NB-IOT / CAT1 security product network intelligent selection method, device, equipment and medium

Abstract

The invention discloses an NB-IOT / CAT1 security product network intelligent selection method and device, equipment and a medium, and the method comprises the steps: obtaining the signal intensity data of a current scene after a product is electrified to find a network, and determining the signal monitoring level and sampling frequency corresponding to the product; turning on a radio frequency end receiving bottom noise detector of the product, performing bandwidth-by-bandwidth frequency sweeping based on a BAND frequency band range supported by the product, and determining an optimal working frequency band; determining the interference degree grade of the current scene according to the same-frequency interference and the environmental electromagnetic interference; and periodically obtaining historical network state information of an area where the current scene is located, and determining a resource avoidance strategy and an optimal reporting time period of network searching in the current scene according to the historical network state information. According to the method, the problems of poor scene adaptability and unbalanced transmission and power consumption application caused by insufficient network pre-judgment capability and network switching strategy rigidness of the current NB-IOT or CAT1 product are effectively solved by surrounding scene judgment, interference positioning, base station resource avoidance and reporting strategy optimization.

Description

Technical Field

[0001] This invention relates to the field of security product technology, and in particular to a method, apparatus, equipment and medium for intelligent network selection of NB-IoT / CAT1 security products. Background Technology

[0002] The network of NB-IoT (Narrow Band Internet of Things) or CAT1 (Smart Connected Terminal) security products on the market generally uses the Reference Signal Received Power (RSRP) to quantize the signal and the signal interference noise ratio (SINR) and the signal quality RSSI to set a certain threshold to determine the current network status and whether to reselect and switch network partitions (usually on a cell-by-cell basis).

[0003] Current NB-IoT / CAT1 security products only focus on the signal itself when selecting networks, without considering scenario characteristics. This limits the adaptability of security products. For example, in many situations such as basements, where RSRP meets the standard but environmental interference is strong, the SINR may be low or negative. Even if the network has deteriorated, the product may not switch over, and the lack of a reselection mechanism will result in untimely network switching, ultimately leading to alarm reporting failure and data loss. Furthermore, the product does not consider the base station load or the device load of the current network partition. This results in reduced resource allocation for high-load areas, and the lack of any network search mechanism means that even when the signal is poor, the product continues to consume power even if a network search fails, ultimately leading to an imbalance in reliability.

[0004] Therefore, there is an urgent need for a network intelligent selection method for NB-IoT / CAT1 security products. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method, apparatus, device and medium for intelligent selection of NB-IoT / CAT1 security product networks that overcomes or at least partially solves the above problems.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to a first aspect of the present invention, a method for intelligent network selection of NB-IoT / CAT1 security products is provided, comprising the following steps: S1. After the product is powered on and searches for a network, it obtains the signal strength data of the current scene. The signal strength data is compared with the preset threshold of the scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product. The signal strength data includes RSSI and RSRP. The signal monitoring level corresponding to the product includes signal stability and weak signal coverage. When the signal monitoring level is signal stability, step S3 is executed. When the signal monitoring level is weak signal coverage, step S2 is executed. S2. Activate the radio frequency receiver noise floor detector of the product, perform a bandwidth-by-band sweep based on the range of BAND frequency bands supported by the product, determine the optimal operating frequency band, and execute step S1 with the optimal operating frequency band. S3. Activate the radio frequency receiver noise floor detector of the product to obtain the co-channel interference and environmental electromagnetic interference in the current scene. Determine the interference level of the current scene based on the co-channel interference and environmental electromagnetic interference. The interference level includes high interference and low interference. When the interference level is high interference, prompt the product installation position to be changed. When the interference level is low interference, execute step S4. S4. Periodically acquire historical network status information of the current scene's location, determine the resource avoidance strategy and optimal reporting time period for network finding in the current scene based on the historical network status information, and execute steps S1-S3 based on the resource avoidance strategy and optimal reporting time period.

[0008] In some embodiments of the present invention, the step of comparing the signal strength data with a preset threshold in a scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product includes: When the current scenario is a closed environment, if RSRP ≥ -105dBm, the signal monitoring level of the product is stable; when the current scenario is a remote area, if RSSI ≤ -110dBm, the signal monitoring level of the product is weak signal coverage. When the signal monitoring level corresponding to the product is stable, the sampling frequency is adjusted to 1 minute; when the signal monitoring level corresponding to the product is weak coverage, the sampling frequency and sampling time are adjusted to 1 second.

[0009] In some embodiments of the present invention, the step of performing a bandwidth-wise frequency sweep based on the product-supported BAND band range to determine the optimal operating frequency band includes: Set the maximum bandwidth R supported by the product's radio frequency terminal. Based on the BAND band supported by the product, scan from the lowest frequency T to the highest frequency Y using a frequency sweep method. When the Nth center frequency point of the frequency sweep is greater than or equal to Y, the frequency sweep ends. The Nth center frequency point of the frequency sweep Sn = T + (N-1)R + R / 2. Obtain and record the maximum power value and corresponding frequency point U within each bandwidth; Filter out the valid frequency points from all frequency points U based on the list of BAND bands that the current product is compatible with; Locate the target frequency Um with the lowest power value among the effective frequency points, use the target frequency Um as the optimal operating frequency band, and repeat step S1.

[0010] In some embodiments of the present invention, the step of periodically acquiring historical network status information of the current scene's location and determining the resource avoidance strategy for finding networks in the current scene and the optimal reporting time period based on the historical network status information includes: The system information block broadcast by the base station of the network partition where the current scenario is located is periodically connected through the radio frequency terminal of the product to extract network resource usage data; By statistically analyzing the usage data of all network resources and the packet data reception rate within a predetermined time period, historical network status information is obtained. Based on the historical network status information, determine the resource avoidance strategy and the optimal reporting time for the network partition where the current scenario is located.

[0011] According to a second aspect of the present invention, an NB-IoT / CAT1 security product network intelligent selection device is provided, the device comprising: The signal acquisition module is used to acquire signal strength data of the current scene after the product is powered on and searches for a network. The signal strength data is compared with a preset threshold in the scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product. The signal strength data includes RSSI and RSRP. The signal monitoring level corresponding to the product includes signal stability and weak signal coverage. When the signal monitoring level is signal stability, the interference identification module is executed. When the signal monitoring level is weak signal coverage, the frequency sweeping module is executed. The frequency sweep module is used to activate the radio frequency receiver noise floor detector of the product, perform a bandwidth-by-band frequency sweep based on the range of the BAND frequency band supported by the product, determine the optimal operating frequency band, and execute the signal acquisition module in the optimal operating frequency band. The interference identification module is used to activate the radio frequency receiver noise floor detector of the product to obtain the co-channel interference and environmental electromagnetic interference in the current scene, determine the interference level of the current scene based on the co-channel interference and environmental electromagnetic interference, the interference level includes high interference and low interference, when the interference level is high interference, prompt the product installation position to be changed, and when the interference level is low interference, execute the network finding and adjustment module. The network search and adjustment module is used to periodically acquire historical network status information of the current scene area, determine the resource avoidance strategy and the best reporting time period for network search in the current scene based on the historical network status information, and execute the signal acquisition module, frequency sweeping module or interference identification module based on the resource avoidance strategy and the best reporting time period.

[0012] In some embodiments of the present invention, the signal acquisition module compares the signal strength data with a preset threshold in a scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product, including: When the current scenario is a closed environment, if RSRP ≥ -105dBm, the signal monitoring level of the product is stable; when the current scenario is a remote area, if RSSI ≤ -110dBm, the signal monitoring level of the product is weak signal coverage. When the signal monitoring level corresponding to the product is stable, the sampling frequency is adjusted to 1 minute; when the signal monitoring level corresponding to the product is weak coverage, the sampling frequency and sampling time are adjusted to 1 second.

[0013] In some embodiments of the present invention, the frequency sweep module performs a bandwidth-wise frequency sweep based on the range of BAND frequency bands supported by the product to determine the optimal operating frequency band, including: Set the maximum bandwidth R supported by the product's radio frequency terminal. Based on the BAND band supported by the product, scan from the lowest frequency T to the highest frequency Y using a frequency sweep method. When the Nth center frequency point of the frequency sweep is greater than or equal to Y, the frequency sweep ends. The Nth center frequency point of the frequency sweep Sn = T + (N-1)R + R / 2. Obtain and record the maximum power value and corresponding frequency point U within each bandwidth; Filter out the valid frequency points from all frequency points U based on the list of BAND bands that the current product is compatible with; Locate the target frequency Um with the lowest power value among the effective frequency points, use the target frequency Um as the optimal operating frequency band, and repeat step S1.

[0014] In some embodiments of the present invention, the network finding adjustment module periodically acquires historical network status information of the current scene's location, and determines the resource avoidance strategy for network finding in the current scene and the optimal reporting time period based on the historical network status information, including: The system information block broadcast by the base station of the network partition where the current scenario is located is periodically connected through the radio frequency terminal of the product to extract network resource usage data; By statistically analyzing the usage data of all network resources and the packet data reception rate within a predetermined time period, historical network status information is obtained. Based on the historical network state information, determine the resource avoidance strategy and the optimal reporting time for the network partition where the current scenario occurs. According to a third aspect of the present invention, a computer device is provided, including a processor and a memory, the memory storing computer program instructions executable by the processor, wherein when the processor executes the computer program instructions, it implements the instructions as described in any of the above methods.

[0015] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein computer program instructions are stored therein, the computer program instructions being loaded and executed by a processor to perform the operations performed by the method described in any of the preceding claims.

[0016] The technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages: This invention provides a method, apparatus, device, and medium for intelligent network selection of NB-IoT / CAT1 security products. The intelligent network selection method for NB-IoT / CAT1 security products described in this invention focuses on scene determination, interference location, base station resource avoidance, and reporting strategy optimization. It effectively solves the problems of insufficient network pre-judgment capability and rigid network switching strategies of current NB-IoT or CAT1 products, resulting in poor scene adaptability and imbalance between transmission and power consumption.

[0017] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a network intelligent selection method for NB-IoT / CAT1 security products provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the principle structure of an NB-IoT / CAT1 security product network intelligent selection device provided in an embodiment of the present invention; Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0020] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings.

[0021] The accompanying drawings illustrate various structural schematics according to embodiments of this application. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0022] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. In the context of this application, similar or identical parts may be represented by the same or similar reference numerals.

[0023] To better understand the above technical solutions, the following will describe the above technical solutions in detail with reference to specific implementation methods. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of the present invention, rather than limitations on the technical solutions of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0024] Figure 1 This is a flowchart illustrating a network intelligent selection method for NB-IoT / CAT1 security products provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the network intelligent selection method for this NB-IoT / CAT1 security product includes the following steps: S1. After the product is powered on and searches for a network, it obtains the signal strength data of the current scene. The signal strength data is compared with the preset threshold of the scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product. The signal strength data includes RSSI (Received Signal Strength Indication) and RSRP (Reference Signal Receiving Power). The signal monitoring level corresponding to the product includes stable signal and weak signal coverage. When the signal monitoring level is stable, step S3 is executed. When the signal monitoring level is weak signal coverage, step S2 is executed. In this embodiment of the invention, the scene determination database stores a preset threshold for determining the signal monitoring level. The preset threshold can be manually defined or calculated based on historical experience data.

[0025] For example, in step S1, this embodiment of the invention compares the signal strength data with a preset threshold in the scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product, including: When the current scenario is a closed environment, if RSRP ≥ -105dBm, the signal monitoring level corresponding to the product is stable signal; when the current scenario is a remote area, if RSSI ≤ -110dBm, the signal monitoring level corresponding to the product is weak signal coverage; the closed environment is, for example, a basement or a densely packed building; the remote area is an area far from the base station or with weak signal coverage. When the signal monitoring level corresponding to the product is stable, the sampling frequency is adjusted to 1 minute; when the signal monitoring level corresponding to the product is weak coverage, the sampling frequency and sampling time are adjusted to 1 second.

[0026] S2. Activate the radio frequency receiver noise floor detector of the product, perform a bandwidth-by-band sweep based on the range of BAND frequency bands supported by the product, determine the optimal operating frequency band, and execute step S1 with the optimal operating frequency band. In this embodiment of the invention, the radio frequency (RF) receiver noise floor detector is a built-in receiver that is conventionally integrated into the RF end of NB-IoT / CAT1 products for demodulating interconnect communication signals. It can be used to achieve accurate interference detection, such as detecting co-channel interference and environmental electromagnetic interference. In complex electromagnetic environments, the RF receiver noise floor detector accurately distinguishes between "useful base station signals" and "various interference signals," and its operation follows the basic architecture of a wireless communication receiver. In this embodiment, the RF receiver noise floor detector first acquires and preprocesses signals, receiving electromagnetic signals in the space through the product antenna. These signals are then processed through three main steps: a front-end low-noise amplifier, a mixer, and a filter, before establishing a connection with the base station. The system performs baseline synchronization processing, demodulates the captured signal after synchronization to extract relevant information, and then extracts its key features through algorithmic filtering. Specifically, it prioritizes signals based on signal strength, first decoding signals with higher strength. If the decoding result conforms to the NB-IoT or CAT1 protocol standard, it is determined to be a useful signal and subtracted from the mixed signal. The remaining unmatched and uneliminated signals are determined to be interference signals. At the same time, the radio frequency receiving noise floor detector performs spectrum analysis on the separated interference signals and extracts core features. The power detection module in the radio frequency receiving noise floor detector directly quantizes and determines the strength of the interference signal, and the frequency sweep method determines the frequency band range of the interference frequency distribution.

[0027] In step S2, this embodiment of the invention performs a bandwidth-wise frequency sweep based on the range of BAND frequency bands supported by the product to determine the optimal operating frequency band, including: Set the maximum bandwidth R supported by the product's radio frequency terminal. Based on the BAND band supported by the product, scan from the lowest frequency T to the highest frequency Y using a frequency sweep method. When the Nth center frequency point of the frequency sweep is greater than or equal to Y, the frequency sweep ends. The Nth center frequency point Sn of the frequency sweep is Sn = T + (N-1)R + R / 2. For example, the first center frequency point S1 = T + R / 2, the second center frequency point S2 = (T + R) + R / 2, the third center frequency point S3 = (T + R + R) + R / 2, etc. Obtain and record the maximum power value and corresponding frequency point U within each bandwidth; Filter out the valid frequency points from all frequency points U based on the list of BAND bands that the current product is compatible with; Locate the target frequency Um with the lowest power value among the effective frequency points, use the target frequency Um as the optimal operating frequency band, and repeat step S1.

[0028] In this embodiment of the invention, the list of BAND bands adapted to the current product can be determined based on the list of supported BAND bands of the operator corresponding to the SIM card used by the product.

[0029] The lower the power value of a frequency point, the higher the signal reception sensitivity and the stronger the anti-interference capability. In this embodiment of the invention, the target frequency point Um with the lowest power value is located among the effective frequency points, and the target frequency point Um is used as the optimal working frequency band.

[0030] S3. Activate the radio frequency receiver noise floor detector of the product to obtain the co-channel interference and environmental electromagnetic interference in the current scene. Determine the interference level of the current scene based on the co-channel interference and environmental electromagnetic interference. The interference level includes high interference and low interference. When the interference level is high interference, prompt the product installation position to be changed. When the interference level is low interference, execute step S4. In step S3, the processing flow of the radio frequency receiving noise floor detector in this embodiment of the invention for co-channel interference and environmental electromagnetic interference is as follows: spatial superposition → mixed capture → separation and identification → maximum value determination; since the co-channel interference and environmental electromagnetic interference have already been superimposed and mixed in the scene space, the radio frequency receiving noise floor detector does not need to perform total power quantization on the mixed signal, but directly compares the intensity of the co-channel interference obtained after separation with the intensity of the environmental electromagnetic interference, and takes the maximum value of the two as the interference intensity of the current scene, thereby simplifying the calculation process, improving decision-making efficiency, and ensuring that the core interference impact is accurately captured.

[0031] For example, when the interference intensity is > -85dBm, its interference level can be defined as high interference, and when the interference intensity is > -95dBm, its interference level can be defined as high-low interference.

[0032] S4. Periodically acquire historical network status information of the current scene's location, determine the resource avoidance strategy and optimal reporting time period for network finding in the current scene based on the historical network status information, and execute steps S1-S3 based on the resource avoidance strategy and optimal reporting time period.

[0033] In step S4, this embodiment of the invention periodically acquires historical network status information of the current scene's location. Determining the resource avoidance strategy and optimal reporting time period for the current scene based on the historical network status information includes: periodically connecting to the system information block broadcast by the base station of the network partition where the current scene is located via the radio frequency terminal of the product to extract network resource usage data; statistically analyzing all the network resource usage data and packet data reception rate within a predetermined time period to obtain historical network status information; and determining the resource avoidance strategy and optimal reporting time period for the network partition where the current scene is located based on the historical network status information.

[0034] Specifically, in this embodiment of the invention, the system information block (SIB) broadcast by the base station of the network partition where the current scenario is located is connected to the radio frequency terminal of the product at regular intervals to extract network resource usage data. The acquisition frequency of the network resource usage data can be, for example, 24 hours. The network resource usage data is, for example, the network resource load rate. The packet data reception rate (PDR) is a key indicator for measuring the performance of data communication networks, representing the ratio of the number of successfully received packets to the total number of sent packets. In this embodiment of the invention, for example, all the network resource usage data and packet data reception rate of the previous day (24 hours) can be statistically analyzed to obtain the historical network status information.

[0035] The resource avoidance strategy set in this embodiment of the invention, for example, is to switch the current network to an adjacent network partition when the network resource load rate is >70%, thereby avoiding reporting delays caused by network congestion during peak data transmission periods such as shopping mall peak hours and community morning peak hours. The definition standard of the network partition is the nearby base station within the coverage area where the product's radio frequency terminal can stably capture signals. Among them, the basic definition follows the 3GPP NB-IoT neighbor cell configuration specification, and the neighbor cell signal strength (RSSI / RSRP) is ≥ the current cell signal strength - 12dB, mainly to ensure signal stability after handover. The enhanced definition is that the product's radio frequency terminal module obtains the pre-configured neighbor cell list through the broadcast system information block, determines potential high-quality neighbor cells by comparing the resource load rate, and can update the product's pre-stored neighbor cell library (in high load scenarios >50%).

[0036] This invention determines the optimal reporting time period by using Packet Data Receiver Rate (PDR) as the core, distinguishing data types and time periods to set standards, such as the product's 24-hour heartbeat, recording the time periods with a reporting success rate ≥90% during the day, and recording the shortest successful reporting time E and the longest successful reporting time F among the network search attempts. The aforementioned indicators are used as core parameters for "effect feedback" to determine the optimal reporting time period for each data type of the product in the current scenario each day, and to set a network policy adjustment timing algorithm to be triggered immediately when the network search time exceeds the shortest time E. When the network search time exceeds the longest time F, it indicates a sudden network deterioration in the current scenario, and the product should exit the network search state to reduce unnecessary power consumption, and trigger the product to start network search and reporting again after 0.5H or 1H, thereby effectively ensuring the daily reporting of data such as the product's heartbeat, and realizing effective monitoring of the product's network.

[0037] It should be noted that the embodiments of the present invention can adopt an update logic that combines historical data benchmarks with real-time deviation verification to ensure that parameters adapt to changes in the scenario. For example, based on the product's daily heartbeat and other reporting mechanisms, each time it is activated, the historical data of the previous day is used as the data reporting period for the current day. The RSSI / RSRP signal values ​​during the reporting process of the current day are compared with the signal strength data of the same period of the previous day in real time. When it is detected that the signal value of the current day is ≥3dBm lower than that of the previous day (3dBm is a floating threshold used to filter the impact of sudden factors such as instantaneous environmental fluctuations and slight product displacement), it is immediately determined that the network status of the current scenario has changed. After the scenario is determined to have changed, the product automatically starts a rescanning mechanism, that is, re-counts the heartbeat reporting success rate of each time period of the day, and re-records the shortest successful reporting time E1 and the longest successful reporting time F1 during the network search process of the day. New high-quality time periods are selected as the best reporting time periods after the update, and these recalibrated indicators are used as the core parameters for the effect feedback of the next day to complete the data iteration update.

[0038] The NB-IoT / CAT1 security product network intelligent selection method described in this invention focuses on scene determination, interference location, base station resource avoidance, and reporting strategy optimization. It effectively solves the problems of insufficient network pre-judgment capability and rigid network switching strategies of current NB-IoT or CAT1 products, resulting in poor scene adaptability and imbalance between transmission and power consumption.

[0039] Based on the above embodiments, as a supplement to the above... Figure 1 The present invention provides an embodiment of an NB-IoT / CAT1 security product network intelligent selection device, which implements the method shown. Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices, see reference. Figure 2 As shown, the NB-IoT / CAT1 security product network intelligent selection device includes: The signal acquisition module 100 is used to acquire signal strength data of the current scene after the product is powered on and searches for a network. The signal strength data is compared with a preset threshold in the scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product. The signal strength data includes RSSI and RSRP. The signal monitoring level corresponding to the product includes signal stability and weak signal coverage. When the signal monitoring level is signal stability, the interference identification module 399 is executed. When the signal monitoring level is weak signal coverage, the frequency sweeping module 200 is executed. The frequency sweep module 200 is used to activate the radio frequency receiver noise floor detector of the product, perform a bandwidth-by-band frequency sweep based on the range of the BAND frequency band supported by the product, determine the optimal operating frequency band, and execute the signal acquisition module 100 in the optimal operating frequency band. Interference identification module 300 is used to activate the radio frequency end receiving noise floor detector of the product to obtain the co-channel interference and environmental electromagnetic interference in the current scene, determine the interference level of the current scene based on the co-channel interference and environmental electromagnetic interference, the interference level includes high interference and low interference, when the interference level is high interference, prompt the product installation position to be changed, and when the interference level is low interference, execute the network finding adjustment module 400. The network search adjustment module 400 is used to periodically acquire historical network status information of the current scene area, determine the resource avoidance strategy and the best reporting time period for network search in the current scene based on the historical network status information, and execute the signal acquisition module 100, the frequency sweeping module 200 or the interference identification module 300 based on the resource avoidance strategy and the best reporting time period.

[0040] In this embodiment of the invention, the signal acquisition module 100 compares the signal strength data with a preset threshold in the scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product, including: When the current scenario is a closed environment, if RSRP ≥ -105dBm, the signal monitoring level of the product is stable; when the current scenario is a remote area, if RSSI ≤ -110dBm, the signal monitoring level of the product is weak signal coverage. When the signal monitoring level corresponding to the product is stable, the sampling frequency is adjusted to 1 minute; when the signal monitoring level corresponding to the product is weak coverage, the sampling frequency and sampling time are adjusted to 1 second.

[0041] In this embodiment of the invention, the frequency sweep module 200 performs a bandwidth-wise frequency sweep based on the range of BAND frequency bands supported by the product to determine the optimal operating frequency band, including: Set the maximum bandwidth R supported by the product's radio frequency terminal. Based on the BAND band supported by the product, scan from the lowest frequency T to the highest frequency Y using a frequency sweep method. When the Nth center frequency point of the frequency sweep is greater than or equal to Y, the frequency sweep ends. The Nth center frequency point of the frequency sweep Sn = T + (N-1)R + R / 2. Obtain and record the maximum power value and corresponding frequency point U within each bandwidth; Filter out the valid frequency points from all frequency points U based on the list of BAND bands that the current product is compatible with; Locate the target frequency Um with the lowest power value among the effective frequency points, use the target frequency Um as the optimal operating frequency band, and repeat step S1.

[0042] In this embodiment of the invention, the network finding adjustment module 400 periodically acquires historical network status information of the current scene's location, and determines the resource avoidance strategy for network finding in the current scene and the optimal reporting time period based on the historical network status information, including: The system information block broadcast by the base station of the network partition where the current scenario is located is periodically connected through the radio frequency terminal of the product to extract network resource usage data; By statistically analyzing the usage data of all network resources and the packet data reception rate within a predetermined time period, historical network status information is obtained. Based on the historical network status information, determine the resource avoidance strategy and the optimal reporting time for the network partition where the current scenario is located.

[0043] Each module in the aforementioned NB-IoT / CAT1 security product network intelligent selection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0044] The NB-IoT / CAT1 security product network intelligent selection device described in this embodiment can execute the NB-IoT / CAT1 security product network intelligent selection method provided in the above embodiments. The NB-IoT / CAT1 security product network intelligent selection device has the corresponding functional steps and beneficial effects of the NB-IoT / CAT1 security product network intelligent selection method described in the above embodiments. For details, please refer to the embodiments of the NB-IoT / CAT1 security product network intelligent selection method described above. The embodiments of this invention will not be repeated here.

[0045] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface, such as a network interface card, is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a congestion control method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0046] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0047] In one exemplary embodiment, a chip is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the above embodiments.

[0048] In one exemplary embodiment, a network interface card is provided, including a chip as described in any of the above embodiments and multiple interfaces, wherein the chip communicates externally through the interfaces.

[0049] In one embodiment, a computer device is also provided, including a processor, a chip in any of the above embodiments, or a network interface card in any of the above embodiments, wherein the chip or the network interface card is used to schedule packets to the processor or the chip or the network interface card itself for processing, and the processor is used to process the packets scheduled by the chip or the network interface card.

[0050] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0051] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0052] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0053] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0054] Similarly, it should be understood that, for the purpose of simplification and aiding understanding of one or more aspects of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention above. Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and it should be noted that the above embodiments are illustrative of the invention and not restrictive, and that alternative embodiments can be devised by those skilled in the art without departing from its scope.

Claims

1. A method for intelligent network selection of NB-IoT / CAT1 security products, characterized in that, Includes the following steps: S1. After the product is powered on and searches for a network, it obtains the signal strength data of the current scene. The signal strength data is compared with the preset threshold of the scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product. The signal strength data includes RSSI and RSRP. The signal monitoring level corresponding to the product includes signal stability and weak signal coverage. When the signal monitoring level is signal stability, step S3 is executed. When the signal monitoring level is weak signal coverage, step S2 is executed. S2. Activate the radio frequency receiver noise floor detector of the product, perform a bandwidth-by-band sweep based on the range of BAND frequency bands supported by the product, determine the optimal operating frequency band, and execute step S1 with the optimal operating frequency band. S3. Activate the radio frequency receiver noise floor detector of the product to obtain the co-channel interference and environmental electromagnetic interference in the current scene. Determine the interference level of the current scene based on the co-channel interference and environmental electromagnetic interference. The interference level includes high interference and low interference. When the interference level is high interference, prompt the product installation position to be changed. When the interference level is low interference, execute step S4. S4. Periodically acquire historical network status information of the current scene's location, determine the resource avoidance strategy and optimal reporting time period for network finding in the current scene based on the historical network status information, and execute steps S1-S3 based on the resource avoidance strategy and optimal reporting time period.

2. The NB-IoT / CAT1 security product network intelligent selection method according to claim 1, characterized in that, The step of comparing the signal strength data with a preset threshold in the scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product includes: When the current scenario is a closed environment, if RSRP ≥ -105dBm, the signal monitoring level of the product is stable; when the current scenario is a remote area, if RSSI ≤ -110dBm, the signal monitoring level of the product is weak signal coverage. When the signal monitoring level corresponding to the product is stable, the sampling frequency is adjusted to 1 minute; when the signal monitoring level corresponding to the product is weak coverage, the sampling frequency and sampling time are adjusted to 1 second.

3. The NB-IoT / CAT1 security product network intelligent selection method according to claim 1, characterized in that, The process of performing a bandwidth-wise frequency sweep based on the product-supported band frequency range to determine the optimal operating frequency band includes: Set the maximum bandwidth R supported by the product's radio frequency terminal. Based on the BAND band supported by the product, scan from the lowest frequency T to the highest frequency Y using a frequency sweep method. When the Nth center frequency point of the frequency sweep is greater than or equal to Y, the frequency sweep ends. The Nth center frequency point of the frequency sweep Sn = T + (N-1)R + R / 2. Obtain and record the maximum power value and corresponding frequency point U within each bandwidth; Filter out the valid frequency points from all frequency points U based on the list of BAND bands that the current product is compatible with; Locate the target frequency Um with the lowest power value among the effective frequency points, use the target frequency Um as the optimal operating frequency band, and repeat step S1.

4. The NB-IoT / CAT1 security product network intelligent selection method according to claim 1, characterized in that, The periodic acquisition of historical network status information for the current scene's location, and the determination of resource avoidance strategies and optimal reporting periods based on this historical network status information, include: The system information block broadcast by the base station of the network partition where the current scenario is located is periodically connected through the radio frequency terminal of the product to extract network resource usage data; By statistically analyzing the usage data of all network resources and the packet data reception rate within a predetermined time period, historical network status information is obtained. Based on the historical network status information, determine the resource avoidance strategy and the optimal reporting time for the network partition where the current scenario is located.

5. A network intelligent selection device for NB-IoT / CAT1 security products, characterized in that, The device includes: The signal acquisition module is used to acquire signal strength data of the current scene after the product is powered on and searches for a network. The signal strength data is compared with a preset threshold in the scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product. The signal strength data includes RSSI and RSRP. The signal monitoring level corresponding to the product includes signal stability and weak signal coverage. When the signal monitoring level is signal stability, the interference identification module is executed. When the signal monitoring level is weak signal coverage, the frequency sweeping module is executed. The frequency sweep module is used to activate the radio frequency receiver noise floor detector of the product, perform a bandwidth-by-band frequency sweep based on the range of the BAND frequency band supported by the product, determine the optimal operating frequency band, and execute the signal acquisition module in the optimal operating frequency band. The interference identification module is used to activate the radio frequency receiver noise floor detector of the product to obtain the co-channel interference and environmental electromagnetic interference in the current scene, determine the interference level of the current scene based on the co-channel interference and environmental electromagnetic interference, the interference level includes high interference and low interference, when the interference level is high interference, prompt the product installation position to be changed, and when the interference level is low interference, execute the network finding and adjustment module. The network search and adjustment module is used to periodically acquire historical network status information of the current scene area, determine the resource avoidance strategy and the best reporting time period for network search in the current scene based on the historical network status information, and execute the signal acquisition module, frequency sweeping module or interference identification module based on the resource avoidance strategy and the best reporting time period.

6. The NB-IoT / CAT1 security product network intelligent selection device according to claim 5, characterized in that, The signal acquisition module compares the signal strength data with a preset threshold in the scene determination database to determine the signal monitoring level and sampling frequency corresponding to the product, including: When the current scenario is a closed environment, if RSRP ≥ -105dBm, the signal monitoring level of the product is stable; when the current scenario is a remote area, if RSSI ≤ -110dBm, the signal monitoring level of the product is weak signal coverage. When the signal monitoring level corresponding to the product is stable, the sampling frequency is adjusted to 1 minute; when the signal monitoring level corresponding to the product is weak coverage, the sampling frequency and sampling time are adjusted to 1 second.

7. The NB-IoT / CAT1 security product network intelligent selection device according to claim 5, characterized in that, The frequency sweep module performs a bandwidth-by-band sweep based on the range of bands supported by the product to determine the optimal operating frequency band, including: Set the maximum bandwidth R supported by the product's radio frequency terminal. Based on the BAND band supported by the product, scan from the lowest frequency T to the highest frequency Y using a frequency sweep method. When the Nth center frequency point of the frequency sweep is greater than or equal to Y, the frequency sweep ends. The Nth center frequency point of the frequency sweep Sn = T + (N-1)R + R / 2. Obtain and record the maximum power value and corresponding frequency point U within each bandwidth; Filter out the valid frequency points from all frequency points U based on the list of BAND bands that the current product is compatible with; Locate the target frequency Um with the lowest power value among the effective frequency points, use the target frequency Um as the optimal operating frequency band, and repeat step S1.

8. The NB-IoT / CAT1 security product network intelligent selection device according to claim 7, characterized in that, The network search adjustment module periodically acquires historical network status information of the current scene's area, and determines the resource avoidance strategy and optimal reporting time period for network search based on the historical network status information, including: The system information block broadcast by the base station of the network partition where the current scenario is located is periodically connected through the radio frequency terminal of the product to extract network resource usage data; By statistically analyzing the usage data of all network resources and the packet data reception rate within a predetermined time period, historical network status information is obtained. Based on the historical network status information, determine the resource avoidance strategy and the optimal reporting time for the network partition where the current scenario is located.

9. A computer device comprising a processor and a memory, characterized in that, The memory stores computer program instructions that can be executed by the processor, and when the processor executes the computer program instructions, it implements the instructions of the method as described in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that are loaded and executed by a processor to perform the operations described in any one of claims 1-4.

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