Communication signal optimization method and device

Abstract

The invention discloses a communication signal optimization method and device. The method comprises the steps that physiological data of a target object is acquired, and the target object is located in a coverage area of the narrowband Internet of Things; feature information of a data acquisition terminal is obtained, the data acquisition terminal is used for acquiring physiological data, and the feature information is used for evaluating the signal quality of the data acquisition terminal; determining a network adjustment state corresponding to the physiological data and the feature information through a signal quality scoring model; and adjusting the network in the coverage area according to the network adjustment state so as to optimize the communication signal of the data acquisition terminal in the coverage area. The technical problems that a static signal coverage mechanism is difficult to adapt to signal transmission path fluctuation caused by frequent movement and attitude change of animals, the network tuning capability is insufficient, signal change in a living body cannot be responded in real time, and the continuity and reliability of data transmission are affected in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things communication technology, and in particular to a method and device for optimizing communication signals. Background Art

[0002] As animal husbandry transforms into a smarter industry, animal health monitoring and environmental data collection increasingly rely on Internet of Things (IoT) technology. Narrowband IoT (NB-IoT), in particular, has been widely used in cattle breeding, wildlife tracking, biological research, and other fields due to its low power consumption, wide coverage, and low cost. However, traditional NB-IoT communication technology faces significant technical challenges when it comes to transmitting signals within living animals:

[0003] 1) Signal attenuation: Animal tissues, such as muscle, bone, and body fluids, severely attenuate NB-IoT signals, up to 60-80dB. This attenuation is particularly pronounced in the rumen of beef cattle, leading to unstable connections between implanted sensors and base stations, significantly reducing signal strength and compromising data transmission quality.

[0004] 2) Inadequate adaptability to dynamic environments: The mobility of cattle, including seasonal migration, changes in pasture environment, and daily posture changes such as standing and lying down, causes frequent fluctuations in signal transmission paths. Existing NB-IoT network optimization strategies are mostly static, making it difficult to achieve real-time network topology adjustments, making it difficult to guarantee sensor signal quality in different states.

[0005] 3) Low data reliability: When signal strength is limited or the network is unstable, the data packet loss rate increases significantly, especially the lack of health monitoring data, which has a serious impact on disease warning, refined management and scientific research analysis of beef cattle breeding.

[0006] 4) Lack of dynamic wireless network optimization capabilities: Current wireless network designs are primarily targeted at standardized human service needs. They lack the ability to monitor channel quality, assess loss, and perform real-time optimization for devices implanted in animals, resulting in insufficient signal transmission reliability in non-steady-state scenarios.

[0007] 5) The contradiction between high transmission power and battery life: To overcome signal attenuation, terminal devices often need to increase transmission power, but this will significantly shorten battery life. Frequent replacement of batteries or equipment not only increases costs but may also cause discomfort to animals and affect their normal physiological activities.

[0008] The above shows the limitations of existing optimization solutions: NB-IoT optimization solutions mostly focus on static industrial scenarios, lack consideration of the signal characteristics inside living organisms, and fail to effectively address the special needs of data collection and transmission inside animals.

[0009] In view of the above problems, the existing technology urgently needs a method that can adapt to the signal transmission characteristics of animals and realize dynamic network optimization, so as to improve the signal coverage quality, enhance the reliability and efficiency of data transmission, and at the same time reduce power consumption and extend the battery life of terminal devices. Summary of the Invention

[0010] The embodiments of the present invention provide a method and device for optimizing communication signals to at least solve the technical problems in related technologies, such as the difficulty of static signal coverage mechanisms in adapting to fluctuations in signal transmission paths caused by frequent movements and posture changes of animals, insufficient network tuning capabilities, and the inability to respond to signal changes in organisms in real time, which affects the continuity and reliability of data transmission.

[0011] According to one aspect of an embodiment of the present invention, a method for optimizing communication signals is provided, comprising: acquiring physiological data of a target object, wherein the target object is located within a coverage area of a narrowband Internet of Things; acquiring characteristic information of a data acquisition terminal, wherein the data acquisition terminal is used to acquire the physiological data, and the characteristic information is used to evaluate the signal quality of the data acquisition terminal; determining a network adjustment state corresponding to the physiological data and the characteristic information through a signal quality scoring model, wherein the signal quality scoring model is obtained through machine learning training using multiple sets of training data, each of the multiple sets of training data comprising: historical physiological data, historical characteristic information, and historical network adjustment states corresponding to the historical physiological data and the historical characteristic information; and adjusting the network within the coverage area according to the network adjustment state to optimize the communication signal of the data acquisition terminal within the coverage area.

[0012] Optionally, acquiring physiological data of the target object includes: acquiring the movement frequency of the target object; counting the drinking frequency of the target object; and acquiring the body posture change frequency of the target object.

[0013] Optionally, obtaining characteristic information of the data acquisition terminal includes: obtaining the signal mean of the data acquisition terminal within a first predetermined time window; obtaining the data packet loss rate of the data acquisition terminal within a second predetermined time window; obtaining the signal standard deviation of the data acquisition terminal within a third predetermined time window; and obtaining the signal-to-noise ratio of the data acquisition terminal within a fourth predetermined time window.

[0014] Optionally, the weight one of the signal mean is greater than the weight two of the data packet loss rate, the weight two of the data packet loss rate is greater than the weight three of the signal standard deviation, and the weight three of the signal standard deviation is greater than the weight four of the signal-to-noise ratio.

[0015] Optionally, before determining the network adjustment state corresponding to the physiological data and the characteristic information through the signal quality scoring model, the communication signal optimization method also includes: obtaining the historical physiological data, the historical characteristic information and the historical network adjustment state corresponding to the historical physiological data and the historical characteristic information within a historical time period; training multiple groups of the training data including the historical physiological data, the historical characteristic information and the historical network adjustment state to obtain the signal quality scoring model.

[0016] Optionally, the network within the coverage area is adjusted according to the network adjustment status to optimize the communication signal of the data acquisition terminal within the coverage area, including: when the network adjustment status indicates that the signal quality of the data acquisition terminal is lower than a first predetermined signal quality, optimizing the data acquisition terminal within an optimization period; when the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than a second predetermined signal quality, or when there is an abnormality in the data acquisition terminal, monitoring the data acquisition terminal; when the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the second predetermined signal quality, determining that there is no need to optimize the communication signal of the data acquisition terminal.

[0017] Optionally, when the signal quality of the data acquisition terminal is lower than the first predetermined signal quality, the signal adjustment priority of the data acquisition terminal is high priority; when the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than the second predetermined signal quality, or when there is an abnormality in the data acquisition terminal, the signal adjustment priority of the data acquisition terminal is medium priority; when the signal quality of the data acquisition terminal is higher than the second predetermined signal quality, the signal adjustment priority of the data acquisition terminal is determined to be low priority; the priority score corresponding to the high priority is greater than or equal to 2.5 points; the priority score corresponding to the medium priority is less than 2.5 points; and the score corresponding to the low priority is less than 1.5 points.

[0018] Optionally, the communication signal optimization method also includes: when the signal adjustment priority is the high priority, determining the signal coverage level of the base station in the coverage area to be adjusted to the low priority; when the signal adjustment priority is the medium priority, determining the signal coverage level of the base station in the coverage area to be adjusted to the medium priority; when the signal adjustment priority is the low priority, determining the signal coverage level of the base station in the coverage area to be adjusted to the high priority.

[0019] Optionally, the network within the coverage area is adjusted according to the network adjustment status to optimize the communication signal of the data acquisition terminal within the coverage area, including: determining the downlink initial repetition number and the maximum allowed repetition number of the data acquisition terminal according to the signal coverage level; adjusting the network within the coverage area according to the downlink initial repetition number and the maximum allowed repetition number to optimize the communication signal of the data acquisition terminal within the coverage area.

[0020] According to another aspect of an embodiment of the present invention, a communication signal optimization device is also provided, including: a first acquisition unit, used to acquire physiological data of a target object, wherein the target object is located in the coverage area of a narrowband Internet of Things; a second acquisition unit, used to acquire characteristic information of a data acquisition terminal, wherein the data acquisition terminal is used to collect the physiological data, and the characteristic information is used to evaluate the signal quality of the data acquisition terminal; a determination unit, used to determine a network adjustment state corresponding to the physiological data and the characteristic information through a signal quality scoring model, wherein the signal quality scoring model is obtained through machine learning training using multiple groups of training data, each of the multiple groups of training data includes: historical physiological data, historical characteristic information, and historical network adjustment states corresponding to the historical physiological data and the historical characteristic information; an optimization unit, used to adjust the network within the coverage area according to the network adjustment state to optimize the communication signal of the data acquisition terminal in the coverage area.

[0021] Optionally, the first acquisition unit includes: a first acquisition module for acquiring the movement frequency of the target object; a statistics module for counting the drinking frequency of the target object; and a second acquisition module for acquiring the body posture change frequency of the target object.

[0022] Optionally, the second acquisition unit includes: a third acquisition module, used to obtain the signal mean of the data acquisition terminal within the first predetermined time window; a fourth acquisition module, used to obtain the data packet loss rate of the data acquisition terminal within the second predetermined time window; a fifth acquisition module, used to obtain the signal standard deviation of the data acquisition terminal within the third predetermined time window; and a sixth acquisition module, used to obtain the signal-to-noise ratio of the data acquisition terminal within the fourth predetermined time window.

[0023] Optionally, the weight one of the signal mean is greater than the weight two of the data packet loss rate, the weight two of the data packet loss rate is greater than the weight three of the signal standard deviation, and the weight three of the signal standard deviation is greater than the weight four of the signal-to-noise ratio.

[0024] Optionally, the communication signal optimization device also includes: a third acquisition unit, used to obtain the historical physiological data, the historical feature information and the historical network adjustment state corresponding to the historical physiological data and the historical feature information within a historical time period before determining the network adjustment state corresponding to the physiological data and the historical feature information through a signal quality scoring model; a training unit, used to train multiple groups of the training data including the historical physiological data, the historical feature information and the historical network adjustment state to obtain the signal quality scoring model.

[0025] The optimization unit includes: an optimization module, which is used to optimize the data acquisition terminal within an optimization period when the network adjustment status indicates that the signal quality of the data acquisition terminal is lower than a first predetermined signal quality; a monitoring module, which is used to monitor the data acquisition terminal when the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than a second predetermined signal quality, or when there is an abnormality in the data acquisition terminal; and a first determination module, which is used to determine that there is no need to optimize the communication signal of the data acquisition terminal when the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the second predetermined signal quality.

[0026] Optionally, when the signal quality of the data acquisition terminal is lower than the first predetermined signal quality, the signal adjustment priority of the data acquisition terminal is high priority; when the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than the second predetermined signal quality, or when there is an abnormality in the data acquisition terminal, the signal adjustment priority of the data acquisition terminal is medium priority; when the signal quality of the data acquisition terminal is higher than the second predetermined signal quality, the signal adjustment priority of the data acquisition terminal is determined to be low priority; the priority score corresponding to the high priority is greater than or equal to 2.5 points; the priority score corresponding to the medium priority is less than 2.5 points; and the score corresponding to the low priority is less than 1.5 points.

[0027] Optionally, the communication signal optimization method also includes: a second determination module, used to determine that the signal coverage level of the base station in the coverage area is adjusted to a low priority when the signal adjustment priority is the high priority; a third determination module, used to determine that the signal coverage level of the base station in the coverage area is adjusted to a medium priority when the signal adjustment priority is the medium priority; and a fourth determination module, used to determine that the signal coverage level of the base station in the coverage area is adjusted to a high priority when the signal adjustment priority is the low priority.

[0028] Optionally, the optimization unit includes: a fifth determination module, used to determine the initial downlink repetition number and the maximum allowed repetition number of the data acquisition terminal according to the signal coverage level; a second optimization module, used to adjust the network within the coverage area according to the initial downlink repetition number and the maximum allowed repetition number to optimize the communication signal of the data acquisition terminal within the coverage area.

[0029] According to another aspect of an embodiment of the present invention, a communication signal optimization system is provided. The communication signal optimization system uses any one of the above-mentioned communication signal optimization methods.

[0030] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned communication signal optimization methods.

[0031] According to another aspect of an embodiment of the present invention, a processor is further provided, wherein the processor is configured to run a program, wherein the program executes any one of the above-mentioned communication signal optimization methods when running.

[0032] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the above-mentioned communication signal optimization methods is executed.

[0033] In an embodiment of the present invention, physiological data of a target object is obtained, wherein the target object is located within a coverage area of a narrowband Internet of Things; characteristic information of a data acquisition terminal is obtained, wherein the data acquisition terminal is used to collect physiological data, and the characteristic information is used to evaluate the signal quality of the data acquisition terminal; a network adjustment state corresponding to the physiological data and the characteristic information is determined through a signal quality scoring model, wherein the signal quality scoring model is obtained through machine learning training using multiple sets of training data, each of the multiple sets of training data includes: historical physiological data, historical characteristic information, and historical network adjustment states corresponding to the historical physiological data and historical characteristic information; and the network within the coverage area is adjusted according to the network adjustment state to optimize the communication signals of the data acquisition terminals within the coverage area. Through the technical solution provided by the present invention, the purpose of determining the signal optimization method for the area where the target object is located based on the dual factors of the physiological data of the target object and the characteristic information of the data acquisition terminal is achieved. This method of adjusting the signal transmission strategy in real time according to the physiological changes of the target object and the signal environment enhances the signal coverage range, so that the signals within the coverage range can respond to signal changes in the organism in real time, improve the continuity and reliability of data transmission, and thus solve the technical problems in related technologies that the static signal coverage mechanism is difficult to adapt to the signal transmission path fluctuations caused by the frequent movement and posture changes of animals, the network tuning capability is insufficient, and it is unable to respond to signal changes in the organism in real time, which affects the continuity and reliability of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0035] Figure 1 This is a hardware structure block diagram of a mobile terminal for a communication signal optimization method according to an embodiment of the present invention;

[0036] Figure 2 is a flow chart of a method for optimizing a communication signal according to an embodiment of the present invention;

[0037] Figure 3 1 is an architecture diagram of a cattle internal data collection terminal communication network according to an embodiment of the present invention;

[0038] Figure 4 This is a schematic diagram of test data according to an embodiment of the present invention. Figure 1 ;

[0039] Figure 5 This is a schematic diagram of test data according to an embodiment of the present invention. Figure 2 ;

[0040] Figure 6This is a schematic diagram of test data according to an embodiment of the present invention. Figure 3 ;

[0041] Figure 7 2 is a schematic diagram of a communication signal optimization device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0043] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0044] As described in the background, the static signal coverage mechanisms used in related technologies struggle to adapt to fluctuations in signal transmission paths caused by animals' frequent movements and posture changes. This leads to insufficient network tuning capabilities and an inability to respond in real time to signal changes within organisms, impacting the continuity and reliability of data transmission. Embodiments of the present invention provide a communication signal optimization method and apparatus, a communication signal optimization system, a computer-readable storage medium, and a processor.

[0045] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0046] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure diagram of a mobile terminal of a communication signal optimization method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0047] The memory 104 can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the communication signal optimization method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned networks include but are not limited to the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0048] For the convenience of description, the nouns or terms in the embodiments of the present invention are explained below:

[0049] Weighted scoring algorithm: Calculates weighted scores by assigning weights to different indicators. It is often used for priority assessment and decision analysis.

[0050] Narrowband Internet of Things (NB-IoT): is a low-power wide-area network (LPWAN) technology defined by the 3GPP standards organization, specifically designed to support efficient, reliable and low-power communications for large-scale IoT devices.

[0051] Reference Signal Received Power (RSRP): The average received power of a base station's reference signal, measured by a terminal (such as a mobile phone), measured in dBm. It is a key indicator for evaluating network coverage quality and is also used for base station switching, network algorithm optimization, and terminal energy-saving strategies.

[0052] Frequency Point: refers to the specific carrier frequency allocated in a wireless communication system. It is the smallest unit of spectrum resources and is used to distinguish different cells or channels to avoid frequency interference.

[0053] Signal-to-noise ratio (SINR): reflects the ratio of signal to noise strength and is used to evaluate channel reliability.

[0054] Physical Downlink Shared Channel (PDSCH): A downlink channel (from base station to terminal) in LTE (Long Term Evolution) and 5G NR (New Radio) communication systems that carries user data and some control information. The PDSCH is designed to deliver data to user equipment (UE) in the most efficient manner using given time and frequency resources while meeting Quality of Service (QoS) requirements.

[0055] The Physical Uplink Shared Channel (PUSCH) is a key channel for uplink data transmission (from user equipment to base station) in LTE and 5G NR communication systems. The PUSCH primarily carries user data, including voice, video, and file uploads, and is also used to transmit some uplink control information.

[0056] 15K (subcarrier spacing): Single-tone mode, suitable for general coverage scenarios.

[0057] 3.75K (subcarrier spacing): Multi-Tone mode, used in extremely low power consumption scenarios.

[0058] Signal attenuation: The decrease in signal strength during electromagnetic wave propagation due to absorption and scattering in the medium. Living tissue can attenuate electromagnetic waves by up to 60-80dB, affecting the penetration capability of traditional communication technologies.

[0059] Example 1

[0060] According to an embodiment of the present invention, a method embodiment of a method for optimizing a communication signal is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0061] Figure 2 is a flow chart of a method for optimizing a communication signal according to an embodiment of the present invention. Figure 2 As shown, the communication signal optimization method includes the following steps:

[0062] Step S202: Acquire physiological data of a target object, wherein the target object is located within a coverage area of the narrowband internet of things.

[0063] Optionally, the target object may be a living organism, such as a cow; and the coverage area may be a pasture.

[0064] In one exemplary scenario, physiological data of cattle in a pasture may be acquired.

[0065] Step S204 : Acquire characteristic information of the data acquisition terminal, wherein the data acquisition terminal is used to acquire physiological data, and the characteristic information is used to evaluate the signal quality of the data acquisition terminal.

[0066] Optionally, the above-mentioned data acquisition terminal can be a bovine rumen terminal acquisition device.

[0067] For example, a rumen terminal device with an integrated NB module is placed in beef cattle. A rumen terminal is an electronic device designed specifically for monitoring and collecting data within the rumen (the first stomach compartment) of cattle (and other ruminants). This terminal typically comes in the form of a capsule, known as a "smart rumen capsule" or "electronic capsule."

[0068] Step S206: Determine the network adjustment state corresponding to the physiological data and the characteristic information through a signal quality scoring model, wherein the signal quality scoring model is obtained through machine learning training using multiple sets of training data, and each set of the multiple sets of training data includes: historical physiological data, historical characteristic information, and historical network adjustment states corresponding to the historical physiological data and historical characteristic information.

[0069] Here, a pre-trained signal quality scoring model can be used to determine the network adjustment status of the base station in the ranch based on the physiological data and characteristic information collected above. This emphasizes the dual importance of physiological data and characteristic information for network signal optimization, as well as the role of the signal quality scoring model in decision-making network adjustment, making the resulting network adjustment method more reasonable.

[0070] Step S208: adjusting the network within the coverage area according to the network adjustment status to optimize the communication signals of the data acquisition terminals within the coverage area.

[0071] Here, the communication signals of the data acquisition terminals in the coverage area can be optimized according to the signal optimization method obtained through machine learning. For example, the number of base stations can be increased in the coverage area.

[0072] Figure 3 : is an architecture diagram of a cattle internal collection terminal communication network according to an embodiment of the present invention, such as Figure 3 As shown, by placing biological rumen capsules inside the cows, the physiological data of the cows can be obtained. This data can be transmitted back to the core network through the wireless network NB-Lot base station and processed using the Internet of Things digital platform.

[0073] As can be seen from the above, in an embodiment of the present invention, the physiological data of the target object can be first obtained, wherein the target object is located in the coverage area of the narrowband Internet of Things; then the characteristic information of the data acquisition terminal is obtained, wherein the data acquisition terminal is used to collect physiological data, and the characteristic information is used to evaluate the signal quality of the data acquisition terminal; then the network adjustment state corresponding to the physiological data and the characteristic information is determined through a signal quality scoring model, wherein the signal quality scoring model is obtained through machine learning training using multiple sets of training data, and each set of the multiple sets of training data includes: historical physiological data, historical characteristic information, and historical network adjustment states corresponding to the historical physiological data and historical characteristic information; and the network in the coverage area is adjusted according to the network adjustment state to optimize the communication signal of the data acquisition terminal in the coverage area, thereby achieving the purpose of determining the signal optimization method for the area where the target object is located based on the dual factors of the physiological data of the target object and the characteristic information of the data acquisition terminal. This method of adjusting the signal transmission strategy in real time according to the physiological changes of the target object and the signal environment enhances the signal coverage range, so that the signal within the coverage range can respond to the signal changes in the organism in real time, thereby improving the continuity and reliability of data transmission.

[0074] Therefore, the technical solution provided by the above-mentioned embodiments of the present invention solves the technical problems in the related technology that the static signal coverage mechanism is difficult to adapt to the fluctuations in the signal transmission path caused by the frequent movement and posture changes of animals, the network tuning capability is insufficient, and it is impossible to respond to signal changes in the organism in real time, which affects the continuity and reliability of data transmission.

[0075] According to the above embodiment of the present invention, acquiring physiological data of the target object includes: acquiring the target object's movement frequency; counting the target object's drinking frequency; and acquiring the target object's posture change frequency.

[0076] Alternatively, the movement frequency may be the number of effective movements of the target object per unit time (e.g., per minute), which may reflect the activity level and behavior pattern of the organism.

[0077] For example, the movement frequency can be obtained in the following manner: movement frequency = effective movement times / statistical duration * 100%.

[0078] Here, if the next position changes by more than 0.5 meters, it is counted as one effective movement, otherwise it is counted as 0; the movement frequency is obtained according to the statistical duration.

[0079] For example: if the moving frequency is 0.85, the corresponding score is 85 points.

[0080] Optionally, the drinking frequency can be calculated by counting the number of effective drinking behaviors per unit time, quantifying the regularity of visits to the drinking area. This can reflect the level of movement of the organism. Calculation method: Drinking frequency = number of effective drinking behaviors / duration of statistics × 100%. If an effective drinking behavior is recorded once per day, the score is 0.8, two times a day a score of 0.9, and more than two times a day a score of 1.

[0081] Optionally, the posture change frequency refers to the number of effective posture changes within a biological unit of time (e.g., every 10 minutes), quantifying the activeness of posture adjustment. It can reflect the activity behavior of a living organism after a posture change. Calculation method: posture change frequency = number of effective changes / statistical duration × 100%. For example, detecting one effective change within 10 minutes will result in 0.8 points, detecting two effective changes within 10 minutes will result in 0.9 points, and detecting three effective changes within 10 minutes will result in 1 point.

[0082] This method identifies the specific components of physiological data, including the animal's movement frequency, water intake frequency, and body posture change frequency, all of which are important inputs to the signal quality scoring model. By monitoring this physiological data, terminal devices can more accurately reflect changes in signal quality in dynamic environments, enabling more precise and effective network adjustments. Monitoring movement frequency helps identify periods of high signal attenuation, monitoring water intake frequency provides additional insight into signal stability, and monitoring body posture change frequency helps predict trends in signal fluctuations, collectively improving the targeted nature of signal optimization.

[0083] According to the above embodiment of the present invention, obtaining characteristic information of the data acquisition terminal includes: obtaining the signal mean of the data acquisition terminal in a first predetermined time window; obtaining the data packet loss rate of the data acquisition terminal in a second predetermined time window; obtaining the signal standard deviation of the data acquisition terminal in a third predetermined time window; and obtaining the signal-to-noise ratio of the data acquisition terminal in a fourth predetermined time window.

[0084] Optionally, the above signal mean (S 信号) is the average value of the signal value within the first predetermined time window (unit: dBm). It can be used to reflect the overall level of signal strength. Its calculation method is: (The original signal value needs to be divided by 10), N represents the number of signal strengths, and i represents the i-th signal strength. Its level mapping is:

[0085] Optionally, the above data packet loss rate (S 丢失率 ) is the mean data loss rate within the window (unit: percentage), which can quantify the reliability of data transmission. The higher the loss rate, the more unstable the network. Calculation method: For example, the mean loss rate is 0.15 → the score is 15 points.

[0086] Optionally, the above signal standard deviation (S 波动性 ) is the standard deviation of the signal value within the window (unit: dBm), which can measure the signal volatility. The larger the standard deviation, the more unstable the signal. Calculation method: (Magnified 10x), Example: Standard deviation 8.2 → Score 82.

[0087] Optionally, the above signal-to-noise ratio (S 信噪比 ) is the mean value of the signal-to-noise ratio (SINR) within the window (unit: dB), which can reflect the signal quality. The higher the SINR, the smaller the noise interference. Level mapping:

[0088] It should be noted that the first predetermined time window, the second predetermined time window, the third predetermined time window and the fourth predetermined time window may be the same or different and may be set according to specific requirements.

[0089] This section describes how to acquire characteristic information from data collection terminals, including signal mean, packet loss rate, standard deviation, and signal-to-noise ratio. These metrics are crucial for evaluating terminal signal quality. By setting different time windows to acquire terminal signal mean, packet loss rate, standard deviation, and signal-to-noise ratio, we can more accurately reflect both transient signal changes and long-term stability, providing a more comprehensive data foundation for the scoring model and enabling more precise network parameter adjustments. For example, using a 1-hour window to monitor signal mean and a 30-minute window to monitor packet loss rate can capture the different signal quality characteristics during peak daytime activity and quiet nighttime periods, enabling more informed optimization decisions.

[0090] According to the above embodiment of the present invention, the weight one of the signal mean is greater than the weight two of the data packet loss rate, the weight two of the data packet loss rate is greater than the weight three of the signal standard deviation, and the weight three of the signal standard deviation is greater than the weight four of the signal-to-noise ratio.

[0091] Table 1 shows the weights of signal mean, data packet loss rate, signal standard deviation, and signal-to-noise ratio.

[0092] Table 1

[0093] parameter Weight illustrate Signal mean 0.4 Core indicators directly determine priorities Data loss rate 0.3 Reflects network stability Signal standard deviation 0.2 Measuring signal volatility Signal-to-noise ratio 0.1 Assist in evaluating signal quality

[0094] As shown in Table 1 above, the signal mean is the core indicator, which directly determines the priority; the data packet loss rate reflects network stability; the signal standard deviation can measure signal volatility; and the signal-to-noise ratio can assist in evaluating signal quality.

[0095] This specifies the weight structure for feature information evaluation: the signal mean is given the highest weight, followed by packet loss rate, signal standard deviation, and finally signal-to-noise ratio. This weight distribution reflects the dominant role of average signal strength in signal quality scoring, while also emphasizing the importance of network stability and anti-interference capabilities in the evaluation system.

[0096] Reasonable weight distribution ensures the model's adaptability in different scenarios. For example, during periods of frequent animal movement, a higher weight for the signal mean can prompt the network to prioritize signal strength. In environments with high signal interference, a higher signal-to-noise ratio weight can prompt network optimization to reduce interference, thereby ensuring the consistency and accuracy of data transmission.

[0097] According to the above embodiment of the present invention, before determining the network adjustment state corresponding to the physiological data and characteristic information through the signal quality scoring model, the communication signal optimization method also includes: obtaining historical physiological data, historical characteristic information and historical network adjustment states corresponding to the historical physiological data and historical characteristic information within a historical time period; training multiple groups of training data including historical physiological data, historical characteristic information and historical network adjustment states to obtain a signal quality scoring model.

[0098] This article describes the training process for the signal quality scoring model, emphasizing the importance of using historical data for training to achieve a more accurate evaluation model. Through continuous training and iteration, the model can be gradually improved, making the prediction and assessment of signal quality more accurate. This allows for faster strategy adjustments, especially when dealing with new animal species or environmental changes, improving the efficiency and effectiveness of network optimization.

[0099] According to the above embodiment of the present invention, the network within the coverage area is adjusted according to the network adjustment status to optimize the communication signal of the data acquisition terminal in the coverage area, including: when the network adjustment status indicates that the signal quality of the data acquisition terminal is lower than the first predetermined signal quality, optimizing the data acquisition terminal within the optimization period; when the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than the second predetermined signal quality, or when the data acquisition terminal has an abnormality, monitoring the data acquisition terminal; when the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the second predetermined signal quality, determining that there is no need to optimize the communication signal of the data acquisition terminal.

[0100] In this embodiment, when it is detected that the signal is extremely poor or fluctuates violently (the signal quality is lower than the first predetermined signal quality), it is determined that the data acquisition terminal needs to be optimized immediately; when it is detected that the signal is medium or there is a potential risk (the signal quality is higher than the first predetermined signal quality and lower than the second predetermined signal quality, or there is an abnormality in the data acquisition terminal), it is determined that monitoring is required; when it is detected that the signal is good (the signal quality is higher than the second predetermined signal quality), it is determined that no adjustment is required.

[0101] For example, by collecting data from terminals inside cattle, correlating the physiological data of beef cattle (water intake, body shape, position, etc.) with the signal strength (rsrp), signal-to-noise ratio (rsrp), frequency band (ECI), data loss rate and other data of the terminals, a mathematical model for signal quality scoring is established to analyze and establish three network adjustment states: high priority (0): the signal is extremely poor or fluctuates violently and requires immediate optimization; medium priority (1): the signal is medium or there is a potential risk and requires continuous monitoring; low priority (2): the signal is good and does not require adjustment. By feeding back the data packet loss ratio of the terminals inside the living animals under the three coverage levels to the base station, the base station optimizes and adjusts the coverage parameters to implement the network communication base station optimization adjustment strategy for differentiated coverage scenarios, thereby enhancing NB network coverage, improving the access success rate of terminals inside the living organisms, reducing the data transmission packet loss rate, reducing the transmission power of NB terminals inside the living animals, the number of data retransmissions and supplementary transmissions, and extending the terminal battery life.

[0102] This article explains how to dynamically adjust network parameters based on the network's state, employing different optimization strategies for varying signal quality conditions. This approach not only enhances network coverage and data transmission reliability, but also avoids unnecessary resource consumption. In low signal quality situations, proactive signal optimization measures can quickly restore data transmission. When signal quality is relatively good, reducing network parameter adjustments helps conserve energy and extend device life.

[0103] According to the above embodiment of the present invention, when the signal quality of the data acquisition terminal is lower than the first predetermined signal quality, the signal adjustment priority of the data acquisition terminal is high priority; when the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than the second predetermined signal quality, or when there is an abnormality in the data acquisition terminal, the signal adjustment priority of the data acquisition terminal is medium priority; when the signal quality of the data acquisition terminal is higher than the second predetermined signal quality, the signal adjustment priority of the data acquisition terminal is determined to be low priority; the priority score corresponding to high priority is greater than or equal to 2.5 points; the priority score corresponding to medium priority is less than 2.5 points; and the score corresponding to low priority is less than 1.5 points.

[0104] Here you can define the adjustment priority as follows: Rule description: High priority: The signal is extremely poor or fluctuates violently and needs to be optimized immediately; Medium priority: The signal is medium or there is potential risk and needs to be monitored; Low priority: The signal is good and no adjustment is required.

[0105] In this embodiment of the present invention, the signal quality scoring model is obtained by associating the physiological data of the cattle (location, water intake, body posture, etc.) with the base station data (frequency, signal, signal-to-noise ratio, frame count, frequency band (ECI), data loss rate). Priority score = 0.1×(0.3×movement frequency + 0.2×water intake frequency + 0.5×body posture change frequency) + 0.9×(0.4·S 信号 +0.3·S 丢失率 +0.2·S 波动性 +0.1·S 信噪比 ).

[0106] This defines the relationship between signal quality score thresholds and priorities. A signal quality score below a threshold is considered high priority, requiring immediate optimization measures; a score between two thresholds is considered medium priority, requiring continuous monitoring; and a score above the second threshold is considered low priority, indicating good signal quality and no adjustments are required. Setting specific priority scores (e.g., high priority >= 2.5, medium priority < 2.5 and > 1.5, and low priority <= 1.5) helps network managers quickly identify and respond to signal issues, ensuring the network optimizes resource allocation across different priorities in a reasonable and efficient manner. This not only enhances signal stability and data transmission efficiency, but also avoids excessive intervention and unnecessary waste of network resources.

[0107] According to the above embodiment of the present invention, the communication signal optimization method may further include: when the signal adjustment priority is high priority, determining the signal coverage level of the base station in the coverage area to be adjusted to low priority; when the signal adjustment priority is medium priority, determining the signal coverage level of the base station in the coverage area to be adjusted to medium priority; when the signal adjustment priority is low priority, determining the signal coverage level of the base station in the coverage area to be adjusted to high priority.

[0108] Here, the concept of determining the base station signal coverage level adjustment strategy based on the signal adjustment priority is proposed, that is, high, medium, and low priority signal adjustments are respectively mapped to low, medium, and high coverage level adjustments, ensuring the accuracy of network parameter adjustments. By matching the signal adjustment priority with the coverage level adjustment strategy, the present invention can effectively respond to scenarios with different signal quality requirements, improving the reliability of signal transmission and the integrity of data acquisition. In emergency situations with high packet loss rates, base station parameters are quickly adjusted to enhance signal coverage, ensuring the timely upload of critical data; and under conditions of good signal quality, base station optimization operations are reduced, avoiding unnecessary disturbances to normal communications, extending equipment operation time, and reducing maintenance costs.

[0109] According to the above embodiment of the present invention, the network within the coverage area is adjusted according to the network adjustment status to optimize the communication signal of the data acquisition terminal in the coverage area, including: determining the downlink initial repetition number and the maximum allowed repetition number of the data acquisition terminal according to the signal coverage level; adjusting the network within the coverage area according to the downlink initial repetition number and the maximum allowed repetition number to optimize the communication signal of the data acquisition terminal in the coverage area.

[0110] Here, we explain how to determine the specific adjustment method of network parameters according to the signal coverage level, especially the adjustment of the initial downlink repetition number and the maximum allowed repetition number, which is one of the key steps to achieve signal optimization. By adjusting the initial downlink repetition number and the maximum allowed repetition number, the method of the present invention can significantly improve the signal arrival rate and the data reception success rate. For example, for high signal adjustment priority (corresponding to low coverage level), the initial downlink repetition number of the base station can be increased from the standard 2 times to 10 times, and the maximum allowed repetition number is increased from 16 times to 64 times, thereby improving the success rate of data transmission in a weak signal environment; and for low priority (corresponding to high coverage level), the number of repetitions can be appropriately reduced to reduce energy consumption, extend the service life of the terminal battery, reduce network congestion, and improve overall communication efficiency.

[0111] For example, you can input terminal collection data according to Table 2:

[0112] Table 2

[0113]

[0114] The calculation steps are as follows: a. Signal processing: b. Time window: Window start: 2024-12-30 18:00, window end: 2024-12-30 20:00; c. Signal mean: d. Data loss rate: S 丢失率 = 0.12×100 = 12%; e. Signal-to-noise ratio: S 信噪比 = 5 (SNR = 18 → 10 < SNR ≤ 20); f. Priority score:

[0115] Combined with the 3 types of priorities obtained previously, three coverage levels of 0 (corresponding to adjusting low priority), 1 (corresponding to adjusting medium priority), and 2 (corresponding to adjusting high priority) can be obtained, which respectively represent 3 different actual situations of the network. 0 indicates good coverage, 1 indicates poor coverage, and 2 indicates extremely poor coverage. Under each coverage level, three cases of packet loss ratio (denoted by p) are distinguished, namely 10% < p < 20%, 20% < p < 30%, and p > 30%. According to the different degrees of packet loss, parameters such as the initial downlink repetition times, the maximum allowed repetition times of PDSCH, the maximum allowed repetition times of 15K 12T PUSCH, the maximum allowed repetition times of 15K 3T PUSCH, the maximum allowed repetition times of 15K 6T PUSCH, the maximum allowed repetition times of 15K ST PUSCH, and the maximum allowed repetition times of 3.75K PUSCH are set to improve the NB network coverage.

[0116] The corresponding network optimization solutions are as follows:

[0117] 1) The network optimization solution for the coverage level of 0 with packet loss is shown in Table 3 below.

[0118] Table 3

[0119]

[0120]

[0121] 2) The network optimization solution for the coverage level of 1 with packet loss is shown in Table 4 below:

[0122] Table 4

[0123]

[0124]

[0125] 3) The network optimization solution for the coverage level of 2 with packet loss is shown in Table 5 below.

[0126] Table 5

[0127]

[0128]

[0129]

[0130] The following describes a specific application scenario.

[0131] 1) Ranch Selection: The project selected Pengyuan Ranch in Xundian, Kunming, Yunnan for the pilot project. Located near the G85 Yinkun Expressway in Huaqingshao, Qingshuigou, Yangjie Town, Xundian Hui and Yi Autonomous County, Pengyuan Ranch is a remote rural area. The ranch's NB network signal coverage previously relied on a nearby NB station (Xundian Xiaohuangpo_Hong Station), approximately 1.5 kilometers away. Test results indicated good NB network coverage for public users within the ranch. However, for this scientific and technological innovation project, precise coverage optimization was required due to NB signal attenuation within living organisms.

[0132] 2) Establishing a NB network wireless environment: Laboratory data showed that the difference in NB signal attenuation between the internal and external parts of cattle's collection terminals was 40dBm-50dBm, with a minimum received signal of -130dBm. The average NB signal before optimization at Xundian Pengyuan Ranch was -85dBm, and the estimated internal NB signal after deployment was -130dBm. The current network signal could not meet the NB signal coverage requirements of the cattle's internal collection terminals. Therefore, a new base station was built within the ranch for subsequent testing. After the base station was completed, the overall NB network coverage within the ranch was good, meeting the basic requirements for NB signal transmission within the cattle. Figure 4 This is a schematic diagram of test data according to an embodiment of the present invention. Figure 1 ,like Figure 4 The test location, coverage and other data are recorded as shown.

[0133] 3. Preparation before capsule release: Before the capsule is released, three cattle data collection terminals are selected for external distance testing of cattle. The distance testing is carried out in an area within 1.5 km of the cattle farm on five routes. The NB signal of the on-site distance testing is normal, and the data collection and data transmission of the cattle internal data collection terminal are normal.

[0134] 4. Capsule deployment: 160 short-term fattening cattle in the ranch were selected for the deployment of internal data collection terminals. After the internal data collection terminals were deployed, the internal detection data (temperature, gastric motility, pH, NB signal RSRP, SINR, etc.) and other data can be transmitted to the platform normally. Figure 5 and Figure 6 Various data, such as ranch name, shed number, etc., are displayed to better manage the data.

[0135] 5. Data collection and transmission: The sensors in the capsule collect the cow's physiological data (temperature, water intake, gastric motility, pH value, etc.), and the NB communication module in the capsule receives NB network signal-related data (cell information, frequency band (ECI), RSRP, PCI, SINR, frequency band, frequency point, etc.). The collected data is transmitted back to the background management and monitoring platform.

[0136] 6. Data Analysis and Network Optimization: Backend data is calculated using a mathematical model to determine three network coverage levels within the ranch, as well as the percentage of terminals experiencing packet loss. Based on these results, the backend optimizes the network to improve NB network coverage and enhance data transmission efficiency. After collecting data, a hierarchical algorithm is used to perform dynamic network parameter optimization. This adjustment significantly improves NB terminal data collection accuracy and significantly reduces packet loss.

[0137] Through the technical solution provided by the above embodiment of the present invention, the wireless signal quality scoring model for beef cattle is obtained by associating the physiological data of cattle (lying, position, etc.) and the data of base stations (frequency, signal, signal-to-noise ratio, frame count, frequency band (ECI), data loss rate). This model has the following advantages: 1) Multi-dimensional evaluation: comprehensive signal strength, stability, and network reliability; 2) Dynamic time window: capture real-time changes to avoid interference from historical data; 3) Subsequent improvement direction: introduce dynamic weights: automatically adjust weight coefficients based on historical data; 4) Increase anomaly detection: identify sudden increase / decrease events in the window. In addition, different network optimization schemes are formulated for different hierarchical states. The signal quality scoring model is used to match three network coverage levels, and three packet loss ratios (represented by p) are distinguished based on each coverage level, which are 10% and 10% respectively. <p<20%、20%<p<30%、p> 30%. According to the degree of packet loss, set the downlink initial repetition number, the maximum allowed PDSCH repetition number, the maximum allowed 15K 12T PUSCH repetition number, the maximum allowed 15K 3T PUSCH repetition number, the maximum allowed 15K 6T PUSCH repetition number, the maximum allowed 15K ST PUSCH repetition number, the maximum allowed 3.75K PUSCH repetition number and other parameters to improve NB network coverage, improve the access success rate of terminals in living organisms, reduce the data transmission packet loss rate, reduce the NB terminal transmit power and the number of data retransmissions and supplementary transmissions in living animals, and extend the terminal battery life.

[0138] This technical solution offers the following advantages: 1) Enhanced network coverage: NB network coverage is increased to 98%, and signal strength in edge areas is increased by 15dBm. 2) Improved data transmission reliability: The proportion of terminals experiencing packet loss is reduced by 10%, meeting the need for real-time health data monitoring during beef cattle breeding. 3) Reduced network construction costs: Compared to traditional base station deployment solutions, this solution saves 30%-50% in infrastructure costs.

[0139] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0140] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0141] Example 2

[0142] According to an embodiment of the present invention, there is also provided a communication signal optimization device for implementing the above communication signal optimization method. Figure 7 is a schematic diagram of a communication signal optimization device according to an embodiment of the present invention, such as Figure 7 As shown, the device includes: a first acquiring unit 701, a second acquiring unit 703, a determining unit 705, and an optimizing unit 707. The device is described below.

[0143] The first acquiring unit 701 is configured to acquire physiological data of a target object, where the target object is located within a coverage area of the narrowband internet of things.

[0144] The second acquiring unit 703 is configured to acquire characteristic information of a data acquisition terminal, wherein the data acquisition terminal is used to acquire physiological data, and the characteristic information is used to evaluate signal quality of the data acquisition terminal.

[0145] A determination unit 705 is configured to determine a network adjustment state corresponding to the physiological data and the characteristic information through a signal quality scoring model, wherein the signal quality scoring model is obtained through machine learning training using multiple sets of training data, each of the multiple sets of training data including: historical physiological data, historical characteristic information, and historical network adjustment states corresponding to the historical physiological data and the historical characteristic information.

[0146] The optimization unit 707 is configured to adjust the network within the coverage area according to the network adjustment status, so as to optimize the communication signals of the data collection terminals within the coverage area.

[0147] It should be noted here that the above-mentioned first acquisition unit 701, second acquisition unit 703, determination unit 705 and optimization unit 707 correspond to steps S202 to S208 in the above-mentioned embodiment. The four units have the same instances and application scenarios as the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiment.

[0148] As can be seen from the above, in the scheme recorded in the above embodiment of the present invention, the first acquisition unit can be used to acquire physiological data of the target object, wherein the target object is located in the coverage area of the narrowband Internet of Things; the second acquisition unit is used to acquire feature information of the data acquisition terminal, wherein the data acquisition terminal is used to collect physiological data, and the feature information is used to evaluate the signal quality of the data acquisition terminal; the determination unit is used to determine the network adjustment state corresponding to the physiological data and the feature information through a signal quality scoring model, wherein the signal quality scoring model is obtained by machine learning training using multiple sets of training data, each of the multiple sets of training data includes: historical physiological data, historical feature information, and historical network adjustment states corresponding to the historical physiological data and historical feature information; the optimization unit is used to adjust the network in the coverage area according to the network adjustment state to optimize the communication signal of the data acquisition terminal in the coverage area, thereby achieving the purpose of determining the signal optimization method for the area where the target object is located based on the dual factors of the target object's physiological data and the feature information of the data acquisition terminal. This method of adjusting the signal transmission strategy in real time based on the physiological changes of the target object and the signal environment enhances the signal coverage range, so that the signal within the coverage area can respond to the signal changes in the organism in real time, thereby improving the continuity and reliability of data transmission.

[0149] Therefore, the technical solution provided by the above-mentioned embodiments of the present invention solves the technical problems in the related technology that the static signal coverage mechanism is difficult to adapt to the fluctuations in the signal transmission path caused by the frequent movement and posture changes of animals, the network tuning capability is insufficient, and it is impossible to respond to signal changes in the organism in real time, which affects the continuity and reliability of data transmission.

[0150] Optionally, the first acquisition unit includes: a first acquisition module for acquiring the movement frequency of the target object; a statistical module for counting the drinking frequency of the target object; and a second acquisition module for acquiring the body posture change frequency of the target object.

[0151] Optionally, the second acquisition unit includes: a third acquisition module, used to obtain the signal mean of the data acquisition terminal within the first predetermined time window; a fourth acquisition module, used to obtain the data packet loss rate of the data acquisition terminal within the second predetermined time window; a fifth acquisition module, used to obtain the signal standard deviation of the data acquisition terminal within the third predetermined time window; and a sixth acquisition module, used to obtain the signal-to-noise ratio of the data acquisition terminal within the fourth predetermined time window.

[0152] Optionally, weight one of the signal mean is greater than weight two of the data packet loss rate, weight two of the data packet loss rate is greater than weight three of the signal standard deviation, and weight three of the signal standard deviation is greater than weight four of the signal-to-noise ratio.

[0153] Optionally, the communication signal optimization device also includes: a third acquisition unit, used to obtain historical physiological data, historical feature information and historical network adjustment states corresponding to the historical physiological data and historical feature information within a historical time period before determining the network adjustment state corresponding to the physiological data and historical feature information through a signal quality scoring model; a training unit, used to train multiple groups of training data including historical physiological data, historical feature information and historical network adjustment states to obtain a signal quality scoring model.

[0154] The optimization unit includes: an optimization module, which is used to optimize the data acquisition terminal within an optimization period when the network adjustment status indicates that the signal quality of the data acquisition terminal is lower than a first predetermined signal quality; a monitoring module, which is used to monitor the data acquisition terminal when the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than a second predetermined signal quality, or when there is an abnormality in the data acquisition terminal; and a first determination module, which is used to determine that there is no need to optimize the communication signal of the data acquisition terminal when the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the second predetermined signal quality.

[0155] Optionally, when the signal quality of the data acquisition terminal is lower than a first predetermined signal quality, the signal adjustment priority of the data acquisition terminal is high priority; when the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than a second predetermined signal quality, or when there is an abnormality in the data acquisition terminal, the signal adjustment priority of the data acquisition terminal is medium priority; when the signal quality of the data acquisition terminal is higher than the second predetermined signal quality, the signal adjustment priority of the data acquisition terminal is determined to be low priority; the priority score corresponding to high priority is greater than or equal to 2.5 points; the priority score corresponding to medium priority is less than 2.5 points; and the score corresponding to low priority is less than 1.5 points.

[0156] Optionally, the communication signal optimization method also includes: a second determination module, used to determine the signal coverage level of the base station in the coverage area to be adjusted to low priority when the signal adjustment priority is high priority; a third determination module, used to determine the signal coverage level of the base station in the coverage area to be adjusted to medium priority when the signal adjustment priority is medium priority; and a fourth determination module, used to determine the signal coverage level of the base station in the coverage area to be adjusted to high priority when the signal adjustment priority is low priority.

[0157] Optionally, the optimization unit includes: a fifth determination module, used to determine the initial downlink repetition number and the maximum allowed repetition number of the data acquisition terminal according to the signal coverage level; a second optimization module, used to adjust the network within the coverage area according to the initial downlink repetition number and the maximum allowed repetition number to optimize the communication signal of the data acquisition terminal in the coverage area.

[0158] According to another aspect of an embodiment of the present invention, a communication signal optimization system is provided. The communication signal optimization system uses any one of the above communication signal optimization methods.

[0159] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned communication signal optimization methods.

[0160] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0161] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: obtaining physiological data of a target object, wherein the target object is located within the coverage area of the narrowband Internet of Things; obtaining characteristic information of a data acquisition terminal, wherein the data acquisition terminal is used to collect physiological data, and the characteristic information is used to evaluate the signal quality of the data acquisition terminal; determining a network adjustment state corresponding to the physiological data and the characteristic information through a signal quality scoring model, wherein the signal quality scoring model is obtained through machine learning training using multiple sets of training data, and each set of the multiple sets of training data includes: historical physiological data, historical characteristic information, and historical network adjustment states corresponding to the historical physiological data and historical characteristic information; adjusting the network in the coverage area according to the network adjustment state to optimize the communication signal of the data acquisition terminal in the coverage area.

[0162] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining the target object's movement frequency; counting the target object's drinking frequency; and obtaining the target object's posture change frequency.

[0163] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining the signal mean of the data acquisition terminal within a first predetermined time window; obtaining the data packet loss rate of the data acquisition terminal within a second predetermined time window; obtaining the signal standard deviation of the data acquisition terminal within a third predetermined time window; and obtaining the signal-to-noise ratio of the data acquisition terminal within a fourth predetermined time window.

[0164] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: before determining the network adjustment state corresponding to the physiological data and feature information through the signal quality scoring model, obtaining historical physiological data, historical feature information and historical network adjustment states corresponding to the historical physiological data and historical feature information within a historical time period; training multiple groups of training data including historical physiological data, historical feature information and historical network adjustment states to obtain a signal quality scoring model.

[0165] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: when the network adjustment status indicates that the signal quality of the data acquisition terminal is lower than a first predetermined signal quality, optimizing the data acquisition terminal within an optimization period; when the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than a second predetermined signal quality, or when there is an abnormality in the data acquisition terminal, monitoring the data acquisition terminal; when the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the second predetermined signal quality, determining that there is no need to optimize the communication signal of the data acquisition terminal.

[0166] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: when the signal adjustment priority is high priority, determining the signal coverage level of the base station within the coverage area to be adjusted to low priority; when the signal adjustment priority is medium priority, determining the signal coverage level of the base station within the coverage area to be adjusted to medium priority; when the signal adjustment priority is low priority, determining the signal coverage level of the base station within the coverage area to be adjusted to high priority.

[0167] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining the initial downlink repetition number and the maximum allowed repetition number of the data acquisition terminal based on the signal coverage level; adjusting the network within the coverage area based on the initial downlink repetition number and the maximum allowed repetition number to optimize the communication signal of the data acquisition terminal in the coverage area.

[0168] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein the program executes any one of the above-mentioned communication signal optimization methods when running.

[0169] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising computer instructions, which, when executed by a processor, execute any one of the above-mentioned communication signal optimization methods.

[0170] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0171] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0172] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0173] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0174] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0175] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0176] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for optimizing a communication signal, characterized in that: include: Acquiring physiological data of a target object, wherein the target object is located within a coverage area of the narrowband internet of things; Acquiring characteristic information of a data acquisition terminal, wherein the data acquisition terminal is used to acquire the physiological data, and the characteristic information is used to evaluate signal quality of the data acquisition terminal; Determining a network adjustment state corresponding to the physiological data and the feature information using a signal quality scoring model, wherein the signal quality scoring model is obtained by machine learning training using multiple sets of training data, each of the multiple sets of training data including: historical physiological data, historical feature information, and historical network adjustment states corresponding to the historical physiological data and the historical feature information; The network within the coverage area is adjusted according to the network adjustment status to optimize the communication signal of the data acquisition terminal within the coverage area.

2. The method for optimizing communication signals according to claim 1, wherein: Acquire physiological data of the target object, including: Acquire the movement frequency of the target object; Counting the drinking frequency of the target subject; Obtaining the body posture change frequency of the target object.

3. The method for optimizing communication signals according to claim 1, wherein: Obtain characteristic information of the data collection terminal, including: Obtaining a signal mean value of the data acquisition terminal within a first predetermined time window; Obtaining a data packet loss rate of the data acquisition terminal within a second predetermined time window; Obtaining a signal standard deviation of the data acquisition terminal within a third predetermined time window; Acquire a signal-to-noise ratio of the data acquisition terminal within a fourth predetermined time window.

4. The method for optimizing communication signals according to claim 3, wherein: The weight one of the signal mean is greater than the weight two of the data packet loss rate, the weight two of the data packet loss rate is greater than the weight three of the signal standard deviation, and the weight three of the signal standard deviation is greater than the weight four of the signal-to-noise ratio.

5. The method for optimizing communication signals according to claim 1, wherein: Before determining the network adjustment state corresponding to the physiological data and the characteristic information through the signal quality scoring model, the method further includes: Acquire the historical physiological data, the historical feature information, and the historical network adjustment status corresponding to the historical physiological data and the historical feature information within a historical time period; Training is performed on multiple groups of the training data including the historical physiological data, the historical feature information, and the historical network adjustment status to obtain the signal quality scoring model.

6. The method for optimizing communication signals according to claim 1, wherein: Adjusting the network within the coverage area according to the network adjustment state to optimize the communication signal of the data acquisition terminal within the coverage area includes: When the network adjustment state indicates that the signal quality of the data acquisition terminal is lower than a first predetermined signal quality, optimizing the data acquisition terminal within an optimization period; When the network adjustment state indicates that the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than the second predetermined signal quality, or when the data acquisition terminal is abnormal, monitoring the data acquisition terminal; When the network adjustment status indicates that the signal quality of the data acquisition terminal is higher than the second predetermined signal quality, it is determined that there is no need to optimize the communication signal of the data acquisition terminal.

7. The method for optimizing communication signals according to claim 6, wherein: When the signal quality of the data acquisition terminal is lower than the first predetermined signal quality, the signal adjustment priority of the data acquisition terminal is high priority; when the signal quality of the data acquisition terminal is higher than the first predetermined signal quality and lower than the second predetermined signal quality, or when there is an abnormality in the data acquisition terminal, the signal adjustment priority of the data acquisition terminal is medium priority; when the signal quality of the data acquisition terminal is higher than the second predetermined signal quality, the signal adjustment priority of the data acquisition terminal is determined to be low priority; the priority score corresponding to the high priority is greater than or equal to 2.5 points; the priority score corresponding to the medium priority is less than 2.5 points; The score corresponding to the low priority is less than 1.5 points.

8. The method for optimizing communication signals according to claim 7, wherein: Also includes: When the signal adjustment priority is the high priority, determining the signal coverage level of the base station in the coverage area to be adjusted to a low priority; When the signal adjustment priority is the medium priority, determining that the signal coverage level of the base station in the coverage area is adjusted to the medium priority; When the signal adjustment priority is the low priority, the signal coverage level of the base station within the coverage area is determined to be adjusted to a high priority.

9. The method for optimizing communication signals according to claim 8, wherein: Adjusting the network within the coverage area according to the network adjustment state to optimize the communication signal of the data acquisition terminal within the coverage area includes: Determine the initial downlink repetition number and the maximum allowed repetition number of the data acquisition terminal according to the signal coverage level; The network within the coverage area is adjusted according to the initial downlink repetition number and the maximum allowed repetition number, so as to optimize the communication signal of the data acquisition terminal within the coverage area.

10. A communication signal optimization device, characterized in that: include: A first acquiring unit is configured to acquire physiological data of a target object, wherein the target object is located within a coverage area of the narrowband internet of things; a second acquiring unit, configured to acquire characteristic information of a data acquisition terminal, wherein the data acquisition terminal is used to acquire the physiological data, and the characteristic information is used to evaluate the signal quality of the data acquisition terminal; a determining unit, configured to determine a network adjustment state corresponding to the physiological data and the feature information using a signal quality scoring model, wherein the signal quality scoring model is obtained by machine learning training using multiple sets of training data, each of the multiple sets of training data including: historical physiological data, historical feature information, and historical network adjustment states corresponding to the historical physiological data and the historical feature information; An optimization unit is used to adjust the network in the coverage area according to the network adjustment state, so as to optimize the communication signal of the data acquisition terminal in the coverage area.

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