Comprehensive pipe gallery inspection system based on 5G

Abstract

The invention discloses a comprehensive pipe gallery inspection system based on 5G, and relates to the technical field of pipe gallery inspection, the comprehensive pipe gallery inspection system comprises a management center, the management center is connected with a data acquisition module, a data processing module, a data analysis module and a comprehensive monitoring module; monitoring data of the comprehensive pipe gallery are collected through the data collection module; performing electric signal conversion on the obtained monitoring data in a data processing module to obtain a monitoring electric signal, and setting a filter end to perform replacement circulation on the monitoring electric signal to obtain a terminal monitoring quantity; the data analysis module constructs a monitoring dynamic graph according to the obtained terminal monitoring quantity and analyzes the monitoring dynamic graph to obtain a monitoring warning quantity and a warning time point; the comprehensive monitoring module carries out time sequencing on the monitoring warning quantity according to the obtained warning time points to obtain a critical check sequence, and generates an inspection strategy according to the critical check sequence; potential safety hazards of the comprehensive pipe gallery can be checked in time, and the reliability of data acquisition in the inspection process is improved.

Description

Technical Field

[0001] This invention relates to the field of utility tunnel inspection technology, specifically a 5G-based integrated utility tunnel inspection system. Background Technology

[0002] Integrated utility tunnel inspection refers to a comprehensive and detailed inspection of the facilities, equipment, and operational status of the integrated utility tunnel to maintain its safe operation and promptly identify and eliminate potential safety hazards. The inspection includes a thorough examination of all facilities and equipment within the tunnel, such as building structure, ventilation systems, power supply systems, lighting systems, fire protection facilities, and monitoring systems, to ensure their normal operation. However, existing integrated utility tunnel inspection methods have several shortcomings: the inspection methods are not intelligent or automated enough, still requiring manual inspection, resulting in time-consuming, labor-intensive, and inefficient processes; the inspection scope and focus are not clearly defined, leading to poor inspection results and an inability to promptly identify and eliminate safety hazards; and the professional qualifications and skill levels of inspection personnel vary, making it difficult to guarantee inspection quality. Therefore, improving the shortcomings of integrated utility tunnel inspection based on 5G technology has significant theoretical and practical implications.

[0003] How to utilize utility tunnel inspection technology to process the collected monitoring data, obtain terminal monitoring data, construct a monitoring dynamic map based on the terminal monitoring data, obtain monitoring warning data and warning time points by analyzing the monitoring dynamic map, sort the monitoring warning data according to the warning time points, and generate inspection strategies based on the sorting results is the problem we need to solve. To this end, we now provide a 5G-based integrated utility tunnel inspection system. Summary of the Invention

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] A 5G-based integrated utility tunnel inspection system includes a management center, which is connected to a data acquisition module, a data processing module, a data analysis module, and a comprehensive monitoring module.

[0006] The process of the data acquisition module acquiring monitoring data includes:

[0007] Inspection sensor points are set up based on the information obtained about the integrated utility tunnel;

[0008] Set up a patrol activity terminal, which includes access sensing points;

[0009] Connect the obtained patrol activity terminal to the inspection sensor point for remote communication.

[0010] The access sensing point is used to match with the inspection sensing point. A capture command is granted to the successfully matched access sensing point. The comprehensive data of the inspection sensing point is captured according to the obtained capture command to obtain monitoring data, and the monitoring data is marked with the capture time.

[0011] The acquired monitoring data is uploaded to the patrol activity terminal via the access sensor point.

[0012] The acquired monitoring data is converted into electrical signals to obtain monitoring electrical signals;

[0013] Set up a filter terminal to mark the obtained monitoring electrical signal as the input signal;

[0014] The obtained weight vector is set according to the filter end, and the obtained weight vector is initialized to obtain the weight zero vector;

[0015] The output monitoring quantity is obtained based on the obtained weight vector and input signal, and the output error quantity is obtained based on the obtained output monitoring quantity.

[0016] The update weight is obtained based on the output error and the input signal.

[0017] The process from initialization to obtaining the updated weight is recorded as one replacement loop, and the number of loop iterations is set according to the obtained filter end.

[0018] The obtained update weight is used as the initial weight vector for the next replacement loop. The obtained initial weight vector is uploaded to the replacement loop to obtain the update weight. The replacement loop is performed again until the number of loop iterations is satisfied.

[0019] The output monitoring quantity obtained after satisfying the number of iterations is marked as the terminal monitoring quantity.

[0020] The process of constructing a monitoring dynamic graph includes:

[0021] Perform a fast Fourier transform on the obtained terminal monitoring data to obtain discrete monitoring data;

[0022] Construct a two-dimensional rectangular coordinate system of time with respect to discrete monitoring quantities based on the obtained capture time;

[0023] Based on the obtained discrete monitoring quantities, a monitoring change curve is generated, and the obtained monitoring change curve is mapped to a two-dimensional rectangular coordinate system to obtain a monitoring dynamic diagram;

[0024] The discrete monitoring quantities are matched with the capture time to obtain the monitoring time points, and the obtained monitoring time points are marked on the monitoring dynamic graph;

[0025] In the monitoring dynamic graph, a sampling sliding interval is set according to the obtained monitoring time points, and the sampling sliding interval is marked as the moving matching interval in the corresponding area of ​​the monitoring dynamic graph.

[0026] Based on the sampling sliding interval, a discrete average value is obtained from the discrete monitoring quantities.

[0027] The discrete standard value is obtained based on the obtained sampling sliding interval, discrete average value, and discrete monitoring quantity;

[0028] Set the monitoring dynamic threshold based on the obtained discrete average value and discrete standard value;

[0029] Upload the obtained dynamic monitoring thresholds to the dynamic monitoring graph;

[0030] Based on the obtained monitoring dynamic thresholds, dynamic safety zones, downward deviation warning zones, and over-limit warning zones are obtained in the monitoring dynamic graph;

[0031] The discrete monitoring quantities of the lower-level warning zone and the over-level warning zone are marked to obtain the monitoring warning quantity, and the monitoring time point corresponding to the monitoring warning quantity is obtained. The obtained monitoring time point is marked as the warning time point.

[0032] Obtain the previous monitoring time point of the warning time point, mark the obtained monitoring time point as the critical time point, and obtain the discrete monitoring quantity corresponding to the critical time point, and mark the obtained discrete monitoring quantity as the critical monitoring quantity;

[0033] Obtain the inspection sensing points corresponding to the critical monitoring quantities, mark the obtained inspection sensing points as early warning sensing points, sort the early warning sensing points according to the time sequence of the critical time points to obtain the critical inspection sequence, and generate the inspection strategy according to the obtained critical inspection sequence.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: the collected monitoring data is converted into electrical signals to obtain monitoring electrical signals, and a filter terminal is set. A weight vector is set according to the filter terminal. The monitoring electrical signals are processed according to the weight vector to obtain output monitoring quantity and output error quantity. The weight vector is updated by the output error quantity to obtain updated weight. The monitoring electrical signals are replaced cyclically by the updated weight to obtain terminal monitoring quantity. By processing the monitoring data, the data quality is improved, which facilitates subsequent analysis.

[0035] The obtained terminal monitoring data is subjected to Fast Fourier Transform to obtain discrete monitoring data. A monitoring dynamic graph is constructed based on the obtained discrete monitoring data, and a sampling sliding interval is set. Discrete average value and discrete standard value are obtained based on the sampling sliding interval. The discrete average value and discrete standard value are dynamically combined to obtain the monitoring dynamic threshold. The monitoring dynamic threshold is divided into regions to obtain the monitoring warning quantity and warning time point. Using the dynamic threshold to divide the warning monitoring quantity and warning time point makes it easier to obtain the most accurate anomaly points, which helps to increase the accuracy of patrol.

[0036] The monitored warning quantities are sorted by time based on the obtained warning time points to obtain a critical inspection sequence, and an inspection strategy is generated based on the critical inspection sequence; this can improve the safety of the integrated utility tunnel. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0039] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] like Figure 1 As shown, a 5G-based integrated utility tunnel inspection system includes a management center, which is connected to a data acquisition module, a data processing module, a data analysis module, and a comprehensive monitoring module.

[0041] The data acquisition module is used to collect monitoring data of the integrated utility tunnel, and the specific process includes:

[0042] Inspection sensor points are set up based on the information obtained about the integrated utility tunnel;

[0043] The inspection sensing points are set up on the pipelines and equipment in the integrated utility tunnel, and the integrated data generated by the pipelines and equipment is recorded through the inspection sensing points;

[0044] Set up a patrol activity terminal, which includes access sensing points;

[0045] Connect the obtained patrol activity terminal to the inspection sensor point for remote communication.

[0046] The access sensing point is used to match with the inspection sensing point. A capture command is granted to the successfully matched access sensing point. The comprehensive data of the inspection sensing point is captured according to the obtained capture command to obtain monitoring data, and the monitoring data is marked with the capture time.

[0047] The monitoring data includes environmental data and facility status data; specifically, based on the various equipment and pipelines in the integrated utility tunnel, including but not limited to: power, communication, water supply, drainage, gas, and heating pipelines; the environmental data includes air quality, light intensity, noise level, and content of harmful substances in the air, etc., while the facility status data includes the operating status of various pipelines, electrical equipment, lighting systems, and other facilities, such as temperature, humidity, pressure, and liquid level.

[0048] The acquired monitoring data is uploaded to the patrol activity terminal via the access sensor point;

[0049] It should be further explained that, in the specific implementation process, the inspection terminal includes intelligent robots, drones, and monitoring sensors; and the inspection terminal can move freely according to the actual structure of the integrated utility tunnel, and can inspect any location in the integrated utility tunnel at any time.

[0050] The data processing module is used to process the acquired monitoring data to obtain the terminal monitoring data. The specific process includes:

[0051] The obtained monitoring data is converted into electrical signals to obtain monitoring electrical signals, which include environmental electrical signals and facility electrical signals. The environmental electrical signals include several environmental classification sub-signals, and the facility electrical signals include several status sub-signals.

[0052] Furthermore, environmental classification sub-signals include air quality sub-signals, light intensity sub-signals, noise level sub-signals, and the content of harmful substances in the air sub-signals. If the environmental data also includes other data, the data obtained after the corresponding data is converted into electrical signals are all sub-signals. Similarly, state sub-signals include temperature sub-signals, humidity sub-signals, pressure sub-signals, and liquid level sub-signals.

[0053] A filter terminal is provided, which is composed of filters and is used to purify the monitoring electrical signal, removing noise and unwanted frequency components.

[0054] The obtained monitoring electrical signal is marked as the input signal and denoted as x(n), where n represents the time step of the input signal;

[0055] The obtained weight vector is set according to the filter end and labeled as W. The weight vector is a column vector. At the same time, the weight vector represents the parameters in the filter and is used to adjust the weight of each component of the input signal. That is, the weight vector W contains multiple weight values, each weight value corresponds to a component of the input signal, and is used to represent the component characteristics of the input signal in multiple dimensions.

[0056] The obtained weight vector is initialized to obtain the zero weight vector;

[0057] It should be further explained that, in the specific implementation process, the initialization is performed before the input signal is processed. It initializes all weight vectors to zero vectors so that the filter end will not affect the input signal in the initial stage, reducing external interference to the input signal reception, increasing the accuracy of signal processing, making the processed monitoring data closer to the original data information, reducing information loss, improving the accuracy of information collection during the monitoring process, and enhancing the accuracy of judging abnormal data.

[0058] The output monitoring quantity is obtained based on the obtained weight vector and input signal, and is labeled as y(n), where y(n) = W T *x(n), W T This is the transpose of the weight vector W, meaning the weight vector is transformed from a column vector into a row vector after transposition.

[0059] The output error is obtained based on the obtained output monitoring quantity and is denoted as e(n), where e(n) = d(n) - y(n), and d(n) is the expected output, which represents the optimal output result and is preset.

[0060] Based on the obtained output error and input signal, the update weight is obtained and marked as w(n)' = w(n-1) + μ*e(n)*x(n), where μ is the learning rate and n-1 refers to the previous time step. Then w(n-1) is the weight vector of the previous time step.

[0061] It should be further explained that, in the specific implementation process, the weight limit is adjusted by the current output error e(n) and the learning rate μ. The adjusted weight vector is the updated weight, so that the output monitoring quantity is closer to the expected output.

[0062] The process from initialization to obtaining the updated weight is recorded as one replacement loop, and the number of loop iterations is set according to the obtained filter end.

[0063] The obtained update weight is used as the initial weight vector for the next replacement loop. The obtained initial weight vector is uploaded to the replacement loop to obtain the update weight, and the replacement loop process is repeated until the number of loop iterations is satisfied.

[0064] The output monitoring quantity obtained after satisfying the number of iterations is marked as the terminal monitoring quantity;

[0065] It should be further explained that, in the specific implementation process, since the monitoring electrical signals include environmental electrical signals and facility electrical signals, the terminal monitoring quantities include environmental monitoring quantities and facility monitoring quantities. Accordingly, the environmental monitoring quantities include air monitoring components, light monitoring components, noise monitoring components, hazardous substance monitoring components, etc., and the facility monitoring quantities include temperature monitoring components, humidity monitoring components, pressure monitoring components, liquid level monitoring components, etc.

[0066] The data analysis module is used to construct a monitoring dynamic map based on the obtained terminal monitoring data, and analyze the monitoring dynamic map to obtain the monitoring warning quantity and warning time point. The specific process includes:

[0067] Perform a fast Fourier transform on the obtained terminal monitoring data to obtain discrete monitoring data;

[0068] Construct a two-dimensional rectangular coordinate system of time with respect to discrete monitoring quantities based on the obtained capture time;

[0069] Based on the obtained discrete monitoring quantities, a monitoring change curve is generated, and the obtained monitoring change curve is mapped to a two-dimensional rectangular coordinate system to obtain a monitoring dynamic diagram;

[0070] It should be further explained that, in the specific implementation process, the monitoring change curve is generated by discrete monitoring quantities, which are obtained by terminal monitoring quantities. Therefore, the monitoring change curve includes air monitoring component curve, light monitoring component curve, noise monitoring component curve, hazardous substance monitoring component curve, temperature monitoring component curve, humidity monitoring component curve, pressure monitoring component curve, liquid level monitoring component curve, etc.

[0071] Based on the acquisition time of the acquired monitoring data, the discrete monitoring quantities are matched with the acquisition time to obtain the monitoring time points. The acquired monitoring time points are marked as e, where e = 1, 2, 3, ..., v1, and v1 is a positive integer.

[0072] The obtained monitoring time points are marked on the monitoring dynamic graph, and the discrete monitoring quantities are marked as P based on the obtained monitoring time points. e ;

[0073] In the monitoring dynamic graph, a sampling sliding interval is set according to the obtained monitoring time points. The obtained sampling sliding interval is marked as T, and the area corresponding to the sampling sliding interval in the monitoring dynamic graph is marked as the moving matching interval. The obtained moving matching interval is numbered and denoted as k, where k = 1, 2, 3, ..., v2, and v2 is a positive integer.

[0074] It should be further explained that, in the specific implementation process, the sampling sliding interval is an interval that can be translated and slid in the monitoring dynamic graph, and the length of the interval is fixed at T. By translating the horizontal axis of the monitoring dynamic graph, data information within the moving matching interval is obtained.

[0075] Based on the sampling sliding interval, a discrete average value is obtained from the discrete monitored quantities. This discrete average value is denoted as M. k ,in k represents the number of the sliding region within the monitoring dynamic graph of the sampling sliding interval, u represents the number of discrete monitoring quantities within the sampling sliding interval, and P u This represents the discrete monitoring quantity within the sampling sliding interval;

[0076] Based on the obtained sampling sliding interval, discrete average value, and discrete monitoring quantity, a discrete standard value is obtained and labeled as LS. k ,in

[0077] A dynamic monitoring threshold is set based on the obtained discrete average value and discrete standard value. The dynamic monitoring threshold includes an upper limit and a lower limit. The obtained upper limit is denoted as ΔD. k上 The obtained lower limit of the monitoring threshold is marked as ΔD. k下 , where ΔD k上 =M k +LS k *α,ΔD k下 =M k -LS k *α, where α is the influence factor, and α > 0;

[0078] Upload the obtained upper and lower limits of the monitoring thresholds to the monitoring dynamic graph;

[0079] It should be further explained that, in the specific implementation process, the upper and lower limits of the monitoring threshold in the monitoring dynamic graph are marked within the corresponding moving matching interval and are a horizontal line segment;

[0080] Based on the obtained upper and lower limits of the monitoring threshold, dynamic safety zones, lower deviation warning zones, and over-limit warning zones are obtained in the monitoring dynamic graph;

[0081] The discrete monitoring quantities of the lower deviation warning zone and the over-quantity warning zone are marked to obtain the monitoring warning quantity, which includes the lower deviation warning quantity and the over-quantity warning quantity. The monitoring time point corresponding to the monitoring warning quantity is obtained and the obtained monitoring time point is marked as the warning time point, which includes the lower deviation time point and the over-quantity time point.

[0082] Furthermore, the dynamic safety zone is the area between the upper and lower limits of the monitoring threshold, and the discrete monitoring quantities within the dynamic safety zone are all not less than the lower limit of the monitoring threshold and not greater than the upper limit of the monitoring threshold; the lower-biased warning zone is the area less than the lower limit of the monitoring threshold, and the discrete monitoring quantities within the lower-biased warning zone are all less than the lower limit of the monitoring threshold; the over-limit warning zone is the area greater than the upper limit of the monitoring threshold, and the discrete monitoring quantities within the over-limit warning zone are all greater than the upper limit of the monitoring threshold.

[0083] The integrated monitoring module is used to conduct safety inspections of the integrated utility tunnel based on the obtained dynamic safety zone, downward deviation warning zone, and excessive warning zone, and to obtain inspection strategies. The specific process includes:

[0084] Obtain the previous monitoring time point of the warning time point, mark the obtained monitoring time point as the critical time point, and obtain the discrete monitoring quantity corresponding to the critical time point, and mark the obtained discrete monitoring quantity as the critical monitoring quantity;

[0085] It should be further explained that, in the specific implementation process, if there are multiple adjacent warning time points, the monitoring time point before the first warning time point is obtained as the critical time point;

[0086] Obtain the inspection sensing points corresponding to the critical monitoring quantities, mark the obtained inspection sensing points as early warning sensing points, sort the early warning sensing points based on the time sequence of the critical time points, and obtain the critical inspection sequence.

[0087] A patrol inspection strategy is generated based on the obtained critical inspection sequence;

[0088] The process of generating the inspection strategy includes:

[0089] When the warning sensing point reaches the critical time point, a warning command is issued. The patrol activity terminal receives the warning command by accessing the sensing point, obtains the monitoring data of the warning sensing point, compares the obtained monitoring data with the monitoring warning quantity, obtains the warning component, conducts a safety inspection on the equipment or pipeline corresponding to the warning component, obtains the cause of the alarm, and cancels the warning command for the warning sensing point that resolves the cause of the alarm.

[0090] For example, if the temperature increases from 40 degrees Celsius to 80 degrees Celsius, and 80 degrees Celsius exceeds the upper limit of the monitoring threshold, then the warning sensing point in the over-limit warning zone will be shut down and a safety inspection will be carried out. The pipelines and corresponding equipment whose temperature exceeds the upper limit of the monitoring threshold will be inspected until the fault point is found and repaired. The warning command will be lifted for the warning monitoring point after the repair is completed.

[0091] Furthermore, after the received warning instruction disappears, the patrol activity terminal moves to the next warning sensing point to conduct a security check. The warning instruction is lifted for the warning sensing point that passes the security check, and the security inspection is completed when all warning instructions are lifted in the critical inspection sequence. This process is recorded as the patrol strategy.

[0092] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A 5G-based integrated utility tunnel inspection system, comprising a management center, characterized in that, The management center is connected to a data acquisition module, a data processing module, a data analysis module, and a comprehensive monitoring module; The data acquisition module is used to collect monitoring data of the integrated utility tunnel; The data processing module is used to convert the acquired monitoring data into electrical signals to obtain monitoring electrical signals, and to set a filter terminal. A weight vector is set according to the filter terminal, and the monitoring electrical signals are processed according to the weight vector to obtain output monitoring quantity and output error quantity. The weight vector is updated by the output error quantity to obtain updated weight weight. The monitoring electrical signals are replaced cyclically by the updated weight weight to obtain terminal monitoring quantity. The data analysis module is used to perform a fast Fourier transform on the obtained terminal monitoring data to obtain discrete monitoring data, construct a monitoring dynamic graph based on the obtained discrete monitoring data, set a sampling sliding interval, obtain discrete average value and discrete standard value based on the sampling sliding interval, dynamically combine the discrete average value and discrete standard value to obtain the monitoring dynamic threshold, divide the monitoring dynamic threshold into regions, and obtain the monitoring warning quantity and warning time point. The integrated monitoring module is used to sort the monitored warning quantities by time according to the obtained warning time points, obtain the critical inspection sequence, and generate an inspection strategy based on the critical inspection sequence.

2. The 5G-based integrated utility tunnel inspection system according to claim 1, characterized in that, The process of the data acquisition module acquiring monitoring data includes: Inspection sensor points are set up based on the information obtained about the integrated utility tunnel; Set up a patrol activity terminal, which includes access sensing points; Connect the obtained patrol activity terminal to the inspection sensor point for remote communication. The access sensing point is used to match with the inspection sensing point. A capture command is granted to the successfully matched access sensing point. The comprehensive data of the inspection sensing point is captured according to the obtained capture command to obtain monitoring data, and the monitoring data is marked with the capture time. The acquired monitoring data is uploaded to the patrol activity terminal via the access sensor point.

3. The 5G-based integrated utility tunnel inspection system according to claim 2, characterized in that, The acquired monitoring data is converted into electrical signals to obtain monitoring electrical signals; Set up a filter terminal to mark the obtained monitoring electrical signal as the input signal; The obtained weight vector is set according to the filter end, and the obtained weight vector is initialized to obtain the weight zero vector; The output monitoring quantity is obtained based on the obtained weight vector and input signal, and the output error quantity is obtained based on the obtained output monitoring quantity. The update weight is obtained based on the output error and the input signal.

4. The 5G-based integrated utility tunnel inspection system according to claim 3, characterized in that, The process from initialization to obtaining the updated weight is recorded as one replacement loop, and the number of loop iterations is set according to the obtained filter end. The obtained update weight is used as the initial weight vector for the next replacement loop. The obtained initial weight vector is uploaded to the replacement loop to obtain the update weight. The replacement loop is performed again until the number of loop iterations is satisfied. The output monitoring quantity obtained after satisfying the number of iterations is marked as the terminal monitoring quantity.

5. A 5G-based integrated utility tunnel inspection system according to claim 4, characterized in that, The process of constructing a monitoring dynamic graph includes: Perform a fast Fourier transform on the obtained terminal monitoring data to obtain discrete monitoring data; Construct a two-dimensional rectangular coordinate system of time with respect to discrete monitoring quantities based on the obtained capture time; Based on the obtained discrete monitoring quantities, a monitoring change curve is generated, and the obtained monitoring change curve is mapped to a two-dimensional rectangular coordinate system to obtain a monitoring dynamic diagram; The discrete monitoring quantities are matched with the capture time to obtain the monitoring time points, and the obtained monitoring time points are marked on the monitoring dynamic graph; In the monitoring dynamic graph, a sampling sliding interval is set according to the obtained monitoring time points, and the sampling sliding interval is marked as the moving matching interval in the corresponding area of ​​the monitoring dynamic graph.

6. A 5G-based integrated utility tunnel inspection system according to claim 5, characterized in that, Based on the sampling sliding interval, a discrete average value is obtained from the discrete monitoring quantities. The discrete standard value is obtained based on the obtained sampling sliding interval, discrete average value, and discrete monitoring quantity; Set the monitoring dynamic threshold based on the obtained discrete average value and discrete standard value; Upload the obtained dynamic monitoring thresholds to the dynamic monitoring graph; Based on the obtained monitoring dynamic thresholds, dynamic safety zones, downward deviation warning zones, and over-limit warning zones are obtained in the monitoring dynamic graph; The discrete monitoring quantities of the lower-level warning zone and the over-level warning zone are marked to obtain the monitoring warning quantity, and the monitoring time point corresponding to the monitoring warning quantity is obtained. The obtained monitoring time point is marked as the warning time point.

7. A 5G-based integrated utility tunnel inspection system according to claim 6, characterized in that, Obtain the previous monitoring time point of the warning time point, mark the obtained monitoring time point as the critical time point, and obtain the discrete monitoring quantity corresponding to the critical time point, and mark the obtained discrete monitoring quantity as the critical monitoring quantity; Obtain the inspection sensing points corresponding to the critical monitoring quantities, mark the obtained inspection sensing points as early warning sensing points, sort the early warning sensing points according to the time sequence of the critical time points to obtain the critical inspection sequence, and generate the inspection strategy according to the obtained critical inspection sequence.

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