Remote data transmission method and device for blood pressure electrical stimulator

Abstract

The invention provides a remote data transmission method and device for a blood pressure electrical stimulator, and relates to the technical field of remote data transmission, and the method comprises the steps: integrating an offline analysis network in the blood pressure electrical stimulator; when measurement is started, local storage is created, and time sequence measurement data are recorded; sending the real-time measurement data to an offline analysis network for value analysis; constructing a time sequence data transmission strategy according to the pre-transmission test data and the value analysis result, and uploading the data; creating a packaging node, packaging and packaging the locally stored record data, and taking a result as verification data; and synchronizing the verification data to a remote data end for consistency verification, and storing the data. The technical problem that in the prior art, due to the fact that the blood pressure electrical stimulator conducts indifference transmission on data, the data transmission efficiency is poor can be solved, the optimized data transmission strategy is constructed according to the data value analysis result and the pre-transmission test data, and the data transmission efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of remote data transmission, and in particular to a remote data transmission method and device for a blood pressure electrical stimulator. Background Art

[0002] The blood pressure electrical stimulator measures blood pressure and pulse rate and assists in the treatment of hypertension through periodic electrical stimulation based on the theory of meridians in traditional Chinese medicine and combined with modern electronic technology. The sensors in the blood pressure electrical stimulator (such as pressure sensors, electrocardiogram sensors, etc.) will continuously collect physiological data such as blood pressure, pulse, heart rate, etc. of the user. The collected data will be stored and analyzed by synchronizing the data to a remote server. In existing technologies, these data are often transmitted continuously without considering the importance and degree of change of the data, resulting in a large amount of redundant or unnecessary data transmission in the network, especially when multiple devices are working at the same time, which is easy to cause network congestion and increase the network burden. Due to the indiscriminate transmission of all data, a lot of unimportant data also occupies precious bandwidth resources, which not only wastes bandwidth, but also may affect the transmission of critical data because network resources are occupied by non-critical data.

[0003] In summary, the prior art has a technical problem that the blood pressure electrical stimulator usually transmits all data indiscriminately, resulting in excessive network burden, bandwidth waste or increased delay, which further affects the data transmission efficiency. Summary of the invention

[0004] The purpose of this application is to provide a remote data transmission method and device for a blood pressure electrical stimulator, so as to solve the technical problem in the prior art that the blood pressure electrical stimulator usually transmits all data indiscriminately, resulting in excessive network burden, bandwidth waste or increased delay, which further affects the data transmission efficiency.

[0005] In view of the above problems, the present application provides a remote data transmission method and device for a blood pressure electrical stimulator.

[0006] In a first aspect, the present application provides a remote data transmission method for a blood pressure electric stimulator, which is implemented by a remote data transmission device for the blood pressure electric stimulator, wherein the remote data transmission method for the blood pressure electric stimulator includes: integrating an offline analysis network in the blood pressure electric stimulator, the offline analysis network is initialized by user data, and the offline analysis network is an evaluation network for measuring the value of data; when the blood pressure electric stimulator is activated and starts to perform measurements, local storage is created, and the local storage is used to record time-series measurement data; the real-time measurement data of the blood pressure electric stimulator is sent to the offline analysis network, data value analysis is performed, and data value analysis results are established; pre-transmission test data is obtained, a time-series data transmission strategy is constructed with the pre-transmission test data and the data value analysis results, and real-time measurement data is uploaded through the time-series data transmission strategy; a packaging node is created according to the working data of the blood pressure electric stimulator, the locally stored recorded data is packaged and packaged with the packaging node, and the packaging and packaging results are used as verification data; the verification data is synchronized to the remote data end, and consistency verification of the real-time measurement data and the verification data is performed at the remote data end, and data is stored according to the consistency verification results.

[0007] In a second aspect, the present application further provides a remote data transmission device for a blood pressure electrical stimulator, which is used to execute the remote data transmission method for a blood pressure electrical stimulator as described in the first aspect, wherein the remote data transmission device for a blood pressure electrical stimulator includes: an offline analysis network integration module, the offline analysis network integration module is used to integrate an offline analysis network in the blood pressure electrical stimulator, the offline analysis network is initialized by user data, and the offline analysis network is an evaluation network for measuring the value of data; a local storage module, the local storage module is used to create local storage when the blood pressure electrical stimulator is activated and starts to perform measurements, and use the local storage to record time-series measurement data; a data value analysis module, the data value analysis module is used to convert the real-time measurement data of the blood pressure electrical stimulator into real-time measurement data. Send to the offline analysis network, perform data value analysis, and establish data value analysis results; data transmission module, the data transmission module is used to obtain pre-transmission test data, build a timing data transmission strategy with the pre-transmission test data and data value analysis results, and upload real-time measurement data through the timing data transmission strategy; data encapsulation module, the data encapsulation module is used to create a packaging node according to the working data of the blood pressure electric stimulator, encapsulate and package the locally stored record data with the packaging node, and use the packaging result as verification data; data verification storage module, the data verification storage module is used to synchronize the verification data to the remote data terminal, perform consistency verification of the real-time measurement data and the verification data at the remote data terminal, and store the data according to the consistency verification result.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: By integrating an offline analysis network in the blood pressure electrical stimulator, the offline analysis network is initialized by user data, and the offline analysis network is an evaluation network for the value of measurement data; when the blood pressure electrical stimulator is activated and starts to perform measurement, a local storage is created, and the local storage is used to record the time series measurement data; the real-time measurement data of the blood pressure electrical stimulator is sent to the offline analysis network, the value analysis of the data is performed, and the data value analysis results are established; the pre-transmission test data is obtained, and a time series data transmission strategy is constructed with the pre-transmission test data and the data value analysis results, and the real-time measurement data is uploaded through the time series data transmission strategy; a packaging node is created according to the working data of the blood pressure electrical stimulator, and the locally stored recorded data is packaged and packaged with the packaging node, and the packaging and packaging results are used as verification data; the verification data is synchronized to the remote data terminal, and the consistency verification of the real-time measurement data and the verification data is performed at the remote data terminal, and the data is stored according to the consistency verification results. In other words, by integrating the offline analysis network to analyze the value of the data, and combining the data value analysis results and the pre-transmission test data to construct an optimized time series data transmission strategy, high-value data will be transmitted first, and lower-value data can be delayed or transmitted at a lower frequency. By using encapsulation nodes to package data and transmit it as verification data, the device is allowed to store large amounts of data locally, and transmit it in batches based on the data value when the network is restored. This ensures the integrity of the data and ensures that the data can remain intact even in the event of a network outage or unstable signal. It can also be transmitted in batches when the network is restored, avoiding data loss and redundant transmission, greatly improving the transmission efficiency of the blood pressure electrical stimulator.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1This is a flow chart of the remote data transmission method for the blood pressure electrical stimulator in this application.

[0012] Figure 2 This is a schematic diagram of the structure of the remote data transmission device used in the blood pressure electrical stimulator of the present application.

[0013] Explanation of the accompanying drawings: offline analysis network integration module 11, local storage module 12, data value analysis module 13, data transmission module 14, data encapsulation module 15, data verification storage module 16. DETAILED DESCRIPTION

[0014] The present application solves the technical problem in the prior art that the blood pressure electrical stimulator usually transmits all data indiscriminately, resulting in excessive network burden, bandwidth waste or increased delay, which further affects the efficiency of data transmission by providing a remote data transmission method and device for the blood pressure electrical stimulator. By integrating the offline analysis network to analyze the value of the data, and combining the data value analysis results and the pre-transmission test data to build an optimized timing data transmission strategy, high-value data will be transmitted first, and lower-value data can be delayed or transmitted at a lower frequency. By using encapsulation nodes to package the data and transmit it as verification data, the device is allowed to store large amounts of data locally, and transmit it in batches according to the value of the data when the network is restored, and ensure the integrity of the data, ensuring that the data can still maintain integrity even in the event of a network outage or unstable signal, and batch transmission when the network is restored to avoid data loss and redundant transmission, greatly improving the transmission efficiency of the blood pressure electrical stimulator.

[0015] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] For example, please refer to the attached Figure 1 The present application provides a remote data transmission method for a blood pressure electrical stimulator, wherein the remote data transmission method for the blood pressure electrical stimulator is implemented by a remote data transmission device for the blood pressure electrical stimulator, and the remote data transmission method for the blood pressure electrical stimulator specifically comprises the following steps: Step 1: Integrate an offline analysis network in the blood pressure electrical stimulator, wherein the offline analysis network is initialized by user data and is a network for evaluating the value of measurement data.

[0017] Specifically, an offline analysis network is integrated in the blood pressure stimulator to evaluate the importance and value of the measured data based on the characteristics of the data (such as the amplitude of change, frequency, etc.). The offline analysis network is an evaluation network that runs inside the device, does not rely on external network connections, and can process and analyze data without Internet access. By integrating the offline analysis network, the blood pressure stimulator can evaluate and filter the collected data in advance independently of the network connection, without relying on the cloud or remote server. This autonomy allows the device to work normally when the network is unstable or disconnected, and complete the data transmission after the network is restored. The offline analysis network is initialized with user data. When it starts running, it will be initialized based on the user's historical data or basic health information to ensure that the network can evaluate the value of the data according to the user's needs. Integrating the offline analysis network and initializing user data enables the blood pressure stimulator to intelligently evaluate the value of the measured data, which helps to reduce unnecessary data transmission, reduce the network burden, and ensure that key data can be identified and transmitted in a timely manner, so that the blood pressure stimulator can monitor the patient's condition in real time without a network connection and respond quickly when abnormal conditions are detected.

[0018] Step 2: When the blood pressure electrical stimulator is activated and starts to perform measurement, a local storage is created and the time series measurement data is recorded using the local storage.

[0019] Specifically, start the blood pressure electrical stimulator to put it into working state, and start to perform measurements and other related functions. Create local storage, which can be the built-in memory of the device or an external memory card, to save the data collected by the device. Local storage means that a part of the storage space will be allocated in the device memory to record the data of each measurement. This space needs to be large enough to store continuous measurement data at multiple time points and to adapt to different measurement cycles (such as blood pressure data records per second or per minute). When the device starts measuring, the time series measurement data is recorded in the local storage. Time series measurement data refers to continuous measurement data of physiological parameters such as blood pressure at different time points, usually including blood pressure, pulse, heart rate, etc., and the timestamps and measurement values ​​of these data are recorded to form a complete time series. Through local storage, it is ensured that the measurement data can be fully recorded even if the network connection is unstable or temporarily interrupted. Continuous time series data recording helps to capture the dynamic changes of blood pressure, especially the fluctuations that may occur in a short period of time, and can provide more comprehensive health information.

[0020] Step 3: Send the real-time measurement data of the blood pressure electrical stimulator to the offline analysis network, perform data value analysis, and establish data value analysis results.

[0021] Specifically, when the blood pressure electrical stimulator starts measuring, the device will collect the user's physiological data (such as blood pressure, pulse, etc.) in real time. These data are timestamped to form a series of time-series measurement data. The real-time measurement data is encapsulated into a format suitable for transmission, the data is converted into a standard format, and sent to the offline analysis network. The offline analysis network receives the data sent by the blood pressure electrical stimulator, performs value analysis on the data, and considers factors such as the magnitude of data changes, frequency and trend, abnormal identification, and association with the user's health status to determine the importance and urgency of the data. In addition, the real-time measurement data can be compared with the historical data to analyze whether there are abnormal fluctuations in the data, or the trend of multiple measurement data can be analyzed to identify long-term abnormal trends. Or, if there is a sharp fluctuation in blood pressure in a short period of time (such as a sudden increase or decrease in blood pressure), it means that the user's health status has changed dramatically, so it can be considered as high-value data. Although some data may not have significant abnormal fluctuations, they may still be considered as high-value data and given priority because they are extremely rare in the user's historical data. According to the analysis results, a value level is assigned to each real-time measurement data, including high-value data, medium-value data, low-value data, etc., to generate data value analysis results, which are usually a quantitative indicator indicating the importance or urgency of the data. By sending data to the offline analysis network in real time and performing value analysis, emergency situations such as sudden changes in blood pressure can be quickly identified, so that timely measures can be taken.

[0022] Step 4: Obtain pre-transmission test data, build a time series data transmission strategy based on the pre-transmission test data and data value analysis results, and upload real-time measurement data through the time series data transmission strategy.

[0023] Specifically, pre-transmission test data refers to test data used to evaluate the current network status before actual data transmission. By sending these test data in different time periods and network conditions, the network transmission performance indicators, including bandwidth utilization, packet loss rate, delay, etc., can be obtained. Through pre-transmission test data, important parameters such as the current network load, available bandwidth, and signal stability can be understood. Pre-test can detect network volatility and help the system predict whether the network is stable. It is evenly divided into multiple transmission frequency bands, and the transmission effect of each transmission frequency band is recorded to establish a high-level priority channel, a normal transmission channel, and a redundant transmission channel. The high-level priority channel, the normal transmission channel, and the redundant transmission channel are matched and called using the data value analysis results to establish a time-series data transmission measurement. Data value analysis is to evaluate the importance of data based on real-time measurement data, distinguish high-value data (such as data important for medical diagnosis) and low-value data (such as redundant data or background data). Data of different values ​​have different priorities in the transmission strategy. The time-series data transmission strategy is a dynamic transmission strategy based on network conditions (provided by pre-transmission test data) and data value (provided by data value analysis results). The timing data transmission strategy can be dynamically adjusted according to the real-time changes in network conditions. When the network load is low, the normal combination channels transmit data, and when the network load is high, the coordination of each combination prioritizes the transmission of high-value data. According to the constructed timing data transmission strategy, start uploading real-time measurement data. By pre-transmitting test data, the transmission strategy is adjusted in real time to avoid forced transmission when the network is congested or unstable, thereby improving the overall transmission success rate. Data value analysis enables high-value data to obtain priority transmission rights, ensuring that key data is uploaded in time. The timing data transmission strategy reduces unnecessary data transmission pressure through intelligent scheduling, so that network resources are reasonably utilized and bandwidth waste is avoided.

[0024] Step 5: Create a packaging node based on the working data of the blood pressure electrical stimulator, use the packaging node to package the locally stored recorded data, and use the packaging result as verification data.

[0025] Specifically, the working data of the blood pressure electric stimulator is organized into a specific data structure, namely, a package node. A package node refers to a logical or structural unit used to encapsulate and package the locally stored data collected by a device (such as a blood pressure electric stimulator), and organize the scattered data to form a data block that is easy to manage, transmit and verify. The creation of the package node is based on the working data of the blood pressure electric stimulator, especially the timestamp, measurement value (such as blood pressure, pulse, heart rate, etc.) and other metadata related to the collected data, including data type, timestamp, data identifier, status information, etc. The measurement data within a period of time is selected from the local storage, including key information such as blood pressure, pulse, and measurement time. According to the time period or batch of the data, the relevant data is divided into multiple nodes for packaging. Each package node contains data for a specific time period and is accompanied by necessary metadata (such as measurement time, data source, data type, etc.). The data in the package node is compressed or packaged into a data packet for easy transmission or verification, and is transmitted to a remote server or cloud as verification data. During the transmission process, the verification data is used to confirm whether the locally stored data is complete and has not been tampered with. By packaging and packaging the data, the management and transmission costs caused by scattered data during the transmission process can be reduced. At the same time, data packaging also helps to compress the data volume and further improve transmission efficiency.

[0026] Step 6: Synchronize the verification data to the remote data terminal, perform consistency verification between the real-time measurement data and the verification data at the remote data terminal, and store the data according to the consistency verification result.

[0027] Specifically, the verification data is synchronized to the remote data terminal, and the remote data terminal verifies the serial number of the real-time measurement data, and generates an encrypted hash value, check code or digital signature to detect whether the data has been tampered with or lost during transmission. On the blood pressure electrical stimulator, the system generates corresponding verification data (such as hash value, check code, etc.) while collecting real-time measurement data to ensure that each batch of data has unique verification information. The remote data terminal receives the real-time measurement data and corresponding verification data from the device, and recalculates the check code or hash value based on the received real-time measurement data to ensure that the data has not been modified or lost during transmission. The regenerated hash value is compared with the synchronized verification data. If the two match, it indicates that the data has maintained consistency during transmission; if they do not match, it indicates that the data may have been damaged or tampered with during transmission. If the consistency verification result shows that the real-time measurement data matches the verification data, the data is considered to be complete and accurate, and the remote data terminal stores the data in a database or other storage system. If the consistency verification fails, it indicates that an error may have occurred during the data transmission process, the failure is recorded, and the process of re-uploading the data is triggered to ensure the integrity of the data. By verifying the consistency of verification data with real-time measurement data, we can ensure that the data remains complete and accurate during transmission and prevent data loss or tampering. Consistency verification provides a mechanism for quickly detecting data errors, and can handle verification failures in a timely manner to ensure the reliability of the final data storage.

[0028] Further, step 4 of this application includes: Perform transmission evaluation on the pre-transmission test data and establish transmission evaluation results; perform threshold segmentation of the transmission evaluation results to establish advanced priority channels, normal transmission channels and redundant transmission channels; use data value analysis results to match and call advanced priority channels, normal transmission channels and redundant transmission channels to establish a timing data transmission strategy.

[0029] Specifically, before the actual data transmission, a test data packet is sent to evenly divide the transmission process into multiple transmission frequency bands, test the transmission effects of different frequency bands, and record and evaluate the transmission effects of different frequency bands, including packet loss rate, delay, loan utilization rate, transmission success rate, etc. Through the evaluation, the transmission evaluation results are established, and the evaluation results of the transmission effect of each transmission frequency band are recorded, including a comprehensive evaluation of indicators such as transmission speed, stability, and error rate, to determine the frequency bands with good effects and the frequency bands with poor effects. Different thresholds are set according to indicators such as packet loss rate, delay, bandwidth usage, etc., and the transmission frequency band is divided into three different channels: advanced priority channel, normal transmission channel and redundant transmission channel. Among them, the advanced priority channel refers to the frequency band with the best transmission effect. Data with high importance and high value are transmitted through this channel first to ensure that critical data can be transmitted quickly and reliably; the normal transmission channel refers to the frequency band with normal and stable transmission effect. It is used to transmit conventional data. The transmission speed and reliability are relatively good, but not as good as the advanced priority channel. It is suitable for general data transmission needs; the redundant transmission channel refers to the frequency band with poor transmission effect, limited bandwidth or unstable bandwidth. It is mainly used to transmit low-priority data or used as a backup channel. It is usually used to transmit data that is not very critical, or it is enabled when there is a problem with the main channel to ensure that data is not lost.

[0030] For example, the test data contains 500 data points, which are divided into 5 frequency bands, each of which contains 100 data points. Frequency band 1: transmission time = 50ms, success rate = 99%, delay = 20ms, frequency band 2: transmission time = 55ms, success rate = 98%, delay = 25ms, frequency band 3: transmission time = 45ms, success rate = 100%, delay = 15ms, frequency band 4: transmission time = 60ms, success rate = 97%, delay = 30ms, frequency band 5: transmission time = 65ms, success rate = 94%, delay = 35ms. The transmission effect of frequency band 3 is the best, while the transmission effect of frequency band 5 is the worst. The performance of other frequency bands is average, meeting the basic transmission requirements. Set the transmission success rate to 95% and the transmission time to 50ms as the threshold. According to the threshold, the frequency bands are classified as follows: advanced priority channel: frequency band 3, normal transmission channel: frequency band 1, frequency band 2, frequency band 4, redundant transmission channel: frequency band 5.

[0031] According to the results of data value analysis, the data is matched to the corresponding transmission channel. Through the real-time network situation, key indicators such as bandwidth, packet loss rate, and delay are collected and analyzed to generate load capture evaluation results. According to the current load status, the data transmission strategy is dynamically adjusted, not only considering the importance of the data itself, but also adjusting the priority according to the real-time changes in the transmission environment. Based on the value analysis of data and the division of transmission channels, a dynamic time-series data transmission strategy is established. The transmission strategy will be adjusted according to the real-time transmission performance of the network (such as bandwidth, delay, packet loss rate, etc.), giving priority to the transmission of high-value data and maximizing the use of currently available network resources. At the same time, according to the priority of different data, it is determined which data should be transmitted immediately, which can be delayed, and which can be transmitted under poor network conditions, so as to achieve the optimal allocation of network resources. Through the channel division based on the transmission evaluation results, high-value data can be transmitted through the optimal channel, ensuring that important information is transmitted in a timely and reliable manner, while reducing the dependence on network instability. The dynamic time-series data transmission strategy can adjust the transmission plan according to the real-time network status to ensure that effective data transmission can be maintained under various network conditions.

[0032] Furthermore, the present application also includes the following steps: A load capture window is established, and the load capture window is used to perform channel load capture evaluation within a predetermined time period of the advanced priority channel and generate a load capture evaluation result, wherein the load capture evaluation result of each advanced priority channel is calculated by the following formula: ; Characterize the load capture evaluation results, Characterizes the maximum bandwidth of the channel, Characterizes the bandwidth usage within the current capture window, Characterizes the maximum packet loss rate tolerance value, is the current packet loss rate, Characterize the current delay, Characterize the maximum tolerable delay; establish joint call constraints based on the load capture evaluation results, and use the joint call constraints to complete the matching calls of high-priority channels, normal transmission channels, and redundant transmission channels.

[0033] Specifically, a load capture window is established to monitor the network status of the advanced priority channel within a predetermined time period and evaluate the load of the channel. By collecting and analyzing key indicators such as bandwidth, packet loss rate, and latency, load capture evaluation results are generated, so that transmission strategies can be dynamically adjusted to ensure that high-priority data obtains the best transmission conditions. The load capture window is a time period (usually a predetermined duration) during which network performance parameters of the advanced priority channel, such as bandwidth utilization, packet loss rate, latency, etc., are continuously monitored to understand the channel usage and load level within a specific time period. The load capture evaluation results are calculated based on the three factors of bandwidth, packet loss rate, and latency. , providing a quantitative evaluation value for the current load status of the channel. α, β, and γ are weighted coefficients for each indicator, which are used to adjust the relative importance of different performance indicators in the overall evaluation and balance the weights of various influencing factors. They can be adjusted according to the specific needs and priorities of the system. For example, if bandwidth utilization is a key factor, the weight of α can be increased to make the impact of bandwidth on load evaluation more significant. It is the maximum bandwidth of the channel, that is, the maximum amount of data transmission that the channel can carry; It is the bandwidth usage rate within the current capture window, that is, the ratio of the current actual bandwidth used to the maximum bandwidth, indicating the amount of bandwidth currently used by the channel; Indicates the bandwidth usage. If the current bandwidth usage is lower, the evaluation result will be higher, indicating that the channel load is lower and there is more available bandwidth. Conversely, if the current bandwidth usage is close to the maximum bandwidth, it means that the bandwidth is close to full capacity.

[0034] is the maximum packet loss rate tolerance value, that is, the maximum packet loss rate that the channel can tolerate under stable operating conditions; is the current packet loss rate, that is, the actual packet loss rate and The ratio indicates the proportion of packet loss during the current data transmission process. Used to evaluate the current packet loss situation. If the current packet loss rate is low (close to 0), the evaluation result will be high, indicating that the channel quality is good and data transmission is more reliable. is the current network delay, which indicates the current delay time of data transmission in the channel; is the maximum tolerable delay, i.e., the maximum delay time that the channel can accept; Evaluate the current network delay. If the current delay is lower, the evaluation result will be higher, indicating that the channel runs more smoothly.

[0035] Combine three independent performance evaluation indicators (bandwidth usage, packet loss rate and latency) into a single performance score according to the given weights, calculate the load capture evaluation results for each high-priority channel, and thus determine the load status of the channel in a specific time period. Establish joint call constraints based on the load capture evaluation results to determine how to match and call different transmission channels. Joint call constraints are a set of rules used to dynamically select and match appropriate transmission channels under different load conditions based on the load capture evaluation results. Constraints ensure that each data transmission task can be matched to the best transmission channel based on its priority and network conditions. The core of joint call constraints is to use the load capture evaluation results to establish reasonable usage rules for each channel, and dynamically adjust according to different network conditions, data priorities and channel load conditions.

[0036] When the load capture evaluation result of the advanced priority channel shows high load (high bandwidth utilization, high packet loss rate, long delay), that is, the load of the advanced priority channel exceeds a certain threshold, the use of the channel will be restricted to avoid overload, and the advanced priority channel will be used to transmit a very small amount of high-value data, while other slightly inferior data will be transferred to the normal transmission channel or redundant transmission channel. When the advanced priority channel is in a medium load state, more data can be allowed to be transmitted through the advanced priority channel, but the total amount of transmission will be limited to prevent overload. When the load capture evaluation result of the advanced priority channel shows low load (abundant bandwidth, low delay, low packet loss rate), the joint call constraint will give priority to the advanced priority channel to transmit high-priority and medium-priority data, and the normal transmission channel and redundant transmission channel will be reduced or used as alternative channels to ensure the rapid transmission of critical data. According to the load capture results of each time window, the call weight of each channel is dynamically adjusted, and data and channels are reasonably allocated to ensure that high-value data obtains high-quality channels and low-value data does not occupy valuable resources, thereby optimizing the overall transmission efficiency and stability of the system.

[0037] Furthermore, the present application also includes the following steps: A comprehensive load capture evaluation result is established based on all load capture evaluation results; whether the comprehensive load capture evaluation result meets the preset load threshold is determined; if the comprehensive load capture evaluation result cannot meet the preset load threshold, a load coordination constraint is generated, and the load coordination constraint is used as a joint call constraint; if the comprehensive load capture evaluation result meets the preset load threshold, a combined call constraint of a normal transmission channel is generated, and the combined call constraint is used as a joint call constraint.

[0038] Specifically, a comprehensive load capture evaluation result is generated by summarizing all load capture evaluation results. Based on the maximum load capacity, performance requirements and actual application scenarios, a load threshold is pre-set to determine whether the load is within an acceptable range. The comprehensive load capture evaluation result is compared with the preset load threshold. If the comprehensive load capture evaluation result does not meet the preset load threshold, it means that the network load is high and cannot meet the transmission requirements; if the comprehensive load capture evaluation result meets the preset load threshold, it indicates that the network load is low and can support normal data transmission. For example, if the preset load threshold is 70% and the comprehensive load capture evaluation result is 60%, which does not exceed the threshold, a combined call constraint for the normal transmission channel is generated as a joint call constraint.

[0039] When the comprehensive load capture evaluation result exceeds the threshold, it means that the network load is large, and it is necessary to optimize the transmission strategy to reduce the network load pressure, and use the load coordination constraint as a joint call constraint. Load coordination constraint is a mechanism for dynamically adjusting the transmission channel, which aims to coordinate the calling method of each channel. Specifically, it may include reducing the data transmission volume of the high-priority channel to avoid overloading it; transferring some secondary important data to the normal channel or redundant channel for transmission to ensure that critical data can still be transmitted first and secondary data will not cause further network overload.

[0040] If the comprehensive load capture evaluation result is within the load threshold, it means that the network condition is good. In this case, data is transmitted according to the predetermined normal strategy, and the combined call constraint is used as the joint call constraint. According to the predetermined priority, each channel is called normally for data transmission without excessive adjustment. High-priority data will be quickly transmitted through the advanced priority channel to ensure its timeliness and reliability. Other data will be transmitted through normal data to ensure that the overall network resources are optimally utilized. The combined call constraint of the normal transmission channel means that under normal load conditions, each channel is called normally according to the current load status and channel performance to maximize the use of network resources. To determine whether the comprehensive load capture evaluation result meets the preset load threshold, the blood pressure electrical stimulator can monitor the system's load condition in real time, and dynamically adjust the transmission strategy to adapt to fluctuations in the network load, ensuring the stability and efficiency of data transmission.

[0041] Furthermore, step six of this application includes: The real-time measurement data uploaded by the remote data terminal is numbered and verified for integrity, and number verification results and integrity verification results are established; transmission feedback is generated based on the number verification results and integrity verification results, and the transmission feedback is synchronized to the blood pressure electric stimulator; the locally stored recorded data of the blood pressure electric stimulator is called, and the associated data is synchronized according to the transmission feedback in the recorded data to re-upload the data.

[0042] Specifically, real-time measurement data will be accompanied by a unique number or timestamp to identify the order and source of the data, ensuring that each piece of data is unique in the system. The remote data end checks the number of each piece of data to ensure that the uploaded data number is consistent with the expected number to prevent data loss, duplication or sequence errors. Integrity verification is used to ensure that the data has not been tampered with or damaged during transmission. Each uploaded verification data will be accompanied by a hash value or checksum as the "fingerprint" of the data packet, which uniquely corresponds to the original data and is used to identify the integrity of the data. The hash value or checksum is calculated for the real-time measurement data, and the calculation result is compared with the uploaded verification data. If the comparison results are consistent, it means that the data has not been modified or lost during transmission, and the verification is passed; if the comparison results are inconsistent, it means that the data may have been tampered with, damaged or lost during transmission, and the verification fails.

[0043] A number verification report is generated based on the comparison results of the number verification, indicating whether the data is uploaded in sequence and without omission. If the data packet number is found to be missing or repeated, the number verification result will be marked as failed. An integrity verification report is generated based on the results of the hash value comparison. If the data is tampered with or damaged during transmission, the integrity verification will fail and an error message will be returned. When the number verification fails, the transmission feedback will include the number and its problem, prompting the blood pressure electrical stimulator that some real-time measurement data has not been uploaded in sequence or some data has been lost; if the integrity verification fails, the feedback will notify the blood pressure electrical stimulator that some real-time measurement data has been damaged or tampered with during transmission and needs to be re-uploaded. When the number and integrity verification are successful, the transmission feedback will include the verified number. The remote data end generates a comprehensive feedback report based on the results of the number verification and integrity verification, summarizes the results and returns them to the blood pressure electrical stimulator in the form of feedback information.

[0044] The transmission feedback will be synchronously sent to the blood pressure stimulator through the network. The blood pressure stimulator will understand which real-time measurement data has problems during the transmission process based on the feedback information received. After receiving the feedback, the blood pressure stimulator will find the corresponding data stored locally according to the number and error mentioned in the feedback, and repackage and upload it. For data packets with incorrect numbers or integrity verification failures, the device will resend these data to ensure that the remote data end receives complete and accurate measurement data. Based on the specific content of the transmission feedback, it can decide whether to re-upload all or only the problematic part of the data. The uploaded real-time measurement data is numbered and verified for integrity through the remote data end, and the verification results are generated and synchronized to the device. The device searches for the locally stored recorded data based on the feedback and re-uploads it, ensuring the integrity and accuracy of the data transmission.

[0045] Furthermore, the present application also includes the following steps: The transmission feedback is used to synchronously perform traceability positioning of the time series data transmission strategy and establish the traceability positioning result; the data features and channel features of the traceability positioning result are extracted, and the abnormal association between the data features and the channel features is established; and the subsequent creation and management of the time series data transmission strategy is performed through the abnormal association.

[0046] Specifically, transmission feedback can provide detailed information about errors that occurred during the upload process, including which data packets were lost, damaged, or transmitted out of sequence. Based on the transmission feedback, the problem area of ​​data transmission can be traced back to identify abnormal behavior or the cause of transmission failure. The time, location, affected data packets, and other information of the problem are recorded to form the traceability positioning results. Tracing positioning means locating the transmission problem of a specific time period, a specific channel, or a specific data packet based on the transmission feedback, helping to identify the bottleneck or anomaly of data transmission. Extract data features, including specific content, data format, data type, data priority, etc. Different types of data may have different requirements for the transmission environment, and some specific data packets may be more prone to loss or transmission errors under specific network conditions. Extract channel features, that is, relevant information about the transmission channel used in the data transmission process, such as bandwidth, delay, packet loss rate, signal strength, etc. By analyzing the extracted data features and channel features, the abnormal associations in the data transmission process are identified, and it is determined which data features and channel features may cause transmission anomalies. For example, a specific type of data packet is more prone to packet loss or transmission errors on certain channels. Analyze the correlation between data features and channel features through data mining or machine learning algorithms, especially in the case of transmission anomalies, find out which data features are directly related to channel features, and clearly show which data features will cause abnormal transmission behavior under specific channel or network conditions.

[0047] According to the abnormal association, adjust or redesign the timing data transmission strategy, such as changing the transmission order of data packets, adjusting the priority, optimizing channel allocation, etc. The timing data transmission strategy will be dynamically adjusted according to the analysis results of the abnormal association to ensure that different types of data can be transmitted under appropriate channels and network conditions, reducing data loss and transmission errors. According to the retroactive positioning and abnormal association, the data transmission situation is continuously monitored, and the transmission strategy is adjusted according to the real-time feedback to ensure that the system can adapt to different network environments and transmission requirements. Retroactive positioning is carried out through transmission feedback to quickly and accurately locate the problem area in data transmission, effectively identify the transmission bottleneck, and effectively improve the accuracy and stability of data transmission. Especially in the case of complex network environment or high data transmission requirements, the strategy can be flexibly adjusted to ensure successful transmission.

[0048] Furthermore, the present application also includes the following steps: Perform a power self-check on the blood pressure electric stimulator and establish the power self-check result; after the blood pressure electric stimulator is activated, perform power adaptation verification according to the user's selected mode and establish the adaptation verification result; use the adaptation verification result to optimize the offline analysis network and report a power reminder.

[0049] Specifically, the blood pressure electrical stimulator performs a power self-test. When the device is started or checked regularly, the current battery power is automatically detected and a self-test result is generated to indicate whether the remaining battery power is sufficient to support the normal operation of the device. When the blood pressure electrical stimulator is turned on or in an idle state, the power self-test program is automatically executed to detect the remaining battery power, battery health status (such as battery aging), battery temperature, etc. through built-in sensors or circuits, and the battery status information is recorded to form a power self-test result, including power percentage, health status indicators, etc. Power adaptation verification is performed according to the operation mode selected by the user (such as low-intensity mode, high-intensity mode, etc.) to ensure that the current power can meet the mode requirements selected by the user. Users can choose different usage modes, and different modes may consume different amounts of power (such as high-intensity mode consumes more power). The device will automatically check whether the current power is sufficient to support the selected mode. If the current power is insufficient to run the selected mode, the mode is adjusted or a low-power reminder is issued. After the verification process is completed, an adaptation verification result is generated to record the pass of the verification and the matching of the power and mode. According to the adaptation verification results, analyze the impact of power on the offline analysis network. If the power is insufficient to support high-power network operations, adjust the network configuration to reduce unnecessary processing and calculations, such as reducing the frequency of data transmission, optimizing the data compression algorithm, and reducing the amount of calculation for real-time analysis. Adjust the offline analysis network according to the optimized configuration to adapt to the power limit. According to the power self-test results and power adaptation verification results, provide power reminders to users to help them understand the current power status of the device and make corresponding adjustments. If the power is lower than the safety threshold, remind the user to charge to prevent the device from running out of power at a critical moment. If the current power cannot support the mode selected by the user, recommend that the user switch to a more suitable mode, or remind the user that the device may not be able to run for too long in the current mode. Through power self-test and adaptation verification, the device can intelligently adjust battery usage to ensure that it can still operate normally or automatically switch to low-power mode when the power is low, improve the device's battery life and user experience, and avoid interruptions due to low power during use.

[0050] In summary, the remote data transmission method for a blood pressure electrical stimulator provided in this application has the following technical effects: By integrating an offline analysis network in the blood pressure electrical stimulator, the offline analysis network is initialized by user data, and the offline analysis network is an evaluation network for the value of measurement data; when the blood pressure electrical stimulator is activated and starts to perform measurement, a local storage is created, and the local storage is used to record the time series measurement data; the real-time measurement data of the blood pressure electrical stimulator is sent to the offline analysis network, the value analysis of the data is performed, and the data value analysis results are established; the pre-transmission test data is obtained, and a time series data transmission strategy is constructed with the pre-transmission test data and the data value analysis results, and the real-time measurement data is uploaded through the time series data transmission strategy; a packaging node is created according to the working data of the blood pressure electrical stimulator, and the locally stored recorded data is packaged and packaged with the packaging node, and the packaging and packaging results are used as verification data; the verification data is synchronized to the remote data terminal, and the consistency verification of the real-time measurement data and the verification data is performed at the remote data terminal, and the data is stored according to the consistency verification results. In other words, by integrating the offline analysis network to analyze the value of the data, and combining the data value analysis results and the pre-transmission test data to construct an optimized time series data transmission strategy, high-value data will be transmitted first, and lower-value data can be delayed or transmitted at a lower frequency. By using encapsulation nodes to package data and transmit it as verification data, the device is allowed to store large amounts of data locally, and transmit it in batches based on the data value when the network is restored. This ensures the integrity of the data and ensures that the data can remain intact even in the event of a network outage or unstable signal. It can also be transmitted in batches when the network is restored, avoiding data loss and redundant transmission, greatly improving the transmission efficiency of the blood pressure electrical stimulator.

[0051] Embodiment 2: Based on the same inventive concept as the remote data transmission method for the blood pressure electrical stimulator in the previous embodiment, the present application also provides a remote data transmission device for the blood pressure electrical stimulator, see the attached Figure 2 , the remote data transmission device for the blood pressure electrical stimulator comprises: The offline analysis network integration module 11 is used to integrate an offline analysis network in the blood pressure electrical stimulator. The offline analysis network is initialized by user data and is a network for evaluating the value of measurement data.

[0052] The local storage module 12 is used to create local storage and record time series measurement data using the local storage when the blood pressure electrical stimulator is activated and starts to perform measurement.

[0053] The data value analysis module 13 is used to send the real-time measurement data of the blood pressure electrical stimulator to the offline analysis network, perform data value analysis, and establish data value analysis results.

[0054] The data transmission module 14 is used to obtain pre-transmission test data, construct a time series data transmission strategy based on the pre-transmission test data and data value analysis results, and upload real-time measurement data through the time series data transmission strategy.

[0055] The data encapsulation module 15 is used to create an encapsulation node according to the working data of the blood pressure electrical stimulator, encapsulate and package the locally stored record data with the encapsulation node, and use the encapsulation and packaging results as verification data.

[0056] The data verification storage module 16 is used to synchronize the verification data to a remote data terminal, perform consistency verification between the real-time measurement data and the verification data at the remote data terminal, and store data according to the consistency verification result.

[0057] Furthermore, the data transmission module 14 in the remote data transmission device for the blood pressure electrical stimulator is also used for: Perform transmission evaluation on the pre-transmission test data and establish transmission evaluation results; perform threshold segmentation of the transmission evaluation results to establish advanced priority channels, normal transmission channels and redundant transmission channels; use data value analysis results to match and call advanced priority channels, normal transmission channels and redundant transmission channels to establish a timing data transmission strategy.

[0058] Furthermore, the data transmission module 14 in the remote data transmission device for the blood pressure electrical stimulator is also used for: A load capture window is established, and the load capture window is used to perform channel load capture evaluation within a predetermined time period of the advanced priority channel and generate a load capture evaluation result, wherein the load capture evaluation result of each advanced priority channel is calculated by the following formula: ; Characterize the load capture evaluation results, Characterizes the maximum bandwidth of the channel, Characterizes the bandwidth usage within the current capture window, Characterizes the maximum packet loss rate tolerance value, is the current packet loss rate, Characterize the current delay, Characterize the maximum tolerable delay; establish joint call constraints based on the load capture evaluation results, and use the joint call constraints to complete the matching calls of high-priority channels, normal transmission channels, and redundant transmission channels.

[0059] Furthermore, the data transmission module 14 in the remote data transmission device for the blood pressure electrical stimulator is also used for: A comprehensive load capture evaluation result is established based on all load capture evaluation results; whether the comprehensive load capture evaluation result meets the preset load threshold is determined; if the comprehensive load capture evaluation result cannot meet the preset load threshold, a load coordination constraint is generated, and the load coordination constraint is used as a joint call constraint; if the comprehensive load capture evaluation result meets the preset load threshold, a combined call constraint of a normal transmission channel is generated, and the combined call constraint is used as a joint call constraint.

[0060] Furthermore, the data verification storage module 16 in the remote data transmission device for the blood pressure electrical stimulator is also used for: The real-time measurement data uploaded by the remote data terminal is numbered and verified for integrity, and number verification results and integrity verification results are established; transmission feedback is generated based on the number verification results and integrity verification results, and the transmission feedback is synchronized to the blood pressure electric stimulator; the locally stored recorded data of the blood pressure electric stimulator is called, and the associated data is synchronized according to the transmission feedback in the recorded data to re-upload the data.

[0061] Furthermore, the data verification storage module 16 in the remote data transmission device for the blood pressure electrical stimulator is also used for: The transmission feedback is used to synchronously perform traceability positioning of the time series data transmission strategy and establish the traceability positioning result; the data features and channel features of the traceability positioning result are extracted, and the abnormal association between the data features and the channel features is established; and the subsequent creation and management of the time series data transmission strategy is performed through the abnormal association.

[0062] Furthermore, the remote data transmission device for the blood pressure electrical stimulator also includes a power self-check reminder module, which is used to: Perform a power self-check on the blood pressure electric stimulator and establish the power self-check result; after the blood pressure electric stimulator is activated, perform power adaptation verification according to the user's selected mode and establish the adaptation verification result; use the adaptation verification result to optimize the offline analysis network and report a power reminder.

[0063] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The remote data transmission method and specific examples for the blood pressure electrical stimulator in the first embodiment are also applicable to the remote data transmission device for the blood pressure electrical stimulator in the present embodiment. Through the above detailed description of the remote data transmission method for the blood pressure electrical stimulator, those skilled in the art can clearly know the remote data transmission device for the blood pressure electrical stimulator in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0064] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0065] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A remote data transmission method for a blood pressure electrical stimulator, characterized in that: include: An offline analysis network is integrated in the blood pressure electrical stimulator, the offline analysis network is initialized by user data, and the offline analysis network is a network for evaluating the value of the measurement data; When the blood pressure electrical stimulator is activated and starts to perform measurement, a local storage is created, and time-series measurement data is recorded using the local storage; Send the real-time measurement data of the blood pressure electrical stimulator to the offline analysis network, perform data value analysis, and establish data value analysis results; Acquire pre-transmission test data, build a time series data transmission strategy based on the pre-transmission test data and data value analysis results, and upload real-time measurement data through the time series data transmission strategy; Create a packaging node according to the working data of the blood pressure electrical stimulator, use the packaging node to package the locally stored recorded data, and use the packaging result as verification data; The verification data is synchronized to a remote data terminal, consistency verification is performed on the remote data terminal between the real-time measurement data and the verification data, and data is stored according to the consistency verification result.

2. The remote data transmission method for a blood pressure electrical stimulator according to claim 1, characterized in that: The method of constructing a time series data transmission strategy based on the pre-transmission test data and the data value analysis result also includes: Performing transmission evaluation on the pre-transmission test data and establishing a transmission evaluation result; Perform threshold segmentation of transmission evaluation results, and establish high-priority channels, normal transmission channels, and redundant transmission channels; The data value analysis results are used to match and call advanced priority channels, normal transmission channels, and redundant transmission channels to establish a time-series data transmission strategy.

3. The remote data transmission method for a blood pressure electrical stimulator according to claim 2, characterized in that: The matching and calling of the advanced priority channel, the normal transmission channel and the redundant transmission channel by using the data value analysis result also includes: A load capture window is established, and the load capture window is used to perform channel load capture evaluation within a predetermined time period of the advanced priority channel and generate a load capture evaluation result, wherein the load capture evaluation result of each advanced priority channel is calculated by the following formula: ； Characterize the load capture evaluation results, Characterizes the maximum bandwidth of the channel, Characterizes the bandwidth usage within the current capture window, Characterizes the maximum packet loss rate tolerance value, is the current packet loss rate, Characterize the current delay, Characterize the maximum tolerable delay; Based on the load capture evaluation results, joint call constraints are established, and the matching calls of high-priority channels, normal transmission channels and redundant transmission channels are completed by the joint call constraints.

4. The remote data transmission method for a blood pressure electrical stimulator according to claim 3, characterized in that: Also includes: Establishing a comprehensive load capture evaluation result based on all load capture evaluation results; Determining whether the comprehensive load capture evaluation result meets a preset load threshold; If the comprehensive load capture evaluation result cannot meet the preset load threshold, a load coordination constraint is generated, and the load coordination constraint is used as a joint call constraint; If the comprehensive load capture evaluation result meets the preset load threshold, a combined call constraint of the normal transmission channel is generated, and the combined call constraint is used as the joint call constraint.

5. The remote data transmission method for a blood pressure electrical stimulator according to claim 1, characterized in that: Also includes: Use the remote data terminal to perform the numbering and integrity verification of the uploaded real-time measurement data, and establish the numbering verification results and integrity verification results; Generate transmission feedback based on the number verification result and the integrity verification result, and synchronize the transmission feedback to the blood pressure electrical stimulator; The recorded data stored locally in the blood pressure electrical stimulator is called, and the associated data is synchronized according to the transmission feedback in the recorded data to re-upload the data.

6. The remote data transmission method for a blood pressure electrical stimulator according to claim 5, characterized in that: Also includes: Use transmission feedback to synchronously perform retrospective positioning of the timing data transmission strategy and establish retrospective positioning results; Extract the data features and channel features of the traceability positioning results, and establish abnormal associations between the data features and the channel features; Subsequent sequential data transmission strategy creation and management are performed through the abnormal association.

7. The remote data transmission method for a blood pressure electrical stimulator according to claim 1, characterized in that: Also includes: Conduct a self-test of the electric quantity of the blood pressure electric stimulator and establish the self-test result of the electric quantity; After the blood pressure electrical stimulator is activated, the power adaptation verification is performed according to the user's selected mode, and the adaptation verification result is established; The offline analysis network is optimized based on the adaptation verification results, and a power reminder is given.

8. A remote data transmission device for a blood pressure electrical stimulator, characterized in that: The steps for implementing the remote data transmission method for a blood pressure electrical stimulator according to any one of claims 1 to 7, wherein the remote data transmission device for a blood pressure electrical stimulator comprises: An offline analysis network integration module, the offline analysis network integration module is used to integrate an offline analysis network in the blood pressure electrical stimulator, the offline analysis network is initialized by user data, and the offline analysis network is an evaluation network for measuring the value of data; A local storage module, wherein the local storage module is used to create a local storage when the blood pressure electrical stimulator is activated and starts to perform measurement, and use the local storage to record time series measurement data; A data value analysis module, which is used to send the real-time measurement data of the blood pressure electrical stimulator to an offline analysis network, perform data value analysis, and establish data value analysis results; A data transmission module, which is used to obtain pre-transmission test data, construct a time series data transmission strategy based on the pre-transmission test data and data value analysis results, and upload real-time measurement data through the time series data transmission strategy; A data encapsulation module, wherein the data encapsulation module is used to create an encapsulation node according to the working data of the blood pressure electrical stimulator, encapsulate and package the locally stored recorded data with the encapsulation node, and use the encapsulation and packaging result as verification data; A data verification storage module is used to synchronize the verification data to a remote data terminal, perform consistency verification between the real-time measurement data and the verification data at the remote data terminal, and store the data according to the consistency verification result.

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