Intelligent target identification method and system

Abstract

The invention discloses an intelligent target identification method and system, and relates to the technical field of target identification. Firstly, an initial data acquisition stage of a robot is analyzed to obtain preset parameters of intelligent target recognition of the robot, so that the influence of external factors on the recognition accuracy of the robot can be reduced, then target data acquisition is performed, a target data acquisition process is analyzed, a target recognition data processing process is optimized, and the recognition accuracy of the robot is improved. And finally, intelligent target identification quality analysis of the robot is carried out after the target identification data processing process is optimized, dynamic feedback adjustment is carried out on intelligent target identification of the robot, and it can be ensured that the robot maintains the optimal identification performance in the constantly changing environment.

Description

Technical Field

[0001] The present invention relates to the field of target recognition technology, and in particular to an intelligent target recognition method and system. Background Art

[0002] In the field of robot intelligent target recognition technology, with the continuous development of artificial intelligence and machine learning technology, the ability of robots to perform tasks in various complex environments has been significantly improved. By optimizing image processing algorithms and target recognition models, robots can more efficiently identify targets, process complex scene information, and improve the accuracy and reliability of recognition task execution. Intelligent target recognition not only improves the robot's autonomous operation capability, but also promotes the popularization and development of robot technology in practical applications, becoming an important part of realizing intelligent robot systems.

[0003] The prior art, such as the invention patent announcement with announcement number: CN107491714B, discloses an intelligent robot and its target object recognition method and device, which include the following steps: the intelligent robot obtains a first image of the target object; the intelligent robot preprocesses the first image to obtain a second image; the intelligent robot obtains color block features of the second image based on a predetermined color combination in a color space; and the intelligent robot matches the color block features of the second image with preset sample records, and determines the target object based on the matching results.

[0004] The prior art, such as the patent application with publication number CN111652069A, discloses a target recognition and positioning method for a mobile robot, which includes a recognition step and a positioning step. The sub-steps of the recognition step include an image acquisition step, a filtering step, an edge detection step, and a feature extraction step. The sub-steps of the positioning step include an imaging modeling step, a distortion correction step, a repositioning step, and a calculation step.

[0005] Combined with the above solutions, it is found that currently in the field of intelligent target recognition, only the recognition process and image matching process are usually analyzed. However, in robot intelligent target recognition, there will be environmental factors that interfere with the accuracy of the image acquisition process, which not only affects the robot's recognition accuracy, but may also lead to recognition failures. There is a risk of misidentification or missed recognition, which affects the robot's recognition accuracy and thus affects the robot's operating efficiency and safety. Summary of the Invention

[0006] In response to the deficiencies in the prior art, the present invention provides a cloud computing-based smart city weak current integrated management method and system, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the first aspect of the present invention is implemented through the following technical solutions: an intelligent target recognition method, including analyzing the initial data acquisition stage of the robot, obtaining the robot's intelligent target recognition preset parameters, and transmitting the robot's intelligent target recognition preset parameters to the robot's central controller.

[0008] The robot receives the preset parameters of the robot's intelligent target recognition through the central controller, collects target data, and then analyzes the target data collection process, thereby optimizing the target recognition data processing process.

[0009] After the target recognition data processing process is optimized, the robot's intelligent target recognition quality analysis is performed, and dynamic feedback adjustment of the robot's intelligent target recognition is performed.

[0010] Furthermore, the initial data collection stage of the robot is analyzed, and the specific analysis process is: the initial data collection stage of the robot is analyzed to obtain the initial data collection characteristic parameters of the robot, and the initial data collection characteristic parameters of the robot include the average value of the ambient light intensity, average vibration frequency, average data transmission frequency and average data transmission delay duration of the robot within a preset data collection period.

[0011] Furthermore, the robot intelligent target recognition preset parameters are obtained, and the specific process is: based on the robot's initial data acquisition characteristic parameters, the robot's initial data acquisition characteristic values are processed, and the robot's initial data acquisition characteristic values represent the robot's average ambient light intensity, average vibration frequency, average data transmission frequency and average data transmission delay duration, which together quantify the degree of data acquisition abnormality.

[0012] According to the initial data collection characteristic values of the robot, the preset parameters of the robot's intelligent target recognition are matched.

[0013] Furthermore, the target data acquisition process is analyzed, and the specific analysis process is: analyzing the target data acquisition process to obtain the robot's target data acquisition deviation data, and the robot's target data acquisition deviation data includes the robot's sensor response time deviation coefficient, data transmission delay average time, line of sight deviation coefficient and motion stability coefficient within a preset data acquisition period.

[0014] The target data acquisition deviation data of the robot is comprehensively processed to obtain the target data acquisition abnormality characteristic value of the robot. The target data acquisition abnormality characteristic value of the robot represents the quantitative result of the robot's sensor response time deviation coefficient, data transmission delay average time, line of sight deviation coefficient and motion stability coefficient on the degree of data acquisition abnormality.

[0015] The robot's initial data acquisition eigenvalues are matched with the target recognition influencing factors corresponding to each initial data acquisition eigenvalue interval stored in the intelligent target recognition database, and the target recognition influencing factors corresponding to the intervals in which the robot's initial data acquisition eigenvalues are located are counted and recorded as the robot's target recognition influencing factors.

[0016] The product of the robot's target data acquisition anomaly characteristic value and the robot's target recognition influencing factor is recorded as the robot's target data acquisition anomaly comprehensive evaluation value. The robot's target data acquisition anomaly comprehensive evaluation value is used to comprehensively quantify the robot's data acquisition anomaly degree, and then the robot's target data acquisition result is obtained according to the robot's target data acquisition anomaly comprehensive evaluation value analysis.

[0017] Furthermore, the target data collection result of the robot is obtained by analyzing the comprehensive evaluation value of the target data collection anomaly of the robot. The specific process is: the target data collection result of the robot includes normal target data collection and abnormal target data collection.

[0018] The robot's target data collection anomaly comprehensive evaluation value is compared with the set target data collection anomaly comprehensive evaluation threshold. If the robot's target data collection anomaly comprehensive evaluation value is higher than the set target data collection anomaly comprehensive evaluation threshold, the robot's target data collection result is marked as target data collection anomaly; otherwise, the robot's target data collection result is marked as target data collection normal. The target recognition data processing process is optimized according to the robot's target data collection result.

[0019] Furthermore, the target recognition data processing process is optimized, and the specific process is: extracting the target data collection result of the robot, if the target data collection result of the robot is that the target data collection is normal, then continuing to optimize the target data processing of the robot at the current data processing rate.

[0020] If the target data collection result of the robot is a target data collection anomaly, the difference between the target data collection anomaly comprehensive evaluation value of the robot and the set target data collection anomaly comprehensive evaluation threshold is extracted and recorded as the target data processing control value of the robot. It is matched with the data processing execution rate corresponding to each target data processing control value interval stored in the intelligent target recognition database, and the data processing execution rate corresponding to the interval in which the target data processing control value of the robot is located is counted and recorded as the target data processing execution rate of the robot. The target data processing of the robot is optimized at the corresponding target data processing execution rate.

[0021] Furthermore, the robot's intelligent target recognition quality analysis is performed after the target recognition data processing process is optimized. The specific analysis process is: the robot's intelligent target recognition quality analysis is performed after the target recognition data processing process is optimized to obtain the robot's intelligent target recognition quality data. The robot's intelligent target recognition quality data includes the robot's historical false detection rate, maximum recognition distance and average target recognition time within a preset data collection period.

[0022] Based on the robot's intelligent target recognition quality data, the robot's intelligent target recognition quality index value is processed. The robot's intelligent target recognition quality index value represents the quantitative result of the robot's historical false detection rate, maximum recognition distance and average target recognition time on the accuracy of data recognition.

[0023] Furthermore, the dynamic feedback adjustment of the robot's intelligent target recognition is specifically carried out as follows: the robot's intelligent target recognition quality index value is imported into the recognition quality analysis model to obtain the robot's intelligent target recognition quality information.

[0024] The robot's intelligent target recognition quality index value is marked as N.

[0025] The threshold of the intelligent target recognition quality indicator in the statistical recognition quality analysis model is marked as .

[0026] After the identification quality analysis model is processed, if , then the robot's intelligent target recognition quality information is marked as qualified intelligent target recognition quality.

[0027] like , the robot's intelligent target recognition quality information is marked as unqualified intelligent target recognition quality.

[0028] The robot's intelligent target recognition quality information is extracted, the frequency of data collection of the robot's intelligent target recognition is dynamically adjusted based on feedback, and early warning control of the robot's intelligent target recognition is performed based on the frequency of data collection of the robot's intelligent target recognition after feedback adjustment.

[0029] Furthermore, the dynamic feedback adjustment of the robot's intelligent target recognition data collection frequency is carried out, and the specific process is: extracting the robot's intelligent target recognition quality information, if the robot's intelligent target recognition quality information is qualified, then continue to perform target recognition with the current robot's intelligent target recognition data collection frequency.

[0030] If the robot's intelligent target recognition quality information shows that the intelligent target recognition quality is unqualified, the current robot's intelligent target recognition data collection frequency is added to the set intelligent target recognition data collection frequency supplementary value to obtain the robot's intelligent target recognition data collection frequency target value, and the current robot's intelligent target recognition data collection frequency is adjusted to the robot's intelligent target recognition data collection frequency target value for subsequent target recognition.

[0031] The second aspect of the present invention provides an intelligent target recognition system, including: a parameter preset value module, which is used to analyze the initial data acquisition phase of the robot, obtain the robot's intelligent target recognition preset parameters, and transmit the robot's intelligent target recognition preset parameters to the robot's central controller.

[0032] The data acquisition optimization module is used for the robot to receive the preset parameters of the robot's intelligent target recognition through the central controller, and to collect target data, and then analyze the target data collection process, thereby optimizing the target recognition data processing process.

[0033] The dynamic feedback adjustment module is used to analyze the quality of the robot's intelligent target recognition after the target recognition data processing process is optimized, and to perform dynamic feedback adjustment on the robot's intelligent target recognition.

[0034] The present invention has the following beneficial effects: (1) The present invention provides an intelligent target recognition method. First, the initial data acquisition stage of the robot is analyzed to help improve the robot's ability to adapt to the target environment. Then, the target data acquisition process is analyzed to identify potential error sources and bottlenecks. Finally, the quality of the robot's intelligent target recognition is analyzed, and dynamic feedback adjustment of the robot's intelligent target recognition is performed to help improve the accuracy of the robot's target recognition.

[0035] (2) The present invention can effectively reduce the impact of environmental factors on recognition results by analyzing the robot's initial data collection stage and presetting the robot's intelligent target recognition parameters. By accurately setting the preset parameters, the robot can adaptively adjust the parameters in a complex and dynamic environment, thereby improving the stability and accuracy of recognition, reducing the impact of factors such as lighting changes on target recognition, and improving the robot's adaptability.

[0036] (3) The present invention analyzes the target data acquisition process and optimizes the target recognition data processing process, which can reduce the negative impact of the data processing link on the recognition results. By optimizing the data processing process, the robot can process the collected data more accurately, thereby improving the accuracy and efficiency of target recognition, reducing the recognition deviation caused by imperfect processing process, and improving the overall performance and reliability of the robot.

[0037] (4) The present invention can significantly improve the accuracy and stability of target recognition, enhance the recognition quality, and optimize the accuracy of the recognition results by analyzing the robot's intelligent target recognition quality after optimizing the target recognition data processing process and performing dynamic feedback adjustment on the robot's intelligent target recognition, thereby effectively improving the quality and efficiency of the overall intelligent target recognition.

[0038] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of the method of the present invention.

[0040] Figure 2 This is a schematic diagram of system module connections of the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] See also Figure 1 As shown, an embodiment of the present invention provides a technical solution: an intelligent target recognition method, comprising analyzing the initial data acquisition phase of the robot, obtaining preset parameters for the robot's intelligent target recognition, and transmitting the preset parameters for the robot's intelligent target recognition to the robot's central controller.

[0043] The robot receives the preset parameters of the robot's intelligent target recognition through the central controller, collects target data, and then analyzes the target data collection process, thereby optimizing the target recognition data processing process.

[0044] After the target recognition data processing process is optimized, the robot's intelligent target recognition quality analysis is performed, and dynamic feedback adjustment of the robot's intelligent target recognition is performed.

[0045] Specifically, the initial data collection stage of the robot is analyzed, and the specific analysis process is: the initial data collection stage of the robot is analyzed to obtain the initial data collection characteristic parameters of the robot, and the initial data collection characteristic parameters of the robot include the average value of the ambient light intensity, average vibration frequency, average data transmission frequency and average data transmission delay duration of the robot within a preset data collection period.

[0046] It should be noted that the average value of the robot's ambient light intensity reflects the changes in the light intensity in the robot's environment. It can be directly measured using a light sensor. Multiple monitoring direction points are selected and the light intensity data is recorded. The average value is taken to obtain the average value of the robot's ambient light intensity. The robot's average vibration frequency reflects the degree of vibration of the robot during movement. It can be measured using a vibration sensor. The vibration frequency data of multiple monitoring time points are recorded and the average value is taken to obtain the average vibration frequency of the robot. The robot's average data transmission frequency refers to the average frequency when the robot system transmits data. The average data transmission frequency can be directly obtained through the robot's data transmission monitoring tool. The average data transmission delay duration of the robot reflects the delay of the data transmission process in the robot system. The data transmission delay duration of multiple data packets can be recorded through the delay measurement tool, and the average value can be taken to obtain the average data transmission delay duration of the robot.

[0047] Specifically, the preset parameters for the robot's intelligent target recognition are obtained. The specific process is: based on the robot's initial data acquisition characteristic parameters, the robot's initial data acquisition characteristic values are processed to obtain the robot's initial data acquisition characteristic values. The robot's initial data acquisition characteristic values represent the robot's average ambient light intensity, average vibration frequency, average data transmission frequency, and average data transmission delay duration, which together quantify the degree of data acquisition abnormality.

[0048] In this embodiment, the initial data collection characteristic values of the robot can be obtained by the following analysis method, and the specific analysis conditions are as follows: , Where, represents the initial data collection characteristic value of the robot, Indicates the average value of the robot’s ambient light intensity, Indicates the set reference ambient light intensity, Indicates the data collection impact factor corresponding to the set average value of ambient light intensity. represents the average vibration frequency of the robot, Indicates the data acquisition impact factor corresponding to the set unit average vibration frequency, represents the average frequency of data transmission by the robot, Indicates the data collection impact factor corresponding to the set unit data transmission average frequency, Indicates the average delay time of the robot's data transmission. It represents the data collection impact factor corresponding to the set average unit data transmission delay duration, and e represents a natural constant.

[0049] It should be explained that the data acquisition influencing factors corresponding to the average value of ambient light intensity, unit average vibration frequency, unit data transmission average frequency and unit data transmission delay average duration are respectively used to adjust the importance of the robot's average value of ambient light intensity, average vibration frequency, average data transmission frequency and average data transmission delay duration in the process of analyzing and obtaining the robot's initial data acquisition characteristic values. For example, in the intelligent target recognition database, there is a preset mapping relationship between the robot's initial data acquisition characteristic parameters and the corresponding data acquisition influencing factors. The data acquisition influencing factors corresponding to the real-time initial data acquisition characteristic parameters of the robot can be matched through the preset mapping relationship. The robot's initial data acquisition characteristic parameters are matched with the preset mapping relationship respectively to obtain the data acquisition influencing factors corresponding to the average value of ambient light intensity, unit average vibration frequency, unit data transmission average frequency and unit data transmission delay average duration.

[0050] In this embodiment, there is a correlation between the robot's average ambient light intensity, average vibration frequency, average data transmission frequency and average data transmission delay time, and they do not exist independently. For example, a strong average vibration frequency may interfere with the stability of the light sensor, thereby affecting the accuracy of the light intensity data. The average ambient light intensity and average vibration frequency may directly affect the average data transmission frequency, causing fluctuations in the average data transmission delay time and a decrease in the average data transmission frequency. Comprehensive analysis of the robot's initial data acquisition characteristic values can be used to evaluate the degree of influence of the environment on the robot's recognition accuracy.

[0051] According to the initial data collection characteristic values of the robot, the preset parameters of the robot's intelligent target recognition are matched.

[0052] It should be noted that the matching obtains the preset parameters of the robot intelligent target recognition. The specific process is to match the initial data acquisition characteristic values of the robot with the preset parameters of the robot intelligent target recognition corresponding to each initial data acquisition characteristic value interval stored in the intelligent target recognition database, and count the preset parameters of the robot intelligent target recognition corresponding to the interval in which the initial data acquisition characteristic value of the robot is located, and record them as the preset parameters of the robot intelligent target recognition.

[0053] It should be added that the preset parameters of the robot's intelligent target recognition include the robot's data processing rate and data collection frequency. The preset parameters of the robot's intelligent target recognition are analyzed based on the robot's initial data collection characteristic parameters, which can reduce the impact of environmental factors and the robot's own performance on the accuracy of target recognition. The larger the robot's initial data collection characteristic value, the greater the degree of abnormality in the data collection environment, and the greater the data processing rate and data collection frequency of the matched robot.

[0054] Specifically, the target data acquisition process is analyzed, and the specific analysis process is: the target data acquisition process is analyzed to obtain the target data acquisition deviation data of the robot, and the target data acquisition deviation data of the robot includes the sensor response time deviation coefficient of the robot within a preset data acquisition period, the average data transmission delay time, the line of sight deviation coefficient and the motion stability coefficient.

[0055] It should be noted that the robot's sensor response time deviation coefficient is used to measure the stability of the sensor response time. The robot's data recorder can be used to measure the sensor's response time at different monitoring time points, and then the average value is taken to obtain the sensor response time average value. The absolute value of the difference between the sensor's response time at different monitoring time points and the sensor response time average value is averaged, and the average value is divided by the sensor response time average value to obtain the robot's sensor response time deviation coefficient. The robot's line of sight deviation coefficient refers to the difference between the actual line of sight and the reference line of sight during the robot's movement. The ultrasonic sensor can be used to measure the actual line of sight at different monitoring time points, and the absolute value of the difference between the actual line of sight and the reference line of sight at different monitoring time points is averaged, and then divided by the reference line of sight to obtain the robot's line of sight deviation coefficient. The robot's motion stability coefficient can measure the robot's stability during movement. An accelerometer can be used to measure the acceleration change during each movement of the robot, and the acceleration change during each movement process is divided by the maximum design acceleration, and the average value is taken to obtain the robot's motion stability coefficient.

[0056] The target data acquisition deviation data of the robot is comprehensively processed to obtain the target data acquisition abnormality characteristic value of the robot. The target data acquisition abnormality characteristic value of the robot represents the quantitative result of the robot's sensor response time deviation coefficient, data transmission delay average time, line of sight deviation coefficient and motion stability coefficient on the degree of data acquisition abnormality.

[0057] In this embodiment, the abnormal characteristic value of the robot's target data collection can be obtained by the following analysis method. The specific analysis conditions are as follows: , Where, Indicates the abnormal characteristic value of the robot's target data collection, Indicates the robot's sensor response time deviation coefficient, Indicates the data acquisition anomaly assessment factor corresponding to the set sensor response time deviation coefficient, Indicates the average delay time of the robot's data transmission. Indicates the data collection anomaly assessment factor corresponding to the set average unit data transmission delay duration. represents the robot’s sight range deviation coefficient, Indicates the data acquisition anomaly assessment factor corresponding to the set sight distance deviation coefficient, represents the robot's motion stability coefficient, It represents the data acquisition anomaly assessment factor corresponding to the set motion stability coefficient, and e represents a natural constant.

[0058] It should be added that, in this embodiment, the data acquisition anomaly assessment factor corresponding to the preset sensor response time deviation coefficient, the data acquisition anomaly assessment factor corresponding to the average unit data transmission delay time, the data acquisition anomaly assessment factor corresponding to the line of sight deviation coefficient, and the data acquisition anomaly assessment factor corresponding to the motion stability coefficient are obtained from the intelligent target recognition database.

[0059] It needs to be explained that the data acquisition anomaly assessment factors corresponding to the sensor response time deviation coefficient, the average time of unit data transmission delay, the line of sight deviation coefficient and the motion stability coefficient are respectively used to adjust the importance of the robot's sensor response time deviation coefficient, the average time of unit data transmission delay, the line of sight deviation coefficient and the motion stability coefficient in the process of analyzing and obtaining the robot's target data acquisition anomaly characteristic value. For example, in the intelligent target recognition database, there is a pre-set mapping relationship between the robot's target data acquisition deviation data and the corresponding data acquisition anomaly assessment factor. The pre-set mapping relationship can be used to match the real-time data acquisition anomaly assessment factor corresponding to the robot's target data acquisition deviation data. The robot's target data acquisition deviation data is matched with the pre-set mapping relationship to obtain the data acquisition anomaly assessment factors corresponding to the sensor response time deviation coefficient, the average time of unit data transmission delay, the line of sight deviation coefficient and the motion stability coefficient.

[0060] In this embodiment, the robot's sensor response time deviation coefficient, data transmission delay average time, line of sight deviation coefficient and motion stability coefficient are correlated and do not exist independently. For example, fluctuations in the average data transmission delay time may lead to inaccurate line of sight measurement, thereby affecting the robot's perception accuracy of the environment. The average data transmission delay time is closely related to motion stability. Longer transmission delays will cause data processing lags, thereby affecting the robot's response speed to environmental changes during movement, and thus affecting the robot's motion stability. Comprehensive analysis can obtain the robot's target data acquisition abnormal characteristic values, which can more accurately evaluate the robot's target data acquisition abnormal characteristic values.

[0061] The robot's initial data acquisition eigenvalues are matched with the target recognition influencing factors corresponding to each initial data acquisition eigenvalue interval stored in the intelligent target recognition database, and the target recognition influencing factors corresponding to the intervals in which the robot's initial data acquisition eigenvalues are located are counted and recorded as the robot's target recognition influencing factors.

[0062] It should be added that the larger the initial data collection characteristic value of the robot, the greater the abnormality of the data collection environment, and the greater the impact on subsequent target recognition. Therefore, the target recognition influence factor of the matched robot is smaller.

[0063] The product of the robot's target data acquisition anomaly characteristic value and the robot's target recognition influencing factor is recorded as the robot's target data acquisition anomaly comprehensive evaluation value. The robot's target data acquisition anomaly comprehensive evaluation value is used to comprehensively quantify the robot's data acquisition anomaly degree, and then the robot's target data acquisition result is obtained according to the robot's target data acquisition anomaly comprehensive evaluation value analysis.

[0064] Specifically, the target data collection result of the robot is obtained according to the comprehensive evaluation value of the target data collection anomaly of the robot. The specific process is: the target data collection result of the robot includes normal target data collection and abnormal target data collection.

[0065] The robot's target data collection anomaly comprehensive evaluation value is compared with the set target data collection anomaly comprehensive evaluation threshold. If the robot's target data collection anomaly comprehensive evaluation value is higher than the set target data collection anomaly comprehensive evaluation threshold, the robot's target data collection result is marked as target data collection anomaly; otherwise, the robot's target data collection result is marked as target data collection normal. The target recognition data processing process is optimized according to the robot's target data collection result.

[0066] Specifically, the target recognition data processing process is optimized. The specific process is: extracting the target data collection result of the robot. If the target data collection result of the robot is normal, then continue to optimize the target data processing of the robot at the current data processing rate.

[0067] If the target data collection result of the robot is a target data collection anomaly, the difference between the target data collection anomaly comprehensive evaluation value of the robot and the set target data collection anomaly comprehensive evaluation threshold is extracted and recorded as the target data processing control value of the robot. It is matched with the data processing execution rate corresponding to each target data processing control value interval stored in the intelligent target recognition database, and the data processing execution rate corresponding to the interval in which the target data processing control value of the robot is located is counted and recorded as the target data processing execution rate of the robot. The target data processing of the robot is optimized at the corresponding target data processing execution rate.

[0068] It should be noted that the larger the robot's target data processing control value, the more abnormal the robot's collected data is, and the faster the robot's target data processing execution rate is. The faster the robot's target data processing execution rate is, the faster the system can process and correct data, thereby improving the accuracy and reliability of the data, and effectively reducing the impact of abnormal data on the overall task execution, which helps to improve the robot's real-time response capability and execution efficiency, and improve the robot's task completion quality.

[0069] Specifically, the robot's intelligent target recognition quality analysis is performed after the target recognition data processing process is optimized. The specific analysis process is: after the target recognition data processing process is optimized, the robot's intelligent target recognition quality analysis is performed to obtain the robot's intelligent target recognition quality data. The robot's intelligent target recognition quality data includes the robot's historical false detection rate, maximum recognition distance and average target recognition time within a preset data collection period.

[0070] It should be noted that the robot's historical false detection rate refers to the probability that the robot misjudges the background or non-target as the target during the target recognition process. The robot's maximum recognition distance refers to the farthest distance at which the robot can accurately identify the target. The robot's historical false detection rate and maximum recognition distance can be obtained from the robot's log system. The robot's average target recognition time refers to the average time required for the robot to complete the target recognition task. The start and end time of target recognition in multiple tasks can be recorded, the recognition time of each task can be calculated, and the recognition time of all tasks can be averaged to obtain the average target recognition time of the robot.

[0071] Based on the robot's intelligent target recognition quality data, the robot's intelligent target recognition quality index value is processed. The robot's intelligent target recognition quality index value represents the quantitative result of the robot's historical false detection rate, maximum recognition distance and average target recognition time on the accuracy of data recognition.

[0072] In this embodiment, the robot's intelligent target recognition quality index value can be obtained through the following analysis method, and the specific analysis conditions are as follows: , Where, Indicates the robot's intelligent target recognition quality index value, represents the robot's historical false positive rate, Indicates the recognition quality analysis factor corresponding to the set false detection rate, Indicates the maximum recognition distance of the robot, Indicates the recognition quality analysis factor corresponding to the set unit maximum recognition distance. represents the average target recognition time of the robot, It represents the recognition quality analysis factor corresponding to the set average time of unit target recognition, and e represents a natural constant.

[0073] It should be added that, in this embodiment, the recognition quality analysis factor corresponding to the preset false detection rate, the recognition quality analysis factor corresponding to the unit maximum recognition distance, and the recognition quality analysis factor corresponding to the unit target recognition average time are obtained from the intelligent target recognition database.

[0074] It needs to be explained that the recognition quality analysis factors corresponding to the false detection rate, unit maximum recognition distance and unit target recognition average time are respectively used to adjust the importance of the robot's historical false detection rate, maximum recognition distance and average target recognition time in the process of analyzing the robot's intelligent target recognition quality index value. For example, in the intelligent target recognition database, there is a pre-set mapping relationship between the robot's intelligent target recognition quality data and the corresponding recognition quality analysis factors. The pre-set mapping relationship can be used to match the recognition quality analysis factors corresponding to the real-time robot's intelligent target recognition quality data. The robot's intelligent target recognition quality data is matched with the pre-set mapping relationship to obtain the recognition quality analysis factors corresponding to the false detection rate, unit maximum recognition distance and unit target recognition average time.

[0075] In this implementation, the robot's historical false detection rate, maximum recognition distance, and average target recognition time are correlated and do not exist independently. For example, a larger recognition distance can improve the robot's coverage range, but it may also result in a higher false detection rate. Especially at longer distances, the accuracy and stability of the sensor may be limited. A longer average target recognition time is related to an increase in the false detection rate. A comprehensive analysis of the robot's intelligent target recognition quality index value can more accurately evaluate the quality of the robot's target recognition.

[0076] Specifically, dynamic feedback adjustment is performed on the robot's intelligent target recognition. The specific process is: importing the robot's intelligent target recognition quality index value into the recognition quality analysis model to obtain the robot's intelligent target recognition quality information.

[0077] It should be noted that the mathematical formula of the pavement paving quality analysis model is: ; Where, Indicates the pavement paving quality information of mastic asphalt pavement construction. It means the road paving quality is qualified. It means the road paving quality is not up to standard. Indicates the pavement paving quality index value of asphalt mastic pavement construction. Indicates the pavement paving quality index threshold.

[0078] The robot's intelligent target recognition quality index value is marked as N.

[0079] The threshold of the intelligent target recognition quality indicator in the statistical recognition quality analysis model is marked as .

[0080] After the identification quality analysis model is processed, if , then the robot's intelligent target recognition quality information is marked as qualified intelligent target recognition quality.

[0081] like , the robot's intelligent target recognition quality information is marked as unqualified intelligent target recognition quality.

[0082] The robot's intelligent target recognition quality information is extracted, the frequency of data collection of the robot's intelligent target recognition is dynamically adjusted based on feedback, and early warning control of the robot's intelligent target recognition is performed based on the frequency of data collection of the robot's intelligent target recognition after feedback adjustment.

[0083] Specifically, the frequency of data collection for intelligent target recognition of the robot is dynamically adjusted through feedback. The specific process is as follows: the quality information of intelligent target recognition of the robot is extracted. If the quality information of intelligent target recognition of the robot is qualified, target recognition is continued at the current frequency of data collection for intelligent target recognition of the robot.

[0084] If the robot's intelligent target recognition quality information shows that the intelligent target recognition quality is unqualified, the current robot's intelligent target recognition data collection frequency is added to the set intelligent target recognition data collection frequency supplementary value to obtain the robot's intelligent target recognition data collection frequency target value, and the current robot's intelligent target recognition data collection frequency is adjusted to the robot's intelligent target recognition data collection frequency target value for subsequent target recognition.

[0085] It needs to be explained that the analysis of the robot's intelligent target recognition quality data can reflect the degree of qualification of the robot's intelligent target recognition quality. If the robot's intelligent target recognition quality is poor, using the original default robot's intelligent target recognition data collection frequency for target recognition may cause inaccurate or lost data during the recognition process, affecting the accuracy and reliability of the recognition results. Therefore, it is necessary to adjust the robot's intelligent target recognition data collection frequency. Increasing the data collection frequency can enhance the accuracy of target recognition.

[0086] The second aspect of the present invention includes an intelligent target recognition system, including: a parameter preset value module, which is used to analyze the initial data collection phase of the robot, obtain the robot's intelligent target recognition preset parameters, and transmit the robot's intelligent target recognition preset parameters to the robot's central controller.

[0087] The data acquisition optimization module is used for the robot to receive the preset parameters of the robot's intelligent target recognition through the central controller, and to collect target data, and then analyze the target data collection process, thereby optimizing the target recognition data processing process.

[0088] The dynamic feedback adjustment module is used to analyze the quality of the robot's intelligent target recognition after the target recognition data processing process is optimized, and to perform dynamic feedback adjustment on the robot's intelligent target recognition.

[0089] It should be noted that an intelligent target recognition system further includes an intelligent target recognition database for storing a first parameter set, a second parameter set, and a third parameter set obtained by analyzing historical data.

[0090] The first parameter set includes the reference ambient light intensity, the data collection influence factor corresponding to the average ambient light intensity, the data collection influence factor corresponding to the unit average vibration frequency, the data collection influence factor corresponding to the unit data transmission average frequency, the data collection influence factor corresponding to the average duration of unit data transmission delay, and the robot intelligent target recognition preset parameters corresponding to each initial data collection characteristic value interval.

[0091] The second parameter set includes the data acquisition anomaly assessment factor corresponding to the sensor response time deviation coefficient, the data acquisition anomaly assessment factor corresponding to the average unit data transmission delay time, the data acquisition anomaly assessment factor corresponding to the line of sight deviation coefficient, the data acquisition anomaly assessment factor corresponding to the motion stability coefficient, the target recognition influencing factor corresponding to each initial data acquisition characteristic value interval, the target data acquisition anomaly comprehensive assessment threshold and the data processing execution rate corresponding to each target data processing control value interval.

[0092] The third parameter set includes the recognition quality analysis factor corresponding to the false detection rate, the recognition quality analysis factor corresponding to the unit maximum recognition distance, the recognition quality analysis factor corresponding to the unit target recognition average time, the pavement paving quality index threshold and the intelligent target recognition data collection frequency supplementary value.

[0093] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0094] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, numerous modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention.

Claims

1. An intelligent target recognition method, characterized in that: include: Analyze the robot's initial data collection phase to obtain preset parameters for the robot's intelligent target recognition, and transmit the preset parameters for the robot's intelligent target recognition to the robot's central controller; The robot receives the preset parameters of the robot's intelligent target recognition through the central controller, and collects target data, and then analyzes the target data collection process to optimize the target recognition data processing process; After the target recognition data processing process is optimized, the robot's intelligent target recognition quality analysis is performed, and dynamic feedback adjustment of the robot's intelligent target recognition is performed.

2. The intelligent target recognition method according to claim 1, characterized in that: The initial data collection phase of the robot is analyzed, and the specific analysis process is as follows: The initial data collection phase of the robot is analyzed to obtain the initial data collection characteristic parameters of the robot, which include the average ambient light intensity, average vibration frequency, average data transmission frequency, and average data transmission delay duration of the robot within a preset data collection period.

3. The intelligent target recognition method according to claim 2, characterized in that: The specific process of obtaining the preset parameters of the robot intelligent target recognition is as follows: Based on the initial data collection characteristic parameters of the robot, the initial data collection characteristic values of the robot are processed, wherein the initial data collection characteristic values of the robot represent the average value of the ambient light intensity, the average vibration frequency, the average data transmission frequency, and the average data transmission delay duration of the robot, which together quantify the degree of data collection abnormality; According to the initial data collection characteristic values of the robot, the preset parameters of the robot's intelligent target recognition are matched.

4. The intelligent target recognition method according to claim 1, characterized in that: The target data collection process is analyzed, and the specific analysis process is as follows: Analyze the target data acquisition process to obtain target data acquisition deviation data of the robot, wherein the target data acquisition deviation data of the robot includes a sensor response time deviation coefficient, an average data transmission delay time, a sight distance deviation coefficient, and a motion stability coefficient of the robot within a preset data acquisition period; Comprehensively processing the target data acquisition deviation data of the robot to obtain the target data acquisition abnormality characteristic value of the robot, wherein the target data acquisition abnormality characteristic value of the robot represents the quantitative result of the degree of data acquisition abnormality of the robot's sensor response time deviation coefficient, average data transmission delay time, sight distance deviation coefficient and motion stability coefficient; Match the robot's initial data collection eigenvalues with the target recognition influence factors corresponding to each initial data collection eigenvalue interval stored in the intelligent target recognition database, and calculate the target recognition influence factors corresponding to the intervals where the robot's initial data collection eigenvalues are located, which are recorded as the robot's target recognition influence factors; The product of the robot's target data acquisition anomaly characteristic value and the robot's target recognition influencing factor is recorded as the robot's target data acquisition anomaly comprehensive evaluation value. The robot's target data acquisition anomaly comprehensive evaluation value is used to comprehensively quantify the robot's data acquisition anomaly degree, and then the robot's target data acquisition result is obtained according to the robot's target data acquisition anomaly comprehensive evaluation value analysis.

5. The intelligent target recognition method according to claim 4, characterized in that: The target data collection result of the robot is obtained by analyzing the comprehensive evaluation value of the target data collection anomaly of the robot. The specific process is as follows: The target data collection results of the robot include normal target data collection and abnormal target data collection; The robot's target data collection anomaly comprehensive evaluation value is compared with the set target data collection anomaly comprehensive evaluation threshold. If the robot's target data collection anomaly comprehensive evaluation value is higher than the set target data collection anomaly comprehensive evaluation threshold, the robot's target data collection result is marked as target data collection anomaly; otherwise, the robot's target data collection result is marked as target data collection normal. The target recognition data processing process is optimized according to the robot's target data collection result.

6. The intelligent target recognition method according to claim 5, characterized in that: The target recognition data processing process is optimized, and the specific process is as follows: Extract the target data collection result of the robot. If the target data collection result of the robot is normal, continue to optimize the target data processing of the robot at the current data processing rate. If the target data collection result of the robot is a target data collection anomaly, the difference between the target data collection anomaly comprehensive evaluation value of the robot and the set target data collection anomaly comprehensive evaluation threshold is extracted and recorded as the target data processing control value of the robot. It is matched with the data processing execution rate corresponding to each target data processing control value interval stored in the intelligent target recognition database, and the data processing execution rate corresponding to the interval in which the target data processing control value of the robot is located is counted and recorded as the target data processing execution rate of the robot. The target data processing of the robot is optimized at the corresponding target data processing execution rate.

7. The intelligent target recognition method according to claim 1, characterized in that: After the target recognition data processing process is optimized, the robot's intelligent target recognition quality analysis is performed. The specific analysis process is as follows: After the target recognition data processing process is optimized, the robot's intelligent target recognition quality analysis is performed to obtain the robot's intelligent target recognition quality data, wherein the robot's intelligent target recognition quality data includes the robot's historical false detection rate, maximum recognition distance, and average target recognition time within a preset data collection period; Based on the robot's intelligent target recognition quality data, the robot's intelligent target recognition quality index value is processed. The robot's intelligent target recognition quality index value represents the quantitative result of the robot's historical false detection rate, maximum recognition distance and average target recognition time on the accuracy of data recognition.

8. The intelligent target recognition method according to claim 7, characterized in that: The specific process of dynamic feedback adjustment of the robot's intelligent target recognition is as follows: Importing the robot's intelligent target recognition quality index value into the recognition quality analysis model to obtain the robot's intelligent target recognition quality information; The robot's intelligent target recognition quality index value is marked as N; The threshold of the intelligent target recognition quality indicator in the statistical recognition quality analysis model is marked as ; After the identification quality analysis model is processed, if , then the robot's intelligent target recognition quality information is marked as qualified; like , then the robot's intelligent target recognition quality information is marked as unqualified; The robot's intelligent target recognition quality information is extracted, the frequency of data collection of the robot's intelligent target recognition is dynamically adjusted based on feedback, and early warning control of the robot's intelligent target recognition is performed based on the frequency of data collection of the robot's intelligent target recognition after feedback adjustment.

9. The intelligent target recognition method according to claim 8, characterized in that: The dynamic feedback adjustment of the robot's intelligent target recognition data collection frequency is carried out in the following specific process: Extract the robot's intelligent target recognition quality information. If the robot's intelligent target recognition quality information indicates that the intelligent target recognition quality is qualified, continue to perform target recognition at the current robot's intelligent target recognition data collection frequency. If the robot's intelligent target recognition quality information shows that the intelligent target recognition quality is unqualified, the current robot's intelligent target recognition data collection frequency is added to the set intelligent target recognition data collection frequency supplementary value to obtain the robot's intelligent target recognition data collection frequency target value, and the current robot's intelligent target recognition data collection frequency is adjusted to the robot's intelligent target recognition data collection frequency target value for subsequent target recognition.

10. An intelligent target recognition system, characterized in that: include: The parameter preset value module is used to analyze the initial data collection phase of the robot, obtain the preset parameters of the robot's intelligent target recognition, and transmit the preset parameters of the robot's intelligent target recognition to the robot's central controller; The data acquisition optimization module is used for the robot to receive the preset parameters of the robot's intelligent target recognition through the central controller, and to collect target data, and then analyze the target data collection process to optimize the target recognition data processing process; The dynamic feedback adjustment module is used to analyze the quality of the robot's intelligent target recognition after the target recognition data processing process is optimized, and to perform dynamic feedback adjustment on the robot's intelligent target recognition.

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