A property security inspection path dynamic optimization system

Abstract

The application discloses a kind of property security inspection path dynamic optimization system, it is related to security inspection path optimization technical field.The property security inspection path dynamic optimization system includes: data acquisition and pre-processing module, for collecting spatial position data and network performance data, obtains historical network data;Wireless signal source dynamic optimization module, for composite determination analysis to network performance data, determine whether to execute signal source switching;Path perception signal source switching decision module, for multidimensional value quantification evaluation, execute signal source switching;Path optimization and rerouting decision module, for constraint-driven path optimization analysis, according to path optimization analysis result executes path switching process;Operation monitoring and closed-loop self-optimization module, for strategy dynamic correction, realize parameter closed-loop adaptive optimization.Signal source switching and path planning disconnection, threshold lack of dynamic self-adaptation lead to link instability and rerouting lag problem are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of security inspection path optimization, in particular to a property security inspection path dynamic optimization system. BACKGROUND

[0002] The property security inspection system is widely used in park management and security fields, and mainly realizes orderly inspection of various facilities, key areas and patrol routes in the park. The existing system is usually equipped with inspection terminal equipment for real-time collection of spatial position information of security personnel, recording of inspection trajectory, and data interaction with the management platform through wireless network to realize task information issuing and real-time feedback of inspection progress, support automatic allocation of inspection tasks, electronic archiving of inspection records and remote synchronization of data, and assist management personnel in visualizing supervision and efficient management of the inspection process. Overall, the existing property security inspection system promotes the informatization and process standardization of security operations and provides basic technical support for park safety management. As a core tool for ensuring park safety and improving management efficiency, the signal source selection and path planning performance of the property security inspection system are directly related to the communication stability, inspection continuity and reliability of task execution. The existing technology generally uses a signal source determination method based on a single signal strength threshold and relies on fixed paths or shortest paths for inspection route planning. Although this scheme has certain applicability in general environments, it often leads to inaccurate signal source switching, damaged inspection path continuity and insufficient dynamic optimization capability under conditions such as multiple signal sources, complex spatial structures and dynamic network interference, making it difficult to effectively guarantee the business quality and stability of security inspection tasks.

[0003] For example, the invention patent with publication number CN118863207A discloses a garden automatic inspection method and system based on path planning, including a data acquisition module, an environment monitoring sensor system, a data analysis module, a path planning module, a dynamic path adjustment module and a task allocation and execution module. The method obtains a three-dimensional model of the garden through a laser radar, a camera and a positioning device, identifies functional areas using machine learning algorithms, and completes path planning using fractal geometry analysis and quantum heuristic optimization algorithms. During the inspection process, hybrid fuzzy neural networks and adaptive genetic algorithms can be used to respond to sudden environmental changes, dynamically adjust the inspection path, and improve the flexibility and adaptability of path generation.

[0004] For example, the invention patent with publication No. CN111798127A discloses a chemical industry park inspection robot path optimization system based on dynamic fire risk intelligent evaluation, which includes a real-time monitoring module, a dynamic fire risk intelligent evaluation module and a real-time path optimization module. The system realizes data interconnection through a local area network, collects environmental and equipment data of warehouses, workshops and public areas in real time, and dynamically determines the fire risk level of each inspection point by using an intelligent evaluation model. The patent has a hierarchical data management mechanism, which classifies and archives original monitoring data, primary risk assessment results and comprehensive fire risk levels, providing support for subsequent risk trend analysis and early warning. The system plans the robot inspection path according to the principles of high risk priority and shortest path distance by combining the comprehensive fire risk level of the inspection point and the geographical layout of the park through the path optimization module, and adjusts the path in real time when the risk level changes, realizing dynamic optimal scheduling of the inspection task.

[0005] The existing inspection path optimization technology mainly focuses on the collection of spatial environment data, the identification of functional areas and the dynamic adjustment of the path under the driving of multiple factors, emphasizes the environmental risk, terrain modeling and emergency response to unexpected situations, and generally ignores the rigid demand of the inspection task for wireless network quality. In the face of multi-source signal interference, network fluctuation or complex topological environment, it is difficult to respond to signal source changes and network quality decline in time, which easily leads to communication interruption, inspection data synchronization delay and business continuity damage, and is difficult to meet the actual needs of high-reliability and high-adaptive property security inspection.

[0006] Therefore, in view of the above problems, there is an urgent need for a property security inspection path dynamic optimization system. SUMMARY

[0007] Technical problems solved

[0008] In view of the deficiencies of the prior art, the present application provides a property security inspection path dynamic optimization system, which solves the problems of disconnection between signal source switching and path planning, and unstable link and detour lag caused by lack of dynamic self-adaptation of threshold.

[0009] Technical scheme

[0010] In order to achieve the above object, the present application is realized by the following technical scheme: a property security inspection path dynamic optimization system, comprising: a data acquisition and preprocessing module, used for acquiring spatial position data and network performance data, obtaining historical network data, and preprocessing the spatial position data, the network performance data and the historical network data; a wireless signal source dynamic optimization module, used for performing composite judgment analysis on the network performance data, performing signal monitoring according to the composite judgment analysis result, and judging whether to perform signal source switching; a path perception signal source switching decision module, used for performing multi-dimensional value quantitative evaluation on the spatial position data, the network performance data and the historical network data, performing signal source switching according to the multi-dimensional value quantitative evaluation result, and entering a path optimization and rerouting decision module; the path optimization and rerouting decision module, used for performing constraint-driven path optimization analysis on the spatial position data, the network performance data and the historical network data, and performing path switching process according to the path optimization analysis result; and a running monitoring and closed-loop self-optimization module, used for improving signal index and false triggering based on network quality data and historical network data statistics, dynamically correcting strategies according to the composite judgment analysis result, the multi-dimensional value quantitative evaluation and the constraint-driven path optimization analysis result, and realizing parameter closed-loop self-adaptive optimization.

[0011] Further, the specific process of acquiring spatial position data and network performance data and obtaining historical network data is: acquiring spatial position data and network performance data in real time, and obtaining network data; the spatial position data includes: positioning data, motion trajectory data, direction and speed, geometric topology of the inspection area, path node and edge length information; the network performance data includes: signal quality, magnetic field fluctuation value, access terminal total rate, link rate, signal source coverage area boundary, time delay, jitter, packet loss rate and freezing rate; and the historical network data includes: historical throughput data, historical link rate and switching time consumption record.

[0012] Further, the specific process of preprocessing the spatial position data, the network performance data and the historical network data is: performing effectiveness verification on the spatial position data by an abnormality identification and boundary constraint algorithm, and eliminating missing values and records not meeting physical boundary conditions; performing noise suppression on the network performance data by a multi-stage filtering algorithm, to reduce instantaneous fluctuations caused by sensor drift, electromagnetic interference and sampling jitter; performing scale unification and interval mapping on the multi-source heterogeneous spatial position data, network performance data and historical network data by a distribution standardization and linear normalization algorithm, to perform standardization and normalization processing; and performing arrangement on the historical throughput data, the historical link rate and the switching time consumption record by a feature extraction and database construction algorithm, to obtain historical maximum bearable throughput, stable throughput, and corresponding historical link rate and switching time consumption data, store the historical maximum bearable throughput, the stable throughput, and the corresponding historical link rate and switching time consumption data, and establish a historical network database.

[0013] Further, the specific process of the composite judgment analysis of the network performance data is: obtaining the signal quality, the magnetic field fluctuation value, the total rate of the access terminal, the maximum bearable throughput, the stable throughput and its corresponding historical link rate, the link rate; performing variance statistics and normalized mapping on the magnetic field fluctuation value and the signal quality to obtain the environmental interference intensity; performing ratio calculation and normalized processing on the total rate of the access terminal and the maximum bearable throughput to obtain the usage rate of the signal source; performing ratio calculation on the stable throughput and its corresponding historical link rate and then performing scale conversion on the real-time link rate to obtain the available bandwidth of the signal source; obtaining the signal source performance value through comprehensive analysis of the signal quality, the environmental interference intensity, the usage rate of the signal source, the available bandwidth of the signal source and the maximum bandwidth of the available signal source; the specific analysis method of the signal source performance value is: calculating the ratio of the available bandwidth of different signal sources and the maximum bandwidth in the available signal sources to obtain a bandwidth normalization coefficient; multiplying the environmental interference intensity and the usage rate of different signal sources after each being added by one to calculate a joint penalty term of the environment and the usage rate; dividing the signal quality of different signal sources by the joint penalty term to obtain a quality occupation correction value; multiplying the quality occupation correction value and the bandwidth normalization coefficient to obtain the signal source performance value.

[0014] Further, the specific process of signal monitoring and judgment of whether to perform signal source switching according to the composite judgment analysis result is: real-time comparison of the signal source performance value and the signal source performance threshold value; when the signal source performance value is greater than or equal to the signal source performance threshold value, maintaining the current signal source and continuously monitoring the change of the signal source performance value over time; when the signal source performance value is less than the signal source performance threshold value, sorting according to the signal source performance value and dividing into three different levels of candidate signal sources: preferred candidate, suboptimal candidate and available candidate, simultaneously boosting the transmission power, and entering the signal source switching process.

[0015] Further, the specific process of multi-dimensional value quantitative evaluation of spatial position data, network performance data and historical network data is: obtaining positioning data, motion trajectory data, direction and speed, geometric topology of inspection area, path node and edge length information, switching time consumption data, time delay, jitter, packet loss rate, stall rate, signal source coverage area boundary, signal source usage rate and environmental interference intensity; performing path discretization algorithm and interpolation mapping processing on spatial position data and network performance data to generate signal source efficiency data sequence, performing mean value calculation on sampling points above the threshold to obtain path average signal source efficiency value in the compliance area; calling average on switching time consumption data to obtain switching time penalty factor; performing segmented regression fitting and robust algorithm processing on switching time penalty factor of different signal sources and time delay, jitter, packet loss and stall rate to obtain time scale constant; performing path prediction algorithm on spatial position data to obtain geometric trajectory of the foresight path segment, and obtaining coverage coincidence degree with the coverage area boundary of the signal source by using geometric accuracy method; obtaining comprehensive occupation interference index by adding the product of usage rate and usage rate weight and the product of environmental interference intensity and environmental interference intensity weight through a weighted summation function according to the weight; obtaining signal source switching evaluation value through comprehensive analysis of path average signal source efficiency value in the compliance area, signal source efficiency threshold, time scale constant, switching time penalty factor, safety compensation constant, coverage coincidence degree and comprehensive occupation interference index: performing normalization processing on the difference between path average signal source efficiency value in the compliance area and signal source efficiency threshold to obtain efficiency improvement amount; constructing an exponential time decay function combining switching time penalty factor, time scale constant, coverage coincidence degree and safety compensation constant to obtain switching time penalty term; performing reverse normalization on comprehensive occupation interference index to obtain interference suppression amount; multiplying efficiency improvement amount, switching time penalty term and interference suppression amount, and amplifying through a proportional coefficient to obtain path signal source switching evaluation value.

[0016] Further, the specific process of performing signal source switching according to the multi-dimensional value quantitative evaluation result is: real-time comparison of the path signal source switching evaluation value and the path signal source switching evaluation multi-level threshold value, entering the signal source switching process: when the path signal source switching evaluation value is greater than or equal to the second threshold value, and the coverage coincidence degree is greater than or equal to the coincidence degree threshold value, immediately performing signal source switching and gradually reducing the transmission power; when the path signal source switching evaluation value is greater than or equal to the second threshold value, and the coverage coincidence degree is less than the coincidence degree threshold value, entering the short-time observation window, continuously performing signal monitoring and increasing the transmission power, and simultaneously preparing for switching in the preferred candidate and the suboptimal candidate according to the current signal source efficiency value size sorting, waiting for the path signal source switching evaluation value to be greater than or equal to the second threshold value, and the coverage coincidence degree to be greater than or equal to the coincidence degree threshold value, and then performing switching; when the path signal source switching evaluation value is greater than or equal to the first threshold value and less than the second threshold value, whether the coverage coincidence degree is greater than the coincidence degree threshold value or not, entering the short-time observation window, determining the prepared switching target in the preferred candidate, continuously performing signal monitoring and increasing the transmission power, waiting for the path signal source switching evaluation value to be greater than or equal to the second threshold value, and the coverage coincidence degree to be greater than or equal to the coincidence degree threshold value, and then performing switching; when the path signal source switching evaluation value is less than the first threshold value, and the coverage coincidence degree is less than the coincidence degree threshold value, only continuously increasing the transmission power, and not performing switching.

[0017] Further, the specific process of the path geometry length index calculated by Dijkstra algorithm and K shortest path algorithm based on spatial position data, and the constraint-driven path optimization analysis combined with network performance data and historical network data is as follows: obtaining the positioning data, motion trajectory data, direction and speed, geometric topology of the inspection area, path node and edge length information, signal quality, magnetic field fluctuation value, total access terminal rate, link rate, time delay, jitter, packet loss rate, stall rate, historical maximum bearable throughput, stable throughput and corresponding historical link rate; generating candidate paths by K shortest path algorithm based on the geometric topology of the inspection area, path node and edge length information, and accumulating the length of each edge segment to obtain the total length of the candidate paths; calculating the total length of the shortest path from the starting point to the ending point by Dijkstra algorithm based on the geometric topology of the inspection area, path node and edge length information, to obtain the shortest path length; performing fixed-step path discretization processing on the geometric trajectory of the front-view path segment to generate virtual sampling points, and accumulating the length of the point segment with an efficiency value greater than or equal to a threshold value to obtain the length of the candidate qualified path; performing section scanning algorithm processing on the signal source efficiency data sequence to statistically obtain the length of the longest continuous low-coverage segment with a signal source efficiency value lower than a signal source efficiency threshold value; performing length statistics on the virtual sampling point section, and performing regression fitting analysis combined with time delay, jitter, packet loss rate and stall rate to obtain a reference dead zone length; obtaining a path switching optimization value based on comprehensive analysis of the total length of the candidate path, the shortest path length, the length of the candidate qualified path, the average signal source efficiency value of the qualified area path, the signal source efficiency threshold value, a zero-protection constant, the length of the longest continuous low-coverage segment and the reference dead zone length; subtracting the shortest path length from the total length of the candidate path, dividing by the shortest path length, and then performing truncated reverse normalization processing to obtain a path redundancy; subtracting the signal source efficiency threshold value from the average signal source efficiency value of the qualified area path, dividing by one minus the signal source efficiency threshold value plus the zero-protection constant, and then performing truncated normalization processing to obtain an efficiency gain; dividing the length of the candidate qualified path by the total length of the candidate path to obtain a coverage ratio; dividing the length of the longest continuous low-coverage segment by the reference dead zone length and performing truncated reverse normalization processing; weighting and summing the efficiency gain, the coverage ratio and the low-coverage suppression according to the path switching weight, and multiplying by the path redundancy to obtain the path switching optimization value.

[0018] Further, the specific process of executing the path switching procedure according to the path optimization analysis result is: comparing the path switching preferred value with the path switching preferred threshold value in real time; when the path switching preferred value is greater than or equal to the path switching preferred threshold value, issuing a rerouting instruction, and prompting the change of the predicted arrival time and the improvement of the signal coverage ratio; when the path switching preferred value is less than the path switching preferred threshold value, maintaining the current path, and continuing to improve the transmission power according to the path perception signal source switching decision module; when a rerouting is just completed, entering a rerouting cooling period, and no longer triggering a new rerouting and switching decision for a small amplitude fluctuation in the cooling period; and returning to the wireless signal source dynamic optimization module for signal monitoring after the cooling period ends.

[0019] Further, based on the signal index improvement degree and the mis-triggering condition of the network quality data and the historical network data statistics, the specific process of dynamically correcting the strategy according to the composite judgment analysis result, the multi-dimensional value quantitative evaluation and the constraint-driven path optimization analysis result, and realizing the closed-loop adaptive optimization of the parameters is: obtaining the statistics of the time delay, the jitter, the packet loss rate and the stall rate before and after the switching in the network quality data, and obtaining the signal index improvement degree through difference calculation; obtaining the proportion of not reaching the target service quality after the switching and the rerouting, and obtaining the mis-triggering condition; combining the results of the composite judgment analysis, the multi-dimensional value quantitative evaluation and the path optimization analysis, and comprehensively correcting the signal source efficiency threshold value, the signal source switching evaluation multi-level threshold value, the coincidence degree threshold value and the path switching preferred threshold value; when the signal index improvement degree is lower than the target and the mis-triggering condition rises, increasing the signal source efficiency threshold value and the coincidence degree threshold value and increasing the path switching preferred threshold value; when the signal index improvement degree stably improves and the mis-triggering condition decreases, decreasing the signal source efficiency threshold value and the path switching preferred threshold value and keeping the coincidence degree threshold value unchanged; when the signal index improvement degree and the mis-triggering condition differentiate, preferentially adjusting the path switching preferred threshold value and slightly correcting the signal source efficiency threshold value; and writing back the corrected threshold value to form a closed-loop adaptive optimization.

[0020] Beneficial effects

[0021] The present application has the following beneficial effects:

[0022] (1) The present application divides the candidate signal sources into preferred candidates, suboptimal candidates and available candidates through hierarchical sorting, different levels correspond to differentiated switching and power adjustment strategies, so that the inspection equipment can preferentially connect the best signal source in a complex signal environment, improve communication continuity and network coverage, and reduce task interruption caused by signal degradation.

[0023] (2) The present application comprehensively reflects the actual income and risk of different switching targets by introducing a multi-dimensional value computer mechanism, identifies the signal source with better coverage and higher income in advance and preferentially completes switching, adopts a delay or observation mechanism when the conditions are insufficient, avoids frequent or false triggering of switching, and significantly improves the accuracy and controllability of switching in complex environments, and reduces interruption and jitter.

[0024] (3) The present application unifies the evaluation of path performance and network stability through constraint-driven path optimization analysis, maintains stable progress when the path quality meets the standard, triggers a detour when the income is significant, improves the accuracy and controllability of the detour decision, balances communication quality and path efficiency, and ensures the continuity and reliability of the inspection process.

[0025] (4) The present application extracts and archives features by using historical network data such as historical throughput, historical link rate and switching time, constructs a historical network database, and provides a basis for system parameter adjustment and decision model optimization, which helps to improve the universality and generalization ability of the inspection strategy in different scenarios and different time periods.

[0026] Of course, any product implementing the present application does not necessarily need to achieve all the advantages described above at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0027] Fig. 1 is a structural diagram of a property security inspection path dynamic optimization system of the present application;

[0028] Fig. 2 is a coverage overlap degree and switching evaluation value change diagram of the present application; DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0030] Please refer to Figs. 1-2The embodiment of the present application provides a technical scheme: a property security inspection path dynamic optimization system, comprising a data acquisition and preprocessing module, which is used for acquiring spatial position data and network performance data, obtaining historical network data, and preprocessing the spatial position data, the network performance data and the historical network data; a wireless signal source dynamic optimization module, which is used for performing composite judgment analysis on the network performance data, performing signal monitoring according to the composite judgment analysis result, and determining whether to perform signal source switching; a path perception signal source switching decision module, which is used for performing multi-dimensional value quantitative evaluation on the spatial position data, the network performance data and the historical network data, performing signal source switching according to the multi-dimensional value quantitative evaluation result, and entering a path optimization and diversion decision module; the path optimization and diversion decision module, which is used for performing constraint-driven path optimization analysis on the spatial position data, the network performance data and the historical network data, and performing path switching process according to the path optimization analysis result; and a running monitoring and closed-loop self-optimization module, which is used for statistically improving signal indexes and false triggering conditions based on network quality data and historical network data, dynamically correcting strategies according to the composite judgment analysis result, the multi-dimensional value quantitative evaluation and the constraint-driven path optimization analysis result, and realizing parameter closed-loop self-adaptive optimization.

[0031] Specifically, the specific process of acquiring spatial position data and network performance data and obtaining historical network data is as follows: real-time acquisition of spatial position data and network performance data, acquisition of network data, which is used to support subsequent inspection path dynamic optimization and signal source selection. The spatial position data includes positioning data, motion trajectory data, direction, speed, geometric topology structure of the inspection area, path node distribution and length of each path segment, which can realize comprehensive recording of the position, moving track, orientation state and speed change of the inspection equipment, and realize accurate modeling and dynamic tracking of the entire inspection environment by combining the structure information of the inspection area and the distance relationship between nodes. The network performance data includes signal quality, magnetic field fluctuation value, total speed of access terminal, link speed, signal source coverage area boundary, time delay, jitter, packet loss rate and freezing rate, which can reflect the current wireless signal strength, environmental interference change, terminal connection load, link physical speed, service range of each signal source, data transmission delay, data packet arrival time fluctuation, data packet loss proportion and service smoothness. The historical network data includes historical throughput data, historical link speed and switching time consumption record, which can record the effective data transmission rate, link physical connection rate and specific time consumed for each signal source switching of each time period, and provide data support for analyzing network performance change trend, switching operation behavior characteristics and subsequent parameter adaptive optimization. Through multi-dimensional real-time acquisition and arrangement of the spatial position data, the network performance data and the historical network data, basic data guarantee is provided for subsequent path dynamic optimization and signal source switching determination.

[0032] In the embodiment, by collecting spatial position data and network performance data in real time, the accurate position, motion state of the inspection equipment and the spatial structure of the inspection area can be dynamically mastered, and the real-time performance and service quality indicators of the wireless network can be comprehensively obtained. Combined with the accumulation and analysis of historical network data, the network performance change trend and switching behavior characteristics can be effectively tracked, providing comprehensive and reliable data support for subsequent path optimization, signal source selection and switching judgment, significantly improving the environmental perception ability and network state monitoring ability of the inspection task, and helping to realize dynamic optimization of the inspection path and signal source selection, and improving the continuity, intelligence and service quality guarantee level of the inspection system.

[0033] Specifically, the specific process of preprocessing the spatial position data, network performance data and historical network data is: the effectiveness of the spatial position data is verified by an abnormality identification and boundary constraint algorithm, and records with missing values and not meeting the physical boundary conditions are removed for positioning data, motion trajectory data, direction, speed, geometric topological structure of the inspection area, path node distribution and length of each path segment, to ensure the integrity and accuracy of the spatial position data. The network performance data is subjected to noise suppression by a multi-stage filtering algorithm, and the instantaneous fluctuations caused by sensor drift, electromagnetic interference and sampling jitter are reduced for signal quality, magnetic field fluctuation value, total access terminal rate, link rate, signal source coverage area boundary, time delay, jitter, packet loss rate and freezing rate, to improve the smoothness of the network performance data. The spatial position data, network performance data and historical network data are subjected to scale unification and interval mapping by a distribution standardization and linear normalization algorithm, and data of different sources and dimensions are standardized and normalized to realize unified management and subsequent analysis of the data. The historical throughput data, historical link rate and switching time consumption records are sorted by a feature extraction and database construction algorithm, and the historical maximum bearable throughput, stable throughput and corresponding historical link rate and switching time consumption data are extracted and stored to establish a historical network database.

[0034] In this embodiment, by implementing validity check on spatial position data, network performance data and historical network data, abnormal or incomplete raw data can be found and eliminated in time, ensuring the authenticity and reliability of all input data. Noise suppression is performed using a multi-stage filtering algorithm to effectively capture instantaneous fluctuations during the process, making the data more stable. Through distribution standardization and linear normalization algorithm, scale unification is achieved for all collected and historical data, solving the compatibility problem of data with different dimensions and sources, and ensuring the rigor and comparability of subsequent analysis steps. Through feature extraction and database construction algorithm, historical throughput data, historical link rate and switching time record are sorted out to obtain historical maximum bearable throughput, stable throughput and corresponding historical link rate and switching time data, and a structured historical network database is constructed, providing a high-quality, structured data foundation, improving the depth of mining historical behavior and the adaptive response capability to dynamic environment, which can greatly enhance the data processing accuracy and intelligent decision-making level of the inspection system in complex scenarios, providing solid data guarantee and technical support for efficient and stable property security inspection.

[0035] Specifically, the specific process of composite judgment analysis on network performance data is as follows: obtaining signal quality, magnetic field fluctuation value, total access terminal rate, maximum bearable throughput, stable throughput, historical link rate corresponding to stable throughput, and link rate. By performing variance statistics and normalization mapping on the magnetic field fluctuation value and the signal quality respectively, the environmental interference intensity is calculated. The total access terminal rate and the maximum bearable throughput are calculated by ratio, and the result is normalized to obtain the usage rate of the signal source. The stable throughput and its corresponding historical link rate are calculated by ratio, and then combined with the real-time link rate for scale conversion to obtain the available bandwidth of the signal source. Based on the signal quality, environmental interference intensity, usage rate of the signal source, available bandwidth of the signal source, and maximum bandwidth of the available signal source, the signal source performance value is obtained through comprehensive analysis. The specific analysis method is as follows: calculate the ratio of the available bandwidth of different signal sources to the maximum bandwidth in the available signal source to obtain the bandwidth normalization coefficient; multiply the environmental interference intensity and the usage rate of different signal sources after adding one respectively to calculate the joint penalty term of environment and usage rate; divide the signal quality of different signal sources by the joint penalty term to obtain the quality occupation correction value; multiply the quality occupation correction value by the bandwidth normalization coefficient to finally obtain the signal source performance value.

[0036] The specific calculation formula of the signal source performance value is as follows:

[0037] ;

[0038] In the formula, The signal source performance value represents the real-time performance of the signal source, and comprehensively evaluates the signal source performance. signal quality of different signal sources, representing link stability; environmental interference intensity of different signal sources, reflecting electromagnetic disturbance and spatial signal fluctuation in the inspection environment; usage rate of different signal sources, representing current load and resource occupation level of the signal source; available bandwidth of different signal sources, reflecting data transmission capability of the signal source in the actual environment; maximum bandwidth of different signal sources, used to represent the upper limit of available bandwidth.

[0039] In the embodiment, through multi-dimensional dynamic evaluation of the signal source performance value, multiple core indicators such as signal quality, environmental interference intensity, usage rate of the signal source, available bandwidth of the signal source, and maximum bandwidth of the available signal source are comprehensively reflected to fully depict the overall performance of each signal source in the actual inspection scene, and dynamically reflect the comprehensive performance of the signal source under the influence of signal quality, environmental interference intensity, usage rate of the signal source, available bandwidth of the signal source, and maximum bandwidth of the available signal source in the actual wireless environment, so that the signal source performance value evaluation is more objective, scientific, and accurate. The formula helps to avoid the interference of single parameter fluctuation on the evaluation result, improve the performance differentiation between different signal sources, enhance the adaptability to complex environment and dynamic network state, and provide a reliable data basis for fine analysis and optimization of network performance in the inspection system.

[0040] Specifically, according to the composite judgment analysis result, the specific process of signal monitoring and judgment whether to perform signal source switching is as follows: the signal source performance value and the signal source performance threshold value are compared in real time. When the signal source performance value is greater than or equal to the signal source performance threshold value, the current signal source is maintained, and the change of the signal source performance value with time is continuously monitored to avoid frequent or unnecessary switching and ensure the continuous stability of the communication state. When the signal source performance value is less than the signal source performance threshold value, all available signal sources are sorted according to the size of the signal source performance value, and the candidate signal sources are divided into three levels of preferred candidate, suboptimal candidate, and available candidate. Specifically, the signal source performance value higher than the median of the quantile of all candidate signal sources is classified as preferred candidate, the signal source performance value between the median and the lower quartile of the quantile is classified as suboptimal candidate, and the signal source performance value lower than the lower quartile of the quantile is classified as available candidate. At the same time, the transmission power is increased to enhance the signal quality of the current link, and the signal source switching process is entered to ensure the continuity and stability of the inspection business. Among them, the signal quality, environmental interference intensity, usage rate of the signal source, available bandwidth, and maximum bandwidth are analyzed comprehensively by using the analytical method of target working condition back calculation to obtain the signal source performance threshold value, and the value range is between 0.6 and 0.65.

[0041] In this embodiment, the timing of signal source switching is accurately controlled through real-time monitoring and dynamic determination of the signal source performance threshold and the signal source performance value, and the signal source performance value is monitored in real time. This can effectively adapt to the complex and variable wireless environment and dynamic changes in signal resources in the inspection scene, avoiding both false switching caused by temporary fluctuations or local interference in the signal source and the failure to switch in time due to long-term low signal source performance, thereby improving the accuracy and adaptability of signal source selection. Overall, the robustness in various working conditions such as weak signal, strong interference, and load change is greatly enhanced, and the communication continuity, business reliability, and overall intelligent level of the inspection task are significantly improved.

[0042] Specifically, the specific process of multi-dimensional value quantitative evaluation is as follows: obtaining positioning data, motion trajectory data, direction, speed, geometric topology of the inspection area, path node and edge length information, switching time data, time delay, jitter, packet loss rate, frame freezing rate, signal source coverage area boundary, signal source usage rate, and environmental interference intensity. The spatial position data and network performance data are processed by path discretization algorithm and interpolation mapping to generate signal source performance data sequence, and the mean value of the sampling points with signal source performance value higher than the signal source performance threshold is calculated to obtain the path average signal source performance value in the compliance area; the path prediction algorithm is applied to the spatial position data to obtain the geometric trajectory of the forward path segment, and the coverage overlap degree is calculated by geometric accurate method combined with the signal source coverage area boundary; the product of the signal source usage rate and the usage rate weight, and the product of the environmental interference intensity and the environmental interference intensity weight are calculated respectively using the weighted summation function, and the two are added to obtain the comprehensive occupation interference index. The difference between the path average signal source performance value in the compliance area and the signal source performance threshold is normalized to obtain the performance improvement amount; the switching time penalty term is obtained by constructing an exponential time decay function combined with the switching time penalty factor, the time scale constant, the coverage overlap degree, and the safety compensation constant; the interference suppression amount is obtained by reverse normalization of the comprehensive occupation interference index; and the performance improvement amount, the switching time penalty term, and the interference suppression amount are multiplied, and then amplified by a proportional coefficient to finally obtain the path signal source switching evaluation value.

[0043] The specific calculation formula of the path signal source switching evaluation value is as follows:

[0044]

[0045] In the formula, represents the path signal source switching evaluation value, which quantitatively evaluates the rationality and timing of candidate path signal source switching; represents the path average signal source performance value in the compliance area, which reflects the overall performance level of the high-quality coverage section of the path; represents the signal source performance threshold value for determining the trigger condition of signal source switching; τ represents the switching time penalty factor, which is obtained by averaging and normalizing the switching time consumption data, and the value range is between 0.05 and 0.1; represents the time scale constant, which is obtained by processing and normalizing the switching time penalty factor and the delay, jitter, packet loss rate, and freezing rate through piecewise regression fitting and robustness algorithm, and the value range is between 0.5 and 2; represents the coverage overlap degree, which is used to quantify the spatial overlap degree of the path and the signal source coverage area; δ represents the safety compensation constant, which is set by simulating the robustness constraint algorithm on the minimum value combination of the coverage overlap degree and the time scale constant, and the value range is between 0.005 and 0.01; represents the comprehensive occupation interference index, represents the usage rate weight, which is obtained by sensitivity analysis and multi-parameter regression optimization algorithm on the signal source usage rate, represents the environmental interference intensity weight, which is obtained by sensitivity analysis and multi-parameter regression optimization algorithm on the environmental interference intensity, and the value range is between 0 and 1; (x, 0, 1) is a data truncation normalization function, which is used to limit the data effectively within the range of 0 to 1, to prevent adverse effects of abnormal values on normalization and subsequent calculation.

[0046] The change trend of the time penalty term and the path signal source switching evaluation value under different coverage overlap degree value conditions is compared and analyzed. Other related parameters are fixed, the coverage overlap degree is gradually increased from 0.2 to 1.0 for interval sampling, the corresponding time penalty term and the final path signal source switching evaluation value are calculated, and the actual influence of the coverage overlap degree in the path signal source switching evaluation process is intuitively displayed, as shown in Table 1.

[0047] Table 1: Coverage overlap degree adjustment switching evaluation value table

[0048]

[0049] As Fig. 2 shown, it is a coverage overlap degree and switching evaluation value change diagram of a property security inspection path dynamic optimization system provided by the embodiment of the application. As shown in Table 1 and Fig. 2 It can be seen that the higher the coverage overlap degree, the higher the time penalty term and the path signal source switching evaluation value, and the switching criterion sensitivity is dynamically adjusted with the coverage overlap degree. This mechanism effectively avoids the risk of mis-switching under low overlap, ensures the scientificity and business continuity of the inspection path switching decision, effectively avoids the risk of mis-switching under low coverage, and ensures that the system only triggers the switching operation when the spatial coverage is sufficient and the link condition is superior, thereby realizing fine and dynamic control of the switching opportunity.

[0050] In the embodiment, the path signal source switching evaluation value is used to quantitatively determine the path switching condition and timing, which can effectively avoid misjudgment caused by short-term fluctuation of signal source performance or extreme working conditions, and improve the scientificity and adaptive level of path switching. The setting of safety compensation constant further enhances the robustness of the algorithm, avoiding the influence of extreme value anomaly on operation safety. Through reasonable allocation of usage weight and environmental interference intensity weight, the dynamic response capability in high resource occupation or high interference scene is improved. Overall, the analysis process significantly improves the accuracy and intelligence of the patrol path optimization, and can continuously provide the optimal path and signal source selection basis for the patrol system in complex environment, ensuring the efficiency of task execution and the stability of network communication quality.

[0051] Specifically, the specific process of signal source switching according to the multi-dimensional value quantitative evaluation result is as follows: real-time comparison of path signal source switching evaluation value and path signal source switching evaluation multi-level threshold value, and entering the signal source switching process. When the path signal source switching evaluation value is greater than or equal to the second threshold value, and the coverage coincidence degree is greater than or equal to the coverage coincidence degree threshold value, the signal source switching operation is immediately performed, and the transmission power is gradually reduced, so that the terminal obtains a high-quality wireless link after signal source switching, and optimizes the energy consumption distribution under the premise of meeting the business demand. When the path signal source switching evaluation value is greater than or equal to the second threshold value, and the coverage coincidence degree is less than the coverage coincidence degree threshold value, a short-time observation window is entered, and the signal source performance value, the path signal source switching evaluation value and the coverage coincidence degree are continuously monitored, and the transmission power is increased to improve the local network coverage. At this time, according to the size of the current signal source performance value, the optimal candidate and suboptimal candidate signal sources are dynamically sorted, and the switching is prepared by selection, and the final signal source switching operation is completed under the condition that the path signal source switching evaluation value is greater than or equal to the second threshold value and the coverage coincidence degree is greater than or equal to the coverage coincidence degree threshold value. When the path signal source switching evaluation value is greater than or equal to the first threshold value and less than the second threshold value, whether the coverage coincidence degree is greater than the coverage coincidence degree threshold value or not, the short-time observation window is entered. During this period, the switching target is determined in the optimal candidate signal source, the key parameters such as signal quality, path signal source switching evaluation value and coverage coincidence degree are continuously monitored, and the transmission power is increased to create more favorable link conditions for subsequent switching, until the path signal source switching evaluation value is greater than or equal to the second threshold value and the coverage coincidence degree is greater than or equal to the coverage coincidence degree threshold value, the signal source switching operation is performed. When the path signal source switching evaluation value is less than the first threshold value, and the coverage coincidence degree is less than the coverage coincidence degree threshold value, only the transmission power is continuously increased, and the signal source switching is not performed, so as to maintain the business availability of the existing link.

[0052] In this embodiment, by real-time judgment of the combination of the path signal source switching evaluation value and the coverage overlap degree, the signal quality and service continuity can be preferentially guaranteed when the wireless environment changes, and the switching will be completed immediately only when the network coverage and performance of the target signal source reach the expected threshold, thereby significantly reducing the risk of service interruption and network jitter during the switching process. In the signal source switching boundary interval, through the short observation window mechanism and the dynamic adjustment of the transmission power, the signal source performance value, the path signal source switching evaluation value and the coverage overlap degree are continuously monitored and timely responded, which can effectively avoid the mis-switching caused by local signal fluctuations, environmental interference or instantaneous network load changes, and improve the stability and accuracy of the switching decision. Through the dynamic sorting and optimization mechanism of the candidate signal source, it is ensured that each switching has sufficient performance improvement space, and smooth switching of business flow and optimal signal connection are realized.

[0053] Specifically, the specific process of constraint-driven path optimization analysis on the inspection path is as follows: obtaining positioning data, motion trajectory data, direction and speed, geometric topology of the inspection area, path node and edge length information, signal quality, magnetic field fluctuation value, total access terminal rate, link rate, time delay, jitter, packet loss rate, freezing rate, historical maximum bearable throughput, stable throughput and corresponding historical link rate. A plurality of candidate paths are generated by using the K shortest path algorithm, and the lengths of the edges are accumulated to obtain the total length of the candidate paths. The Dijkstra algorithm is used to calculate the total length of the shortest path from the starting point to the ending point, which provides a reference for subsequent path optimization. Fixed-step path discretization processing is performed on the geometric trajectory of the forward path segment to generate high-density virtual sampling points, and the cumulative length of the point segment with a signal source performance value greater than or equal to a signal source performance threshold is obtained to obtain the length of the candidate qualified path. The signal source performance data sequence is applied to the section scanning algorithm to continuously count the length of the section with a signal source performance value lower than the signal source performance threshold, and the maximum value is extracted to obtain the length of the longest continuous low coverage section. The time delay, jitter, packet loss rate and freezing rate are modeled by regression fitting analysis to obtain the dead zone reference length. The total length of the candidate path is subtracted from the shortest path length, and then divided by the shortest path length to obtain the path redundancy after truncation and reverse normalization. The average signal source performance value of the qualified area path is subtracted from the signal source performance threshold, and then divided by the sum of the signal source performance threshold and the zero protection constant to obtain the performance gain after truncation normalization. The length of the candidate qualified path is divided by the total length of the candidate path to obtain the coverage ratio. The length of the longest continuous low coverage section is divided by the dead zone reference length to obtain the low coverage suppression after truncation and reverse normalization. The performance gain, coverage ratio and low coverage suppression are weighted and summed according to the path switching weight, and then multiplied by the path redundancy to finally obtain the path switching optimization value, which realizes the fine quantitative evaluation of the path switching priority.

[0054] The specific calculation formula of the path switching optimization value is:

[0055]

[0056] In the formula, represents the path switching preference value, which comprehensively evaluates the pros and cons of the candidate path and the switching priority; represents the candidate path length, which quantifies the spatial distribution of the high-quality section of the path; represents the total length of the candidate path, which represents the spatial distance of the path and the overall length of the inspection travel; represents the shortest path length, which measures the optimal path distance from the starting point to the end point; represents the average signal source performance value of the qualified section, which reflects the overall performance level of the high-quality coverage section of the path; represents the signal source performance threshold, which is used to determine the trigger condition for signal source switching; represents the length of the longest continuous low-coverage section, which accurately reflects the continuity of the weak signal area of the path; represents the dead zone reference length, which provides a standard for determining the low-coverage section of the path; represents the zero-exclusion protection constant, which is obtained by the denominator minimum value robustness constraint algorithm for the signal source performance threshold and the average signal source performance value of the qualified section, and the value range is between 0.005 and 0.01; the path switching weight includes: : performance gain weight, : coverage proportion weight, : low-coverage suppression weight, which is obtained by multi-index sensitivity analysis and regression optimization algorithm for historical throughput data, historical link rate data, and latency, jitter, packet loss rate, and lag rate before and after switching; the coverage proportion weight is obtained by multi-index sensitivity analysis and multi-objective regression optimization algorithm for path node and edge length information, signal source coverage area boundary data, and historical switching time record; the low-coverage suppression weight is obtained by multi-index sensitivity analysis and multi-objective regression optimization algorithm for signal quality, magnetic field fluctuation value, and the length of the longest continuous low-coverage section, and the value range is between 0 and 1, which is used to balance the comprehensive influence of different key indicators in the path selection process; (x, 0, 1) is a data truncation normalization function, which is used to limit the data effectively within the range of 0 to 1, preventing abnormal values from adversely affecting normalization and subsequent calculations.

[0057] In the embodiment, the scientificity and decision accuracy of the dynamic optimization of the inspection path are improved significantly through the quantitative determination based on the path switching preference value, the multi-dimensional influences of the path space structure, the signal high-quality coverage section, the continuous low-coverage risk and the network performance are fully considered, the intelligent discrimination of the advantages and disadvantages of different candidate paths is realized, and the path switching preference value output finally not only effectively avoids the link instability caused by the signal weak area, but also guarantees the business continuity and high availability of the inspection task throughout, and the self-adaptive ability of the inspection system to the complex scene is greatly enhanced.

[0058] Specifically, the specific process of executing the path switching process according to the path optimization analysis result is that: the path switching preference value and the path switching preference threshold are compared in real time, and it is ensured that each path switching decision is based on data and the threshold is the criterion. When the path switching preference value is greater than or equal to the path switching preference threshold, the rerouting instruction is issued in time, and the user is prompted about the change of the expected arrival time and the improvement of the signal coverage ratio, so that the dynamic balance between the communication quality improvement and the arrival efficiency is realized in the inspection process. When the path switching preference value is less than the path switching preference threshold, the current path is maintained unchanged, and the transmission power is continued to be improved according to the strategy of the path perception signal source switching decision module, so as to guarantee the signal quality and business continuity of the inspection task under the current path. In addition, in order to avoid frequent path switching caused by continuous small fluctuations, a rerouting cooling period is automatically entered after completing a rerouting. During the cooling period, the short-term small fluctuations of the path switching preference value no longer trigger new rerouting and switching decisions. Only when the cooling period is over, the wireless signal source dynamic optimization module is re-entered to carry out a new round of signal monitoring.

[0059] In the embodiment, through the real-time comparison of the path switching preference value and the path switching preference threshold, combined with the cooling period mechanism after the path switching, it is guaranteed that the optimal rerouting decision can be made flexibly when facing the network quality change and the business demand adjustment, the repeated switching caused by short-term fluctuations is effectively avoided, the reliability of the path switching operation is improved, the resource consumption and business interruption caused by frequent rerouting are reduced, the signal coverage quality and the communication link stability on the inspection path can be continuously maintained, and the inspection task can be always guaranteed to proceed smoothly in the best path in the complex inspection environment.

[0060] Specifically, based on the network quality data, the historical network data statistical signal indicator improvement degree and the false triggering condition, the strategy is dynamically corrected according to the composite decision analysis result, the multi-dimensional value quantitative evaluation and the constraint driven path optimization analysis result, and the specific process of parameter closed loop adaptive optimization is as follows: the delay, jitter, packet loss rate and stall rate in the network quality data are counted before and after switching, the signal indicator improvement degree is quantified through differential analysis, and the improvement amplitude of the service quality for each path switching or signal source switching is accurately reflected. The proportion of not reaching the target service quality after switching and rerouting is synchronously counted, and the false triggering condition is determined to provide a reliable basis for subsequent parameter adjustment. Combined with the composite decision analysis, the multi-dimensional value quantitative evaluation and the path optimization analysis result, the signal source efficiency threshold, the signal source switching evaluation multi-level threshold, the coincidence degree threshold and the path switching optimization threshold are dynamically corrected. When the signal indicator improvement degree is lower than the target and the false triggering condition rises, the signal source efficiency threshold and the coincidence degree threshold are adjusted upward, and the path switching optimization threshold is increased, the switching criterion is tightened, and the false switching risk is reduced. When the signal indicator improvement degree continuously improves and the false triggering condition decreases, the signal source efficiency threshold and the path switching optimization threshold are adjusted downward, the coincidence degree threshold remains unchanged, the sensitivity and response ability are improved. When the signal indicator improvement degree and the false triggering condition differentiate, the path switching optimization threshold is preferentially adjusted, and the signal source efficiency threshold is slightly corrected, so that the decision criterion flexibly adapts to the complex network environment. The corrected threshold parameters are written back to the wireless signal source dynamic optimization module, the path aware signal source switching module and the path optimization and rerouting decision module in real time, forming a parameter adaptive closed loop optimization mechanism, which ensures the self-regulation ability of the inspection path and the signal source switching decision in the dynamic network environment, and continuously improves the service continuity and the signal quality guarantee level.

[0061] In the embodiment, through dynamic correction and real-time writing of key threshold parameters, the decision conditions of path switching and signal source switching are continuously optimized according to the comprehensive feedback of network quality and historical performance, the adaptive ability of the decision process to network environment changes and service quality fluctuations is effectively enhanced, the service risk caused by false switching and criterion lag is significantly reduced, the inspection task always obtains high quality signal guarantee and continuous service continuity in various complex scenes, and the running efficiency and intelligent level of the inspection system are simultaneously improved.

[0062] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other presenters can develop. It is also possible, however, that only a single element can be present. It is further noted that such a term as "comprising" is intended to mean that the embodiments include the recited elements, but not excluding other elements. "Consisting essentially of when used herein in relation to a composition, means that the composition includes the recited elements, and can include additional elements, so long as the additional elements do not materially alter the basic and novel characteristics of the claimed composition. "Consisting of" when used herein in relation to a composition, means that the composition includes the recited elements, and no additional elements.

[0063] The preferred embodiments of the application disclosed above are only to help explain the principles of the present application. The preferred embodiments do not describe all the details of the present application, nor limit the present application to only the specific embodiments described. It is apparent that many modifications and variations can be made to the present application based on the content of the present disclosure. The present disclosure selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. A dynamic optimization system for property security patrol routes, characterized in that, Comprise: Data acquisition and pretreatment module, for collecting spatial position data and network performance data, obtaining historical network data, preprocessing spatial position data, network performance data and historical network data; Wireless signal source dynamic optimization module, for composite decision analysis on network performance data, signal monitoring according to composite decision analysis result and decision whether to execute signal source switching; The specific process of the composite decision analysis on network performance data is: Obtain signal quality, magnetic field fluctuation value, access terminal total rate, maximum loadable throughput, stable throughput and its corresponding historical link rate, link rate; variance statistics and normalized mapping are carried out on magnetic field fluctuation value and signal quality to obtain environmental interference intensity; The ratio calculation and normalization processing are carried out on access terminal total rate and maximum loadable throughput to obtain the usage rate of signal source; The ratio calculation is carried out on stable throughput and its corresponding historical link rate, and then the real-time link rate is combined to carry out scale conversion to obtain the available bandwidth of signal source; The signal source performance value is obtained through comprehensive analysis of signal quality, environmental interference intensity, usage rate of signal source, available bandwidth of signal source and maximum bandwidth of available signal source; The specific analysis method of the signal source performance value is: the ratio of the available bandwidth of different signal sources to the maximum bandwidth in the available signal sources is calculated to obtain the bandwidth normalization coefficient; the environmental interference intensity and the usage rate of different signal sources are each added one and then multiplied to calculate the joint penalty term of environment and usage rate; the signal quality of different signal sources is divided by the joint penalty term to obtain the quality occupation correction value; the quality occupation correction value is multiplied by the bandwidth normalization coefficient to obtain the signal source performance value; Path perception signal source switching decision module, for multi-dimensional value quantitative evaluation on spatial position data, network performance data and historical network data, signal source switching is executed according to multi-dimensional value quantitative evaluation result, and path optimization and rerouting decision module is entered; The specific process of the multi-dimensional value quantitative evaluation on spatial position data, network performance data and historical network data is: Obtain positioning data, motion trajectory data, direction and speed, geometric topology of inspection area, path node and edge length information, switching time consumption data, time delay, jitter, packet loss rate, freezing rate, signal source coverage area boundary, usage rate of signal source and environmental interference intensity; Path discretization algorithm and interpolation mapping processing are carried out on spatial position data and network performance data to generate signal source performance data sequence, mean value calculation is carried out on sampling points above threshold to obtain path average signal source performance value of qualified area; Switching time consumption data is called to obtain switching time penalty factor; The switching time penalty factor of different signal sources and the time delay, jitter, packet loss, and stall rate are subjected to segmented regression fitting and robust algorithm processing to obtain a time scale constant; the geometric trajectory of the forward path segment is obtained by path prediction algorithm on the spatial position data, and the coverage overlap degree is obtained by using the geometric accurate method on the coverage area boundary of the signal source; the product of the usage rate and the usage rate weight and the product of the environmental interference intensity and the environmental interference intensity weight are added by a weighted summation function according to the weight to obtain a comprehensive occupation interference index; the signal source switching evaluation value is obtained by comprehensive analysis of the path average signal source efficiency value in the compliance area, the signal source efficiency threshold value, the time scale constant, the switching time penalty factor, the safety compensation constant, the coverage overlap degree, and the comprehensive occupation interference index; The difference between the path average signal source efficiency value in the compliance area and the signal source efficiency threshold value is subjected to normalization processing to obtain an efficiency improvement amount; an exponential time decay function is constructed by combining the switching time penalty factor, the time scale constant, the coverage overlap degree, and the safety compensation constant to obtain a switching time penalty term; the interference suppression amount is obtained by reverse normalization on the comprehensive occupation interference index; the product of the efficiency improvement amount, the switching time penalty term, and the interference suppression amount is multiplied by a proportional coefficient to obtain the path signal source switching evaluation value; The path optimization and rerouting decision module is used for constraint-driven path optimization analysis on the spatial position data, network performance data, and historical network data, and executes the path switching process according to the path optimization analysis result; The specific process of the constraint-driven path optimization analysis is as follows: The positioning data, motion trajectory data, direction and speed, geometric topology of the inspection area, path node and edge length information, signal quality, magnetic field fluctuation value, access terminal total rate, link rate, time delay, jitter, packet loss rate, stall rate, historical maximum bearable throughput, stable throughput, and corresponding historical link rate are obtained; the candidate paths are generated by the K-shortest path algorithm on the geometric topology of the inspection area, path node, and edge length information, and the total length of each edge segment is accumulated to obtain the total length of the candidate path; the shortest path length is obtained by calculating the shortest path length from the starting point to the ending point of the geometric topology of the inspection area, path node, and edge length information by the Dijkstra algorithm; The geometric trajectory of the forward path segment is subjected to fixed-step path discretization processing to generate virtual sampling points, and the length of the point segment with an efficiency value greater than or equal to the threshold value is accumulated to obtain the candidate compliance path length; The signal source efficiency data sequence is subjected to section scanning algorithm processing, the length of the continuous section with a signal source efficiency value lower than the signal source efficiency threshold value is counted, and the maximum value is taken as the longest continuous low coverage segment length; the length of the virtual sampling point section is counted, and regression fitting analysis is performed in combination with the time delay, jitter, packet loss rate, and stall rate to obtain a reference dead zone length; The path switching optimization value is obtained by comprehensive analysis of the total length of the candidate path, the shortest path length, the candidate compliance path length, the path average signal source efficiency value in the compliance area, the signal source efficiency threshold value, the zero-protection constant, the longest continuous low coverage segment length, and the dead zone reference length. The path redundancy is obtained by subtracting the shortest path length from the total length of the candidate path, dividing by the shortest path length, and performing truncated reverse normalization processing; the performance gain is obtained by subtracting the signal source performance threshold from the average signal source performance value of the path in the compliance area, dividing by a constant that is the signal source performance threshold plus a zero protection constant, and performing truncated normalization processing; the coverage ratio is obtained by dividing the length of the candidate compliance path by the total length of the candidate path; the low coverage suppression is obtained by dividing the length of the longest continuous low coverage section by the reference dead zone length and performing truncated reverse normalization processing; and the path switching optimization value is obtained by weighting and summing the performance gain, the coverage ratio, and the low coverage suppression according to the path switching weight, and multiplying by the path redundancy. The running monitoring and closed-loop self-optimization module is configured to improve the signal indicators based on the network quality data and the historical network data statistics, and to dynamically correct the strategy based on the composite judgment analysis result, the multi-dimensional value quantitative evaluation, and the constraint-driven path optimization analysis result, so as to realize closed-loop adaptive optimization of parameters.

2. The system of claim 1, wherein: The specific process of collecting spatial position data and network performance data and obtaining historical network data is as follows: Real-time collection of spatial position data and network performance data to obtain network data; The spatial position data includes positioning data, motion trajectory data, direction and speed, geometric topology of the inspection area, path node and edge length information; The network performance data includes signal quality, magnetic field fluctuation value, total access terminal rate, link rate, signal source coverage area boundary, time delay, jitter, packet loss rate, and freezing rate; The historical network data includes historical throughput data, historical link rate, and switching time consumption records.

3. The system of claim 1, wherein: The specific process of preprocessing the spatial position data, network performance data, and historical network data is as follows: The effectiveness of the spatial position data is verified through an abnormality identification and boundary constraint algorithm to eliminate missing values and records that do not meet the physical boundary conditions; the network performance data is subjected to noise suppression through a multi-stage filtering algorithm to reduce instantaneous fluctuations caused by sensor drift, electromagnetic interference, and sampling jitter; the multi-source heterogeneous spatial position data, network performance data, and historical network data are subjected to scale unification and interval mapping through distribution standardization and linear normalization algorithms for standardization and normalization processing; the historical throughput data, historical link rate, and switching time consumption records are arranged through a feature extraction and database construction algorithm to obtain historical maximum bearable throughput, stable throughput, and corresponding historical link rate and switching time data, which are stored to establish a historical network database.

4. The system of claim 1, wherein: The specific process of signal monitoring and determining whether to perform signal source switching according to the composite judgment analysis result is as follows: Real-time comparison of the signal source performance value and the signal source performance threshold: When the signal source performance value is greater than or equal to the signal source performance threshold, the current signal source is maintained, and the signal source performance value is continuously monitored over time; When the signal source performance value is less than the signal source performance threshold value, the candidate signal sources are sorted according to the signal source performance value and divided into three different levels: preferred candidate, suboptimal candidate and available candidate, the transmitting power is increased, and the signal source switching process is entered.

5. The system of claim 1, wherein: The specific process of performing the signal source switching according to the multi-dimensional value quantitative evaluation result is as follows: The path signal source switching evaluation value and the path signal source switching evaluation multi-level threshold value are compared in real time, and the signal source switching process is entered: When the path signal source switching evaluation value is greater than or equal to the second-level threshold value and the coverage overlap degree is greater than or equal to the overlap degree threshold value, the signal source switching is immediately performed and the transmitting power is gradually reduced; When the path signal source switching evaluation value is greater than or equal to the second-level threshold value and the coverage overlap degree is less than the overlap degree threshold value, a short-time observation window is entered, the signal monitoring is continuously performed and the transmitting power is increased, the switching is dynamically prepared in the preferred candidate and the suboptimal candidate according to the current signal source performance value, and the switching is performed when the path signal source switching evaluation value is greater than or equal to the second-level threshold value and the coverage overlap degree is greater than or equal to the overlap degree threshold value; When the path signal source switching evaluation value is greater than or equal to the first-level threshold value and less than the second-level threshold value, whether the coverage overlap degree is greater than the overlap degree threshold value or not, a short-time observation window is entered, the switching target is determined in the preferred candidate, the signal monitoring is continuously performed and the transmitting power is increased, and the switching is performed when the path signal source switching evaluation value is greater than or equal to the second-level threshold value and the coverage overlap degree is greater than or equal to the overlap degree threshold value; When the path signal source switching evaluation value is less than the first-level threshold value and the coverage overlap degree is less than the overlap degree threshold value, only the transmitting power is continuously increased, and the switching is not performed.

6. The system of claim 1, wherein: The specific process of performing the path switching process according to the path optimization analysis result is as follows: The path switching preferred value and the path switching preferred threshold value are compared in real time: When the path switching preferred value is greater than or equal to the path switching preferred threshold value, a rerouting instruction is issued, and the change of the predicted arrival time and the improvement of the signal coverage ratio are prompted; When the path switching preferred value is less than the path switching preferred threshold value, the current path is maintained, and the transmitting power is continuously increased according to the path perception signal source switching decision module; When a rerouting is just completed, a rerouting cooling period is entered, in the cooling period, a new rerouting and switching decision is not triggered for a small amplitude fluctuation, and after the cooling period is over, the signal monitoring is performed again in the wireless signal source dynamic optimization module.

7. The system of claim 1, wherein: The specific process of performing the strategy dynamic correction according to the composite decision analysis result, the multi-dimensional value quantitative evaluation and the constraint-driven path optimization analysis result based on the signal index improvement degree and the mis-triggering condition obtained by statistically analyzing the network quality data and the historical network data, and realizing the parameter closed-loop adaptive optimization is as follows: The signal index improvement degree is obtained by differentiating the switching before and after the network quality data of the time delay, the jitter, the packet loss rate and the stall rate, and the mis-triggering condition is obtained by statistically analyzing the proportion of the target service quality not reached after the switching and the rerouting. The signal source efficiency threshold, the signal source switching evaluation multi-level threshold, the coincidence threshold and the path switching optimization threshold are comprehensively corrected according to the results of the composite judgment analysis, the multi-dimensional value quantitative evaluation and the path optimization analysis. When the signal index improvement degree is lower than the target and the false triggering condition rises, the signal source efficiency threshold and the coincidence threshold are increased, and the path switching optimization threshold is also increased. When the signal index improvement degree stably improves and the false triggering condition decreases, the signal source efficiency threshold and the path switching optimization threshold are decreased, and the coincidence threshold remains unchanged. When the signal index improvement degree and the false triggering condition differentiate, the path switching optimization threshold is preferentially adjusted, and the signal source efficiency threshold is slightly corrected. The corrected thresholds are written back to form a closed-loop adaptive optimization.

Images