False alarm rate processing method and device, equipment, storage medium and computer program product

Abstract

The invention discloses a false alarm rate processing method and device, equipment, a storage medium and a computer program product. The false alarm rate processing method comprises the following steps: constructing a target sensing reference curve; determining a test false alarm rate of the target area based on the target perception reference curve; based on a target false alarm rate, evaluating the determined test false alarm rate to obtain an evaluation result; the target false alarm rate is related to a sensing service in the target area.

Description

Technical Field

[0001] This application relates to the field of target detection technology, and in particular to a method, apparatus, device, storage medium, and computer program product for processing false alarm rates. Background Technology

[0002] With the continuous development of the low-altitude economy, the demand for wide-area exploration and low-altitude flight path perception by low-altitude drones is increasing, and these business scenarios require a higher false alarm rate from the sensor-based commercial network system. The false alarm rate typically refers to the probability that, within a certain period of time, the system incorrectly identifies the presence of a target when no actual target is present.

[0003] However, in real-world environments, the inability to create clear airspace makes it impossible to accurately acquire and assess situations where the sensory commercial network system detects false targets, thus hindering timely and accurate assessment of the false alarm rate. Summary of the Invention

[0004] To address the technical problems existing in related technologies, embodiments of this application provide a false alarm rate processing method, apparatus, device, storage medium, and computer program product.

[0005] To achieve the above objectives, the technical solution of this application embodiment is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a method for processing false alarm rates, the method comprising:

[0007] Construct a target perception reference curve;

[0008] Based on the target perception reference curve, the false alarm rate of the target area is determined.

[0009] Based on the target false alarm rate, the determined test false alarm rate is evaluated to obtain the evaluation result; the target false alarm rate is related to the sensing services in the target area.

[0010] Secondly, embodiments of this application also provide a false alarm rate processing device, the device comprising:

[0011] Construction unit, used to construct target perception reference curve;

[0012] The determining unit is used to determine the false alarm rate of the target area based on the target perception reference curve.

[0013] An evaluation unit is used to evaluate the determined test false alarm rate based on the target false alarm rate and obtain an evaluation result; the target false alarm rate is related to the sensing services in the target area.

[0014] Thirdly, embodiments of this application also provide a false alarm rate processing device, including: a processor and a memory for storing a computer program capable of running on the processor;

[0015] When the processor runs the computer program, it executes the steps of the false alarm rate processing method described in the embodiments of this application.

[0016] Fourthly, embodiments of this application also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the false alarm rate processing method described in embodiments of this application.

[0017] Fifthly, embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the false alarm rate processing method described in embodiments of this application.

[0018] The false alarm rate processing method, apparatus, device, storage medium, and computer program product provided in this application embodiment construct a target perception reference curve; determine the test false alarm rate of a target area based on the target perception reference curve; evaluate the determined test false alarm rate based on the target false alarm rate to obtain an evaluation result; the target false alarm rate is related to the perception services within the target area. By adopting the technical solution of this application embodiment, the false alarm rate of a target area in the current environment is tested using the constructed target perception reference curve, thus obtaining the test false alarm rate of the target area, and the test false alarm rate is evaluated based on the target false alarm rate. The target perception reference curve is obtained by constructing clearance conditions in a laboratory environment and establishing the relationship between the false alarm rate and noise, thereby realizing the global control function of the false alarm rate of the target area. This provides an effective means for false alarm rate performance testing for commercial sensing network systems, enabling timely and accurate evaluation of the false alarm rate. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the false target identification situation that occurred during field testing of related technologies;

[0020] Figure 2 This is a flowchart illustrating the false alarm rate processing method according to an embodiment of this application. Figure 1 ;

[0021] Figure 3 This is a flowchart illustrating the false alarm rate processing method according to an embodiment of this application. Figure 2 ;

[0022] Figure 4 This is a schematic diagram of the construction perception reference curve in an embodiment of this application;

[0023] Figure 5 This is a schematic diagram of the inter-network information interaction process according to an embodiment of this application;

[0024] Figure 6 This is a schematic diagram of the joint correction sensing reference curve in an embodiment of this application;

[0025] Figure 7 This is a schematic diagram illustrating the process of iterative updating of the environment database according to an embodiment of this application;

[0026] Figure 8 This is a schematic diagram of the change-aware reference curve in an embodiment of this application;

[0027] Figure 9 This is a flowchart illustrating the optimization of false alarm rate in an embodiment of this application.

[0028] Figure 10 This is a schematic diagram of the composition structure of the false alarm rate processing device according to an embodiment of this application;

[0029] Figure 11 This is a schematic diagram of the hardware composition of the false alarm rate processing device according to an embodiment of this application. Detailed Implementation

[0030] The present application will now be described in further detail with reference to the accompanying drawings and embodiments.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0032] Currently, with the continuous development of the low-altitude economy, the demand for wide-area exploration and low-altitude flight path perception by low-altitude drones is increasing. Their business scenarios require a high false alarm rate from the sensing commercial network system. However, actual testing has revealed that the industry still lacks the means to assess the false alarm rate.

[0033] Generally speaking, the false alarm rate refers to the probability that a commercially available sensory network system incorrectly identifies the presence of a target when there is no actual target within a certain period of time. However, in real-world environments, the inability to create clear airspace makes it impossible to accurately acquire and assess situations where the system perceives false targets, thus hindering the timely and accurate assessment and reduction of the false alarm rate.

[0034] In related technologies, the false alarm rate is obtained as accurately as possible by manually selecting periods with less public activity (such as the early morning hours) to approximate airspace conditions. However, this method has the following problems: Firstly, it cannot avoid the influence of high-clutter environments such as dense buildings and highways (e.g., Figure 1 The diagram shown illustrates the false target recognition situation in the field test of the related technology. Figure 1As shown by the circles in the image, environmental clutter in this area is misjudged as false targets, resulting in a large area of ​​false alarm trajectories. Furthermore, this method's detection time is not comprehensive enough and it cannot promptly address the impact of increased noise floor in real-world communication scenarios (such as high daytime pedestrian traffic). Therefore, the current network urgently needs an effective means for assessing the false alarm rate to solve the technical problem of not being able to assess the false alarm rate in a timely and accurate manner.

[0035] Based on this, this application proposes a false alarm rate processing method. In various embodiments of this application, the false alarm rate of the target area in the current environment is tested by constructing a target perception reference curve, that is, the test false alarm rate of the target area is obtained, and the test false alarm rate is evaluated based on the target false alarm rate. The target perception reference curve is obtained by constructing a clearance condition in a laboratory environment and establishing the relationship between the false alarm rate and noise, thereby realizing the global control function of the false alarm rate of the target area. This provides an effective means for testing the performance of the false alarm rate of the communication and sensing commercial network system, so as to be able to evaluate the false alarm rate in a timely and accurate manner.

[0036] This application provides a method for processing false alarm rates, which is applied to a false alarm rate processing device. Figure 2 This is a flowchart illustrating the false alarm rate processing method according to an embodiment of this application. Figure 1 ;like Figure 2 As shown, the false alarm rate processing method includes:

[0037] Step 201: Construct a target perception reference curve.

[0038] In this embodiment, the false alarm rate processing device can be a newly added false alarm control unit in the sensing commercial network system. That is, the false alarm rate processing method of this application is executed by the false alarm control unit. The false alarm control unit can be located in a first network device, a second network device, or other network element devices. The first network device can be a base station device, such as a next-generation base station (gNB) in a 5G system or a New Radio (NR) system. The second network device can be a sensing function (SF) device, i.e., a network element device for core network sensing control and sensing measurement data processing.

[0039] For example, when the false alarm control unit is set in other network element devices, it can be set between the first network device and the second network device, such as between the gNB and the SF device. In this way, the false alarm control unit can interact with the gNB and the SF device respectively.

[0040] In practical applications, the false alarm rate processing device can obtain the target perception reference curve by correcting the initial perception reference curve, i.e., the source perception reference curve.

[0041] Based on this, in one embodiment, constructing the target sensing reference curve includes: acquiring a source sensing reference curve; and correcting the source sensing reference curve based on data from different clutter environments to obtain the target sensing reference curve.

[0042] In practical applications, in one embodiment, obtaining the source-aware reference curve includes:

[0043] Obtain the performance relationship curve between the Reference Signal Receiving Power (RSRP) and the false alarm rate at the current noise level;

[0044] The performance relationship curves between RSRP and false alarm rate under all noise levels are summarized to obtain the source sensing reference curve; the source sensing reference curve represents the performance relationship curve between RSRP and false alarm rate under different noise levels.

[0045] Here, the target perception reference curve is used to characterize the result of correcting the performance relationship curve between RSRP and false alarm rate under different noise levels. The source perception reference curve can be constructed in a laboratory environment, and the construction method includes, but is not limited to, the following process: first, establish clearance conditions in the laboratory environment; then, under clearance conditions, obtain the performance relationship curves between RSRP and false alarm rate under different noise levels; and finally, summarize the performance relationship curves between RSRP and false alarm rate for all noise levels to obtain the source perception reference curve.

[0046] It should be noted that the performance relationship curve between RSRP and false alarm rate at the current noise level can be understood as the performance relationship curve between RSRP and false alarm rate under a single environment, i.e., the performance curve of RSRP and false alarm rate under a single environment. In one embodiment, obtaining the performance relationship curve between RSRP and false alarm rate at the current noise level includes: determining the RSRP corresponding to the increased transmit power and determining the current false alarm rate when there is no target in the test environment; continuously increasing the number of targets in the test environment and repeating the above steps until the set number of simulations is reached, thereby obtaining the performance relationship curve between RSRP and false alarm rate at the current noise level.

[0047] Here, the false alarm rate processing device first ensures that there are no targets in the test environment (i.e., the aforementioned laboratory environment), for example, no moving targets. Then, it activates the base station's sensing function and continuously increases the received echo energy, such as increasing the transmit power, to obtain the RSRP corresponding to the increased transmit power. Additionally, it obtains the current number of false samples and the number of real samples, and determines the current false alarm rate based on the ratio of the current number of false samples to the number of real samples. Next, it continuously increases the number of targets in the test environment, repeating the above steps until the set number of simulations is reached, to obtain the performance relationship curve between RSRP and false alarm rate at the current noise level. It should be noted that the number of simulations can be preset according to actual conditions, for example, it can be set to 1000 times; this embodiment does not limit this.

[0048] Here, the performance relationship curves between RSRP and false alarm rate at different noise levels can be understood as the performance relationship curves between RSRP and false alarm rate under complex environments, i.e., the performance curves of RSRP and false alarm rate under complex environments. In one embodiment, the step of summarizing the performance relationship curves between RSRP and false alarm rate under all noise levels to obtain the source sensing reference curve includes: obtaining the performance relationship curves between RSRP and false alarm rate under all noise levels while increasing the background noise of the test environment; summarizing the performance relationship curves between RSRP and false alarm rate under all noise levels to obtain the source sensing reference curve.

[0049] Here, after obtaining the performance relationship curve between RSRP and false alarm rate at the current noise level, the false alarm rate processing device increases the background noise of the test environment and repeats the above test content. That is, it tests and obtains the false alarm rate corresponding to different RSRP at all noise levels, and summarizes the false alarm rates corresponding to different RSRP at all noise levels to obtain the performance relationship curve between RSRP and false alarm rate at different noise levels, that is, to obtain the source sensing reference curve.

[0050] In practical applications, after obtaining the source sensing reference curve based on the above method, the false alarm rate processing device can use data from different clutter environments, such as the noise floor rise value of different clutter environments, to jointly correct the source sensing reference curve and obtain the target sensing reference curve. The noise floor rise value of different clutter environments can be understood as the noise floor rise value introduced by actual clutter in different environments (also called the noise floor rise amount), and the noise floor rise value can be obtained based on the environmental database of the target area.

[0051] Step 202: Determine the false alarm rate of the target area based on the target perception reference curve.

[0052] In this embodiment, the test false alarm rate of the target area can also be called the measurement false alarm rate of the target area. It can generally be understood as the probability of false detection of targets within the target area under the influence of environmental noise level, background noise level, and the received noise level of the sensing base station within a certain period of time. The test false alarm rate ranges from 0 to 100%.

[0053] In practical applications, the false alarm rate processing device obtains the test false alarm rate of the current commercial sensing network system based on the jointly corrected target perception reference curve, and determines whether the current commercial sensing network meets the requirements.

[0054] Based on this, in one embodiment, determining the test false alarm rate of the target area based on the target perception reference curve includes: acquiring first information reported by the first network device; matching the target perception reference curve based on the first information, and determining the false alarm rate corresponding to the first information as the test false alarm rate of the target area.

[0055] In practical applications, in one embodiment, obtaining the first information reported by the first network device includes: sending a sensing data reporting threshold to the first network device; and receiving the first information periodically reported by the first network device based on the sensing data reporting threshold.

[0056] Here, the first network device can be a base station device, such as a gNB in ​​a 5G or NR system. The first information may include, but is not limited to, the noise floor level and received echo energy level of the target area currently sensed by the base station. In practical applications, the false alarm rate processing device, i.e., the false alarm control unit, configures the sensing data reporting threshold (i.e., the sensing data reporting threshold of the base station) and sends the configured sensing data reporting threshold to the base station. Then, based on the sensing data reporting threshold, the base station periodically reports information such as the noise floor level and received echo energy level of the current target area to the false alarm control unit. That is, the false alarm control unit can obtain the first information reported by the base station at this time.

[0057] In practical applications, after obtaining the first information, the false alarm rate processing device matches the current base station perception reference curve, that is, matches the target perception reference curve (the target perception reference curve includes multiple perception reference curves). Based on the first information, it selects a perception reference curve from the target perception reference curves and determines the false alarm rate corresponding to the selected perception reference curve as the test false alarm rate of the target area, that is, the false alarm rate corresponding to the first information.

[0058] Step 203: Based on the target false alarm rate, evaluate the determined test false alarm rate to obtain the evaluation result.

[0059] In practical applications, before evaluating the test false alarm rate, the false alarm rate processing device needs to request the target false alarm rate from a second network device. This second network device can be an SF device, i.e., a network element device for core network perception control and perception measurement data processing.

[0060] Based on this, in one embodiment, before evaluating the determined test false alarm rate based on the target false alarm rate to obtain the evaluation result, the method further includes: acquiring the target false alarm rate.

[0061] In this embodiment, the target false alarm rate is related to the sensing services within the target area; that is, the target false alarm rate will vary depending on the sensing services within the target area. The target false alarm rate can be understood as the expected false alarm rate requirement under different service scenarios.

[0062] In practical applications, in one embodiment, obtaining the target false alarm rate includes: sending a first request to a second network device; the first request is used to request the acquisition of the target false alarm rate; and obtaining the target false alarm rate issued by the second network device based on the sensing services within the target area in response to the first request.

[0063] Here, the second network device, such as the SF device, pre-configures corresponding target false alarm rates based on different service scenarios. When obtaining the target false alarm rate corresponding to the sensing service of the current commercial sensing network, the false alarm rate processing device, i.e., the false alarm control unit, first sends a first request to the SF device. This first request is used to request the acquisition of the target false alarm rate corresponding to the current sensing service. The first request carries the type of the current sensing service. After receiving the first request, the SF device responds to the first request by selecting the corresponding target false alarm rate based on the type of sensing service in the target area and sends the selected target false alarm rate to the false alarm control unit.

[0064] In one embodiment, the step of evaluating the determined test false alarm rate based on the target false alarm rate to obtain an evaluation result includes: comparing the test false alarm rate with the target false alarm rate to obtain a comparison result; evaluating the determined test false alarm rate based on the comparison result to obtain an evaluation result; the evaluation result characterizes whether the test false alarm rate meets the requirement of the target false alarm rate.

[0065] Here, the target false alarm rate corresponding to the current sensing service is used as the benchmark to determine whether the monitored test false alarm rate meets the target false alarm rate requirement. That is, the test false alarm rate is compared with the target false alarm rate. When the test false alarm rate is less than or equal to the target false alarm rate, it indicates that the test false alarm rate meets the target false alarm rate requirement. At this time, there is no need to adjust the test false alarm rate. Instead, the test false alarm rate of the sensing network in the target area is continuously monitored. When the monitored test false alarm rate is greater than the target false alarm rate, it indicates that the test false alarm rate does not meet the target false alarm rate requirement. At this time, the test false alarm rate needs to be optimized.

[0066] In related technologies, there are currently no effective means to reduce the false alarm rate when the detected false alarm rate is greater than the target false alarm rate; that is, there is a lack of means to optimize the false alarm rate in related technologies. Therefore, this application proposes a method to reduce the perceived false alarm rate, i.e., to reduce the test false alarm rate.

[0067] Based on this, in one embodiment, the method further includes: optimizing the test false alarm rate when the evaluation result indicates that the test false alarm rate does not meet the target false alarm rate requirement.

[0068] In practical applications, in one embodiment, optimizing the test false alarm rate includes: determining whether the test false alarm rate is affected by environmental clutter; updating the current target perception reference curve if the determination result indicates that the test false alarm rate is affected by environmental clutter; and redetermining the test false alarm rate of the target area based on the updated target perception reference curve, until the redetermined test false alarm rate meets the requirement of the target false alarm rate.

[0069] Here, when the test false alarm rate does not meet the target false alarm rate requirement (i.e., the monitored test false alarm rate is greater than the target false alarm rate), it is first necessary to rule out the possibility that the error is caused by clutter due to changes in the current environment. Therefore, the false alarm rate processing device, i.e., the false alarm control unit, needs to determine whether the test false alarm rate is affected by environmental clutter. When the determination result is that the test false alarm rate is affected by environmental clutter, the false alarm control unit triggers a request to the second network device, such as the SF device, to obtain the current environmental clutter level. Based on the current environmental clutter level fed back by the SF device, the current target perception reference curve is updated to obtain the updated target perception reference curve. Then, the test false alarm rate of the target area is re-obtained using the updated target perception reference curve, and the re-obtained test false alarm rate is compared with the target false alarm rate. If the re-obtained test false alarm rate is greater than the target false alarm rate, the test false alarm rate of the target area is re-obtained. If the re-obtained test false alarm rate is less than or equal to the target false alarm rate, it indicates that the re-obtained test false alarm rate meets the target false alarm rate requirement, and the monitoring of the test false alarm rate of the target area is stopped.

[0070] Here, if the judgment result indicates that the test false alarm rate is affected by environmental clutter, the false alarm control unit can also reconfigure the sensing data reporting threshold (i.e., the sensing data reporting threshold of the base station). Specifically, when the judgment result indicates that the test false alarm rate is affected by environmental clutter, the false alarm control unit triggers a request to the second network device, such as the SF device, to obtain the current environmental clutter level. Based on the current environmental clutter level fed back by the SF device, it selects the noise floor rise value with reference to the clutter database and reconfigures the sensing data reporting threshold.

[0071] In practical applications, when the judgment result indicates that the false alarm rate is affected by environmental clutter, the false alarm rate processing device can also update the environmental database while updating the current target perception reference curve.

[0072] Based on this, in one embodiment, the method further includes: updating the environmental database when the judgment result indicates that the false alarm rate of the test is affected by environmental clutter.

[0073] In practical applications, in one embodiment, updating the environment database includes: sending a second request to a second network device; the second request is used to request the acquisition of the current environmental clutter level; acquiring the current environmental clutter level sent by the second network device in response to the second request; and updating the environment database based on the current environmental clutter level.

[0074] Here, the second network device can be an SF device, namely, a network element device for core network perception control and perception measurement data processing. Sending the second request to the second network device includes: periodically or based on event triggering.

[0075] It should be noted that sending a second request to the second network device periodically could be done, for example, by sending a second request to the second network device every night. Sending a second request based on an event could also be triggered by detecting changes in the environment, such as people or vehicles.

[0076] In practical applications, the false alarm rate processing device periodically or based on event triggering sends a second request to a second network device, such as an SF device, to request the current environmental clutter level, that is, to request the quantitative index of the clutter level in the target area, such as the detection noise floor rise range value; after receiving the second request, the SF device responds to the second request by sending the current environmental clutter level to the false alarm rate processing device; the false alarm rate processing device combines the response result (i.e., the current environmental clutter level) to correct the current environmental database, that is, to update the current environmental database.

[0077] The technical solution of this application embodiment uses a constructed target perception reference curve to test the false alarm rate of the target area in the current environment, thereby obtaining the test false alarm rate of the target area. The test false alarm rate is then evaluated based on the target false alarm rate. The target perception reference curve is obtained by constructing clearance conditions in a laboratory environment and establishing the relationship between the false alarm rate and noise. This enables global control of the false alarm rate of the target area, providing an effective means for testing the performance of the false alarm rate in a sensory commercial network system, thereby enabling timely and accurate evaluation of the false alarm rate.

[0078] This application also provides another method for processing false alarm rates, which is applied to a false alarm rate processing device. Figure 3 This is a flowchart illustrating the false alarm rate processing method according to an embodiment of this application. Figure 2 ;like Figure 3 As shown, the false alarm rate processing method includes:

[0079] Step 301: Obtain the source sensing reference curve.

[0080] In one embodiment, obtaining the source sensing reference curve includes: obtaining the performance relationship curve between RSRP and false alarm rate at the current noise level; summarizing the performance relationship curves between RSRP and false alarm rate at all noise levels to obtain the source sensing reference curve; the source sensing reference curve represents the performance relationship curve between RSRP and false alarm rate at different noise levels.

[0081] Step 302: Based on data from different clutter environments, the source sensing reference curve is corrected to construct the target sensing reference curve.

[0082] Step 303: Based on the target perception reference curve, determine the false alarm rate of the target area.

[0083] In one embodiment, determining the false alarm rate of a target area based on the target perception reference curve includes: acquiring first information reported by a first network device; matching the target perception reference curve based on the first information, and determining the false alarm rate corresponding to the first information as the false alarm rate of the target area.

[0084] Here, the first network device can be a base station device, such as a gNB in ​​a 5G system or NR system.

[0085] Step 304: Obtain the target false alarm rate, compare the test false alarm rate with the target false alarm rate, and obtain the comparison result.

[0086] In this embodiment of the application, the target false alarm rate is related to the sensing services within the target area.

[0087] In one embodiment, obtaining the target false alarm rate includes: sending a first request to a second network device; the first request is used to request obtaining the target false alarm rate; obtaining the target false alarm rate issued by the second network device based on the sensing services within the target area in response to the first request.

[0088] Here, the second network device can be an SF device, namely a core network sensing control and sensing measurement data processing network element device.

[0089] Step 305: Based on the comparison results, evaluate the determined false alarm rate of the test to obtain the evaluation results.

[0090] In this embodiment of the application, the evaluation result characterizes whether the test false alarm rate meets the target false alarm rate requirement.

[0091] Step 306: If the evaluation result indicates that the test false alarm rate does not meet the target false alarm rate requirement, optimize the test false alarm rate.

[0092] In one embodiment, optimizing the test false alarm rate includes: determining whether the test false alarm rate is affected by environmental clutter; updating the current target perception reference curve if the determination result indicates that the test false alarm rate is affected by environmental clutter; and redetermining the test false alarm rate of the target area based on the updated target perception reference curve until the redetermined test false alarm rate meets the requirement of the target false alarm rate.

[0093] In one embodiment, the method further includes updating the environmental database when the determination result indicates that the false alarm rate of the test is affected by environmental clutter.

[0094] In practical applications, in one embodiment, updating the environment database includes: sending a second request to a second network device; the second request is used to request the acquisition of the current environmental clutter level; acquiring the current environmental clutter level sent by the second network device in response to the second request; and updating the environment database based on the current environmental clutter level.

[0095] It should be noted that the specific processing procedure for the false alarm rate processing device to process the false alarm rate has been detailed above and will not be repeated here.

[0096] The technical solution of this application embodiment uses a constructed target perception reference curve to test the false alarm rate of a target area in the current environment, thus obtaining the test false alarm rate of the target area. The test false alarm rate is then evaluated based on the target false alarm rate. The target perception reference curve is obtained by constructing clearance conditions in a laboratory environment and establishing the relationship between the false alarm rate and noise. This achieves a global control function for the false alarm rate of the target area, providing an effective means for performance testing of the false alarm rate in commercial sensing network systems, thereby enabling timely and accurate evaluation of the false alarm rate. Furthermore, this application embodiment also considers actual environmental conditions to reduce the test false alarm rate, thus providing an effective means for optimizing the test false alarm rate in commercial sensing network systems, thereby improving the effectiveness of optimizing the test false alarm rate.

[0097] The present application will be described below with reference to application examples.

[0098] To address the problem of the inability to timely and accurately determine and reduce the false alarm rate in related technologies, this application proposes a false alarm rate processing method. This method adds a false alarm control unit (corresponding to the aforementioned false alarm rate processing device) between the base station (corresponding to the first network device mentioned above) and the SF device (corresponding to the second network device mentioned above). The false alarm control unit establishes the relationship between the sensed echo signal, the false alarm rate, and noise, and realizes the function of global control of the false alarm rate of the sensing commercial network system. Specifically, it involves both information interaction process and false alarm rate optimization scheme based on the actual environment, thereby providing an effective means for the false alarm rate performance detection and optimization of the sensing commercial network.

[0099] The false alarm control unit proposed in this application is used to implement functions such as updating and iterating the environmental clutter database (corresponding to the aforementioned environmental database), judging the false alarm rate of the current environment, and reducing the false alarm rate. This false alarm control unit can be installed in a base station (e.g., gNB), SF equipment, or other network element equipment, and its specific functions are as follows:

[0100] 1. Information exchange

[0101] Interaction 1 - Maintaining the Environmental Database: The false alarm control unit periodically (e.g., every night) or when triggered by events (e.g., changes in the environment such as people and vehicles) requests the SF device to obtain the quantitative index of the clutter level in the area (corresponding to the current environmental clutter level mentioned above, such as the detection noise floor rise range value). Based on the response result (i.e., the quantitative index of the clutter level in the area), the current environmental database is corrected, that is, the current environmental database is updated.

[0102] Interaction 2 - False Alarm Assessment and Optimization: The false alarm control unit configures the sensing data reporting threshold and periodically obtains information such as the current noise floor level and received echo energy level transmitted by the gNB (corresponding to the first information mentioned above); the false alarm control unit requests the target false alarm rate based on the sensing service from the SF device; the false alarm control unit determines whether the test false alarm rate of the current sensing commercial network system meets the requirements based on the predefined sensing reference curve (corresponding to the source sensing reference curve mentioned above).

[0103] Here, the predefined sensing reference curve can be constructed through a laboratory environment. The construction methods include, but are not limited to, establishing clearance conditions in the laboratory environment and obtaining the relationship curves of various factors related to the false alarm rate under those conditions. The specific implementation steps are as follows:

[0104] Step 1: Ensure there are no targets in the test environment, turn on the base station's sensing function, and continuously increase the received echo energy level (e.g., increase the transmit power to check the corresponding RSRP), obtain the current number of false samples and the number of real samples, and calculate the current false alarm rate.

[0105] Step 2: Continuously increase the number of targets in the test environment and repeat the above steps until the set number of simulations is reached (e.g., 1000 simulations), and obtain the performance relationship curve between RSRP and false alarm rate at the current noise level.

[0106] Step 3: Increase the background noise of the test environment, repeat the above test content, summarize the false alarm rate corresponding to different RSRP under all noise levels, and obtain the performance relationship curve between RSRP and false alarm rate under different noise levels (corresponding to the aforementioned source sensing reference curve).

[0107] Figure 4 This is a schematic diagram of the construction perception reference curve in an embodiment of this application, as shown below. Figure 4 As shown, the performance relationship curves between RSRP and false alarm rate under three different noise levels are presented. N0 to N2 represent different background noise levels (i.e., different noise levels) in the target area, and P represents the false alarm rate. RSRP under different noise levels corresponds to different false alarm rates.

[0108] Example 1: Inter-network information exchange process

[0109] The following example illustrates the inter-network information exchange process, using the scenario where the false alarm control unit is located in a network element other than the gNB and SF devices, and this other network element is located between the gNB and SF devices. Figure 5 This is a schematic diagram of the inter-network information interaction process according to an embodiment of this application, such as... Figure 5 As shown, the inter-network information exchange process includes the following steps:

[0110] Step 1: The false alarm control unit has a built-in perception reference curve and periodically (e.g., every early morning) or triggered by an event (due to an abnormal false alarm rate) sends a database update request (corresponding to the second request mentioned above) to the SF device to obtain the current environmental clutter level in the area.

[0111] Step two: The SF device responds to the database update request and reports the current environmental clutter level to the false alarm control unit;

[0112] Step 3: The false alarm control unit updates the environmental database based on the current environmental clutter level, determines the sensing reference curve corresponding to the clutter based on the current environmental database, and notifies the gNB to change the reporting threshold, etc. to control the false alarm rate.

[0113] Step 4: The gNB periodically reports information such as the current gNB noise floor level and received echo energy level to the false alarm control unit (corresponding to the first information mentioned above);

[0114] Step 5: The false alarm control unit requests the SF device to issue the target false alarm rate based on the sensing service.

[0115] Step 6: The SF device sends the target false alarm rate configuration back to the false alarm control unit;

[0116] Step 7: The false alarm control unit matches the current sensing reference curve to obtain the test false alarm rate;

[0117] Step 8: The false alarm control unit optimizes the false alarm rate (i.e., optimizes the test false alarm rate) based on the current noise floor level and environmental database.

[0118] 2. Optimize the false alarm rate based on the actual environment.

[0119] As described above, the false alarm control unit has a predefined perception reference curve, and can continuously iterate the environmental database of the region based on the first interaction in the above information exchange. By using the noise floor rise value of different clutter environments (corresponding to the data of the different clutter environments mentioned above), the predefined perception reference curve (corresponding to the source perception reference curve mentioned above) is jointly corrected to obtain the corrected perception reference curve (corresponding to the target perception reference curve mentioned above).

[0120] Figure 6 This is a schematic diagram of the joint correction sensing reference curve in an embodiment of this application, as shown below. Figure 6 As shown, the left figure represents the sensing reference curve before correction, and the right figure represents the sensing reference curve after correction. It can be seen that the corrected sensing reference curve is obtained by setting corresponding sensing reference curves based on the current environmental clutter disturbance (I0, I1), thus obtaining six performance relationship curves between RSRP and false alarm rate under different noise levels. Here, N0 to N2 represent different noise levels in the target area, P represents the false alarm rate, and different RSRP levels correspond to different false alarm rates.

[0121] The following is an overview of the false alarm rate assessment and optimization process, using the example of a false alarm control unit located between the gNB and the SF equipment:

[0122] First, the false alarm control unit configures the reporting threshold of the sensing base station and notifies the gNB. The gNB periodically reports information such as the current gNB noise floor level and received echo energy level to the false alarm control unit. Second, the false alarm control unit requests and sets the target false alarm rate issued by the SF device as the judgment benchmark and continuously monitors the test false alarm rate in the region. Based on the aforementioned jointly corrected sensing reference curve (corresponding to the aforementioned target sensing reference curve), the current test false alarm rate is obtained. Finally, it is determined whether the current sensing network meets the target false alarm rate requirement: if the test false alarm rate meets the target false alarm rate requirement, no adjustment is needed, and the test false alarm rate of the sensing network in the region is continuously monitored until the test false alarm rate is higher than the target false alarm rate. When the test false alarm rate is higher than the target false alarm rate, it is necessary to first rule out the error caused by clutter due to changes in the current environment (corresponding to the aforementioned judgment on whether the test false alarm rate is affected by environmental clutter). At this time, it can trigger a request to the SF device for the current environmental clutter level, update the current sensing reference curve based on feedback, select the noise floor rise value with reference to the clutter database, and reconfigure the base station reporting threshold, etc. The modification of the base station reporting threshold includes, but is not limited to: raising the threshold when the power of components in the entire sensing power spectrum exceeds a certain threshold of background interference noise. Based on the updated sensing reference curve, the false alarm rate of the region is re-acquired and compared with the target false alarm rate until the target false alarm rate requirement is met.

[0123] Example 2: Iterative Update of Environment Database

[0124] Suppose that the false alarm rate reported by the rain and snow system in a certain area is abnormally high at night. The false alarm control unit requests the SF equipment to provide feedback on the current environmental clutter level and determines that the current environmental clutter disturbance (I0~I3) is insufficient to trigger a large-scale false alarm.

[0125] By increasing the number of cumulative reported samples and changing (i.e., extending) the cumulative time of the base station's sensing and receiving window, the possibility of unknown environmental clutter causing a rise in noise floor was ruled out. If the false alarm rate remained unchanged before and after testing, it was determined that environmental clutter was causing the judgment error. Therefore, it was necessary to change the element relationship reference curve, reconfigure the base station reporting threshold for the region, and update the noise floor rise I4 (also known as clutter disturbance) to the environmental database, as shown in the environmental database diagram below:

[0126] Environmental level Clutter disturbance (dB) Dense urban areas I0 water surface I1 Motor vehicle flow I2 flock of birds I3 Hail and rain / snow weather impact I4

[0127] The following example illustrates the process of iteratively updating the environmental database in this application, using the example of a false alarm control unit being set between the gNB and the SF device. Figure 7This is a schematic diagram of the iterative update process of the environment database according to an embodiment of this application, as shown below. Figure 7 As shown, the process for iteratively updating this environment database includes the following steps:

[0128] Step 1: The false alarm control unit triggers a database update request (triggered by an abnormal false alarm rate) to request the SF device to obtain the current environmental clutter level in the area;

[0129] Step two: The SF device responds to the database update request and reports the current environmental clutter level to the false alarm control unit;

[0130] Step 3: The false alarm control unit checks the current environment database and finds that there is no clutter disturbance data corresponding to the current clutter level. It then configures the base station to change the number of reported samples or extend the base station's sensing and receiving time window, and notifies the gNB of this configuration information.

[0131] Step 4: The gNB periodically reports information such as the current gNB noise floor level and received echo energy level to the false alarm control unit (corresponding to the first information mentioned above);

[0132] Step 5: The false alarm control unit judges the changes in background noise and the additional impact of environmental clutter. When it is determined that environmental clutter causes judgment error, the current gNB sensing reference curve is changed and the clutter disturbance amount is updated to the current environmental database.

[0133] Step 6: The false alarm control unit notifies the gNB of the current noise floor level, the change in the base station reporting threshold, and other information.

[0134] Example 3: Application of Reducing False Alarm Rate

[0135] Figure 8 This is a schematic diagram of the change perception reference curve in an embodiment of this application. Assume that the detected false alarm rate corresponding to the business demand in a certain region is 5%, and the target false alarm rate configured for the SF equipment is 5%. The false alarm control unit selects a reference curve (N1, I0) from the perception reference curve based on the background noise level (N1) and environmental factors (I0) of the region. Figure 8 (Left figure), the base station reporting threshold is configured to be 13dB. When the base station reports RSRP and the current test false alarm rate is obtained as 8%, it can be seen that the test false alarm rate is higher than the target false alarm rate. Therefore, the false alarm control unit triggers a request to the SF device to obtain the current environmental clutter level. Based on this request, the SF device feeds back to the false alarm control unit that the current environmental clutter level is water surface influence (I1), and reselects the current sensing reference curve (N1, I1) (corresponding to...). Figure 8(See the right figure). Reconfigure the base station reporting threshold to 15dB. At this time, the base station's reported RSRP can be monitored again, and the current false alarm rate is 5%, which meets the target false alarm rate requirement. Stop monitoring the false alarm rate.

[0136] The following example illustrates the false alarm rate optimization process of this application, using the false alarm control unit set between the gNB and the SF device as an example. Figure 9 This is a flowchart illustrating the optimization of false alarm rate in an embodiment of this application, as shown below. Figure 9 As shown, the process for optimizing the false alarm rate includes the following steps:

[0137] Step 1: Configure the SF equipment to achieve a target false alarm rate of 5%.

[0138] Step 2: Based on the acquisition request from the false alarm control unit, the gNB reports the current gNB noise floor level and received echo energy level to the false alarm control unit.

[0139] Step 3: The false alarm control unit matches the current gNB perception reference curve to obtain the current test false alarm rate of 8%. After comparing it with the target false alarm rate, it is determined that the current test false alarm rate does not meet the requirements.

[0140] Step 4: In order to eliminate the influence of environmental clutter, the false alarm control unit requests the current environmental clutter level of the area from the SF equipment;

[0141] Step 5: The SF equipment reports the current environmental clutter level to the false alarm control unit;

[0142] Step 6: When it is determined that the current environmental clutter is causing the judgment error, the false alarm control unit updates / extracts relevant information from the environmental database and changes the current gNB sensing reference curve;

[0143] Step 7: The false alarm control unit notifies the gNB of the current noise floor level, the change in the base station reporting threshold, and other information.

[0144] Step 8: The gNB re-reports the current gNB noise floor level and received echo energy level to the false alarm control unit;

[0145] Step nine: Based on the re-reported current gNB noise floor level and received echo energy level, the false alarm control unit re-matches the current gNB sensing reference curve to obtain the current test false alarm rate of 5%. After comparing it with the target false alarm rate, it is determined that the current test false alarm rate meets the requirements.

[0146] Compared with the solutions of related technologies, the solution of this application has the following advantages:

[0147] This application introduces a method to reduce the false alarm rate of sensing. By adding a false alarm control unit, it realizes the updating and iteration of the environmental clutter database, the judgment of the false alarm rate of the current environment, and the reduction of the false alarm rate. It provides an effective means for the performance detection and optimization of false alarm rate of sensing commercial networks. It can not only evaluate the false alarm rate in a timely and accurate manner, but also improve the effectiveness of optimizing the test false alarm rate.

[0148] To implement the false alarm rate processing method of this application embodiment, this application embodiment also provides a false alarm rate processing device. Figure 10 This is a schematic diagram of the composition structure of the false alarm rate processing device according to an embodiment of this application, as shown below. Figure 10 As shown, the false alarm rate processing device includes:

[0149] Construction unit 1001 is used to construct the target perception reference curve;

[0150] The determining unit 1002 is used to determine the false alarm rate of the target area based on the target perception reference curve.

[0151] Evaluation unit 1003 is used to evaluate the determined test false alarm rate based on the target false alarm rate and obtain an evaluation result; the target false alarm rate is related to the sensing services in the target area.

[0152] In one embodiment, the construction unit 1001 includes a first acquisition unit and a correction unit; wherein,

[0153] The first acquisition unit is used to acquire the source sensing reference curve;

[0154] The correction unit is used to correct the source sensing reference curve based on data from different clutter environments to obtain the target sensing reference curve.

[0155] In one embodiment, the first acquisition unit is specifically used for:

[0156] Obtain the performance relationship curve between RSRP and false alarm rate at the current noise level; summarize the performance relationship curves between RSRP and false alarm rate at all noise levels to obtain the source sensing reference curve; the source sensing reference curve represents the performance relationship curve between RSRP and false alarm rate at different noise levels.

[0157] In one embodiment, the determining unit 1002 is specifically used for:

[0158] Obtain the first information reported by the first network device; match the target perception reference curve based on the first information, and determine the false alarm rate corresponding to the first information as the test false alarm rate of the target area.

[0159] In one embodiment, the false alarm rate processing device may further include a second acquisition unit; wherein,

[0160] The second acquisition unit is used to acquire the target false alarm rate before the evaluation unit 1003 evaluates the determined test false alarm rate based on the target false alarm rate and obtains the evaluation result.

[0161] In one embodiment, the second acquisition unit is specifically used for:

[0162] Send a first request to the second network device; the first request is used to request the acquisition of the target false alarm rate; acquire the target false alarm rate issued by the second network device based on the sensing services in the target area in response to the first request.

[0163] In one embodiment, the evaluation unit 1003 is specifically used for:

[0164] The test false alarm rate is compared with the target false alarm rate to obtain a comparison result; based on the comparison result, the determined test false alarm rate is evaluated to obtain an evaluation result; the evaluation result indicates whether the test false alarm rate meets the requirements of the target false alarm rate.

[0165] In one embodiment, the false alarm rate processing device may further include an optimization unit; wherein,

[0166] The optimization unit is used to optimize the test false alarm rate when the evaluation result indicates that the test false alarm rate does not meet the target false alarm rate requirement.

[0167] In one embodiment, the optimization unit is specifically used for:

[0168] Determine whether the test false alarm rate is affected by environmental clutter; if the determination result indicates that the test false alarm rate is affected by environmental clutter, update the current target perception reference curve; based on the updated target perception reference curve, redetermine the test false alarm rate of the target area until the redetermined test false alarm rate meets the requirement of the target false alarm rate.

[0169] In one embodiment, the false alarm rate processing device may further include an update unit; wherein,

[0170] The update unit is used to update the environmental database when the judgment result indicates that the false alarm rate of the test is affected by environmental clutter.

[0171] In one embodiment, the update unit is specifically used for:

[0172] Send a second request to a second network device; the second request is used to request the current environmental clutter level; obtain the current environmental clutter level sent by the second network device in response to the second request; update the environmental database based on the current environmental clutter level.

[0173] In practical applications, the construction unit 1001, the determination unit 1002, and the evaluation unit 1003 can be implemented by the processor in the false alarm rate processing device.

[0174] It should be noted that the false alarm rate processing device provided in the above embodiments is only illustrated by the division of the above-described program modules. In practical applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the false alarm rate processing device and the false alarm rate processing method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the false alarm rate processing method embodiments, which will not be repeated here.

[0175] Based on the hardware implementation of the above program modules, and in order to implement the false alarm rate processing method of this application embodiment, this application embodiment also provides a false alarm rate processing device. Figure 11 This is a schematic diagram of the hardware composition structure of the false alarm rate processing device according to an embodiment of this application, as shown below. Figure 11 As shown, the false alarm rate processing device 1100 includes:

[0176] The communication interface 1101 is capable of exchanging information with other devices (such as the first network device and the second network device);

[0177] The processor 1102 is connected to the communication interface 1101 to enable information interaction with other devices (such as the first network device and the second network device). When running a computer program, it executes the false alarm rate processing method provided above, and the computer program is stored in the memory 1103.

[0178] Specifically, the processor 1102 is used to construct a target perception reference curve; determine the test false alarm rate of the target area based on the target perception reference curve; evaluate the determined test false alarm rate based on the target false alarm rate to obtain an evaluation result; the target false alarm rate is related to the perception services in the target area.

[0179] In one embodiment, the processor 1102 is specifically used for:

[0180] Obtain the source sensing reference curve; based on data from different clutter environments, correct the source sensing reference curve to obtain the target sensing reference curve.

[0181] In one embodiment, the communication interface 1101 is specifically used for:

[0182] Obtain the performance relationship curve between RSRP and false alarm rate at the current noise level; summarize the performance relationship curves between RSRP and false alarm rate at all noise levels to obtain the source sensing reference curve; the source sensing reference curve represents the performance relationship curve between RSRP and false alarm rate at different noise levels.

[0183] In one embodiment, the processor 1102 is specifically used for:

[0184] Obtain the first information reported by the first network device; match the target perception reference curve based on the first information, and determine the false alarm rate corresponding to the first information as the test false alarm rate of the target area.

[0185] In one embodiment, the communication interface 1101 is used to acquire the target false alarm rate before the processor 1102 evaluates the determined test false alarm rate based on the target false alarm rate and obtains the evaluation result.

[0186] In one embodiment, the communication interface 1101 is specifically used for:

[0187] Send a first request to the second network device; the first request is used to request the acquisition of the target false alarm rate; acquire the target false alarm rate issued by the second network device based on the sensing services in the target area in response to the first request.

[0188] In one embodiment, the processor 1102 is specifically used for:

[0189] The test false alarm rate is compared with the target false alarm rate to obtain a comparison result; based on the comparison result, the determined test false alarm rate is evaluated to obtain an evaluation result; the evaluation result indicates whether the test false alarm rate meets the requirements of the target false alarm rate.

[0190] In one embodiment, the processor 1102 is further configured to:

[0191] If the evaluation result indicates that the test false alarm rate does not meet the target false alarm rate requirement, the test false alarm rate is optimized.

[0192] In one embodiment, the processor 1102 is specifically used for:

[0193] Determine whether the test false alarm rate is affected by environmental clutter; if the determination result indicates that the test false alarm rate is affected by environmental clutter, update the current target perception reference curve; based on the updated target perception reference curve, redetermine the test false alarm rate of the target area until the redetermined test false alarm rate meets the requirement of the target false alarm rate.

[0194] In one embodiment, the processor 1102 is further configured to:

[0195] If the judgment result indicates that the false alarm rate of the test is affected by environmental clutter, the environmental database is updated.

[0196] In one embodiment, the processor 1102 is specifically used for:

[0197] Send a second request to a second network device; the second request is used to request the current environmental clutter level; obtain the current environmental clutter level sent by the second network device in response to the second request; update the environmental database based on the current environmental clutter level.

[0198] It should be noted that the specific processing procedures of communication interface 1101 and processor 1102 can be understood by referring to the above-mentioned false alarm rate processing method.

[0199] Of course, in practical applications, the various components in the false alarm rate processing device 1100 are coupled together through the bus system 1104. It is understood that the bus system 1104 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 1104 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 11 The general designated all buses as Bus System 1104.

[0200] The memory 1103 in this embodiment is used to store various types of data to support the operation of the false alarm rate processing device 1100. Examples of such data include any computer program used to operate on the false alarm rate processing device 1100.

[0201] The false alarm rate processing method disclosed in the above embodiments of this application can be applied to the processor 1102, or implemented by the processor 1102. The processor 1102 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above false alarm rate processing method can be completed by the integrated logic circuit of the hardware in the processor 1102 or by instructions in the form of software. The processor 1102 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 1102 can implement or execute the various false alarm rate processing methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the false alarm rate processing method disclosed in the embodiments of this application can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in memory 1103. The processor 1102 reads the information in memory 1103 and, in conjunction with its hardware, completes the steps of the aforementioned false alarm rate processing method.

[0202] In an exemplary embodiment, the false alarm rate processing device 1100 may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned false alarm rate processing method.

[0203] It is understood that the memory 1103 in this embodiment can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 1103 described in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0204] In an exemplary embodiment, this application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a memory 1103 storing a computer program. This computer program can be executed by a processor 1102 in the false alarm rate processing device 1100 to complete the steps of the false alarm rate processing method described in the aforementioned embodiment. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM.

[0205] In an exemplary embodiment, this application also provides a computer program product, including a computer program that can be executed by a processor 1102 in a false alarm rate processing device 1100 to complete the steps of the false alarm rate processing method described in the aforementioned embodiment.

[0206] It should be noted that terms such as "first" and "second" are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0207] Furthermore, the technical solutions described in the embodiments of this application can be combined arbitrarily without conflict.

[0208] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for processing false alarm rates, characterized in that, The method includes: Construct a target perception reference curve; Based on the target perception reference curve, the false alarm rate of the target area is determined. Based on the target false alarm rate, the determined test false alarm rate is evaluated to obtain the evaluation result; the target false alarm rate is related to the sensing services in the target area.

2. The method according to claim 1, characterized in that, The construction of the target perception reference curve includes: Obtain the source-aware reference curve; Based on data from different clutter environments, the source sensing reference curve is corrected to obtain the target sensing reference curve.

3. The method according to claim 2, characterized in that, The acquisition of the source sensing reference curve includes: Obtain the performance relationship curve between the reference signal received power RSRP and the false alarm rate at the current noise level; The performance relationship curves between RSRP and false alarm rate under all noise levels are summarized to obtain the source sensing reference curve; the source sensing reference curve represents the performance relationship curve between RSRP and false alarm rate under different noise levels.

4. The method according to claim 1, characterized in that, The determination of the false alarm rate of the target area based on the target perception reference curve includes: Obtain the first information reported by the first network device; Based on the first information, the target perception reference curve is matched, and the false alarm rate corresponding to the first information is determined as the test false alarm rate of the target area.

5. The method according to claim 1, characterized in that, Before evaluating the determined test false alarm rate based on the target false alarm rate to obtain the evaluation result, the method further includes: Obtain the false alarm rate of the target; The acquisition of the target false alarm rate includes: Send a first request to the second network device; the first request is used to request the acquisition of the target false alarm rate; The second network device obtains the target false alarm rate based on the sensing services issued within the target area in response to the first request.

6. The method according to claim 1, characterized in that, The evaluation of the determined test false alarm rate based on the target false alarm rate, to obtain the evaluation result, includes: The test false alarm rate is compared with the target false alarm rate to obtain the comparison result; Based on the comparison results, the determined false alarm rate is evaluated to obtain an evaluation result; the evaluation result indicates whether the false alarm rate meets the target false alarm rate requirement.

7. The method according to claim 6, characterized in that, The method further includes: If the evaluation result indicates that the test false alarm rate does not meet the target false alarm rate requirement, the test false alarm rate is optimized.

8. The method according to claim 7, characterized in that, The optimization of the test false alarm rate includes: Determine whether the false alarm rate of the test is affected by environmental clutter; If the judgment result indicates that the false alarm rate of the test is affected by environmental clutter, update the current target perception reference curve; Based on the updated target perception reference curve, the false alarm rate of the target area is re-determined until the re-determined false alarm rate meets the target false alarm rate requirement.

9. The method according to claim 8, characterized in that, The method further includes: If the judgment result indicates that the false alarm rate of the test is affected by environmental clutter, the environmental database is updated.

10. The method according to claim 9, characterized in that, The updated environment database includes: Send a second request to the second network device; the second request is used to request the current environmental clutter level; Obtain the current environmental clutter level sent by the second network device in response to the second request; The environmental database is updated based on the current environmental clutter level.

11. A false alarm rate processing device, characterized in that, The device includes: Construction unit, used to construct target perception reference curve; The determining unit is used to determine the false alarm rate of the target area based on the target perception reference curve. An evaluation unit is used to evaluate the determined test false alarm rate based on the target false alarm rate and obtain an evaluation result; the target false alarm rate is related to the sensing services in the target area.

12. A false alarm rate processing device, characterized in that, include: A processor and a memory for storing computer programs capable of running on the processor; When the processor is used to run the computer program, it performs the steps of the method according to any one of claims 1 to 10.

13. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 10.

14. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 10.

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