Blood pressure data transmission method and system, computing device, storage medium and product

Abstract

The embodiment of the invention relates to the field of blood pressure data processing, and provides a blood pressure data transmission method and system, computing equipment, a storage medium and a product, and the method comprises the steps that multiple pieces of blood pressure data of a blood pressure monitor are obtained, and the blood pressure data comprise blood pressure values and moments corresponding to the blood pressure values; performing wavelet transform or short-time Fourier transform decomposition on the blood pressure data to obtain a fluctuation period, a first time period and a second time period of the blood pressure data; the time period when the blood pressure value fluctuation frequency is greater than a first threshold value in the fluctuation period is a first time period. First indication information is sent, the first indication information indicates that the first blood pressure data collected in the first time period are compressed through the first compression ratio, the second blood pressure data collected in the second time period are compressed through the second compression ratio, and the first compression ratio is smaller than the second compression ratio. And receiving the compressed new blood pressure data. According to the embodiment of the invention, a small compression ratio can be adopted for the blood pressure data with large fluctuation. Therefore, resources consumed by blood pressure data transmission are saved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of blood pressure data processing, and more specifically to a blood pressure data transmission method, system, computing device, storage medium and product. Background Art

[0002] With the development of wearable devices and remote medical technology, the real-time monitoring and transmission of blood pressure data have become increasingly common. However, the traditional method of obtaining blood pressure data is usually that a blood pressure data collection device continuously collects blood pressure data and transmits the collected data to a server for processing. However, when continuous blood pressure monitoring is required, a large amount of blood pressure data will be collected. At this time, sending a large amount of blood pressure data will consume a large amount of transmission resources. Summary of the Invention

[0003] The embodiments of the present application provide a blood pressure data transmission method, system, computing device, storage medium and product, which can consume less transmission resources and obtain more blood pressure data information.

[0004] In a first aspect, the embodiments of the present application provide a blood pressure data transmission method, which can be applied to a blood pressure data processing device. The method includes: obtaining a plurality of blood pressure data of a blood pressure monitor, where the blood pressure data includes a blood pressure value and a moment corresponding to the blood pressure value, and the moments corresponding to the blood pressure values in the plurality of blood pressure data are the moments between a first moment and a second moment, and the time interval between the first moment and the second moment is more than 1 week; performing wavelet transform or short-time Fourier transform decomposition on the blood pressure data to obtain a fluctuation period, a first time period and a second time period of the blood pressure data of the blood pressure monitor; a time period in the fluctuation period of the blood pressure data of the blood pressure monitor where the fluctuation frequency of the blood pressure value is greater than a first threshold is a first time period, and the blood pressure data in the first time period is first blood pressure data; a time period other than the first time period in the fluctuation period of the blood pressure data of the blood pressure monitor is a second time period, and the blood pressure data in the second time period is second blood pressure data; sending a first indication information, where the first indication information is used to indicate that the first blood pressure data is compressed using a first compression ratio and the second blood pressure data is compressed using a second compression ratio, and the first compression ratio is less than the second compression ratio; receiving the new compressed blood pressure data, where the new blood pressure data in the first time period is obtained by compressing using the first compression ratio, and the new blood pressure data in the second time period is obtained by compressing using the second compression ratio.

[0005] In some implementations, before receiving the new compressed blood pressure data, it further includes sending a second indication message, where the second indication message indicates that new blood pressure data is collected at a first frequency during a first time period of the blood pressure fluctuation cycle of the blood pressure monitor, and new blood pressure data is collected at a second frequency during a second time period of the blood pressure fluctuation cycle of the blood pressure monitor; the first frequency is greater than the second frequency.

[0006] In some implementations, before sending the first indication message, it further includes: obtaining environmental data and activity data of the blood pressure monitor; where the activity data includes meal time and exercise time; the environmental data includes environmental temperature and environmental humidity; inputting the blood pressure fluctuation cycle of the blood pressure monitor, the first time period, the second time period, the activity data, the environmental data, and multiple blood pressure data of the blood pressure monitor into a prediction model to obtain predicted blood pressure data, a new blood pressure fluctuation cycle of the blood pressure monitor, a new first time period, and a new second time period.

[0007] In some implementations, before sending the first indication message, it further includes: determining the number of acquisitions of the first blood pressure data according to the first frequency and the first time period; determining the number of acquisitions of the second blood pressure data according to the second frequency and the second time period; determining the proportion of the second blood pressure data in all the blood pressure data according to the number of acquisitions of the first blood pressure data and the number of acquisitions of the second blood pressure data; where all the blood pressure data is the sum of the first blood pressure data and the second blood pressure data; multiplying M times the proportion of the second blood pressure data in all the blood pressure data by the total coding space of the blood pressure data to obtain the coding space of the second blood pressure data; where M is a positive number greater than 0 and less than 1; subtracting the coding space of the second blood pressure data from the total coding space of the blood pressure data to obtain the coding space of the first blood pressure data; obtaining the first compression ratio for compressing the first blood pressure data according to the coding space of the first blood pressure data and the total coding space of the blood pressure data; obtaining the second compression ratio for compressing the second blood pressure data according to the coding space of the second blood pressure data and the total coding space of the blood pressure data; after receiving the new compressed blood pressure data, it further includes: decompressing the blood pressure data of the first time period according to the first compression ratio; decompressing the blood pressure data of the second time period according to the second compression ratio.

[0008] In some implementations, the new blood pressure data in the first time period is compressed using the first compression ratio, including: the new blood pressure data in the first time period is compressed using one or more of Huffman coding, run-length coding, and differential coding; the new blood pressure data in the second time period is compressed using the second compression ratio, including: the new blood pressure data in the second time period is compressed using predictive coding.

[0009] In some implementations, the blood pressure data includes systolic pressure and diastolic pressure.

[0010] In a second aspect, a method for transmitting blood pressure data is provided. This method can be applied to a blood pressure data acquisition device. The method includes: acquiring blood pressure data of a blood pressure monitor, where the blood pressure data includes a blood pressure value and the moment corresponding to the blood pressure value; the moment corresponding to the blood pressure value in the blood pressure data is a moment between a first moment and a second moment; transmitting the blood pressure data; receiving first indication information, where the first indication information is used to indicate that the first blood pressure data is compressed using a first compression ratio and the second blood pressure data is compressed using a second compression ratio; the first blood pressure data is the blood pressure data in a first time period, and the first time period is a time period in the fluctuation cycle of the blood pressure data of the blood pressure monitor where the fluctuation frequency of the blood pressure value is greater than a first threshold; the second blood pressure data is the blood pressure data in a second time period, and the second time period is a time period outside the first time period in the fluctuation cycle of the blood pressure data of the blood pressure monitor; the first compression ratio is less than the second compression ratio; acquiring new blood pressure data; compressing and transmitting the compressed new blood pressure data; the new blood pressure data acquired in the first time period is compressed using the first compression ratio, and the new blood pressure data acquired in the second time period is compressed using the second compression ratio.

[0011] In a third aspect, a blood pressure data transmission system is provided, characterized in that the system includes: a blood pressure data acquisition device and a server, the blood pressure data acquisition device executes the method described in the second aspect, and the server executes the method described in any item of the first aspect.

[0012] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which includes instructions that, when running on a computer, cause the computer to execute the blood pressure data transmission method described in the first aspect.

[0013] In a fifth aspect, an embodiment of the present application provides a computing device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method described in the first aspect is implemented.

[0014] Sixth aspect, an embodiment of the present application provides a chip, which includes a processor coupled to a transceiver and is used to execute the technical solution provided in the first aspect of the embodiments of the present application. In a possible design, the chip can also be a dedicated hardware structure for implementing the technical solution provided in the above first aspect. For example, the processing related to the neural network model can be implemented by a dedicated neural network processor or a graphics processor.

[0015] Seventh aspect, an embodiment of the present application provides a chip system, which includes a processor for implementing the functions involved in the above first aspect. For example, generating or processing the information involved in the method provided in the above first aspect.

[0016] In a possible design, the above chip system further includes a memory, which is connected to the processor through a circuit structure. The memory is used to store the program instructions and data necessary for the terminal. The chip system can be composed of chips or can include chips and other discrete devices. Further optionally, the chip further includes a communication interface, and the processor is connected to the communication interface. The communication interface is used to receive the data and / or information that needs to be processed. The processor obtains the data and / or information from the communication interface, processes the data and / or information, and outputs the processing result through the communication interface. The communication interface can be an input / output interface.

[0017] Eighth aspect, an embodiment of the present application provides a computer program product containing instructions. When the computer program product runs on a computer, it causes the computer to execute the blood pressure data transmission method provided in the above first aspect.

[0018] Compared with the prior art, in the process of collecting blood pressure in the embodiments of the present application, the blood pressure data processing device can determine the time when the blood pressure fluctuates greatly based on the fluctuation of the blood pressure of the blood pressure monitor. A large blood pressure fluctuation is of great significance for blood pressure monitoring. By using a lower compression ratio for the blood pressure with a large fluctuation, that is, performing less compression on the blood pressure data with a large fluctuation, more useful information can be transmitted with less communication resources, saving the resources consumed by blood pressure data transmission. Description of the Drawings

[0019] By referring to the accompanying drawings and reading the detailed description of the embodiments of the present application, the objectives, features, and advantages of the embodiments of the present application will become easy to understand. Among them: Figure 1 is a schematic diagram of the blood pressure data transmission system in the embodiments of the present application; Figure 2 is a schematic flowchart of the blood pressure data transmission method provided in the embodiments of the present application; Figure 3 is another schematic flowchart of the blood pressure data transmission method provided in the embodiments of the present application Figure 4 Structural schematic diagram of the blood pressure data processing device according to an embodiment of the present application; Figure 5 Structural schematic diagram of a computing device according to an embodiment of the present application; Figure 6 Structural schematic diagram of a mobile phone according to an embodiment of the present application; Figure 7 Structural schematic diagram of a server according to an embodiment of the present application.

[0020] In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed implementation manners

[0021] In the description of the embodiments of the present application, the terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects (for example, the first time period and the second time period respectively represent different time periods, and other similar situations), and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that shown or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or modules does not necessarily limit to the clearly listed steps or modules, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. The division of modules in the embodiments of the present application is only a logical division, and there may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, and the indirect coupling between modules, the communication connection may be in an electrical or other similar form, which are not limited in the embodiments of the present application. And the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed to multiple circuit modules, and some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.

[0022] An embodiment of the present application provides a blood pressure data transmission method, system, computing device, storage medium, and product. The method includes: a blood pressure data acquisition device acquires blood pressure data of a blood pressure monitor. Correspondingly, a blood pressure data processing device obtains multiple blood pressure data of the blood pressure monitor. The blood pressure data includes a blood pressure value and the time corresponding to the blood pressure value. Among the multiple blood pressure data, the time corresponding to the blood pressure value is the time between a first moment and a second moment, and the time interval between the first moment and the second moment is more than one week. The blood pressure data processing device performs wavelet transform or short-time Fourier transform decomposition on the blood pressure data to obtain the fluctuation period of the blood pressure data of the blood pressure monitor and the first time period in the fluctuation period where the fluctuation frequency of the blood pressure value is greater than a first threshold. The blood pressure data in the first time period is the first blood pressure data. It is determined that the time period outside the first time period in the fluctuation period of the blood pressure data of the blood pressure monitor is the second time period, and the blood pressure data in the second time period is the second blood pressure data. The blood pressure data processing device sends the first time period, the first compression ratio, the second time period, and the second compression ratio. Correspondingly, the blood pressure data acquisition device receives the first time period, the first compression ratio, the second time period, and the second compression ratio. Wherein, the first compression ratio is used to indicate that the first blood pressure data is compressed using the first compression ratio, and the second compression ratio is used to indicate that the second blood pressure data is compressed using the second compression ratio, and the first compression ratio is less than the second compression ratio. The blood pressure data acquisition device acquires new blood pressure data. Correspondingly, the blood pressure data processing device receives the new blood pressure data. The new blood pressure data is obtained according to the first compression ratio or the new blood pressure data is compressed according to the second compression ratio.

[0023] Through the above method, during the process of collecting blood pressure, the blood pressure data processing device can determine the time when the blood pressure fluctuates greatly based on the fluctuation of the blood pressure of the blood pressure monitor. Blood pressure data with large fluctuations is of great significance for monitoring blood pressure abnormalities. By using a lower compression ratio for blood pressure with large fluctuations, that is, less compression of blood pressure data with large fluctuations, and a higher compression ratio for blood pressure with small fluctuations, that is, more compression of blood pressure data with small fluctuations, more useful information can be transmitted with less communication resources, saving the resources consumed in blood pressure data transmission.

[0024] The blood pressure data transmission method provided by the embodiment of the present application can be applied to a blood pressure data transmission system in a scenario capable of transmitting blood pressure data. Referring to Figure 1 , the blood pressure data transmission system may include a blood pressure data acquisition device and a blood pressure data processing device. The blood pressure data acquisition device and the blood pressure data processing device are connected by wire or wirelessly.

[0025] A blood pressure data acquisition device (an example of a computing device) is used to acquire blood pressure data and transmit the acquired blood pressure data to a blood pressure data processing device. The blood pressure data acquisition device can be implemented as a terminal device, such as a blood pressure monitor, a blood pressure monitoring bracelet, a blood pressure monitoring watch, and other terminal devices. The blood pressure data acquisition device includes a processor, a memory, a blood pressure sensor, a data transceiver interface, etc. The blood pressure sensor can acquire blood pressure. The transceiver interface can perform operations of receiving or sending data. The processor can control the acquisition frequency of blood pressure data in multiple future time periods and compress the acquired blood pressure data. The processor can also implement the relevant operations performed by the processor in the blood pressure data acquisition device in the following embodiments. Exemplarily, the processor can be a central processing unit, a field programmable gate array, a single-chip microcomputer, etc.

[0026] A blood pressure data processing device (an example of a computing device) is used to receive the blood pressure data acquired by the blood pressure data acquisition device and process the blood pressure data. The blood pressure data processing device is also used to determine the acquisition frequency of blood pressure data in multiple future time periods according to the processing result of the blood pressure data and send the acquisition frequency of blood pressure data in multiple future time periods to the blood pressure data acquisition device. The blood pressure data processing device can be implemented as a server, a computer, a mobile phone, and other terminal devices. The blood pressure data processing device includes a processor, a data transceiver interface, a memory, etc. The memory can store the received blood pressure data. The processor can process the blood pressure data and determine the acquisition frequency of blood pressure data in multiple future time periods. The processor can also implement the relevant operations performed by the processor in the blood pressure data processing device in the following embodiments. The transceiver interface can perform operations such as receiving the blood pressure data from the blood pressure data acquisition device and sending the acquisition frequency of blood pressure data in multiple future time periods to the blood pressure data acquisition device. Exemplarily, the processor can be a central processing unit, a field programmable gate array, a single-chip microcomputer, etc.

[0027] Exemplarily, the blood pressure data transmission system of the embodiments of the present application can be applied to a remote health monitoring scenario. The blood pressure data acquisition device is a blood pressure monitoring bracelet, and the blood pressure data processing device is a cloud server.

[0028] The server involved in the embodiments of the present application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0029] Refer to Figure 2 , Figure 2Schematic flowchart of a blood pressure data transmission method provided by an embodiment of this application. Taking the blood pressure data acquisition device implemented as a blood pressure monitoring bracelet and the blood pressure data processing device implemented as a cloud server (referred to as the cloud) as an example, this method will be introduced. This method includes steps 101-106.

[0030] Step 101, the blood pressure monitoring bracelet collects the blood pressure data of the blood pressure monitor.

[0031] The blood pressure data includes the blood pressure value and the corresponding moment of the blood pressure value. Among multiple blood pressure data, the moments corresponding to the blood pressure values are the moments between the first moment and the second moment, and the time interval between the first moment and the second moment is more than 1 week.

[0032] When the blood pressure monitoring bracelet collects blood pressure data, it will record the collected blood pressure value and the corresponding moment of the blood pressure value. Among them, the blood pressure value can include systolic blood pressure and diastolic blood pressure. For example, taking the collection of blood pressure data once per second as an example, the blood pressure data is shown in Table 1. Table 1 shows an example of measuring systolic blood pressure data and diastolic blood pressure data between the first moment (taking 2025-01-01 08:10 as an example) and the second moment (taking 2025-01-08 08:10 as an example).

[0033] Table 1 Blood Pressure Data Table

[0034] In some embodiments, the time interval between the first moment and the second moment is 2 weeks, 3 weeks, 4 weeks, etc.

[0035] Step 102, the blood pressure monitoring bracelet sends the blood pressure data of the blood pressure monitor. Correspondingly, the cloud obtains multiple blood pressure data of the blood pressure monitor.

[0036] In some embodiments, the cloud can send a message requesting blood pressure data to the blood pressure monitoring bracelet. After receiving the message requesting blood pressure data, the blood pressure monitoring bracelet sends the blood pressure data to the cloud.

[0037] In some other embodiments, a preset moment can be set between the cloud and the blood pressure monitoring bracelet, and the blood pressure monitoring bracelet can send the blood pressure data to the cloud at the preset moment.

[0038] Step 103, the cloud performs wavelet transform or short-time Fourier transform decomposition on the blood pressure data to obtain the fluctuation period, the first time period, and the second time period of the blood pressure data of the blood pressure monitor.

[0039] Among them, the fluctuation period of the blood pressure data of the blood pressure monitor includes a first time period in which the fluctuation frequency of the blood pressure value is greater than a first threshold. The blood pressure data in the first time period is the first blood pressure data. Determine the time period other than the first time period in the fluctuation period of the blood pressure data of the blood pressure monitor as the second time period, and the blood pressure data in the second time period is the second blood pressure data.

[0040] The cloud performs wavelet transform or short-time Fourier transform decomposition on the blood pressure data, and can obtain the fluctuation period of the blood pressure data of the blood pressure monitor. Assuming that the fluctuation period of the blood pressure data of the blood pressure monitor is one week, the fluctuation frequencies from Monday to Friday are similar, and the fluctuation frequencies on Saturday and Sunday are similar. From 2:00 to 3:00, from 5:30 to 7:30, from 12:00 to 14:00, and from 16:00 to 18:00 every day from Monday to Friday are the time periods when the fluctuation frequency of the blood pressure value is greater than the first threshold, and from 3:00 to 3:30, from 8:00 to 10:00, and from 17:00 to 19:00 every day from Saturday to Sunday are the time periods when the fluctuation frequency of the blood pressure value is greater than the first threshold. Then it can be determined that the above time periods belong to the first time period, and the time periods other than the above time periods in one week belong to the second time period.

[0041] In some embodiments, the time window length of the short-time Fourier transform is more than 5 seconds, such as 5 seconds, 30 seconds, 2 minutes, etc. Considering that the blood pressure fluctuation value is affected by breathing, and the breathing cycle of adults is generally about 3 - 5 seconds, by selecting a time window length of more than 5 seconds, a complete breathing cycle can be covered, so that the fluctuation of the blood pressure value can be measured more accurately.

[0042] The cloud can determine whether a time period is the first time period based on the number of time windows in which the fluctuation frequency of the blood pressure value is greater than the first threshold in a time period. Exemplarily, the time window length for the cloud to perform short-time Fourier transform decomposition on the blood pressure data is 1 minute. Assuming that there are a total of 50 time windows of blood pressure data from 2:00 to 3:00 on Monday, if the fluctuation frequency of the blood pressure value in more than 50% of the time windows, that is, more than 26 time windows, is greater than the first threshold, then it is determined that the time period from 2:00 to 3:00 on Monday is the first time period.

[0043] Step 104, the cloud sends a first indication message. Correspondingly, the blood pressure monitoring bracelet receives the first indication message.

[0044] Among them, the first indication message is used to indicate that the first compression ratio is used to compress the first blood pressure data, and the second compression ratio is used to compress the second blood pressure data, and the first compression ratio is less than the second compression ratio.

[0045] The compression ratio refers to the proportional relationship between the size of the data after being compressed by the compression algorithm and the size of the original data. The larger the compression ratio, the more the data is compressed, and the greater the possible data distortion.

[0046] In some embodiments, the first indication information includes a first time period, a first compression ratio, a second time period, and a second compression ratio in the fluctuation cycle of the blood pressure data of the blood pressure monitor. For example, the first time period in the fluctuation cycle of the blood pressure data of the blood pressure monitor is from 2:00 to 3:00 and from 5:30 to 7:30 on Monday every week. The first compression ratio is 50:1. The second time period in the fluctuation cycle of the blood pressure data of the blood pressure monitor is the time period other than from 2:00 to 3:00 and from 5:30 to 7:30 on Monday every week. The second compression ratio is 100:1. Then when the blood pressure monitoring bracelet receives the first time period and the first compression ratio, the blood pressure monitoring bracelet can compress the blood pressure data collected in the first time period by the first compression ratio. Similarly, when the blood pressure monitoring bracelet receives the second time period and the second compression ratio, the blood pressure monitoring bracelet can compress the blood pressure data collected in the second time period by the second compression ratio.

[0047] The blood pressure monitoring bracelet can compress the blood pressure data by the first compression ratio in various ways. For example, use one or more of Huffman coding, run-length coding, and differential coding to compress the blood pressure data.

[0048] Similarly, the blood pressure monitoring bracelet can compress the blood pressure data by the second compression ratio in various ways. For example, use predictive coding to compress the blood pressure data.

[0049] In other embodiments, the first indication information includes a first time period, a first compression method, a second time period, and a second compression method. Wherein the compression ratio of the first compression method is the first compression ratio, and the compression ratio of the second compression method is the second compression ratio. The blood pressure monitoring bracelet and the cloud can determine the corresponding compression ratio according to the data compression method. When the blood pressure monitoring bracelet receives the first time period and the first compression method, the blood pressure monitoring bracelet can compress the blood pressure data collected in the first time period by the first compression method, and the compression ratio corresponding to the first compression method is the first compression ratio. Similarly, when the blood pressure monitoring bracelet receives the second time period and the second compression method, the blood pressure monitoring bracelet can compress the blood pressure data collected in the second time period by the second compression method, and the compression ratio corresponding to the second compression method is the second compression ratio.

[0050] Exemplarily, the first compression method is a lossless compression method or a method approximating lossless compression. For example, Huffman coding, run-length coding, differential coding. The second compression method is a lossy compression method. For example, predictive coding.

[0051] Step 105, the blood pressure monitoring bracelet collects new blood pressure data.

[0052] Step 106, the blood pressure monitoring bracelet compresses and sends the compressed new blood pressure data. Correspondingly, the cloud receives the compressed new blood pressure data.

[0053] Among them, the new blood pressure data collected in the first time period is compressed using the first compression ratio, and the new blood pressure data collected in the second time period is compressed using the second compression ratio.

[0054] The blood pressure monitoring bracelet can continuously collect the blood pressure data of the detector. After receiving the first indication information, the blood pressure data collected in the first time period of the blood pressure fluctuation cycle of the blood pressure monitor can be compressed using the first compression ratio, and the blood pressure data collected in the second time period of the blood pressure fluctuation cycle of the blood pressure monitor can be compressed using the second compression ratio and the compressed blood pressure data can be sent. For example, taking the first time period including 2:00 to 3:00, 5:30 to 7:30, 12:00 to 14:00, and 16:00 to 18:00 every day from Monday to Friday, and 3:00 to 3:30, 8:00 to 10:00, 17:00 to 19:00 every day from Saturday to Sunday as an example. If the blood pressure monitoring bracelet collects new blood pressure data at 6:00 on a certain day from Monday to Friday, then the blood pressure data is compressed using the first compression ratio. If the blood pressure monitoring bracelet collects new blood pressure data at 9:00 on Saturday or Sunday, then the blood pressure data is compressed using the second compression ratio.

[0055] It can be understood that the blood pressure monitoring bracelet can send blood pressure data regularly or irregularly. For example, the blood pressure monitoring bracelet can send the compressed blood pressure data once every 30 minutes. Exemplarily, the blood pressure monitoring bracelet at 1:31 on Monday compresses and sends the data collected from 1:00 to 1:30 using the second compression ratio. At 2:01 on Monday, it compresses and sends the data collected from 1:30 to 2:00 using the second compression ratio. At 2:31 on Monday, it compresses and sends the data collected from 2:00 to 2:30 using the first compression ratio, and so on.

[0056] For another example, the blood pressure monitoring bracelet can compress and send the K blood pressure data after collecting K blood pressure data. Among them, K is a positive integer.

[0057] In the solution of the embodiment of the present application, the first blood pressure data is blood pressure data with large fluctuations and is relatively important medical data, which can better reflect the blood pressure situation of the blood pressure monitor. By instructing the blood pressure data acquisition device to compress the first blood pressure data using the first compression ratio and the second blood pressure data using the second compression ratio, the relatively important blood pressure data can be better preserved. By compressing the second blood pressure data with a larger compression ratio, the second blood pressure data occupies less space and saves transmission resources.

[0058] The above embodiments introduced a solution for compressing blood pressure data collected at different times with different compression ratios according to the volatility of blood pressure. Next, some other embodiments will be introduced. These embodiments can adjust the collection frequency of blood pressure data according to the fluctuation of blood pressure, adopting a higher collection frequency during the time period with large blood pressure volatility and a lower collection frequency during the time period with smaller blood pressure volatility, so as to reduce the amount of blood pressure data and transmit less blood pressure data. Before step 106 of the above embodiments, step 201 is further included.

[0059] Step 201, the cloud sends a second indication message. Correspondingly, the blood pressure monitoring bracelet receives the second indication message.

[0060] Among them, the second indication message indicates to collect new blood pressure data at the first frequency during the first time period of the blood pressure data fluctuation cycle of the blood pressure monitor, and to collect new blood pressure data at the second frequency during the second time period of the blood pressure data fluctuation cycle of the blood pressure monitor; the first frequency is greater than the second frequency.

[0061] After receiving the second indication message, when the blood pressure monitoring bracelet is about to send the newly collected blood pressure data, it can compress the blood pressure data according to the time period in which the blood pressure data is located in the blood pressure data fluctuation cycle of the blood pressure monitor. For example, still taking the first time period including 2:00-3:00, 5:30-7:30, 12:00-14:00 and 16:00-18:00 every day from Monday to Friday, and 3:00-3:30, 8:00-10:00, 17:00-19:00 every day from Saturday to Sunday as an example. Suppose the blood pressure monitoring bracelet receives the second indication message at 08:11:00 on January 8, 2025 (Wednesday), the first frequency is 1 time per second, and the second frequency is 6 times per minute. Then the blood pressure output collection device collects blood pressure data at a frequency of 6 times per minute between 8:11 and 12:00 (excluding 12:00) on January 8, and collects blood pressure data at a frequency of 1 time per second between 12:00 and 14:00 (excluding 14:00). And so on, collect blood pressure data at a frequency of 6 times per minute from 0:00 to 3:00 on Saturday, and collect blood pressure data at a frequency of 1 time per second from 3:00 to 3:30.

[0062] Since the time periods with large blood pressure fluctuation frequencies are the same from Monday to Friday, and the time periods with large blood pressure fluctuation frequencies are the same on Saturday and Sunday. Then when judging which frequency to use to measure blood pressure data, it is possible to first determine which day of the week the current date is. If the current date is one of Monday to Friday, then continue to determine which time periods from Monday to Friday are the first time periods. If the current date is Saturday or Sunday, then continue to determine which time periods on Saturday or Sunday are the first time periods.

[0063] In some other embodiments of the present application, the cloud inputs the existing blood pressure data of the blood pressure monitor into the prediction model to predict the future blood pressure data. The fluctuation period, the first time period, and the second time period of the blood pressure data of the blood pressure monitor are determined according to the predicted blood pressure. The cloud also calibrates the prediction model based on the actual blood pressure data of the blood pressure monitor at a future moment, so as to adjust the prediction model. In this way, the cloud can more accurately distinguish the first time period and the second time period, so as to achieve the transmission of more useful blood pressure data with fewer resources. Before the above step 104, steps 301 and 302 are further included.

[0064] Step 301, the cloud obtains environmental data and the activity data of the blood pressure monitor.

[0065] Among them, the environmental data includes environmental temperature and environmental humidity. The activity data includes eating time and exercise time. In some embodiments, the blood pressure monitoring bracelet can collect environmental data and the activity data of the blood pressure monitor and send this data to the cloud. Exemplarily, the blood pressure monitoring bracelet can judge whether it is in a diet state and an exercise state (such as standing, lying, running) by collecting the heart rate of the blood pressure monitor. Another example is that the blood pressure monitor can set the exercise time and eating time on the blood pressure monitoring bracelet by itself, and the bracelet records this time.

[0066] In some other embodiments, the cloud can be connected to a temperature and humidity sensor other than the blood pressure monitoring bracelet to obtain the temperature and humidity of the environment where the blood pressure monitor is located.

[0067] The environment and activity state of the blood pressure monitor will affect its blood pressure value. For example, in an environment with a high temperature, the human blood pressure will increase, and in an environment with a low temperature, the human blood pressure will decrease. Another example is that the blood pressure fluctuation will increase in a cold environment (such as a temperature < 15 °C) or within 1 hour after a meal. And the blood pressure of different people may be affected by the environmental temperature differently. For example, in a high-temperature and high-humidity environment, the blood pressure of some people may increase, while the blood pressure of some people may decrease. Therefore, in the embodiments of the present application, the prediction model of blood pressure data is trained according to the environment and activity state of the blood pressure monitor, so as to increase the accuracy of predicting the blood pressure data of the blood pressure monitor.

[0068] Step 302, input the fluctuation period, the first time period, the second time period, the activity data, the environmental data, and multiple blood pressure data of the blood pressure monitor into the prediction model to obtain the predicted blood pressure data, the new fluctuation period of the blood pressure data of the blood pressure monitor, the new first time period, and the new second time period.

[0069] Exemplarily, the prediction model can be a neural network model. In some embodiments, the prediction model is a long short-term memory network model. In some other embodiments, the prediction model is a self-attention model. In some other embodiments, the prediction model is a temporal convolutional network model. The prediction model can output predicted blood pressure data, a new fluctuation period of the blood pressure data of the blood pressure monitor, a new first time period, and a new second time period based on the fluctuation period of the blood pressure data of the blood pressure monitor, the first time period, the second time period, activity data, environmental data, and multiple blood pressure data of the blood pressure monitor.

[0070] After receiving the compressed new blood pressure data, it further includes: inputting the newly received compressed new blood pressure data into the prediction model. Thereby increasing the training data set of the prediction model and increasing the prediction accuracy of the prediction model.

[0071] In some embodiments, the prediction model outputs a first time period, a first frequency for collecting the first blood pressure data, a second time period, and a second frequency for collecting the second blood pressure data. The cloud sends these data to the blood pressure monitoring bracelet, and the blood pressure monitoring bracelet collects new blood pressure data according to these data. Taking the blood pressure data, activity data, and environmental data between 8:00 on January 1, 2025 and 11:00 on January 8, 2025 as an example to predict the systolic blood pressure between 11:30 on January 8, 2025 and 12:30 on January 8, 2025. Among them, in the first time period of the above time, the blood pressure data is collected at a frequency of 6 times per minute, and in the second time period, the blood pressure data is collected at a frequency of 1 time per minute.

[0072] Assume that the period between 11:30 on January 8, 2025 and 12:00 on January 8, 2025 is the first time period, and the period between 12:30 on January 8, 2025 and 12:30 on January 8, 2025 is the second time period. Some of the data between 11:30 on January 8, 2025 and 12:30 on January 8, 2025 predicted by the above blood pressure data is shown in Table 2. If the predicted blood pressure value is the predicted systolic blood pressure 1 shown in Table 2, it can be seen that the difference between the predicted blood pressure value and the actual systolic blood pressure is relatively large. Assume that the difference between the predicted blood pressure value and the actual systolic blood pressure is greater than the second threshold, and the prediction model outputs to increase the frequency of collecting blood pressure data. For example, increase the collection frequency of blood pressure data in the first time period from 6 times per minute to 10 times per minute, and increase the collection frequency of blood pressure data in the second time period from 1 time per minute to 3 times per minute. Thereby increasing the collection of blood pressure data, increasing the amount of input data of the prediction model, and thus increasing the accuracy of the prediction model.

[0073] Table 2 Predicted Blood Pressure Data Table

[0074] If the predicted blood pressure value is the predicted systolic blood pressure 2 shown in Table 2, it can be seen that the difference between the predicted blood pressure value and the actual systolic blood pressure is relatively small. Assuming that the difference between the predicted blood pressure value and the actual systolic blood pressure is less than the third threshold, the prediction model reduces the frequency of collecting blood pressure data. Among them, the third threshold is less than the second threshold. For example, the collection frequency of blood pressure data in the first time period is reduced from 6 times per minute to 4 times per minute, and the collection frequency of blood pressure data in the second time period is reduced from 1 time per minute to 30 times per hour. Since the error between the predicted blood pressure value and the actual systolic blood pressure is less than the third threshold, it indicates that the predicted blood pressure value is relatively accurate. By reducing the collection of blood pressure data and the amount of transmitted blood pressure data, the resources consumed by transmitting blood pressure data can be reduced to a greater extent.

[0075] In some embodiments, when the difference between the predicted blood pressure value and the actual systolic blood pressure is greater than the third threshold and less than the second threshold, the prediction model does not change the frequency of collecting blood pressure data. Thus, by adjusting the frequency of blood pressure collection, a balance can be achieved between the accuracy of predicting blood pressure data and the resources consumed by transmitting blood pressure data.

[0076] Exemplarily, that the difference between the predicted blood pressure value and the actual systolic blood pressure is greater than the second threshold can be implemented as comparing that the mean absolute error between the predicted blood pressure value and the actual systolic blood pressure is greater than a preset mean absolute error (a way of implementing the second threshold).

[0077] Exemplarily again, that the difference between the predicted blood pressure value and the actual systolic blood pressure is greater than the second threshold can be implemented as comparing that the root mean square error between the predicted blood pressure value and the actual systolic blood pressure is greater than a preset root mean square error (a way of implementing the second threshold).

[0078] In some embodiments, referring to Figure 3 , before the above step 104, steps 401 to 405 are further included.

[0079] Step 401, the cloud determines the number of collections of the first blood pressure data according to the first frequency and the first time period, and determines the number of collections of the second blood pressure data according to the second frequency and the second time period.

[0080] Taking the first frequency of 1 time per second and the second frequency of 6 times per minute as an example. Still taking the first time period including 2:00 to 3:00, 5:30 to 7:30, 12:00 to 14:00, and 16:00 to 18:00 every day from Monday to Friday, and 3:00 to 3:30, 8:00 to 10:00, and 17:00 to 19:00 every day from Saturday to Sunday as an example. Then, there are 7 hours per day from Monday to Friday belonging to the first time period and 17 hours belonging to the second time period. The collection times in the first time period are 3600 times per hour, and the collection times in the second time period are 360 times per hour.

[0081] Similarly, it is obtained that there are 4.5 hours per day from Saturday to Sunday belonging to the first time period and 19.5 hours belonging to the second time period.

[0082] Step 402: The cloud determines the proportion of the second blood pressure data in all the blood pressure data based on the number of acquisitions of the first blood pressure data and the number of acquisitions of the second blood pressure data.

[0083] Among them, all the blood pressure data is the sum of the first blood pressure data and the second blood pressure data.

[0084] Continuing with the above example, the total number of all blood pressure data collected every day from Monday to Friday is 3600×7 + 360×17 = 25200 + 6120 = 31320 times. The proportion of the second blood pressure data in all the blood pressure data is 6120 divided by 31320, approximately equal to 0.1954. The cloud can also obtain that the proportion of the first blood pressure data in all the blood pressure data is 25200 divided by 31320, approximately equal to 0.8046.

[0085] The total number of all blood pressure data collected every day on Saturday and Sunday is 3600×4.5 + 360×19.5 = 16200 + 7020 = 23220 times. The proportion of the first blood pressure data in all the blood pressure data is 16200 divided by 23220, approximately equal to 0.6977. The proportion of the second blood pressure data in all the blood pressure data is 7020 divided by 23220, approximately equal to 0.3023.

[0086] Step 403: The cloud multiplies M times the proportion of the second blood pressure data in all the blood pressure data by the total coding space of the blood pressure data to obtain the coding space of the second blood pressure data.

[0087] Among them, M is a positive number greater than 0 and less than 1.

[0088] Exemplarily, taking the blood pressure monitoring bracelet sending compressed blood pressure data to the cloud every day as an example. Suppose the cloud allocates a total coding space of 20 kilobytes for the blood pressure data of each day. That is to say, the blood pressure data of each day is compressed and sent to the cloud through a 20 - kilobyte size. Taking M as 0.7, the coding space of the second blood pressure data from Monday to Friday every week is 20×0.7×0.1954 = 2.7356 kilobytes. The coding space of the second blood pressure data from Saturday to Sunday every week is 20×0.7×0.3023 = 4.2322 kilobytes.

[0089] By setting the coefficient M (such as 0.7), the encoding space is no longer evenly distributed to the first blood pressure data and the second blood pressure data according to the amount of blood pressure data. Instead, after evenly distributing the encoding space to the first blood pressure data and the second blood pressure data according to the amount of blood pressure data, the 1 - M (such as 0.3) encoding space allocated to the second blood pressure data is allocated to the first blood pressure data. As a result, the encoding space of the second blood pressure data occupies less space in the total encoding space of the blood pressure data, and the encoding space of the first blood pressure data occupies more space in the total encoding space of the blood pressure data. Consequently, the first blood pressure data has a smaller compression ratio, and the second blood pressure data has a larger compression ratio. This enables the first blood pressure data, which can better reflect the blood pressure situation of the blood pressure monitor, to be less affected by compression, and the second blood pressure data can save more transmission resources.

[0090] Step 404: The cloud subtracts the encoding space of the second blood pressure data from the total encoding space of the blood pressure data to obtain the encoding space of the first blood pressure data.

[0091] Continuing with the above example, the encoding space of the first blood pressure data from Monday to Friday is 20 - 2.7356 = 17.2644 kilobytes per week. The encoding space of the first blood pressure data from Saturday to Sunday is 20 - 4.2322 = 15.7678 kilobytes per week. It can be seen that from Monday to Friday, the proportion of the first blood pressure data in the total blood pressure data is 0.8046, and the encoding space occupied by the first blood pressure data in the encoding space occupied by the total blood pressure data is 0.86322. From Saturday to Sunday, the proportion of the first blood pressure data in the total blood pressure data is 0.6977, and the encoding space occupied by the first blood pressure data in the encoding space occupied by the total blood pressure data is 0.78839.

[0092] Step 405: The cloud obtains the first compression ratio for compressing the first blood pressure data based on the encoding space of the first blood pressure data and the total encoding space of the blood pressure data. The second compression ratio for compressing the second blood pressure data is obtained based on the encoding space of the second blood pressure data and the total encoding space of the blood pressure data.

[0093] Assume that the space occupied by the first blood pressure data per day from Monday to Friday is 100 kilobytes, and the space occupied by the second blood pressure data is 24 kilobytes. Compressing the first blood pressure data to 17.2644 kilobytes gives a compression ratio of 100 divided by 17.2644 = 5.7923. Compressing the second blood pressure data to 2.7356 kilobytes gives a compression ratio of 24 divided by 2.7356 = 8.7732.

[0094] Assume that the space occupied by the first blood pressure data for each of Saturday and Sunday is 64 kilobytes, and the space occupied by the second blood pressure data is 28 kilobytes. Compress the first blood pressure data to 15.7678 kilobytes, and the compression ratio is 64 divided by 15.7678 = 4.0589. Compress the second blood pressure data to 4.2322 kilobytes, and the compression ratio is 28 divided by 4.2322 = 6.6159.

[0095] In some embodiments, the first compression ratio for compressing the first blood pressure data collected from Monday to Friday and the first compression ratio for compressing the first blood pressure data collected on Saturday or Sunday are different during the fluctuation period of the blood pressure monitor's blood pressure data. Then the first indication information includes the first compression ratio corresponding to each first time period and the second compression ratio corresponding to each second time period. For example, the first indication information includes the first time period 1 from Monday to Friday, the first compression ratio 5.7923 corresponding to the first time period 1, the second time period 1 from Monday to Friday, the second compression ratio 8.7732 corresponding to the second time period 1, the first time period 2 on Saturday and Sunday, the first compression ratio 4.0589 corresponding to the first time period 2, the second time period 2 on Saturday and Sunday, and the second compression ratio 6.6159 corresponding to the second time period 2. Thus, the blood pressure monitoring bracelet can compress the blood pressure data collected during the first time period 1 from Monday to Friday according to the first time period 1 from Monday to Friday and the first compression ratio 5.7923 in the first indication information. Compress the blood pressure data collected during the second time period 1 from Monday to Friday according to the second time period 1 from Monday to Friday and the second compression ratio 8.7732 in the first indication information, and so on.

[0096] In other embodiments, the first compression ratio for compressing the first blood pressure data collected from Monday to Friday and the first compression ratio for compressing the first blood pressure data collected on Saturday or Sunday are different during the fluctuation period of the blood pressure monitor's blood pressure data. Then the cloud sends the larger compression ratio. For example, the first compression ratio corresponding to the first time period 1 is 5.7923, and the first compression ratio corresponding to the first time period 2 is 4.0589. The second compression ratio corresponding to the second time period 1 is 8.7732, and the second compression ratio corresponding to the second time period 2 is 6.6159. Then the first indication information includes the first compression ratio of 5.7923 and the second compression ratio of 8.7732. Thus, after receiving the first indication information, the blood pressure monitoring bracelet compresses the received blood pressure data using the first compression ratio of 5.7923 during the first time period from Monday to Sunday, and compresses the received blood pressure data using the second compression ratio of 8.7732 during the second time period from Monday to Sunday.

[0097] Step 406, the cloud decompresses the blood pressure data of the first time period according to the first compression ratio. Decompress the blood pressure data of the second time period according to the second compression ratio. Step 406 is located after receiving the new compressed blood pressure data.

[0098] Thus, through the above steps 401 to 406, the cloud can determine the coding space occupied by the blood pressure data in the first time period and the coding space occupied by the blood pressure data in the second time period. Thus, when the cloud receives the blood pressure data from the blood pressure monitoring bracelet, it can determine in which data storage spaces the data sent is the blood pressure data of the first time period and in which positions the data sent is the blood pressure data of the second time period. For example, taking lossless compression for the data in the first time period and lossy compression for the data in the second time period as an example. Assume that the blood pressure monitoring bracelet sends blood pressure data once a day. And it is agreed between the cloud and the blood pressure monitoring bracelet that the blood pressure monitoring bracelet sends the blood pressure data of the first time period first and then the blood pressure data of the second time period. Then, if the cloud receives 2 kilobytes of data sent by the blood pressure monitoring bracelet, the cloud can determine that the first 1.5 kilobytes of data are obtained by compressing the blood pressure data of the first time period using the lossless compression method. And it is determined that the last 0.5 kilobytes of data are obtained by compressing the blood pressure data of the second time period using the lossy compression method. Thus, the cloud can decompress the first 1.5 kilobytes of data using the corresponding method of lossless compression and decompress the last 0.5 kilobytes of data using the corresponding method of lossy compression. Thus, the cloud can obtain the blood pressure data sent by the blood pressure monitoring bracelet.

[0099] In some embodiments, referring to steps 401 to 405, the blood pressure monitoring bracelet can also determine the coding space occupied by the blood pressure data in the first time period and the coding space occupied by the blood pressure data in the second time period based on the maximum blood pressure value in the blood pressure data, the minimum blood pressure value in the blood pressure data, the maximum blood pressure value in the first time period, and the minimum blood pressure value in the first time period. Thus, the blood pressure data in the first time period and the blood pressure data in the second time period are compressed according to this coding space.

[0100] In some other embodiments, the cloud sends a first quantity of coding spaces and a second quantity of coding spaces. Correspondingly, the blood pressure monitoring bracelet receives the first quantity of coding spaces and the second quantity of coding spaces. Thus, the blood pressure monitoring bracelet encodes the blood pressure data in the first time period according to the first quantity of coding spaces, encodes the blood pressure data in the second time period according to the first quantity of coding spaces, and sends the encoded blood pressure data.

[0101] In some embodiments, the cloud can perform data preprocessing on the received blood pressure data. For example, if there are missing blood pressure data in the blood pressure data, the missing values can be supplemented. If there are outliers in the blood pressure data, such as a blood pressure value differing from the average of its previous 5 and next 5 blood pressure values by more than 20% of the average, then that blood pressure value is replaced with the average of its previous 5 and next 5 blood pressure values. If there are a small number of missing values in the blood pressure data (e.g., the continuously missing blood pressure data is less than 5), then the interpolation method is used to estimate the missing blood pressure data. If there are a large number of consecutive missing values in the blood pressure data, then the data for that missing time period is removed. For another example, the cloud can normalize the received data, such as mapping the blood pressure data to the [0, 1] interval using the min-max normalization method.

[0102] In some embodiments, the cloud can also instruct the blood pressure monitoring bracelet on the accuracy of collecting blood pressure data. For example, high-precision blood pressure data is collected in the first time period, and low-precision blood pressure data is collected in the second time period. For instance, the high precision is 16 bits and the low precision is 8 bits. For another example, the high precision is 10 bits and the low precision is 5 bits.

[0103] It can be understood that the embodiments of the present application can be applied in various scenarios such as the sports health detection of smart bracelets, the real-time monitoring of patients in hospitals, and the long-term health monitoring of patients in medical centers.

[0104] The above has described the blood pressure data transmission method in the embodiments of the present application. The following will separately introduce the blood pressure data processing device (such as a server) that executes the above method.

[0105] Refer to Figure 4 As Figure 4 shown in the structural schematic diagram of a blood pressure data processing device, the blood pressure data processing device in the embodiments of the present application can implement the steps corresponding to the blood pressure data transmission method executed in the corresponding embodiments in the above Figure 2 . The functions implemented by the blood pressure data processing device can be achieved through hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, and the modules can be software and / or hardware. The blood pressure data processing device may include an input / output module 601 and a processing module 602. The function implementations of the processing module 602 and the input / output module 601 can refer to the operations executed in the corresponding embodiments in Figure 2 , which will not be elaborated here. For example, the processing module 602 can be used to control operations such as the transceiver and acquisition of the input / output module 601. The input / output module 601 is configured to obtain multiple blood pressure data of the blood pressure monitor. Send the first indication information. Receive the compressed new blood pressure data.

[0106] The processing module 602 is configured to perform wavelet transform or short-time Fourier transform decomposition on the blood pressure data to obtain the fluctuation period, the first time period, and the second time period of the blood pressure data of the blood pressure monitor.

[0107] Among them, the blood pressure data includes blood pressure values and the corresponding moments of the blood pressure values. The moments corresponding to the blood pressure values in multiple blood pressure data are the moments between the first moment and the second moment, and the time interval between the first moment and the second moment is more than 1 week. The time period in the fluctuation period of the blood pressure data of the blood pressure monitor with a blood pressure value fluctuation frequency greater than the first threshold is the first time period, and the blood pressure data in the first time period is the first blood pressure data; the time period other than the first time period in the fluctuation period of the blood pressure data of the blood pressure monitor is the second time period, and the blood pressure data in the second time period is the second blood pressure data. The first indication information is used to indicate that the first blood pressure data is compressed with the first compression ratio and the second blood pressure data is compressed with the second compression ratio, and the first compression ratio is less than the second compression ratio. The new blood pressure data in the first time period is compressed with the first compression ratio, and the new blood pressure data in the second time period is compressed with the second compression ratio.

[0108] In some embodiments, the input / output module 601 is further configured to send second indication information. The second indication information indicates that new blood pressure data is collected at the first frequency in the first time period of the fluctuation period of the blood pressure data of the blood pressure monitor, and new blood pressure data is collected at the second frequency in the second time period of the fluctuation period of the blood pressure data of the blood pressure monitor. Among them, the first frequency is greater than the second frequency.

[0109] In some embodiments, the input / output module 601 is further configured to obtain environmental data and activity data of the blood pressure monitor. Among them, the activity data includes meal time and exercise time; the environmental data includes environmental temperature and environmental humidity; The fluctuation period of the blood pressure data of the blood pressure monitor, the first time period, the second time period, the activity data, the environmental data, and the multiple blood pressure data of the blood pressure monitor are input into a prediction model to obtain predicted blood pressure data, a new fluctuation period of the blood pressure data of the blood pressure monitor, a new first time period, and a new second time period.

[0110] In some embodiments, the processing module 602 is further configured to determine the number of times of collecting the first blood pressure data according to the first frequency and the first time period, determine the number of times of collecting the second blood pressure data according to the second frequency and the second time period, and determine the proportion of the second blood pressure data in all the blood pressure data according to the number of times of collecting the first blood pressure data and the number of times of collecting the second blood pressure data. Wherein, all the blood pressure data is the sum of the first blood pressure data and the second blood pressure data. Multiply M times the proportion of the second blood pressure data in all the blood pressure data by the total coding space of the blood pressure data to obtain the coding space of the second blood pressure data. Wherein, M is a positive number greater than 0 and less than 1. Subtract the coding space of the second blood pressure data from the total coding space of the blood pressure data to obtain the coding space of the first blood pressure data. Obtain the first compression ratio for compressing the first blood pressure data according to the coding space of the first blood pressure data and the total coding space of the blood pressure data. Obtain the second compression ratio for compressing the second blood pressure data according to the coding space of the second blood pressure data and the total coding space of the blood pressure data. Decompress the blood pressure data in the first time period according to the first compression ratio. Decompress the blood pressure data in the second time period according to the second compression ratio.

[0111] In some embodiments, the new blood pressure data in the first time period is compressed by using one or more of Huffman coding, run-length coding, and differential coding; the new blood pressure data in the second time period is compressed by using predictive coding.

[0112] In the above application embodiments, the processing module 602 compresses the blood pressure data in the first time period and the second time period by using different compression ratios, thereby saving the resources consumed for transmitting the blood pressure data.

[0113] The embodiments of the present application further provide a blood pressure data collection device, which includes an input / output module and a processing module. The input / output module is configured to collect the blood pressure data of a blood pressure monitor, send the blood pressure data, receive the first indication information, collect the new blood pressure data, and compress and send the compressed new blood pressure data.

[0114] The processing module is configured to compress the new blood pressure data collected in the first time period by using the first compression ratio, and compress the new blood pressure data collected in the second time period by using the second compression ratio.

[0115] Among them, the blood pressure data includes a blood pressure value and the moment corresponding to the blood pressure value. The moment corresponding to the blood pressure value in the blood pressure data is the moment between the first moment and the second moment. Among them, the first indication information is used to indicate that the first blood pressure data is compressed using a first compression ratio, and the second blood pressure data is compressed using a second compression ratio; the first time period is the time period in the fluctuation cycle of the blood pressure data of the blood pressure monitor where the fluctuation frequency of the blood pressure value is greater than the first threshold, and the blood pressure data in the first time period is the first blood pressure data; the second time period is the time period outside the first time period in the fluctuation cycle of the blood pressure data of the blood pressure monitor, and the blood pressure data in the second time period is the second blood pressure data; the first compression ratio is less than the second compression ratio.

[0116] For other operations performed by the input / output module and the processing module of the blood pressure data acquisition device, reference can be made to the steps performed by the blood pressure monitoring bracelet in the above method embodiments.

[0117] The blood pressure data processing device 60 and the blood pressure data acquisition device in the embodiments of the present application have been described above from the perspective of modular functional entities. Below, the blood pressure data processing device and the blood pressure data acquisition device in the embodiments of the present application will be described from the perspective of hardware processing.

[0118] It should be noted that Figure 4 The entity device corresponding to the shown input / output module 601 can be a transceiver, a radio frequency circuit, a communication module, an input / output (I / O) interface, etc., and the entity device corresponding to the processing module 602 can be a processor.

[0119] Figure 4 The shown devices can all have a structure as Figure 5 shown. When Figure 4 the shown blood pressure data processing device 60 has a structure as Figure 5 shown, Figure 5 the processor and the transceiver in it can implement the same or similar functions of the processing module 602 and the input / output module 601 provided by the corresponding device embodiment of the device, Figure 5 and the memory in it stores the computer program that the processor needs to call when executing the method of the above blood pressure data processing device.

[0120] The embodiments of the present application also relate to a chip system, which includes at least one processor and an interface circuit. The processor includes a plurality of vector storage units. The processor is used to execute the interaction of instructions and / or data through the interface circuit, so that the chip system executes the method of any of the above embodiments. In a possible implementation manner, the chip system can also directly include a memory, and the memory stores a computer program or computer instructions. Exemplarily, the memory can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). An embodiment of the present application further relates to a processor, which includes a plurality of storage units for calling a computer program or computer instructions stored in a memory, so that the processor executes the method described in any one of the above embodiments. Exemplarily, in an embodiment of the present application, the processor is an integrated circuit chip with the ability to process signals. For example, the processor can be an FPGA, a general-purpose processor, a DSP, an ASIC, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, an SoC, a CPU, a network processor (NP), a microcontroller unit (MCU), a PLD, or other integrated chips, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. In a possible implementation manner, an embodiment of the present application further provides a computer-readable storage medium, and the computer-readable storage medium stores program code, and when the program code runs on the computer, the computer is caused to execute the above method embodiment.

[0121] An embodiment of the present application further provides a terminal device, such as Figure 6As shown, for ease of illustration, only the parts related to the embodiments of the present application are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The terminal device can be any terminal device including a mobile phone, a tablet computer, a personal digital assistant (PDA), a point of sales (POS) terminal device, an in-vehicle computer, etc. Taking the terminal device as a mobile phone as an example: Figure 6 The block diagram of a part of the structure of the mobile phone related to the terminal device provided by the embodiments of the present application is shown. Refer to Figure 6 , the mobile phone includes: a radio frequency (RF) circuit 1010, a memory 1020, an input unit 1030, a display unit 1040, a sensor 1050, an audio circuit 1060, a Wi-Fi module 1070, a processor 1080, and a power supply 1090 and other components. Those skilled in the art can understand that Figure 6 the structure of the mobile phone shown in

[0122] does not limit the mobile phone, and it may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements. Figure 6 The following specifically introduces each component of the mobile phone in combination with The RF circuit 1010 can be used for receiving and sending signals during information reception or call processes. Specifically, after receiving the downlink information from the base station, it is given to the processor 1080 for processing; in addition, the designed uplink data is sent to the base station. Generally, the RF circuit 1010 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc. In addition, the RF circuit 1010 can also communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to the Global System of Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0123] The memory 1020 can be used to store software programs and modules. The processor 1080 executes various functional applications and data processing of the mobile phone by running the software programs and modules stored in the memory 1020. The memory 1020 may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory 1020 may include high-speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0124] The input unit 1030 can be used to receive input digital or character information and generate key signal inputs related to the user settings and function controls of the mobile phone. Specifically, the input unit 1030 may include a touch panel 1031 and other input devices 1032. The touch panel 1031, also known as a touch screen, can collect touch operations of the user on or near it (such as operations of the user using a finger, a stylus, or any suitable object or accessory on or near the touch panel 1031), and drive the corresponding connection device according to a pre-set program. Optionally, the touch panel 1031 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the touch orientation of the user, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor 1080, and can receive and execute commands sent by the processor 1080. In addition, various types such as resistive, capacitive, infrared, and surface acoustic wave can be used to implement the touch panel 1031. In addition to the touch panel 1031, the input unit 1030 may further include other input devices 1032. Specifically, the other input devices 1032 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, power on / off keys, etc.), a trackball, a mouse, a joystick, etc.

[0125] The display unit 1040 can be used to display information input by the user or information provided to the user, as well as various menus of the mobile phone. The display unit 1040 may include a display panel 1041. Optionally, the display panel 1041 can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. Further, a touch panel 1031 can cover the display panel 1041. When the touch panel 1031 detects a touch operation on or near it, it is transmitted to the processor 1080 to determine the type of touch event. Subsequently, the processor 1080 provides a corresponding visual output on the display panel 1041 according to the type of touch event. Although in Figure 6 , the touch panel 1031 and the display panel 1041 are implemented as two independent components to realize the input and input functions of the mobile phone, but in some embodiments, the touch panel 1031 and the display panel 1041 can be integrated to realize the input and output functions of the mobile phone.

[0126] The mobile phone may further include at least one sensor 1050, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. Among them, the ambient light sensor can adjust the brightness of the display panel 1041 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 1041 and / or the backlight when the mobile phone is moved to the ear. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity, and can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors that the mobile phone can also be configured with, they will not be elaborated here.

[0127] The audio circuit 1060, the speaker 1061, and the microphone 1062 can provide an audio interface between the user and the mobile phone. The audio circuit 1060 can transmit the electrical signal converted from the received audio data to the speaker 1061, and the speaker 1061 converts it into a sound signal for output; on the other hand, the microphone 1062 converts the collected sound signal into an electrical signal, which is received by the audio circuit 1060 and then converted into audio data. After the audio data is output to the processor 1080 for processing, it is sent to another mobile phone, for example, through the RF circuit 1010, or the audio data is output to the memory 1020 for further processing.

[0128] Wi-Fi belongs to short-range wireless transmission technology. Through the Wi-Fi module 1070, a mobile phone can help users send and receive emails, browse the web, and access streaming media, etc. It provides users with wireless broadband Internet access. Although Figure 6 the Wi-Fi module 1070 is shown, it can be understood that it does not belong to an essential component of the mobile phone and can be completely omitted within the scope of not changing the essence of the invention as needed.

[0129] The processor 1080 is the control center of the mobile phone, connecting various parts of the entire mobile phone through various interfaces and circuits. By running or executing software programs and / or modules stored in the memory 1020, and by invoking data stored in the memory 1020, it executes various functions of the mobile phone and processes data, thereby monitoring the mobile phone as a whole. Optionally, the processor 1080 may include one or more processing units; optionally, the processor 1080 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor 1080 either.

[0130] The mobile phone also includes a power supply 1090 (such as a battery) for supplying power to each component. Optionally, the power supply can be logically connected to the processor 1080 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system.

[0131] Although not shown, the mobile phone may also include a camera, a Bluetooth module, etc., which will not be elaborated here.

[0132] In the embodiment of the present application, the processor 1080 included in the mobile phone also has the function of controlling the execution of the blood pressure data transmission method process executed by the above-mentioned blood pressure data processing device.

[0133] The embodiment of the present application also provides a server. Please refer to Figure 7 , Figure 7FIG. 0 is a schematic structural diagram of a server provided by an embodiment of the present application. The server 1100 may vary greatly due to different configurations or performances, and may include one or more central processing units (CPU) 1122 (for example, one or more processors) and a memory 1132, and one or more storage media 1130 (for example, one or more mass storage devices) for storing application programs 1142 or data 1144. Among them, the memory 1132 and the storage media 1130 may be transient storage or persistent storage. The program stored in the storage media 1130 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the server. Further, the central processing unit 1122 may be configured to communicate with the storage media 1130 and execute a series of instruction operations in the storage media 1130 on the server 1100.

[0134] The server 1100 may further include one or more power supplies 1126, one or more wired or wireless network interfaces 1150, one or more input / output interfaces 1158, and / or one or more operating systems 1141, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and so on.

[0135] The steps performed by the server in the above embodiments may be based on the Figure 7 structure of the server 1100 shown. For example, for example, the steps performed by the blood pressure data processing device 60 shown in the above embodiments may be based on the Figure 4 structure of the server shown. For example, the central processing unit 1122 performs the following operations by calling instructions in the memory 1132: Figure 7 Obtain a plurality of blood pressure data of the blood pressure monitor through the input / output interface 1158. It is also possible to send the first indication information and receive the compressed new blood pressure data through the input / output interface 1158.

[0136]

[0137] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0138] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above may refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0139] ​In several embodiments provided by the embodiments of the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, indirect couplings or communication connections of devices or modules, and can be in electrical, mechanical, or other forms.

[0140] The modules described as separate components may or may not be physically separated. The components displayed as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, in each embodiment of the embodiments of the present application, each functional module can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0142] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product.

[0143] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more integrated available media. The available medium may be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0144] The technical solutions provided in the embodiments of the present application have been introduced in detail above. Specific examples are used in the embodiments of the present application to illustrate the principles and implementation manners of the embodiments of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the embodiments of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the embodiments of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the embodiments of the present application.

Claims

1. A blood pressure data transmission method, characterized in that, The method includes: Obtaining multiple blood pressure data of a blood pressure monitor, where the blood pressure data includes a blood pressure value and a corresponding moment of the blood pressure value. The moments corresponding to the blood pressure values in the multiple blood pressure data are the moments between a first moment and a second moment, and the time interval between the first moment and the second moment is more than 1 week; Performing wavelet transform or short-time Fourier transform decomposition on the blood pressure data to obtain the fluctuation period, a first time period, and a second time period of the blood pressure data of the blood pressure monitor; the time period in the fluctuation period of the blood pressure data of the blood pressure monitor where the fluctuation frequency of the blood pressure value is greater than a first threshold is the first time period, and the blood pressure data in the first time period is the first blood pressure data; the time period other than the first time period in the fluctuation period of the blood pressure data of the blood pressure monitor is the second time period, and the blood pressure data in the second time period is the second blood pressure data; Sending a first indication message, where the first indication message is used to indicate that the first blood pressure data is compressed using a first compression ratio and the second blood pressure data is compressed using a second compression ratio, and the first compression ratio is less than the second compression ratio; Receiving the new compressed blood pressure data, where the new blood pressure data in the first time period is obtained by compressing using the first compression ratio, and the new blood pressure data in the second time period is obtained by compressing using the second compression ratio.

2. The method according to claim 1, characterized in that Before receiving the new compressed blood pressure data, it further includes sending a second indication message, where the second indication message indicates that new blood pressure data is collected in the first time period of the fluctuation period of the blood pressure data of the blood pressure monitor at a first frequency, and new blood pressure data is collected in the second time period of the fluctuation period of the blood pressure data of the blood pressure monitor at a second frequency; the first frequency is greater than the second frequency.

3. The method according to claim 1 or 2, characterized in that Before sending the first indication message, it further includes: Obtaining environmental data and activity data of the blood pressure monitor; where the activity data includes meal time and exercise time; the environmental data includes environmental temperature and environmental humidity; Inputting the fluctuation period of the blood pressure data of the blood pressure monitor, the first time period, the second time period, the activity data, the environmental data, and the multiple blood pressure data of the blood pressure monitor into a prediction model to obtain predicted blood pressure data, a new fluctuation period of the blood pressure data of the blood pressure monitor, a new first time period, and a new second time period.

4. The method according to claim 2, wherein Before sending the first indication message, it further includes: Determining the collection times of the first blood pressure data according to the first frequency and the first time period; Determining the collection times of the second blood pressure data according to the second frequency and the second time period; Determining the proportion of the second blood pressure data in all the blood pressure data according to the collection times of the first blood pressure data and the collection times of the second blood pressure data; where all the blood pressure data is the sum of the first blood pressure data and the second blood pressure data; Multiplying M times the proportion of the second blood pressure data in all the blood pressure data by the total coding space of the blood pressure data to obtain the coding space of the second blood pressure data; where M is a positive number greater than 0 and less than 1; Subtract the coding space of the second blood pressure data from the total coding space of the blood pressure data to obtain the coding space of the first blood pressure data; Obtain the first compression ratio for compressing the first blood pressure data based on the coding space of the first blood pressure data and the total coding space of the blood pressure data; Obtain the second compression ratio for compressing the second blood pressure data based on the coding space of the second blood pressure data and the total coding space of the blood pressure data; After receiving the new compressed blood pressure data, it further includes: decompressing the blood pressure data of the first time period according to the first compression ratio; decompressing the blood pressure data of the second time period according to the second compression ratio.

5. The method according to claim 1 or 2, characterized in that, The new blood pressure data of the first time period is compressed using the first compression ratio, including: the new blood pressure data of the first time period is compressed using one or more of Huffman coding, run-length coding, and differential coding; The new blood pressure data of the second time period is compressed using the second compression ratio, including: the new blood pressure data of the second time period is compressed using predictive coding.

6. A blood pressure data transmission method, characterized in that, It includes: Collect the blood pressure data of the blood pressure monitor, where the blood pressure data includes blood pressure values and the corresponding moments of the blood pressure values; the moments corresponding to the blood pressure values in the blood pressure data are the moments between the first moment and the second moment; Send the blood pressure data; Receive the first indication information, where the first indication information is used to indicate that the first blood pressure data is compressed using the first compression ratio and the second blood pressure data is compressed using the second compression ratio; the first blood pressure data is the blood pressure data in the first time period, and the first time period is the time period in the fluctuation cycle of the blood pressure data of the blood pressure monitor where the fluctuation frequency of the blood pressure value is greater than the first threshold; the second blood pressure data is the blood pressure data in the second time period, and the second time period is the time period outside the first time period in the fluctuation cycle of the blood pressure data of the blood pressure monitor; the first compression ratio is less than the second compression ratio; Collect new blood pressure data; Compress and send the compressed new blood pressure data; the new blood pressure data collected in the first time period is compressed using the first compression ratio, and the new blood pressure data collected in the second time period is compressed using the second compression ratio.

7. A blood pressure data transmission system, characterized in that, The system includes: a blood pressure data acquisition device and a server, the blood pressure data acquisition device executes the method according to claim 6, and the server executes the method according to any one of claims 1-5.

8. A computing device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method according to any one of claims 1-5 or claim 6.

9. A computer-readable storage medium, characterized in that, It includes instructions that, when running on a computer, cause the computer to execute the method according to any one of claims 1-5 or claim 6.

10. A computer program product, comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, the method according to any one of claims 1-5 or claim 6 is implemented.

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