Implantable electronic medical device status data updating method and system

Abstract

The present application discloses a method and system for updating the status data of an implantable electronic medical device, and belongs to the field of data processing technology. The present application achieves accurate grasp of the device data update timing by acquiring neural regulation sensor monitoring information and performing timing label recognition on the implantable electronic medical device index, thereby effectively improving the efficiency of data processing. At the same time, by deeply mining the monitoring task jump data and storage update execution records of the target medical device, a sensor monitoring feature relationship network is constructed, which fully reveals the internal connections and dependencies between devices, and provides a rich information basis for data update decisions. Finally, based on the importance weight of the target medical device index in the feature relationship network, a local disassembly is performed to obtain a clearer and more focused data update reference, which provides strong support for the formulation of accurate data update strategies.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and specifically relates to a method and system for updating status data of an implantable electronic medical device. Background Art

[0002] In the current field of implantable electronic medical device (chip) management, with the increase in device types and the complexity of functions, how to efficiently and accurately process and analyze the massive data generated by the devices has become a technical problem that needs to be solved. Traditional data management methods often ignore the grasp of data update timing and the mining of internal connections and dependencies between devices, resulting in low data processing efficiency and inaccurate update strategy formulation. Summary of the invention

[0003] The present application provides a method and system for updating status data of an implantable electronic medical device, which can solve or partially solve the technical problems involved in the above-mentioned background technology.

[0004] The present application embodiment provides a method for updating status data of an implantable electronic medical device, which is applied to a data updating system. The method includes:

[0005] Acquire neural regulation sensor monitoring information, perform a timing label recognition operation on an implantable electronic medical device index based on the neural regulation sensor monitoring information, obtain a data update timing label corresponding to the implantable electronic medical device index, and migrate the implantable electronic medical device index whose data update timing label meets the state data update index to a target medical device index group; wherein the implantable electronic medical device index is associated with the neural regulation sensor monitoring information;

[0006] Acquire monitoring task jump data and storage update execution record corresponding to the target medical device index in the target medical device index group, and generate a structured feature relationship network for the target medical device index in the target medical device index group based on the monitoring task jump data and the storage update execution record to obtain a sensor monitoring feature relationship network;

[0007] Based on the importance weights of the target medical device indexes in the target medical device index group in the sensor monitoring feature relationship network, a local feature relationship network disassembly operation is performed on the sensor monitoring feature relationship network to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group; wherein X is a positive integer.

[0008] In one implementation, the obtaining of neural regulation sensor monitoring information includes:

[0009] Acquire a temporary storage space set of original monitoring information; wherein the temporary storage space set of original monitoring information includes at least one encryption server or at least one information management middle station;

[0010] If it is determined that the original monitoring information temporary storage space in the original monitoring information temporary storage space set is related to the firmware upgrade prompt, the original monitoring information temporary storage space in the original monitoring information temporary storage space set that contains the firmware upgrade prompt is determined as the target monitoring information temporary storage space, and the neural regulation sensor monitoring information is determined from the cache information corresponding to the target monitoring information temporary storage space.

[0011] In one implementation, the step of performing a timing label recognition operation on an implantable electronic medical device index based on the neural regulation sensor monitoring information to obtain a data update timing label corresponding to the implantable electronic medical device index includes:

[0012] Obtaining device state characteristics, sensor control transmission characteristics, and sensor monitoring analysis characteristics corresponding to the implantable electronic medical device index associated with the neural control sensor monitoring information;

[0013] Acquire a data update discrimination mode, determine a storage update discrimination score of the implantable electronic medical device index in the data update discrimination mode according to the device state characteristics, the sensor control transmission characteristics, and the sensor monitoring analysis characteristics, and determine a first upgrade optimization weight corresponding to the implantable electronic medical device index based on the storage update discrimination score;

[0014] Mapping the device state feature, the sensor control transmission feature, and the sensor monitoring analysis feature into a global storage update decision vector corresponding to the implantable electronic medical device index, and outputting a second upgrade optimization weight corresponding to the global storage update decision vector through a deep decision tree network;

[0015] The first upgrade optimization weight and the second upgrade optimization weight are integrated to obtain a target upgrade optimization weight corresponding to the implantable electronic medical device index, and a data update timing label of the implantable electronic medical device index is determined according to the target upgrade optimization weight.

[0016] In one implementation, the data update determination mode includes a device state determination sub-mode, a sensor control transmission determination sub-mode, and a sensor monitoring analysis determination sub-mode;

[0017] The step of determining the storage update discrimination score of the implantable electronic medical device index in the data update discrimination mode according to the device state feature, the sensor control transmission feature, and the sensor monitoring analysis feature includes:

[0018] Obtaining a first discrimination point of view of the device state feature in the device state discrimination sub-mode, obtaining a second discrimination point of view of the sensor regulation transmission feature in the sensor regulation transmission discrimination sub-mode, and obtaining a third discrimination point of view of the sensor monitoring analysis feature in the sensor monitoring analysis discrimination sub-mode;

[0019] The first discriminant opinion, the second discriminant opinion and the third discriminant opinion are fused by using the discriminant opinion fusion rule contained in the data update discriminant mode to obtain the storage update discriminant score of the implantable electronic medical device index in the data update discriminant mode.

[0020] In one implementation, the structured feature relationship network is generated for the target medical device index in the target medical device index group according to the monitoring task jump data and the storage update execution record to obtain the sensor monitoring feature relationship network, including:

[0021] According to the monitoring task jump data and the storage update execution record, the target medical device index in the target medical device index group is associated and derived to obtain a target medical device derivative index; if the data update timing label corresponding to the target medical device derivative index meets the status data update indicator, the target medical device derivative index is migrated to the target medical device index group;

[0022] If the monitoring task jump data or the storage update execution record indicates that there is a connection between the target medical device index u and the target medical device index v in the target medical device index group, a data conduction vector between the target medical device index u and the target medical device index v is generated;

[0023] Determining, according to the stored update execution record, an involvement coefficient between target medical device indexes in the target medical device index group;

[0024] The sensor monitoring feature relationship network is constructed by taking the target medical device index in the target medical device index group as the knowledge feature unit, taking the data conduction vector between the target medical device indexes in the target medical device index group as the relationship pointer, and taking the involvement coefficient between the target medical device indexes in the target medical device index group as the relationship pointer influencing factor.

[0025] In one implementation, the method further includes:

[0026] Obtaining global relationship pointer impact factors corresponding to each target medical device index in the sensor monitoring feature relationship network, and determining a target medical device index whose global relationship pointer impact factor is less than a first set impact factor as a medical device index to be updated;

[0027] Taking the relationship chain corresponding to the index of the to-be-updated medical device in the sensor monitoring feature relationship network as the to-be-processed relationship chain, extracting the to-be-processed relationship chain from the sensor monitoring feature relationship network, and obtaining a target update feature relationship chain;

[0028] Then, based on the importance weight of the target medical device index in the target medical device index group in the sensor monitoring feature relationship network, a local feature relationship network disassembly operation is performed on the sensor monitoring feature relationship network to obtain X target local feature relationship networks for representing the data update reference corresponding to the target medical device index group, including:

[0029] Based on the importance weight of the target medical device index in the target medical device index group in the target update feature relationship chain, a local feature relationship network disassembly operation is performed on the target update feature relationship chain to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group.

[0030] In one implementation, based on the importance weight of the target medical device index in the target medical device index group in the sensor monitoring feature relationship network, the sensor monitoring feature relationship network is subjected to a local feature relationship network disassembly operation to obtain X target local feature relationship networks for representing data update references corresponding to the target medical device index group, including:

[0031] Obtaining a set of associated medical device indexes of a target medical device index r in the target medical device index group, and obtaining importance weights between the target medical device index r and each associated medical device index in the set of associated medical device indexes;

[0032] Determine the associated medical device index corresponding to the largest importance weight in the associated medical device index set as the target associated medical device index, and if the importance weight between the target medical device index r and the target associated medical device index is greater than a second set impact factor, combine the target medical device index r and the target associated medical device index into a first original index set;

[0033] Obtaining a set of associated index sets of a first original index set in the sensor monitoring feature relationship network, and obtaining importance weights between the first original index set and each associated index set in the set of associated index sets;

[0034] Determine the associated index set corresponding to the largest importance weight in the associated index set set as the target associated index set, and if the importance weight between the first original index set and the target associated index set is greater than the second set impact factor, combine the first original index set and the target associated index set into a second original index set;

[0035] Based on the second original index set contained in the sensor monitoring feature relationship network, a local feature relationship network disassembly operation is performed on the sensor monitoring feature relationship network to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group; wherein the importance weights between different target local feature relationship networks are not greater than the second set impact factor.

[0036] In one implementation, the associated medical device index set includes a target medical device index t;

[0037] The obtaining of the importance weights between the target medical device index r and each associated medical device index in the associated medical device index set includes:

[0038] Obtaining a feature relationship attention value corresponding to the sensor monitoring feature relationship network, obtaining a relationship pointer influence factor between the target medical device index r and the target medical device index t, obtaining a global relationship pointer influence factor corresponding to the target medical device index r, and obtaining a global relationship pointer influence factor corresponding to the target medical device index t;

[0039] Determine a first operation result between the global relationship pointer influence factor corresponding to the target medical device index r and the global relationship pointer influence factor corresponding to the target medical device index t as a first original importance weight, and determine a second operation result between the first original importance weight and the feature relationship attention value corresponding to the sensor monitoring feature relationship network as a second original importance weight;

[0040] Determine a third operation result between the relationship pointer influence factor between the target medical device index r and the target medical device index t and the second original importance weight as a third original importance weight;

[0041] The importance weight between the target medical device index r and the target medical device index t is determined based on the feature relationship attention value corresponding to the sensor monitoring feature relationship network and the third original importance weight.

[0042] In one implementation, the method further includes:

[0043] According to the X target local feature relationship networks used to characterize the data update reference corresponding to the target medical device index group, the state data update processing is performed on the selected implantable electronic medical device.

[0044] An embodiment of the present application provides a data update system, comprising at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the above method.

[0045] An embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the above method are implemented.

[0046] The embodiment of the present application obtains neuromodulatory sensor monitoring information and performs timing tag identification on the index of implantable electronic medical devices, thereby achieving accurate grasp of the device data update timing and effectively improving the efficiency of data processing. At the same time, by deeply mining the monitoring task jump data and storage update execution records of the target medical device, a sensor monitoring feature relationship network is constructed, which fully reveals the internal connections and dependencies between devices, providing a rich information basis for data update decisions. Finally, based on the importance weight of the target medical device index in the feature relationship network, a local disassembly is performed to obtain a clearer and more focused data update reference, which provides strong support for the formulation of accurate data update strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flowchart of a method for updating status data of an implantable electronic medical device provided in an embodiment of the present application.

[0048] Figure 2 A structural diagram of a data updating system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are usually a class, and the number of objects is not limited. For example, the first object can be one or more. In addition, in this application, "and / or" represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0051] Figure 1 A method for updating status data of an implantable electronic medical device is shown, which is applied to a data updating system. The method includes the following steps 110 to 130.

[0052] In the field of smart healthcare, in order to ensure that implantable electronic medical devices can operate stably and effectively, and to update and process their status data in a timely manner, the embodiment of the present application will introduce the implementation steps of steps 110 to 130 in detail through a specific implantable neural regulation application scenario example.

[0053] Step 110, obtain neural regulation sensor monitoring information, perform timing label recognition operation on the implantable electronic medical device index based on the neural regulation sensor monitoring information, obtain the data update timing label corresponding to the implantable electronic medical device index, and migrate the implantable electronic medical device index whose data update timing label meets the status data update index to the target medical device index group; wherein, the implantable electronic medical device index is related to the neural regulation sensor monitoring information.

[0054] In the application scenario of implantable neuromodulation, electronic medical devices implanted in patients (such as implantable chips such as deep brain stimulators) continuously monitor neural signals and transmit these signals as neuromodulation sensor monitoring information to external receivers. The primary task of the data update system is to obtain this sensor monitoring information.

[0055] After obtaining the neuromodulation sensor monitoring information, the data update system will perform a timing tag recognition operation on the implantable electronic medical device index. This is because, during the long-term monitoring process, in order to effectively manage and track data, each implantable electronic medical device will be assigned a unique index, and the data it transmits will also be marked with a timing tag to record the time sequence of data generation.

[0056] The core of the timing tag recognition operation is to determine which implantable electronic medical device data needs to be updated by comparing the neuromodulation sensor monitoring information with the preset status data update indicators. Specifically, the data update system will check whether the data corresponding to each timing tag meets the update conditions, such as whether the data change amplitude exceeds the threshold, whether there is abnormal fluctuation, etc.

[0057] After completing the timing tag identification, the data update system will migrate the implantable electronic medical device indexes whose data update timing tags meet the status data update indicators to the target medical device index group. The purpose of this step is to screen out the devices that really need data update, so as to carry out more refined management and processing later.

[0058] Step 120, obtain the monitoring task jump data and storage update execution record corresponding to the target medical device index in the target medical device index group, and generate a structured feature relationship network for the target medical device index in the target medical device index group based on the monitoring task jump data and the storage update execution record to obtain a sensor monitoring feature relationship network.

[0059] After obtaining the target medical device index group, the data update system will further obtain the monitoring task jump data and storage update execution records corresponding to these indexes. The monitoring task jump data reflects the changes in the task status of the implantable electronic medical device during the monitoring process, such as the adjustment of the monitoring frequency, the modification of the monitoring parameters, etc.; while the storage update execution record records the historical situation of the device data update, such as the update time, update content, etc.

[0060] Based on the monitoring task jump data and storage update execution records, the data update system generates a structured feature relationship network for each target medical device index in the target medical device index group. The purpose of this step is to discover the inherent connections and rules between implantable electronic medical devices and between them and monitoring tasks.

[0061] Specifically, the data update system analyzes the monitoring task jump data of each target medical device index and extracts the characteristics of task state changes; at the same time, it also analyzes the storage update execution records and extracts the characteristics of data updates. Then, these features are associated and fused to form a sensor monitoring feature relationship network containing multiple nodes and edges. In this sensor monitoring feature relationship network, nodes represent implantable electronic medical devices or monitoring tasks, and edges represent the internal connections between them.

[0062] Step 130: Based on the importance weights of the target medical device indexes in the target medical device index group in the sensor monitoring feature relationship network, perform a local feature relationship network disassembly operation on the sensor monitoring feature relationship network to obtain X target local feature relationship networks for characterizing data update references corresponding to the target medical device index group; wherein X is a positive integer.

[0063] Furthermore, after obtaining the sensor monitoring feature relationship network, the data update system will perform a local feature relationship network disassembly operation based on the importance weight of the target medical device index in the target medical device index group in the network. The purpose of this step is to disassemble the complex sensor monitoring feature relationship network into multiple simple local networks, so as to more intuitively analyze and extract data update references.

[0064] The calculation of importance weight is based on the node importance evaluation method in network analysis, such as PageRank algorithm, node degree calculation, etc. By calculating the importance weight of each target medical device index in the sensor monitoring feature relationship network, the data update system can determine which devices or monitoring tasks occupy a core position in the network, so as to focus on analyzing and processing them.

[0065] After the local feature network is disassembled, the data update system will obtain X target local feature networks (X is a positive integer). Each target local feature network contains a set of interrelated implantable electronic medical devices or monitoring tasks, as well as the internal connections and rules between them. Through these local networks, the data update system can more accurately extract the information used to characterize the data update reference corresponding to the target medical device index group.

[0066] Specifically, the data update system analyzes the nodes and edges in each target local feature relationship network and extracts feature information related to data updates, such as the frequency of data updates, the content of updates, and the time distribution of updates. Then, these feature information are integrated and summarized to form a complete data update reference solution. This solution can provide strong support for equipment maintenance personnel, helping them to more accurately manage and maintain the status data of implantable electronic medical devices.

[0067] In detail, step 110 describes how to extract key data from the neuromodulation sensor monitoring information, and perform effective timing tag identification on the index of the implantable electronic medical device, and then filter out the device index that meets the specific state data update index. First, the system needs to obtain the neuromodulation sensor monitoring information. This information is transmitted in real time by the implantable electronic medical device and contains important data about the patient's neural state. This data is the basis for all subsequent processing steps, so it is crucial to ensure the accuracy and integrity of the data. Secondly, based on the acquired neuromodulation sensor monitoring information, the system will perform a timing tag identification operation on the index of the implantable electronic medical device. Each implantable electronic medical device has a unique index for identifying and tracking the device. The timing tag is used to record the time sequence of data generation to ensure the timing of the data. Through the timing tag identification operation, the system can assign a corresponding data update timing tag to each implantable electronic medical device index, which reflects the time information of the device data update. Then, the system will check the data update timing tag corresponding to each implantable electronic medical device index to determine whether it meets the preset state data update index. These indicators are set in advance according to medical needs and device performance characteristics to determine when the device's status data needs to be updated. If a device's data update timing label meets these indicators, its index will be selected as a candidate for further processing. Finally, the system migrates the implantable electronic medical device indexes whose data update timing labels meet the status data update indicators to the target medical device index group. This index group is a temporary collection for storing all device indexes that need to be updated. Through such migration operations, the system can effectively screen out devices that really need to be updated, providing convenience for subsequent data processing and management. Therefore, by obtaining neural regulation sensor monitoring information, performing timing label recognition operations on implantable electronic medical device indexes, and screening out device indexes that meet the status data update indicators, effective management and timely updating of implantable electronic medical device status data are achieved.

[0068] In addition, step 120 describes how to generate a structured feature relationship network, i.e., a sensor monitoring feature relationship network, based on the information in the target medical device index group. First, the system needs to obtain the monitoring task jump data and storage update execution record corresponding to each target medical device index in the target medical device index group. The monitoring task jump data reflects the changes in the task state of the implantable electronic medical device during the monitoring process, such as the adjustment of the monitoring frequency, monitoring parameters, etc. The storage update execution record records the history of device data updates in detail, including key information such as update time and update content. Secondly, the system conducts in-depth analysis and processing of each target medical device index in the target medical device index group based on the acquired monitoring task jump data and storage update execution record. The purpose of this step is to dig out the intrinsic connections and laws between implantable electronic medical devices and between them and monitoring tasks, and provide strong data support for the subsequent feature relationship network generation. Then, the system begins to build a structured feature relationship network. It will use implantable electronic medical devices or monitoring tasks as nodes and the intrinsic connections between them as edges to form a complex network containing multiple nodes and edges. In this sensor monitoring feature relationship network, each node represents an implantable electronic medical device or monitoring task, and each edge represents a certain connection or relationship between them. Finally, through continuous analysis and optimization, the system obtains a complete sensor monitoring feature relationship network. This sensor monitoring feature relationship network clearly shows the internal connection and rules between implantable electronic medical devices and between them and monitoring tasks, providing strong support for equipment maintenance personnel.

[0069] Further, step 130 describes in detail how to disassemble the feature relationship network based on the importance weight of the target medical device index in the sensor monitoring feature relationship network, so as to obtain multiple target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group. First, the system needs to evaluate the importance weight of each index in the target medical device index group in the sensor monitoring feature relationship network. This step is critical because it determines the focus and direction of the subsequent disassembly operation. The calculation of the importance weight may be based on a variety of factors, such as node degree, betweenness centrality, close centrality, etc., which are all commonly used node importance evaluation indicators in network analysis. Through calculation, the system can obtain the relative importance of each target medical device index in the feature relationship network. Secondly, the system performs a local feature relationship network disassembly operation on the sensor monitoring feature relationship network according to the calculated importance weight. The purpose of disassembly is to decompose the complex overall network into multiple simple local networks so as to more deeply analyze and understand the internal connections and laws in each local network. During the disassembly process, the system will give priority to retaining those target medical device indexes with higher importance weights and their related edges and nodes because they are more likely to contain information that has an important impact on data update. Then, through continuous disassembly and optimization, the system finally obtained X target local feature relationship networks (X is a positive integer). Each target local feature relationship network contains a set of interrelated implantable electronic medical devices or monitoring tasks, as well as the internal connections and laws between them. These local networks are more concise and clear, which is convenient for doctors or medical equipment maintenance personnel to conduct in-depth analysis and processing. Finally, through the analysis of these target local feature relationship networks, the system can extract key information for characterizing the data update reference corresponding to the target medical device index group. This information may include the frequency of data updates, the content of updates, the time distribution of updates, etc., which is of great reference value for equipment maintenance personnel. They can formulate more accurate and personalized data update strategies based on this information to ensure the stable operation of implantable electronic medical devices and the safety of patients.

[0070] It is worth mentioning that in the implantable neural regulation system, in order to optimize the data update strategy and improve the system efficiency, a sensor monitoring feature relationship network is constructed. This sensor monitoring feature relationship network has implantable neural stimulators, related monitoring tasks, and the data interaction relationship between them as nodes and edges.

[0071] For example, a patient with epilepsy has a neurostimulator implanted in his body to regulate abnormal brain electrical activity to reduce epileptic seizures. The neurostimulator regularly collects the patient's brain electrical signals and transmits them to the data processing system of the medical center.

[0072] In the embodiment of the present application, a sensor monitoring feature relationship network is constructed. The nodes in the sensor monitoring feature relationship network include a neural stimulator (node ​​A), an EEG signal acquisition task (node ​​T1), a data update task (node ​​T2), etc. The edges represent the data interaction and dependency between these nodes. For example, node A transmits the collected EEG signal to node T1, and node T2 is responsible for updating the parameters of the neural stimulator according to the data of node T1.

[0073] Each edge has a weight value, which indicates the frequency or importance of data interaction. For example, the weight value of edge AT1 is 0.9, which means that the neurostimulator (node ​​A) often transmits data to the EEG signal acquisition task (node ​​T1); the weight value of edge T1T2 is 0.7, which means that the EEG signal acquisition task (node ​​T1) is highly dependent on the data update task (node ​​T2).

[0074] Each node also has an importance weight value, which indicates the importance of the node in the network. In this example, the importance weight value of node A is 1.0 because it is the source of data; the importance weight value of node T1 is 0.8, indicating the criticality of the EEG signal acquisition task to the overall data flow; the importance weight value of node T2 is 0.6, indicating that the data update task is important but relatively dependent on the data of other nodes.

[0075] By sensing and monitoring the feature relationship network, the system can analyze the optimal strategy for data updates. For example, if the system finds that the amount of data at node T1 suddenly increases and the weight value of edge AT1 also changes, the system may decide to increase the update frequency of node T2 to adapt to the change in data volume. At the same time, the system will also consider the importance weight of the node to ensure that data updates of key nodes are given priority.

[0076] It can be seen that the sensor monitoring feature relationship network performs update decision analysis at the data level through the connection of nodes and edges and the corresponding weight values, which helps to optimize the data update strategy, improve system efficiency, and ensure the normal operation of the implantable neural stimulator.

[0077] To facilitate the implementation of the above technical solution, the implementation of steps 110 to 130 is explained below through programming codes.

[0078] import numpy as np

[0079] from collections import defaultdict

[0080] #Set the database or data structure

[0081] neural_regulation_data={

[0082] #Implantable electronic medical device index: [Sensor monitoring information time series]

[0083] 'device1': [0.1, 0.2, 0.3, ...],

[0084] 'device2': [0.2, 0.1, 0.4, ...],

[0085] #...

[0086] }

[0087] device_index_to_update=[] #List of medical device indexes to be updated

[0088] #Time series label recognition function

[0089] def identify_temporal_labels(device_data):

[0090] #Simplified example, complex algorithms can be adjusted in actual applications

[0091] labels=[]

[0092] for data_point in device_data:

[0093] if data_point>0.25: #set threshold

[0094] labels.append('high')

[0095] else:

[0096] labels.append('low')

[0097] return labels

[0098] #Structured feature relationship network generation function

[0099] def generate_feature_relation_network(target_devices):

[0100] feature_relations=defaultdict(list)

[0101] for device in target_devices:

[0102] #Set get_monitoring_task_data and get_storage_update_records to already defined functions

[0103] task_data=get_monitoring_task_data(device)

[0104] update_records=get_storage_update_records(device)

[0105] #Generate feature relationships

[0106] for task in task_data:

[0107] for record in update_records:

[0108] if task['type'] == record['type']:

[0109] feature_relations[device].append((task,record))

[0110] return feature_relations

[0111] #Local feature network decomposition function

[0112] def dismantle_local_feature_networks(feature_relations, target_devices):

[0113] local_networks=[]

[0114] for device in target_devices:

[0115] #Disassemble the local network according to the importance weight

[0116] local_network={device:feature_relations[device][:2]} #Set to take only the first two most important feature relations

[0117] local_networks.append(local_network)

[0118] return local_networks

[0119] #Main function

[0120] def main():

[0121] global device_index_to_update

[0122] #Timing label recognition

[0123] for device, data in neural_regulation_data.items():

[0124] temporal_labels=identify_temporal_labels(data)

[0125] if 'high' in temporal_labels: #Set the 'high' label to match the state data update indicator

[0126] device_index_to_update.append(device)

[0127] #Structured feature relationship network generation

[0128] feature_relations=generate_feature_relation_network(device_index_to_update)

[0129] #Disassembly of local feature relationship network

[0130] local_networks=dismantle_local_feature_networks(feature_relations,device_index_to_update)

[0131] # Output results

[0132] for i, network in enumerate(local_networks):

[0133] print(f"Target Local Feature Relation Network {i+1}:")

[0134] for device, relations in network.items():

[0135] print(f"Device: {device}, Relations: {relations}")

[0136] if __name__ == "__main__":

[0137] main()

[0138] The embodiment of the present application significantly improves the efficiency and accuracy of implantable electronic medical device data management by implementing a series of innovative technical steps. Specifically, by first acquiring the neural regulation sensor monitoring information and performing timing tag identification on the implantable electronic medical device index, the device data update timing is accurately grasped. This step ensures that only the implantable electronic medical device index that meets the status data update index will be included in further analysis and processing, thereby effectively avoiding unnecessary data processing overhead.

[0139] Next, the embodiment of the present application targets the selected target medical device index group, deeply mines its monitoring task jump data and storage update execution records, and then generates a sensor monitoring feature relationship network. The construction of this structured feature relationship network not only fully reveals the internal connections and dependencies between the target medical devices, but also provides a rich information basis for subsequent data update decisions.

[0140] Finally, the embodiment of the present application partially disassembles the feature relationship network based on the importance weight of the target medical device index in the sensor monitoring feature relationship network to obtain multiple target local feature relationship networks. This step provides medical professionals with a clearer and more focused data update reference, allowing them to more accurately formulate data update strategies, thereby improving the operating efficiency of implantable electronic medical devices.

[0141] In the field of smart healthcare, data update and processing of implantable electronic medical devices is a highly integrated and complex process, which is not only related to the effective operation of the equipment, but also directly related to the safety of patients. For the X target local feature relationship networks used to characterize the data update reference corresponding to the target medical device index group, how to guide the data update process can be further explained from the following aspects.

[0142] 1. Refined formulation of data update strategy

[0143] First, the target local feature relationship network provides the data update system with deep insights into the interactions and dependencies between target medical devices. Based on these networks, the data update system can:

[0144] (1) Priority sorting: Set the priority of data update according to the importance weight of the device in the network. Devices with high importance weight, such as equipment that directly affects the monitoring or treatment of patients' vital signs, will be given priority for data update to ensure the timeliness and accuracy of the data;

[0145] (2) Dependency optimization: Analyze the dependency paths in the network and optimize the order and timing of data updates. For example, the update of some devices may need to rely on the data of other devices as a prerequisite. Through reasonable scheduling, data waiting time and transmission conflicts can be reduced;

[0146] (3) Resource allocation: Reasonably allocate communication bandwidth, storage space, and computing resources based on the network structure and update requirements. For devices with large amounts of data and frequent updates, more resource support may be required.

[0147] II. Measures to ensure data integrity and security

[0148] During the data update process, it is critical to ensure the integrity and security of the data:

[0149] (1) Data encryption: Use advanced encryption technology to encrypt data during transmission to prevent data from being stolen or tampered with during transmission. At the same time, stored data should also be encrypted to prevent unauthorized access;

[0150] (2) Integrity check: At the data receiving end, an integrity check mechanism is implemented to ensure that the received data is completely consistent with the sender, without packet loss or damage;

[0151] (3) Access control: Establish a strict access control mechanism to clearly define the data access rights of different users or systems. Only authorized personnel or systems can access and process sensitive data.

[0152] III. Evaluation and feedback of update effects

[0153] After the data update is completed, timely evaluation and feedback on the update effect is carried out to continuously optimize the update strategy:

[0154] (1) Effect evaluation: By comparing and analyzing the data quality, real-time performance, equipment operation status, and patient treatment effects before and after the update, the actual effect of the data update can be evaluated. This helps to identify potential problems and improvement points;

[0155] (2) User feedback: Collect feedback from doctors and patients to understand their satisfaction and suggestions on data update frequency, accuracy, and device performance. This feedback will provide important reference for subsequent optimization work;

[0156] (3) Strategy adjustment: Adjust the data update strategy in a timely manner based on the evaluation results and user feedback. This may include measures such as optimizing the update algorithm, adjusting the update frequency, and enhancing device performance.

[0157] 4. Continuous technical support and iterative upgrades

[0158] In order to maintain the efficiency and accuracy of data update processing, continuous technical support and iterative upgrades are required:

[0159] (1) Technical support: Establish a professional technical support team to answer user questions, solve technical problems, and provide remote assistance and other services. This helps ensure that the equipment is always in optimal condition during operation.

[0160] (2) Iterative upgrade: With the continuous advancement of technology and the continuous changes in medical needs, implantable electronic medical devices and their data update processing systems should be iteratively upgraded regularly. This includes improvements in firmware updates, algorithm optimization, and security enhancement.

[0161] It can be seen that data update processing based on the target local feature relationship network is a complex process involving multiple links and aspects. Through the implementation of measures such as fine-tuning update strategies, ensuring data integrity and security, evaluating update effects, and continuous technical support and iterative upgrades, the data update system can continuously improve the efficiency and accuracy of data updates and provide patients with safer and more effective medical services.

[0162] In some optional embodiments, the obtaining of neural regulation sensor monitoring information includes: obtaining a set of original monitoring information temporary storage spaces; wherein the set of original monitoring information temporary storage spaces includes at least one encryption server or at least one information management middle station; if it is determined that the original monitoring information temporary storage space in the set of original monitoring information temporary storage spaces is related to the firmware upgrade prompt, then the original monitoring information temporary storage space in the set of original monitoring information temporary storage spaces that contains the firmware upgrade prompt is determined as the target monitoring information temporary storage space, and the neural regulation sensor monitoring information is determined from the cache information corresponding to the target monitoring information temporary storage space.

[0163] In actual application, the process of obtaining neuromodulatory sensor monitoring information is a comprehensive task involving multiple links and considerations. The first step in this process is to obtain a temporary storage space set for raw monitoring information, which plays the role of storing and preliminarily processing monitoring data. The composition of the temporary storage space set for raw monitoring information can be diverse, but it must include at least one encryption server or at least one information management middle platform. Such a design ensures data security and ease of management, because the encryption server can provide an additional layer of data protection, while the information management middle platform facilitates centralized processing and analysis of data.

[0164] After determining the set of raw monitoring information temporary storage spaces, the next key step is to identify the elements in these spaces that are related to the firmware upgrade prompt. The firmware upgrade prompt is an important signal that the relevant monitoring information may need to be updated or adjusted to adapt to new technical conditions or optimize data processing efficiency. The implementation of this step requires a mechanism to detect and parse the firmware upgrade prompt in order to accurately identify which raw monitoring information temporary storage spaces are affected.

[0165] Once the original monitoring information temporary storage spaces associated with the firmware upgrade prompts are determined, these spaces are marked as target monitoring information temporary storage spaces. This marking process is based on an in-depth analysis and understanding of the firmware upgrade prompts, which ensures that subsequent data processing can focus on the information that really needs attention. By narrowing the processing scope, the efficiency and accuracy of data processing can be improved, and unnecessary waste of resources can be avoided.

[0166] After determining the temporary storage space for target monitoring information, the next task is to extract the neuromodulatory sensor monitoring information from the cache information in these spaces. Neuromodulatory sensor monitoring information is a special type of data that reflects subtle changes in nervous system activity and has important reference value for medical diagnosis and treatment. Extracting this information requires specific data processing techniques, including signal decoding, noise filtering, and feature extraction. The application of these technologies can ensure that neuromodulatory sensor monitoring information is accurately extracted from the original cache information, providing a high-quality data foundation for subsequent analysis and application.

[0167] However, the process of obtaining neuromodulatory sensor monitoring information is not static, and it needs to be flexibly adjusted and optimized according to the actual application scenarios and needs. For example, in some cases, it may be necessary to extract data from the target monitoring information temporary storage space in real time in order to monitor and respond to changes in the nervous system immediately. In other cases, more attention may be paid to the integrity and accuracy of the data, so batch processing will be chosen after a certain amount of data has been accumulated.

[0168] In addition, the process of obtaining neuromodulatory sensor monitoring information also needs to consider data security and privacy protection. Since this data involves personal physiological information and health status, relevant laws, regulations and ethical standards must be strictly followed when processing and using it. This includes encrypting and storing data, restricting access rights, and obtaining necessary authorization and consent when using data for research or development.

[0169] In summary, obtaining neuromodulatory sensor monitoring information is a complex and meticulous process, which involves multiple links and multiple aspects of consideration. By rationally designing and implementing this process, we can ensure that high-quality, accurate and reliable neuromodulatory sensor monitoring information is obtained, providing strong support for medical diagnosis and treatment. At the same time, it can also provide valuable data resources for the research and development of neuroscience, and promote continuous progress and innovation in related fields.

[0170] In practical applications, the specific implementation methods of obtaining neuromodulation sensor monitoring information may vary depending on different needs and conditions. For example, in the medical field, it may be necessary to use implantable electronic medical devices to collect patients' neural signals in real time and transmit these signals to encrypted servers or information management platforms for storage and processing through wireless communication technology.

[0171] Regardless of the application scenario, obtaining neuromodulatory sensor monitoring information needs to follow certain principles and processes. First, it is necessary to clarify the source and collection method of the data to ensure the reliability and validity of the data. Secondly, it is necessary to select a suitable data storage and processing platform to effectively manage and analyze the data. Finally, it is necessary to use appropriate data processing technologies and methods to extract useful neuromodulatory sensor monitoring information from the raw data for further analysis and application.

[0172] In the process of achieving this, we also need to pay attention to some key technical challenges and solutions. For example, due to the complexity and weakness of neural signals, high-precision sensors and amplifiers are needed to collect and process neural signals, and noise filtering and signal enhancement are required. At the same time, due to the huge amount of data and real-time requirements, efficient data compression and transmission technology is needed to ensure timely transmission and processing of data.

[0173] In other optional embodiments, the timing label recognition operation is performed on the implantable electronic medical device index based on the neural regulation sensor monitoring information to obtain the data update timing label corresponding to the implantable electronic medical device index, including: obtaining the device state characteristics, sensor regulation transmission characteristics and sensor monitoring analysis characteristics corresponding to the implantable electronic medical device index associated with the neural regulation sensor monitoring information; obtaining a data update judgment mode, and determining the storage update judgment score of the implantable electronic medical device index in the data update judgment mode based on the device state characteristics, the sensor regulation transmission characteristics and the sensor monitoring analysis characteristics; The storage update discrimination score determines the first upgrade optimization weight corresponding to the implantable electronic medical device index; the device state characteristics, the sensor control transmission characteristics, and the sensor monitoring analysis characteristics are mapped into a global storage update decision vector corresponding to the implantable electronic medical device index, and a second upgrade optimization weight corresponding to the global storage update decision vector is output through a deep decision tree network; the first upgrade optimization weight and the second upgrade optimization weight are integrated to obtain a target upgrade optimization weight corresponding to the implantable electronic medical device index, and a data update timing label of the implantable electronic medical device index is determined according to the target upgrade optimization weight.

[0174] Furthermore, the process of performing a timing label recognition operation on the implantable electronic medical device index based on the neuromodulation sensor monitoring information to obtain the data update timing label corresponding to the implantable electronic medical device index is a complex process that integrates data analysis, pattern recognition and weight optimization. The core of this process is to accurately determine the data update timing of each device index by comprehensively considering the various characteristics of the implantable electronic medical device and the performance of these data characteristics in the data update discrimination mode.

[0175] First, the data update system needs to obtain three key features corresponding to the implantable electronic medical device index associated with the neural regulation sensor monitoring information: device state feature, sensor regulation transmission feature, and sensor monitoring analysis feature. The device state feature reflects the current working state and operating status of the device, and is an important basis for judging whether data update is needed. The sensor regulation transmission feature involves the transmission quality of sensor data and the execution of control instructions, which affects the timeliness and accuracy of data update. The sensor monitoring analysis feature focuses on the analysis capability and accuracy of sensor monitoring data, which determines the difficulty of extracting useful information from the original monitoring data.

[0176] In detail, the device status feature mainly reflects the current working state and operating status of the implantable electronic medical device. This feature can be represented by a series of numerical values, such as the device's operating temperature, battery power, operating time, etc. For example, the device status feature may include: operating temperature (40°C), battery power (80%), operating time (120 hours). These values ​​can intuitively tell the data update system whether the device is in a normal working state and whether maintenance or replacement is required. The sensor control transmission feature involves the transmission quality of the sensor data and the execution of the control instructions. This feature can also be quantified by numerical values, such as data transmission rate, transmission error rate, control instruction response time, etc. For example, the sensor control transmission feature may include: data transmission rate (10Mbps), transmission error rate (0.1%), control instruction response time (5ms). These values ​​reflect the transmission efficiency and accuracy of the sensor data, as well as the device's response speed to the control instructions, and are important bases for judging the timeliness and accuracy of data updates. The sensor monitoring analysis feature focuses on the analysis capability and accuracy of the sensor monitoring data. This feature can also be characterized by relevant numerical values, such as data analysis speed, analysis error rate, monitoring data accuracy, etc. For example, sensor monitoring analysis characteristics may include: data analysis speed (100KB / s), analysis error rate (0.05%), and monitoring data accuracy (99%). These values ​​measure the device's ability to extract useful information from raw monitoring data, as well as the accuracy and reliability of the extracted information.

[0177] Next, the data update system obtains the data update discrimination mode, which is a preset mode for evaluating the data update requirements of implantable electronic medical devices. Based on the previously extracted device state features, sensor control transmission features, and sensor monitoring analysis features, the data update system can calculate the storage update discrimination score of each implantable electronic medical device index in the data update discrimination mode. This score reflects the urgency and importance of data update for the device index under the current feature combination.

[0178] Based on the storage update discrimination score, the data update system can further determine the first upgrade optimization weight corresponding to the implantable electronic medical device index. This weight is a quantitative indicator to measure the device index in the data update priority, which helps the data update system preliminarily determine which device index data update is more critical.

[0179] However, it is not enough to rely solely on the storage update discrimination score to determine the data update timing, because the data update system also needs to consider the global storage update decision. Therefore, the data update system maps the device state characteristics, sensor control transmission characteristics, and sensor monitoring analysis characteristics into a global storage update decision vector corresponding to the implantable electronic medical device index. This vector contains the decision information about the device index for data update in the global scope.

[0180] In order to extract useful decision information from the global storage update decision vector, the data update system processes it through a deep decision tree network and outputs the second upgrade optimization weight corresponding to the global storage update decision vector. The deep decision tree network is a powerful machine learning model that can capture complex patterns and relationships in the data, thereby providing the data update system with a more accurate basis for data update decisions.

[0181] Finally, the data update system integrates the first upgrade optimization weight and the second upgrade optimization weight to obtain the target upgrade optimization weight corresponding to the implantable electronic medical device index. This weight combines local and global data update requirements and provides a reliable basis for the data update system to determine the final data update timing label. Based on the target upgrade optimization weight, the data update system can accurately assign a data update timing label to each implantable electronic medical device index, thereby guiding subsequent data update operations.

[0182] It can be seen that the process of performing timing tag identification operations on implantable electronic medical device indexes based on neuromodulation sensor monitoring information is a complex and delicate task. It requires the data update system to fully consider the various characteristics of the device and the performance of these characteristics in the data update judgment mode and global storage update decision. By accurately calculating and optimizing weights, the data update system can accurately assign a suitable data update timing tag to each device index, thereby ensuring timely and accurate data updates.

[0183] In some examples, the data update judgment mode includes a device state judgment sub-mode, a sensor control transmission judgment sub-mode and a sensor monitoring analysis judgment sub-mode; then, determining the storage update judgment score of the implantable electronic medical device index in the data update judgment mode based on the device state characteristics, the sensor control transmission characteristics and the sensor monitoring analysis characteristics includes: obtaining a first judgment point of view of the device state characteristics in the device state judgment sub-mode, obtaining a second judgment point of view of the sensor control transmission characteristics in the sensor control transmission judgment sub-mode, and obtaining a third judgment point of view of the sensor monitoring analysis characteristics in the sensor monitoring analysis judgment sub-mode; using the judgment point of view fusion rule contained in the data update judgment mode, the first judgment point of view, the second judgment point of view and the third judgment point of view are fused to obtain the storage update judgment score of the implantable electronic medical device index in the data update judgment mode.

[0184] In some examples, in order to comprehensively and accurately evaluate the data update needs of implantable electronic medical devices, the data update system builds a comprehensive data update judgment mode. This mode is not a single structure, but consists of three important sub-modes: device state judgment sub-mode, sensor control transmission judgment sub-mode, and sensor monitoring analysis judgment sub-mode. Each sub-mode focuses on evaluating a key aspect of the device and together provides comprehensive support for data update decisions.

[0185] The core task of the device status judgment sub-mode is to evaluate the current working state and operating status of the implantable electronic medical device. To achieve this goal, the data update system inputs the device status characteristics into this sub-mode and obtains the first judgment opinion of its output. This first judgment opinion is actually a comprehensive evaluation of the device status characteristics, which reflects the urgency of data update of the device in the current state. For example, if the operating temperature of the device is too high or the battery power is too low, the first judgment opinion may indicate that the device needs to be updated as soon as possible to maintain its normal operation.

[0186] The sensor control transmission discrimination sub-mode focuses on the transmission quality of the sensor data and the execution of the control instructions. Similarly, the data update system inputs the sensor control transmission characteristics into this sub-mode and obtains the second discrimination opinion of its output. This second discrimination opinion is a comprehensive evaluation of the sensor control transmission characteristics, which reveals the efficiency and accuracy of data transmission and the response speed of the device to the control instructions. If the data transmission rate is low or the transmission error rate is high, the second discrimination opinion may emphasize the importance of data update to ensure the improvement of transmission quality.

[0187] The core task of the sensor monitoring analysis and discrimination sub-mode is to evaluate the analysis capability and accuracy of the sensor monitoring data. The data update system inputs the sensor monitoring analysis features into this sub-mode and obtains the third discrimination point of view of its output. This third discrimination point of view is a comprehensive evaluation of the sensor monitoring analysis features, which reflects the ability of the device to extract useful information from the raw monitoring data. If the data analysis speed is slow or the analysis error rate is high, the third discrimination point of view may indicate that the monitoring performance of the device needs to be improved through data update.

[0188] After obtaining these three discriminant opinions, the data update system also needs to fuse them together to form a comprehensive storage update discriminant score. To achieve this goal, the data update system uses the discriminant opinion fusion rule contained in the data update discriminant model. This fusion rule is a carefully designed algorithm that can organically combine the first discriminant opinion, the second discriminant opinion, and the third discriminant opinion, and consider their mutual influence and weight distribution. By fusing these three discriminant opinions, the data update system can obtain a more comprehensive and accurate storage update discriminant score.

[0189] This score is actually a quantitative assessment of the data update needs of implantable electronic medical devices. It comprehensively considers multiple aspects such as the working status of the device, the transmission quality of sensor data, and the parsing ability of monitoring data. Through this score, the data update system can more intuitively understand the urgency of data update for each device index and provide strong support for subsequent data update operations.

[0190] Therefore, it is a complex and delicate process to determine the storage update discrimination score of the implantable electronic medical device index in the data update discrimination mode based on the device state characteristics, sensor control transmission characteristics and sensor monitoring analysis characteristics. It requires the data update system to comprehensively consider multiple aspects of the device and use advanced discrimination viewpoints and fusion rules to form a comprehensive and accurate evaluation result. Through this score, the data update system can make data update decisions more scientifically, providing a strong guarantee for the stable operation and effective monitoring of implantable electronic medical devices.

[0191] In some preferred embodiments, the target medical device index in the target medical device index group is subjected to structured feature relationship network generation based on the monitoring task jump data and the storage update execution record to obtain a sensor monitoring feature relationship network, including: according to the monitoring task jump data and the storage update execution record, the target medical device index in the target medical device index group is subjected to association and derivation to obtain a target medical device derivative index; if the data update timing label corresponding to the target medical device derivative index meets the status data update indicator, the target medical device derivative index is migrated to the target medical device index group; if the monitoring task jump data or the storage update execution record indicates If it is shown that there is an involvement between the target medical device index u and the target medical device index v in the target medical device index group, a data conduction vector between the target medical device index u and the target medical device index v is generated; based on the storage update execution record, the involvement coefficient between the target medical device indexes in the target medical device index group is determined; the target medical device indexes in the target medical device index group are used as knowledge feature units, the data conduction vector between the target medical device indexes in the target medical device index group are used as relationship pointers, and the involvement coefficient between the target medical device indexes in the target medical device index group is used as a relationship pointer influencing factor to construct the sensor monitoring feature relationship network.

[0192] In the actual application process, in order to more effectively manage and maintain implantable electronic medical devices, a structured feature relationship network generation method based on monitoring task jump data and storage update execution records is proposed. The core of this method is to reveal the internal connection and mutual influence between the devices in the target medical device index group by deeply mining and analyzing the changes in monitoring tasks and the execution of storage updates, and then build a comprehensive sensor monitoring feature relationship network.

[0193] First, the data update system associates and derives each target medical device index in the target medical device index group based on the monitoring task jump data and storage update execution records. The purpose of this step is to find other derivative indexes associated with the target medical device index. These derivative indexes may be other devices that participate in data updates with the target medical device when the monitoring task changes, or devices that interact with the target medical device during the storage update execution process. Through association derivation, the data update system can more comprehensively understand the context of the target medical device during the data update process, providing a rich information foundation for the subsequent relationship network construction.

[0194] In the process of association derivation, the data update system needs to strictly screen and evaluate each target medical device derivative index. Specifically, the data update system will check whether the data update timing label corresponding to the derivative index meets the preset status data update index. This indicator is a standard that comprehensively considers the timeliness, importance and urgency of data updates, and is used to evaluate whether the derived index needs to be updated. If the data update timing label of the derived index meets this indicator, the data update system will migrate it to the target medical device index group as an important node for the subsequent relationship network construction.

[0195] In addition to association derivation, the data update system also needs to pay attention to the involvement between the devices in the target medical device index group. This involvement may be due to their joint role in the monitoring task, or their mutual dependence in the storage update process. In order to reveal this involvement, the data update system will carefully analyze the monitoring task jump data and storage update execution records, looking for clues indicating that there is an involvement between the target medical device index u and the target medical device index v. Once such a clue is found, the data update system will generate a data conduction vector between u and v. This vector describes the interaction mode and intensity between device u and device v during the data update process, and is an important bridge for building a relationship network.

[0196] After determining the data transmission vectors between the devices in the target medical device index group, the data update system needs to further analyze the involvement coefficients between these devices. The involvement coefficient is a quantitative indicator used to measure the strength and importance of the involvement between devices. The data update system will analyze which devices often appear together during the data update process, or what kind of dependencies exist between their update operations based on the storage update execution records. This information helps the data update system determine the involvement coefficient more accurately, providing strong support for the subsequent relationship network construction.

[0197] Finally, the data update system will use the devices in the target medical device index group as knowledge feature units, the data transmission vectors between them as relationship pointers, and the involvement coefficients as relationship pointer influencing factors to construct a sensor monitoring feature relationship network. This relationship network is a complex and sophisticated structure that reveals the inherent connections and mutual influences between devices, providing a new perspective for the data update system to examine the data update behavior of implantable electronic medical devices. Through this relationship network, the data update system can more intuitively understand which devices are closely connected during the data update process, which devices' update operations are interdependent, and which devices have the strongest involvement. This information is of great significance for formulating reasonable data update strategies, optimizing the interaction between devices, and improving the overall performance of medical systems.

[0198] In this way, by building a sensor monitoring feature relationship network, the data update system can more comprehensively understand the data update behavior of implantable electronic medical devices, provide more powerful support for subsequent data update decisions, and help the data update system more effectively manage and maintain implantable electronic medical devices. At the same time, it also provides new ideas and methods for the research and development and design of medical devices.

[0199] In some other optional embodiments, the method further includes: obtaining the global relationship pointer influence factors corresponding to each target medical device index in the sensor monitoring feature relationship network, determining the target medical device index whose global relationship pointer influence factor is less than the first set influence factor as the medical device index to be updated; taking the relationship chain corresponding to the medical device index to be updated in the sensor monitoring feature relationship network as the relationship chain to be processed, extracting the relationship chain to be processed from the sensor monitoring feature relationship network, and obtaining the target update feature relationship chain. Then, based on the importance weights of the target medical device indexes in the target medical device index group in the sensor monitoring feature relationship network, performing a local feature relationship network disassembly operation on the sensor monitoring feature relationship network to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group includes: based on the importance weights of the target medical device indexes in the target medical device index group in the target update feature relationship chain, performing a local feature relationship network disassembly operation on the target update feature relationship chain to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group.

[0200] Based on this embodiment, the data update strategy based on the sensor monitoring feature relationship network is further expanded. The core of this expansion is that the data update system not only pays attention to the structure and dynamics of the entire relationship network, but also goes deep into each node in the relationship network, that is, the target medical device index, and the relationship chain between them, to perform more refined analysis and operations.

[0201] First, the data update system obtains the global relationship pointer influence factor corresponding to each target medical device index from the sensor monitoring feature relationship network. This influence factor is a quantitative indicator that reflects the importance and influence of the target medical device index in the entire relationship network. By comparing the global relationship pointer influence factor with the preset first setting influence factor, the data update system can screen out those target medical device indexes with less influence and may need data update, and determine them as medical device indexes to be updated.

[0202] After determining the indexes of the medical devices to be updated, the data update system uses the relationship chains corresponding to these indexes in the sensor monitoring feature relationship network as the relationship chains to be processed. These relationship chains are bridges connecting the medical device indexes to be updated with other related device indexes, and they reveal the interactions and dependencies in the data update process. In order to gain a deeper understanding of these relationship chains, the data update system extracts the relationship chains to be processed from the sensor monitoring feature relationship network to obtain the target update feature relationship chains.

[0203] Next, the data update system performs a local feature relationship network disassembly operation on the target update feature relationship chain based on the importance weight of the target medical device index in the target medical device index group in the target update feature relationship chain. The purpose of this step is to disassemble the complex relationship chain into multiple relatively simple local feature relationship networks, each of which focuses on a specific set of target medical device indexes and the direct and indirect relationships between them. Through such disassembly, the data update system can more clearly understand the role and importance of each target medical device index in the data update process, as well as their mutual influence with other device indexes.

[0204] Each of the X target local feature relationship networks obtained by disassembly represents a set of data update references corresponding to the target medical device indexes. These local feature relationship networks not only reveal the direct relationship between device indexes, but also reflect the indirect relationship and involvement between them through relationship pointers and relationship pointer influence factors. Through these local feature relationship networks, the data update system can more accurately determine which device indexes need to be updated, as well as the priority and order of the update.

[0205] At the global level, the data update system screens the medical device indexes to be updated by comparing the global relationship pointer influencing factors; at the local level, the data update system deeply understands the characteristics and behaviors of each target medical device index and its relationship chain by disassembling the target update feature relationship chain. This combination enables the data update system to grasp the status data update requirements of implantable electronic medical devices more comprehensively and accurately, providing strong support for subsequent data update decisions.

[0206] In practical applications, the data update system can adjust the global relationship pointer impact factor and the first set impact factor according to specific medical needs and device characteristics to screen out more suitable medical device indexes to be updated. At the same time, the data update system can also select the number and range of the disassembled local feature relationship network as needed to obtain a more detailed or generalized data update reference.

[0207] In this way, through a series of steps such as obtaining the global relationship pointer influencing factor, screening the index of the medical device to be updated, extracting the relationship chain to be processed, and disassembling the target update feature relationship chain, the refined analysis and operation of the sensor monitoring feature relationship network is realized. It provides a new perspective for the data update system to examine the state data update requirements of implantable electronic medical devices, provides powerful tools and methods for the subsequent data update strategy formulation and optimization, and will promote the management and maintenance of implantable electronic medical devices to a more efficient and intelligent direction.

[0208] In some alternative embodiments, based on the importance weight of the target medical device index in the target medical device index group in the sensor monitoring feature relationship network, a local feature relationship network disassembly operation is performed on the sensor monitoring feature relationship network to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group, including: obtaining a set of associated medical device indexes of the target medical device index r in the target medical device index group, and obtaining the importance weights between the target medical device index r and each associated medical device index in the associated medical device index set; determining the associated medical device index corresponding to the largest importance weight in the associated medical device index set as the target associated medical device index; if the importance weight between the target medical device index r and the target associated medical device index is greater than a second set influencing factor, then the target medical device index r is associated with the target associated medical device index. The method comprises the following steps: combining the first original index set and the target medical device index into a first original index set; obtaining the associated index set set of the first original index set in the sensor monitoring feature relationship network, and obtaining the importance weights between the first original index set and each associated index set in the associated index set set; determining the associated index set corresponding to the largest importance weight in the associated index set set as the target associated index set, and if the importance weight between the first original index set and the target associated index set is greater than the second set impact factor, combining the first original index set and the target associated index set into a second original index set; based on the second original index set included in the sensor monitoring feature relationship network, performing a local feature relationship network disassembly operation on the sensor monitoring feature relationship network to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group; wherein the importance weights between different target local feature relationship networks are not greater than the second set impact factor.

[0209] In this embodiment, the data update system focuses on the state data update level of implantable electronic medical devices, and proposes a technical solution for disassembling local feature relationship networks based on sensor monitoring feature relationship networks. The core of this solution is that by deeply analyzing the importance weight of the target medical device index in the sensor monitoring feature relationship network, the data update system can accurately disassemble X target local feature relationship networks used to characterize the target medical device index group data update reference.

[0210] First, the data update system selects a target medical device index r from the target medical device index group and obtains its associated medical device index set. This set contains all medical device indexes that are directly or indirectly associated with the target medical device index r in the sensor monitoring feature relationship network. Then, the data update system calculates the importance weights between the target medical device index r and these associated medical device indexes, which reflects the degree of association and mutual influence between them.

[0211] Then, the data update system finds the associated medical device index with the largest importance weight with the target medical device index r from the associated medical device index set, and determines it as the target associated medical device index. The purpose of this step is to identify the medical device index that is most closely related to the target medical device index r. Next, the data update system compares the importance weight between the target medical device index r and the target associated medical device index with the size of the second set impact factor. If the weight is greater than the second set impact factor, it means that the degree of correlation between the two indexes is very high, and the data update system combines them into the first original index set.

[0212] Subsequently, the data update system obtains the associated index set set of the first original index set in the sensor monitoring feature relationship network, and this set includes all index sets that are directly or indirectly associated with the first original index set. Next, the data update system calculates the importance weights between the first original index set and these associated index sets respectively to evaluate the degree of association between them. Similarly, the data update system finds the associated index set with the largest importance weight with the first original index set from the associated index set set, and determines it as the target associated index set. Then, the data update system compares the importance weight between the first original index set and the target associated index set with the size of the second set impact factor. If the weight is greater than the second set impact factor, it means that the degree of association between the two index sets is very high, and the data update system combines them into the second original index set.

[0213] Finally, the data update system performs a local feature network disassembly operation on the sensor monitoring feature relationship network based on the second original index set contained in the sensor monitoring feature relationship network. The purpose of this step is to disassemble the complex sensor monitoring feature relationship network into multiple relatively simple local feature relationship networks, each of which focuses on a specific set of target medical device indexes and their associated indexes, which are used to characterize the data update reference corresponding to the target medical device index group. Through such disassembly, the data update system can more clearly understand the role and importance of each target medical device index and its associated index in the data update process, as well as their mutual influence. At the same time, the data update system ensures that the importance weights between different target local feature relationship networks are not greater than the second set influence factor, so as to ensure that the disassembled local feature relationship networks are relatively independent and representative.

[0214] At the global level, the data update system identifies the target medical device index and its associated indexes by calculating importance weights; at the local level, the data update system disassembles the sensor monitoring feature relationship network to gain a deep understanding of the characteristics and behaviors of each target medical device index and its associated indexes. This combination enables the data update system to more comprehensively and accurately grasp the status data update needs of implantable electronic medical devices.

[0215] In this way, through a series of steps such as obtaining the associated medical device index set, calculating the importance weight, identifying the target associated medical device index and the target associated index set, and disassembling the sensor monitoring feature relationship network, the refined analysis and operation of the implantable electronic medical device status data update requirements are realized. It provides a new perspective for the data update system to examine the correlation and mutual influence in the data update process, and provides powerful tools and methods for the subsequent data update strategy formulation and optimization.

[0216] In some other examples, the associated medical device index set includes a target medical device index t; the obtaining of the importance weights between the target medical device index r and each associated medical device index in the associated medical device index set includes: obtaining a feature relationship attention value corresponding to the sensor monitoring feature relationship network, obtaining a relationship pointer influence factor between the target medical device index r and the target medical device index t, obtaining a global relationship pointer influence factor corresponding to the target medical device index r, and obtaining a global relationship pointer influence factor corresponding to the target medical device index t; comparing the global relationship pointer influence factor corresponding to the target medical device index r with the target medical device index t. The first operation result between the global relationship pointer influence factor corresponding to index t is determined as the first original importance weight, and the second operation result between the first original importance weight and the feature relationship attention value corresponding to the sensor monitoring feature relationship network is determined as the second original importance weight; the third operation result between the relationship pointer influence factor between the target medical device index r and the target medical device index t and the second original importance weight is determined as the third original importance weight; based on the feature relationship attention value corresponding to the sensor monitoring feature relationship network and the third original importance weight, the importance weight between the target medical device index r and the target medical device index t is determined.

[0217] This embodiment relates to how to accurately evaluate the importance weights between target medical device indexes. First, the set of associated medical device indexes includes the target medical device index t, which means that when evaluating the importance weights between the target medical device index r and other associated medical device indexes, the data update system must also take the target medical device index t into consideration. This is because the target medical device index t may be directly or indirectly associated with the target medical device index r, and the update of its status data may also affect the target medical device index r.

[0218] Next, the data update system enters the specific steps of obtaining importance weights. First, the data update system needs to obtain the feature relationship attention value corresponding to the sensor monitoring feature relationship network. This value reflects the degree of association and mutual influence between each node (i.e., medical device index) in the sensor monitoring feature relationship network. The higher the feature relationship attention value, the closer the association between the nodes and the greater the mutual influence.

[0219] Then, the data update system needs to obtain the relationship pointer influence factor between the target medical device index r and the target medical device index t. This factor reflects the degree of direct association between the two indexes in the sensor monitoring feature relationship network. The larger the relationship pointer influence factor, the closer the direct association between the two indexes.

[0220] Next, the data update system also needs to obtain the global relationship pointer influence factor corresponding to the target medical device index r and the global relationship pointer influence factor corresponding to the target medical device index t. These two factors reflect the global influence of these two indexes in the sensor monitoring feature relationship network. The larger the global relationship pointer influence factor, the greater the influence of the index in the entire relationship network.

[0221] With the above basic data, the data update system can start calculating the importance weight. First, the data update system performs a first operation (such as subtraction or division) on the global relationship pointer influence factor corresponding to the target medical device index r and the global relationship pointer influence factor corresponding to the target medical device index t to obtain the first original importance weight. This weight reflects the difference in the global influence of the two indexes.

[0222] Then, the data update system performs a second operation (such as multiplication or addition) on the first original importance weight and the feature relationship attention value corresponding to the sensor monitoring feature relationship network to obtain a second original importance weight. This weight comprehensively considers the global influence difference and the degree of association between nodes, and more comprehensively reflects the relative importance between the two indexes.

[0223] Next, the data updating system performs a third operation (e.g., multiplication or addition) on the relationship pointer influence factor between the target medical device index r and the target medical device index t and the second original importance weight to obtain a third original importance weight. This weight further incorporates the degree of direct correlation between the two indexes, making the evaluation result more accurate.

[0224] Finally, the data update system determines the importance weight between the target medical device index r and the target medical device index t based on the feature relationship attention value corresponding to the sensor monitoring feature relationship network and the third original importance weight through a certain algorithm or model (such as weighted average or neural network). This weight combines multiple factors such as global influence, the degree of association between nodes, and the degree of direct association, and can more accurately reflect the relative importance of the two indexes in the data update process.

[0225] This design not only considers the global influence of the target medical device index, but also the degree of direct and indirect correlation between them. Through such comprehensive considerations, the data update system can more accurately evaluate the importance weights between the target medical device indexes, providing strong support for the subsequent data update strategy formulation.

[0226] In this way, by comprehensively considering multiple factors such as the global influence, the degree of correlation between nodes, and the degree of direct correlation, an accurate assessment of the importance weights between target medical device indexes in the process of updating the status data of implantable electronic medical devices is achieved.

[0227] In an independent embodiment, the method further includes: performing a state data update process on the selected implantable electronic medical device according to X target local feature relationship networks used to characterize the data update reference corresponding to the target medical device index group.

[0228] It can be understood that by performing a detailed analysis of the target medical device index group, especially based on its corresponding X target local feature relationship network, to achieve effective update of the status data of the selected implantable electronic medical device not only requires the data update system to have an in-depth understanding of the current status of the medical device, but also requires the data update system to be able to accurately predict and judge its future status change trend, thereby ensuring the accuracy and timeliness of the status data update.

[0229] Among them, the target medical device index group refers to a group of implantable electronic medical devices that have been specifically screened and evaluated and are considered critical or important due to their specific functions, locations, usage conditions or patient needs. By monitoring and analyzing these devices, the data update system can obtain important information about their operating status and performance, thereby providing strong support for subsequent data update processing. The X target local feature relationship networks are constructed based on in-depth analysis and mining of the target medical device index group, which reveal the complex relationships between devices and between devices and the external environment. These relationships may include interactions between devices, response patterns of devices to specific stimuli, and trends in device status over time. Through the study of these relationship networks, the data update system can have a more comprehensive understanding of the overall status of the target medical device index group and provide a more accurate reference for subsequent status data updates.

[0230] Based on this, the state data update processing of the selected implantable electronic medical device according to these target local feature relationship networks can be divided into the following key steps:

[0231] 1. The data update system needs to conduct an in-depth interpretation and analysis of the target local feature relationship network. This includes understanding the meaning of each node and edge in the relationship network, as well as the interactions and associations between them. Through this step, the data update system can reveal the key features and influencing factors of the selected medical device in its current state, thereby providing a strong basis for subsequent data updates;

[0232] Second, based on the analysis results of the relationship network, the data update system needs to accurately evaluate the current status of the selected medical device. This includes a comprehensive review and judgment of its functional status, performance, and possible risks or problems. Through this step, the data update system can ensure an accurate grasp of the status of the medical device and provide a reliable starting point for subsequent data updates;

[0233] 3. The data update system needs to predict and judge the future status of the selected medical device based on the characteristics and trends in the relationship network. This includes reasonable speculation and evaluation of its possible status changes, performance fluctuations, and potential risks or problems. Through this step, the data update system can provide forward-looking guidance for data updates to ensure the timeliness and effectiveness of updates.

[0234] Next, based on the results of the above analysis, evaluation and prediction, the data update system can actually update the status data of the selected implantable electronic medical device. This includes the correction of its status parameters, the adjustment of performance indicators, and the marking of risks or problems. Through this step, the data update system can ensure that the status data of the medical device can accurately reflect its current and future conditions.

[0235] In this way, by deeply analyzing and mining the target local feature relationship network corresponding to the target medical device index group, the state data of the selected implantable electronic medical device is effectively updated. This process not only improves the data update system's ability to accurately grasp the state of medical devices, but also provides strong data support for subsequent medical decision-making and treatment. At the same time, it also shows that in modern medical technology, through in-depth analysis and mining of large amounts of data, the data update system can achieve accurate management and optimization of the state of medical devices.

[0236] It is worth mentioning that this embodiment also requires the data update system to fully consider the diversity and complexity of medical devices. Different types of medical devices may have different characteristics, functions and performances, so when updating their status data, the data update system needs to adopt targeted methods and strategies. At the same time, the data update system also needs to pay attention to the interaction and impact between medical devices and patients, ensuring that the actual needs and conditions of patients can be fully considered when updating status data.

[0237] In other independent embodiments, the state data update processing of the selected implantable electronic medical device based on the X target local feature relationship networks used to characterize the data update reference corresponding to the target medical device index group includes: performing structured disaster recovery backup of the neural regulation sensor monitoring sampling data corresponding to the selected implantable electronic medical device based on the X target local feature relationship networks.

[0238] It is understandable that the sampling data of neuromodulation sensor monitoring is a key indicator reflecting the operating status and performance of medical devices. By performing structured disaster recovery backup on these data, the data update system can not only ensure the integrity and security of the data, but also provide a solid foundation for subsequent data analysis and updates.

[0239] In order to achieve this core step, the data update system first needs to extract key features and relationships related to the selected medical device from the X target local feature relationship networks. These features and relationships may include specific functional states of the medical device, performance parameters, interaction patterns with other medical devices, etc. By deeply analyzing and mining this information, the data update system can more comprehensively understand the operating status and performance of the selected medical device, providing powerful guidance for subsequent data backup and update.

[0240] Secondly, based on the extracted key features and relationships, the data update system needs to finely process and organize the sampling data of the neuromodulation sensor monitoring corresponding to the selected medical device. This includes unifying and standardizing the data format, classifying and archiving the data, and establishing associations and indexes between the data. Through these tasks, the data update system can ensure the structuring and ordering of the data, creating favorable conditions for subsequent data backup and updating.

[0241] Then, the data update system enters the actual operation stage of structured disaster recovery backup. At this stage, the data update system needs to adopt advanced backup technologies and strategies to ensure that the neuromodulation sensor monitoring sampling data corresponding to the selected medical device can be safely and reliably backed up to the designated storage medium or cloud platform. At the same time, the data update system also needs to establish a complete disaster recovery mechanism to deal with possible data loss, damage or catastrophic events. This includes formulating a detailed data recovery plan, regularly verifying and testing data backups, and establishing a multi-level data protection system.

[0242] Finally, after the structured disaster recovery backup is completed, the data update system also needs to effectively manage and maintain the backup data. This includes regularly checking and updating the backup data to ensure the timeliness and accuracy of the data. At the same time, the data update system also needs to establish a complete access control mechanism to ensure that only authorized personnel can access and use the backup data. Through these measures, the data update system can ensure the integrity and security of the backup data and provide strong support for subsequent data analysis and updates.

[0243] In some other independent embodiments, the structured disaster recovery backup of the neural regulation sensor monitoring sampling data corresponding to the selected implantable electronic medical device according to the X target local feature relationship networks includes: mining the device performance trend vector and the monitoring object state vector of the neural regulation sensor monitoring sampling data in combination with the X target local feature relationship networks, wherein the device performance trend vector is used to characterize the device performance information in the neural regulation sensor monitoring sampling data, and the monitoring object state vector is used to characterize the object state information in the neural regulation sensor monitoring sampling data; for the device performance trend vector, based on the information in the monitoring object state vector that has an intersection with the device performance trend vector, A first key backup annotation vector is obtained based on the vector elements of the fork, and the first key backup annotation vector is used to characterize the device performance trend vector that is integrated with the monitoring object state vector; for the monitoring object state vector, based on the vector elements in the device performance trend vector that intersect with the monitoring object state vector, a second key backup annotation vector is obtained, and the second key backup annotation vector is used to characterize the monitoring object state vector that is integrated with the device performance trend vector; the first key backup annotation vector is spliced ​​with the second key backup annotation vector to obtain a structured disaster recovery annotation vector; based on the structured disaster recovery annotation vector, a structured disaster recovery backup is performed on the neural regulation sensor monitoring sampling data.

[0244] It can be understood that the core of the technical solution for performing structured disaster recovery backup of the neural regulation sensor monitoring sampling data corresponding to the selected implantable electronic medical device based on X target local feature relationship networks is to deeply mine the device performance trend vector and the monitoring object state vector in the neural regulation sensor monitoring sampling data by combining the target local feature relationship network, thereby realizing refined management and efficient backup of the data.

[0245] First, the X target local feature relationship networks are constructed based on the in-depth analysis and mining of implantable electronic medical devices, which reveal the complex relationship between medical devices and between medical devices and the external environment. The neuromodulation sensor monitoring sampling data is an important indicator reflecting the operating status and performance of medical devices, and is the basis for the data update system to update status data and perform disaster recovery backup. The device performance trend vector and the monitoring object state vector are the two core elements extracted by the data update system from the neuromodulation sensor monitoring sampling data, which are used to characterize the performance information of the device and the state information of the monitoring object, respectively.

[0246] Next, the data update system combines the X target local feature relationship networks to conduct in-depth mining and analysis of the neural regulation sensor monitoring sampling data. In this process, the data update system focuses on how to accurately extract the device performance trend vector and the monitoring object state vector, and understand the intrinsic relationship between them. The device performance trend vector can tell the data update system how the performance of the medical device changes over time, while the monitoring object state vector reflects the state changes of the object monitored by the medical device (such as a certain physiological parameter of the patient). The intersection of these two vectors, that is, the vector elements that contain both device performance information and monitoring object state information, is the key to the subsequent analysis of the data update system.

[0247] After obtaining the equipment performance trend vector and the monitored object state vector, the data update system begins to construct the first key backup annotation vector and the second key backup annotation vector. For the equipment performance trend vector, the data update system obtains the first key backup annotation vector based on the vector elements in the monitored object state vector that intersect with it. This vector integrates the state information of the monitored object, allowing the data update system to have a more comprehensive understanding of the performance of the medical device. Similarly, for the monitored object state vector, the data update system obtains the second key backup annotation vector based on the vector elements in the equipment performance trend vector that intersect with it. This vector integrates the performance information of the medical device, allowing the data update system to more accurately grasp the state changes of the monitored object.

[0248] After obtaining the first key backup annotation vector and the second key backup annotation vector, the data update system splices them to obtain a structured disaster recovery annotation vector. This vector contains both the performance information of the medical device and the status information of the monitored object, and is an important basis for the data update system to perform structured disaster recovery backup. Through this vector, the data update system can ensure that the backed-up data is both comprehensive and accurate, providing strong support for subsequent data recovery and analysis.

[0249] Finally, based on the structured disaster recovery annotation vector, the data update system performs structured disaster recovery backup of the neural regulation sensor monitoring sampling data. The implementation of this step requires the use of advanced backup technologies and strategies to ensure that the data can be safely and reliably backed up to the designated storage medium or cloud platform. At the same time, the data update system also needs to establish a complete disaster recovery mechanism and data recovery plan to deal with possible data loss, damage or catastrophic events. Through the implementation of these measures, the data update system can ensure that the status data of medical devices is effectively managed and protected, providing strong data support for subsequent medical decision-making and treatment.

[0250] In this way, it is not just a simple backup of the neural regulation sensor monitoring sampling data, but through deep mining and analysis of the data, key information and features are extracted and then backed up in a structured manner. This method not only improves the comprehensiveness and accuracy of the backup data, but also makes the data update system more convenient and efficient in subsequent data recovery and analysis.

[0251] In addition, with the continuous development and innovation of medical technology, the types and functions of implantable electronic medical devices are also increasing and expanding. Therefore, when implementing this technical solution, the data update system needs to maintain sensitivity and foresight to new technologies, and constantly explore and apply new methods and tools to improve the update and backup capabilities of medical device status data. For example, the data update system can use advanced technologies such as machine learning and artificial intelligence to automatically identify and extract key features and relationships of medical devices, further improving the efficiency and accuracy of data processing; the data update system can also use advanced technologies such as distributed storage and cloud computing to improve data storage and backup capabilities, and ensure data security and reliability.

[0252] It can be seen that by combining the X target local feature relationship networks and performing structured disaster recovery backup on the neural control sensor monitoring sampling data corresponding to the selected implantable electronic medical device, the data update system realizes the efficient management and protection of the medical device status data. This solution not only improves the data update system's ability to accurately grasp the status of medical devices, but also provides strong data support for subsequent medical decision-making and treatment. At the same time, it also demonstrates the important role and value of data analysis and mining in the management and optimization of medical device status in modern medical technology.

[0253] Further, Figure 2 Schematic diagram of a data updating system 200 provided in an embodiment of the present application. Figure 2 The data updating system 200 shown includes a processor 210, and the processor 210 can call and run a computer program from a memory to implement the method in the embodiment of the present application.

[0254] Alternatively, if Figure 2 As shown, the data update system 200 may further include a memory 230. The processor 210 may call and run a computer program from the memory 230 to implement the method in the embodiment of the present application.

[0255] The memory 230 may be a separate device independent of the processor 210 , or may be integrated into the processor 210 .

[0256] Alternatively, if Figure 2As shown, the data update system 200 may further include a transceiver 220, and the processor 210 may control the transceiver 220 to interact with other devices, specifically, to send information or data to other devices, or to receive information or data sent by other devices.

[0257] Optionally, the data update system 200 may implement the corresponding processes corresponding to the storage engine or components in the storage engine (such as a processing module) or a device deployed with a storage engine in each method of the embodiments of the present application, which will not be described here for the sake of brevity.

[0258] It should be understood that the processor of the embodiment of the present application may be an integrated circuit chip with signal processing capabilities.

[0259] It is understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. It should be noted that the memory of the systems and methods described herein is intended to include but is not limited to suitable types of memory.

[0260] Based on the above, a readable storage medium is provided, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the above method are implemented.

[0261] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0262] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0263] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the present application, all of which are within the protection of the present application.

Claims

1. A method for updating status data of an implantable electronic medical device, characterized in that: The method is applied to a data updating system, and the method comprises: Acquire neural regulation sensor monitoring information, perform a timing label recognition operation on an implantable electronic medical device index based on the neural regulation sensor monitoring information, obtain a data update timing label corresponding to the implantable electronic medical device index, and migrate the implantable electronic medical device index whose data update timing label meets the state data update index to a target medical device index group; wherein the implantable electronic medical device index is associated with the neural regulation sensor monitoring information; Acquire monitoring task jump data and storage update execution record corresponding to the target medical device index in the target medical device index group, and generate a structured feature relationship network for the target medical device index in the target medical device index group based on the monitoring task jump data and the storage update execution record to obtain a sensor monitoring feature relationship network; Based on the importance weights of the target medical device indexes in the target medical device index group in the sensor monitoring feature relationship network, a local feature relationship network disassembly operation is performed on the sensor monitoring feature relationship network to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group; wherein X is a positive integer; The method of performing a timing label identification operation on the implantable electronic medical device index based on the neural regulation sensor monitoring information to obtain a data update timing label corresponding to the implantable electronic medical device index includes: obtaining a device state feature, a sensor regulation transmission feature, and a sensor monitoring analysis feature corresponding to the implantable electronic medical device index associated with the neural regulation sensor monitoring information; obtaining a data update discrimination mode, determining a storage update discrimination score of the implantable electronic medical device index in the data update discrimination mode according to the device state feature, the sensor regulation transmission feature, and the sensor monitoring analysis feature, and determining a first upgrade optimization weight corresponding to the implantable electronic medical device index based on the storage update discrimination score; mapping the device state feature, the sensor regulation transmission feature, and the sensor monitoring analysis feature into a global storage update decision vector corresponding to the implantable electronic medical device index, and outputting a second upgrade optimization weight corresponding to the global storage update decision vector through a deep decision tree network; integrating the first upgrade optimization weight and the second upgrade optimization weight to obtain a target upgrade optimization weight corresponding to the implantable electronic medical device index, and determining a data update timing label of the implantable electronic medical device index according to the target upgrade optimization weight; The structured feature relationship network is generated for the target medical device index in the target medical device index group according to the monitoring task jump data and the storage update execution record to obtain the sensor monitoring feature relationship network, including: according to the monitoring task jump data and the storage update execution record, the target medical device index in the target medical device index group is associated and derived to obtain the target medical device derivative index; if the data update timing label corresponding to the target medical device derivative index meets the status data update indicator, the target medical device derivative index is migrated to the target medical device index group; if the monitoring task jump data or the storage update execution record indicates that the target medical device is If there is an involvement between the target medical device index u and the target medical device index v in the medical device index group, a data conduction vector between the target medical device index u and the target medical device index v is generated; according to the storage update execution record, the involvement coefficient between the target medical device indexes in the target medical device index group is determined; the target medical device indexes in the target medical device index group are used as knowledge feature units, the data conduction vector between the target medical device indexes in the target medical device index group are used as relationship pointers, and the involvement coefficient between the target medical device indexes in the target medical device index group are used as relationship pointer influencing factors to construct the sensor monitoring feature relationship network; The method further includes: obtaining global relationship pointer influence factors corresponding to each target medical device index in the sensor monitoring feature relationship network, and determining the target medical device index whose global relationship pointer influence factor is less than the first set influence factor as the medical device index to be updated; using the relationship chain corresponding to the medical device index to be updated in the sensor monitoring feature relationship network as the relationship chain to be processed, and extracting the relationship chain to be processed from the sensor monitoring feature relationship network to obtain the target update feature relationship chain; Then, based on the importance weights of the target medical device indexes in the target medical device index group in the sensor monitoring feature relationship network, a local feature relationship network disassembly operation is performed on the sensor monitoring feature relationship network to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group, including: based on the importance weights of the target medical device indexes in the target medical device index group in the target update feature relationship chain, a local feature relationship network disassembly operation is performed on the target update feature relationship chain to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group.

2. The method according to claim 1, characterized in that The obtaining of neural regulation sensor monitoring information includes: Acquire a temporary storage space set of original monitoring information; wherein the temporary storage space set of original monitoring information includes at least one encryption server or at least one information management middle station; If it is determined that the original monitoring information temporary storage space in the original monitoring information temporary storage space set is related to the firmware upgrade prompt, the original monitoring information temporary storage space in the original monitoring information temporary storage space set that contains the firmware upgrade prompt is determined as the target monitoring information temporary storage space, and the neural regulation sensor monitoring information is determined from the cache information corresponding to the target monitoring information temporary storage space.

3. The method according to claim 1, characterized in that The data update discrimination mode includes a device state discrimination sub-mode, a sensor control transmission discrimination sub-mode and a sensor monitoring analysis discrimination sub-mode; The step of determining the storage update discrimination score of the implantable electronic medical device index in the data update discrimination mode according to the device state feature, the sensor control transmission feature, and the sensor monitoring analysis feature includes: Obtaining a first discrimination point of view of the device state feature in the device state discrimination sub-mode, obtaining a second discrimination point of view of the sensor regulation transmission feature in the sensor regulation transmission discrimination sub-mode, and obtaining a third discrimination point of view of the sensor monitoring analysis feature in the sensor monitoring analysis discrimination sub-mode; The first discriminant opinion, the second discriminant opinion and the third discriminant opinion are fused by using the discriminant opinion fusion rule contained in the data update discriminant mode to obtain the storage update discriminant score of the implantable electronic medical device index in the data update discriminant mode.

4. The method according to claim 1, characterized in that: The method of performing a local feature relationship network disassembly operation on the sensor monitoring feature relationship network based on the importance weight of the target medical device index in the target medical device index group in the sensor monitoring feature relationship network to obtain X target local feature relationship networks for representing data update references corresponding to the target medical device index group includes: Obtaining a set of associated medical device indexes of a target medical device index r in the target medical device index group, and obtaining importance weights between the target medical device index r and each associated medical device index in the set of associated medical device indexes; Determine the associated medical device index corresponding to the largest importance weight in the associated medical device index set as the target associated medical device index, and if the importance weight between the target medical device index r and the target associated medical device index is greater than a second set impact factor, combine the target medical device index r and the target associated medical device index into a first original index set; Obtaining a set of associated index sets of a first original index set in the sensor monitoring feature relationship network, and obtaining importance weights between the first original index set and each associated index set in the set of associated index sets; Determine the associated index set corresponding to the largest importance weight in the associated index set set as the target associated index set, and if the importance weight between the first original index set and the target associated index set is greater than the second set impact factor, combine the first original index set and the target associated index set into a second original index set; Based on the second original index set contained in the sensor monitoring feature relationship network, a local feature relationship network disassembly operation is performed on the sensor monitoring feature relationship network to obtain X target local feature relationship networks for characterizing the data update reference corresponding to the target medical device index group; wherein the importance weights between different target local feature relationship networks are not greater than the second set impact factor.

5. The method according to claim 4, characterized in that The associated medical device index set includes a target medical device index t; The obtaining of the importance weights between the target medical device index r and each associated medical device index in the associated medical device index set includes: Obtaining a feature relationship attention value corresponding to the sensor monitoring feature relationship network, obtaining a relationship pointer influence factor between the target medical device index r and the target medical device index t, obtaining a global relationship pointer influence factor corresponding to the target medical device index r, and obtaining a global relationship pointer influence factor corresponding to the target medical device index t; Determine a first operation result between the global relationship pointer influence factor corresponding to the target medical device index r and the global relationship pointer influence factor corresponding to the target medical device index t as a first original importance weight, and determine a second operation result between the first original importance weight and the feature relationship attention value corresponding to the sensor monitoring feature relationship network as a second original importance weight; Determine a third operation result between the relationship pointer influence factor between the target medical device index r and the target medical device index t and the second original importance weight as a third original importance weight; The importance weight between the target medical device index r and the target medical device index t is determined based on the feature relationship attention value corresponding to the sensor monitoring feature relationship network and the third original importance weight.

6. The method according to claim 1, characterized in that The method further comprises: According to the X target local feature relationship networks used to characterize the data update reference corresponding to the target medical device index group, the state data update processing is performed on the selected implantable electronic medical device.

7. A data updating system, characterized in that: The method comprises at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the method according to any one of claims 1 to 6.

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