Big Dipper positioning-based anti-riot gun anti-loss management system, method and platform

Abstract

The invention discloses an anti-riot gun anti-loss management system, method and platform based on Beidou positioning, and relates to the technical field of anti-riot device management, and the method comprises the following steps: setting a clear initial activity range, namely an initial electronic fence range, for each anti-riot gun, and automatically triggering an alarm to remind a manager when the anti-riot gun exceeds the initial activity range; the geographic position of the riot gun is obtained in real time through the Beidou positioning unit installed on the riot gun. Potential track abnormity can be recognized in time by acquiring the position information of the riot gun in real time and combining historical track data analysis, even if the riot gun is still in a preset range, track data are deeply analyzed and predicted by using a machine learning model, the range of the electronic fence is dynamically adjusted when abnormity is detected, and the safety of the riot gun is improved. And the monitoring density is enhanced, so that potential risks are quickly responded, abuse or loss of the riot gun is prevented, the safety management capability of the riot gun is improved, and more intelligent and accurate real-time monitoring and risk prevention are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of riot control device management, and in particular to a Beidou positioning-based riot control gun anti-loss management system, method and platform. Background Art

[0002] Riot control devices are non-lethal equipment used to respond to and control violent incidents and are widely used in law enforcement and security. Common riot control equipment includes riot shields, riot guns, tear gas, rubber bullets, and pepper spray, etc., which are designed to effectively control riots or violent behavior while minimizing permanent damage. For example, riot shields can be used to resist projectiles and close-range attacks, riot guns fire rubber bullets or beanbag rounds to stop criminals, and tear gas and pepper spray are used to disperse crowds. The system installs Beidou positioning terminals on riot control devices to obtain their precise locations in real time and transmit data to the monitoring center, thereby achieving comprehensive tracking and management of the equipment.

[0003] A riot gun is a non-lethal law enforcement equipment, mainly used to control violence and riots. It can effectively curb the violent behavior or sabotage activities of lawbreakers by firing rubber bullets, bean bag rounds, tear gas or other non-lethal ammunition. The role of a riot gun is to quickly make the target lose its attack capability or be stopped through precise shooting, thereby avoiding escalation of confrontation and harming innocent people. It is usually used by security personnel, law enforcement agencies and riot control forces to deal with violent conflicts, riots or illegal gatherings. Compared with traditional firearms, the design of riot guns pays more attention to reducing personal injuries and adopts non-lethal methods to achieve the purpose of controlling the situation.

[0004] The prior art has the following deficiencies: Existing technology performs anti-loss management by presetting the activity range of riot guns. When the riot gun exceeds the range, the system will automatically trigger an alarm and alert the management personnel. However, when the riot gun is still within the preset range, but its historical trajectory is abnormal, the system will not automatically issue an alarm. In this case, the riot gun may be stolen or moved to other locations without authorization. Once violent conflicts escalate, riot guns, as key equipment, will not only fail to effectively control the situation, but may also intensify violence, cause a large number of casualties, and even cause social panic.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a management system, method and platform for preventing the loss of riot guns based on Beidou positioning. By acquiring the location information of riot guns in real time and combining historical trajectory data analysis, it is possible to timely identify potential trajectory anomalies. Even if the riot gun is still within the preset range, the trajectory data is deeply analyzed and predicted using a machine learning model. When anomalies are detected, the range of the electronic fence is dynamically adjusted, and the monitoring density is enhanced, so as to quickly respond to potential risks and prevent the abuse or loss of riot guns. Through this solution, the safety management capabilities of riot guns are improved, more intelligent and accurate real-time monitoring and risk prevention are achieved, the management efficiency and emergency response capabilities of riot guns are greatly enhanced, and the risk of illegal use or loss of equipment is effectively reduced to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for preventing the loss of riot guns based on Beidou positioning, comprising the following steps: Set a clear starting range for each anti-riot gun, that is, the starting electronic fence range. When the anti-riot gun exceeds the starting range, an alarm will be automatically triggered to remind the management personnel; The Beidou positioning unit installed on the riot gun obtains its geographic location in real time, and transmits the obtained location information to the monitoring center through the wireless communication network to monitor the real-time location of the riot gun; The location information of the riot gun is recorded in chronological order to form complete historical trajectory data; The historical trajectory is divided into several sub-segments according to the number of time points, and matched with the preset path. For the historical trajectory of each sub-segment, the features reflecting the potential position trajectory anomalies of the riot gun are extracted. The extracted features are deeply analyzed through feature engineering technology to evaluate their correlation with the abuse or loss of the riot gun. The analyzed features are used as feature vectors and input into a pre-trained and used machine learning model for prediction. The results output by the machine learning model are used to determine whether there is an abnormality in the trajectory of the current riot gun. When the machine learning model identifies anomalies in the historical trajectory of the riot gun, it dynamically narrows the activity range of the riot gun based on the analysis results, that is, adjusts the range of the electronic fence to respond to potential risks in a timely manner; at the same time, it increases the monitoring density to ensure real-time tracking of the riot gun.

[0008] Preferably, a clear starting range of activity is set for each riot control gun, and the specific steps are as follows: Determine the working area of ​​the riot gun based on actual usage scenarios and needs; Through geographic information systems or mapping tools, the specific range of the electronic fence is defined according to the boundaries of the area to ensure that the riot gun moves within the preset starting activity range.

[0009] Preferably, for each sub-segment of the historical trajectory, features reflecting the potential position trajectory anomalies of the riot gun are extracted therefrom, wherein the extracted features include the degree of deviation of the riot gun's motion trajectory from its preset task path and the density between the riot gun's motion trajectory points. The degree of deviation of the riot gun's motion trajectory from its preset task path and the density between the riot gun's motion trajectory points are deeply analyzed through feature engineering technology, and a frequent deviation from the preset path index and a motion path sparsity index are generated respectively. The frequent deviation from the preset path index and the motion path sparsity index are used to evaluate their correlation with the abuse or loss of the riot gun.

[0010] Preferably, for each sub-segment of the historical trajectory, the specific steps of deeply analyzing the deviation degree between the anti-riot gun movement trajectory and its preset task path through feature engineering technology to generate the frequent deviation from the preset path index are as follows: For each sub-segment's historical trajectory points and preset path points, the Euclidean distance from the historical trajectory points to the preset path points is first calculated. The Euclidean distance is used to represent the deviation between the historical trajectory points and the preset path points. Based on the deviation, it is determined whether it belongs to the "deviation from path" state. When the deviation is greater than the deviation threshold, it is considered to be off-path. Calculate the number of deviations and total deviation time of each trajectory segment, and consider the time interval between trajectory points, that is, the time difference between each trajectory point update, to calculate the frequent deviation frequency. The calculation expression is: , in: is an indicator function, indicating that when the deviation Greater than the deviation threshold When the value is 1, it means that the trajectory point deviates from the path. is the time interval between trajectory points, Indicates frequent deviation frequency, i represents the index of the trajectory point, N represents the total number of trajectory points on each subsegment; Taking into account the frequent deviation frequency, the deviation between the historical trajectory points and the preset path points, and the total time of the entire trajectory, the frequent deviation index of the preset path is calculated. The calculation expression is: , in: Indicates frequent deviations from the intended path indicator, is the total time of the entire trajectory, To adjust the deviation degree of impact.

[0011] Preferably, for each sub-segment of the historical trajectory, the specific steps of generating the motion path sparsity index by in-depth analysis of the density between the motion trajectory points of the riot gun through feature engineering technology are as follows: The time distribution and point density of the historical trajectory of the riot gun are evaluated. For each sub-segment of trajectory data, the time interval between the timestamp of each motion trajectory point and the previous and next trajectory points is analyzed, and the "trajectory time sparsity" is defined to represent the sparsity of trajectory points within a certain period of time. The formula is as follows: , in: and Respectively i and i +1 track point timestamp, and are the maximum and minimum timestamps of the sub-segment trajectory, represents the temporal sparsity of the trajectory; The weighting coefficient is introduced to consider the spatial distribution characteristics of the trajectory points and their temporal sparsity. By combining the temporal sparsity and the distribution changes between the trajectory points, the path sparsity index is comprehensively generated. The generated expression is: , in: represents the motion path sparsity index, and Respectively i The spatial coordinates of the trajectory points, and Respectively i +1 spatial coordinates of the trajectory point, is the weighted attenuation coefficient of the spatial distribution, which controls the influence of the spatial distribution between trajectory points.

[0012] Preferably, the analyzed frequent deviation from the predetermined path index and motion path sparsity index are input as feature vectors into a pre-trained and used machine learning model, and a trajectory anomaly risk index is generated based on the machine learning model. The trajectory anomaly risk index is used to determine whether there is an abnormality in the current trajectory of the riot control gun.

[0013] Preferably, the trajectory anomaly risk index generated when predicting the trajectory anomaly of the current anti-riot gun by a pre-trained and put into use machine learning model is compared and analyzed with a pre-set risk threshold to determine whether the trajectory of the current anti-riot gun is abnormal. The specific steps are as follows: If the trajectory anomaly risk index is greater than the risk threshold, it is determined that the current trajectory of the riot gun is abnormal; if the trajectory anomaly risk index is less than or equal to the risk threshold, it is determined that the current trajectory of the riot gun is not abnormal.

[0014] Preferably, when the machine learning model identifies that the historical trajectory of the riot gun is abnormal, the specific steps of dynamically reducing the activity range of the riot gun and increasing the monitoring density are as follows based on the analysis results: After identifying the abnormal trajectory, the activity range of the riot gun is dynamically adjusted according to the trajectory abnormality risk index, that is, the range of the electronic fence is adjusted. The initial activity range of the riot gun is set to , according to the trajectory anomaly risk index and risk thresholds Calculate adjusted range of motion , the calculation expression is: , in: It is the preset maximum trajectory abnormality risk index, which is used to control the extent of the reduction of the activity range; After adjusting the electronic fence, the real-time tracking of the riot gun is strengthened by increasing the monitoring density. The enhancement of monitoring density is achieved by increasing the location update frequency. The default location update frequency of the riot gun is set to , after trajectory anomaly detection, the enhanced update frequency Calculated by the following formula: , in, is the adjustment factor used to control the increase in the update frequency.

[0015] A Beidou positioning-based riot gun anti-loss management system, including an initial activity range setting module, a real-time location monitoring module, a historical trajectory recording module, a historical trajectory analysis and feature extraction module, a machine learning prediction and abnormality judgment module, and a dynamic response and monitoring adjustment module; The initial activity range setting module sets a clear starting activity range for each riot gun, that is, the starting electronic fence range. When the riot gun exceeds the starting activity range, an alarm is automatically triggered to remind the management personnel; The real-time location monitoring module obtains the geographic location of the riot gun in real time through the Beidou positioning unit installed on the riot gun, and transmits the obtained location information to the monitoring center through the wireless communication network to monitor the real-time location of the riot gun; The historical trajectory recording module records the location information of the riot gun in chronological order to form complete historical trajectory data; The historical trajectory analysis and feature extraction module divides the historical trajectory into several sub-segments according to the number of time points and matches them with the preset path. For each sub-segment of the historical trajectory, it extracts features that reflect the potential position trajectory anomalies of the riot gun. The extracted features are deeply analyzed through feature engineering technology to evaluate their correlation with the abuse or loss of the riot gun. The machine learning prediction and anomaly judgment module uses the analyzed features as feature vectors and inputs them into a pre-trained and used machine learning model for prediction. The output of the machine learning model is used to determine whether there is an anomaly in the trajectory of the current riot gun. Dynamic response and monitoring adjustment module: When the machine learning model identifies that there are anomalies in the historical trajectory of the riot gun, the activity range of the riot gun is dynamically reduced according to the analysis results, that is, the range of the electronic fence is adjusted to respond to potential risks in a timely manner; at the same time, the monitoring density is enhanced to ensure real-time tracking of the riot gun.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention can timely identify potential trajectory anomalies by acquiring the location information of the riot gun in real time and combining it with historical trajectory data analysis. Even if the riot gun is still within the preset range, the machine learning model is used to conduct in-depth analysis and prediction of the trajectory data, dynamically adjust the range of the electronic fence when anomalies are detected, and enhance the monitoring density, so as to quickly respond to potential risks and prevent the abuse or loss of the riot gun. Through this solution, the safety management capability of the riot gun is improved, more intelligent and accurate real-time monitoring and risk prevention are achieved, the management efficiency and emergency response capability of the riot gun are greatly enhanced, and the risk of illegal use or loss of the equipment is effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 The present invention is a method flow chart of a method for preventing loss of riot guns based on Beidou positioning.

[0019] Figure 2 This is a module schematic diagram of a riot gun anti-loss management system based on Beidou positioning in the present invention. DETAILED DESCRIPTION

[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0021] The present invention provides Figure 1 A method for preventing the loss of riot guns based on Beidou positioning is shown, comprising the following steps: Set a clear starting range for each anti-riot gun, that is, the starting electronic fence range. When the anti-riot gun exceeds the starting range, an alarm will be automatically triggered to remind the management personnel, ensuring that the anti-riot gun is always in the authorized area to prevent unauthorized movement or potential abuse risks; To set a clear starting range for each riot gun, you first need to determine the working area of ​​the riot gun based on the actual usage scenario and needs. For example, when law enforcement officers use it, the starting range can be set around a specific law enforcement area, patrol route or public activity area. Then, through the geographic information system (GIS) or map tools, define the specific range of the electronic fence according to the boundaries of the area to ensure that the riot gun moves within the preset starting range. If the riot gun exceeds the set range, the system will automatically trigger an alarm.

[0022] The Beidou positioning unit installed on the anti-riot gun obtains its geographic location in real time, and transmits the acquired location information to the monitoring center through wireless communication networks (such as GSM, Wi-Fi, etc.), so as to realize real-time location monitoring of the anti-riot gun and provide data support for subsequent trajectory analysis; The location information of the riot gun is recorded in chronological order to form complete historical trajectory data; These historical trajectory data include each location update, dwell time, and movement path of the riot gun. The role of this step is to provide basic data for subsequent trajectory analysis and anomaly detection.

[0023] The historical trajectory is divided into several sub-segments according to the number of time points, and matched with the preset path. For the historical trajectory of each sub-segment, the features reflecting the potential position trajectory anomalies of the riot gun are extracted. The extracted features are deeply analyzed through feature engineering technology to evaluate their correlation with the abuse or loss of the riot gun. Dividing the historical trajectory into several sub-segments according to the number of time points and matching them with the preset path means dividing the historical movement path of the riot gun (including the timestamp and coordinates of each position) into several small segments, each of which contains a certain number of time points or location information. Each sub-segment represents the movement of the riot gun over a period of time. Then, these sub-segments are compared with the preset path of the riot gun (that is, the ideal route that the riot gun should follow) to check whether the actual trajectory in each sub-segment conforms to the predetermined route or range of activity. The purpose of this process is to more accurately identify abnormal behavior of the riot gun by refining the historical trajectory and comparing it with the preset path.

[0024] For the historical trajectory of each sub-segment, features reflecting the potential position trajectory anomalies of the riot gun are extracted, where the extracted features include the degree of deviation of the riot gun's motion trajectory from its preset task path and the density between the riot gun's motion trajectory points. The degree of deviation of the riot gun's motion trajectory from its preset task path and the density between the riot gun's motion trajectory points are deeply analyzed through feature engineering technology, and the frequent deviation from the preset path index and the motion path sparsity index are generated respectively. The frequent deviation from the preset path index and the motion path sparsity index are used to evaluate their correlation with the abuse or loss of the riot gun.

[0025] The higher the deviation of the riot gun's historical trajectory from its preset mission path, it usually indicates that the riot gun is at a higher risk of being misused or lost. This is because riot guns should follow predetermined paths when performing tasks, such as patrol routes, guard areas, or specific mission locations. If the historical trajectory shows that the riot gun frequently deviates from the normal path, especially when there is no clear mission requirement, this may indicate that the riot gun has been illegally moved or abused. For example, if a riot gun suddenly enters an unauthorized area or continuously deviates from the preset working range, it may mean that the equipment has been stolen or used improperly. Therefore, trajectory deviation not only reflects the physical movement of the equipment, but can also be an obvious sign of abuse, theft, or unauthorized use, indicating that the security of the equipment has been threatened.

[0026] For each sub-segment’s historical trajectory, the specific steps for generating frequent deviation indicators from the preset path by deeply analyzing the deviation degree between the anti-riot gun’s trajectory and its preset task path through feature engineering technology are as follows: For each sub-segment's historical trajectory point and preset path point (the corresponding point of the preset path), first calculate the Euclidean distance from the historical trajectory point to the preset path point. The Euclidean distance is used to represent the deviation between the historical trajectory point and the preset path point. Based on the deviation, determine whether it belongs to the "deviation from path" state. When the deviation is greater than the deviation threshold, it is considered to be off-path. The purpose of this step is to identify possible abnormal behaviors in the trajectory, such as device theft or abuse, and to discover potential risks in a timely manner.

[0027] Calculate the number of deviations and total deviation time of each trajectory segment, and consider the time interval between trajectory points, that is, the time difference between each trajectory point update, to calculate the frequent deviation frequency. The calculation expression is: , in: is an indicator function, indicating that when the deviation Greater than the deviation threshold When the value is 1, it means that the trajectory point deviates from the path. is the time interval between trajectory points, Indicates frequent deviation frequency,i represents the index of the trajectory point, N represents the total number of trajectory points on each subsegment; The frequency and degree of trajectory deviation are measured by calculating the severity of each deviation point in the historical trajectory of the riot gun and combining the time difference between each trajectory point.

[0028] Finally, the frequent deviation frequency, the deviation between the historical trajectory points and the preset path points, and the total time of the entire trajectory are comprehensively considered to calculate the frequent deviation index of the preset path. The calculation expression is: , in: Indicates frequent deviations from the intended path indicator, is the total time of the entire trajectory, To adjust the deviation degree of impact.

[0029] It can be seen from the frequent deviation from the predetermined path indicator that for each sub-segment's historical trajectory, the greater the performance value of the frequent deviation from the predetermined path indicator generated by in-depth analysis of the deviation between the riot gun's motion trajectory and its preset task path through feature engineering technology, the higher the deviation between the riot gun's historical trajectory and its preset task path, which in turn reflects the higher risk of abuse or loss of the riot gun. When the riot gun's motion trajectory frequently deviates from the preset path, it usually means that the device has been illegally used, moved, or deviated from the original mission area. For example, the riot gun may be stolen and transferred to an unauthorized area, or maliciously operated and used for non-predetermined purposes. Frequent deviations usually lead to trajectory anomalies. The system can effectively assess whether the riot gun has a high risk of abuse or loss by analyzing the frequency and severity of these deviations to generate the frequent deviation from the predetermined path indicator. If the frequent deviation from the predetermined path indicator is high, it means that there is a significant anomaly in the trajectory of the riot gun, which increases the possibility of abuse, loss, or illegal use.

[0030] The sparser the density between the historical trajectory points of the riot gun, the higher the correlation between the historical trajectory of the riot gun and abuse or loss, and the greater the risk. Generally, when a riot gun is performing a task, its movement trajectory should be continuous and relatively stable. If the intervals between the trajectory points are too large, or there is no location information update for a long time in certain areas, it may mean that the riot gun has not been active during this period, or its signal has been lost, resulting in incomplete trajectory records. This sparse trajectory may indicate that the riot gun has been illegally parked, disassembled, or moved to a place that cannot be monitored, or even stolen. Therefore, the sparse trajectory density is a warning signal that the riot gun may be being used illegally or has been lost, which requires high attention and further safety inspections.

[0031] For each sub-segment of the historical trajectory, the specific steps of generating the motion path sparsity index by deeply analyzing the density between the motion trajectory points of the riot gun through feature engineering technology are as follows: The time distribution and point density of the historical trajectory of the riot gun are evaluated. For each sub-segment of trajectory data, the time stamp of each motion trajectory point and the time interval between the previous and next trajectory points are analyzed. By evaluating the time difference, the movement frequency of the riot gun in the sub-segment is derived. The "trajectory time sparsity" is defined to represent the sparsity of trajectory points within a certain period of time. The formula is as follows: , in: and Respectively i and i +1 track point timestamp, and are the maximum and minimum timestamps of the sub-segment trajectory, represents the temporal sparsity of the trajectory; This step evaluates the sparsity of the trajectory by calculating the ratio of the time interval between every two trajectory points to the total time period. The greater the time sparsity, the longer the time interval between the trajectory points, indicating that the motion trajectory is more sparse, which may reflect that the device is stagnant or loses contact at some stage. The evaluation of time distribution provides a basic framework for subsequent sparsity calculations.

[0032] The weighting coefficient is introduced to consider the spatial distribution characteristics of trajectory points and their temporal sparsity. By combining the temporal sparsity and the distribution changes between trajectory points (for example, the relationship between the temporal sparsity in each sub-segment and the spatial distribution of trajectory points), the path sparsity index is comprehensively generated. The generated expression is: , in: represents the motion path sparsity index, and Respectively i The spatial coordinates of the trajectory points, and Respectively i +1 spatial coordinates of the trajectory point, is the weighted attenuation coefficient of the spatial distribution, which controls the influence of the spatial distribution between trajectory points; By introducing the attenuation of spatial distance and considering the actual distribution of trajectory points, the farther the trajectory points are, the greater the impact. The motion path sparsity index finally calculated reflects the combined effect of the temporal sparsity and spatial distribution of trajectory points. The larger the value, the higher the trajectory sparsity and the greater the risk of abuse or loss of riot guns. The purpose of this step is to comprehensively consider the time and space factors, and improve the sensitivity of the motion path sparsity index through the weighted attenuation function to ensure that abnormal trajectories can be identified in a timely manner.

[0033] It can be seen from the motion path sparsity index that for each sub-segment of the historical trajectory, the larger the performance value of the motion path sparsity index generated by in-depth analysis of the density between the motion trajectory points of the riot gun through feature engineering technology, the higher the correlation between the historical trajectory of the riot gun and abuse or loss, and the greater the risk. Specifically, a larger motion path sparsity index means that the riot gun has obvious trajectory point sparsity in certain time periods, that is, in certain areas or time periods, the riot gun has not undergone normal movement or update, which may cause the equipment to be lost or illegally parked. This sparsity reflects that the riot gun may have lost contact or been illegally moved for a period of time, thereby reducing its traceability in normal areas and increasing the risk of abuse or loss. Therefore, the motion path sparsity index can be used as an effective indicator to help timely detect potential safety hazards.

[0034] The analyzed features are used as feature vectors and input into a pre-trained and used machine learning model for prediction. The results output by the machine learning model are used to determine whether there is an abnormality in the trajectory of the current riot gun. The analyzed frequent deviation from the predetermined path index and motion path sparsity index are input as feature vectors into a pre-trained and put into use machine learning model. A trajectory anomaly risk index is generated based on the machine learning model. The trajectory anomaly risk index is used to determine whether there is an abnormality in the current trajectory of the riot control gun.

[0035] A pre-trained and used machine learning model refers to a model that is trained, learned, and optimized through historical data, and is designed to predict and determine whether there are anomalies in the trajectory of the riot gun. The key feature of this model is that it has been extensively trained on historical data sets to obtain effective parameters and weights so that it can identify patterns and features related to trajectory anomalies. By training on labeled trajectory data, the machine learning model learns how to judge the abnormality of the trajectory based on the characteristics of the trajectory (such as frequent deviation from the predetermined path indicator and motion path sparsity indicator). This model usually uses supervised learning methods, specifically, classification algorithms (such as support vector machines, random forests, or neural networks, etc.), and builds a classifier by learning the normal and abnormal patterns of historical trajectories, which can automatically predict new riot gun trajectory data. The model training process includes extracting features from a large amount of historical trajectory data, marking which trajectories are normal and which are abnormal, and then adjusting the parameters in the model so that it can accurately judge the abnormality of the trajectory of the riot gun when new data is input. Finally, the trained and verified machine learning model is put into use, which can automatically process real-time data and trigger alarms when the trajectory of the riot gun is abnormal.

[0036] The core advantage of the machine learning model in use lies in its adaptability and efficiency. Through training, the model can identify complex patterns in the trajectory that are difficult to detect through simple rules. For example, traditional methods may only rely on a preset trajectory deviation range or threshold to determine whether the trajectory is normal, while machine learning models can discover more complex abnormal patterns by analyzing historical trajectory data, such as sparse distribution of trajectory points, frequent deviations from normal paths, and other subtle changes, especially those involving the combination of multi-dimensional data (such as time, space, and motion patterns). Through input based on feature vectors (such as frequent deviations from the predetermined path index and motion path sparsity index), the machine learning model can comprehensively consider the influence of multiple factors to make more accurate predictions and classifications. This intelligent judgment not only improves the accuracy of detection, but also can promptly detect potential risks of abuse or loss, and improve the overall safety and emergency response capabilities of the riot gun management system.

[0037] The machine learning model is not specifically limited here, and can achieve the goal of frequently deviating from the predetermined path indicators. and motion path sparsity index Conduct comprehensive analysis to generate trajectory anomaly risk index In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; the trajectory abnormality risk index The generated expression is: , where , They are the frequent deviation from the planned path indicators and motion path sparsity index The preset scaling factor of , The preset scaling factor refers to the parameter used in a machine learning model or algorithm to weight the contribution of different input features to the final result. Specifically, in the formula, the preset scaling factor is used to adjust the contribution of each feature (such as frequent deviation from the predetermined path indicator) to the final result. and motion path sparsity index ) for the final output (i.e., trajectory anomaly risk index ). These coefficients determine the importance of each input feature and are set as constants in the model to balance the relative contributions of each feature. For example, Indicates frequent deviation from the path index The weight of Represents the motion path sparsity index weight, and usually By adjusting these proportional coefficients, the model can be made more sensitive or more balanced to a certain feature in different situations, thus affecting the final prediction effect.

[0038] It can be seen from the trajectory anomaly risk index that, for the historical trajectory of each sub-segment, the greater the performance value of the frequent deviation from the predetermined path index generated after in-depth analysis of the degree of deviation between the riot gun's motion trajectory and its preset task path through feature engineering technology, the greater the performance value of the motion path sparsity index generated after in-depth analysis of the density between the riot gun's motion trajectory points through feature engineering technology, that is, the greater the performance value of the trajectory anomaly risk index generated when the current riot gun's trajectory anomaly is predicted by a pre-trained and put into use machine learning model, the greater the risk of the riot gun having a trajectory anomaly, and vice versa.

[0039] The trajectory anomaly risk index generated when predicting the current anti-riot gun's trajectory anomaly through the pre-trained and put into use machine learning model is compared and analyzed with the pre-set risk threshold to determine whether the current anti-riot gun's trajectory is abnormal. The specific steps are as follows: If the trajectory anomaly risk index is greater than the risk threshold, it is determined that the current trajectory of the riot gun is abnormal; if the trajectory anomaly risk index is less than or equal to the risk threshold, it is determined that the current trajectory of the riot gun is not abnormal.

[0040] When the machine learning model identifies anomalies in the historical trajectory of the riot gun, it dynamically reduces the activity range of the riot gun based on the analysis results, that is, adjusts the range of the electronic fence to respond to potential risks in a timely manner; at the same time, it increases the monitoring density to ensure real-time tracking of the riot gun; When the machine learning model identifies anomalies in the historical trajectory of the riot gun, the specific steps for dynamically narrowing the activity range of the riot gun and increasing the monitoring density are as follows based on the analysis results: After identifying the abnormal trajectory, the activity range of the riot gun is dynamically adjusted according to the trajectory abnormality risk index, that is, the range of the electronic fence is adjusted. The initial activity range of the riot gun is set to , according to the trajectory anomaly risk index and risk thresholds Calculate adjusted range of motion , the calculation expression is: , in: It is the preset maximum trajectory abnormality risk index (indicating the maximum value of completely abnormal trajectory), which is used to control the extent of reduction of activity range; This step is to calculate the trajectory abnormality risk index and risk thresholds The difference is used as an adjustment factor to reduce the activity range of the riot gun. The higher the trajectory anomaly risk index, the more serious the trajectory anomaly, the greater the reduction of the electronic fence, and the smaller the activity range of the riot gun, thereby limiting its activities in potential risk areas. The purpose of this step is to reduce the risk of riot guns entering unsafe or unauthorized areas by dynamically adjusting the electronic fence.

[0041] After adjusting the electronic fence, the real-time tracking of the riot gun is strengthened by increasing the monitoring density. The enhancement of monitoring density is achieved by increasing the location update frequency. The default location update frequency of the riot gun is set to , after trajectory anomaly detection, the enhanced update frequency Calculated by the following formula: , in, is the adjustment coefficient, which is used to control the increase of the update frequency; When the trajectory anomaly risk index is high, the enhanced update frequency This allows the location of the riot gun to be updated at a higher frequency, ensuring that the system can monitor its position changes in real time and respond to abnormal situations in a timely manner. By increasing the monitoring density, the system can track the location of the riot gun more accurately, ensuring that any potential dangers or abuses can be quickly discovered and handled. The purpose of this step is to improve the real-time tracking capabilities of the riot gun, ensuring rapid response and accurate judgment of trajectory abnormalities.

[0042] By dynamically adjusting the range of riot guns and enhancing monitoring density, we can respond to abnormal trajectories in a timely manner and reduce potential risks. By reducing the range of the electronic fence, the activities of riot guns are restricted to a smaller and safer area, reducing the possibility of entering risky areas or unauthorized areas. At the same time, by increasing the frequency of location updates, enhanced monitoring density ensures that the dynamic changes of riot guns are tracked in real time with higher accuracy, so as to quickly detect any abnormal behavior or potential threats. This comprehensive response mechanism helps to strengthen the safety of riot guns and ensure that rapid response measures can be taken in the event of abnormal trajectories to prevent serious consequences such as abuse or loss.

[0043] The above-mentioned Beidou positioning-based anti-riot gun anti-loss management method can effectively solve the limitation of the existing technology that anti-riot guns only rely on the preset activity range for monitoring. This method obtains the location information of the anti-riot gun in real time and combines it with historical trajectory data analysis to promptly identify potential trajectory anomalies, even if the anti-riot gun is still within the preset range. The machine learning model is used to conduct in-depth analysis and prediction of trajectory data, dynamically adjust the range of the electronic fence when anomalies are detected, and enhance the monitoring density, so as to quickly respond to potential risks and prevent the abuse or loss of anti-riot guns. This solution improves the safety management capabilities of anti-riot guns, realizes more intelligent and accurate real-time monitoring and risk prevention, greatly enhances the management efficiency and emergency response capabilities of anti-riot devices, and effectively reduces the risk of illegal use or loss of equipment.

[0044] The present invention provides Figure 2 The anti-riot gun anti-loss management system based on Beidou positioning shown includes an initial activity range setting module, a real-time location monitoring module, a historical trajectory recording module, a historical trajectory analysis and feature extraction module, a machine learning prediction and abnormality judgment module, and a dynamic response and monitoring adjustment module; The initial activity range setting module sets a clear starting activity range for each riot gun, that is, the starting electronic fence range. When the riot gun exceeds the starting activity range, an alarm is automatically triggered to remind the management personnel; The real-time location monitoring module obtains the geographic location of the riot gun in real time through the Beidou positioning unit installed on the riot gun, and transmits the obtained location information to the monitoring center through the wireless communication network to monitor the real-time location of the riot gun; The historical trajectory recording module records the location information of the riot gun in chronological order to form complete historical trajectory data; The historical trajectory analysis and feature extraction module divides the historical trajectory into several sub-segments according to the number of time points and matches them with the preset path. For each sub-segment of the historical trajectory, it extracts features that reflect the potential position trajectory anomalies of the riot gun. The extracted features are deeply analyzed through feature engineering technology to evaluate their correlation with the abuse or loss of the riot gun. The machine learning prediction and anomaly judgment module uses the analyzed features as feature vectors and inputs them into a pre-trained and used machine learning model for prediction. The output of the machine learning model is used to determine whether there is an anomaly in the trajectory of the current riot gun. Dynamic response and monitoring adjustment module: When the machine learning model identifies that there are anomalies in the historical trajectory of the riot gun, the activity range of the riot gun is dynamically reduced according to the analysis results, that is, the range of the electronic fence is adjusted to respond to potential risks in a timely manner; at the same time, the monitoring density is enhanced to ensure real-time tracking of the riot gun.

[0045] The anti-riot gun anti-loss management method based on Beidou positioning provided in the embodiment of the present invention is realized by the anti-riot gun anti-loss management system based on Beidou positioning. The specific methods and processes of the anti-riot gun anti-loss management system based on Beidou positioning are detailed in the embodiment of the anti-riot gun anti-loss management method based on Beidou positioning, which will not be repeated here.

[0046] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0047] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0048] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0049] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for preventing the loss of riot guns based on Beidou positioning, characterized in that: The following steps are involved: Set a clear starting range for each anti-riot gun, that is, the starting electronic fence range. When the anti-riot gun exceeds the starting range, an alarm will be automatically triggered to remind the management personnel; The Beidou positioning unit installed on the anti-riot gun obtains its geographic location in real time, and transmits the obtained location information to the monitoring center through the wireless communication network to monitor the real-time location of the anti-riot gun; The location information of the riot gun is recorded in chronological order to form complete historical trajectory data; The historical trajectory is divided into several sub-segments according to the number of time points, and matched with the preset path. For the historical trajectory of each sub-segment, the features reflecting the potential position trajectory anomalies of the riot gun are extracted. The extracted features are deeply analyzed through feature engineering technology to evaluate their correlation with the abuse or loss of the riot gun. The analyzed features are used as feature vectors and input into a pre-trained and used machine learning model for prediction. The results output by the machine learning model are used to determine whether there is an abnormality in the trajectory of the current riot gun. When the machine learning model identifies anomalies in the historical trajectory of the riot gun, it dynamically narrows the activity range of the riot gun based on the analysis results, that is, adjusts the range of the electronic fence to respond to potential risks in a timely manner; at the same time, it increases the monitoring density to ensure real-time tracking of the riot gun.

2. According to claim 1, a Beidou positioning-based anti-riot gun loss prevention management method is characterized in that: Set a clear starting range for each riot gun. The specific steps are as follows: Determine the working area of ​​the riot gun based on actual usage scenarios and needs; Through geographic information systems or mapping tools, the specific range of the electronic fence is defined according to the boundaries of the area to ensure that the riot gun moves within the preset starting activity range.

3. The method for preventing loss of riot guns based on Beidou positioning according to claim 1 is characterized in that: For the historical trajectory of each sub-segment, features reflecting the potential position trajectory anomalies of the riot gun are extracted, where the extracted features include the degree of deviation of the riot gun's motion trajectory from its preset task path and the density between the riot gun's motion trajectory points. The degree of deviation of the riot gun's motion trajectory from its preset task path and the density between the riot gun's motion trajectory points are deeply analyzed through feature engineering technology, and the frequent deviation from the preset path index and the motion path sparsity index are generated respectively. The frequent deviation from the preset path index and the motion path sparsity index are used to evaluate their correlation with the abuse or loss of the riot gun.

4. The method for preventing loss of riot guns based on Beidou positioning according to claim 3 is characterized in that: For each sub-segment’s historical trajectory, the specific steps for generating frequent deviation indicators from the preset path by deeply analyzing the deviation degree between the anti-riot gun’s trajectory and its preset task path through feature engineering technology are as follows: For each sub-segment's historical trajectory points and preset path points, the Euclidean distance from the historical trajectory points to the preset path points is first calculated. The Euclidean distance is used to represent the deviation between the historical trajectory points and the preset path points. Based on the deviation, it is determined whether it belongs to the "deviation from path" state. When the deviation is greater than the deviation threshold, it is considered to be off-path. Calculate the number of deviations and total deviation time of each trajectory segment, and consider the time interval between trajectory points, that is, the time difference between each trajectory point update, to calculate the frequent deviation frequency. The calculation expression is: ， in: is an indicator function, indicating that when the deviation Greater than the deviation threshold When the value is 1, it means that the trajectory point deviates from the path. is the time interval between trajectory points, Indicates frequent deviation frequency, i represents the index of the trajectory point, N represents the total number of trajectory points on each subsegment; Taking into account the frequent deviation frequency, the deviation between the historical trajectory points and the preset path points, and the total time of the entire trajectory, the frequent deviation index of the preset path is calculated. The calculation expression is: ， in: Indicates frequent deviations from the intended path indicator, is the total time of the entire trajectory, To adjust the deviation degree of impact.

5. The method for preventing loss of riot guns based on Beidou positioning according to claim 4 is characterized in that: For each sub-segment of the historical trajectory, the specific steps of generating the motion path sparsity index by deeply analyzing the density between the motion trajectory points of the riot gun through feature engineering technology are as follows: The time distribution and point density of the historical trajectory of the riot gun are evaluated. For each sub-segment of trajectory data, the time interval between the timestamp of each motion trajectory point and the previous and next trajectory points is analyzed, and the "trajectory time sparsity" is defined to represent the sparsity of trajectory points within a certain period of time. The formula is as follows: ， in: and Respectively i and i +1 track point timestamp, and are the maximum and minimum timestamps of the sub-segment trajectory, represents the temporal sparsity of the trajectory; The weighting coefficient is introduced to consider the spatial distribution characteristics of the trajectory points and their temporal sparsity. By combining the temporal sparsity and the distribution changes between the trajectory points, the path sparsity index is comprehensively generated. The generated expression is: ， in: represents the motion path sparsity index, and Respectively i The spatial coordinates of the trajectory points, and Respectively i +1 spatial coordinates of the trajectory point, is the weighted attenuation coefficient of the spatial distribution, which controls the influence of the spatial distribution between trajectory points.

6. The method for preventing loss of riot guns based on Beidou positioning according to claim 3 is characterized in that: The analyzed frequent deviation from the predetermined path index and motion path sparsity index are input as feature vectors into a pre-trained and put into use machine learning model. A trajectory anomaly risk index is generated based on the machine learning model. The trajectory anomaly risk index is used to determine whether there is an abnormality in the current trajectory of the riot control gun.

7. The method for preventing the loss of riot guns based on Beidou positioning according to claim 6 is characterized in that: The trajectory anomaly risk index generated when predicting the current anti-riot gun's trajectory anomaly through the pre-trained and put into use machine learning model is compared and analyzed with the pre-set risk threshold to determine whether the current anti-riot gun's trajectory is abnormal. The specific steps are as follows: If the trajectory anomaly risk index is greater than the risk threshold, it is determined that the trajectory of the current riot gun is abnormal; If the trajectory anomaly risk index is less than or equal to the risk threshold, it is determined that there is no anomaly in the trajectory of the current riot gun.

8. The method for preventing the loss of riot guns based on Beidou positioning according to claim 7 is characterized in that: When the machine learning model identifies anomalies in the historical trajectory of the riot gun, the specific steps for dynamically narrowing the activity range of the riot gun and increasing the monitoring density are as follows based on the analysis results: After identifying the abnormal trajectory, the activity range of the riot gun is dynamically adjusted according to the trajectory abnormality risk index, that is, the range of the electronic fence is adjusted. The initial activity range of the riot gun is set to , according to the trajectory anomaly risk index and risk thresholds Calculate adjusted range of motion , the calculation expression is: ， in: It is the preset maximum trajectory abnormality risk index, which is used to control the extent of the reduction of the activity range; After adjusting the electronic fence, the real-time tracking of the riot gun is strengthened by increasing the monitoring density. The enhancement of monitoring density is achieved by increasing the location update frequency. The default location update frequency of the riot gun is set to , after trajectory anomaly detection, the enhanced update frequency Calculated by the following formula: ， in, is the adjustment factor used to control the increase in the update frequency.

9. A Beidou-based anti-riot gun loss prevention management system, used to implement the Beidou-based anti-riot gun loss prevention management method described in any one of claims 1 to 8, characterized in that: It includes the initial activity range setting module, real-time location monitoring module, historical trajectory recording module, historical trajectory analysis and feature extraction module, machine learning prediction and abnormality judgment module, and dynamic response and monitoring adjustment module; The initial activity range setting module sets a clear starting activity range for each riot gun, that is, the starting electronic fence range. When the riot gun exceeds the starting activity range, an alarm is automatically triggered to remind the management personnel; The real-time location monitoring module obtains the geographic location of the riot gun in real time through the Beidou positioning unit installed on the riot gun, and transmits the obtained location information to the monitoring center through the wireless communication network to monitor the real-time location of the riot gun; The historical trajectory recording module records the location information of the riot gun in chronological order to form complete historical trajectory data; The historical trajectory analysis and feature extraction module divides the historical trajectory into several sub-segments according to the number of time points and matches them with the preset path. For each sub-segment of the historical trajectory, it extracts features that reflect the potential position trajectory anomalies of the riot gun. The extracted features are deeply analyzed through feature engineering technology to evaluate their correlation with the abuse or loss of the riot gun. The machine learning prediction and anomaly judgment module uses the analyzed features as feature vectors and inputs them into a pre-trained and used machine learning model for prediction. The output of the machine learning model is used to determine whether there is an anomaly in the trajectory of the current riot gun. Dynamic response and monitoring adjustment module: When the machine learning model identifies that there are anomalies in the historical trajectory of the riot gun, the activity range of the riot gun is dynamically reduced according to the analysis results, that is, the range of the electronic fence is adjusted to respond to potential risks in a timely manner; at the same time, the monitoring density is enhanced to ensure real-time tracking of the riot gun.

10. A Beidou-based anti-riot gun loss prevention management platform, comprising the Beidou-based anti-riot gun loss prevention management system described in claim 9.

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