Sensor hop point detection method, device, equipment and medium

Abstract

The present invention provides a method, device, equipment and medium for detecting sensor jump points, which relates to the technical field of high-precision positioning, and includes: obtaining sensor data collected by a sensor to be detected, and obtaining status information when the sensor to be detected collects the sensor data; determining a dynamically adjustable threshold by using the status information; detecting whether the sensor to be detected has a jump point based on the sensor data and the dynamically adjustable threshold through a pre-constructed set of sensor jump point detection models, and obtaining initial jump point detection results output by each sensor jump point detection model in the set of sensor jump point detection models; determining a target jump point detection result corresponding to the sensor to be detected according to the initial jump point detection results output by each sensor jump point detection model. The present invention can significantly improve the sensor jump point detection ability in various speed change scenarios, and enhance the robustness and sensitivity of jump point detection.

Description

Technical Field

[0001] The present invention relates to the field of high-precision positioning technology, and in particular, to a method, device, equipment and medium for detecting sensor jump points. Background Art

[0002] The phenomenon of sensor jump points refers to the sudden change or error of sensor observation values due to certain sudden factors. Taking RTK (Real-time kinematic) as an example, although RTK technology has been very mature, in some specific scenarios, such as scenarios with large height changes and fast speed changes, the accuracy and real-time performance of RTK jump point detection are limited, and RTK jump points cannot be effectively identified, thus affecting the positioning accuracy and system performance. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for detecting sensor jump points, which can significantly improve the sensor jump point detection ability in various speed change scenarios and enhance the robustness and sensitivity of jump point detection.

[0004] In a first aspect, the present invention provides a method for detecting sensor jump points, including:

[0005] Obtaining sensor data collected by a sensor to be detected, and obtaining status information when the sensor to be detected collects the sensor data;

[0006] Determining a dynamically adjustable threshold using the status information;

[0007] Based on the sensor data and the dynamically adjustable threshold, detecting whether the sensor to be detected has a jump point through a pre-constructed set of sensor jump point detection models, and obtaining initial jump point detection results output by each sensor jump point detection model in the set of sensor jump point detection models;

[0008] Determining a target jump point detection result corresponding to the sensor to be detected according to the initial jump point detection results output by each sensor jump point detection model.

[0009] In an implementation manner, the status information includes sensor speed information and sensor speed change rate; determining a dynamically adjustable threshold using the status information includes:

[0010] Determining a real-time observation standard deviation corresponding to the sensor to be detected according to the observation accuracy range corresponding to the sensor to be detected and the sensor speed information;

[0011] Determining a dynamically adjustable threshold based on the real-time observation standard deviation; or determining a dynamically adjustable threshold based on the real-time observation standard deviation and the sensor speed change rate.

[0012] In one embodiment, the status information further includes the sensor update frequency; determining the dynamically adjustable threshold based on the real-time observation standard deviation and the sensor speed change rate includes:

[0013] Correcting the sensor speed change rate by using the sensor update frequency to obtain the real sensor speed change rate;

[0014] Determining the dynamically adjustable threshold based on the real-time observation standard deviation and the real sensor speed change rate.

[0015] In one embodiment, by using a pre-constructed set of sensor jump point detection models, based on the sensor data and the dynamically adjustable threshold, detecting whether a sensor to be detected has a jump point includes:

[0016] For any sensor jump point detection model in the pre-constructed set of sensor jump point detection models, using this sensor jump point detection model to perform the following operations:

[0017] Insert the sensor data at the end of the sensor data sequence, control the counter to increment, and when the value of the counter is greater than or equal to the size of the sliding window, set the value of the counter to the size of the sliding window;

[0018] Control the sliding window to slide on the sensor data sequence;

[0019] Determine the reference benchmark based on the historical sensor data located within the slid sliding window;

[0020] Detect whether the sensor to be detected has a jump point according to the deviation between the sensor data and the reference benchmark and the dynamically adjustable threshold, and obtain the initial jump point detection result output by this sensor jump point detection model.

[0021] In one embodiment, the sensor jump point detection model is a sliding window difference detection model; detecting whether the sensor to be detected has a jump point according to the deviation between the sensor data and the reference benchmark and the dynamically adjustable threshold, and obtaining the initial jump point detection result output by this sensor jump point detection model includes:

[0022] If the deviation between the sensor data and the reference benchmark is greater than the dynamically adjustable threshold, determine that the initial jump point detection result is that the probability value of the sensor to be detected having a jump point is greater than the preset probability threshold.

[0023] In one embodiment, the sensor jump point detection model is a cumulative sum detection model; detecting whether the sensor to be detected has a jump point according to the deviation between the sensor data and the reference benchmark and the dynamically adjustable threshold, and obtaining the initial jump point detection result output by this sensor jump point detection model includes:

[0024] Based on a dynamically adjustable threshold, the deviations between the sensor data and the reference benchmark are respectively accumulated in the positive direction and the negative direction to obtain a positive-direction cumulative sum and a negative-direction cumulative sum;

[0025] If either the positive-direction cumulative sum or the negative-direction cumulative sum is greater than the dynamically adjustable threshold, it is determined that the probability value of a jump point occurring in the sensor to be detected in the initial jump point detection result is greater than the preset probability threshold.

[0026] In one implementation manner, according to the initial jump point detection results output by each sensor jump point detection model, determining the target jump point detection result corresponding to the sensor to be detected includes:

[0027] When the initial jump point detection results output by each sensor jump point detection model are all that the probability value of a jump point occurring in the sensor to be detected is greater than the preset probability threshold, it is determined that the target jump point detection result is that a jump point occurs in the sensor to be detected.

[0028] In a second aspect, the present invention further provides a sensor jump point detection device, including:

[0029] An acquisition module, which acquires the sensor data collected by the sensor to be detected and the status information when the sensor to be detected collects the sensor data;

[0030] A threshold adjustment module, which is used to determine a dynamically adjustable threshold by using the status information;

[0031] A jump point detection module, which is used to detect whether a jump point occurs in the sensor to be detected based on the sensor data and the dynamically adjustable threshold through a pre-constructed set of sensor jump point detection models, and obtain the initial jump point detection results output by each sensor jump point detection model in the set of sensor jump point detection models;

[0032] A result determination module, which is used to determine the target jump point detection result corresponding to the sensor to be detected according to the initial jump point detection results output by each sensor jump point detection model.

[0033] In a third aspect, the present invention further provides an electronic device, including a processor and a memory, where the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of the first aspect.

[0034] In a fourth aspect, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer executable instructions, and when the computer executable instructions are called and executed by the processor, the computer executable instructions cause the processor to implement the method according to any one of the first aspect.

[0035] A method, device, equipment and medium for detecting sensor jump points provided by the present invention first obtain sensor data collected by a sensor to be detected and state information when the sensor to be detected collects the sensor data, so as to determine a dynamically adjustable threshold by using the state information; then, through a pre-constructed set of sensor jump point detection models, based on the sensor data and the dynamically adjustable threshold, detect whether the sensor to be detected has a jump point, and obtain an initial jump point detection result output by each sensor jump point detection model in the set of sensor jump point detection models; finally, according to the initial jump point detection results output by each sensor jump point detection model, determine the target jump point detection result corresponding to the sensor to be detected. The above method adaptively determines a dynamically adjustable threshold by using the state information when the sensor to be detected collects the sensor data, so as to improve the sensor jump point detection ability in various speed change scenarios, and comprehensively determines the target jump point detection result according to the initial jump point detection results output by each sensor jump point detection model in the set of sensor jump point detection models, which helps to enhance the robustness and sensitivity of jump point detection.

[0036] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims and drawings.

[0037] To make the above objectives, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0038] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0039] Figure 1 It is a flowchart of a method for detecting sensor jump points provided by an embodiment of the present invention;

[0040] Figure 2 It is a flowchart of a method for detecting RTK jump points provided by an embodiment of the present invention;

[0041] Figure 3 It is a comprehensive jump point detection logic diagram provided by an embodiment of the present invention;

[0042] Figure 4 It is an initialization or update sliding window logic diagram provided by an embodiment of the present invention;

[0043] Figure 5 A logic diagram of sliding window differential jump point detection provided by an embodiment of the present invention;

[0044] Figure 6 A logic diagram of CUSUM jump point detection provided by an embodiment of the present invention;

[0045] Figure 7 A schematic structural diagram of a sensor jump point detection device provided by an embodiment of the present invention;

[0046] Figure 8 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0048] Currently, the prior art mainly uses a fixed-threshold jump detection method, which mainly has the following problems: (1) Fixed-threshold limitation: It is difficult to adapt to different speed change scenarios. For example, from hovering to full-speed lifting and lowering, the position change rate is very different, resulting in a decline in jump detection accuracy; (2) Lack of multi-method fusion: A single method has insufficient robustness to noise and short-term jumps, and is prone to false alarms or missed alarms.

[0049] Based on this, the embodiments of the present invention provide a sensor jump point detection method, device, equipment, and medium, which can significantly improve the sensor jump point detection ability in various speed change scenarios and enhance the robustness and sensitivity of jump point detection.

[0050] To facilitate the understanding of this embodiment, first, a sensor jump point detection method disclosed in the embodiments of the present invention will be introduced in detail. Refer to Figure 1 The flowchart of a sensor jump point detection method shown, and this method mainly includes the following steps S102 to step S108:

[0051] Step S102, obtain the sensor data collected by the sensor to be detected, and obtain the status information when the sensor to be detected collects the sensor data. Among them, the status information includes at least the sensor speed information, and may also include the sensor speed change rate, the sensor update frequency, etc.

[0052] Step S104, determine a dynamically adjustable threshold using the status information.

[0053] Among them, the dynamically adjustable threshold refers to a critical parameter that changes in real time according to the current sensor speed information of the sensor to be detected (such as hovering, full-speed lifting, etc.), and this critical parameter is called the jump point detection threshold.

[0054] In one example, the dynamically adjustable threshold can be determined only based on the sensor speed information; in another example, the dynamically adjustable threshold can be determined by combining the sensor speed information and the sensor speed change rate; in another example, the sensor update frequency can be used to correct the sensor speed change rate, and then the dynamically adjustable threshold can be determined by combining the sensor speed information and the corrected sensor speed change rate.

[0055] Step S106: Based on the sensor data and the dynamically adjustable threshold, detect whether a jump point occurs in the sensor to be detected through a pre-constructed set of sensor jump point detection models, and obtain the initial jump point detection results output by each sensor jump point detection model in the set of sensor jump point detection models. Among them, the set of sensor jump point detection models includes at least a sliding window difference detection model and a cumulative sum (CUSUM) detection model, and may also include other detection models, such as a detection model based on machine learning, a detection model based on a deep neural network, etc.; the initial jump point detection result is used to characterize whether the probability value of a jump point occurring in the sensor to be detected is greater than a preset probability threshold, that is, whether a jump point is suspected to occur.

[0056] In one implementation manner, after the latest sensor data is obtained, it can be inserted at the end of the existing sensor data sequence, and each sensor jump point detection model in the set of sensor jump point detection models can detect whether a jump point occurs in the sensor to be detected based on the sensor data, the sensor data sequence, and the dynamically adjustable threshold, and output the corresponding initial jump point detection result.

[0057] Step S108: Determine the target jump point detection result corresponding to the sensor to be detected according to the initial jump point detection results output by each sensor jump point detection model. Among them, the target jump point detection result is used to characterize whether a jump point occurs in the sensor to be detected.

[0058] In one implementation manner, when the initial jump point detection results output by each sensor jump point detection model are all that a jump point is suspected to occur in the sensor to be detected, it is determined that a jump point occurs in the sensor to be detected. In another implementation manner, a fixed or adjustable confidence level can be configured for each sensor jump point detection model, and based on this confidence level, the initial jump point detection results output by each sensor jump point detection model are comprehensively considered, and then it is determined whether a jump point occurs in the detected sensor.

[0059] The sensor jump point detection method provided by the embodiments of the present invention adaptively determines a dynamically adjustable threshold using the state information during the acquisition of sensor data by the sensor to be detected, so as to improve the sensor jump point detection ability in various speed change scenarios, and comprehensively determines the target jump point detection result according to the initial jump point detection results output by each sensor jump point detection model in the sensor jump point detection model set, which helps to enhance the robustness and sensitivity of jump point detection.

[0060] For easy understanding, the embodiments of the present invention provide a specific implementation manner of a sensor jump point detection method.

[0061] For the aforementioned step S104, the embodiments of the present invention provide a specific implementation manner for determining the dynamically adjustable threshold using the state information, including the following steps 1a to 1b:

[0062] Step 1a, determine the real-time observation standard deviation corresponding to the sensor to be detected according to the observation accuracy range corresponding to the sensor to be detected and the sensor speed information. Among them, the observation accuracy range includes the accuracy when the sensor is stationary and the accuracy when the sensor is moving at high speed. .

[0063] The sensor speed information can be used as an indication of the signal drift rate for dynamically adjusting the threshold (i.e., the aforementioned dynamically adjustable threshold). During the actual use of the sensor, different working conditions may be involved; taking RTK as an example, the judgment threshold for jumps in the static working condition of RTK is obviously not the same number as the judgment threshold for RTK in the highly mobile working condition (such as the high-speed flight of a 15m / s drone), and the latter should be much larger than the former. If the speed is small but the position data has changed significantly, then this kind of jump is more likely to be abnormal; if the speed is large, the position change may be a natural result of the movement itself and should not be misjudged as a jump.

[0064] Similarly, in the sensor jump point detection model (such as the CUSUM detection model), the RTK speed information can be used to adjust the change rate of the cumulative sum. For example, when the RTK speed is high, the change rate of the cumulative sum is allowed to be faster to adapt to the intense movement environment; when the speed is low, a smoother change is required to prevent false detection.

[0065] In the present invention, the threshold is determined according to the accuracy characteristics of RTK itself. When it is stationary, its accuracy is centimeter-level. Assume that the accuracy in the RTK hardware manual is ; when moving at high speed, its accuracy is ; then according to the speed from 0 to , within the observation accuracy range to Establish a mapping relationship to obtain the real-time observation standard deviation :

[0066] ;

[0067] Wherein, is the real-time observation standard deviation, is the observation accuracy when the sensor is stationary, is the observation accuracy when the sensor is moving at high speed, is the maximum speed of the sensor movement, is the real-time speed of the sensor movement.

[0068] Step 1b, determine the dynamically adjustable threshold based on the real-time observation standard deviation; or, determine the dynamically adjustable threshold based on the real-time observation standard deviation and the sensor speed change rate.

[0069] Method 1, only based on the real-time observation standard deviation Determine the dynamically adjustable threshold , the real-time observation standard deviation is an important reference for calculating the real-time threshold . Optionally, the relationship between the dynamically adjustable threshold and the real-time observation standard deviation is determined based on the Hypothesis Theory. The Hypothesis Theory is a method in statistics for judging whether sample data supports a certain hypothesis (referred to as the null hypothesis H). Its core idea is to use probability to evaluate the possibility of random errors, ensure that the conclusion has statistical significance, and avoid making wrong inferences due to sample bias. It evaluates whether the deviation between the sample data and the expected value is large enough to reject the null hypothesis by comparing the deviation. Usually includes the following steps:

[0070] 1. Propose a hypothesis: Set the null hypothesis H0 and the alternative hypothesis H1. H0 usually represents no significant difference or a certain default state, and H1 represents the conclusion that the researcher hopes to prove. In the embodiment of the present invention, the null hypothesis H0 is that the sensor is normal, and the alternative hypothesis H1 is that the sensor has a jump point.

[0071] 2. Select a test method and a significance level: Determine the type of statistical test (such as t-test, z-test) and the significance level α, usually 0.05 or 0.01.

[0072] 3. Calculate the test statistic: Calculate the test statistic (such as z value, t value) using the sample data and determine its probability under the assumed distribution.

[0073] 4. Determine the rejection region: According to the significance level, find the critical value of the rejection region (such as the z value of the normal distribution).

[0074] 5. Make a decision: If the test statistic falls into the rejection region, reject H0 and accept H1; if the test statistic is not in the rejection region, H0 cannot be rejected.

[0075] In specific implementation, since the sensor value may jump in both positive and negative directions, the embodiment of the present invention adopts a two-tailed test. The embodiment of the present invention exemplarily provides a significant level as shown in Table 1, A mapping table of Z-score and rejection region range.

[0076] Table 1 Significant level, Mapping table of value (Z-score) and rejection region range

[0077]

[0078] Based on the above, since RTK is a high-precision sensor, the real-time observation standard deviation The significance level is 0.1%, then the threshold can be adjusted dynamically Standard deviation from real-time observation The relationship between them is:

[0079] .

[0080] in, Represents the real-time observation standard deviation Corresponding For example, at a significance level of 0.1%, determine the real-time observed standard deviation Corresponding The value (Z-score) is 3.29, which means that The embodiment of the present invention can achieve the following effects through the above method: the sensitivity of detection is set according to the sensor speed information (for example, a larger change is allowed at a higher speed); if the sensor speed is close to zero but a significant jump is detected, it may be a signal abnormality, and a jump point is determined; if the sensor speed is large, the signal change may be caused by motion and should not be misjudged, thereby improving the reliability of jump point detection.

[0081] Method 2: Determine the dynamically adjustable threshold based on the real-time observation standard deviation and the sensor speed change rate. In specific implementation, first determine the initial dynamically adjustable threshold according to the aforementioned Method 1, and then adjust the initial dynamically adjustable threshold using the sensor speed change rate to obtain the final dynamically adjustable threshold. For example, determine the product of the sensor speed change rate and the fault tolerance parameter (set to 2.0), and use the sum of this product and the initial dynamically adjustable threshold as the final dynamically adjustable threshold. Therefore, the dynamically adjustable threshold is expressed as follows:

[0082] ;

[0083] wherein, is the sensor speed change rate.

[0084] Furthermore, the sensor update frequency can be used to correct the sensor speed change rate to obtain the real sensor speed change rate, and then the dynamically adjustable threshold is determined based on the real-time observation standard deviation and the real sensor speed change rate. Exemplarily, the sensor speed change rate can be corrected according to the following formula : ; wherein, is the sensor update frequency. On this basis, the final dynamically adjustable threshold is obtained according to the above Method 2.

[0085] For the aforementioned step S106, an embodiment of the present invention provides a specific implementation manner for detecting whether a sensor to be detected has a jump point based on sensor data and a dynamically adjustable threshold through a pre-constructed sensor jump point detection model set, and obtaining the initial jump point detection result output by each sensor jump point detection model. For any sensor jump point detection model in the pre-constructed sensor jump point detection model set, the following steps 2a to 2d are performed using this sensor jump point detection model:

[0086] Step 2a: Insert the sensor data to the end of the sensor data sequence, control the counter to increase, and when the value of the counter is greater than or equal to the size of the sliding window, set the value of the counter to the size of the sliding window;

[0087] Step 2b: Control the sliding window to slide on the sensor data sequence;

[0088] Step 2c: Determine the reference benchmark based on the historical sensor data located within the slid sliding window;

[0089] Step 2d: Detect whether the sensor to be detected has a jump point according to the deviation between the sensor data and the reference benchmark and the dynamically adjustable threshold, and obtain the initial jump point detection result output by this sensor jump point detection model.

[0090] In one example, when the sensor jump point detection model is a sliding window differential detection model, if the deviation between the sensor data and the reference benchmark is greater than a dynamically adjustable threshold, the initial jump point detection result is determined to be a probability value of a jump point occurring in the sensor to be detected that is greater than a preset probability threshold.

[0091] The sliding window differential detection model is used to detect the deviation between the current position and the sliding window mean, and quickly determine whether a significant jump occurs. It detects whether the new sensor data has a jump by comparing the deviation between the average value (or median) of the data in the sliding window and the new sensor data. The principle of the sliding window differential detection model is: the sliding window saves the sensor data sequence of the most recent period; calculates the average value (or median) of the sensor data sequence in the sliding window as a reference benchmark; calculates the deviation between the new sensor data and the reference benchmark; if the deviation exceeds the dynamically adjustable threshold, it is determined that the sensor to be detected is suspected of having a jump. The sliding window differential detection model has the advantages of being simple and easy to implement, small amount of calculation, and sensitive to the detection of sudden changes with a single large change. In addition, the sliding window differential detection model is more sensitive to signal noise, so it is easily affected by single-point outliers, and it is impossible to distinguish whether the sudden change is a single anomaly or a trend change. Therefore, the sliding window differential detection model is suitable for detecting single emergencies or short-term outliers. Among them, the preset probability threshold can be 0. When the probability value of the sensor to be detected having a jump is greater than the preset probability threshold, it can be determined that the sensor to be detected is suspected of having a jump.

[0092] Based on this, the embodiment of the present invention provides a specific implementation method of using a sliding window difference detection model to detect whether a sensor has a jump point, including:

[0093] 1. Use a sliding window of length WINDOW_SIZE to calculate the mean of the data in the window , the mean The reference benchmark is:

[0094] ;

[0095] 2. For new sensor data With the mean The difference between To perform the test:

[0096] ;

[0097] 3. Determine whether the difference exceeds the dynamically adjustable threshold:

[0098] ;

[0099] If the difference exceeds the dynamically adjustable threshold, it is determined that the probability value of the sensor to be detected having a jump point is greater than the preset probability threshold; otherwise, it is determined that the sensor to be detected has not had a jump point.

[0100] In one example, when the sensor jump point detection model is a cumulative sum detection model, based on the dynamically adjustable threshold, the deviations between the sensor data and the reference benchmark are cumulatively calculated in the positive and negative directions respectively to obtain the positive cumulative sum and the negative cumulative sum; if either the positive cumulative sum or the negative cumulative sum is greater than the dynamically adjustable threshold, it is determined that the initial jump point detection result is that the probability value of the sensor to be detected having a jump point is greater than the preset probability threshold.

[0101] The cumulative sum detection model is used to further detect the cumulative deviation and filter out the influence of noise, thereby improving the reliability and robustness of jump detection. It detects the change trend and jump of new sensor data by accumulating the deviation amounts in the positive and negative directions. The principle of the cumulative sum detection model is as follows: calculate the deviation between the new sensor data and the reference mean value within the sliding window; divide the deviation into positive and negative directions and accumulate them respectively (referred to as positive cumulative sum and negative cumulative sum); if any one of the cumulative sums exceeds the set threshold, it is determined that a jump has occurred in the signal; once a jump is detected, reset the cumulative sum so as to start detecting subsequent signals again. The cumulative sum detection model has the advantages of being more sensitive to trend changes (such as the signal gradually deviating from the reference benchmark), being more robust to noise, and being able to filter out the influence of single-point outliers. However, compared with the sliding window difference model, the cumulative sum detection model has a slightly larger computational amount, requires reasonable setting of deviation parameters and thresholds, and is more complex to adjust. Therefore, the cumulative sum detection model can be applied to scenarios such as detecting the trend change or long-term drift of signals, and scenarios with large noise or stable trend changes.

[0102] Based on this, the embodiments of the present invention provide a specific implementation manner for detecting whether a sensor has a jump point by using the cumulative sum detection model, including:

[0103] 1. Update the positive cumulative sum and the negative cumulative sum:

[0104] cusumPos = max(0, cusumPos + (newData − meanValue) − ) ;

[0105] cusumNeg = max(0, cusumNeg + (meanValue − (newData + ) ;

[0106] wherein, cusumPos and cusumNeg are the positive cumulative sum and the negative cumulative sum respectively, newData is the new sensor data, and meanValue is the reference benchmark. is a dynamically adjustable threshold.

[0107] 2. Jump compensation:

[0108] If the jump detection function returns true and both the previous and current frames are in a fixed solution state, calculate the jump compensation value compensation:

[0109] compensation=(window[2]−window[1]);

[0110] where window[2] is the sensor data of the previous frame, and window[1] is the new sensor data (i.e., the sensor data of the current frame);

[0111] Limit the range of the compensation value:

[0112] compensation=LIMIT(compensation,−1000.0,1000.0);

[0113] Then use the compensation value to compensate the new sensor data.

[0114] For the foregoing step S108, an embodiment of the present invention provides an implementation manner for determining the target jump point detection result corresponding to the sensor to be detected according to the initial jump point detection result output by each sensor jump point detection model, that is: when the initial jump point detection results output by each sensor jump point detection model are all that the probability value of the sensor to be detected having a jump point is greater than the preset probability threshold, determine the target jump point detection result as the sensor to be detected having a jump point, which helps to avoid the misjudgment of a single sensor jump point detection model.

[0115] The sensor jump point detection method provided by the embodiment of the present invention can be used to detect the inevitable jump point situation of important sensors on an unmanned aerial vehicle, such as a low number of RTK satellites or RTK being interfered. This method has the following characteristics: providing RTK jump detection capabilities adapted to multiple scenarios, from low-speed hovering to high-speed ascending and descending (such as 10 m / s); innovatively combining the sliding window difference method and the CUSUM method to enhance the robustness and sensitivity of jump detection; designing an adaptive mechanism to adjust the critical parameters of jump detection in real time to adapt to different speed change scenarios.

[0116] Exemplarily, an embodiment of the present invention takes RTK as an example and provides an application example of a sensor jump point detection method. Refer to Figure 2 the flow schematic diagram of a RTK jump point detection method shown below, including the following (1) to (9):

[0117] (1) Input RTK data rtk_data_height and RTK update frequency rtk_data_vel_down.

[0118] (2) Determine whether initialization is completed; if not, execute (3) and skip subsequent detections, and the process ends; if so, execute (4).

[0119] (3) Call the sensor jump point detection initialization program detect_change_point_init and skip subsequent detections.

[0120] (4) Calculate the true sensor change rate : .

[0121] (5) Call the sensor jump point detection model detect_change_poin to determine whether a jump is detected; if so, execute (7); if not, execute (6).

[0122] An embodiment of the present invention provides a specific implementation manner of calling a sensor jump point detection model detect_change_poin to determine whether a jump is detected. Refer to Figure 3 As shown in a comprehensive jump point detection logic diagram, it includes the following steps: initialize or update the sliding window; calculate the sliding window difference diff; determine whether a jump is detected in the sliding window; if so, determine that a jump is detected in the sliding window; if not, determine that no jump is detected in the sliding window; calculate the CUSUM cumulative sum; determine whether a jump is detected by the CUSUM method; if so, determine that a jump is detected by the CUSUM method; if not, determine that no jump is detected by the CUSUM method; if jumps are detected by both methods, return true; if no jump is detected by any method, return false.

[0123] Further, an embodiment of the present invention also provides a specific implementation manner of initializing or updating the sliding window. Refer to Figure 4 As shown in a logic diagram for initializing or updating a sliding window, it includes the following steps: input new sensor data newData; determine whether the sliding window is full; if so, return true; if not, insert the new sensor data newData into the sliding window, and the initialization counter increases; determine whether the value of the counter is less than the window size; if so, return false; if not, set the value of the counter to the window size and return true.

[0124] Further, an embodiment of the present invention also provides as Figure 5 As shown in a logic diagram for detecting jump points in the sliding window difference, it includes the following steps: input new sensor data newData and the sensor speed change rate ; Determine the dynamically adjustable threshold ; Calculate the mean value within the sliding window ; Calculate the difference diff between the sensor data newData and the mean value within the sliding window ; Determine whether the difference diff is greater than the dynamically adjustable threshold ; If so, the sliding window detects a jump and returns true; if not, the sliding window does not detect a jump and returns false.

[0125] Furthermore, an embodiment of the present invention also provides a CUSUM jump point detection logic diagram as shown in Figure 6 which includes the following steps: Input new sensor data newData and the sensor speed change rate ; Determine the dynamically adjustable threshold ; Calculate the mean value within the sliding window ; Update the cumulative sums cusumPos and cusumNeg; Determine whether cusumPos > or cusumNeg < ; If so, reset cusumPos and cusumNeg, determine that the CUSUM method detects a jump, and return true; if not, determine that the CUSUM method does not detect a jump and return false.

[0126] (VI) The jump detection is not triggered.

[0127] (VII) Calculate the compensation value compensation.

[0128] (VIII) Constrain the range of the compensation value.

[0129] (IX) Update the fixed solution rtk_data_fix_last, and the process ends.

[0130] In summary, the sensor jump point detection method provided by the embodiment of the present invention has at least the following characteristics: Multi-scenario adaptability: The adaptive mechanism adjusts the jump detection parameters in real time to meet the requirements of different speed change scenarios; High detection accuracy: The sliding window difference method quickly responds to single-frame jumps, and the CUSUM method captures trend changes, reducing the missed detection rate and false detection rate under double guarantees; Real-time performance: The algorithm optimizes the processing speed to adapt to high-frequency (10Hz) RTK data streams and meets the real-time performance requirements; Enhanced robustness: The fault tolerance mechanism combines noise processing to improve the robustness of the algorithm in complex environments.

[0131] Based on the foregoing embodiments, an embodiment of the present invention provides a sensor jump point detection device. Refer to the structural schematic diagram of a sensor jump point detection device as shown in Figure 7 The device mainly includes the following parts:

[0132] An acquisition module 702, configured to acquire sensor data collected by a sensor to be detected, and acquire status information when the sensor to be detected collects the sensor data;

[0133] A threshold adjustment module 704, configured to determine a dynamically adjustable threshold by using the status information, where the dynamically adjustable threshold is a jump point detection threshold;

[0134] A jump point detection module 706, configured to detect whether a jump point occurs in the sensor to be detected based on the sensor data and the dynamically adjustable threshold through a pre-constructed set of sensor jump point detection models, and obtain an initial jump point detection result output by each sensor jump point detection model in the set of sensor jump point detection models;

[0135] A result determination module 708, configured to determine a target jump point detection result corresponding to the sensor to be detected according to the initial jump point detection results output by each sensor jump point detection model.

[0136] The sensor jump point detection device provided by the embodiment of the present invention adaptively determines a dynamically adjustable threshold by using the status information when the sensor to be detected collects the sensor data, so as to improve the sensor jump point detection ability in various speed change scenarios, and comprehensively determines the target jump point detection result according to the initial jump point detection results output by each sensor jump point detection model in the set of sensor jump point detection models, which helps to enhance the robustness and sensitivity of the jump point detection.

[0137] In an implementation manner, the status information includes sensor speed information and a sensor speed change rate; the threshold adjustment module 704 includes:

[0138] A standard deviation determination unit, configured to: determine a real-time observation standard deviation corresponding to the sensor to be detected according to the observation accuracy range corresponding to the sensor to be detected and the sensor speed information;

[0139] A threshold determination unit, configured to: determine a dynamically adjustable threshold based on the real-time observation standard deviation; or determine a dynamically adjustable threshold based on the real-time observation standard deviation and the sensor speed change rate.

[0140] In an implementation manner, the status information further includes a sensor update frequency; specifically, the threshold determination unit is configured to:

[0141] correct the sensor speed change rate by using the sensor update frequency to obtain a real sensor speed change rate;

[0142] determine a dynamically adjustable threshold based on the real-time observation standard deviation and the real sensor speed change rate.

[0143] In an implementation manner, the jump point detection module 706 is specifically configured to:

[0144] For any sensor jump point detection model in the pre-constructed set of sensor jump point detection models, perform the following operations using this sensor jump point detection model:

[0145] Insert the sensor data at the end of the sensor data sequence, control the counter to increment, and when the value of the counter is greater than or equal to the size of the sliding window, set the value of the counter to the size of the sliding window;

[0146] Control the sliding window to slide on the sensor data sequence;

[0147] Determine a reference benchmark based on the historical sensor data located within the slid sliding window;

[0148] Detect whether a jump point occurs in the sensor to be detected according to the deviation between the sensor data and the reference benchmark and the dynamically adjustable threshold, and obtain the initial jump point detection result output by this sensor jump point detection model.

[0149] In one implementation, the sensor jump point detection model is a sliding window difference detection model, and the jump point detection module 706 is specifically configured to:

[0150] If the deviation between the sensor data and the reference benchmark is greater than the dynamically adjustable threshold, determine that the initial jump point detection result is that the probability value of a jump point occurring in the sensor to be detected is greater than the preset probability threshold.

[0151] In one implementation, the sensor jump point detection model is a cumulative sum detection model, and the jump point detection module 706 is specifically configured to:

[0152] Based on the dynamically adjustable threshold, accumulate the deviation between the sensor data and the reference benchmark in the positive and negative directions respectively to obtain the positive direction cumulative sum and the negative direction cumulative sum;

[0153] If any of the positive direction cumulative sum and the negative direction cumulative sum is greater than the dynamically adjustable threshold, determine that the initial jump point detection result is that the probability value of a jump point occurring in the sensor to be detected is greater than the preset probability threshold.

[0154] In one implementation, the result determination module 708 is specifically configured to:

[0155] When the initial jump point detection result output by each sensor jump point detection model is that the probability value of a jump point occurring in the sensor to be detected is greater than the preset probability threshold, determine that the target jump point detection result is that a jump point occurs in the sensor to be detected.

[0156] The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment. For the sake of brief description, for the parts not mentioned in the device embodiment, reference may be made to the corresponding content in the foregoing method embodiment.

[0157] The embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device; a computer program is stored on the storage device, and when the computer program is run by the processor, it executes the method described in any one of the foregoing embodiments.

[0158] Figure 8 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device 100 includes: a processor 80, a memory 81, a bus 82, and a communication interface 83. The processor 80, the communication interface 83, and the memory 81 are connected through the bus 82; the processor 80 is used to execute an executable module stored in the memory 81, such as a computer program.

[0159] Among them, the memory 81 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 83 (which may be wired or wireless), a communication connection is realized between the system network element and at least one other network element, and the Internet, a wide area network, a local area network, a metropolitan area network, etc. can be used.

[0160] The bus 82 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 8 only a bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0161] Among them, the memory 81 is used to store a program. After receiving an execution instruction, the processor 80 executes the program. The method executed by the device defined by the flow process disclosed in any one of the foregoing embodiments of the present invention can be applied to the processor 80 or implemented by the processor 80.

[0162] The processor 80 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 80 or the instructions in the form of software. The above-mentioned processor 80 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 81, and the processor 80 reads the information in the memory 81 and combines its hardware to complete the steps of the above method.

[0163] The computer program product of the readable storage medium provided by the embodiments of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For the specific implementation, reference can be made to the foregoing method embodiments, and details are not described herein again.

[0164] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0165] Finally, it should be noted that the above-mentioned embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A sensor jump point detection method, characterized in that: include: Acquire sensor data collected by the sensor to be detected, and acquire state information of the sensor to be detected when collecting the sensor data, wherein the state information includes sensor speed information and sensor speed change rate; determining a dynamically adjustable threshold using the state information; Using a pre-built sensor jump point detection model set, based on the sensor data and the dynamically adjustable threshold, detecting whether the sensor to be detected has a jump point, and obtaining an initial jump point detection result output by each sensor jump point detection model in the sensor jump point detection model set; Determine a target jump point detection result corresponding to the sensor to be detected according to the initial jump point detection result output by each sensor jump point detection model; Determining a dynamically adjustable threshold using the state information includes: determining a real-time observation standard deviation corresponding to the sensor to be detected based on an observation accuracy range corresponding to the sensor to be detected and the sensor speed information; The dynamically adjustable threshold is determined based on the real-time observed standard deviation, or the dynamically adjustable threshold is determined based on the real-time observed standard deviation and the sensor speed change rate.

2. The sensor jump point detection method according to claim 1, characterized in that: The state information also includes a sensor update frequency; determining a dynamically adjustable threshold based on the real-time observation standard deviation and the sensor speed change rate includes: Correcting the sensor speed change rate using the sensor update frequency to obtain a true sensor speed change rate; A dynamically adjustable threshold is determined based on the real-time observed standard deviation and the true sensor speed change rate.

3. The sensor jump point detection method according to claim 1, characterized in that: By using a pre-built sensor jump point detection model set, based on the sensor data and the dynamically adjustable threshold, detecting whether the sensor to be detected has a jump point, including: For any sensor jump point detection model in the pre-built sensor jump point detection model set, use the sensor jump point detection model to perform the following operations: Inserting the sensor data to the end of the sensor data sequence, controlling a counter to increase, and when the value of the counter is greater than or equal to the size of the sliding window, setting the value of the counter to the size of the sliding window; Controlling the sliding window to slide on the sensor data sequence; Determining a reference benchmark based on historical sensor data within the sliding window after sliding; According to the deviation between the sensor data and the reference benchmark and the dynamically adjustable threshold, whether a jump point occurs in the sensor to be detected is detected to obtain an initial jump point detection result output by the sensor jump point detection model.

4. The sensor jump point detection method according to claim 3, characterized in that: The sensor jump point detection model is a sliding window difference detection model; according to the deviation between the sensor data and the reference benchmark and the dynamically adjustable threshold, whether the sensor to be detected has a jump point is detected, and an initial jump point detection result output by the sensor jump point detection model is obtained, including: If the deviation between the sensor data and the reference datum is greater than the dynamically adjustable threshold, then the initial jump point detection result is determined to be that the probability value of the jump point occurring in the sensor to be detected is greater than a preset probability threshold.

5. The sensor jump point detection method according to claim 3, characterized in that: The sensor jump point detection model is an accumulation and detection model; according to the deviation between the sensor data and the reference benchmark and the dynamically adjustable threshold, whether the sensor to be detected has a jump point is detected, and an initial jump point detection result output by the sensor jump point detection model is obtained, including: Based on the dynamically adjustable threshold, the deviation between the sensor data and the reference datum is accumulated in a positive direction and a negative direction respectively to obtain a positive direction cumulative sum and a negative direction cumulative sum; If any of the cumulative sum in the positive direction and the cumulative sum in the negative direction is greater than the dynamically adjustable threshold, it is determined that the initial jump point detection result is that the probability value of the jump point of the sensor to be detected is greater than the preset probability threshold.

6. The sensor jump point detection method according to claim 1, characterized in that: Determining a target jump point detection result corresponding to the sensor to be detected according to the initial jump point detection result output by each sensor jump point detection model includes: When the initial jump point detection results output by each of the sensor jump point detection models are all those in which the probability value of the jump point of the sensor to be detected is greater than a preset probability threshold, it is determined that the target jump point detection result is that the jump point of the sensor to be detected occurs.

7. A sensor jump point detection device, characterized in that: include: An acquisition module, used to acquire sensor data collected by the sensor to be detected, and to acquire state information of the sensor to be detected when collecting the sensor data, wherein the state information includes sensor speed information and sensor speed change rate; A threshold adjustment module, configured to determine a dynamically adjustable threshold using the state information; A jump point detection module is used to detect whether a jump point occurs in the sensor to be detected based on the sensor data and the dynamically adjustable threshold through a pre-built sensor jump point detection model set, and obtain an initial jump point detection result output by each sensor jump point detection model in the sensor jump point detection model set; A result determination module, configured to determine a target jump point detection result corresponding to the sensor to be detected according to the initial jump point detection result output by each sensor jump point detection model; The threshold adjustment module is specifically used to: determine the real-time observation standard deviation corresponding to the sensor to be detected according to the observation accuracy range corresponding to the sensor to be detected and the sensor speed information; determine the dynamically adjustable threshold based on the real-time observation standard deviation, or determine the dynamically adjustable threshold based on the real-time observation standard deviation and the sensor speed change rate.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 6.

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