Low-illumination closed space structure detection method and system based on mobile-fixed cooperative guidance

Abstract

The invention provides a low-illumination closed space structure detection method and system based on mobile-fixed cooperative guidance, and relates to the technical field of closed space structure disease detection. The low-illumination closed space structure detection method based on mobile-fixed cooperative guidance is realized by a fixed sensor node network in a mobile-fixed cooperative system and an unmanned aerial vehicle detection system, and comprises the following steps: the fixed sensor node network collects image data of a low-illumination closed space and generates a basic disease data map; transmitting the basic disease data map to an unmanned aerial vehicle detection system, performing global path planning through a dual-mode adaptive closed space disease detection path planning algorithm, and generating an accurate detection path and a supplementary detection path; and the unmanned aerial vehicle detection system detects the diseases of the low-illumination closed space through a machine learning algorithm according to the precise detection path and the supplementary detection path to obtain a disease map result. By adopting the method, the high efficiency and the accuracy of disease detection of the low-illumination closed space structure can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of confined space structure disease detection, and in particular to a low-illuminance confined space structure detection method and system based on mobile-solid collaborative guidance. Background Art

[0002] With the acceleration of urbanization, the development and utilization of confined space structures, including tunnels and towers, are becoming more and more extensive. However, confined space structures are in a high-pressure and humid environment for a long time, which is prone to cracks and leakage structural defects, threatening structural safety. Traditional manual inspection methods are inefficient and difficult to detect potential risks in a timely manner. Therefore, it is of great significance to develop efficient and accurate confined structure space defect detection methods.

[0003] In the existing technology, fixed sensor networks and mobile detection equipment are two commonly used detection methods. Fixed sensor networks can monitor for a long time, but the coverage is limited and it is difficult to achieve comprehensive detection. Mobile detection equipment such as drones are highly flexible, but the detection effect is limited in low-light and weak-textured underground space environments, affecting the detection results. The above two detection methods often operate independently and lack an effective coordination mechanism. Summary of the invention

[0004] In order to solve the technical problems of the existing technology, such as limited coverage, inability to conduct comprehensive detection, limited detection effect in low-light and weak-texture underground space environments, affecting detection results, and lack of coordination mechanism between fixed sensor networks and mobile detection equipment, the embodiment of the present invention provides a low-light enclosed space structure detection method and system based on mobile-fixed coordinated guidance. The technical solution is as follows:

[0005] On the one hand, a low-light confined space structure detection method based on mobile-solid collaborative guidance is provided, and the method is implemented by a low-light confined space structure detection device based on mobile-solid collaborative guidance, and the method includes:

[0006] S1, the fixed sensor node network collects image data of low-light enclosed spaces and generates a basic disease data map;

[0007] S2, the fixed sensor node network transmits the basic disease data map to the drone detection system, performs global path planning through a dual-mode adaptive confined space disease detection path planning algorithm, and generates an accurate detection path and a supplementary detection path;

[0008] S3. The UAV detection system detects the diseases of the low-light enclosed space according to the precise detection path and the supplementary detection path through the U-Net deep learning algorithm to obtain the disease map result of the low-light enclosed space structure.

[0009] Optionally, the damage map result of the low-illuminance enclosed space structure includes: damage results not covered by fixed sensors and damage results covered by fixed sensors;

[0010] The disease results include: the location of the disease, the type of disease and the severity of the disease.

[0011] Optionally, the S2 performs global path planning through a dual-mode adaptive confined space disease detection path planning algorithm to generate an accurate detection path and a supplementary detection path, including:

[0012] S21. Obtaining basic disease data maps and a three-dimensional map model of the confined space;

[0013] S22, dividing the confined space into a plurality of sections according to the basic disease data map and the three-dimensional map model of the confined space; determining the characteristics of each section according to the distribution of fixed sensors and the distribution of known diseases;

[0014] S23, determining a task type for each section according to the characteristics of each section;

[0015] S24. According to the task type of each section, plan the detection of each section, obtain the detection plan and supplement the detection plan;

[0016] S25. Generate accurate detection paths and supplementary detection paths according to the detection plan and the supplementary detection plan;

[0017] S26. Use an obstacle avoidance algorithm to optimize the precise detection path to obtain an optimized precise detection path; use an obstacle avoidance algorithm to optimize the supplementary detection path to obtain an optimized supplementary detection path.

[0018] Optionally, after the step of the UAV detection system in S3 detecting the diseases of the low-light confined space according to the precise detection path and the supplementary detection path by using a machine learning algorithm to obtain the disease map result of the low-light confined space structure, the step further includes:

[0019] The mobile-fixed collaborative system adopts the information decentralization method to send notification messages to specific fixed sensor nodes; the specific fixed sensor nodes receive the notification messages, shorten the perception cycle of the mobile-fixed collaborative system by adjusting the perception cycle of the fixed sensors, and obtain a new perception cycle of the mobile-fixed collaborative system.

[0020] Optionally, the mobile-fixed cooperative system adopts an information decentralization method to send a notification message to a specific fixed sensor node; the specific fixed sensor node receives the notification message, and shortens the perception cycle of the mobile-fixed cooperative system by adjusting a perception cycle of the fixed sensor, thereby obtaining a new perception cycle of the mobile-fixed cooperative system, including:

[0021] The mobile-solid collaborative system determines the impact range of each detected disease based on the disease map results of the low-light enclosed space structure detected by the drone detection system;

[0022] Determine the fixed set of sensors to be notified based on the impact area of ​​each detected disease;

[0023] According to the fixed sensor set notified, a communication function is constructed; according to the communication function, a sensing period adjustment function is constructed;

[0024] According to the sensing cycle adjustment function, the sensing cycle of each fixed sensor is adjusted, and the adjusted sensing cycle of each fixed sensor is output;

[0025] According to the adjusted sensing period of each fixed sensor, the sensing period of the mobile-fixed cooperative system is shortened to obtain a new sensing period of the mobile-fixed cooperative system.

[0026] Optionally, the mobile-fixed coordination system updates the fixed sensor node network deployment plan according to the disease results not covered by the fixed sensors to obtain a new fixed sensor node network deployment plan, including:

[0027] According to the disease results not covered by fixed sensors, a utility function for fixed sensor deployment is constructed;

[0028] Define a fixed sensor coverage function; construct an optimization problem based on the fixed sensor coverage function;

[0029] The Monte Carlo tree search algorithm is used to solve the optimization problem and obtain a new fixed sensor node network deployment scheme.

[0030] Optionally, after the step of obtaining a new fixed sensor node network deployment solution, the method further includes:

[0031] Construct a deployment decision function; evaluate the new fixed sensor node network deployment plan according to the deployment decision function and obtain the evaluation result;

[0032] Determine whether to execute a new fixed sensor node network deployment plan based on the evaluation result; if the evaluation result is 1, execute the new fixed sensor node network deployment plan and update the fixed sensor network topology; if the evaluation result is 0, retain the original fixed sensor node network deployment plan.

[0033] On the other hand, a low-light confined space structure detection system based on mobile-solid collaborative guidance is provided, and the system is applied to a low-light confined space structure detection method based on mobile-solid collaborative guidance, and the system includes:

[0034] The fixed sensor node network is used for collecting image data of low-light enclosed spaces and generating a basic disease data map; the fixed sensor node network transmits the basic disease data map to the drone detection system;

[0035] The drone detection system is used to perform global path planning through a dual-mode adaptive confined space disease detection path planning algorithm to generate a precise detection path and a supplementary detection path; the drone detection system detects diseases in low-illuminance confined spaces based on the precise detection path and the supplementary detection path through a U-Net deep learning algorithm to obtain a disease map result of a low-illuminance confined space structure.

[0036] Optionally, the damage map result of the low-illuminance enclosed space structure includes: damage results not covered by fixed sensors and damage results covered by fixed sensors;

[0037] The disease results include: the location of the disease, the type of disease and the severity of the disease.

[0038] Optionally, the global path planning is performed by using a dual-mode adaptive confined space disease detection path planning algorithm to generate a precise detection path and a supplementary detection path, including:

[0039] Obtain basic disease data maps and three-dimensional map models of confined spaces;

[0040] According to the basic disease data map and the three-dimensional map model of the confined space, the confined space is divided into multiple sections; according to the distribution of fixed sensors and known disease distribution, the characteristics of each section are determined;

[0041] Determine the task type for each segment based on the characteristics of each segment;

[0042] According to the task type of each section, plan the inspection for each section, obtain the inspection plan and supplement the inspection plan;

[0043] Generate precise detection paths and supplementary detection paths based on detection plans and supplementary detection plans;

[0044] An obstacle avoidance algorithm is used to optimize the precise detection path to obtain an optimized precise detection path; an obstacle avoidance algorithm is used to optimize the supplementary detection path to obtain an optimized supplementary detection path.

[0045] Optionally, after the step of detecting the damage of the low-light confined space by a machine learning algorithm according to the precise detection path and the supplementary detection path, and obtaining the damage map result of the low-light confined space structure, the drone detection system further includes:

[0046] The mobile-fixed collaborative system adopts the information decentralization method to send notification messages to specific fixed sensor nodes; the specific fixed sensor nodes receive the notification messages, shorten the perception cycle of the mobile-fixed collaborative system by adjusting the perception cycle of the fixed sensors, and obtain a new perception cycle of the mobile-fixed collaborative system.

[0047] Optionally, the mobile-fixed cooperative system adopts an information decentralization method to send a notification message to a specific fixed sensor node; the specific fixed sensor node receives the notification message, and shortens the perception cycle of the mobile-fixed cooperative system by adjusting a perception cycle of the fixed sensor, thereby obtaining a new perception cycle of the mobile-fixed cooperative system, including:

[0048] The mobile-solid collaborative system determines the impact range of each detected disease based on the disease map results of the low-light enclosed space structure detected by the drone detection system;

[0049] Determine the fixed set of sensors to be notified based on the impact area of ​​each detected disease;

[0050] According to the fixed sensor set notified, a communication function is constructed; according to the communication function, a sensing period adjustment function is constructed;

[0051] According to the sensing cycle adjustment function, the sensing cycle of each fixed sensor is adjusted, and the adjusted sensing cycle of each fixed sensor is output;

[0052] According to the adjusted sensing period of each fixed sensor, the sensing period of the mobile-fixed cooperative system is shortened to obtain a new sensing period of the mobile-fixed cooperative system.

[0053] Optionally, the mobile-fixed coordination system updates the fixed sensor node network deployment plan according to the disease results not covered by the fixed sensors to obtain a new fixed sensor node network deployment plan, including:

[0054] According to the disease results not covered by fixed sensors, a utility function for fixed sensor deployment is constructed;

[0055] Define a fixed sensor coverage function; construct an optimization problem based on the fixed sensor coverage function;

[0056] The Monte Carlo tree search algorithm is used to solve the optimization problem and obtain a new fixed sensor node network deployment scheme.

[0057] Optionally, after the step of obtaining a new fixed sensor node network deployment solution, the method further includes:

[0058] Construct a deployment decision function; evaluate the new fixed sensor node network deployment plan according to the deployment decision function and obtain the evaluation result;

[0059] Determine whether to execute a new fixed sensor node network deployment plan based on the evaluation result; if the evaluation result is 1, execute the new fixed sensor node network deployment plan and update the fixed sensor network topology; if the evaluation result is 0, retain the original fixed sensor node network deployment plan.

[0060] On the other hand, a low-light confined space structure detection device based on the coordinated guidance of moving and fixing is provided, and the low-light confined space structure detection device based on the coordinated guidance of moving and fixing comprises: a processor; a memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, any one of the low-light confined space structure detection methods based on the coordinated guidance of moving and fixing is implemented.

[0061] On the other hand, a computer-readable storage medium is provided, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned low-illumination confined space structure detection methods based on mobile-solid collaborative guidance.

[0062] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0063] The embodiment of the present invention first collects image data of low-light enclosed spaces through a fixed sensor node network to generate a basic disease data map; secondly, the fixed sensor node network transmits the basic disease data map to the drone detection system, guides the drone to perform the detection task, and performs global path planning through a dual-mode adaptive enclosed space disease detection path planning algorithm to generate a precise detection path and a supplementary detection path; finally, the drone detection system detects diseases in low-light enclosed spaces based on the precise detection path and the supplementary detection path through a machine learning algorithm to obtain a disease map result of the low-light enclosed space structure. The embodiment of the present invention detects diseases in low-light enclosed spaces through the collaborative work of the drone detection system and the fixed sensor node network, thereby improving the efficiency and accuracy of structural disease detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0065] like Figure 1 The figure provides an overall flow chart of a low-light confined space structure detection method based on mobile-solid collaborative guidance;

[0066] Figure 2 It is a flow chart of a low-light enclosed space structure detection method based on mobile-solid collaborative guidance provided by an embodiment of the present invention;

[0067] Figure 3 It is a flow chart of a dual-mode adaptive confined space disease detection path planning algorithm provided by an embodiment of the present invention;

[0068] Figure 4 It is a visualization schematic diagram of a dual-mode adaptive confined space disease detection path planning algorithm provided by an embodiment of the present invention;

[0069] Figure 5 It is a schematic diagram of the structure of a multifunctional detection system for unmanned aerial vehicles provided by an embodiment of the present invention;

[0070] Figure 6 It is a block diagram of a low-light enclosed space structure detection system based on mobile-solid collaborative guidance provided by an embodiment of the present invention;

[0071] Figure 7 It is a structural schematic diagram of a low-illuminance enclosed space structure detection device based on mobile-solid collaborative guidance provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0073] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0074] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0075] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0076] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0077] The embodiment of the present invention provides a low-light confined space structure detection method based on mobile-fixed collaborative guidance, which can be implemented by a low-light confined space structure detection device based on mobile-fixed collaborative guidance, and the low-light confined space structure detection device based on mobile-fixed collaborative guidance can be a terminal or a server. Figure 1 As shown in FIG. 1 , an overall flow chart of a low-light confined space structure detection method based on mobile-solid collaborative guidance is provided; Figure 2 The flowchart of the low-light confined space structure detection method based on mobile-solid collaborative guidance is shown in FIG. The processing flow of the method may include the following steps:

[0078] S1. The fixed sensor node network collects image data of low-light enclosed spaces and generates basic disease data maps.

[0079] Among them, low-light enclosed spaces include tunnels, water channels and towers.

[0080] The fixed sensor node network includes a plurality of fixed sensor nodes.

[0081] S2. The fixed sensor node network transmits the basic disease data map to the UAV detection system, and performs global path planning through the dual-mode adaptive confined space disease detection path planning algorithm to generate accurate detection paths and supplementary detection paths.

[0082] Among them, the DACSDD algorithm (Dual-mode Adaptive Confined Space Disease Detection Path Planning Algorithm, DACSDD) is a dual-mode adaptive confined space disease detection path planning algorithm.

[0083] Among them, Figure 3The figure shows a flow chart of a dual-mode adaptive confined space disease detection path planning algorithm; in a feasible implementation method, sensor positions and disease data are obtained; the sensor positions and disease data are imported into a three-dimensional map model of the confined space; the confined space is divided into sections and section characteristics are determined; the sensor coverage is calculated and the known disease density is evaluated; the section task type is determined based on the sensor coverage and the known disease density; the section task types include: precise detection and supplementary detection; the implementation process of precise detection planning includes: extracting a point set, optimizing the access sequence using the TSP algorithm, and setting scanning parameters; generating a precise detection path based on the scanning parameters; the implementation process of supplementary detection planning includes: identifying uncovered areas; domain; according to the uncovered area, obtain the coverage path planning and set the flight parameters; according to the flight parameters, generate the supplementary detection path; according to the precise detection path and the supplementary detection path, determine whether there is a path conflict detection; if there is a conflict, adjust the path, solve the path conflict and optimize it, and after solving the path conflict and optimizing the path, perform task scheduling optimization; if there is no path conflict, perform global task scheduling optimization, calculate the minimized total flight distance, use the obstacle avoidance algorithm, optimize the precise detection path and the supplementary detection path, and obtain the optimized precise detection path and the optimized supplementary detection path; according to the optimized precise detection path and the optimized supplementary detection path, perform the detection task and generate the fusion data set and disease map results.

[0084] Optionally, the specific implementation process of S2 may include S21-S26:

[0085] S21. Obtaining basic disease data maps and a three-dimensional map model of the confined space;

[0086] In a feasible implementation, a location set of fixed sensor nodes is obtained; the location set of fixed sensor nodes is imported into a three-dimensional map model of a confined space; a basic disease data map is obtained, and the confined space is divided into networks according to the basic disease data map.

[0087] Among them, the basic disease data map includes the location information of each known disease point, the type of disease, the degree of damage and other information.

[0088] S22, dividing the confined space into a plurality of sections according to the basic disease data map and the three-dimensional map model of the confined space; determining the characteristics of each section according to the distribution of fixed sensors and the distribution of known diseases;

[0089] S23, determining a task type for each section according to the characteristics of each section;

[0090] Among them, the task types include: precise detection and supplementary detection; the basis for confirming the task type includes but is not limited to: sensor coverage, known disease density and section importance.

[0091] S24. Plan the detection of each section according to the task type of each section, and obtain an accurate detection plan and a supplementary detection plan;

[0092] Among them, the precise detection planning is for the precise detection section; in a feasible implementation mode, the specific steps of executing the precise detection planning and generating the precise detection path include:

[0093] (1) Extracting a set of points that require precise detection from the basic disease data map;

[0094] (2) Based on the point set, the TSP algorithm is used to optimize the access order of the points in the point set to obtain the optimized path;

[0095] Among them, the TSP algorithm can be solved by genetic algorithm.

[0096] (3) According to the optimized path, set scanning parameters for each detection point to generate an accurate detection path; the scanning parameters include: hovering time and scanning accuracy.

[0097] The supplementary inspection plan is for the section of the supplementary inspection; the specific steps of executing the supplementary inspection plan and generating the supplementary inspection path include:

[0098] (1) Identify areas not covered by fixed sensor networks;

[0099] (2) Based on the uncovered area, the improved Boustrophedon algorithm is used to generate the path to the covered area;

[0100] (3) According to the path of the coverage area, set the scanning flight parameters and generate a supplementary detection path; the flight parameters include: flight speed and flight altitude.

[0101] S25. Generate a precise detection path and a supplementary detection path according to the precise detection plan and the supplementary detection plan;

[0102] S26. Use an obstacle avoidance algorithm to optimize the precise detection path to obtain an optimized precise detection path; use an obstacle avoidance algorithm to optimize the supplementary detection path to obtain an optimized supplementary detection path.

[0103] Among them, the obstacle avoidance algorithm can be used , RRT and Lite and other methods, the present invention adopts The method optimizes the conversion path between segments to minimize the total flight distance. The process of minimizing the total flight distance can be expressed by the following formula (1):

[0104] (1)

[0105] in, Indicates segment arrive distance; Indicates the total flight distance; Indicates the segments that divide a confined space.

[0106] S3. The UAV detection system detects the diseases of the low-light enclosed space according to the precise detection path and the supplementary detection path through the U-Net deep learning algorithm to obtain the disease map result of the low-light enclosed space structure.

[0107] In a feasible implementation, a fused data set is obtained based on the data collected by the UAV detection system and the data collected by the fixed sensor. Based on the fused data set, the process of using the machine learning algorithm for analysis can be expressed by the following formula (2):

[0108] (2)

[0109] in, Indicates Identified diseases; represents the fused dataset; Indicates data samples; Represents the U-Net deep learning algorithm.

[0110] Among them, according to the disease map results of the low-light confined space structure, the detection results are visualized to generate a heat map of the health status of the low-light confined space, which can be expressed by the following formula (3):

[0111] (3)

[0112] in, The heat value indicating health status; represents the heat map generation function; The damage map results representing low-light confined space structures; ,in, Indicates the location of the disease; Indicates the type of disease; Indicates the severity of the disease.

[0113] Optionally, the damage map results of the low-light enclosed space structure include: damage results not covered by the fixed sensors and damage results covered by the fixed sensors;

[0114] Among them, the disease results include: the location of the disease, the type of disease and the severity of the disease.

[0115] Optionally, after the step of detecting the damage of the low-light confined space by the U-Net deep learning algorithm according to the precise detection path and the supplementary detection path, and obtaining the damage map result of the low-light confined space structure, the UAV detection system of S3 further includes:

[0116] The mobile-fixed collaborative system adopts the information decentralization method to send notification messages to specific fixed sensor nodes; the specific fixed sensor nodes receive the notification messages, shorten the perception cycle of the mobile-fixed collaborative system by adjusting the perception cycle of the fixed sensors, and obtain a new perception cycle of the mobile-fixed collaborative system.

[0117] Among them, the information decentralization method refers to a method of delegating decision-making power and management power from a higher level of management to a lower level of management or place in the fields of administrative management, corporate management, etc. It is a method mastered by technical personnel in this field and will not be further elaborated in this application.

[0118] Optionally, the mobile-fixed cooperative system adopts an information decentralization method to send a notification message to a specific fixed sensor node; the specific fixed sensor node receives the notification message, and shortens the perception cycle of the mobile-fixed cooperative system by adjusting the perception cycle of the fixed sensor, thereby obtaining a new perception cycle of the mobile-fixed cooperative system, including:

[0119] The mobile-solid collaborative system determines the impact range of each detected disease based on the disease map results of the low-light enclosed space structure detected by the drone detection system;

[0120] In a feasible implementation, the impact range of each detected disease can be expressed by the following formula (4):

[0121] (4)

[0122] in, Indicates each disease detected; represents the distance function, Indicates a predetermined threshold.

[0123] Determine the fixed set of sensors to be notified based on the impact area of ​​each detected disease;

[0124] According to the fixed sensor set notified, a communication function is constructed; according to the communication function, a sensing period adjustment function is constructed;

[0125] The communication function can be expressed by the following formula (5):

[0126] (5)

[0127] in, represents a fixed set of sensors, in, Indicates The location coordinates of the sensors; represents the power set of a fixed set of sensors, Represents the message space; Represents a collection of diseases.

[0128] The sensing period adjustment function can be expressed by the following formula (6):

[0129] (6)

[0130] in, represents the original perception cycle; Indicates the generated notification message; represents the set of positive real numbers.

[0131] According to the sensing cycle adjustment function, the sensing cycle of each fixed sensor is adjusted, and the adjusted sensing cycle of each fixed sensor is output;

[0132] In a feasible implementation, the generated notification message is sent to each sensor; the sensing period adjustment function is used to adjust the sensing period of each fixed sensor to obtain an updated sensing period, which can be expressed by the following formula (7):

[0133] (7)

[0134] in, represents the sensing period of each fixed sensor update; Represents the original sensing period of each fixed sensor.

[0135] According to the adjusted perception cycle of each fixed sensor, the perception cycle of the mobile-fixed cooperative system is shortened to obtain a new perception cycle of the mobile-fixed cooperative system.

[0136] In a feasible implementation, by shortening the sensing period of the mobile-fixed cooperative system, the sensing probability of the mobile-fixed cooperative system can be improved. According to the new sensing period of the mobile-fixed cooperative system, the improvement of system performance can be evaluated by using the following formula (8):

[0137] (8)

[0138] in, Indicates the system performance value after adopting the new sensing cycle; Indicates the system performance value when the original sensing cycle is used; Indicates the improvement in system performance.

[0139] Optionally, the mobile-fixed collaborative system updates the fixed sensor node network deployment plan according to the disease results not covered by the fixed sensors to obtain a new fixed sensor node network deployment plan, including:

[0140] According to the disease results not covered by fixed sensors, a utility function for fixed sensor deployment is constructed;

[0141] In a feasible implementation, the fixed sensor deployment utility function can be expressed by the following formula (9):

[0142] (9)

[0143] in, Indicates the disease results that are not covered by the fixed sensor, Represents the i-th uncovered disease, containing multiple attributes, Indicates the location of the disease; Represents the type; Indicates severity; represents the detection probability; Represents the set of real numbers.

[0144] Among them, the sensor deployment utility function is used to evaluate the effectiveness of deploying sensors at location s for detection The effect of treating diseases.

[0145] Define a fixed sensor coverage function; construct an optimization problem based on the fixed sensor coverage function;

[0146] The sensor coverage function can be expressed by the following formula (10):

[0147] (10)

[0148] in, Indicates sensor coverage; Represents the power set of three-dimensional real number space; Represents a collection of sensors.

[0149] The optimization problem can be expressed by the following formula (11):

[0150]

[0151] in, represents the detection probability threshold; represents the detection probability; Indicates the maximum number of sensors that can be deployed.

[0152] The Monte Carlo tree search algorithm is used to solve the optimization problem and obtain a new fixed sensor node network deployment scheme.

[0153] Among them, the Monte Carlo tree search algorithm is a conventional technical means and will not be further elaborated in this application.

[0154] Optionally, after the step of obtaining a new fixed sensor node network deployment solution, the method further includes:

[0155] Construct a deployment decision function; evaluate the new fixed sensor node network deployment plan according to the deployment decision function and obtain the evaluation result;

[0156] In a feasible implementation, the deployment decision function can be expressed by the following formula (12):

[0157] (12)

[0158] in, Represents the cost function of the new fixed sensor node network deployment scheme.

[0159] Determine whether to execute a new fixed sensor node network deployment plan based on the evaluation result; if the evaluation result is 1, execute the new fixed sensor node network deployment plan and update the fixed sensor network topology; if the evaluation result is 0, retain the original fixed sensor node network deployment plan.

[0160] In a feasible implementation, whether to implement a new fixed sensor node network deployment plan is determined based on the evaluation results. , execute the new fixed sensor node network deployment plan and update the fixed sensor network topology; when , the original fixed sensor node network deployment scheme is retained; the process of updating the fixed sensor network topology structure can be expressed by the following formula (13):

[0161] (13)

[0162] in, Represents a graph structure, which is used to represent the connection relationship between sensors.

[0163] According to the new fixed sensor node network deployment scheme, the overall perception probability of the system is re-evaluated by the following formula (14):

[0164] (14)

[0165] in, Represents a new fixed sensor node network deployment scheme.

[0166] In a feasible implementation, the mobile-fixed collaborative system is called fixed-mobile network convergence, which means that through the integration and cooperation between fixed networks and mobile networks, full-service and converged service operations are achieved, providing users with diverse and high-quality communication, information, entertainment and other services.

[0167] like Figure 4 : is a dual-mode adaptive confined space disease detection path planning algorithm path planning visualization schematic diagram provided by an embodiment of the present invention; wherein the mobile-fixed collaborative system in the present application includes: a fixed sensor node network and a drone detection system; the fixed sensor node network includes: a water leakage sensor, a tilt sensor and a joint sensor; such as Figure 5 It is a structural schematic diagram of a multifunctional detection system for unmanned aerial vehicles provided by an embodiment of the present invention; the unmanned aerial vehicle detection system includes: an unmanned aerial vehicle body, a propulsion system, a processor, a memory, a communication system and a general sensor system.

[0168] Among them, a remotely adjustable LED lighting system can be set on the drone body. The rotatable head installed on the drone body contains multiple LED light sources. The rotatable head can rotate 360 ​​degrees to provide all-round lighting.

[0169] Wherein, for different illumination environments, the processor is used to intelligently adjust the system brightness to adapt to different environments; wherein the intelligent brightness adjustment includes: optimal lighting angle and optimal lighting intensity; wherein the brightness intelligent adjustment optimization problem can be expressed by the following formulas (15)-(17):

[0170] (15)

[0171] (16)

[0172] (17)

[0173] in, represents the area of ​​the shaded region; Indicates the degree of glare Indicates the richness of image details; represents the first weight coefficient; represents the second weight coefficient; represents the third weight coefficient; Indicates the minimum permissible lighting angle; Indicates the maximum permissible lighting angle; Indicates the minimum permissible lighting intensity; Indicates the maximum permissible lighting intensity; Indicates the lighting angle; Indicates the lighting intensity.

[0174] The processor is used to calculate and control the remote adjustment LED lighting system, including:

[0175] (1) Calculate the optimal lighting angle and optimal lighting intensity based on environmental data;

[0176] (2) Control the rotatable head to rotate to the optimal lighting angle;

[0177] (2) Control the output light intensity of the LED light source to the optimal lighting intensity;

[0178] The processor is also used to perform fusion processing and disease detection on multi-source data collected by the general sensor system, including:

[0179] (1) Based on the image data collected by the full HD camera, a deep convolutional neural network based on the ResNet50 architecture is used to extract image features;

[0180] (2) Extract temperature anomaly features based on the thermal image data collected by the thermal imaging camera;

[0181] (3) Extract infrared features based on infrared image data collected by a low-light infrared camera;

[0182] (4) Fusing the image features, temperature anomaly features, and infrared features to obtain fused features;

[0183] (5) A deep convolutional neural network based on the ResNet50 architecture pre-trained on a large-scale structural damage dataset is used to classify the fused features and identify the types and locations of structural damage in confined spaces.

[0184] In a feasible implementation, the drone detection system can be equipped with a multi-sensor joint perception system including: a full HD camera, a thermal imaging camera and a low-light infrared camera; wherein the full HD camera is used to capture clear structural surface images; the thermal imaging camera is used to detect temperature anomalies and identify potential leakage or structural problems; the low-light infrared camera is used to use infrared information to expand image information in low-light environments.

[0185] The embodiment of the present invention first collects image data of low-light enclosed spaces through a fixed sensor node network to generate a basic disease data map; secondly, the fixed sensor node network transmits the basic disease data map to the drone detection system, guides the drone to perform the detection task, and performs global path planning through a dual-mode adaptive enclosed space disease detection path planning algorithm to generate a precise detection path and a supplementary detection path; finally, the drone detection system detects diseases in low-light enclosed spaces based on the precise detection path and the supplementary detection path through a machine learning algorithm to obtain a disease map result of the low-light enclosed space structure. The embodiment of the present invention detects diseases in low-light enclosed spaces through the collaborative work of the drone detection system and the fixed sensor node network, thereby improving the efficiency and accuracy of structural disease detection.

[0186] Figure 6 The block diagram of a low-light confined space structure detection system based on mobile-solid collaborative guidance according to an exemplary embodiment is shown. The system is used for a low-light confined space structure detection method based on mobile-solid collaborative guidance. Figure 6 The system includes a fixed sensor node network 610 and a drone detection system 620. Among them:

[0187] The fixed sensor node network 610 is used for collecting image data of low-light enclosed spaces and generating a basic disease data map; the fixed sensor node network transmits the basic disease data map to the drone detection system;

[0188] The drone detection system 620 is used to perform global path planning through a dual-mode adaptive confined space disease detection path planning algorithm to generate a precise detection path and a supplementary detection path; the drone detection system detects diseases in low-illuminance confined spaces based on the precise detection path and the supplementary detection path through a U-Net deep learning algorithm to obtain a disease map result of the low-illuminance confined space structure.

[0189] Optionally, the damage map result of the low-illuminance enclosed space structure includes: damage results not covered by fixed sensors and damage results covered by fixed sensors;

[0190] The disease results include: the location of the disease, the type of disease and the severity of the disease.

[0191] Optionally, the global path planning is performed by using a dual-mode adaptive confined space disease detection path planning algorithm to generate a precise detection path and a supplementary detection path, including:

[0192] Obtain basic disease data maps and three-dimensional map models of confined spaces;

[0193] According to the basic disease data map and the three-dimensional map model of the confined space, the confined space is divided into multiple sections; according to the distribution of fixed sensors and known disease distribution, the characteristics of each section are determined;

[0194] Determine the task type for each segment based on the characteristics of each segment;

[0195] According to the task type of each section, plan the inspection for each section, obtain the inspection plan and supplement the inspection plan;

[0196] Generate precise detection paths and supplementary detection paths based on detection plans and supplementary detection plans;

[0197] An obstacle avoidance algorithm is used to optimize the precise detection path to obtain an optimized precise detection path; an obstacle avoidance algorithm is used to optimize the supplementary detection path to obtain an optimized supplementary detection path.

[0198] Optionally, after the step of detecting the damage of the low-light confined space by a machine learning algorithm according to the precise detection path and the supplementary detection path, and obtaining the damage map result of the low-light confined space structure, the drone detection system further includes:

[0199] The mobile-fixed collaborative system adopts the information decentralization method to send notification messages to specific fixed sensor nodes; the specific fixed sensor nodes receive the notification messages, shorten the perception cycle of the mobile-fixed collaborative system by adjusting the perception cycle of the fixed sensors, and obtain a new perception cycle of the mobile-fixed collaborative system.

[0200] Optionally, the mobile-fixed cooperative system adopts an information decentralization method to send a notification message to a specific fixed sensor node; the specific fixed sensor node receives the notification message, and shortens the perception cycle of the mobile-fixed cooperative system by adjusting a perception cycle of the fixed sensor, thereby obtaining a new perception cycle of the mobile-fixed cooperative system, including:

[0201] The mobile-solid collaborative system determines the impact range of each detected disease based on the disease map results of the low-light enclosed space structure detected by the drone detection system;

[0202] Determine the fixed set of sensors to be notified based on the impact area of ​​each detected disease;

[0203] According to the fixed sensor set notified, a communication function is constructed; according to the communication function, a sensing period adjustment function is constructed;

[0204] According to the sensing cycle adjustment function, the sensing cycle of each fixed sensor is adjusted, and the adjusted sensing cycle of each fixed sensor is output;

[0205] According to the adjusted sensing period of each fixed sensor, the sensing period of the mobile-fixed cooperative system is shortened to obtain a new sensing period of the mobile-fixed cooperative system.

[0206] Optionally, the mobile-fixed coordination system updates the fixed sensor node network deployment plan according to the disease results not covered by the fixed sensors to obtain a new fixed sensor node network deployment plan, including:

[0207] According to the disease results not covered by fixed sensors, a utility function for fixed sensor deployment is constructed;

[0208] Define a fixed sensor coverage function; construct an optimization problem based on the fixed sensor coverage function;

[0209] The Monte Carlo tree search algorithm is used to solve the optimization problem and obtain a new fixed sensor node network deployment scheme.

[0210] Optionally, after the step of obtaining a new fixed sensor node network deployment solution, the method further includes:

[0211] Construct a deployment decision function; evaluate the new fixed sensor node network deployment plan according to the deployment decision function and obtain the evaluation result;

[0212] Determine whether to execute a new fixed sensor node network deployment plan based on the evaluation result; if the evaluation result is 1, execute the new fixed sensor node network deployment plan and update the fixed sensor network topology; if the evaluation result is 0, retain the original fixed sensor node network deployment plan.

[0213] The embodiment of the present invention first collects image data of low-light enclosed spaces through a fixed sensor node network to generate a basic disease data map; secondly, the fixed sensor node network transmits the basic disease data map to the drone detection system, guides the drone to perform the detection task, and performs global path planning through a dual-mode adaptive enclosed space disease detection path planning algorithm to generate a precise detection path and a supplementary detection path; finally, the drone detection system detects diseases in low-light enclosed spaces based on the precise detection path and the supplementary detection path through a machine learning algorithm to obtain a disease map result of the low-light enclosed space structure. The embodiment of the present invention detects diseases in low-light enclosed spaces through the collaborative work of the drone detection system and the fixed sensor node network, thereby improving the efficiency and accuracy of structural disease detection.

[0214] Figure 7is a structural schematic diagram of a low-light enclosed space structure detection device based on mobile-solid collaborative guidance provided by an embodiment of the present invention, such as Figure 7 As shown, the low-light confined space structure detection equipment based on mobile-solid collaborative guidance may include the above Figure 6 The low-light confined space structure detection system based on mobile-fixed collaborative guidance is shown. Optionally, the low-light confined space structure detection device 710 based on mobile-fixed collaborative guidance may include a first processor 2001 .

[0215] Optionally, the low-light enclosed space structure detection device 710 based on mobile-solid collaborative guidance may also include a memory 2002 and a transceiver 2003 .

[0216] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0217] Combine the following Figure 7 The various components of the low-light enclosed space structure detection device 710 based on mobile-solid collaborative guidance are specifically introduced:

[0218] The first processor 2001 is the control center of the low-light confined space structure detection device 710 based on mobile-solid collaborative guidance, which can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0219] Optionally, the first processor 2001 can perform various functions of the low-illumination enclosed space structure detection device 710 based on mobile-solid collaborative guidance by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002.

[0220] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 7 CPU0 and CPU1 are shown in FIG.

[0221] In a specific implementation, as an embodiment, the low-light confined space structure detection device 710 based on mobile-solid collaborative guidance may also include multiple processors, such as Figure 7 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0222] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0223] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be connected to the computer through the interface circuit ( Figure 7 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0224] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0225] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 7 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0226] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 7 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0227] It should be noted that Figure 7 The structure of the low-light enclosed space structure detection device 710 based on mobile-solid collaborative guidance shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0228] In addition, the technical effects of the low-light enclosed space structure detection device 710 based on the coordinated guidance of moving and fixing can refer to the technical effects of the low-light enclosed space structure detection method based on the coordinated guidance of moving and fixing described in the above method embodiment, and will not be repeated here.

[0229] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0230] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0231] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0232] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0233] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0234] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0235] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0236] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0237] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of the system or unit, which can be electrical, mechanical or other forms.

[0238] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0239] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0240] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0241] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A low-light enclosed space structure detection method based on mobile-solid collaborative guidance, characterized in that: The low-light confined space structure detection method based on mobile-solid collaborative guidance is implemented by a fixed sensor node network and a drone detection system in a mobile-solid collaborative system; the method includes: S1, the fixed sensor node network collects image data of low-light enclosed spaces and generates a basic disease data map; S2, the fixed sensor node network transmits the basic disease data map to the drone detection system, performs global path planning through a dual-mode adaptive confined space disease detection path planning algorithm, and generates an accurate detection path and a supplementary detection path; S3. The UAV detection system detects the diseases of the low-light enclosed space according to the precise detection path and the supplementary detection path through the U-Net deep learning algorithm to obtain the disease map result of the low-light enclosed space structure.

2. The low-light enclosed space structure detection method based on mobile-solid collaborative guidance according to claim 1 is characterized in that: The disease map results of the low-light enclosed space structure include: disease results not covered by fixed sensors and disease results covered by fixed sensors; The disease results include: the location of the disease, the type of disease and the severity of the disease.

3. The low-light enclosed space structure detection method based on mobile-solid collaborative guidance according to claim 1 is characterized in that: The S2 performs global path planning through a dual-mode adaptive confined space disease detection path planning algorithm to generate accurate detection paths and supplementary detection paths, including: S21. Obtaining basic disease data maps and a three-dimensional map model of the confined space; S22, dividing the confined space into a plurality of sections according to the basic disease data map and the three-dimensional map model of the confined space; determining the characteristics of each section according to the distribution of fixed sensors and the distribution of known diseases; S23, determining a task type for each section according to the characteristics of each section; S24. According to the task type of each section, plan the detection of each section, obtain the detection plan and supplement the detection plan; S25. Generate accurate detection paths and supplementary detection paths according to the detection plan and the supplementary detection plan; S26. Use an obstacle avoidance algorithm to optimize the precise detection path to obtain an optimized precise detection path; use an obstacle avoidance algorithm to optimize the supplementary detection path to obtain an optimized supplementary detection path.

4. The low-light enclosed space structure detection method based on mobile-solid collaborative guidance according to claim 1 is characterized in that: After the step of detecting the damage of the low-light enclosed space by the U-Net deep learning algorithm according to the precise detection path and the supplementary detection path, and obtaining the damage map result of the low-light enclosed space structure, the UAV detection system in S3 further includes: The mobile-fixed collaborative system adopts the information decentralization method to send notification messages to specific fixed sensor nodes; the specific fixed sensor nodes receive the notification messages, shorten the perception cycle of the mobile-fixed collaborative system by adjusting the perception cycle of the fixed sensors, and obtain a new perception cycle of the mobile-fixed collaborative system.

5. The low-light enclosed space structure detection method based on mobile-solid collaborative guidance according to claim 4 is characterized in that: The mobile-fixed cooperative system adopts an information decentralization method to send a notification message to a specific fixed sensor node; the specific fixed sensor node receives the notification message, shortens the perception cycle of the mobile-fixed cooperative system by adjusting the perception cycle of the fixed sensor, and obtains a new perception cycle of the mobile-fixed cooperative system, including: The mobile-solid collaborative system determines the impact range of each detected disease based on the disease map results of the low-light enclosed space structure detected by the drone detection system; Determine the fixed set of sensors to be notified based on the impact area of ​​each detected disease; According to the fixed sensor set notified, a communication function is constructed; according to the communication function, a sensing period adjustment function is constructed; According to the sensing cycle adjustment function, the sensing cycle of each fixed sensor is adjusted, and the adjusted sensing cycle of each fixed sensor is output; According to the adjusted sensing period of each fixed sensor, the sensing period of the mobile-fixed cooperative system is shortened to obtain a new sensing period of the mobile-fixed cooperative system.

6. The low-light enclosed space structure detection method based on mobile-solid collaborative guidance according to claim 1 is characterized in that: The mobile-fixed collaborative system updates the fixed sensor node network deployment plan according to the disease results not covered by the fixed sensors to obtain a new fixed sensor node network deployment plan, including: According to the disease results not covered by fixed sensors, a utility function for fixed sensor deployment is constructed; Define a fixed sensor coverage function; construct an optimization problem based on the fixed sensor coverage function; The Monte Carlo tree search algorithm is used to solve the optimization problem and obtain a new fixed sensor node network deployment scheme.

7. The low-light enclosed space structure detection method based on mobile-solid collaborative guidance according to claim 6 is characterized in that: After the step of obtaining a new fixed sensor node network deployment solution, the method further includes: Construct a deployment decision function; evaluate the new fixed sensor node network deployment plan according to the deployment decision function and obtain the evaluation result; Determine whether to execute a new fixed sensor node network deployment plan based on the evaluation result; if the evaluation result is 1, execute the new fixed sensor node network deployment plan and update the fixed sensor network topology; if the evaluation result is 0, retain the original fixed sensor node network deployment plan.

8. A low-light confined space structure detection system based on mobile-solid collaborative guidance, the low-light confined space structure detection system based on mobile-solid collaborative guidance is used to implement the low-light confined space structure detection method based on mobile-solid collaborative guidance as claimed in any one of claims 1 to 7, characterized in that: The system comprises: The fixed sensor node network is used for collecting image data of low-light enclosed spaces and generating a basic disease data map; the fixed sensor node network transmits the basic disease data map to the drone detection system; The drone detection system is used to perform global path planning through a dual-mode adaptive confined space disease detection path planning algorithm to generate a precise detection path and a supplementary detection path; the drone detection system detects diseases in low-illuminance confined spaces based on the precise detection path and the supplementary detection path through a U-Net deep learning algorithm to obtain a disease map result of a low-illuminance confined space structure.

9. A low-light enclosed space structure detection device based on mobile-solid collaborative guidance, characterized in that: The low-light enclosed space structure detection equipment based on mobile-solid collaborative guidance includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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