tree with the pre-backed-up parameter nodes, the changes in the ultrasonic parameters are automatically monitored according to the comparison results, replacing manual monitoring, reducing the dependence on highly professional talents, and saving labor costs and time costs.

[0011] In an optional implementation, the ultrasound parameters are divided and classified to obtain a classified parameter structure, including:

[0012] Classifying the ultrasound parameters according to the ultrasound imaging mode to obtain the first layer of ultrasound parameters;

[0013] Classify the ultrasonic parameters according to the parameter action range to obtain the second layer of ultrasonic parameters;

[0014] Classify the ultrasonic parameters according to the specific services related to the parameters to obtain the third-layer ultrasonic parameters;

[0015] The three-layer ultrasound parameters consisting of the first-layer ultrasound parameters, the second-layer ultrasound parameters and the third-layer ultrasound parameters are determined as the classified ultrasound parameters.

[0016] The present invention classifies ultrasound parameters according to ultrasound mode imaging mode, parameter scope, and parameter specific related business, and combines the classified first-layer ultrasound parameters, second-layer ultrasound parameters, and third-layer ultrasound parameters to complete the classification of ultrasound parameters, so as to facilitate the analysis of ultrasound parameters.

[0017] In an optional implementation, the classified parameter structure is used to generate a parameter tree in a node-layered manner, including:

[0018] The three layers of ultrasound parameters are connected in series to generate parameter trees corresponding to each ultrasound imaging mode;

[0019] Determine whether the linkage parameters have branch nodes corresponding to the three-layer ultrasound parameters;

[0020] If the linkage parameters do not have a branch node corresponding to the three-layer ultrasound parameters, a corresponding branch node is added to the three-layer ultrasound parameters.

[0021] The present invention generates a parameter tree corresponding to each ultrasound imaging mode by connecting three layers of ultrasound parameters in series, and adds corresponding branch nodes in the three layers of ultrasound parameters when there are no branch nodes corresponding to the three layers of ultrasound parameters in the linkage parameters, so as to establish a complete parameter tree, thereby facilitating the monitoring of the parameter nodes stored in the parameter tree.

[0022] In an optional implementation, before comparing the parameter nodes stored in the parameter tree with the pre-backed-up parameter nodes, the method further includes:

[0023] Use comparison tables to preset linkage paths for linkage parameters;

[0024] Traverse the comparison table to determine the linkage path corresponding to the linkage parameters.

[0025] The present invention uses a comparison table to preset linkage paths of linkage parameters, and determines linkage paths corresponding to linkage parameters by traversing comparison table results, so as to compare parameter nodes.

[0026] In an optional implementation, determining a linkage path corresponding to a linkage parameter includes:

[0027] Use smoke scripts to randomly adjust parameters;

[0028] If the triggered linkage path does not exist in the comparison table, the linkage path will be inserted into the comparison table, and the comparison table will be used to count the triggers. After the comparison table is trained and perfected to reach a stable state, useless records in the comparison table will be removed to obtain the specific path of parameter linkage.

[0029] The present invention utilizes a smoke script to perform random parameter adjustment. The smoke script automatically performs random parameter adjustment without manual intervention, thereby improving the efficiency of parameter adjustment. When a triggered linkage path does not exist in a comparison table, the linkage path is inserted into the comparison table to obtain a completed linkage path. After the comparison table training reaches a stable state, useless records in the comparison table are removed to avoid interference caused by useless records in determining a specific path for parameter linkage.

[0030] In an optional embodiment, automatically monitoring the change of the ultrasound parameter according to the comparison result includes:

[0031] If the comparison result is consistent, it is determined that the ultrasound parameters have not changed;

[0032] If the comparison result is inconsistent, it is determined that the ultrasound parameters have changed.

[0033] The present invention determines whether the ultrasonic parameters have changed by comparing the current parameters with the backup parameters, so as to monitor the ultrasonic parameters when the ultrasonic parameters have changed.

[0034] In an optional embodiment, after determining that the ultrasound parameter has changed, the method further includes:

[0035] Automatically monitor changes in ultrasonic parameters, obtain monitoring results, and update backup parameters based on the monitoring results.

[0036] The present invention updates the backup parameters according to the monitoring results of the automatically monitored ultrasonic parameter changes, so that when the parameters are compared again, the data source is the latest backup parameters, thereby improving the accuracy of the parameter comparison results.

[0037] In a second aspect, the present invention provides an ultrasonic parameter monitoring device, the device comprising:

[0038] A classification module is used to extract ultrasound parameters, divide and classify the ultrasound parameters, and obtain a classified parameter structure;

[0039] A generation module is used to generate a parameter tree according to the node layering method of the classified parameter structure;

[0040] A comparison module is used to compare the parameter nodes stored in the parameter tree with the pre-backed-up parameter nodes to obtain a comparison result;

[0041] The monitoring module is used to automatically monitor changes in ultrasound parameters based on the comparison results.

[0042] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the ultrasonic parameter monitoring method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the ultrasonic parameter monitoring method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 is a schematic flow chart of an ultrasonic parameter monitoring method according to an embodiment of the present invention;

[0046] Figure 2 is a flow chart of another ultrasonic parameter monitoring method according to an embodiment of the present invention;

[0047] Figure 3 is a structural block diagram of an ultrasonic parameter monitoring device according to an embodiment of the present invention;

[0048] Figure 4 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0050] In related technologies, the parameters of the ultrasound system are sent to the FPGA (Field Programmable Gate Array) so that the FPGA can configure ultrasound signals and other operations according to the parameters, and it is also convenient for users to adjust the parameters. This process involves parameter changes and linkage calculation, sending, and storage. This process is very cumbersome and prone to errors, and some detailed errors are difficult to detect.

[0051] According to an embodiment of the present invention, an embodiment of an ultrasonic parameter monitoring method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0052] In this embodiment, an ultrasonic parameter monitoring method is provided, which can be used in a mobile terminal. Figure 1 is a flow chart of an ultrasonic parameter monitoring method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0053] Step S101, extracting ultrasound parameters, and classifying the ultrasound parameters to obtain a classified parameter structure.

[0054] In the embodiment of the present invention, the staff performs ultrasonic testing through ultrasonic testing equipment such as ultrasonic testing instruments, and a large number of ultrasonic parameters are generated during the human-computer interaction process. The ultrasonic parameters generated by the ultrasonic testing equipment during the ultrasonic testing process are extracted. The ultrasonic parameters include detection parameters related to the ultrasonic probe and parameters common to the ultrasonic system. The ultrasonic parameters are divided and classified to obtain a classified parameter structure, so as to facilitate the analysis of the ultrasonic parameters.

[0055] Step S102: Generate a parameter tree by arranging the classified parameter structure in a node-layered manner.

[0056] In the embodiment of the present invention, the classified parameter structure is used as a branch node, and a parameter tree is generated in a node hierarchical manner. Specifically, the parameter tree refers to a structure formed by the parameters according to a logical structure during the data analysis process, and the tree structure of the parameter tree is determined by the logical relationship between the parameters. The parameter tree corresponding to the ultrasound parameters is established with the classified parameter structure as a branch node.

[0057] Since the parameter tree has a hierarchical structure, the parameter tree can be used to monitor the impact of changes in upper-level parameters on changes in lower-level parameters, and to monitor parameter linkage. The parameter tree can compare parameter linkage input, output, and process parameters, and verify the correctness of parameter linkage. It can easily and quickly locate the error point and report the error path to facilitate locating the error node and improve debugging efficiency. At the same time, the introduction of the parameter tree can generate a parameter linkage sequence table for the linkage process, and compare and verify it with the parameter linkage table obtained by the traditional manual combing of the linkage range. The automatic monitoring of the parameter linkage range and the manual combing of the linkage range complement each other and verify each other, thereby reducing the probability of errors.

[0058] Step S103: perform node comparison between the parameter nodes stored in the parameter tree and the parameter nodes backed up in advance to obtain a comparison result.

[0059] In an embodiment of the present invention, parameter changes are monitored by comparing parameter nodes, and the parameter nodes stored in the parameter tree and the pre-backed-up parameter nodes are compared. The parameter nodes stored in the parameter tree and the pre-backed-up parameter nodes are used as data sources for comparison to obtain comparison results.

[0060] Step S104: Automatically monitor changes in ultrasound parameters according to the comparison results.

[0061] In an embodiment of the present invention, changes in ultrasound parameters are monitored based on comparison results between parameter nodes stored in the parameter tree and pre-backed-up parameter nodes, thereby automatically monitoring changes in ultrasound parameters to perform operations such as parameter calculation, distribution and storage.

[0062] The ultrasonic parameter monitoring method provided in the present embodiment divides and classifies the extracted ultrasonic parameters, generates a parameter tree according to the node layering method for the classified ultrasonic parameters, stores parameter nodes by using the parameter tree, and uses the hierarchical structure of the parameter tree itself to monitor the impact of upper-layer parameter changes on lower-layer parameter changes, and compares the parameter nodes stored in the parameter tree with the pre-backed-up parameter nodes to automatically monitor the changes in ultrasonic parameters based on the comparison results, instead of manual monitoring, thereby reducing dependence on highly professional talents and saving labor costs and time costs.

[0063] In this embodiment, an ultrasonic parameter monitoring method is provided, which can be used in the above-mentioned mobile terminal. Figure 2 is a flow chart of an ultrasonic parameter monitoring method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0064] Step S201, extracting ultrasound parameters, and classifying the ultrasound parameters to obtain a classified parameter structure.

[0065] Specifically, in the above step S201, the ultrasound parameters are divided and classified, and the classified parameter structure includes:

[0066] Step S2011, classifying the ultrasound parameters according to the ultrasound imaging mode to obtain the first layer of ultrasound parameters.

[0067] Step S2012: classify the ultrasound parameters according to the parameter action range to obtain the second layer of ultrasound parameters.

[0068] Step S2013: classify the ultrasonic parameters according to the specific services related to the parameters to obtain the third layer of ultrasonic parameters.

[0069] Step S2014: determining three layers of ultrasound parameters consisting of the first layer ultrasound parameters, the second layer ultrasound parameters and the third layer ultrasound parameters as classified ultrasound parameters.

[0070] In the embodiment of the present invention, the ultrasound parameters are divided and classified into three structures.

[0071] The first layer is classified according to the ultrasound imaging mode, which is divided into B mode, C mode, PW mode, CW mode and M mode, etc. Among them, the B mode uses the brightness mode to display the echo strength. The B mode scan can obtain a grayscale image that reflects the body's tissue structure, that is, a black and white B frame image; the C mode scan can obtain a C frame image that reflects the blood flow distribution information in the body; the PW mode uses the pulse Doppler mode to transmit and receive pulse signals to obtain blood flow information; the CW mode uses continuous Doppler technology for inspection; the M mode is recorded on a fixed sampling line to observe the changes in tissues and organs over time.

[0072] The second layer is classified according to the scope of parameter application, which can be divided into front-end parameters and back-end parameters. The front-end parameters are related to FPGA and involve imaging, while the back-end parameters are related to image algorithms and involve image post-processing.

[0073] The third layer classifies parameters according to their specific related services. Front-end parameters are divided into transmission-related, beamforming, extraction, timing, signal processing, and upload, etc. Back-end parameters are mostly generated offline, and only need to read parameters according to rules. Some parameters that require simple calculations are divided into two categories: direct reading and calculation generation.

[0074] Specifically, emission correlation refers to the parameters generated in the initial stage of ultrasonic imaging, which are usually the parameters generated by the ultrasonic probe (or ultrasonic transducer) converting electrical energy into ultrasonic energy for emission. Beamforming refers to the parameters generated during the beamforming process when the ultrasonic wave encounters different tissue interfaces after emission and the ultrasonic probe receives the reflected signal. Extraction refers to the parameters generated during the process of removing noise and enhancing signals from the transmitted wave. Timing refers to the parameters generated when calculating the round-trip time of the ultrasonic wave during ultrasonic imaging. Signal processing refers to the parameters generated by the processing of ultrasonic waves and data during ultrasonic imaging. Upload refers to uploading the parameters to the cloud or ultrasound system.

[0075] The ultrasound parameters are divided and classified mainly based on the ultrasound system solution document. The ultrasound system solution document is parsed by a preset script to automatically generate labels for the meaning of each layer of parameters to facilitate subsequent binding of parameters.

[0076] Ultrasound parameters are classified according to ultrasound mode imaging mode, parameter scope, and parameter-specific related services, and the classified first-layer ultrasound parameters, second-layer ultrasound parameters, and third-layer ultrasound parameters are combined to complete the classification of ultrasound parameters, so as to facilitate analysis of ultrasound parameters.

[0077] Step S202: Generate a parameter tree by arranging the classified parameter structure in a node-layered manner.

[0078] Specifically, the above step S202 includes:

[0079] Step S2021: connect the three layers of ultrasound parameters in series to generate a parameter tree corresponding to each ultrasound imaging mode.

[0080] Step S2022, determining whether the linkage parameters have branch nodes corresponding to the three-layer ultrasound parameters.

[0081] Step S2023: If the linkage parameters do not have a branch node corresponding to the three-layer ultrasound parameters, a corresponding branch node is added to the three-layer ultrasound parameters.

[0082] In an embodiment of the present invention, the three layers of ultrasound parameters divided above are connected in series by encoding in the initial stage. Since ultrasound has different imaging modes, the parameters corresponding to each ultrasound imaging mode are divided according to the ultrasound imaging mode to generate a parameter tree. In the process of human-computer interaction parameter linkage, several nodes of each ultrasound imaging mode parameter tree will be passed through. For each ultrasound imaging mode parameter tree, these passed nodes constitute a sub-parameter tree. For the human-computer interaction parameters, the above sub-parameter trees are combined to construct a parameter tree.

[0083] Each linkage parameter has a corresponding trigger condition for the entire parameter model, that is, the trigger of the parameter after the user's operation is the change of the gear adjusted by the user, and the change of the gear adjusted by the user is reflected in the three-layer ultrasound parameter model. Therefore, it is necessary to determine whether the linkage parameter exists with the three-layer ultrasound parameter. If not, it proves that the three-layer ultrasound parameter is true, and it is necessary to insert the node to improve the three-layer ultrasound parameter.

[0084] First, determine whether the linkage parameters have branch nodes corresponding to the three-layer ultrasound parameters. If there are branch nodes corresponding to the three-layer ultrasound parameters in the pre-existing branch nodes of the linkage parameters, skip the branch node. If there are no branch nodes corresponding to the three-layer ultrasound parameters in the pre-existing branch nodes of the parameter tree, add the corresponding branch nodes in the three-layer ultrasound parameters and update the parameter tree.

[0085] By connecting the three layers of ultrasound parameters in series, a parameter tree corresponding to each ultrasound imaging mode is generated, and when the linkage parameters do not have branch nodes corresponding to the three layers of ultrasound parameters, corresponding branch nodes are added to the three layers of ultrasound parameters to establish a complete parameter tree, thereby facilitating the monitoring of the parameter nodes stored in the parameter tree.

[0086] Step S203: Preset the linkage path of the linkage parameters using the comparison table.

[0087] Step S204, traverse the comparison table to determine the linkage path corresponding to the linkage parameter.

[0088] In the embodiment of the present invention, during the human-computer interaction process, the linkage path of the preset linkage parameters is recorded using a comparison table to facilitate data analysis. The comparison table can also be traversed to determine the linkage path corresponding to the linkage parameter in combination with the parameter linkage general interface.

[0089] The comparison table may be generated during the human-computer interaction operation. New nodes may be added during each human-computer interaction operation, and it is necessary to store the new nodes and update the comparison table. The comparison table may also be pre-configured.

[0090] The linkage path of the linkage parameter is preset by using the comparison table, and the linkage path corresponding to the linkage parameter is determined by traversing the comparison table results, so as to compare the parameter nodes.

[0091] Specifically, the above step S204 includes:

[0092] Step S2041, using the smoke script to perform random parameter adjustments.

[0093] Step S2042, if the triggered linkage path does not exist in the comparison table, the linkage path is inserted into the comparison table, and the trigger count is performed using the comparison table. After the comparison table is trained and perfected to reach a stable state, useless records in the comparison table are removed to obtain the specific path of parameter linkage.

[0094] In an embodiment of the present invention, the smoke script is a smoke test script, and the smoke script can be used to automatically adjust the parameters randomly without manual intervention to achieve automatic parameter adjustment. If the triggered linkage path does not exist in the above-mentioned comparison table, the linkage path is inserted into the above-mentioned comparison table, and the trigger count is performed on the comparison table to facilitate comparison of parameter nodes. After the comparison table is trained and perfected and reaches a stable state, useless records in the comparison table are removed to avoid interference caused by useless records in determining the specific path of parameter linkage.

[0095] The human-computer interaction operation process is recorded, and these operations are randomly shuffled and combined. The script is used to replace the manual operation, and the complete path node is determined in advance. The path node is the path node of the combined parameter tree of each human-computer interaction.

[0096] When comparing parameters, it is possible that the parameters on the path have not changed, but the parameters on the path may affect the changes of other parameters. Therefore, comparing by path can avoid the situation where parameter changes cannot be monitored.

[0097] The parameters are automatically adjusted randomly through the smoke script without manual intervention, which improves the efficiency of parameter adjustment. When the triggered linkage path does not exist in the comparison table, the linkage path is inserted into the comparison table to obtain the completed linkage path. After the comparison table training reaches a stable state, the useless records in the comparison table are removed to avoid interference with the specific path of determining the parameter linkage caused by useless records.

[0098] Step S205: perform node comparison between the parameter nodes stored in the parameter tree and the parameter nodes backed up in advance to obtain a comparison result.

[0099] For details, please see Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0100] Step S206: Automatically monitor changes in ultrasound parameters based on the comparison results.

[0101] Specifically, the above step S206 includes:

[0102] Step S2061: If the comparison result is consistent, it is determined that the ultrasound parameters have not changed.

[0103] Step S2062: If the comparison result is inconsistent, it is determined that the ultrasound parameters have changed.

[0104] In the embodiment of the present invention, parameter changes are monitored by comparing parameters, using the current parameters and backup parameters as the data source for comparison, comparing the trigger parameter nodes of the source, and comparing the parameters in layers to improve efficiency. If the comparison results of the current parameters and the backup parameters are consistent, it is determined that the ultrasonic parameters have not changed. If the comparison results of the current parameters and the backup parameters are inconsistent, it is determined that the ultrasonic parameters have changed, so as to realize the monitoring of ultrasonic parameter changes.

[0105] By comparing the current parameters with the backup parameters, it is determined whether the ultrasonic parameters have changed, so that the ultrasonic parameters can be monitored when they have changed.

[0106] Step S2063, automatically monitor the changes of ultrasonic parameters, obtain monitoring results, and update backup parameters according to the monitoring results.

[0107] In an embodiment of the present invention, changes in ultrasonic parameters are automatically monitored to obtain monitoring results. When ultrasonic parameters change, corresponding parameter linkage is triggered, and backup parameters are updated according to the monitoring results, so that the latest data source is used when comparing parameters to achieve the purpose of monitoring parameter changes.

[0108] The ultrasonic parameter monitoring method provided in this embodiment updates the backup parameters according to the monitoring results of the ultrasonic parameter changes automatically monitored, so that when the parameters are compared again, the data source is the latest backup parameters, thereby improving the accuracy of the parameter comparison results.

[0109] In this embodiment, an ultrasonic parameter monitoring device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0110] This embodiment provides an ultrasonic parameter monitoring device, such as Figure 3 As shown, including:

[0111] The classification module 301 is used to extract ultrasound parameters, and classify the ultrasound parameters to obtain a classified parameter structure.

[0112] The generating module 302 is used to generate a parameter tree according to the node layering method based on the classified parameter structure.

[0113] The comparison module 303 is used to compare the parameter nodes stored in the parameter tree with the pre-backed-up parameter nodes to obtain a comparison result.

[0114] The monitoring module 304 is used to automatically monitor changes in ultrasound parameters according to the comparison results.

[0115] In some optional implementations, the classification module 301 includes:

[0116] The first classification unit is used to classify the ultrasound parameters according to the ultrasound imaging mode to obtain the first layer of ultrasound parameters.

[0117] The second classification unit is used to classify the ultrasound parameters according to the parameter action range to obtain the second layer of ultrasound parameters.

[0118] The third classification unit is used to classify the ultrasonic parameters according to specific services related to the parameters to obtain the third layer of ultrasonic parameters.

[0119] The first determining unit is used to determine three layers of ultrasound parameters consisting of the first layer ultrasound parameters, the second layer ultrasound parameters and the third layer ultrasound parameters as classified ultrasound parameters.

[0120] In some optional implementations, the generating module 302 includes:

[0121] The generating unit is used to connect the three layers of ultrasound parameters in series to generate a parameter tree corresponding to each ultrasound imaging mode.

[0122] The judgment unit is used to judge whether there are branch nodes corresponding to the three-layer ultrasound parameters in the linkage parameters.

[0123] A new unit is added, which is used to add a corresponding branch node in the three-layer ultrasound parameter if the linkage parameter does not have a branch node corresponding to the three-layer ultrasound parameter.

[0124] In some optional embodiments, the device further comprises:

[0125] The preset module is used to preset the linkage path of the linkage parameters by using the comparison table.

[0126] The traversal module is used to traverse the comparison table and determine the linkage path corresponding to the linkage parameters.

[0127] In some optional implementations, the traversal module includes:

[0128] The parameter adjustment unit is used to perform random parameter adjustment using the smoke script.

[0129] The insertion unit is used to insert the linkage path into the comparison table if the triggered linkage path does not exist in the comparison table, and use the comparison table to perform trigger counting. After the comparison table is trained and perfected to reach a stable state, useless records in the comparison table are removed to obtain the specific path of parameter linkage.

[0130] In some optional embodiments, the monitoring module 304 includes:

[0131] The second determining unit is used to determine that the ultrasound parameter has not changed if the comparison result is consistent.

[0132] The third determining unit is configured to determine that the ultrasound parameter has changed if the comparison result is inconsistent.

[0133] In some optional embodiments, the device further comprises:

[0134] The update module is used to automatically monitor changes in ultrasonic parameters, obtain monitoring results, and update backup parameters based on the monitoring results.

[0135] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0136] The ultrasonic parameter monitoring device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0137] The embodiment of the present invention also provides a computer device having the above Figure 3 The ultrasonic parameter monitoring device shown.

[0138] See also Figure 4 , Figure 4 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 4 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0139] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0140] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0141] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0142] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0143] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0144] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, etc. The output device 40 can include a display device, etc.

[0145] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0146] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. An ultrasonic parameter monitoring method, characterized in that: The method comprises: Extracting ultrasound parameters, and classifying the ultrasound parameters to obtain a classified parameter structure; Generating a parameter tree by layering the classified parameter structure in a node-by-node manner; Compare the parameter nodes stored in the parameter tree with the pre-backed-up parameter nodes to obtain a comparison result; The changes of the ultrasonic parameters are automatically monitored according to the comparison results.

2. The method according to claim 1, characterized in that The ultrasonic parameters are divided and classified to obtain a classified parameter structure, including: Classifying the ultrasound parameters according to ultrasound imaging modes to obtain first-layer ultrasound parameters; Classifying the ultrasonic parameters according to the parameter action range to obtain the second layer of ultrasonic parameters; Classifying the ultrasonic parameters according to specific services related to the parameters to obtain third-layer ultrasonic parameters; The three-layer ultrasound parameters consisting of the first-layer ultrasound parameters, the second-layer ultrasound parameters and the third-layer ultrasound parameters are determined as the classified ultrasound parameters.

3. The method according to claim 2, characterized in that The step of generating a parameter tree by layering the classified parameter structure in a node-by-node manner includes: Connecting the three layers of ultrasound parameters in series to generate parameter trees corresponding to each ultrasound imaging mode; Determine whether the linkage parameters have branch nodes corresponding to the three-layer ultrasound parameters; If the linkage parameter does not have a branch node corresponding to the three-layer ultrasound parameter, a corresponding branch node is added to the three-layer ultrasound parameter.

4. The method according to claim 1, characterized in that: Before comparing the parameter nodes stored in the parameter tree with the pre-backed-up parameter nodes, the method further includes: Use comparison tables to preset linkage paths for linkage parameters; The comparison table is traversed to determine the linkage path corresponding to the linkage parameter.

5. The method according to claim 4, characterized in that The step of determining a linkage path corresponding to the linkage parameter includes: Use smoke scripts to adjust parameters randomly; If the triggered linkage path does not exist in the comparison table, the linkage path is inserted into the comparison table, and the comparison table is used to perform trigger counting. After the comparison table is trained and perfected to reach a stable state, useless records in the comparison table are removed to obtain the specific path of parameter linkage.

6. The method according to claim 1, characterized in that The automatically monitoring the change of the ultrasonic parameter according to the comparison result comprises: If the comparison result is consistent, it is determined that the ultrasound parameter has not changed; If the comparison result is inconsistent, it is determined that the ultrasound parameter has changed.

7. The method according to claim 6, characterized in that After determining that the ultrasound parameter has changed, the method further includes: Automatically monitor changes in the ultrasonic parameters, obtain monitoring results, and update backup parameters according to the monitoring results.

8. An ultrasonic parameter monitoring device, characterized in that: The device comprises: A classification module, used for extracting ultrasound parameters, and classifying the ultrasound parameters to obtain a classified parameter structure; A generating module, used for generating a parameter tree according to the node layering method of the classified parameter structure; A comparison module, used to compare the parameter nodes stored in the parameter tree with the pre-backed-up parameter nodes to obtain a comparison result; A monitoring module is used to automatically monitor changes in the ultrasound parameters according to the comparison results.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the ultrasonic parameter monitoring method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the ultrasonic parameter monitoring method according to any one of claims 1 to 7.

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