Confidence level assessment method and apparatus
By constructing a mapping relationship between repeated binary string permutations and decimal values, the computational complexity and real-time performance issues of sensor data evaluation are resolved, achieving efficient and accurate confidence level evaluation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING JINGWEI HIRAIN TECH CO INC
- Filing Date
- 2022-12-28
- Publication Date
- 2026-06-26
AI Technical Summary
Existing confidence level assessment methods are computationally complex and have poor real-time output, making them unable to effectively handle false data generated by sensors in complex environments.
By constructing repeated permutations of N-digit binary strings, a mapping relationship between decimal values and confidence levels is established. The confidence level of the data to be evaluated is directly determined through the mapping relationship, avoiding the need for training with a large amount of sample data and constructing probability distribution functions.
It simplifies the confidence level assessment process, improves the real-time performance and accuracy of the assessment, reduces storage space requirements, and is suitable for sensor data assessment in robot navigation and intelligent assisted driving.
Smart Images
Figure CN116186345B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of data processing technology, and in particular relates to a confidence level assessment method and apparatus. Background Technology
[0002] In fields such as robot navigation and intelligent assisted driving, sensors are essential devices for sensing environmental information. When sensors perceive environmental information, they are inevitably affected by various interference factors in the environment, such as obstacle obstruction, multiple target intersections, multipath scattering, and echo power fluctuations. This can lead to false data detected by the sensors. Before further analysis of the target data, a confidence assessment is needed, assigning a confidence level to each data point to evaluate its reliability.
[0003] In related technologies, confidence assessment is based on Bayesian probability models, time series models, or antenna pattern compensation models. However, using the aforementioned models for confidence level assessment has the problems of large data computation and poor real-time output of confidence levels. Summary of the Invention
[0004] This application provides a confidence level assessment method and apparatus that can solve the problems of existing confidence level assessment methods being computationally complex and having poor real-time performance in outputting confidence levels.
[0005] In a first aspect, embodiments of this application provide a confidence level assessment method, the method comprising:
[0006] Construct repeated permutations of an N-digit binary string, and based on these permutations, establish a first mapping relationship between decimal values and confidence levels. The decimal values in this first mapping relationship correspond to the numbers 2 and 3 in the repeated permutations. N Each binary string corresponds to a specific binary string, where N is an integer greater than or equal to 2.
[0007] Obtain the data to be evaluated, convert it to a decimal comparison value, and then convert the data to N-digit binary data.
[0008] Based on the first mapping relationship, determine the decimal value that is the same as the decimal comparison value, and set the confidence level corresponding to the decimal value as the confidence level of the data to be evaluated.
[0009] In some embodiments, prior to obtaining the data to be evaluated, the following steps are included:
[0010] Detection values are obtained at each detection interval. These values are processed according to preset rules to obtain data to be evaluated. The data to be evaluated includes N detection elements generated sequentially. The preset rules include:
[0011] If the detected value is not null, the detected element is written as the first element into the preset storage sequence corresponding to the data to be evaluated.
[0012] If the detected value is empty, the detected element is written as the second element into the preset storage sequence corresponding to the data to be evaluated.
[0013] In this case, one of the first and second elements is element "0", and the other is element "1".
[0014] In some embodiments, the preset rules also include:
[0015] If all the preset storage sequences have been written with detection elements, delete the detection element located at the first end of the preset storage sequence, and write the detection element corresponding to the current detection value to the second end of the preset storage sequence. In the preset storage sequence, N detection elements are arranged from the first end to the second end according to the order of writing time.
[0016] In some embodiments, constructing a repeating permutation of an N-digit binary string, and constructing a first mapping relationship between decimal values and confidence levels based on the repeating permutation, includes:
[0017] Construct repeating permutations of an N-digit binary string;
[0018] Count the occurrences of the first element in each binary string, sort the repeated binary strings according to their occurrence counts, and obtain the first sorted result.
[0019] Based on the first sorting result, a second mapping relationship is constructed between binary strings and confidence levels, where each confidence level corresponds to one or more binary strings.
[0020] Calculate the decimal value corresponding to the binary string, and generate the first mapping relationship based on the decimal value and the second mapping relationship. The first mapping relationship includes the decimal value corresponding to each binary string and the confidence level corresponding to each decimal value.
[0021] In some embodiments, calculating the decimal value corresponding to the binary string and generating the first mapping relationship based on the decimal value and the second mapping relationship includes:
[0022] Calculate the decimal value corresponding to the binary string.
[0023] Initial query data is generated based on decimal values and a second mapping relationship. The initial query data includes binary strings, their corresponding decimal values, and their corresponding confidence levels. Binary strings with the same frequency of occurrence correspond to the same confidence level.
[0024] Sort multiple first strings based on their decimal values to obtain a second sorted result, where the first strings are binary strings that appear the same number of times.
[0025] Based on the second sorting result, update the confidence level of at least one second string by increasing or decreasing it by one level to obtain the first mapping relationship. Multiple first strings include second strings.
[0026] In some embodiments, updating the confidence level corresponding to at least one second string by increasing or decreasing it by one level based on the second sorting result to obtain a first mapping relationship includes:
[0027] When the first element is 1, the confidence level corresponding to at least one second string is increased by one level according to the second sorting result, resulting in the first mapping relationship, where the decimal value corresponding to the second string is A. i The decimal value of the binary strings other than the second string in the first string is A. j A i <A j ,
[0028] Alternatively, if the first element is 1, update the confidence level of at least one second string by decreasing it by one level based on the second sorting result to obtain the first mapping relationship, where A i >A j .
[0029] In some embodiments, constructing a second mapping relationship between binary strings and confidence levels based on the first sorting result includes:
[0030] Based on the first sorting result, the binary string that appears N or N-1 times is compared with the highest confidence level P. K correspond,
[0031] Based on the first sorting result, the binary string that appears 0 times is associated with the lowest confidence level P1;
[0032] Based on the first sorting result, the binary string with the number of occurrences (Nm) is matched with the confidence level P. K-m Correspondingly, m and K are positive integers, and 2≤m≤N-1.
[0033] In some embodiments, the binary string is (b N ...b2, b1), calculate the decimal value corresponding to the binary string using the following formula:
[0034]
[0035] Where A is the decimal value, i is any number of digits in the binary string, and b i Let i be the element with the number of bits in the binary string, and N be the number of bits in the binary string.
[0036] In some embodiments, prior to constructing the repeating permutation of the N-bit binary string, the method further includes:
[0037] The detection period for acquiring target data is determined by the number of bits N based on the preset evaluation duration and detection period.
[0038] Secondly, embodiments of this application provide a confidence level assessment device, the device comprising:
[0039] The module is used to construct repeating permutations of an N-digit binary string. Based on the repeating permutations, a first mapping relationship is established between decimal values and confidence levels. The decimal values in the first mapping relationship are associated with the numbers 2 and 3 in the repeating permutations. N Each binary string corresponds to a specific binary string, where N is an integer greater than or equal to 2.
[0040] The acquisition module is used to acquire the data to be evaluated, convert the data to a decimal comparison value, and the data to be evaluated is N-digit binary data.
[0041] The generation module is used to determine the decimal value that is the same as the decimal comparison value according to the first mapping relationship, and set the confidence level corresponding to the decimal value as the confidence level of the data to be evaluated.
[0042] Thirdly, embodiments of this application provide a confidence level assessment device, the device comprising: a processor and a memory storing computer program instructions.
[0043] The confidence level assessment method described above is implemented when the processor executes computer program instructions.
[0044] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the confidence level assessment method described above.
[0045] Fifthly, embodiments of this application provide a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor, they implement the confidence level assessment method described above.
[0046] The confidence level assessment method provided in this application does not require training with a large amount of sample data, nor does it require constructing a probability distribution function. The generation process of the first mapping relationship is simple, and the accuracy of the confidence level generated by the first mapping relationship is not affected by the target data. In this application, by constructing a repeating permutation of binary strings, and constructing a first mapping relationship between decimal values and confidence levels based on the repeating permutation, the first mapping relationship can store only decimal values and confidence levels, resulting in a small storage space. By converting the data to be evaluated into decimal comparison values, the confidence level corresponding to the decimal comparison values can be obtained through the first mapping relationship. Compared with inputting binary strings for comparison or inputting the data to be evaluated for calculation, the comparison speed of decimal data is faster, and the real-time output of the confidence level is better. Attached Figure Description
[0047] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0048] Figure 1 This is a flowchart illustrating a confidence level assessment method provided in an embodiment of this application.
[0049] Figure 2 This is a flowchart illustrating a confidence level assessment method provided in another embodiment of this application.
[0050] Figure 3 This is a flowchart illustrating a confidence level assessment method provided in another embodiment of this application.
[0051] Figure 4 One embodiment of this application provides a binary string and a confidence level line graph.
[0052] Figure 5 This is a schematic diagram of the hardware structure of a confidence level assessment device provided in an embodiment of this application.
[0053] Figure 6 This is a schematic diagram of the confidence level assessment device provided in one embodiment of this application. Detailed Implementation
[0054] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.
[0055] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.
[0056] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The embodiments will now be described in detail with reference to the accompanying drawings.
[0057] In fields such as robot navigation and intelligent assisted driving, sensors are essential devices for sensing environmental information. When sensors perceive environmental information, they are inevitably affected by various interference factors in the environment, such as obstacle obstruction, multiple target intersections, multipath scattering, and echo power fluctuations. This can lead to false data detected by the sensors. Before further analysis of the target data, a confidence assessment is needed, assigning a confidence level to each data point to evaluate its reliability.
[0058] In related technologies, confidence assessment is based on Bayesian probability models, time series models, or antenna pattern-based compensation models. Constructing a Bayesian probability model requires establishing a probability distribution function that strictly corresponds to the characteristics of the target data. However, the target data in real-world applications is complex and variable, making it difficult to establish a sustainable and accurate Bayesian probability model. Constructing a time series model requires acquiring sample data over multiple consecutive, relatively long measurement periods to ensure accurate output. However, using this time series model also requires inputting target data from many measurement periods, resulting in poor real-time performance of the output confidence level. Constructing an antenna pattern-based compensation model requires acquiring a large amount of sample data under different measurement environments for the same antenna, and performing statistical analysis based on this sample data, resulting in a huge computational workload.
[0059] As can be seen from the above, using the evaluation model in the relevant technology to evaluate the confidence level has the problems of large data calculation volume and poor real-time output confidence level.
[0060] To address the problems of existing technologies, this application provides a confidence level assessment method. The confidence level assessment method provided in this application is described below.
[0061] Figure 1 A flowchart illustrating a confidence level assessment method according to an embodiment of this application is shown. The method includes the following steps:
[0062] S110, construct a repeating permutation of an N-digit binary string, and based on the repeating permutation, construct the first mapping relationship between decimal values and confidence levels. The decimal value in the first mapping relationship corresponds to the 2 in the repeating permutation. N Each binary string corresponds to a specific binary string, where N is an integer greater than or equal to 2.
[0063] S120: Obtain the data to be evaluated, convert the data to be evaluated into a decimal comparison value, and the data to be evaluated is N-digit binary data.
[0064] S130, determine the decimal value that is the same as the decimal comparison value according to the first mapping relationship, and set the confidence level corresponding to the decimal value as the confidence level of the data to be evaluated.
[0065] The confidence level assessment method provided in this application can first generate a first mapping relationship, and then generate a confidence level corresponding to the data to be assessed through the first mapping relationship. For example, a higher confidence level indicates better reliability of the data to be assessed, and a lower confidence level indicates lower reliability of the data to be assessed. The data to be assessed can be direct data obtained directly from sensor detection, such as electromagnetic wave data obtained periodically by radar, infrared data obtained periodically by infrared sensors, and temperature data obtained periodically by temperature sensors. The data to be assessed can also be indirect data obtained indirectly through sensor detection, such as measurement information of the target object's radial distance, radial Doppler velocity, azimuth, and height obtained by processing electromagnetic wave data detected by radar.
[0066] Those skilled in the art can set the number of bits N as needed. The larger the value of N, the more input data to be evaluated the generated first mapping relationship corresponds to, and the worse the real-time performance and the higher the accuracy of the confidence level output by the first mapping relationship. The smaller the value of N, the smaller the input data to be evaluated the generated first mapping relationship corresponds to, and the better the real-time performance and the lower the accuracy of the confidence level output by the first mapping relationship.
[0067] Please refer to Table 1 to construct repeating permutations of an N-digit binary string. This means the repeating permutations include all N-digit binary strings formed by arranging elements "1" and / or "0". When constructing the binary string, the first bit is chosen from "1" and "0", the second bit from "1" and "0", and so on, until the Nth bit is chosen from "1" and "0". Therefore, the total number of possible permutations should be 2. N One. The 2 N The binary strings form a repeating permutation. See Table 1.
[0068] Table 1
[0069]
[0070]
[0071] For example, when N is 2, the repeated permutations include the binary string {(1,1),(1,0),(0,1),(0,0)}, totaling 2^n. 2 There are 2 binary strings; when N is 3, the repeated permutations include the binary strings {(1,1,1),(1,0,1),(1,1,0),(0,1,1),(1,0,0),(0,1,0),(0,0,1)(0,0,0)}, for a total of 2. 3A binary string. This principle applies to cases where N is 4, 5, or higher, but will not be elaborated upon in this application.
[0072] The constructed repeating permutations are deductions for all possible scenarios of the data to be evaluated. To clearly explain the principle of the confidence level evaluation method provided in this application, the following describes how the first mapping relationship provided in this application generates confidence levels for the data to be evaluated.
[0073] Please see Figure 2 In one embodiment, S110 includes:
[0074] S210, construct a repeating permutation of an N-digit binary string;
[0075] S220: Count the occurrences of the first element in each binary string, sort the binary strings in the repeated permutations according to the occurrence count, and obtain the first sort result.
[0076] S230, construct a second mapping relationship between binary strings and confidence levels based on the first sorting result, where each confidence level corresponds to one or more binary strings.
[0077] S240, calculate the decimal value corresponding to the binary string, and generate the first mapping relationship based on the decimal value and the second mapping relationship. The first mapping relationship includes the decimal value corresponding to each binary string and the confidence level corresponding to each decimal value.
[0078] The data to be evaluated is binary data corresponding to the target data. If the target data is not binary, it can be binary-coded using preset preprocessing rules to obtain the data to be evaluated. If the target data is binary, the binary-coding process can be omitted; the N most recently written detection values from the target data can be used to obtain the data to be evaluated. Since the target data is sensor-acquired data with temporal characteristics, the data to be evaluated also has temporal characteristics. In this embodiment, the N detection elements in the data to be evaluated are arranged sequentially.
[0079] Preprocessing may include classifying the detection values in the target data into two categories according to preset preprocessing rules. Detection values classified into category A are assigned the element "0", and detection values classified into category B are assigned the element "1", resulting in evaluation data corresponding to the target data. This evaluation data is binary data composed of elements "0" and / or elements "1". If the first element is 0 or 1, the more times the first element appears in the evaluation data, the more frequently the classification of the detection value corresponding to the first element occurs in the target data.
[0080] For example, based on whether the detected value is null, the detected values are divided into two categories: non-nullable detected values are assigned the element "1", and detected values that were not detected or were null are assigned the element "0", thus obtaining the data to be evaluated corresponding to the target data. When the first element is "1", the greater the frequency of the element "1" in the data to be evaluated, the more data was collected in the target data corresponding to that data, and the fewer cases where no data was collected.
[0081] For example, based on whether the detected value is within the abnormal range, the detected values are divided into two categories: those not within the abnormal range are assigned a value of "1", and those within the abnormal range are assigned a value of "0", thus obtaining the data to be evaluated corresponding to the target data. When the first element is "1", the greater the frequency of the element "1" in the data to be evaluated, the more normal data and the fewer abnormal data there is in the target data corresponding to the binary string.
[0082] Since the constructed repeating permutation is a deduction of all possible cases of the target data, for the binary string in the repeating permutation, the higher the frequency of the first element in the binary string, the higher the frequency of the detection value assigned to the first element in the target data.
[0083] The first sorting result can be based on the frequency of occurrence from largest to smallest, or vice versa. The frequency of occurrence and the confidence level can be positively or negatively correlated, depending on whether a positive detection value in the target data is assigned as the first element. A positive detection value is one that improves the reliability of the target data, such as a detection value within the normal range, a detection value with a jump amplitude within a preset threshold, or a non-null value. Conversely, a negative detection value is one that reduces the reliability of the target data, such as a detection value within the abnormal range, a detection value with a jump amplitude above a preset threshold, or a null value. If a positive detection value in the target data is assigned as the first element, then the frequency of occurrence and the confidence level are positively correlated in generating the first mapping relationship. If a negative detection value in the target data is assigned as the first element, then the frequency of occurrence and the confidence level are negatively correlated in generating the first mapping relationship.
[0084] In one embodiment, the greater the frequency of the first element, the higher the corresponding confidence level. For example, when assigning the non-empty detection value in the target data as the first element, according to the first sorting result, the greater the frequency of the binary string, the higher the confidence level of the binary string, and the higher the reliability of the target data matching the binary string. See Table 2.
[0085] Table 2
[0086]
[0087] In one embodiment, the fewer times the first element appears, the higher the corresponding confidence level. For example, when the detection value in the abnormal interval of the target data is assigned as the first element, according to the first sorting result, the fewer times the binary string appears, the higher the confidence level of the binary string, and the higher the reliability of the target data matching the binary string.
[0088] There is a definite mapping relationship between binary strings and decimal values, and a definite mapping relationship between binary strings and confidence levels. Therefore, the first mapping relationship between decimal values and confidence levels can be obtained from binary strings. This first mapping relationship includes the correspondence between the decimal value and the confidence level. Since the arrangement of binary strings in a repeating permutation is different, the decimal values converted from each binary string will inevitably be different. Therefore, when evaluating the confidence level of target data, the target data can first be processed into binary data to be evaluated. Then, using the same binary string to decimal value conversion formula as in the first mapping relationship generation process, the data to be evaluated can be converted into a decimal comparison value. This ensures that, given the same decimal value and decimal comparison value, the binary string corresponding to the decimal value is the same as the data to be evaluated corresponding to the decimal comparison value. In the first mapping relationship, only the decimal value that matches the decimal comparison value needs to be found; the confidence level corresponding to that decimal value can then be determined as the confidence level corresponding to the data to be evaluated.
[0089] The confidence level assessment method provided in this application does not require training with a large amount of sample data, nor does it require constructing a probability distribution function. The generation process of the first mapping relationship is simple, and the accuracy of the confidence level generated by the first mapping relationship is not affected by the target data. In this application, by constructing a repeating permutation of binary strings, and constructing a first mapping relationship between decimal values and confidence levels based on the repeating permutation, the first mapping relationship can store only decimal values and confidence levels, resulting in a small storage space. By converting the data to be evaluated into decimal comparison values, the confidence level corresponding to the decimal comparison values can be obtained through the first mapping relationship. Compared with inputting binary strings for comparison or inputting the data to be evaluated for calculation, the comparison speed of decimal data is faster, and the real-time output of the confidence level is better.
[0090] In one embodiment, step S120 includes:
[0091] Detection values are obtained at each detection interval. These values are processed according to preset rules to obtain data to be evaluated. The data to be evaluated includes N detection elements generated sequentially. The preset rules include:
[0092] If the detected value is not null, the detected element is written as the first element into the preset storage sequence corresponding to the data to be evaluated.
[0093] If the detected value is empty, the detected element is written as the second element into the preset storage sequence corresponding to the data to be evaluated.
[0094] In this case, one of the first and second elements is element "0", and the other is element "1".
[0095] The detection cycle is the detection cycle of the target data, so the sensor collects the detection value at each detection cycle interval. The preset storage sequence is the storage space pre-set by those skilled in the art for the data to be evaluated. It can be understood that if the data to be evaluated has N elements, then the preset storage sequence only needs to set N storage bits, with one storage bit corresponding to one element.
[0096] In this embodiment, the first element or the second element is assigned to the detection value by determining whether the detection value is empty. Optionally, the first element is element "1" and the second element is element "0".
[0097] In one embodiment, the preset rules further include:
[0098] If all the preset storage sequences have been written with detection elements, delete the detection element located at the first end of the preset storage sequence, and write the detection element corresponding to the current detection value to the second end of the preset storage sequence. In the preset storage sequence, N detection elements are arranged from the first end to the second end according to the order of writing time.
[0099] The first and second ends can be the beginning or end of a pre-stored sequence, respectively. If each storage bit in the pre-stored sequence contains an element, the first element written in the pre-stored sequence is deleted, and the element assigned to the latest detected value is written. Optionally, the first end is the leftmost end of the pre-stored sequence, and the second end is the rightmost end. For example, if the data to be evaluated stored in the pre-stored sequence is "0, 0, 0, 0", and the element assigned to the latest detected value is "1", then the leftmost element "0" is deleted, and the element "1" is written, resulting in the data to be evaluated stored in the pre-stored sequence being "0, 0, 0, 1".
[0100] Please see Figure 3 In some embodiments, S240 includes:
[0101] S310, calculates the decimal value corresponding to the binary string;
[0102] S320, Generate initial query data based on decimal values and the second mapping relationship. The initial query data includes binary strings, decimal values corresponding to each binary string, and confidence levels corresponding to each binary string. Binary strings with the same number of occurrences correspond to the same confidence level.
[0103] S330, sort multiple first strings according to their decimal values to obtain a second sorting result, where the first strings are binary strings that appear the same number of times;
[0104] S340, based on the second sorting result, update the confidence level of at least one second string by increasing or decreasing it by one level to obtain the first mapping relationship, where multiple first strings include second strings.
[0105] The decimal value is a binary string represented in decimal, which can be calculated using the following formula:
[0106]
[0107] Where A is the decimal value, i is any number of digits in the binary string, and b i Let i be the element with the number of bits in the binary string, and N be the number of bits in the binary string.
[0108] For example: the binary string (0,1,1,1,1,) has 5 bits, where b1 is 0, b2 is 1, b3 is 1, b4 is 1, b5 is 1, and A is 0. 2 +1*2 1 +1*2 2 +1*2 3 +1*2 4 =0+2+4+8+16=30.
[0109] Initial query data is generated based on decimal values and the first mapping relationship. In this initial query data, since the confidence level is generated according to the order of occurrence, binary strings with the same occurrence frequency correspond to the same confidence level. Therefore, adding a new element to the leftmost or rightmost position of the binary string in the initial query data, resulting in an increase or decrease of the first element, will cause a change in the confidence level, leading to a jump in the confidence level. Furthermore, since the repeated binary strings represent all possible scenarios of the target data, and the positions of the elements in the binary strings correspond to the temporally ordered detection values in the target data, meaning the elements in the binary strings have a temporal characteristic, binary strings with the same occurrence frequency correspond to the same confidence level. This means the resulting confidence level does not reflect the temporal characteristic. As can be seen from the formula for converting binary strings to decimal values, the different positions of the same element in the binary string have different effects on the magnitude of the decimal value. Therefore, the magnitude of the decimal value reflects the temporal sequence characteristics. By sorting the decimal values corresponding to binary strings that have the same frequency of occurrence, and adjusting the confidence level of some binary strings based on the second sorting result, the confidence level of the binary strings can reflect the temporal sequence characteristics, reducing or avoiding jumps in confidence level.
[0110] For example, if the target data for a certain detection period N5 corresponds to the data to be evaluated (0,0,1,1,1,), and the detection value for the next detection period N6 is assigned the element "0", and the newly added element "0" is recorded on the rightmost side of the data to be evaluated for the detection period N5, then the target data for the next detection period N6 corresponds to the data to be evaluated (0,1,1,1,0,). If the detection value for the next detection period N7 is assigned the element "0", and the newly added element "0" is recorded on the rightmost side of the data to be evaluated for the detection period N6, then the target data for the next detection period N7 corresponds to the data to be evaluated (1,1,1,0,0,). If the element "1" is taken as the first element, and the detection values that are not empty are assigned the element "1", then the data to be evaluated for the detection periods N5, N6, and N7 should all correspond to the confidence level P3 corresponding to the occurrence frequency of 3. From the data to be evaluated (0,0,1,1,1,), (0,1,1,1,0,), and (1,1,1,0,0,), it can be unequivocally determined that the most recent three detection periods for (0,0,1,1,1,) have all collected detection values, while the most recent two detection periods for (1,1,1,0,0,) have not collected detection values. Therefore, the confidence level corresponding to (0,0,1,1,1,) should be greater than or equal to the confidence level corresponding to (0,1,1,1,0,), and the confidence level corresponding to (0,1,1,1,0,) should be greater than or equal to the confidence level corresponding to (1,1,1,0,0,). The decimal values corresponding to (0,0,1,1,1,), (0,1,1,1,0,) and (1,1,1,0,0,) are 7, 14, and 28, respectively. Therefore, in the process of generating the first mapping relationship, based on the second sorting result of the decimal values, the confidence level corresponding to (0,0,1,1,1,) with the smaller decimal value can be increased by one level to P4, and the confidence level corresponding to (1,1,1,0,0,) with the smaller decimal value can be decreased by one level to P2.
[0111] Since only the element "1" increases the decimal value when converting a binary string to a decimal value, those skilled in the art can determine whether to increase or decrease the confidence level of the higher-ranked decimal value in the second sorting result by one level, or increase or decrease the confidence level of the lower-ranked decimal value in the second sorting result by one level, depending on whether the element "0" or "1" is set as the first element, whether the elements "0" and "1" are assigned positive or negative detection values respectively, and whether the second sorting result is sorted from largest to smallest or smallest to largest.
[0112] In one embodiment, S340 includes:
[0113] When the first element is 1, the confidence level corresponding to at least one second string is increased by one level according to the second sorting result, resulting in the first mapping relationship, where the decimal value corresponding to the second string is A. i The decimal value of the binary strings other than the second string in the first string is A. j A i <A j ;
[0114] Alternatively, if the first element is 1, update the confidence level of at least one second string by decreasing it by one level based on the second sorting result to obtain the first mapping relationship, where A i >A j .
[0115] In this embodiment, the first element is 1. That is, the more "1" elements in a binary string, the higher the corresponding confidence level. Among binary strings with the same frequency of occurrence, the larger the position of the "1" element (the later the "1" element is written into the binary string), the smaller its decimal value. Therefore, increasing the confidence level of the binary string with the smaller decimal value by one level and decreasing the confidence level of the binary string with the larger decimal value by one level can avoid or reduce jumps in confidence level. Those skilled in the art will understand that when using the first mapping relationship provided in this embodiment, the positive detection value needs to be assigned the element "1".
[0116] Please refer to Table 3 below. For example, when N is 5 and the first element is 1, repeating the permutation can produce 32 possible binary strings as shown in the table. There are four confidence levels: binary strings appearing 5 times and 4 times correspond to confidence level P4; binary strings appearing 3 times correspond to confidence level P3; binary strings appearing 2 times correspond to confidence level P2; and binary strings appearing 1 times and 0 times correspond to confidence level P1. Initial query data is generated based on decimal values and the first mapping relationship, where binary strings with the same frequency of occurrence correspond to the same confidence level.
[0117] Table 3
[0118]
[0119] Please refer to Table 4 below. For example, when N is 5 and the first element is 1, repeating the permutation can produce 32 possible binary strings as shown in the table. There are four confidence levels: binary strings appearing 5 times and 4 times correspond to confidence level P4; binary strings appearing 3 times correspond to confidence level P3; binary strings appearing 2 times correspond to confidence level P2; and binary strings appearing 1 times and 0 times correspond to confidence level P1. The decimal values corresponding to the binary strings appearing 3 times are sorted in ascending order, and the first two decimal values "7" and "11" are assigned confidence levels up to P4. Similarly, the decimal values corresponding to the binary strings appearing 2 times are sorted in ascending order, and the first two decimal values "3" and "5" are assigned confidence levels up to P3. Sort the decimal values corresponding to binary strings that appear 1 time in ascending order, and increase the confidence level of the first two decimal values "1" and "2" to P2.
[0120] Table 4
[0121]
[0122]
[0123] Please see Figure 4 The graph shows the change curve for assessing the confidence level of the target data using the first mapping relationship provided in this application. Hollow circles represent confidence levels, and solid dots represent the written elements "1" or "0". As can be seen from the graph, the confidence level of the target data does not experience sudden jumps; instead, it increases or decreases gradually based on quantification levels.
[0124] In one embodiment, S230 includes:
[0125] Based on the first sorting result, the binary string that appears N or N-1 times is compared with the highest confidence level P. K correspond,
[0126] Based on the first sorting result, the binary string that appears 0 times is associated with the lowest confidence level P1;
[0127] Based on the first sorting result, the binary string with the number of occurrences (Nm) is matched with the confidence level P. K-m Correspondingly, m and K are positive integers, and 2≤m≤N-1.
[0128] Please refer to Table 4, where the binary strings appearing 4 and 5 times correspond to the highest confidence level P4. The binary strings appearing a partial number of times (5-2) correspond to confidence levels P. (5-2)Correspondence. Binary strings with partial occurrences (5-3) and confidence levels P. (5-3) Correspondence. The binary string that appears 0 times corresponds to the confidence level P1.
[0129] In one embodiment, the binary string is (b N ...b2, b1), calculate the decimal value corresponding to the binary string using the following formula:
[0130]
[0131] Where A is a decimal value, i is any number of digits in the binary string, bi is the element with digit i in the binary string, and N is the number of digits in the binary string.
[0132] Since the binary string in the repeated permutation is a deduction of all possible cases of the target data, and the elements of each position in the binary string correspond to the detection values arranged in time in the target data, in this embodiment, the decimal value is calculated starting from the right side of the binary string. This is so that when the target data is converted into the data to be evaluated, only the element assigned to the latest detection value needs to be added to the right side of the data to be evaluated.
[0133] In one embodiment, the process further includes the following step before S110:
[0134] The detection period for acquiring target data is determined by the number of bits N based on the preset evaluation duration and detection period.
[0135] Those skilled in the art can set a preset evaluation duration according to the real-time requirements of the target data. A shorter preset evaluation duration results in better real-time performance, while a longer preset evaluation duration results in poorer real-time performance. Optionally, the preset evaluation duration can be less than or equal to 300ms. A larger bit depth N results in a larger actual amount of target data stored, and a smaller bit depth N results in a larger actual amount of target data stored. Optionally, the bit depth N can be less than or equal to 8, meaning the target data does not exceed 1 byte (8 bits). For example, if the target data is radar data with a detection period T of 50ms and a preset evaluation duration of 300ms, then 300ms / 50ms = 6, and the bit depth N can be set to 6 or 5.
[0136] Based on the confidence level assessment method provided in the above embodiments, this application also provides specific implementation methods of the confidence level assessment device. Please refer to the following embodiments.
[0137] First see Figure 5 The confidence level assessment device 500 provided in this application embodiment includes the following modules:
[0138] Module 51 is used to construct repeating permutations of an N-digit binary string, and to construct a first mapping relationship between decimal values and confidence levels based on the repeating permutations. The decimal values in the first mapping relationship are associated with the numbers 2 and 3 in the repeating permutations. N Each binary string corresponds to a specific binary string, where N is an integer greater than or equal to 2.
[0139] Module 52 is used to acquire the data to be evaluated, convert the data to be evaluated into a decimal comparison value, and the data to be evaluated is N-digit binary data.
[0140] The generation module 53 is used to determine the decimal value that is the same as the decimal comparison value according to the first mapping relationship, and set the confidence level corresponding to the decimal value as the confidence level of the data to be evaluated.
[0141] The confidence level assessment device provided in this application does not require training with a large amount of sample data, nor does it require constructing a probability distribution function. The generation process of the first mapping relationship is simple, and the accuracy of the confidence level generated by the first mapping relationship is not affected by the target data. In this application, by constructing a repeating permutation of binary strings, and constructing a first mapping relationship between decimal values and confidence levels based on the repeating permutation, the first mapping relationship can store only decimal values and confidence levels, resulting in a small storage space. By converting the data to be evaluated into decimal comparison values, the confidence level corresponding to the decimal comparison values can be obtained through the first mapping relationship. Compared with inputting binary strings for comparison or inputting the data to be evaluated for calculation, the comparison speed of decimal data is faster, and the real-time output of the confidence level is better.
[0142] As one implementation of this application, the confidence level assessment device 500 further includes:
[0143] Preprocessing module 54 is used to detect values obtained at each detection interval, process the detection values according to preset rules to obtain data to be evaluated, the data to be evaluated includes N detection elements generated along the time sequence, wherein the preset rules include:
[0144] If the detected value is not null, the detected element is written as the first element into the preset storage sequence corresponding to the data to be evaluated.
[0145] If the detected value is empty, the detected element is written as the second element in the preset storage sequence corresponding to the data to be evaluated. In this case, one of the first element and the second element is element "0" and the other is element "1".
[0146] As one implementation of this application, the preset rules also include:
[0147] If all the preset storage sequences have been written with detection elements, delete the detection element located at the first end of the preset storage sequence, and write the detection element corresponding to the current detection value to the second end of the preset storage sequence. In the preset storage sequence, N detection elements are arranged from the first end to the second end according to the order of writing time.
[0148] As one implementation of this application, the aforementioned construction module 51 is also used for:
[0149] Construct repeating permutations of an N-digit binary string;
[0150] Count the occurrences of the first element in each binary string, sort the repeated binary strings according to their occurrence counts, and obtain the first sorted result.
[0151] Based on the first sorting result, a second mapping relationship is constructed between binary strings and confidence levels, where each confidence level corresponds to one or more binary strings.
[0152] Calculate the decimal value corresponding to the binary string, and generate the first mapping relationship based on the decimal value and the second mapping relationship. The first mapping relationship includes the decimal value corresponding to each binary string and the confidence level corresponding to each decimal value.
[0153] As one implementation of this application, the aforementioned construction module 51 is also used for:
[0154] Calculate the decimal value corresponding to the binary string.
[0155] Initial query data is generated based on decimal values and a second mapping relationship. The initial query data includes binary strings, their corresponding decimal values, and their corresponding confidence levels. Binary strings with the same frequency of occurrence correspond to the same confidence level.
[0156] Sort multiple first strings based on their decimal values to obtain a second sorted result, where the first strings are binary strings that appear the same number of times.
[0157] Based on the second sorting result, update the confidence level of at least one second string by increasing or decreasing it by one level to obtain the first mapping relationship. Multiple first strings include second strings.
[0158] As one implementation of this application, the aforementioned construction module 51 is also used for:
[0159] When the first element is 1, the confidence level corresponding to at least one second string is increased by one level according to the second sorting result, resulting in the first mapping relationship, where the decimal value corresponding to the second string is A.i The decimal value of the binary strings other than the second string in the first string is A. j A i <A j ,
[0160] Alternatively, if the first element is 1, update the confidence level of at least one second string by decreasing it by one level based on the second sorting result to obtain the first mapping relationship, where A i >A j .
[0161] As one implementation of this application, the aforementioned construction module 51 is also used for:
[0162] Based on the first sorting result, the binary string that appears N or N-1 times is compared with the highest confidence level P. K correspond,
[0163] Based on the first sorting result, the binary string that appears 0 times is associated with the lowest confidence level P1;
[0164] Based on the first sorting result, the binary string with the number of occurrences (Nm) is matched with the confidence level P. K-m Correspondingly, m and K are positive integers, and 2≤m≤N-1.
[0165] As one implementation of this application, the binary string is (b N ...b2, b1), calculate the decimal value corresponding to the binary string using the following formula:
[0166]
[0167] Where A is a decimal value, i is any number of digits in the binary string, bi is the element with digit i in the binary string, and N is the number of digits in the binary string.
[0168] As one implementation of this application, the confidence level assessment device 500 further includes:
[0169] The determination module 55 is used to obtain the detection cycle of the target data and determine the number of bits N according to the preset evaluation duration and detection cycle.
[0170] The confidence level assessment device provided in this embodiment of the invention can implement the steps in the above method embodiments, and will not be repeated here to avoid repetition.
[0171] Figure 6 A schematic diagram of the hardware structure of the confidence level assessment device provided in an embodiment of this application is shown.
[0172] The confidence level assessment device may include a processor 701 and a memory 702 storing computer program instructions.
[0173] Specifically, the processor 701 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.
[0174] Memory 702 may include mass storage for data or instructions. For example, and not limitingly, memory 702 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 702 may include removable or non-removable (or fixed) media. Where appropriate, memory 702 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 702 is non-volatile solid-state memory.
[0175] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.
[0176] The processor 701 reads and executes computer program instructions stored in the memory 702 to implement any of the confidence level assessment methods in the above embodiments.
[0177] In one example, the confidence level assessment device may also include a communication interface 703 and a bus 710. Wherein, as Figure 6 As shown, the processor 701, memory 702, and communication interface 703 are connected through bus 710 and complete communication with each other.
[0178] The communication interface 703 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.
[0179] Bus 710 includes hardware, software, or both, that couples components of a confidence level assessment device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 710 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.
[0180] The confidence level assessment device can be based on the above embodiments to realize the combination of the confidence level assessment method and apparatus described above.
[0181] Furthermore, in conjunction with the confidence level assessment methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions, which, when executed by a processor, implement any of the confidence level assessment methods in the above embodiments and achieve the same technical effect. To avoid repetition, further details are omitted here. The aforementioned computer-readable storage medium may include non-transitory computer-readable storage media, such as read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, etc., and is not limited thereto.
[0182] In addition, this application also provides a computer program product, including computer program instructions, which, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.
[0183] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.
[0184] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.
[0185] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.
[0186] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.
[0187] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.
Claims
1. A confidence level assessment method, characterized in that, The method includes: Construct a repeating permutation of an N-digit binary string, and based on the repeating permutation, construct a first mapping relationship between decimal values and confidence levels. The decimal value in the first mapping relationship is associated with the number 2 in the repeating permutation. N Each of the given binary strings corresponds one-to-one, where N is an integer greater than or equal to 2. Acquire the data to be evaluated, and convert the data to be evaluated into a decimal comparison value. The data to be evaluated is N-bit binary data obtained by processing the detection values collected by the sensor. The binary data is used to characterize the detection status of the sensor in N consecutive detection cycles. Based on the first mapping relationship, a decimal value that is the same as the decimal comparison value is determined, and the confidence level corresponding to the decimal value is set as the confidence level of the data to be evaluated. The confidence level is used to evaluate the reliability of the data to be evaluated obtained by the sensor processing. The step of constructing a repeating permutation of an N-digit binary string, and constructing a first mapping relationship between decimal values and confidence levels based on the repeating permutation, includes: Construct repeating permutations of an N-digit binary string; The occurrence count of the first element in each of the binary strings is counted, and the binary strings in the repeated permutations are sorted according to the occurrence count to obtain a first sorting result, wherein the first element is element "0" or element "1". Based on the first sorting result, a second mapping relationship between the binary string and the confidence level is constructed, wherein the confidence level corresponds to one or more of the binary strings, and the number of occurrences is positively or negatively correlated with the confidence level; Calculate the decimal value corresponding to the binary string, and generate a first mapping relationship based on the decimal value and the second mapping relationship. The first mapping relationship includes the decimal value corresponding to each of the binary strings and the confidence level corresponding to each of the decimal values.
2. The confidence level assessment method according to claim 1, characterized in that, Before obtaining the data to be evaluated, the following steps are included: Detection values are obtained at each detection interval. These detection values are then processed according to preset rules to obtain the data to be evaluated. The data to be evaluated includes N detection elements generated sequentially over time. The preset rules include: If the detected value is not null, the detected element is written as the first element into a preset storage sequence corresponding to the data to be evaluated. If the detected value is empty, the detected element is written as the second element into the preset storage sequence corresponding to the data to be evaluated. Among them, one of the first element and the second element is element "0" and the other is element "1".
3. The confidence level assessment method according to claim 2, characterized in that, The preset rules also include: When all the preset storage sequences have the detection elements written to them, the detection element located at the first end of the preset storage sequence is deleted, and the detection element corresponding to the current detection value is written to the second end of the preset storage sequence. In the preset storage sequence, N detection elements are arranged from the first end to the second end according to the order of writing time.
4. The confidence level assessment method according to claim 1, characterized in that, The calculation of the decimal value corresponding to the binary string, and the generation of the first mapping relationship based on the decimal value and the second mapping relationship, include: Calculate the decimal value corresponding to the binary string. Initial query data is generated based on the decimal values and the second mapping relationship. The initial query data includes the binary strings, the decimal values corresponding to each of the binary strings, and the confidence levels corresponding to each of the binary strings. Binary strings with the same number of occurrences correspond to the same confidence level. A second sorting result is obtained by sorting multiple first strings according to the decimal values, wherein the first strings are binary strings that have the same number of occurrences. The confidence level of at least one second string is increased or decreased by one level based on the second sorting result to obtain the first mapping relationship, wherein the plurality of first strings include the second string.
5. The confidence level assessment method according to claim 4, characterized in that, The step of updating the confidence level of at least one second string by increasing or decreasing it by one level according to the second sorting result to obtain the first mapping relationship includes: When the first element is 1, the confidence level corresponding to at least one second string is updated by one level according to the second sorting result to obtain the first mapping relationship, wherein the decimal value corresponding to the second string is A. i The decimal value corresponding to the other binary strings among the plurality of first strings, excluding the second string, is A. j A i <A j , Alternatively, if the first element is 1, update the confidence level of at least one second string by decreasing it by one level according to the second sorting result to obtain the first mapping relationship, wherein A i >A j .
6. The confidence level assessment method according to claim 1, characterized in that, The step of constructing a second mapping relationship between the binary string and the confidence level based on the first sorting result includes: Based on the first sorting result, the binary string that appears N or N-1 times is compared with the highest confidence level P. K correspond, Based on the first sorting result, the binary string that appears 0 times is associated with the lowest confidence level P1; Based on the first sorting result, the binary string with an occurrence count of (Nm) is compared with the confidence level P. K-m Correspondingly, m and K are positive integers, and 2≤m≤N-1.
7. The confidence level assessment method according to claim 1, characterized in that, The binary string is (b N ...b2, b1), calculate the decimal value corresponding to the binary string using the following formula: Where A is the decimal value, and i is any number of digits in the binary string. Let i be the element with the number of bits in the binary string, and N be the number of bits in the binary string.
8. The confidence level assessment method according to claim 1, characterized in that, Before constructing the repeating permutation of the binary string of bit length N, the method further includes: The detection period for acquiring target data is determined by the number of bits N based on the preset evaluation duration and the detection period.
9. A confidence level assessment device, characterized in that, The device includes: A construction module is used to construct repeating permutations of an N-digit binary string, and based on the repeating permutations, to construct a first mapping relationship between decimal values and confidence levels, wherein the decimal value in the first mapping relationship is related to the 2 in the repeating permutation. N Each of the given binary strings corresponds one-to-one, where N is an integer greater than or equal to 2. The acquisition module is used to acquire the data to be evaluated and convert the data to be evaluated into a decimal comparison value. The data to be evaluated is N-bit binary data obtained by processing the detection values collected by the sensor. The binary data is used to characterize the detection status of the sensor in N consecutive detection cycles. The generation module is used to determine the decimal value that is the same as the decimal comparison value according to the first mapping relationship, and set the confidence level corresponding to the decimal value as the confidence level of the data to be evaluated. The confidence level is used to evaluate the reliability of the data to be evaluated obtained by the sensor processing. The step of constructing a repeating permutation of an N-digit binary string, and constructing a first mapping relationship between decimal values and confidence levels based on the repeating permutation, includes: Construct repeating permutations of an N-digit binary string; The occurrence count of the first element in each of the binary strings is counted, and the binary strings in the repeated permutations are sorted according to the occurrence count to obtain a first sorting result, wherein the first element is element "0" or element "1". Based on the first sorting result, a second mapping relationship between the binary string and the confidence level is constructed, wherein the confidence level corresponds to one or more of the binary strings, and the number of occurrences is positively or negatively correlated with the confidence level; Calculate the decimal value corresponding to the binary string, and generate a first mapping relationship based on the decimal value and the second mapping relationship. The first mapping relationship includes the decimal value corresponding to each of the binary strings and the confidence level corresponding to each of the decimal values.